Learning Factories: Featuring New Concepts, Guidelines, Worldwide Best-Practice Examples 3031464273, 9783031464270

Citation preview

Eberhard Abele · Joachim Metternich · Michael Tisch · Antonio Kreß

Learning Factories Featuring New Concepts, Guidelines, Worldwide Best-Practice Examples Second Edition

Learning Factories

Eberhard Abele · Joachim Metternich · Michael Tisch · Antonio Kreß

Learning Factories Featuring New Concepts, Guidelines, Worldwide Best-Practice Examples Second Edition

Eberhard Abele Institute for Production Management, Technology and Machine Tools (PTW) Technical University of Darmstadt Darmstadt, Germany

Joachim Metternich Institute for Production Management, Technology and Machine Tools (PTW) Technical University of Darmstadt Darmstadt, Germany

Michael Tisch Institute for Production Management, Technology and Machine Tools (PTW) Technical University of Darmstadt Darmstadt, Germany

Antonio Kreß Institute for Production Management, Technology and Machine Tools (PTW) Technical University of Darmstadt Darmstadt, Germany

ISBN 978-3-031-46427-0 ISBN 978-3-031-46428-7 (eBook) https://doi.org/10.1007/978-3-031-46428-7 1st edition: © Springer Nature Switzerland AG 2019 2nd edition: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Preface

Empowering Production in the Face of Current Global Challenges Production-related competencies remain important for economic growth and longterm competitiveness. As societies face pressing challenges like climate change, political crises, and the rising importance of artificial intelligence, there is a growing need to develop competencies to cope with these challenges successfully. However, the complexity of production systems can be hardly understood with classical lectures based on theory only. Consequently, more practical approaches are needed. Learning factories offer the chance to develop these competencies in a realistic environment. Production employees, engineers, and students will be enabled to improve manufacturing systems using established methods in different fields. Furthermore, learning factories facilitate the development of essential social and personal competencies such as team building and leadership. Considering the success achieved so far, the adoption of learning factories worldwide increased notably in the last years with a strong community. In the light of these developments, this book aims to provide a comprehensive overview of the state of the art in learning factories. The latest concepts are explained in a practical way. In addition, guidelines for planning, developing, and improving learning factories are presented step by step. Forty-six existing Best Practice Examples of learning factories, their overall goals, equipment, and products, as well as their operational concepts are given. We firmly believe that learning factories significantly contribute to the excellence of future generations of production employees, engineers, and students. Darmstadt, Germany

Eberhard Abele Joachim Metternich Michael Tisch Antonio Kreß

v

Contents

1

Challenges for Future Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 New Global Value Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Digitalisation of Value Streams and Artificial Intelligence . . . . . 1.3 Uncertainty and New Pandemics . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Scarcity of Natural Resources and Circular Economy . . . . . . . . . 1.5 Learning and Knowledge Society . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Demographic Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Wrap-Up of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 7 9 14 15 18 20 23 24

2

Competences for Future Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Importance of Competences for Competitiveness . . . . . . . . . . . . 2.2 The Concept of Competence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Qualification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Competence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Learning Targets in Learning Factories . . . . . . . . . . . . . . . . . . . . . 2.3.1 Classification of Competences . . . . . . . . . . . . . . . . . . . . . 2.3.2 Addressed Competences in Learning Factories . . . . . . . 2.4 Competence Development and Learning Target Tracking . . . . . . 2.5 Learning Factories as Part of a Competence Strategy . . . . . . . . . 2.6 Wrap-Up of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27 27 30 31 31 32 34 34 38 45 47 48 48

3

Learning in Production, Learning for Production . . . . . . . . . . . . . . . . 3.1 Definition of Basic Terms and Notions . . . . . . . . . . . . . . . . . . . . . 3.2 Historical Development of Work-Related Learning . . . . . . . . . . . 3.3 Forms of Work-Related Learning for Production . . . . . . . . . . . . . 3.4 Types of Perceived Learning Concepts in Production . . . . . . . . . 3.5 Need for Learning Factories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Wrap-Up of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51 51 57 60 63 70 73 75 vii

viii

4

5

6

Contents

Historical Development, Terminology, and Definition of Learning Factories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Historical Development of the Learning Factory Concepts . . . . 4.2 Terminology of Learning Factories . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Definition of Learning Factories . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Wrap-Up of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Variety of Learning Factory Concepts . . . . . . . . . . . . . . . . . . . . . . . 5.1 Learning Factory Morphology: Dimension 1 “Operational Model” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Economic or Financial Sustainability of the Learning Factory Concept . . . . . . . . . . . . . . . . . . . 5.1.2 Content-Related or Thematic Sustainability of the Learning Factory Concept . . . . . . . . . . . . . . . . . . . 5.1.3 Personal Sustainability of the Learning Factory Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Learning Factory Morphology: Dimension 2 “Targets and Purpose” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Learning Factory Morphology: Dimension 3 “Process” . . . . . . . 5.4 Learning Factory Morphology: Dimension 4 “Setting” . . . . . . . . 5.5 Learning Factory Morphology: Dimension 5 “Product” . . . . . . . 5.6 Learning Factory Morphology: Dimension 6 “Didactics” . . . . . . 5.7 Learning Factory Morphology: Dimension 7 “Metrics” . . . . . . . 5.8 Learning Factory Morphology: Dimension 8 “Research” . . . . . . 5.9 Database for Learning Factories . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.10 Wrap-Up of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Life Cycle of Learning Factories for Competence Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Learning Factory Planning and Design . . . . . . . . . . . . . . . . . . . . . 6.1.1 Overview Planning and Design Approaches . . . . . . . . . 6.1.2 The IALF Approach to Competence-Oriented Planning and Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Learning Factory Built-Up, Sales, and Acquisition . . . . . . . . . . . 6.2.1 Analysis and Concept Definition for the Built-Up of a Learning Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Built-Up of Standardised Turnkey Learning Factories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Design and Built-Up of Customer-Individual Learning Factories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

81 81 85 86 93 93 97 99 100 103 104 105 107 109 113 116 118 118 118 122 124 129 130 130 134 152 152 153 159

Contents

ix

6.3

Learning Factory Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Offer of Learning Factory Trainings for Industrial Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Training Management for Learning Factories in Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Learning Factory Evaluation and Improvement . . . . . . . . . . . . . . 6.4.1 Quality System for Learning Factories Based on a Maturity Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Evaluation of the Success of Learning Factories . . . . . . 6.5 Remodelling Learning Factory Concepts . . . . . . . . . . . . . . . . . . . 6.6 Wrap-Up of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

162

Overview on Existing Learning Factory Concepts . . . . . . . . . . . . . . . . 7.1 Learning Factories in Education . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Active Learning in Learning Factories . . . . . . . . . . . . . . 7.1.2 Action-Oriented Learning in Learning Factories . . . . . . 7.1.3 Experiential Learning and Learning Factories . . . . . . . . 7.1.4 Game-Based Learning in Learning Factories and Gamification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.5 Problem-Based Learning in Learning Factories . . . . . . 7.1.6 Project-Based Learning in Learning Factories . . . . . . . . 7.1.7 Research-Based Learning in Learning Factories . . . . . . 7.1.8 Best Practice Examples for Education . . . . . . . . . . . . . . 7.1.9 Example: Learning Factories for Industrie 4.0 Vocational Education in Baden-Württemberg . . . . . . . . 7.1.10 MecLab—A Learning Factory for Secondary Schools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Learning Factories in Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Developing Competences in Learning Factories . . . . . . 7.2.2 Best Practice Examples for Training . . . . . . . . . . . . . . . . 7.2.3 Success Factors for Learning Factories . . . . . . . . . . . . . . 7.2.4 Learning Factory Trainings as a Part of Change Management Approaches . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.5 Technology and Innovation Transfer in Course of Learning Factory Trainings . . . . . . . . . . . . . . . . . . . . . 7.3 Learning Factories in Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Learning Factories as Research Objects . . . . . . . . . . . . . 7.3.2 Learning Factories as Platforms for Production-Oriented Research . . . . . . . . . . . . . . . . . . 7.4 Wrap-Up of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

213 228 230 231 232

7

162 164 165 166 170 200 203 206

233 240 242 244 245 247 249 255 257 260 261 265 266 267 268 269 277 279

x

8

9

Contents

Overview on Learning Factory Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Learning Factories for Lean Production . . . . . . . . . . . . . . . . . . . . 8.2 Learning Factories for Industrie 4.0 . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Learning Factories for Resource and Energy Efficiency . . . . . . . 8.4 Learning Factories for Industrial Engineering . . . . . . . . . . . . . . . 8.5 Learning Factories for Product Development . . . . . . . . . . . . . . . . 8.6 Other Topics Addressed in Learning Factories . . . . . . . . . . . . . . . 8.6.1 Learning Factories for Additive Manufacturing . . . . . . 8.6.2 Learning Factories for Automation . . . . . . . . . . . . . . . . . 8.6.3 Changeability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6.4 Complete Product Creation Processes . . . . . . . . . . . . . . . 8.6.5 Global Production Networks . . . . . . . . . . . . . . . . . . . . . . 8.6.6 Intralogistics and Logistics . . . . . . . . . . . . . . . . . . . . . . . . 8.6.7 Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6.8 Worker’s Participation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7 Learning Factories for Specific Industry Branches or Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.8 Wrap-Up of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview on Potentials and Limitations of Existing Learning Factory Concept Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Potentials of Learning Factories . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Limitations of Learning Factories . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Learning Factory Concept Variations of Learning Factories in the Narrow Sense—Advantages and Disadvantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 The Learning Factory Core Concept . . . . . . . . . . . . . . . . 9.3.2 Model Scale Learning Factories . . . . . . . . . . . . . . . . . . . . 9.3.3 Physical Mobile Learning Factories . . . . . . . . . . . . . . . . 9.3.4 Low-Cost Learning Factories . . . . . . . . . . . . . . . . . . . . . . 9.3.5 Digitally and Virtually Supported Learning Factories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.6 Producing Learning Factories . . . . . . . . . . . . . . . . . . . . . . 9.4 Learning Factory Concept Variations of Learning Factories in the Broader Sense—Advantages and Disadvantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.1 Digital, Virtual, and Hybrid Learning Factories . . . . . . 9.4.2 Remotely Accessible Learning Factories and Teaching Factories . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Wrap-Up of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

287 287 294 299 303 305 307 307 309 310 312 314 314 315 317 318 319 321 327 327 327

335 335 336 339 339 340 345

347 347 359 362 368

Contents

xi

10 International Association of Learning Factories . . . . . . . . . . . . . . . . . . 10.1 History of the IALF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Mission of the IALF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Working Groups of the IALF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Conferences on Learning Factories (CLF) . . . . . . . . . . . . . . . . . . 10.5 Past Activities of the IALF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6 Wrap-Up of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

373 375 377 380 380 382 389 389

11 Best Practice Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Overview Best Practice Examples . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.1 Best Practice Example 1: 5G Learning Factory at AMTC, Tongji University, China . . . . . . . . . . . . . . . . 11.2 Best Practice Example 2: Aalto Factory of the Future at Dept. of Electrical Engineering and Automation, Aalto University, Finland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Best Practice Example 3: Additive Manufacturing Center (AMC) at TU Darmstadt, Germany . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Best Practice Example 4: A Distributed Learning Factory with a Central Hub (SEPT LF) at McMaster University, Hamilton, Canada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5 Best Practice Example 5: Aquaponics 4.0 Learning Factory (AllFactory) at University of Alberta, Canada . . . . . . . . 11.6 Best Practice Example 6: Demonstration Factory Aachen DFA at WZL & FIR, RWTH Aachen University, Germany . . . . 11.7 Best Practice Example 7: Digital Capability Center Aachen Led by ITA Academy GmbH Aachen, Germany . . . . . . 11.8 Best Practice Example 8: Die Lernfabrik at IWF, TU Braunschweig, Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.9 Best Practice Example 9: E|Drive-Center at FAPS, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.10 Best Practice Example 10: ETA-Factory at PTW, TU Darmstadt, Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.11 Best Practice Example 11: Fábrica do Futuro at University of São Paulo (USP), Brazil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.12 Best Practice Example 12: FIM Learning Factory at Faculty of Industrial Management, Universiti Malaysia Pahang, Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.13 Best Practice Example 13: FlowFactory at PTW, TU Darmstadt, Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.14 Best Practice Example 14: Globale Learning Factory at wbk, Karlsruhe Institute of Technology, Karlsruhe, Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

391 391 393

397 403

408 416 420 425 431

435 440 444

449 457

461

xii

Contents

11.15 Best Practice Example 15: Global McKinsey Innovation & Learning Center Network (ILC) . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.16 Best Practice Example 16: Hybrid Teaching Factory for Personalised Education—Towards Teaching Factory 5.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.17 Best Practice Example 17: IFA-Learning Factory, Leibniz University Hannover (LUH), Germany . . . . . . . . . . . . . . . . . . . . . 11.18 Best Practice Example 18: Industry 4.0 Lab at the Politecnico di Milano, Italy . . . . . . . . . . . . . . . . . . . . . . . . . . 11.19 Best Practice Example 19: LEAD Factory at IIM, TU Graz, Austria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.20 Best Practice Example 20: LEAN-Factory at Fraunhofer IPK, Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.21 Best Practice Example 21: Lean Learning Factory at FESB, University of Split, Croatia . . . . . . . . . . . . . . . . . . . . . . . 11.22 Best Practice Example 22: Lean School at Faculty of Industrial Engineering, University of Valladolid, Spain . . . . . 11.23 Best Practice Example 23: Learning and Research Factory (LFF) at the Chair of Production Systems, Ruhr-University Bochum, Germany . . . . . . . . . . . . . . . . . . . . . . . . 11.24 Best Practice Example 24: Learning Factory (CUBE) at the Department of Design, Production and Management (Faculty of Engineering Technology), University of Twente, Enschede, The Netherlands . . . . . . . . . . . . 11.25 Best Practice Example 25: Learning Factory jumpING at Heilbronn University, Germany . . . . . . . . . . . . . . . . . . . . . . . . . 11.26 Best Practice Example 26: Learning Factory of Advanced Industrial Engineering aIE (LF aIE) at IFF, University of Stuttgart, Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.27 Best Practice Example 27: Learning Factory SUM Mostar, Bosnia, and Herzegovina . . . . . . . . . . . . . . . . . . . . . . . . . . 11.28 Best Practice Example 28: Lernfabrik für schlanke Produktion (LSP) at the iwb, Technical University of Munich (TUM), Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.29 Best Practice Example 29: Manufacturing Systems Learning Factory (iFactory) at University of Windsor, Canada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.30 Best Practice Example 30: Model Factory @ Singapore Institute of Manufacturing Technology, Singapore . . . . . . . . . . . 11.31 Best Practice Example 31: MPS Lernplattform at Mercedes-Benz AG in Sindelfingen, Germany . . . . . . . . . . . . . 11.32 Best Practice Example 32: Operational Excellence at Department of Engineering, University of Luxembourg, Luxembourg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

465

470 477 482 486 491 495 501

506

511 515

520 525

531

535 540 545

549

Contents

11.33 Best Practice Example 33: Pilotfabrik Industry 4.0 at TU Wien, Austria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.34 Best Practice Example 34: Process Learning Factory CiP at PTW, TU Darmstadt, Germany . . . . . . . . . . . . . . . . . . . . . . . . . . 11.35 Best Practice Example 35: Recycling Atelier Augsburg at the Institut für Textiltechnik Augsburg and University Augsburg for Applied Sciences, Germany . . . . . . . . . . . . . . . . . . 11.36 Best Practice Example 36: SDFS Smart Demonstration Factory Siegen at PROTECH, University Siegen, Germany . . . . 11.37 Best Practice Example 37: Smart Factory AutFab at h_da, University of Applied Sciences Darmstadt, Germany . . . . 11.38 Best Practice Example 38: Smart Factory at SZTAKI (Institute for Computer Science and Control), Budapest, Hungary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.39 Best Practice Example 39: SmartFactory-KL at the German Research Center for Artificial Intelligence (DFKI), Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.40 Best Practice Example 40: Smart Mini Factory, Free University of Bozen-Bolzano, Italy . . . . . . . . . . . . . . . . . . . . . . . . 11.41 Best Practice Example 41: Stellenbosch Learning Factory (SLF), Department of Industrial Engineering, Stellenbosch University, South Africa . . . . . . . . . . . . . . . . . . . . . . 11.42 Best Practice Example 42: SZTAKI Industry 4.0 Learning Factory, Gy˝or, Hungary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.43 Best Practice Example 43: The Centre for Industry 4.0 at Chair of Business Informatics, esp. Processes and Systems, University of Potsdam, Germany . . . . . . . . . . . . . . 11.44 Best Practice Example 44: The Learning Factory at Penn State University, Pennsylvania, USA . . . . . . . . . . . . . . . . . . . . . . . 11.45 Best Practice Example 45: The Purdue Learning Factory Ecosystem—Preparing Future Engineers, West Lafayette, USA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.46 Best Practice Example 46: Werk150, ESB Business School, Reutlingen University, Germany . . . . . . . . . . . . . . . . . . . . 11.47 List of Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xiii

553 559

565 570 575

581

585 591

597 602

607 612

616 622 629 635

12 Conclusion and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642

About the Authors

Prof. Dr.-Ing. Prof. E.h. Eberhard Abele born in 1953, studied mechanical engineering. He is a research fellow and head of the Department “Industrial Automation” at the Fraunhofer Institute IPA in Stuttgart. From 1986 to 1999, he worked in the automotive supply industry as the head of manufacturing technology and technical director for factories in Spain and France. The focus of his industrial activities was on production management of highly automated factories and systematical productivity improvement. From 2000 to 2021, he was the head of the Institute PTW, which today is managed by Prof. Metternich and Prof. Weigold. Professor Abele contributed to more than 200 publications in the field of manufacturing organisation, machine tool technology, and manufacturing processes. The Process Learning Factory (CIP), which he initiated in 2008, and the Learning factory for energy efficiency (ETA-Factory, opened in 2013) have shown a novel path in the long-term qualification of university graduates and employees from companies. He is a founding member of the International Association of Learning Factories (IALF). He is a fellow in the Scientific Society for Production Engineering (WGP), CIRP, acatech, and a member of the supervisory board of EIT Manufacturing, Festo-Didactic, Zahoransky AG, and Datron AG. He is a fellow in the Scientific Society for Production Engineering (WGP), in the International Academy for Production Engineering (CIRP) and acatech.

xv

xvi

About the Authors

He was a member of the supervisory board of European Institute of Innovation & Technology (EIT) Manufacturing and Festo Didactic. Currently, he is member of the supervisory board of Zahoransky AG and Datron AG. Prof. Dr.-Ing. Joachim Metternich studied industrial engineering at the Technical University of Darmstadt and received his doctorate in 2001. After his time as an assistant of the CEO of TRUMPF Werkzeugmaschinen GmbH, he headed a production group at Bosch Diesel s.r.o. in the Czech Republic. He then took over the responsibility for the worldwide lean production system of Knorr-Bremse SfS GmbH, a manufacturer of systems for rail vehicles. Since 2012, he has been a director of the Institute of Production Management, Technology and Machine Tools (PTW). His research interests include lean manufacturing and its digital upgrading and the use of learning factories for competence development in manufacturing. He is the author and co-author of more than 200 articles and book chapters. He is also a member and past president of the International Association of Learning Factories (IALF) and the Scientific Society for Production Engineering (WGP). Dr.-Ing. Michael Tisch studied industrial engineering with a technical specialisation in mechanical engineering at the Technical University of Darmstadt. Until July 2018, he worked as a chief engineer at the Institute of Production Management, Technology and Machine Tools at the Technical University of Darmstadt. In his research and in his doctoral thesis (2018), he dealt with the design of learning factories for lean production. Since 2018, he has been at MTU Maintenance Hannover GmbH and currently as a senior manager of a repair department for engine parts.

About the Authors

xvii

Dr.-Ing. Antonio Kreß studied industrial engineering with a technical specialisation in mechanical engineering at the Technical University of Darmstadt. Until June 2023, he worked as a research associate and postdoc at the Institute of Production Management, Technology and Machine Tools at the Technical University of Darmstadt. In his research and in his doctoral thesis (2022), he dealt with the configuration of learning factories. Since 2023, he has been at Schunk Holding GmbH and currently as a senior manager for global operations.

List of Figures

Fig. 1.1 Fig. 1.2 Fig. 1.3 Fig. 1.4 Fig. 1.5

Fig. 1.6

Fig. 1.7 Fig. 1.8 Fig. 1.9 Fig. 1.10

Fig. 1.11 Fig. 1.12

Effect of education and training, to be considered in different time spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qualification for e-mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sectoral analysis of enterprise size in manufacturing (Eurostat, 2023) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Megatrends with crucial importance for production and products (Abele & Reinhart, 2011) . . . . . . . . . . . . . . . . . . . Identified megatrends in literature shown in Adolph et al. (2014), based on Abele and Reinhart (2011), Arndt (2008), Graf (2000), Grömling and Haß (2009), Herrmann (2010), Jovane et al. (2009), Krys (2011), Warnecke (1999), and Wartenberg and Haß (2005) . . . . . . . . . Chinese acquisitions in Europe in recent years (will) lead to various challenges for production, data from China-EU-FDI Radar (Datenna, 2023) . . . . . . . . . . . . . . . Exemplary Industrie 4.0 concepts implemented in the Process Learning Factory CiP in Darmstadt . . . . . . . . . . Additive process chain changes the possibilities and requirements of manufacturing processes . . . . . . . . . . . . . Aim of the research project ETA-Factory (PTW, TU Darmstadt, 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Current challenges in production technology require efficient forms of knowledge and competence management, with slight changes according to Abele and Reinhart (2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Population pyramid in the EU from 2007 to 2022 (Eurostat, 2020) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Age structure of the national and non-national populations in EU-28, January 2016 (in %) (Eurostat, 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 2 4 5

6

8 11 13 17

19 21

22

xix

xx

Fig. 1.13 Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 2.5 Fig. 2.6 Fig. 2.7 Fig. 2.8 Fig. 2.9 Fig. 2.10 Fig. 3.1

Fig. 3.2 Fig. 3.3

Fig. 3.4 Fig. 3.5 Fig. 3.6 Fig. 3.7 Fig. 3.8 Fig. 4.1

Fig. 4.2

Fig. 4.3

List of Figures

Extended production targets: Always affected by megatrends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of the structure of this structure . . . . . . . . . . . . . . . . Human–technology–organisation approach . . . . . . . . . . . . . . . Survey on learning success of further education (Achenbach et al., 2022) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Relation of competence, qualification, skills, and knowledge (Heyse & Erpenbeck, 2009) . . . . . . . . . . . . . . . Knowledge stair according to North (2011) . . . . . . . . . . . . . . . Exemplary qualification matrix for deployment and development of production staff . . . . . . . . . . . . . . . . . . . . . Professional and interdisciplinary qualification as the basis for vocational professional competence . . . . . . . . Catalogue of competences (Erpenbeck & Rosenstiel, 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cognitive, affective, and psychomotor domains can be addressed in learning factories . . . . . . . . . . . . . . . . . . . . . . . . . . Curriculum for modern lean production of the Process Learning Factory CiP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview over the structure of this chapter on the learning in production and the learning for production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning factory concept between formal and informal learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vocational training workshop of German company AEG in Mühlheim-Saarn, approximately taken in the year 1956 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coaching in industrial environments as a form of work integrated learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Historical development of work-related learning approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning approaches using enriched working processes . . . . . Job instruction approach in production . . . . . . . . . . . . . . . . . . . Learning approaches using simulated production processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of the structure of this chapter regarding the historical development, terminology, and definition of learning factories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Historical development of learning factory approaches and the number of indexed documents on Google Scholar regarding learning and teaching factories (based on Tisch and Metternich, 2017 and extended) . . . . . . . . . . . . . Key characteristics of learning factories and learning factories in the narrow sense (Abele et al., 2015 developed by Metternich) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23 28 29 30 31 31 32 34 35 36 46

52 53

58 59 61 66 67 68

82

85

91

List of Figures

Fig. 4.4

Fig. 5.1 Fig. 5.2

Fig. 5.3 Fig. 5.4 Fig. 5.5 Fig. 5.6

Fig. 5.7

Fig. 5.8 Fig. 5.9

Fig. 5.10

Fig. 5.11

Fig. 5.12

Fig. 5.13

xxi

Key characteristics of learning factories and learning factories in the broader sense (Abele et al., 2015, developed by Metternich) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview over the structure of this chapter regarding the variety of learning factory concepts . . . . . . . . . . . . . . . . . . . Learning factory morphology, dimension 1: operational model, according to Tisch et al. (2015b), Tisch (2018) and Kreß et al. (2023) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Three sustainability dimensions of learning factory operational models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Common types of ongoing learning factory financing for academic operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Common types of ongoing learning factory financing for industrial operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dependencies of research, transfer, education and training, industry projects, and business creation in learning factories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning factory morphology, dimension 2: targets and purpose, with small adaptions according to Tisch et al. (2015b), Tisch (2018), and Kreß et al. (2023) . . . . . . . . . Current and future application areas of learning factories in producing and non-producing sectors . . . . . . . . . . . . . . . . . . Learning factory morphology, dimension 3: process, according to Tisch et al. (2015b), Tisch (2018), and Kreß et al. (2023) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life cycles of production (phases of production highlighted in red) according to Tisch (2018) based on Bauernhansl et al. (2014), Westkämper (2006), Westkämper et al. (2006), Umeda et al. (2012), Schenk et al. (2014), Grundig (2015), Schuh (2006), Dürr (2013) . . . . Learning factory morphology, dimension 4: setting, according to Tisch et al. (2015b), Tisch (2018), and Kreß et al. (2023) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Examples for physical, virtual, life-size, and scaled-down learning environments in learning factories based on Tisch (2018), from left to right and top to bottom pictures were taken from Festo Didactic (2017a, 2017b, 2017c), PTW, TU Darmstadt (2017a, 2017b), BMW (2015), Jäger et al. (2015), Hammer (2014), IFA (2017), FBK (2015), and Görke et al. (2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning factory morphology, dimension 5: product, according to Tisch et al. (2015b), Tisch (2018) and Kreß et al. (2023) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

92 98

100 101 102 103

104

106 107

108

109

110

111

113

xxii

Fig. 5.14 Fig. 5.15

Fig. 5.16 Fig. 5.17

Fig. 5.18 Fig. 5.19 Fig. 5.20 Fig. 6.1 Fig. 6.2 Fig. 6.3

Fig. 6.4 Fig. 6.5 Fig. 6.6 Fig. 6.7 Fig. 6.8 Fig. 6.9 Fig. 6.10 Fig. 6.11 Fig. 6.12

Fig. 6.13

Fig. 6.14

List of Figures

Examples for learning factory products (Abele et al., 2017b) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of traditional product design process (a) and the product design process for learning factories (b,c), with changes inspired by Wagner et al. (2014), similar also in Abele et al. (2017a) . . . . . . . . . . . . . . . . . . . . . . Learning factory morphology, dimension 6: didactics, according to Tisch et al. (2015a) and Kreß et al. (2023) . . . . . Learning factory morphology, dimension 7: learning factory metrics, according to Tisch et al. (2015a) and Kreß et al. (2023) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning factory morphology, dimension 8: research, according to Kreß et al. (2023) . . . . . . . . . . . . . . . . . . . . . . . . . Screenshot of the “learning factories morphology web application” (LMS, 2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Screenshot of the map function of the “learning factories morphology web application” (LMS, 2015) . . . . . . . . . . . . . . . Learning factory life cycle (Tisch & Metternich, 2017) . . . . . . Levels of learning factory design (Tisch et al., 2015a) . . . . . . . Conceptual relationships in the two didactic transformations in learning factory design (Kreß & Metternich, 2020; Tisch et al., 2015a) . . . . . . . . . . . . . . . . . . . . Simplified learning factory design process at the macro level according to Tisch (2018) . . . . . . . . . . . . . . . . . . . . . . . . . Morphological description for the general target and framework definition (Kreß et al., 2023; Tisch, 2018) . . . Components of the competence formulation . . . . . . . . . . . . . . . Subdivision of a learning factory into factory areas and configuration alternatives (Kreß, 2022) . . . . . . . . . . . . . . . Procedure for the configuration of learning factories (Kreß, 2022) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simplified learning factory design process on meso level according to Tisch (2018) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Checklist for the learning module framework definition (Tisch, 2018) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roles in the shopfloor management; tasks and activities of a shopfloor management expert . . . . . . . . . . . . . . . . . . . . . . . Structure of the competence transformation for the design and redesign of learning modules according to Tisch et al. (2013) . . . . . . . . . . . . . . . . . . . . . . . . . Extract from the competence transformation chart of the learning module “Quality Techniques of Lean Production” (Enke et al., 2016) . . . . . . . . . . . . . . . . . . . . . . . . . Possible sequences of activities, according to Abele et al. (2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

114

115 117

119 120 121 122 130 135

136 137 138 139 140 140 143 144 145

146

148 148

List of Figures

Fig. 6.15

Fig. 6.16

Fig. 6.17 Fig. 6.18 Fig. 6.19 Fig. 6.20

Fig. 6.21 Fig. 6.22 Fig. 6.23 Fig. 6.24 Fig. 6.25

Fig. 6.26 Fig. 6.27 Fig. 6.28 Fig. 6.29 Fig. 6.30 Fig. 6.31

Fig. 6.32 Fig. 6.33

Fig. 6.34

xxiii

Overview of sequence steps and application areas of sequencing strategies for learning factory modules (Tisch, 2018) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sequence of activities for the sub-competence “ability to develop an andon-concept for production” (Enke et al., 2016) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simplified learning factory design process on micro level according to Tisch (2018) . . . . . . . . . . . . . . . . . . . . . . . . . Business models for the built-up of learning factories . . . . . . . Overview of operators and target groups of the turnkey learning factory projects mentioned (Enke et al., 2017b) . . . . . Examples for turnkey learning factories built up around the world after the model of the Process Learning Factory CiP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phases of typical turnkey learning factory projects according to Enke et al. (2017b) . . . . . . . . . . . . . . . . . . . . . . . . Success barriers and countermeasures regarding turnkey learning factory projects according to Enke et al. (2017b) . . . . Exemplary questions for the design of company-specific learning factories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scope of support by external learning factory experts (Abele et al., 2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exemplary value-adding architecture for the design and construction of individualised learning factories (Abele et al., 2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Offer of learning factory trainings for industry (Abele et al., 2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Issues for training management in learning factories . . . . . . . . Challenges for a quality system for learning factories in the operation phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structure of the maturity model (Enke et al., 2017a) . . . . . . . . Structure of maturity and capability level relations of the learning factory maturity model (Enke et al., 2018) . . . Definition of capability levels for all statements to enable a classification of particular learning factories (Enke et al., 2017a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation in learning factories along the CIPP model according to Stufflebeam (1972) . . . . . . . . . . . . . . . . . . . . . . . . Evaluation possibilities for the effects of learning factories according to Tisch et al. (2014) based on Alliger et al. (1997), Becker et al. (2010), Gessler (2005) and Kirkpatrick (1998) . . . . . . . . . . . . . . . . . . . . . . . . . . Two variants of feedback sheets for the use in learning factory trainings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

150

151 151 153 155

156 157 158 160 161

162 163 165 166 168 169

170 171

173 174

xxiv

Fig. 6.35 Fig. 6.36 Fig. 6.37 Fig. 6.38 Fig. 6.39 Fig. 6.40 Fig. 6.41 Fig. 6.42

Fig. 6.43 Fig. 6.44

Fig. 6.45 Fig. 6.46

Fig. 6.47 Fig. 7.1 Fig. 7.2 Fig. 7.3 Fig. 7.4 Fig. 7.5 Fig. 7.6 Fig. 7.7

Fig. 7.8

List of Figures

Exemplary subjective self-evaluation sheet for different competence classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General possible experimental designs for learning success evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Competence-oriented learning success evaluation approaches in learning factories . . . . . . . . . . . . . . . . . . . . . . . . . Exemplary operationalised competences of a learning module for “flexible production systems” . . . . . . . . . . . . . . . . . Exemplary knowledge tests regarding different knowledge levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Combination of knowledge and performance perspective to evaluate learning success in learning factories . . . . . . . . . . . Integrated learning factory training and transfer concept . . . . . Process of planning, implementation, and evaluation using learning factory modules in combination with transfer projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simplified example for the parallel planning of learning modules and transfer projects . . . . . . . . . . . . . . . . . . . . . . . . . . . Training- and project-based approach for the implementation phase of the integrated training and transfer concept . . . . . . . . . . . . . . . . . . . . . . . . . . . Monetary, indirect monetary, and non-monetary effects of learning factories on the cost and benefit side . . . . . . . . . . . Categorised indirect and non-monetary effects of learning factories on individual, academic, industrial, and societal level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning factory remodelling cycle . . . . . . . . . . . . . . . . . . . . . . Structure of the overview of existing learning factories in this chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Detailed structure of the overview on existing learning factory concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Use of learning factories in education in connection with stand-alone and industry-partnered projects . . . . . . . . . . . Long-cycled and short-cycled steered courses in connection with learning factories in education . . . . . . . . . . Most important active learning concepts in the field of engineering education in learning factories . . . . . . . . . . . . . Experiential learning cycle in learning factories . . . . . . . . . . . . Framework and example of game elements used for gamification purposes according to Werbach and Hunter (2012) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Game-based learning and gamification in learning factories. Classification according to Deterding et al. (2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

178 180 181 182 183 186 189

189 190

192 199

200 203 227 228 229 230 231 234

235

236

List of Figures

Fig. 7.9 Fig. 7.10 Fig. 7.11 Fig. 7.12

Fig. 7.13

Fig. 7.14

Fig. 7.15

Fig. 7.16

Fig. 7.17 Fig. 7.18 Fig. 7.19 Fig. 7.20 Fig. 7.21 Fig. 7.22 Fig. 7.23 Fig. 7.24

Fig. 7.25

Fig. 7.26

xxv

Civil and military paper airplanes and respective process steps of the paper airplane game . . . . . . . . . . . . . . . . . . . . . . . . RoboCup Logistics League 2016 in Leipzig, Germany. Screenshot taken from RoboCup (2016) . . . . . . . . . . . . . . . . . . Steps of a learning method using gamification elements, according to Böhner et al. (2015) . . . . . . . . . . . . . . . . . . . . . . . . Advanced Design Project regarding the optimisation of a Lean Machining Line in the Process Learning Factory CiP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classification of forms of teaching linked with research—the research-teaching nexus (Healey, 2005) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research process for research-based learning in learning factories according to Blume et al. (2015), adapted from Creswell (2008) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sixteen learning factories in Baden-Württemberg (Germany) on the map (Ministerium für Wirtschaft, Arbeit und Wohnungsbau Baden-Württemberg, 2017) . . . . . . Impression of learning factory 4.0 in Balingen as example for one of the 16 learning factories 4.0 in Baden-Württemberg, pictures taken from Festo Didactic (2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Three stations: handling, conveyor, and stack magazine . . . . . MecLab workpiece: top and bottom parts of a cylinder in three different colours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Setup with a MecLab station and laptop . . . . . . . . . . . . . . . . . . Samples for the production lines created by the pupils during Ideenpark 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning factories in training to speed up transformation and project implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of learning factories in “information assimilation” and “experiential learning” (Tisch & Metternich, 2017) . . . . . Learning as a feedback process according to Sterman (1994) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extended feedback loop using the learning factory as virtual world, shown in Abele et al. (2017), inspired by Sterman (1994) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General process of integrating the learning factory concept in a change management approach according to Dinkelmann et al. (2014) and Dinkelmann (2016) . . . . . . . . Research process of applied sciences and the integration of practical experience through learning factories (Abele et al., 2017), according to Schuh and Warschat (2013) on the basis of Ulrich et al. (1984) . . . . . . . . . . . . . . . . . . . . . . .

237 238 239

243

245

246

248

249 251 252 253 256 256 258 262

263

265

270

xxvi

Fig. 7.27 Fig. 7.28 Fig. 7.29 Fig. 7.30 Fig. 7.31

Fig. 8.1 Fig. 8.2 Fig. 8.3

Fig. 8.4 Fig. 8.5 Fig. 8.6 Fig. 8.7 Fig. 8.8 Fig. 9.1 Fig. 9.2

Fig. 9.3 Fig. 9.4

Fig. 9.5 Fig. 9.6

Fig. 9.7

List of Figures

Learning factories as research enablers according to Seifermann et al. (2014a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problem identification and abstraction of the problem in a learning factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Solution finding, realisation into practice, and verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning factories as a research enabler with the example of artificial intelligence in manufacturing . . . . . . . . . . . . . . . . . Combining the advantages of field and laboratory experiments for research in learning factories according to Schuh et al. (2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Detailed structure of the overview on content of existing learning factories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Process Learning Factory CiP at PTW, TU Darmstadt . . . . . . . Overview over the regional Mittelstand-Digital competence centers (left) and the structure and impressions of the Hessen Digital Competence Center in Darmstadt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part of the Festo Learning Factory Scharnhausen . . . . . . . . . . . Learning factory ETA at TU Darmstadt (PTW, TU Darmstadt, 2016) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Green Factory Bavaria locations for resource and energy efficient production (FAPS, 2018) . . . . . . . . . . . . . Impression of the teaching course in the Learning and Research Factory at RU Bochum . . . . . . . . . . . . . . . . . . . . Impressions from the learning module at the Learning and Innovation Factory, TU Wien . . . . . . . . . . . . . . . . . . . . . . . Structure of the overview over concept variations of existing learning factories . . . . . . . . . . . . . . . . . . . . . . . . . . . Required resources along the learning factory life cycle (Tisch & Metternich, 2017), life cycle similar to general product life cycle according to VDI (1993) . . . . . . . . . . . . . . . Exemplary limits regarding space- and time-related mapping ability (Abele et al., 2017b; Tisch, 2018) . . . . . . . . . . Virtual factories and learning factories from McKinsey&Company, Festo, and Siemens (Abele et al., 2017b) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advantages and disadvantages of the learning factory core concept (learning factories in the narrow sense) . . . . . . . . Advantages and disadvantages of model scale learning environments compared to the learning factory core concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advantages and disadvantages of physical mobile learning factories compared to the learning factory core concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

270 271 273 274

275 288 289

295 298 300 301 306 313 328

329 331

333 337

337

340

List of Figures

Fig. 9.8 Fig. 9.9

Fig. 9.10

Fig. 9.11

Fig. 9.12 Fig. 9.13 Fig. 9.14 Fig. 9.15

Fig. 9.16

Fig. 9.17 Fig. 9.18 Fig. 9.19 Fig. 9.20

Fig. 9.21

Fig. 9.22

Fig. 10.1 Fig. 10.2

xxvii

Advantages and disadvantages of low-cost learning factories compared to the learning factory core concept . . . . . Tec2Screen® enables individual learning paths related to learning speed and preferred media, picture taken from Festo Didactic (2018) . . . . . . . . . . . . . . . . . . . . . . . . . . . . Use of virtual extensions and simulation depending on the implementation effort for a physical environment and the feedback time to actions of the learners based on Thiede et al. (2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advantages and disadvantages of digitally and virtually supported learning factories compared to the learning factory core concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advantages and disadvantages of producing learning factories compared to the learning factory core concept . . . . . Virtual model of the FlowFactory at PTW; visTable®touch Software (visTABLE, 2017b) . . . . . . . . . . . . . Virtual learning factory XPRES at KTH . . . . . . . . . . . . . . . . . . Real assembly environment and its virtual model of the ESB Logistics Learning Factory (LLF) (Abele et al., 2017a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advantages and disadvantages of digital and virtual learning factories compared to the learning factory core concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Infrastructure and interfaces of physical, digital, and virtual learning factories (Abele et al., 2017a) . . . . . . . . . . Interrelation of digital, physical, and hybrid learning factories (Abele et al., 2017a) . . . . . . . . . . . . . . . . . . . . . . . . . . Advantages and disadvantages of hybrid learning factories compared to the learning factory core concept . . . . . Teaching Factory sessions for factory-to-classroom and lab-to-factory knowledge communication, shown in Abele et al. (2017a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advantages and disadvantages of remotely accessible learning and teaching factories compared to the learning factory core concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of concept variations and methods and approaches along the learning factory life cycle on current limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning factory networks, starting from local efforts in 1980s to a worldwide association . . . . . . . . . . . . . . . . . . . . . Structure of projects and groups related to learning factories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

341

342

344

345 346 349 352

354

357 358 359 360

361

362

367 374 375

xxviii

Fig. 10.3

Fig. 10.4

Fig. 10.5 Fig. 10.6 Fig. 10.7 Fig. 10.8 Fig. 10.9 Fig. 11.1 Fig. 11.2 Fig. 11.3 Fig. 11.4 Fig. 11.5 Fig. 11.6 Fig. 11.7 Fig. 11.8 Fig. 11.9 Fig. 11.10 Fig. 11.11 Fig. 11.12 Fig. 11.13 Fig. 11.14

Fig. 11.15 Fig. 11.16 Fig. 11.17 Fig. 11.18

List of Figures

Founding members of the Initiative on European Learning Factories (from left to right: Professor Laszlo Monostori, Professor Wilfried Sihn, Professor Friedrich Bleicher, Professorin Vera Hummel, Professor Kurt Matyas, Professor Eberhard Abele, Dr. Thomas Lundholm, Dr. Dimitris Mavrikios, Christian Morawetz, Professor Ivica Veza, Professor Toma Udiljak, Jan Cachay, Professor Bengt Lindberg. Not in the picture: Professor Gunther Reinhart, Professor Pedro Cunha) . . . . . . . Names of the founding members of the Initiative on European Learning Factories in 2011 and their institutes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Presidencies in the IALF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Group picture of the members of the International Association of Learning Factories in Darmstadt, 2017 . . . . . . Impressions of the conferences on learning factories from 2011 to 2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impressions of the conferences on learning factories from 2019 to 2023 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Students in the NIL winter school playing a logistics game (Bauer, 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Value stream of the 5G Learning Factory . . . . . . . . . . . . . . . . . Teaching model in the 5G Learning Factory . . . . . . . . . . . . . . . Aalto Factory of the Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cylindrical piece . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mobile phone or battery module replica . . . . . . . . . . . . . . . . . . QR code for the Aalto Factory of the Future . . . . . . . . . . . . . . . Additive Manufacturing Center and the connected institutes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Value stream of the Additive Manufacturing Center . . . . . . . . Workshop program for qualification along the process chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Equipment installed in the SEPT LF . . . . . . . . . . . . . . . . . . . . . Electronic screwdriver and solenoid valve . . . . . . . . . . . . . . . . SEPT LF Architecture and SEPT Learning Factory Hub . . . . . Focusing on products assembly reduced the cost of running the W. Booth Learning Factory . . . . . . . . . . . . . . . . Laboratory and learning factory infrastructure for teaching vibrations-based machine condition monitoring competence-based learning modules . . . . . . . . . . . Production systems, factory design, and virtual models in AllFactory, University of Alberta . . . . . . . . . . . . . . . . . . . . . Fish and leafy green plants production at AllFactory . . . . . . . . Service portfolio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Factory layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

375

376 377 379 385 386 387 395 396 400 400 401 402 404 405 407 410 411 412 414

415 419 420 422 424

List of Figures

Fig. 11.19

Fig. 11.20 Fig. 11.21 Fig. 11.22 Fig. 11.23

Fig. 11.24 Fig. 11.25 Fig. 11.26 Fig. 11.27 Fig. 11.28 Fig. 11.29 Fig. 11.30 Fig. 11.31 Fig. 11.32 Fig. 11.33 Fig. 11.34 Fig. 11.35 Fig. 11.36 Fig. 11.37 Fig. 11.38 Fig. 11.39 Fig. 11.40 Fig. 11.41 Fig. 11.42 Fig. 11.43 Fig. 11.44 Fig. 11.45 Fig. 11.46 Fig. 11.47 Fig. 11.48 Fig. 11.49

xxix

Smart wristband for human–machine interaction. Operators personal log in for smart assistance system and ergonomic workspaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview on digital solutions along the textile value chain . . . Modular system for workshops at the DCC Aachen . . . . . . . . Excerpt from training portfolio: clients can choose their individual training modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . Didactic approach of ITA Academy’s consulting model: identify clients digitisation level and create customised project approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Organisational structure and core topics of Die Lernfabrik and its three laboratories . . . . . . . . . . . . . . . . . . . . . Impressions and results of survey among the participants of the events HoloHack and GameJam at Die Lernfabrik . . . . Electric machine learning factory . . . . . . . . . . . . . . . . . . . . . . . Process chain of hairpin stators . . . . . . . . . . . . . . . . . . . . . . . . . Manufactured products in the Process Learning Factory E|Drive-Center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Building of the ETA-Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . ETA-Factory shopfloor—Greenfield process chain . . . . . . . . . Learning factory for energy productivity (LEP)—Brownfield process chain . . . . . . . . . . . . . . . . . . . . . . . Overview of existing workshop modules in the ETA-Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Layout and equipment at Fábrica do Futuro . . . . . . . . . . . . . . . Designed and assembled product at Fábrica do Futuro . . . . . . Information technology environment and support software at Fábrica do Futuro . . . . . . . . . . . . . . . . . . . . . . . . . . . Process flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Products in the Learning Factory . . . . . . . . . . . . . . . . . . . . . . . . Workstation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Busbar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D printer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Teaching framework (class lecture, practical learning, benchmarking, project presentation) . . . . . . . . . . . . . . . . . . . . . Production concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MES architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . System in the Learning Factory . . . . . . . . . . . . . . . . . . . . . . . . . Smart office station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impressions of the FlowFactory . . . . . . . . . . . . . . . . . . . . . . . . . Different variants of the electrical gear drive assembled in the learning factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exemplary configuration of the learning factory in Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . McKinsey Innovation & Learning Center network . . . . . . . . . .

426 427 428 429

430 433 435 437 438 439 441 442 443 444 447 448 448 452 452 453 453 454 454 455 455 456 459 461 464 464 467

xxx

Fig. 11.50 Fig. 11.51 Fig. 11.52 Fig. 11.53 Fig. 11.54 Fig. 11.55 Fig. 11.56 Fig. 11.57 Fig. 11.58 Fig. 11.59 Fig. 11.60 Fig. 11.61 Fig. 11.62 Fig. 11.63 Fig. 11.64 Fig. 11.65 Fig. 11.66 Fig. 11.67 Fig. 11.68 Fig. 11.69 Fig. 11.70 Fig. 11.71 Fig. 11.72 Fig. 11.73 Fig. 11.74 Fig. 11.75 Fig. 11.76 Fig. 11.77

List of Figures

“From-to” journey covering lean, digital, and sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selected support formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model Factory in a Box (MFIB) concept . . . . . . . . . . . . . . . . . Cloud-based education model . . . . . . . . . . . . . . . . . . . . . . . . . . a AR assembly GUI, b Real-time Remote-Control Car Assembly, c Real-time Field of View of Technician . . . . . . . . Teaching Factory concept as a closed-loop control system . . . Architecture of the Cloud-based Personalised Learning Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Product(s) within the IFA-Learning Factory . . . . . . . . . . . . . . . Impressions of the IFA-Learning Factory . . . . . . . . . . . . . . . . . Industry 4.0 Lab at the Politecnico di Milano . . . . . . . . . . . . . . Exploded view of the IIM scooter; Connection plate after cutting, drilling, and electroplating . . . . . . . . . . . . . . . . . . Layout and selected technologies of the LEAD Factory . . . . . Comparison of “current state” and “digital state” of the LEAD Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Layout, process, equipment, and products of the production environment . . . . . . . . . . . . . . . . . . . . . . . . . . Didactical design of the LEAN-Factory . . . . . . . . . . . . . . . . . . Layout of Lean Learning Factory at FESB, University of Split . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assembly line for gearbox in accordance with Industry 4.0 trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assembly line of Karet with vertical integration of ERP and MES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General view of the Lean School . . . . . . . . . . . . . . . . . . . . . . . . Two basic versions of the educational toy car (minivan and pickup) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Initial layout of the Lean School to manufacture cars (L34N) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Basic training process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evolution of the main learning KPIs . . . . . . . . . . . . . . . . . . . . . Learning and Research Factory (LFF) of the Chair of Production Systems (LPS) . . . . . . . . . . . . . . . . . . . . . . . . . . . Manufactured products in the Learning and Research Factory (LFF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Examples of digitisation solutions in the LFF . . . . . . . . . . . . . Chronological development of the Learning and Research Factory, LFF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Envisaged layout for part of the generic workshop in the “CUBE” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

468 470 470 473 474 474 475 479 480 484 489 489 491 494 495 498 498 499 503 503 504 505 506 508 510 510 511 514

List of Figures

Fig. 11.78

Fig. 11.79 Fig. 11.80 Fig. 11.81 Fig. 11.82 Fig. 11.83 Fig. 11.84 Fig. 11.85 Fig. 11.86 Fig. 11.87 Fig. 11.88 Fig. 11.89 Fig. 11.90 Fig. 11.91 Fig. 11.92 Fig. 11.93 Fig. 11.94

Fig. 11.95

Fig. 11.96 Fig. 11.97 Fig. 11.98 Fig. 11.99 Fig. 11.100 Fig. 11.101 Fig. 11.102 Fig. 11.103 Fig. 11.104 Fig. 11.105 Fig. 11.106 Fig. 11.107 Fig. 11.108 Fig. 11.109

xxxi

Students of different educational programmes interacting with the learning factory—and with each other—at different levels of aggregation . . . . . . . . . . . . . . . . . . Floor layout and arrangement of equipment . . . . . . . . . . . . . . . Examples of typical learning factory products with QR code to video . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Project timeline with due dates and milestones . . . . . . . . . . . . Development of competences in jumpING students . . . . . . . . . Modules of the Learning Factory aIE, picture from Festo Didactic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Product of the Learning Factory aIE . . . . . . . . . . . . . . . . . . . . . Layout of the initial situation in the Learning Factory aIE . . . Learning by doing simulation game in the Learning Factory aIE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FSRE Learning Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FSRE Learning Factory structure . . . . . . . . . . . . . . . . . . . . . . . Lifting platform-3D model and real product . . . . . . . . . . . . . . . Overview of the main areas and the equipment of the LSP . . . Manufactured products in the LSP . . . . . . . . . . . . . . . . . . . . . . Overview of research and development projects in the LSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modular and reconfigurable iFactory at the IMS center . . . . . . Integrated products and systems design, planning, and control demonstrated within the learning Factory environment at the IMS center . . . . . . . . . . . . . . . . . . . . . . . . . . Assembled products families in the Integrated Systems Learning Factory (automobile belt tensioners family variants and desk set family variants) . . . . . . . . . . . . . . . . . . . . Manufactured products in the Model Factory@SIMTech . . . . Smart Engineering System (SES) . . . . . . . . . . . . . . . . . . . . . . . Smart Engineering System (SES) product range and applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Layout and impressions of the MPS Lernplattform in Sindelfingen at Daimler AG . . . . . . . . . . . . . . . . . . . . . . . . . . MPSfactory at Daimler AG in Sindelfingen . . . . . . . . . . . . . . . Process sequences for (dis)assembly of hole puncher . . . . . . . Augmented reality Manual Work Instructions . . . . . . . . . . . . . QR code to a video of the Operational Excellence Factory . . . Machining area in TU Wien Pilot Factory . . . . . . . . . . . . . . . . TU Wien Pilot Factory and its simulation . . . . . . . . . . . . . . . . . Manufactured products in the TU Wien Pilotfabrik . . . . . . . . . Exemplary use cases in the TU Wien Pilotfabrik . . . . . . . . . . . Building of the Process Learning Factory CiP . . . . . . . . . . . . . Value stream of the Process Learning Factory CiP . . . . . . . . . .

515 517 518 519 520 522 523 523 524 527 528 530 533 533 534 537

538

539 542 543 544 548 549 552 552 553 556 556 557 558 561 562

xxxii

Fig. 11.110 Fig. 11.111 Fig. 11.112 Fig. 11.113 Fig. 11.114

Fig. 11.115 Fig. 11.116 Fig. 11.117 Fig. 11.118 Fig. 11.119 Fig. 11.120 Fig. 11.121 Fig. 11.122

Fig. 11.123 Fig. 11.124 Fig. 11.125 Fig. 11.126 Fig. 11.127 Fig. 11.128 Fig. 11.129 Fig. 11.130 Fig. 11.131 Fig. 11.132 Fig. 11.133

Fig. 11.134

Fig. 11.135

Fig. 11.136

List of Figures

Manufactured products in the Process Learning Factory CiP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alternative value stream for trainings in the field of Industrie 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extensions of the Process Learning Factory CiP . . . . . . . . . . . Seven stations of the Recycling Atelier Augsburg . . . . . . . . . . Card sliver made from recycled post-consumer jeans (left) and pre-consolidated nonwoven made from recycled carbon fibres (right) . . . . . . . . . . . . . . . . . . . . . . Textile cycle envisioned by the Recycling Atelier . . . . . . . . . . Trainings conducted at the Recycling Atelier . . . . . . . . . . . . . . Production facility of the SDFS Smart Demonstration Factory Siegen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Grand opening of the Campus Buschhütten with the partners of the SDFS . . . . . . . . . . . . . . . . . . . . . . . . . . Working stations of the Learning Factory AutFab of h_da . . . Industrie 4.0-techologies in the AutFab . . . . . . . . . . . . . . . . . . . General view of the SZTAKI Smart Factory . . . . . . . . . . . . . . . Recent additions to the Smart Factory infrastructure. Left: mobile robot with workpiece rack; right: embedded computing framework on a test stand . . . . . . . . . . . . . . . . . . . . AR extension for the material handling robot added recently to the Smart Factory infrastructure . . . . . . . . . . . . . . . Factory concept of the SmartFactory-KL . . . . . . . . . . . . . . . . . Asset administration shell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logo of the project “smartMA-X” . . . . . . . . . . . . . . . . . . . . . . . Logo of the project TWIN4TRUCKS . . . . . . . . . . . . . . . . . . . . Exemplary pictures of the lab . . . . . . . . . . . . . . . . . . . . . . . . . . . Pneumatic cylinder from Kuhnke . . . . . . . . . . . . . . . . . . . . . . . Products in the SLF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Layout and equipment of the SLF assembly facility . . . . . . . . Layout and equipment of the STC manufacturing facility . . . . One of several configurable collaborative assembly stations installed in the SZTAKI Industry 4.0 Learning Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Left: replica of the robot gripper sent to students attending remotely. Right: ball valve subject to assembly, with parts pre-packaged for supporting remote attendance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Left: elements used in the assembly fixtures—different colours are marking their function. Right: lightweight assembly pallet for manual tests during remote attendance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Screenshot captured during a hybrid session of the summer school course . . . . . . . . . . . . . . . . . . . . . . . . . . .

563 563 564 567

567 568 569 572 575 578 579 583

584 584 589 590 591 591 594 595 600 600 601

604

605

606 606

List of Figures

Fig. 11.137 Fig. 11.138 Fig. 11.139 Fig. 11.140 Fig. 11.141 Fig. 11.142

Fig. 11.143 Fig. 11.144 Fig. 11.145

Fig. 11.146 Fig. 11.147 Fig. 11.148

Fig. 11.149 Fig. 11.150

Fig. 11.151 Fig. 11.152 Fig. 11.153 Fig. 11.154 Fig. 11.155 Fig. 11.156

xxxiii

Environment of the ZIP4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Principle of the hybrid simulation . . . . . . . . . . . . . . . . . . . . . . . Example of a basic structure in the ZIP4.0 . . . . . . . . . . . . . . . . AR glasses in a learning scenario . . . . . . . . . . . . . . . . . . . . . . . Didactical concept of the ZIP4.0 . . . . . . . . . . . . . . . . . . . . . . . . A cross-section rendering of our new Engineering Design and Innovation building showing the layout of the Learning Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Metal shop has five CNC mills and four CNC lathes, each with a digital twin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24-h access makerspace features 120 butcher block tables, 3D printers, and low-risk hand and power tools . . . . . . A typical design studio in the Penn State Learning Factory. There is no “front” to the room and students sit with their team. Studios open into adjacent makerspaces for hands-on instruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hybrid Skateboard/Scooter Parts and Smart Factory Production System Layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prototype subassemblies (a–g); and product (h) manufactured in the Smart Factory . . . . . . . . . . . . . . . . . . . Smart Warehouse region of the Smart Factory and the operation path of the Veloce and Dynamo autonomous robots for delivery and pickup across the production line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Layout of the automated and connected worker assembly stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Foundry Production Layout: a Sinto FDNX-1 moulding machine; b shakeout station; c Thermtronix dipout furnace, d Inductotherm induction furnace; e conveyor system; and f return sand conveyor system with a 6-ton sand capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A large-scale and modular industrial process manufacturing system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Student curricular progression through Industry 4.0 themes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Way to the Werk150 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . City Scooter (left) and mobile Working Hub (center and left) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Factory Werk150 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Physical Factory Werk150 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

608 609 610 610 611

614 614 615

616 618 619

619 620

621 621 622 624 625 626 628

List of Tables

Table 1.1

Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 3.1

Table 3.2 Table 3.3 Table 4.1 Table 6.1

Table 6.2

Table 6.3

Table 6.4

Significant differences depending on the size of the company regarding perception and the processes for further training (Eurostat, 2021) . . . . . . . . . . . . . . . . . . . . . . Taxonomies of cognitive, affective, and psychomotor learning targets according . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Competence classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Competence areas and examples . . . . . . . . . . . . . . . . . . . . . . . . . Competence foci and examples . . . . . . . . . . . . . . . . . . . . . . . . . . Perspectives of the learning theories taken from Tisch (2018) based to Kerres (2012) with additions according to Ertmer and Newby (1993), Schunk (1996), and Jonassen (1991) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Work-related learning approaches for production (based on Adolph et al., 2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Types of work-related learning in production in the overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scope of learning factory definitions (Tisch, 2018) . . . . . . . . . . Characteristics of subjective and objective measuring approaches according to Clasen (2010) with orientation among others at Bommer et al. (1995) and Harris and Schaubroeck (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Observation criteria and indicators for the performance evaluation in the simulated, complex problem situation (Tisch et al., 2015b) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Checklist for non-recurring and operating direct costs of learning factories according to Abele et al. (2015) based on Zangemeister (1993) . . . . . . . . . . . . . . . . . . . . . . . . . . . Checklist for direct monetary benefits of learning factories, non-recurring, and operating benefits (Abele et al., 2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 36 39 40 41

56 64 69 89

177

185

194

194

xxxv

xxxvi

Table 6.5

Table 6.6 Table 6.7

Table 6.8

Table 6.9

Table 7.1 Table 7.2

Table 10.1 Table 10.2 Table 10.3 Table 11.1 Table 11.2

List of Tables

Detailed overview of expected non-recurring costs and direct monetary benefits of the Future Learning Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Detailed overview of expected operating costs and direct monetary benefits of the Future Learning Factory . . . . . . . . . . . Calculated NPV for the Future Learning Factory depending on interest rate i and the number of regarded periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amortisation periods in years for learning factory investments depending on R0 and Rt , based on a discount rate of 5% . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distinction between directly monetary, indirectly monetary and non-monetary effects based on Heinrich and Lehner (2005) and Hanssen (2010) . . . . . . . . . . . . . . . . . . . Overview on existing learning factories around the globe (extract) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advantages and disadvantages of the two opposed learning process sequences based on Coleman (1982), Keeton et al. (2002), Kolb (1984), Tisch (2018) . . . . . . . . . . . . . Important dates of the IALF . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ongoing working groups of the IALF . . . . . . . . . . . . . . . . . . . . . Dates and topics of the CLFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation of Hybrid TF Case Studies . . . . . . . . . . . . . . . . . . . . Equipment and pedagogical capabilities of the new facility . . .

195 196

197

198

199 214

259 378 381 383 476 614

Chapter 1

Challenges for Future Production

A skilled workforce and continuous training are essential for a prosperous and successful economy. Figure 1.1 shows the effects of education and training in different time spans. All education and training measures have numerous positive effects on the individual, the companies for which they work, and society as a whole.1 This relationship has been known for a long time,2 yet often only the short-term effects are noticed and not the medium- and long-term successes. For companies in the manufacturing sector in particular, education and training play a crucial role. The economic success of companies today and in future depends on the competences of the entire workforce, not just engineers and managers.3 The lack of these competences, such as entrepreneurial, managerial, or scientific management skills, significantly reduces the ability to innovate in terms of fundamentally new products, process efficiency, productivity, and quality.4 Studies predict a significant shift in labour demand towards future jobs, that require advanced competences.5 The literature on educational control states that the return on training is almost always positive, can be very high, and can take many forms, including a higher level of value-added activities, greater flexibility, and a better ability to innovate.6 In the short, mid and long term, education and training are crucial for the competitiveness of the economy and the ability to adapt to technological change.7

1

See Gylfason (2001). See Barro (1996). 3 See O’Sullivan et al. (2011). 4 See Tether et al. (2005). 5 See CEDEFOP (2010). 6 See Smith (2001). 7 See Hanushek and Woessmann (2007). 2

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 E. Abele et al., Learning Factories, https://doi.org/10.1007/978-3-031-46428-7_1

1

2

1 Challenges for Future Production

nation

affected level

sustainable economic performance and growth

organization

less waste in work processes greater flexibility and efficiency of labor force

individual

competiveness

sustainable economic success

more innovations

increased individual income job satisfaction and thus better health

short-term

mid-term

long-term

time span of effects Fig. 1.1 Effect of education and training, to be considered in different time spans

Fig. 1.2 Qualification for e-mobility

This applies, for example, to the transition from the internal combustion engine to e-mobility, with all its challenges and opportunities for OEMs8 and the supply industry, that depend on broad-based education activities,9 see Fig. 1.2. For any nation, the industrial sector is an important factor in wealth creation. For example, the manufacturing sector will employ more than 29 million people in the 8 9

Original Equipment Manufacturer. See Bogdanovs et al. (2022).

1 Challenges for Future Production

3

European Union (EU).10 Moreover, in Europe, more than 26% of the value added in the non-financial business economy will be generated by the manufacturing sector.11 In 2021, the value of production sold in the EU amounts to 5209 billion e, an increase of almost 14% compared to 4581 billion e in 2020.12 Despite the often published shift from an industrial to a service and information society, the facts show that manufacturing is still the backbone of the developed world’s prosperity.13 In Germany, more than 8 million jobs are directly related to manufacturing.14 In addition, some 6 million people are employed in productionrelated business services such as logistics and information technology.15 This means that 14 out of a total of 40 million employees in Germany are directly linked to the production sector. Some estimates suggest that overall, 70% of jobs and 75% of GDP in Europe are linked to manufacturing.16 More than 2 million enterprises were classified as “manufacturing” in the EU in 2020: When looking at the perception and processes of training, there are very significant differences depending on the size of the company, see Fig. 1.3.17 Any manufacturing company that defines its vision and strategic development must also define the strategy of the training and qualification of its employees. At the very beginning of these strategic processes, therefore, these following fundamental questions should be posed (Table 1.1): • What innovations will influence our product portfolio tomorrow? • Which megatrends can be identified today that will influence the market, technology, and manufacturing systems?18 • What specific competences will be needed for our factory of tomorrow? Learning and training systems must address current and future developments in manufacturing. These developments are accompanied by economic, environmental, and social megatrends that we are currently recognising. Megatrends are huge economic, social, political, and technological changes that are likely to affect our lives for many years (7–10 or more).19 Although megatrends may temporarily overshadow short-term developments, in the longer term, they determine the direction of change in organisational, technological, and human issues.20 These changes in production need to be addressed with breakthrough innovations in production 10

See Eurostat (2023). See Eurostat (2016). 12 See Eurostat (2022). 13 See Abele and Reinhart (2011). 14 See DESTATIS (2016). 15 See DESTATIS (2016). 16 See O’Sullivan et al. (2011). 17 See Eurostat (2023). 18 See Abele and Reinhart (2011). 19 See Naisbitt (1982). 20 See Abele and Reinhart (2011). 11

Fig. 1.3 Sectoral analysis of enterprise size in manufacturing (Eurostat, 2023)

4 1 Challenges for Future Production

1 Challenges for Future Production

5

Table 1.1 Significant differences depending on the size of the company regarding perception and the processes for further training (Eurostat, 2021) Micro

Enterprise category

Small

Medium sized Large

Head count

< 10

< 50

< 250

> 250

Turnover in million e

≤e2

≤ e 10

≤ e 50

> e 50

Budget for training

Not existing or not defined

Rarely defined

2–4% of turnover

2–6% of turnover

Internal Mostly not coordination of training/ processes and goals well defined

Hardly or rarely defined

Depends on branch

Special focus in HR department

Mostly not yet Contact and qualification activities within learning factories

Mostly not yet

Some have experiences

Most have experiences with int. or external learning factories

Competences for new products and value chains

Competences for future production systems

New Global Value Stream

E-Mobility New Pandemics

Learning Society

New Energies Quality of Life - Medtech

Artificial Intelligence Digitalization of Value Stream

Demographic Change

Climate Change

Circular Economy

Fig. 1.4 Megatrends with crucial importance for production and products (Abele & Reinhart, 2011)

processes, products, services, and technologies.21 An overview of the megatrends22 is given in Fig. 1.4. Taking account all these megatrends leads to rapidly increasing uncertainty and complexity in manufacturing companies. On the other hand, looking at the megatrends is a valuable way for companies to develop their own growth scenarios. 21 22

See Grömling and Haß (2009). See also Abele and Reinhart (2011).

Wartenberg, Haß (2005)

Warnecke (1999)

Schneidermann (2006)

Krys (2011)

Herrmann (2010)

Jovane, Westkämper (2009)

Grömling (2009)

Identified Megatrends

Globalization Shortening of Product Life Cycles New Technologies Digitalization / Networking Scarcity of Resources Knowledge Society Increasing Customer Demands Services in an Industrial Environment Demografic Change Risk of instability & Security Investment and Infrastructure Climate Change / Environmental Degradation Mobility Welfare Orientation Market of the Future Quality of Life Product

Graf (2000)

Production

Arndt (2008)

1 Challenges for Future Production

Abele, Reinhart (2011)

6

Explicit mention of the megatrends Implicit mention or used on the edge of a development

Fig. 1.5 Identified megatrends in literature shown in Adolph et al. (2014), based on Abele and Reinhart (2011), Arndt (2008), Graf (2000), Grömling and Haß (2009), Herrmann (2010), Jovane et al. (2009), Krys (2011), Warnecke (1999), and Wartenberg and Haß (2005)

Recruiting and—more importantly—developing competent employees are critical competitive factors for companies that determine success or failure in a dynamic world and rapidly changing markets. Figure 1.5 shows the result of a literature review of ten individual studies on the influence of megatrends on the future of production. The overview also shows whether the identified megatrends have an impact on the future design of production processes or product characteristics.23 The trends at the top of the chart have a greater impact on production, while those at the bottom have a corresponding impact on product design. Globalisation and the resulting intensification of competition, dynamic product life cycles, the emergence of new technologies, digitalisation and networking, resource scarcity, the importance of knowledge, the risk of instability and demographic change are identified as the main challenges for industrial production.24

23 24

See also Abele and Reinhart (2011). See Adolph et al. (2014).

1.1 New Global Value Streams

7

1.1 New Global Value Streams Globalisation is not a new phenomenon.25 The interconnectedness of the world economy has been developing for centuries, with companies gradually expanding beyond national borders. What is new is the dramatic acceleration of this process by 2022? However, the war in Ukraine has abruptly interrupted this dynamic and reopened the question of globalisation strategy in every company, especially in Europe and North America. In the meantime, internationally active manufacturing companies reassess their supply chain options. A return to purely national production is not a new option for reasons of market presence, but also for cost reasons. The task of qualifying and preparing managers for an international assignment is therefore still important.26 The trend towards globalisation can also be seen in international acquisitions of (mainly industrial) companies: The annual number of acquisitions from China in Europe increased by 48% from 2015 to 2016—and has increased more than sevenfold in the last ten years.27 This phenomenon leads, among other things, to. • the need for international cooperation, • globally networked value chains that need to be designed and managed, and • a strong need for global standardisation of production systems. Figure 1.6 shows the number of acquisitions or investments by Chinese companies in Europe, as well as some prominent European companies acquired by China.28 The example of German industry shows that the production of high-quality goods in international networks has a positive effect on national industrial employment as long as the core production focus remains in the home country. Keeping jobs in the home country is only possible with an educated and well-trained workforce. Consequently, the megatrend of globalisation poses several challenges for industrial companies in high-wage countries: • achieving internationally leading productivity, • availability of a well-educated and highly trained workforce—think globally, act locally, • ensuring the highest quality of goods in the production network as a prerequisite, • achieving high levels of adaptability and flexibility in production systems, • ensuring a reasonable degree of supply security, short lead times and resilience, and • protecting intellectual property and core competences. In our discussions with production and HR managers on the globalisation megatrend, it was often argued that future workers and employees will to: 25

See Naisbitt (1982). See Rahman (2022). 27 See Sun and Kron (2017). 28 See Datenna (2023). 26

8

1 Challenges for Future Production Chinese aquisitions in Europe (based on the China-EU FDI Radar)

manage global production networks

achieve global standardization of production systems

master worldwide cooperation

Exemplary acquisitions in recent years from China in Europe

Legend

Syngenta, Agriculture, Switzerland. In 2016, $43 billion bid. In 2017, China Chem collected 80% of shares

Supercell Oy, High Technology, Finland. In 2016, Tencent bought Supercell for $8.6 billion

KUKA, Robotics, Germany In 2016, $5 billion bid, Midea collected over 95% of shares

Windmw GMBH, Energy, Germany. In 2016, China Three Gorges bought 80% of shares for $1.9 billion

Krauss Maffei, Machine manufacturer, Germany In 2016, $1 billion takeover from Chem China

Skyscanner holdings, High Technology, Great Britain. In 2016, Ctrip.com International Ltd buys skyscanner for $1.7 billion

High state influence

Medium state influence

Low state influence

High state influence

Fig. 1.6 Chinese acquisitions in Europe in recent years (will) lead to various challenges for production, data from China-EU-FDI Radar (Datenna, 2023)

1.2 Digitalisation of Value Streams and Artificial Intelligence

9

• need opportunities to develop and enhance their intercultural skills, • be exposed to global sourcing processes at an earlier stage, and • be exposed to optimisation and Best Practice Examples of production processes, production systems and value networks. Managers facing a new intercultural challenge can be prepared for their new position abroad in a modern learning system (such as a learning factory29 ) in the specific country and may be in an academic or industrial area.

Current megatrend “new global value streams”: Corresponding chances for the setup of new learning factories Idea Learning content Learning factory for digitalisation of global value streams

• Modelling and comparison of alternative configurations of the global production network • IT-based global production planning • Ramp up and scalable automation • Piracy and legal issues • Cultural differences, language, and market conditions • Currency and payment risks and internal resistance against global production

Learning factory for preparing existing staff on • Quality standards and processes a higher proportion of foreign workers • Supplier evaluation • Lean management • Key performance indicators as backbone for continuous improvement

1.2 Digitalisation of Value Streams and Artificial Intelligence Every manufacturing company has either already faced digital transformation or is actively facing it. All universities must therefore take this into account in their bachelor, master, and doctoral programs. Many companies have successfully completed the first steps of this journey, and there are important lessons to be learned from their experiences.30 However, no matter how much digital transformation has been achieved, the work is not yet done: Digital transformation is an ongoing process that requires constant 29 30

See Chap. 4. See Hilbert (2022).

10

1 Challenges for Future Production

commitment and focus on improvement. It is truly a journey for every company, but also for every person involved in engineering education. Digitalisation will help to create a dynamic and agile organisation.31 Gone are the days of creating an annual plan at the beginning of the year and then watching it slowly fall apart as market conditions change and user expectations diverge. Today, a business must be agile, constantly adapting to market needs, and responding to unexpected complexities. The “Industry 4.0” project envisages a factory of connected devices, where every product knows or even finds its way to completion.32 As a result, the role of humans in production systems can change if they are relieved of routine activities, while optimal decision-making is made possible based on extensive data. Artificial intelligence (AI) can be applied to production data to improve failure prediction and maintenance planning.33 This results in less costly maintenance for production lines. Many other applications and benefits of AI in manufacturing are possible, including more accurate demand forecasting and less material waste. Digitalisation and AI in manufacturing are quite complex challenges if these areas are to be trained in a learning factory: Why? A university institute may have the expertise in either manufacturing technologies or manufacturing processes but often does not have the expertise in all the digital domains. To solve this problem, an interdisciplinary team is needed that simultaneously looks at production from the point of view of: • mechanical engineering, • business management and, above all, • the current state of information technology. Figure 1.7 shows some examples of implemented Industry 4.0 concepts and use cases in the Process Learning Factory “Center for Industrial Productivity” (CiP)34 at the Technical University Darmstadt35 : • Components as information carriers: In order to achieve efficient and futureoriented production in the sense of Industry 4.0, the collection and processing of data generated during the value-added process is of particular importance. In addition to the integration of the necessary sensors into the production process, communication between all the systems and equipment involved is also necessary for the implementation of media-free, digital and, as a rule, automatic data acquisition. • Tool tracking and tracing: By integrating innovative sensor technology into the tool holder, the tool can be monitored, and the entire tool circuit can be networked.

See Del˙ioglu and Uysal (2022). See Promotorengruppe Kommunikation der Forschungsunion Wirtschaft—Wissenschaft (2013). 33 See Papadopoulos et al. (2022). 34 See Best Practice Example 34 in Chap. 11. 35 A detailed description of the implemented use cases can be found in Abele et al. (2015) and Kreß and Metternich (2022). 31 32

1.2 Digitalisation of Value Streams and Artificial Intelligence Predictive Quality

Digital Backbone

Product steers process

Milkrun 4.0

Energy Monitoring

Digital Shopfloor Management

11

Components as information carriers

Predictive Maintenance

Intelligent worker assistance systems

Fig. 1.7 Exemplary Industrie 4.0 concepts implemented in the Process Learning Factory CiP in Darmstadt

•

•

•

•

The track-and-trace system at the control level makes it possible to actively optimise route planning, inventory management, procurement, storage location and storage size. Condition and energy monitoring: Condition and energy monitoring uses data from production machines to provide a real-time picture of the quality or energy consumption of the production process. The quality of the process condition includes the control of product condition, process condition, and machine condition. Product controls the process: The product variant is defined by the customer using a product configurator; the information is stored directly on the component. Prior to assembly, the component uses RFID to call up a type-specific nonlinear assistant system for the respective operator, which enables the desired engine configuration to be built. Data generated during the process, such as assembly and bolt logs, is stored in the cloud and can be accessed via the data of the RFID sensor. In this way, the data is always available. Digital shopfloor management: In the context of the connected factory, workers are confronted with complex IT systems and autonomously operating machines. At the same time, they need to be flexible and creative problem-solvers. One tool to support workers in this process is shopfloor management, enabled by the real-time data that is now available. This serves as a central communication and collaboration platform for shopfloor workers in their daily tasks. Digital twin: A digital value stream map is used to obtain all relevant information about the process in real time. All relevant information flows along the entire value stream. The user-friendly visualisation and linkage of this data, which was previously collected and used separately, provides the basis for the rapid identification of potential improvements.

12

1 Challenges for Future Production

• Paperless quality assurance: A paperless, reliable, and automated quality assurance system is demonstrated in the manual assembly of the pneumatic cylinder. An electronic screwdriving station is only activated when the upstream quality control approves the component. The screwdriving station selects the appropriate screwdriving program based on the current variant, and a work instruction is displayed to the operator. During the assembly of the cylinder, additional process characteristics for quality detection, such as torque or yield points, are assigned to the identification number of the pneumatic cylinder being processed. Consistent documentation of product quality and test results throughout the process enables complete traceability at the product level. • Predictive Quality: Based on sensor data such as force, acceleration, sound or current consumption, predictions about product quality can be made by training machine learning (ML) algorithms. In a further stage of expansion, manufacturing systems automatically adjust their parameters based on ML prediction. This can save measurement effort and reduce defects. • Intelligent worker assistance systems: Assembly information is generated from the 3D CAD system and made available interactively for the assembly of small batch sizes. The implementation includes intelligent networking of all components of the assembly workplace, as well as systems for visual support and control of the assembly process. A bidirectional flow of information between the system and the operator is enabled. An important key technology, where digital technologies are the backbone, is additive manufacturing technology. Processes need to be developed and designed from CAD data creation through preprocessing, the additive manufacturing process itself and post-processing.36 This process chain requires technological knowledge and a wide range of additive manufacturing skills to fully integrate and exploit the potential of additive manufacturing. This technology can be seen as an ideal field of work to explain the digital process chain to students in a learning factory. Based on our experience, the main advantage will be: • manageable investment, especially for non-metal process chain, • much lower risk potential for students compared to a machining process, and • quick detection of the effects on the whole process chain in short cycles. Figure 1.8 illustrates a typical additive manufacturing process chain that can be part of a learning factory for additive manufacturing.37

36 37

See Vayre et al. (2012), Huang et al. (2013). E.g., it is part of the Best Practice Example 3 in Chap. 11.

1.2 Digitalisation of Value Streams and Artificial Intelligence

CAD Design

13

Support Generation / CAM Operations

Slicing

challenge for a learning factory: manage the interdisciplinarity of additive manufacturing Finished Product

Post Processing

Building Process

Fig. 1.8 Additive process chain changes the possibilities and requirements of manufacturing processes

The experience of the learning factories at the Technical University of Darmstadt38 has shown that learning environments are needed in which product engineers, IT specialists, and production workers can access and experience these key technologies together.

Current megatrend “digitalisation of value streams and artificial intelligence”: Corresponding chances for the setup of new learning factories Idea Learning content Digital shopfloor management with quality, productivity, and delivery KPIs

• Advantage paperless production • KPIs: what is behind this compass?

Additive manufacturing in a batch production with preprocess and post-processes

• Get deep insights into advantages, regarding cost structure, flexibility, and delivery time

AI in quality assurance, data analytics along the value chain

• Link all quality data coming from end of line inspection, random check, and customers and automated, link with historical data and generation of proposals

38

See Best Practice Examples 3, 10, 13, and 34 in Chap. 11.

14

1 Challenges for Future Production

1.3 Uncertainty and New Pandemics As the COVID-19 pandemic demonstrated, manufacturing agility is more important than ever. Supply chains have been disrupted, demand for different products has shifted dramatically, and many factories have been temporarily closed to protect workers and comply with government pandemic regulations.39 Indeed, the sheer scale of change and uncertainty facing businesses is at least unusual, if not unprecedented. A recent report by McKinsey & Company highlights three core operational areas that manufacturers need to focus on in terms of plant operations in response to the COVID crisis.40 These include. • protecting the workforce with appropriate social distancing measures, • managing risk to ensure business continuity, and • finding new ways to increase productivity, even with large parts of the workforce working remotely. This is hardly a new story, but in today’s age of disruption, robust analytics is crucial. Processes are becoming more complex, not less. To better coordinate between the shopfloor and the top floor, organisations need analytics that can help them gain data insights.41 “You can’t manage what you can’t measure.” This quote displays important insights into the uncertain world of manufacturing. Modern learning systems, like learning factories, should also be a platform for confronting management and employees with the scenario of new pandemics. The first steps start with simple hygiene measures, but more importantly continuous production should be simulated with a high proportion of work from home. The scenario of a new pandemic is only one aspect of uncertainty. Changes in the political, technological, economic, and environmental landscape—such as radical technological advances, data breaches, natural disasters, or new business regulations—can create business uncertainty. Recent cyber-attacks42 have shown that entire production plants can suddenly be shut down for two weeks and regular operations disrupted for several months. This growing risk will lead companies to replace traditional large-scale production in one location with decentralised, agile production units.43 These developments lead to increasing demands on the changeability and adaptability of companies, their production systems, and their employees.44 It is argued that investments in the lifelong competence development of employees are crucial for the flexibility and adaptability

39

See Bastas and Garza-Reyes (2022). See LaBerge et al. (2020). 41 See Longard et al. (2022). 42 See Duo et al. (2022). 43 Additive manufacturing technology in particular will contribute significantly to this development. 44 See Abele and Reinhart (2011), Westkämper and Zahn (2008), Arndt (2013). 40

1.4 Scarcity of Natural Resources and Circular Economy

15

of processes and organisations.45 Work integrated learning methods are needed to accelerate learning curves.46

Current megatrend “uncertainty and new pandemics”: Corresponding chances for the setup of new learning factories Learning content Idea The hygiene factory: anticipating a new pandemic

• Delivery and efficiency despite the pandemic • Maintaining productivity and quality despite new processes, new attendance rules and working from home

The super-agile factory

• Erratic variation of the production program, to which extend possible? • What are consequences for the equipment and the degree of automation? • Change in the work content for operative workers

1.4 Scarcity of Natural Resources and Circular Economy Demand for water, food, energy, land, and minerals is rising sharply, making natural resources increasingly scarce and expensive.47 As the world’s population grows and global economic prosperity increases, resource scarcity will continue to play a major role in production. The availability of natural resources such as energy, metals and other industrial materials is reaching critical levels. The growing demand for batteries and other components of electric vehicles, and the potential for shortages and supply risks are issues of public concern.48 Manufacturing companies need to manage these resources efficiently and sustainably to reduce environmental impact and remain competitive in the long term. By strategically integrating sustainable, future-oriented approaches, and technologies into their production, industrial companies can make more efficient use of natural resources. From sourcing to production, from distribution to disposal, recycling, and circular economy, there is scope for improved resource efficiency at every stage of industry.

45

See Wagner et al. (2010), Adolph et al. (2014). See Nixdorf et al. (2022). 47 See Abele and Reinhart (2011). 48 Lithium, cobalt, nickel, graphite, and copper are the minerals of particular concern. 46

16

1 Challenges for Future Production

Furthermore, innovation in the field of product design and the development of alternative materials are helping to alleviate competition for limited resources and generate added value both commercially and ecologically.49 How can a future-oriented curriculum in a modern learning system, like a learning factory, meet this challenge? Four main questions need to be considered: • What resource scarcity will most affect tomorrow’s production in our geographical region, in our industrial area? • How can a learning factory provide a better understanding of the complex issue compared to a lecture? • What specific shortages will a learning factory address (energy, materials, machinery)? • What will be the best learning content to prepare product development and production engineering for the reduction or shift of resources? In recent years, energy efficiency has become a topic of great interest in society, politics, and business. In particular, interdisciplinary aspects of energy efficiency have not yet been considered in industry, research, and education. For example, the aim of the ETA-Factory50 is to operate a model factory that integrates various interdisciplinary approaches to reducing energy consumption and CO2 emissions from industrial production processes.51 Figure 1.9 gives an overview of the objectives and solutions for more energyefficient production processes. Furthermore, in view of the energy transition and the associated challenge of a high share of wind and solar energy, the production and consumption of electrical energy must be coordinated in a timely manner. This can be achieved through innovative energy storage or through demand-side management, i.e., more flexible energy consumption. Therefore, innovative and adapted technologies for future industrial processes are required.52 Demand-side management in an industrial environment is also part of a learning program in the ETA-Factory at the Technical University of Darmstadt. The experts interviewed identified the following needs for education, training, and research in the field of limited natural resources, among others: • Learning environments capable of making energy-efficient production tangible, which is a challenge as energy flows are mostly invisible to the eye. • Research environments are needed in which innovative technologies and processes for efficient and flexible energy use can be developed, evaluated, and transferred to industry. • Raising awareness of resource and energy efficiency through the integration of these issues into curricula.

49

See Abele and Reinhart (2011). See PTW, TU Darmstadt (2017). 51 See Best Practice Example 10 in Chap. 10. 52 See BMBF (2018). 50

1.4 Scarcity of Natural Resources and Circular Economy

17

Today: Isolated optimization of different sub-systems of a factory Process chain

Building 25%

Machine

20%

30%

Savings
5

data processing (e.g., AI)

data management & visualization own location

scaleability

IT after SOP (PPS, ERP, MES) 3-5

traceability

network

organization

2-3

data acquisition

purely virtual (planning + execution

industrial robots

robots light weight robots

radio based technology

more than application

wearable robots

optical technology

Fig. 5.11 Learning factory morphology, dimension 4: setting, according to Tisch et al. (2015b), Tisch (2018), and Kreß et al. (2023)

• The factory environment can also be implemented as a purely virtual representation (learning factory in the broad sense).23 To combine both, physical and virtual environments, hybrid learning factories offer more possibilities.24 Furthermore, the factory setting can feature either full-sized equipment used in real factories25 or scaled-down versions of the equipment.26 The equipment can be physical or virtual, and a combination of life-sized and scaled-down versions 23

See for example Haghighi et al. (2014) and FBK (2015). See Sect. 4.3. 25 See, e.g., Abele et al. (2010). 26 See for example Festo Didactic (2016) and Kaluza et al. (2015). 24

virtual factory environment

physical learning environment

5.4 Learning Factory Morphology: Dimension 4 “Setting”

scaled down robot in LF, Festo Didactic

iFactory, IFF, Stuttgart and Festo Didactic

virtual robot learning factory, Festo Didactic

virtual learning factory (AFB), Festo Didactic

scaled-down factory environment

111

physical machining area, PTW, TU Darmstadt

physical assembly area, IFA, Hannover

intralogistics area, WPSCenter, BMW

ETA Factory, PTW, TU Darmstadt

virtual machining area, PTW, TU Darmstadt

virtual learning factory FBK, TU Kaiserslautern

virtual learning factory, McKinsey

virtual assembly area, IFA, Hannover

life-size factory environment

Fig. 5.12 Examples for physical, virtual, life-size, and scaled-down learning environments in learning factories based on Tisch (2018), from left to right and top to bottom pictures were taken from Festo Didactic (2017a, 2017b, 2017c), PTW, TU Darmstadt (2017a, 2017b), BMW (2015), Jäger et al. (2015), Hammer (2014), IFA (2017), FBK (2015), and Görke et al. (2017)

can be used in a learning factory. An example of this can be seen in Fig. 5.12, which showcases a selection of physical, virtual, full-sized, and scaled-down learning factories. By definition,27 learning factories include more than just a single workplace or a single machine. Thus, the mapping of learning factories can range from single manufacturing or assembly cells, over entire factories, to even to factory networks. The respective subordinated factory levels are also part of the learning factory setup.28 Furthermore, the flexibility and changeability of the learning environment is of great importance for learning factories. The used terms flexibility and changeability

27 28

See Sect. 4.3. The factory levels used in the morphology are according to Wiendahl et al. (2009).

112

5 The Variety of Learning Factory Concepts

in the context of learning factories are in line with the generally accepted distinction between the concepts29 : • Flexibility allows a rapid organised conversion of the factory environment within trainings (in reference to a planned learning path). • Changeability refers to the capability to adjust the training environment to various unexpected alterations or unexpected suggestions from the learners.30 The dimensions in respect of which the factory environment has to be flexible and changeable can be distinguished in product, process, organisation, and layout.31 In order to ensure the ability to change, analogous to real production systems, the learning factory environment needs special properties. These properties are referred to as change enablers.32 The primary change enablers are mobility, modularity, compatibility, scalability, and universality. With regard to IT support in learning factories, different IT systems can be distinguished depending on the relation to the production phase33 : • Before the start of production there are systems like CAD and CAM. • After the start of production there are systems like ERP and MES. • And after the production phase systems like CRM and PLM are used. To address learners’ prior knowledge, many learning factories consider different states, for example, one state with a lot of waste and one with less waste, or a non-digitised or digitised state. This leads to the fact that the mapped value stream for different learning modules must be adapted to the respective states. It is also possible in research projects to compare different states, for example, the impact of a particular technology. Meanwhile, a wide range of technologies have been integrated into learning factories, such as technologies for data acquisition, traceability, data processing, and so on. During the design of a learning factory, an additional question arises regarding the physical location of the learning factory. On the one hand, this can be a separate location; on the other hand, the learning factory can also be integrated into an existing factory or other building. Virtual learning factories require a digital location on a server or in a cloud. Also, it should be clarified which persons take the role of the operator: do the participants take this role, does it involve employed persons from the own organisation, do the trainers or paid personnel take this role. For lectures or group work, a meeting room is often used, which is either integrated into the shopfloor or structurally represents a separated room. The morphology also contains possible design elements for the automation pyramid, the ICT protocol, possible assistance systems, and component traceability.

29

See, e.g., Nyhuis et al. (2008). See Tisch (2018). 31 See Wiendahl et al. (2007). 32 According to Hernández Morales (2003). 33 See Tisch et al. (2015b). 30

5.5 Learning Factory Morphology: Dimension 5 “Product”

113

5.5 Learning Factory Morphology: Dimension 5 “Product” In dimension 5 “Product,” the characteristics of the products, which are produced within the simulated factory environments, are described. The complete morphological description of this dimension is shown in Fig. 5.13. The product forms an important aspect of the learning factory concept. The characteristics of the product and its variants must be adapted to the overarching concept of the learning factory, especially regarding the research and learning goals. The selection of the product has influences on • the degree of complexity of the learning factory scenarios and the duration required by the learners to get into the represented processes, • the material and personnel cost for operation and maintenance of the learning factory, and Design dimension 5: product #

design element

5.1 materiality

5.2 form of product

5.3 product origin

5.4 marketability

5.5 functionality

5.6

no. of different product

5.7 no. of variants

5.8

no. of components

5.9

further product use

5.10

weight of the product

characteristics material (physical product)

immaterial (service)

digital (data, software)

general cargo

bulk goods

flow products

development by participants (changing ideas)

own development

available on the market

partial development

available on the market but not available on the market didactically simplified didactically adapted product with limited functionality

functional product

1 2 3-4 >4 product products products products

without function/application, for demonstration only

flexible, developed by participants

1 2-4 5-20 >20 flexible, depending variant variants variants variants on participants 1 comp.

2-5 comp.

re-use/ recycling ≤ 1kg

external development

6-20 comp.

exhibition/ display

give-away

1 kg – 10 kg

determined by real orders

51-100 comp.

21-50 comp.

acceptance of real order

>100 comp. sale

10 kg – 25 kg

customizable

customizable

disposal

≥ 25 kg

physical 5.11 components

mechanical

electric

eletronic

digital

Fig. 5.13 Learning factory morphology, dimension 5: product, according to Tisch et al. (2015b), Tisch (2018) and Kreß et al. (2023)

114

5 The Variety of Learning Factory Concepts

budget / costs

Go-kart, wzl, Aachen Integrated Drilling Machine, Integrated Learning Factory, Bochum Desk set, IFF, Stuttgart slot car, imw, Vienna 3D-printer, Pilot Factory, Vienna Microcontroller, Festo Didactic, MPS LF

Single Acting Cylinder, Festo Didactic MPS LF

Helicopter, IFA, Hannover

Pneumatic Cylinder, PTW, Darmstadt

Bottle Cap, LPS, Bochum Give away, PTW, Darmstadt

next to reality

Fig. 5.14 Examples for learning factory products (Abele et al., 2017b)

• the possibilities of modelled value-adding processes.34 The products used in learning factories are fundamentally in most cases based or oriented on the products of real factories. In most cases, material products are used,35 whereby the use of intangible products (services) can also be observed, e.g., maintenance. Material products in general can be subdivided into general cargo, bulk goods, and flow products.36 Figure 5.14 exemplarily shows the range of products used in learning factories today. In contrast to the usual product development process,37 the product used in the learning factory is either selected specifically from the products available on the market38 or even specially developed for the use in a learning factory,39 see also Fig. 5.15.40 For products specifically created for learning factories, a distinction can be made between those that are commercially available but have been pedagogically simplified and those that are not available for purchase. Furthermore, learning factory products can also be categorised based on their functionality, ranging from fully functional products to didactically adapted products with reduced functionality, to products with no function. These products, specifically designed for learning factories, can be divided into proprietary creations by the operator, externally commissioned developments, or even developments created by learners during the learning sessions. In 34

See Tisch (2018). See learning factory in the narrow sense. 36 See Schenk et al. (2014). 37 See Fig. 5.15a. 38 See Fig. 5.15c. 39 See Fig. 5.15b. 40 See Metternich et al. (2013), Wagner et al. (2015), Tisch et al. (2015a), Tisch (2018). 35

5.5 Learning Factory Morphology: Dimension 5 “Product” Sequence Constraints

Needed Variants

Customer Needs

115

Production System Design & Operation A2

Product Design A1 Products Features

System Configuration Process Plan

Manufacturing Processes

(a) Traditional product design process: The traditional product development process shown is used for traditional product and production system design. The development of the product on the basis of customer requirements forms the starting point. Based on this, the production system and production operations are designed. For learning factories, this approach to product and production system development is not effective. Sequence Constraints Production System Configuration Scenarios

Potential Product Features

Production System Analysis B1

Product Design

Process Variants

B2

Manufacturing Processes (b) Possible learning factory product design process 1: Realizable production systems are analyzed based on constrains, different configurations and included manufacturing processes. Based on the predefined production system potential product features and finally a suiting learning factory product is defined. This approach reverses the traditional product development process.

Potential Product Features Candidate Product families

Pool of Features

Sequence Constraints Product Variants

Product Selection C1 Selection Criteria

Production System Design & Operations C2

Process Plan System Configuration

Manufacturing processes

(c) Possible learning factory product design process 2: First potential product features and product families are collected and based on defined selection criteria a suiting product for the learning factory concept is selected. Based on this, similar to the traditional product design process the production system is designed.

Fig. 5.15 Comparison of traditional product design process (a) and the product design process for learning factories (b, c), with changes inspired by Wagner et al. (2014), similar also in Abele et al. (2017a)

116

5 The Variety of Learning Factory Concepts

most learning factories, modular products are used due to cost considerations, so that individual components or as many as possible can be reused. A few learning factory operators sell the products produced during the learning sessions.41 In the Best Practice Examples shown in Chap. 11, several different concepts for the selection of a product can be identified, among other there are • an own developed fantasy product in the IFA-Learning Factory, see Best Practice Example 17, • own developed products that are produced in the learning factory for the market in the Demonstration Factory at WZL in Aachen, see Best Practice Example 6, also in the FlowFactory, see Best Practice Example 13, and • a product available on the market from an industrial company for example in the Process Learning Factory CiP, see Best Practice Example 34.

5.6 Learning Factory Morphology: Dimension 6 “Didactics” The dimension 6 “Didactics” are an integral part of learning factory concepts, which address one of the primary purposes of education and training, see Fig. 5.16.42 Regarding the didactical concept, the following questions are important43 : • What should be learned in terms of competence classes and different learning objectives? Four competence classes can be distinguished: technical and methodological, social and communication, personal and activity-oriented competences. Most often technical and methodological competences are addressed, e.g., the influence of technologies on production, or the application of lean methods. • How should be learned regarding the learning scenario, the degree of autonomy of the learners, the format of the learning modules and any standardisation, the embedding of the systematising elements into the learning module as well as the role of the trainer in this? • Where should be learned in relation to the type of learning environment or the communication channel? • How should we evaluate based on the evaluation levels and the type of evaluation instrument? Possible evaluation approaches are, e.g., the feedback of participants and methods to measure competences on the shopfloor. Methods for the evaluation are for example written knowledge tests, written reports, and practical exams. Moreover, different didactical extensions are possible to enrich the scope of the learning environment, e.g., with a simulation, temporal impacts of implemented 41

See as a rare example Kreimeier et al. (2014). See Abele et al. (2015). 43 See Tisch (2018). 42

5.6 Learning Factory Morphology: Dimension 6 “Didactics”

117

Design dimension 6: pedagogy # 6.1

6.2

design element competence class

characteristics technical and methodological social and communication competences competences personal competences

dimension of learning targets

learning 6.3 scenario strategy

activity-oriented competences

cognitive

instruction

affective

demonstration

closed scenario

greenfield (development of factory environment)

6.4

type of learning environment

6.5

communication channel

6.6

degree of autonomy

6.7

role of the trainer

6.8

type of learning activities

6.9

standardization of trainings

6.10

theoretical foundation

6.11

evaluation levels

feedback of participants

6.12

evaluation methods

written test

6.13

learning factory extensions

6.14

degree of personalization

6.15

participation capability

psychomotor

brownfield (improvement of existing factory environment)

onsite learning (in factory environment) instructed

remote connection (to the factory environment)

moderator

practical seminar workshop lab course

standardized learning modules prerequisite

self-determined/ selforganized

self-guided/ self-regulated

presenter

tutorial

open scenario

coach project work

competency measurement oral test

written report

case study participant personalization in-person participation

transfer to real factory

business scenario (e.g., product life cycle)

flipped classroom

modular learning modules

customized learning modules

in advance (en bloc)

knowledge test

instructor

alternating with practical parts

based on demand

economic impact of training

return on training

oral presentation

practical exam

role play

360-degree assessment

simulation group personalization

hybrid participation

none none none

remote participation

Fig. 5.16 Learning factory morphology, dimension 6: didactics, according to Tisch et al. (2015a) and Kreß et al. (2023)

solutions can be shown. With the concept of personalised learning, it is possible to address individual learners in a subject-oriented way, which is possible, for example, using virtual learning environments. The setting can also be designed to be either exclusively in-person, hybrid, or virtual.

118

5 The Variety of Learning Factory Concepts

5.7 Learning Factory Morphology: Dimension 7 “Metrics” Dimension 7 “Metrics” presents quantitative characteristics of learning factory concepts, such as the number of participants per learning module, the average duration of individual learning modules, or the available learning area. The different parameters provide a rough framework for the learning factory concepts related to selected quantitative figures. Individual parameters could also be assigned to the other dimensions of the morphology. The complete morphological description of dimension 7 “Learning Factory Metrics” is shown in Fig. 5.17.

5.8 Learning Factory Morphology: Dimension 8 “Research” In dimension 8 “Research,” the possible purpose of research is detailed, shown in Fig. 5.18. Distinct research topics can be identified in learning factories, analogous to the possible learning content in dimension 2 “target and purpose”, e.g., lean management, energy, and resource efficiency, and so on. The research object itself can be distinguished between technologies, processes, methods, management tools, materials, didactical methods, or fundamental discoveries. Involved persons in the research process range from top grade researchers (e.g., full professors) to first stage researcher (e.g., Ph.D. students). A distinction can be made between whether the learning factory serves as a research object or research enabler in the sense of a research laboratory: • On the one hand, the use of a learning factory as a research object involves exploring and improving the concept of the learning factory itself. This could involve developing new designs for learning factories or studying the growth and adoption of these facilities. • On the other hand, when a learning factory is used as a research enabler, it provides a platform to conduct production-related research, e.g., about the effectivity of digital assistance systems.

5.9 Database for Learning Factories Based on the dimensions and the first version of the morphology, a database for the collection of different learning factory concepts has been established in course of the CIRP CWG on learning factories44 in order to create an overview on existing learning factory concepts around the globe, to classify existing approaches systematically, 44

See Mavrikios et al. (2017).

5.9 Database for Learning Factories

119

Design dimension 7: metrics # design element characteristics no. of 7.1 participants per 1-5 6-10 10-15 16-20 >20 learning module no. of stand. >10 modularized 7.2 learning 1 2-4 5-10 modules no. of integrated undergraduate program graduate program 7.3 learning modumodu>10 >10 1 2-4 5-10 1 2-4 5-10 modules larized larized aver. duration of ≤ 0.5 0,5 days – 1 day – 2 days – 2 days – 5 days – 10 days – > 20 days 7.4 a learning 1 days 5 days 5 days 10 days 20 days day 2 days module no. of highly 201-500 501-1000 >1000 7.5 qualified people < 50 50-200 trained per year 7.6 size of LF FTE in LF 7.7 (trainers, operators etc.)

< 50 sqm

50 sqm 100 sqm

100 sqm 300 sqm

300 sqm 500 sqm

500 sqm 1000 sqm

> 1000 sqm

1 million

25

< 10,000 $

10,000 $ 1 million $

1 million $ 20 million $

> 20 million

Fig. 5.17 Learning factory morphology, dimension 7: learning factory metrics, according to Tisch et al. (2015a) and Kreß et al. (2023)

and maybe identify certain characteristics of different learning factory types. The structure of the database consists of three main entities: the user, the associated facility, and the application scenarios45 :

45

An entity relationship diagram describing those three entities and its relations can be found in Mavrikios et al. (2017).

120

5 The Variety of Learning Factory Concepts

Design dimension 8: research design # element

characteristics

lean energy & resource industrial global Industry 4.0 Industry 5.0 management efficiency engineering production 8.1

research topics

product creation circular sustainability/ business artificial smart information process economy social impact engineering intelligence logistics object recognition

engineering education

digital twin/ service twin/ human twin

factory planning

additive manufacturing

new new new new didactical fundamental management materials methods discoveries tools

8.2

research object

new new new technologies processes methods

8.3

involved persons in the research process

top grade researcher senior researcher (e.g., recognized researcher (e.g., full professor, associate professor, (e.g., assistant director of research) senior researcher) professor, post-doc)

8.4 research scope

LF as research object

workers participation

first stage researcher (e.g., PhD student)

LF as research enabler

Fig. 5.18 Learning factory morphology, dimension 8: research, according to Kreß et al. (2023)

• User: The user can log in to the database, view, and create new entries for their own learning factory, and newly created facilities are associated with the user. • Associated facility: The associated facility describes the structure and use of the “learning factory concept” where the user can create various application scenarios and associate them with individual facilities. The user can also define and associate learning factory equipment to each facility. • Application scenario: Various application scenarios can be created by the user that consider the structure and use of specific learning factories, which can vary widely depending on the scenario, such as research, teaching, further education, and others. Each application scenario created by the user includes a set of the design dimensions of the morphology with a video storage feature. The database is accessible via a web application called “Learning Factories Morphologies application” that enables editing and visualising data. The technical implementation is described in detail in Mavrikios et al. (2017). The application is available under: http://syrios.mech.upatras.gr/LF/ (LMS, 2015).

At the top of the website the user can navigate through all the functions of the application, see also Fig. 5.19. The functions of the application are shortly described in the following: • Home: In the home screen information on already inserted learning factories are shown. The user can see the information on the specific learning factory by selecting the learning factory name directly in the dropdown menu on the left entitled “Facility:” with applicable filters.

5.9 Database for Learning Factories

121

Fig. 5.19 Screenshot of the “learning factories morphology web application” (LMS, 2015)

• Browse: The home screen displays information on already inserted learning factories and allows users to view information on a specific learning factory by selecting the name from a dropdown menu on the left, with the option to filter by country or application scenario of interest. Users can also switch from visualised graph to a printable version of the information, which shows all values of the selected learning factory in a table format. • Map: The map function of the application gives a geographic overview on the locations of the learning factories registered in the database. A screenshot of the current status is seen in Fig. 5.19. • Help: The help button leads to the user’s manual that gives general information on the application and shortly describes the functionalities of the web application. • Login: At the login screen, a registered user is able to log in in order to edit existing or create new learning factory facilities and learning factory application scenarios. • Register: In order to register for the Learning Factory Morphology Application, the user has to fill in name, organisation, email-address, and a short description of the own learning factory. • Search: Terms of interest can be typed in the “search” field. The application displays learning factories based on the search. Currently, twenty learning factories from nine countries are registered in the database. Since the use of the application increases greatly with the increasing number of users and consequently more characterised facilities, all learning factory operators should be encouraged to register and characterise their current learning factory

122

5 The Variety of Learning Factory Concepts

Fig. 5.20 Screenshot of the map function of the “learning factories morphology web application” (LMS, 2015)

concepts using the Learning Factory Morphology Application: http://syrios.mech. upatras.gr/LF/register (Fig. 5.20).

5.10 Wrap-Up of This Chapter This chapter provides a comprehensive presentation of the learning factory concept’s diversity from existing learning factories across the globe. The existing learning factories are shown to exhibit a wide range of characteristics along eight dimensions of the learning factory system. The morphological model is employed within these dimensions to refine the systematic understanding of learning factory systems. Dimension 1: The operational model of a learning factory is crucial to maintaining ongoing competence development and innovation. Most learning factories are run by academic organisations or profit-oriented businesses, and variations are also operated in vocational schools. The sustainability of a learning factory requires economic or financial, content-related, or thematic, and sustainability of the learning factory concept dimensions. To sustain operation, financial factors such as securing funding and generating value for partners are important. Financing methods include internal funding, public funding, and seeking investment from third-party sources. The most common financing types for universities are internal funding, cooperation with industry, and publicly funded projects. For industrial operators, the most common types are company-internal funding and course fees for trainings. Different combinations of financing methods can be used to secure financing for the learning factory’s non-recurring and operating costs.

5.10 Wrap-Up of This Chapter

123

Dimension 2: Learning factories have three main purposes: educating students, training industrial personnel, and conducting production-related research. They can also serve secondary purposes such as demonstration, technology testing and transfer, industrial production, and positive public image. In recent years, demonstration and technology transfer have become more significant, especially in relation to digitalisation and Industrie 4.0. Learning factories can address different target groups with varying levels of technical knowledge and company affiliation. They can also focus on specific industrial branches such as automotive, pharmaceutical, or textile. The objectives of learning factories vary depending on the learning content. Dimension 3: In the “Process” dimension of a learning factory, the production processes are specified in more detail. The boundaries of the system are defined by identifying four production-related life cycles: product, factory, order, and technology. The mapped life cycle phases depend on the intended learning content, and the production processes are described in terms of material flow, process type, production organisation, degree of automation, and production processes and technologies. The concept of the learning factory is versatile, and indirect functions are also identified. Dimension 4: The “Setting” dimension in learning factories refers to the characteristics of the learning environment, which can be physical, virtual, or a combination of both. The flexibility and changeability of the learning environment is important, and special properties called “change enablers” are needed to ensure the ability to change. IT systems are used in different phases of production. Learners’ prior knowledge is addressed by considering different states, and a wide range of technologies have been integrated into learning factories. The physical location of the learning factory and the role of the operator should also be considered during design. Possible design elements for automation, ICT protocol, assistance systems, and component traceability are also included in the morphology. Dimension 5: In the learning factory concept, the characteristics of the products used in simulated factory environments should be described. The product selection influences the complexity of learning scenarios, cost of operation and maintenance, and possibilities of modelled value-adding processes. Learning factory products are usually based on real factory products but can be specifically developed for learning purposes. They can be fully functional or didactically adapted with reduced functionality and can be proprietary creations, externally commissioned, or developed by learners during sessions. Modular products are often used for cost considerations, and some operators sell the products produced during learning sessions. Best practices for product selection include using own-developed fantasy products, producing own-developed products for the market, or using products available on the market from industrial companies. Dimension 6: The dimension 6 “Didactics” in learning factories involve determining what competences and learning objectives should be addressed, how learners should

124

5 The Variety of Learning Factory Concepts

be taught, where they should learn, and how to evaluate their progress. Four competence classes can be distinguished: technical and methodological, social and communication, personal and activity-oriented competences. Different didactical extensions are also possible, such as personalised learning and simulations. The learning setting can be in-person, hybrid, or virtual. Dimension 7: Dimension 7, “Metrics,” deals with quantitative aspects of learning factory concepts, including the number of participants, the duration of individual learning modules, and the learning area available. Dimension 8: Dimension 8 “Research” in learning factory concepts deals with the possible research purposes, topics, and involved individuals. The research topics can be similar to the learning content in dimension 2. The learning factory can serve as a research object, which means exploring and improving the concept of the learning factory itself, or as a research enabler, which provides a platform to conduct production-related research. Involved individuals range from top grade researchers to first stage researchers. A database for collecting different learning factory concepts has been established based on the morphology, consisting of three main entities: the user, the associated facility, and the application scenarios, which is accessible through a web application. The application allows users to view and edit information on learning factories, create and associate application scenarios, and visualise data in different formats.

References Abele, E., Cachay, J., Heb, A., & Scheibner, S. (Eds.). (2011). 1st Conference on Learning Factories, Darmstadt. Institute of Production Management, Technology and Machine Tools (PTW). Abele, E., Chryssolouris, G., Sihn, W., Metternich, J., ElMaraghy, H. A., Seliger, G., Sivard, G., ElMaraghy, W., Hummel, V., Tisch, M., & Seifermann, S. (2017a). Learning factories for future oriented research and education in manufacturing. CIRP Annals—Manufacturing Technology, 66(2), 803–826. Abele, E., Chryssolouris, G., Sihn, W., Metternich, J., ElMaraghy, H. A., Seliger, G., Sivard, G., ElMaraghy, W., Hummel, V., Tisch, M., & Seifermann, S. (2017b, August). Learning factories for future oriented research and education in manufacturing. Presentation CIRP STC-O KeynotePaper, GA 2017. CIRP General Assembly 2017, Lugano, Switzerland. Abele, E., Metternich, J., Tisch, M., Chryssolouris, G., Sihn, W., ElMaraghy, H. A., Hummel, V., & Ranz, F. (2015). Learning factories for research, education, and training. In 5th CIRPSponsored Conference on Learning Factories. Procedia CIRP, 32, 1–6. https://doi.org/10.1016/ j.procir.2015.02.187 Abele, E., Tenberg, R., Wennemer, J., & Cachay, J. (2010). Kompetenzentwicklung in Lernfabriken für die Produktion. Zeitschrift für wirtschaftlichen Fabrikbetrieb (ZWF), 105(10), 909–913. Bauernhansl, T., Siegert, J., Groß, E., Dinkelmann, M., Abele, E., Metternich, J., & Reinhart, G. (2014). Kompetenzbildung in der Wertschöpfung. Werkstattstechnik Online: wt, 104(11/12), 776–780. BMW. (2015). WPS-Center der BMW Group: Lernwerkstatt auf 1500 m2 Fläche. Retrieved from https://www.press.bmwgroup.com/deutschland/pressDetail.html?title=wps-center-der-bmwgroup-lernwerkstatt-auf-1500-m%C2%B2-fl%C3%A4che&outputChannelId=7&id=T02145 44DE&left_menu_item=node__5247

References

125

Dürr, P. (2013). Modell zur Bewertung der Effizienz der IT-Unterstützung im Auftragsabwicklungsprozess von produzierenden KMU. Univ., Diss., Stuttgart. Stuttgarter Beiträge zur Produktionsforschung: Vol. 16. Stuttgart: Fraunhofer Verlag. Erol, S., Jäger, A., Hold, P., Ott, K., & Sihn, W. (2016). Tangible industry 4.0: A scenariobased approach to learning for the future of production. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 13–18. FBK, T. U. K. (2015). Virtuelle Lernfabrik. Retrieved from http://www.wgp.de/fileadmin/Produk tionsakademie/wgp-KL2.pdf Festo Didactic. (2015). Festo: Schulterschluss für die Fachkräfte der Zukunft: Kooperation mit der Gewerblichen Schule Göppingen—Lernfabrik eingeweiht. Esslingen. Retrieved from https:// www.festo.com/net/SupportPortal/Files/354477/CC_S_1_15_Kooperation.rtf Festo Didactic. (2016). MPS®: The Modular Production System: Das Konzept im Detail. Retrieved from http://www.festo-didactic.com/de-de/lernsysteme/lernfabriken,cim-fms-systeme/cp-fac tory/mps-transfer-factory-das-konzept-im-detail.htm?fbid=ZGUuZGUuNTQ0LjEzLjE4LjEy OTMuNzY0Mw Festo Didactic. (2017a). iFactory: Innovative training factory: For advanced Industrial Engineering (aIE). Retrieved from http://www.festo-didactic.com/int-en/news/ifactory-innovative-trainingfactory.htm?fbid=aW50LmVuLjU1Ny4xNy4xNi4yOTUy Festo Didactic. (2017b). Individuelle Lösungen: AFB factory Hybride Produktion. Retrieved from http://www.festo-didactic.com/de-de/lernsysteme/lernfabriken,cim-fms-systeme/afb-factoryhybride-produktion/individuelle-loesungen-afb-factory-hybride-produktion.htm?fbid=ZGU uZGUuNTQ0LjEzLjE4Ljk5Ny43Nzc4 Festo Didactic. (2017c). Robot Vision Cell: Trends der Robotik im Fokus. Retrieved from http:// www.festo-didactic.com/de-de/lernsysteme/lernfabriken,cim-fms-systeme/robot-vision-cell/ robot-vision-cell-trends-der-robotik-im-fokus.htm?fbid=ZGUuZGUuNTQ0LjEzLjE4LjEy NzEuNzYzMw Görke, M., Bellmann, V., Busch, J., & Nyhuis, P. (2017). Employee qualification by digital learning games. Procedia Manufacturing, 9, 229–237. https://doi.org/10.1016/j.promfg.2017.04.040 Grundig, C.-G. (2015). Fabrikplanung [Elektronische Ressource]: Planungssystematik—Methoden—Anwendungen Claus-Gerold Grundig. Carl Hanser. Haghighi, A., Shariatzadeh, N., Sivard, G., Lundholm, T., & Eriksson, Y. (2014). Digital learning factories: Conceptualization, review and discussion. In The 6th Swedish Production Symposium (SPS14). Retrieved from http://conferences.chalmers.se/index.php/SPS/SPS14/paper/viewFile/ 1729/401 Hammer, M. (2014, August). Making operational transformations successful with experiential learning. In CIRP Collaborative Working Group—Learning Factories for Future Oriented Research and Education in Manufacturing. CIRP General Assembly, Nantes, France. Hernández Morales, R. (2003). Systematik und Wandlungsfähigkeit in der Fabrikplanung (Als Ms. gedr). Fortschritt-Berichte/VDI. Reihe 16, Technik und Wirtschaft: Nr. 149. VDI-Verl. Herrmann, S., & Stäudel, T. (2014). Learn and experience VPS in the BMW learning factory. In 4th Conference on Learning Factories, Stockholm, Sweden (pp. 1–18). IFA. (2017). IFA Lernfabrik. Retrieved from http://www.ifa-lernfabrik.de/ Jäger, A., Sihn, W., Hummel, V., & Ranz, F. (2015, August). Physical and digital learning factories—Differentiation and collaboration. In CIRP CWG “Learning Factories for Future Oriented Research and Education in Manufacturing”, Kapstadt, Südafrika. Kaluza, A., Juraschek, M., Neef, B., Pittschellis, R., Posselt, G., Thiede, S., & Herrmann, C. (2015). Designing learning environments for energy efficiency through model scale production processes. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 41–46. https://doi.org/10.1016/j.procir.2015.02.114 Kreß, A., Hummel, V., Ahmad, R., Hulla, M., Quadrini, W., Callupe, M., Gärtner, Q., Weyand, A., Barth, J., Riemann, T., Fumagalli, L., Ramsauer, C., & Metternich, J. (2023). Revision of the learning factory morphology. In Proceedings of the 13th Conference on Learning Factories

126

5 The Variety of Learning Factory Concepts

(CLF 2023). Available at SSRN: https://ssrn.com/abstract=4458050 or https://doi.org/10.2139/ ssrn.4458050 Kreimeier, D. (Ed.). (2015). 5th Conference on Learning Factories. Procedia CIRP, 32. Kreimeier, D., Morlock, F., Prinz, C., Krückhans, B., Bakir, D. C., & Meier, H. (2014). Holistic learning factories—A concept to train lean management, resource efficiency as well as management and organization improvement skills. In 47th CIRP Conference on Manufacturing Systems. Procedia CIRP, 17, 184–188. KTH. (2014). 4th Conference on Learning Factories. Retrieved from https://www.kth.se/en/itm/ inst/iip/4clf/presentations/presentations-1.459320 Küsters, D., Praß, N., & Gloy, Y.-S. (2017). Textile learning factory 4.0—Preparing Germany’s textile industry for the digital future. Procedia Manufacturing, 9, 214–221. https://doi.org/10. 1016/j.promfg.2017.04.035 LMS. (2015). Learning Factory Morphology Application. Retrieved from http://syrios.mech.upa tras.gr/LF/ Martinsen, K. (Ed.). (2016). 6th CIRP Conference on Learning Factories. Procedia CIRP, 54. Mavrikios, D., Sipsas, K., Smparounis, K., Rentzos, L., & Chryssolouris, G. (2017). A web-based application for classifying teaching and learning factories. In 7th CIRP-Sponsored Conference on Learning Factories. Procedia Manufacturing. Metternich, J., Abele, E., & Tisch, M. (2013). Current activities and future challenges of the process learning factory CiP. In G. Reinhart, P. Schnellbach, C. Hilgert, & S. L. Frank (Eds.), 3rd Conference on Learning Factories, Munich, May 7, 2013 (pp. 94–107). Augsburg. Metternich, J., & Glass, R. (Eds.). (2017). 7th Conference on Learning Factories, CLF 2017. Procedia Manufacturing, 9. Micheu, H.-J., & Kleindienst, M. (2014). Lernfabrik zur praxisorientierten Wissensvermittlung: Moderne Ausbildung im Bereich Maschinenbau und Wirtschaftswissenschaften. Zeitschrift für wirtschaftlichen Fabrikbetrieb (ZWF), 109(6), 403–407. Nyhuis, P., Reinhart, G., & Abele, E. (Eds.). (2008). Wandlungsfähige Produktionssysteme: Heute die Industrie von morgen gestalten. PZH Produktionstechnisches Zentrum. Oberthuer, C. (2013). Integration of process simulations into the CIP of energy efficiency at Daimler trucks. In G. Reinhart, P. Schnellbach, C. Hilgert, & S. L. Frank (Eds.), 3rd Conference on Learning Factories, Munich, May 7, 2013 (pp. 38–47). Augsburg. Plorin, D. (2016). Gestaltung und Evaluation eines Referenzmodells zur Realisierung von Lernfabriken im Objektbereich der Fabrikplanung und des Fabrikbetriebes. Dissertation, Chemnitz. Wissenschaftliche Schriftenreihe des Instituts für Betriebswissenschaften und Fabriksysteme: Heft 120. Chemnitz: Techn. Univ. Inst. für Betriebswiss. und Fabriksysteme. Porter, M. E. (2008). Competitive advantage: Creating and sustaining superior performance (2nd ed.). Free Press. PTW, TU Darmstadt. (2017a). Prozesslernfabrik CiP: Der Weg zur operativen Exzellenz. Retrieved from http://www.prozesslernfabrik.de/ PTW, TU Darmstadt. (2017b). Welcome to ETA-Factory: The energy efficient model factory of the future. Retrieved from http://www.eta-fabrik.tu-darmstadt.de/eta/index.en.jsp Reinhart, G., Schnellbach, P., Hilgert, C., & Frank, S. L. (Eds.). (2013). 3rd Conference on Learning Factories, Munich, May 7, 2013. Augsburg. Riffelmacher, P. (2013). Konzeption einer Lernfabrik für die variantenreiche Montage. Dissertation, Stuttgart. Stuttgarter Beiträge zur Produktionsforschung (Vol. 15). Stuttgart: Fraunhofer Verlag. Rybski, C., & Jochem, R. (2016). Benefits of a learning factory in the context of lean management for the pharmaceutical industry. Procedia CIRP, 54, 31–34. https://doi.org/10.1016/j.procir.2016. 05.106 Schenk, M., Wirth, S., & Müller, E. (2014). Fabrikplanung und Fabrikbetrieb: Methoden für die wandlungsfähige, vernetzte und ressourceneffiziente Fabrik (2., vollst. überarb. und erw. Aufl.). VDI-Buch. Springer-Vieweg. Schuh, G. (Ed.). (2006). Produktionsplanung und -steuerung: Grundlagen, Gestaltung und Konzepte (3., völlig neu bearbeitete Auflage). VDI-Buch. Springer.

References

127

Schuh, G., Gartzen, T., Rodenhauser, T., & Marks, A. (2015, July). Promoting work-based learning through industry 4.0. In 5th Conference on Learning Factories, Bochum, Germany. Sihn, W., & Jäger, A. (Eds.). (2012). 2nd Conference on Learning Factories—Competitive Production in Europe Through Education and Training. Tisch, M. (2018). Modellbasierte Methodik zur kompetenzorientierten Gestaltung von Lernfabriken für die schlanke Produktion. Dissertation, Darmstadt. Aachen: Shaker. Tisch, M., Hertle, C., Abele, E., Metternich, J., & Tenberg, R. (2015a). Learning factory design: A competency-oriented approach integrating three design levels. International Journal of Computer Integrated Manufacturing, 29(12), 1355–1375. https://doi.org/10.1080/0951192X. 2015.1033017 Tisch, M., Ranz, F., Abele, E., Metternich, J., & Hummel, V. (2015b). Learning factory morphology: Study on form and structure of an innovative learning approach in the manufacturing domain. TOJET, July 2015(Special Issue 2 for International Conference on New Horizons in Education 2015), 356–363. Tisch, M., & Metternich, J. (2017). Potentials and limits of learning factories in research, innovation transfer, education, and training. In 7th CIRP-Sponsored Conference on Learning Factories. Procedia Manufacturing. Umeda, Y., Takata, S., Kimura, F., Tomiyama, T., Sutherland, J. W., Kara, S., Herrmann, C., & Duflou, J. R. (2012). Toward integrated product and process life cycle planning—An environmental perspective. CIRP Annals—Manufacturing Technology, 61, 681–702. Wagner, U., AlGeddawy, T., ElMaraghy, H. A., & Müller, E. (2014). Product family design for changeable learning factories. In 47th CIRP Conference on Manufacturing Systems. Procedia CIRP, 17, 195–200. https://doi.org/10.1016/j.procir.2014.01.119 Wagner, U., AlGeddawy, T., ElMaraghy, H. A., & Müller, E. (2015). Developing products for changeable learning factories. CIRP Journal of Manufacturing Science and Technology, 9, 146– 158. Wank, A., Adolph, S., Anokhin, O., Arndt, A., Anderl, R., & Metternich, J. (2016). Using a learning factory approach to transfer Industrie 4.0 approaches to small- and medium-sized enterprises. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 89–94. https:// doi.org/10.1016/j.procir.2016.05.068 Werz, F. (2012). Excellent qualified and trained employees: The key for successful implementation of lean production. In W. Sihn & A. Jäger (Eds.), 2nd Conference on Learning Factories— Competitive Production in Europe Through Education and Training (pp. 106–123). Westkämper, E. (2006). Einführung in die Organisation der Produktion. Springer-Lehrbuch. Springer. Westkämper, E., Constantinescu, C., & Hummel, V. (2006). New paradigm in manufacturing engineering: Factory life cycle. Production Engineering, 13(1), 143–146. Wiendahl, H.-P., ElMaraghy, H. A., Nyhuis, P., Zäh, M. F., Wiendahl, H.-H., Duffie, N., & Brieke, M. (2007). Changeable manufacturing—Classification, design and operation. CIRP Annals— Manufacturing Technology, 56(2), 783–809. https://doi.org/10.1016/j.cirp.2007.10.003 Wiendahl, H.-P., Reichardt, J., & Nyhuis, P. (2009). Handbuch Fabrikplanung: Konzept, Gestaltung und Umsetzung wandlungsfähiger Produktionsstätten. Carl Hanser. Zinn, B. (2014). Lernen in aufwendigen technischen Real-Lernumgebungen. Die Berufsbildende Schule, 66(1), 23–26.

Chapter 6

The Life Cycle of Learning Factories for Competence Development

A variety of models and other approaches in the field of learning factories have been developed and published since learning factories first emerged. The current approaches to the design, operation, and implementation of learning factories are classified into the following categories1 : • • • • • •

description of single learning factories or learning modules (use cases), description models of the learning factory system,2 methods for the design of learning factories,3 methods for the design of learning modules,4 didactic-medial design of learning arrangements,5 success measurements as well as methods for success measurement and evaluation.6

This chapter deals with planning, designing, evaluating, and improving competence development in learning factories referring among others to the approaches and models named above. The structure of the chapter can be explained along the learning factory life cycle, see also Fig. 6.1.

1

See Tisch (2018). See e.g. Steffen et al. (2013b) and Tisch et al. (2015c). 3 See e.g. Reiner (2009), Tisch et al. (2015a) and Kaluza et al. (2015). 4 See e.g. Enke et al. (2015), Abele et al. (2015) and Plorin (2016). 5 See e.g. Pittschellis (2015) and Tvenge et al. (2016). 6 See e.g. Cachay et al. (2012), Tisch et al. (2014, 2015b) and Enke et al. (2017a). 2

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 E. Abele et al., Learning Factories, https://doi.org/10.1007/978-3-031-46428-7_6

129

130

6 The Life Cycle of Learning Factories for Competence Development Market / need / problem

Business potentials / goals

Chapter 6.1: Learning factory planning and design

Learning factory development

Learning factory built-up

Sales — acquisition

Requirements / Goals

Product tracking / monitoring

Learning factory planning

Use of learning factory / trainings (Use / maintenance) Learning factory remodeling / recycling

Chapter 6.2: Learning factory built-up, sales, and acquisition (depending on the business model) Chapter 6.3: Learning factory operation Chapter 6.4: evaluation, and improvement Chapter 6.5: Learning factory remodeling

Fig. 6.1 Learning factory life cycle (Tisch & Metternich, 2017)

6.1 Learning Factory Planning and Design 6.1.1 Overview Planning and Design Approaches In the planning and design phase, learning factory systems (or subsystems) are developed from two perspectives, while a combination of the two perspectives is also recognised: 1. From a factory perspective: Learning factories can be interpreted as an idealised representation of real production environments.7 A variety of design approaches take up this perspective.8 Only a few approaches focus exclusively on this perspective.9 2. From a learning perspective: Learning factories can be interpreted as a complex learning environment.10 A variety of approaches can be identified that design learning factories as complex learning environments.11

7

Tisch et al. (2015a). See e.g. Tisch et al. (2015a), Plorin (2016), Dinkelmann (2016), Kaluza et al. (2015) or Rentzos et al. (2014). 9 As representatives of this Kaluza et al. (2015), Gebbe et al. (2015) and Rentzos et al. (2014) can be named. 10 Tisch et al. (2015a). 11 See e.g. Reiner (2009), Riffelmacher (2013), Tisch et al. (2013, 2015a), Abele et al. (2015) and Plorin (2016). 8

6.1 Learning Factory Planning and Design

131

3. From the combined perspective: By combining the “factory” and “learning” perspectives, learning factories are considered as learning environments that address issues of sociotechnical systems and that integrate a model of a sociotechnical system. For the design and configuration of the entire learning factory concept (including the factory environment, the learning modules, and the learning situations), didactical, social, and technological implications have to be kept in mind.12 In recent years, several linear, sequential approaches have been proposed, which are similar to product life phases of well-known VDI guidelines13 : Reiner (2009) describes the integration of the learning factory concept into a lean transformation and uses a generic three-step approach for learning factory design: “requirements,” “development and setup,” and “operation and use.” Likewise, Doch et al. (2015) use similar phases: “analysis of requirements,” “conceptualisation,” and “design and implementation.” Riffelmacher (2013) describes a learning factory concept for the high-mix assembly using a physical as well as a virtual learning environment, while based on this specific development no general design process for learning factories is derived. Doch et al. (2015) show a three-step procedure (needs analysis, conception, design, and implementation) for the development of learning modules in learning factories and design of a learning factory for the pharmaceutical industry in which tablet production is depicted. However, the design of the socio-technical infrastructure is not covered. Dinkelmann (2016) describes a method for the participation of employees in the change management of multi-variant series production using learning factories. Within this change management approach, problems that are actually occurring in industrial companies are mapped in learning factories, where they are resolved by employees with the aim of transferring motivation and solution ideas into the daily work environment. Plorin (2016) describes a design approach based on a reference model, consisting of structure, design, integration, and quality model, for the realisation of learning factories for factory planning and factory operation. The comprehensive approach addresses a broad field of applications, which particularly describes the didacticmedial arrangement of the learning arrangements and the design of individual learning modules. With regard to the design of the learning environment, the model particularly addresses a learning module-induced adaptation of existing environments. In the approach developed first in Darmstadt by Tisch et al. (2015a, 2015b, 2015c) and then followed by the IALF,14 the intended competences crucially influence the learning factory design process, since domain-specific competence development is

12

Tisch et al. (2015a). See e.g. VDI (1993). 14 Tisch et al. (2015a). 13

132

6 The Life Cycle of Learning Factories for Competence Development

in general seen as key objective of learning factories.15 The competence-oriented learning factory design follows a holistic approach in two major transformations on three design levels.16 The IALF learning factory design approach is presented in the following Sect. 6.1.2 in detail. Other approaches focus on parts of the learning factory design. Wagner et al. (2014, 2015) describe a product development approach for learning factories, arguing that, compared to traditional product development, learning factory products must be designed to complement the learning factory concept. Kaluza et al. (2015) describe a procedure for creating scaled-down learning environments using the example of energy efficiency. The focus is on the exact mapping of the technical system to ensure that the scaled-down environment has the same behaviour or characteristics of the real factory environment. Gebbe et al. (2015) mention that existing design approaches focus on learning factories used for education and training, while a systematic procedure for the design of demonstration and research factories is not available. Tvenge et al. (2016) propose opening up the learning factory approach to a broader modern workplace learning framework17 that enables continuous, autonomous social learning on demand and on the go. Baena et al. (2017) describe a four-step approach in their publication (product, value chain, use of ICT technologies, integration of the Industry 4.0 approach). This emphasises the use of new technologies. Karre et al. (2017) present a five-step design approach to extend an existing learning factory with the Industrie 4.0 approach. Küsters (2018) describes another design approach for learning factories, with which the learning factory Digital Capability Center Aachen was designed. The approach focuses on learning factories for the digital transformation of production and refers to the approach presented by the IALF. The design of learning factories takes place in five steps: 1. First, the goals and scope are defined. Here, the operating organisation and the offer of the learning factory are specified, and the target group is described. The result is a list of requirements based on use cases. 2. The training content and the curriculum of the learning factory are derived by designing a business simulation. 3. The learning facility infrastructure is then planned. Here, value streams are defined in several states, products and processes are selected, and the necessary IT infrastructure is determined. 4. The location and building of the learning factory are then determined. 5. Finally, the organisational structure is discussed and the economic viability of the learning factory is determined. 15

See Tisch et al. (2013, 2015a), Reiner (2009), Abele et al. (2010), Abel et al. (2013), Steffen et al. (2013a), Jäger et al. (2013) and Matt et al. (2014). 16 See e.g. Tisch et al. (2015a). 17 See Hart (2015).

6.1 Learning Factory Planning and Design

133

Sadaj et al. (2020) show a three-phase approach that describes for the first time how a learning factory in the broad sense can be designed for services instead of products. Tschandl et al. (2020) show a four-step approach (analysis, concept, validation, sign) that focuses on digital transformation and Industry 4.0 in the design of learning factories. Design approaches for learning factories should specifically address the five weaknesses of previous approaches, namely18 : 1. Learning factories often prioritise engineering over didactic principles when designing learning systems. A learning factory that focuses solely on engineering principles may produce technically advanced training programs but neglect to consider the most effective way to teach those principles to learners. Without a focus on didactic principles, learners may struggle to understand the material, which can impede their development of competences. 2. Often no established model for the structured design of learning factories is used, resulting in significant redesign efforts. If an organisation develops a learning factory without following a structured design model, it may realise that certain aspects of the training program are not effective after implementation. This may require significant redesign efforts and waste resources that could have been avoided by following a proven design model. 3. Learning modules in learning factories frequently lack goal or competence orientation, despite competence development being the primary objective of such activities. A learning factory that is not designed with competence orientation may produce training modules that are not aligned with the skills and knowledge required by the target group. This could result in learners not acquiring the right competences. 4. Due to a lack of clearly defined objectives, there are no practical methods or procedures for monitoring goal attainment. If there are no defined objectives for a learning factory’s training program, there is no way to measure the success of the program. For example, if the objective is to reduce the time it takes for employees to complete a task, but there are no methods in place to measure progress, it will be difficult to know if the training program is effective. 5. The transfer of knowledge from learning factories to real factory environments is often hindered by a lack of user or target group focus. A learning factory that does not consider the target audience or end-users may produce training modules that are not applicable in real-world situations. For instance, if a learning factory develops a training program for machine operators 18

See Tisch et al. (2013) and Tisch (2018).

134

6 The Life Cycle of Learning Factories for Competence Development

but does not consider the specific machines or work environment of the target audience, it may not be possible to transfer the acquired knowledge to the actual workplace.

6.1.2 The IALF Approach to Competence-Oriented Planning and Design To address the weaknesses in the design of learning factories, an approach has been developed that focuses on developing competences. This approach is structured into three design levels: macro, meso, and micro, as shown in Fig. 6.2. These design levels are based on the structure of other learning programs,19 and they are used to structure the complex learning factory system in a clear and appropriate way. Using this approach, the learning factory is designed at each level to address different design elements.20 • The macro level focuses on the development of a comprehensive curriculum. In the context of a learning factory, this level involves more than traditional or mediaassisted learning programs, as the curriculum is physically depended on the factory environment. The macro level of the learning factory model also includes the socio-technical learning factory infrastructure (such as the production processes, products, and employees) as well as the overall content and didactic concept of the learning factory program. In designing the learning factory curriculum, the learning targets, target groups (or learners), and other stakeholders (trainers, operators) must be taken into consideration. • At the meso level, the focus is on designing specific learning modules. For example, in a learning factory for lean management, a module might be designed to teach workers how to improve set-up processes. This module would be located at the meso level, which is part of the overall learning factory curriculum (at the macro level) and utilises the infrastructure established there. The meso level of the learning factory includes various learning modules, e.g., those related to improve the material flow, shopfloor management, and so on. The design of a learning module encompasses not only the sequencing of learning processes but also the planning of how the socio-technical factory infrastructure can be modified or adapted to support the learning objectives in each of the learning modules. • At the micro level, the focus is on designing specific learning situations and learning resources. For example, in a learning factory for lean management, a learning module might be designed to teach how to improve value streams. At the micro level, specific learning situations would be created, such as a simulation of a production line that learners must improve. These learning situations have a variety of characteristics, ranging from exploratory and experimental to systematising 19 20

See Seufert and Euler (2005). See Tisch (2018).

6.1 Learning Factory Planning and Design

135

Fig. 6.2 Levels of learning factory design (Tisch et al., 2015a)

and reflective.21 Within the learning modules, the learning situations are highly interdependent and must be tailored to the learning objectives of the module as well as to other situations within the module. The design of the learning situations includes not only the didactic and methodological aspects of each phase but also the preparation of the factory environment as a learning support, the learning material, medium, and product.22 The procedure for the design of learning factories can be subdivided into two didactic transformations within all design levels (macro, meso, and micro): • The first transformation relates to the derivation of intended competences that are part of a curriculum and structured in a competence matrix. In the first didactic transformation, the determination of competences is based on the organisational environment and targets. Those competence requirements are highly individualised to the target group and industry. The competence requirements must be formulated as learning targets, so that they can then be conceptually implemented in the second transformation. Example: An industrial company has the target to reduce the throughput time and increase quality based on the principles of lean production. Therefore, the intended competences are derived from the lean principles, e.g., “the production managers are able to systematically transform value streams based on the lean principles.” • The second transformation details the conceptual implementation of the intended competences. In anticipation of specific learning processes, learning methods are established which accentuate the relevant products and production processes in such a way that a purposeful alternation of action and understanding is triggered. 21 22

See Abele et al. (2015). See Abele et al. (2015).

136

6 The Life Cycle of Learning Factories for Competence Development 1st didactic transformation

organizational requirements

2nd didactic transformation configuration & design

learning targets

organizational environment

process +

factory elements

product

organizational targets

infrastructure intended competences

teaching methods

learning process

+

target group

media

didactics

Fig. 6.3 Conceptual relationships in the two didactic transformations in learning factory design (Kreß & Metternich, 2020; Tisch et al., 2015a)

Example: An industrial company decided to establish a learning factory for lean production to train their production managers. It implements some of the already used factory elements and products, creates learning media (e.g., a presentation), and specifies the teaching methods and the learning process (e.g., practical exercises combined with theory sessions). The division into the two didactic transformations is useful because thereby first the relevant competences and learning targets are systematically derived and only subsequently the infrastructure and didactics are designed. The conceptual relationships inside the two didactic transformations are visualised in Fig. 6.3. In the following sections, the approach on the individual design levels—macro, meso, and micro—is described.23

6.1.2.1

Learning Factory Design on the Macro Level

At the macro level, the didactic-methodological concept, the socio-technical learning environment, and the overall curriculum (i.e., the learning factory program) are developed. An overview of the design steps at the macro level is presented in Fig. 6.4.24 In the course of the general target and framework definition, requirements, targets, and framework conditions are defined: 23

A detailed process model in BPMN 2.0 and a description of the design process on the three design levels can be found in Tisch (2018). 24 A detailed description is given in Tisch (2018).

6.1 Learning Factory Planning and Design

137

Fig. 6.4 Simplified learning factory design process at the macro level according to Tisch (2018)

• In the first step, learning factory-specific framework conditions, company goals and organisational requirements are identified in connection with the learning system. Moreover, further relevant requirements are analysed. The other noncompetence-related requirement sources are the target work system, the target group, and the content. • In the next step, the learning factory’s targets, evaluation criteria, and project plan are established by identifying specific requirements and analysing the relevant requirements sources of the first step. To support the definition of learning factory-specific framework conditions, the morphological description of the learning factory system presented in Sects. 5.1– 5.8 is helpful. For this purpose, the morphology is converted into a standardised sheet that can be used in consultation with the relevant stakeholders to structure their requirements for the learning factory design. In this context, it is discussed and defined which characteristic of individual design elements should be part of the system. Therefore, a distinction can be made between mandatory (green) and optional requirements (yellow) based on the specifications (Fig. 6.5).25 In the definition of learning targets, the overarching intended competences are defined. The learning targets are based on the results of the previous steps, especially the organisational targets. The aim is to identify and formulate the general dispositions that are necessary to successfully cope with relevant problems. The identification and formulation of the competences are strongly domain- and content-specific (especially for technical and methodical competences). The competence formulation includes the following components: • First, a reference to the learner and the targeted goal is made. These steps ensure that the target group is clearly defined. As an important didactical principle, the prior knowledge of the target group should be considered. 25

See Tisch (2018).

138

6 The Life Cycle of Learning Factories for Competence Development

Fig. 6.5 Morphological description for the general target and framework definition (Kreß et al., 2023; Tisch, 2018)

• The cognitive level of the learning target should be described. The respective key terms from relevant learning taxonomies26 can be used for assistance.27 • Furthermore, the related content should be described specifically. Figure 6.6 summarises the components of a learning target. An example of a formulated learning target is: After participating in the course “value stream design,” the production managers are able to analyse, design, and evaluate value streams

26 27

Bloom et al. (1956) and Anderson et al. (2001). See inter alia Schermutzki (2007).

Target group

Cognitive level

Content

Description

139

Reference to the learner (often as an introductory phrase)

Description of the addressed cognitive level of the learning target

Specific description of the learning target related content

Example

6.1 Learning Factory Planning and Design

After participating the course “Value Stream Design”, the production managers are able to...

…to analyze, design and evaluate…

…value streams including underlying processes, key performance indicators, material and information flows as well as improvement potentials.

Fig. 6.6 Components of the competence formulation

including underlying processes, key performance indicators, material, and information flows as well as improvement potentials. This competence formulation should be done for all intended learning modules at this stage. In the conceptual and detail planning phase, based on the previous step identified competences, the socio-technical infrastructure is configured and the general didactic-methodological concept is designed. Here, all modelled manufacturing processes, the products used in the concept, as well as the general didactic-methodological framework are aligned to the intended competences. The configuration of a learning factory refers to the selection of suitable factory elements and products. The selected factory elements—as part of the infrastructure— determine the subsequent application potential of the learning factory. To configure a learning factory, it is divided into different factory areas, such as an area for sawing, an area for assembly, and so on. For each area, different configuration alternatives are available. Alternatives can be chosen based on intuition. However, this exposes one to the risk of not achieving the maximum benefit from the available budget in terms of the intended competences. Therefore, we propose an optimisation approach to achieve maximum utility values in relation to the intended competences (u) at certain resource inputs (w), as, for example, costs. Figure 6.7 displays a learning factory with three factory areas as well as three configuration alternatives for factory area 2. The associated configuration process can be divided into four subsequent steps, which are explained below (Fig. 6.8). Configuration step 1: Requirements for the configuration First, requirements for the configuration are derived from the framework conditions and the intended research and learning objectives. For further structuring, these can be classified on the one hand into mandatory and optional requirements. On the other hand, the requirements can be divided according to the respective configuration level to indicate whether the requirements relate to the entire factory, a factory area, a factory element, or the used product.

140

6 The Life Cycle of Learning Factories for Competence Development

Learning factory with different factory areas

Configuration alternatives for the factory area 2 Configuration alternative 2.1

Factory area 1 Configuration alternative 2.2 Factory area 2

Configuration alternative 2.3 Factory area 3

Fig. 6.7 Subdivision of a learning factory into factory areas and configuration alternatives (Kreß, 2022) Configuration step I

Requirements for the configuration Configuration step II

Identification of possible configuration alternatives Configuration step III

Evaluation of the possible configuration alternatives Configuration step IV

Selection and analysis of configuration alternatives Fig. 6.8 Procedure for the configuration of learning factories (Kreß, 2022)

Configuration step 2: Identification of possible configuration alternatives Based on the mandatory requirements, possible factory elements and the appropriate product can be selected. In general, two ways can be identified in the literature to the appropriate learning factory product28 : • industrial products that are available on the market are selected (and optionally didactically modified)—or— • learning factory products that are specifically developed for fitting into the learning factory concept. For the selection or development of learning factory products, it is important to consider that decisions on the mapped processes and the manufactured product are interwoven with each other. A decision regarding the product always affects needed or possible production processes and vice versa. 28

Metternich et al. (2013), Wagner et al. (2014, 2015) and Tisch et al. (2015a).

6.1 Learning Factory Planning and Design

141

Based on the learning factory product, necessary production processes can be derived. Furthermore, the production processes can be dedicated to several factory areas that can contain one or more factory elements like machines, assembly stations, and so on. Therefore, several factory elements can be combined into different configuration alternatives that represent a factory area completely. Generally, more than one configuration alternative is available for each factory area that fulfils the mandatory requirements. Configuration step 3: Evaluation of possible configuration alternatives The identified configuration alternatives are then evaluated with regard to the optional requirements. For this purpose, evaluation criteria are first derived from the optional requirements. These should be operationalised in such a way that the evaluation is as objective and valid as possible, for example, quantitatively by variables. The evaluation criteria are to be weighted based on this, for instance with the pair comparison method. The sum product of the weighting and the evaluation results in utility values for the configuration alternatives. The more of the optional requirements are fulfilled, the higher the utility value. Configuration step 4: Selection and analysis of the configuration After all configuration alternatives have been evaluated, the appropriate configuration alternative is selected in each factory area. The selection should be made in such a way that the highest possible utility values are achieved while complying with the existing restrictions, such as a budget and the factory measurements. Excursion for the Use of an Optimisation Model In addition to the intuitive selection of configuration alternatives, an optimisation model can be used. Optimisation models consist of three elements: the decision variables, the target function, and the constraints. The decision variables are to indicate, which configuration alternative is selected in the respective factory areas. This is possible with the binary variable x ij , which is 1, if the configuration alternative j was selected in the factory area i, and otherwise 0. xi j ∈ {0; 1}. By the target function, the best possible configuration with the highest possible total utility U should be able to be determined. For this, the sum product of the decision variables and the utility values over all factory areas and configuration alternatives should be maximised. U=

I ∑ J ∑ i=1 j=1

u i j xi j → max .

142

6 The Life Cycle of Learning Factories for Competence Development

The constraints result, among other things, from the mandatory requirements. These include, for example, compliance with the budget and the factory dimensions. In order to keep the budget, the sum product from the decision variables and the costs of the configuration alternatives should be smaller than a certain value. To check the factory dimensions, the creation of a first layout is necessary, for example, by the triangle method or the layout optimisation by the facility layout problem. J ∑

xi j = 1

j=1 I ∑ J ∑

wi j,r xi j ≤ Cr .

i=1 j=1

The optimisation model can be solved algorithm-based. The only algorithm to date for solving the corresponding facility configuration problem is described in Kreß (2022) and is based on the branch-and-bound approach.29 To apply the optimisation model, a configuration system was developed. After all the available data on the resistances and the evaluation of the configuration alternatives have been entered, the configuration system determines the best possible configuration on the basis of algorithms. For this purpose, a number of additional assistance functions are available, such as algorithms for layout generation, pair comparison for weighting, intuitive selection, and comparison of different configurations. The comparison with the intuitive approach shows that30 : • The optimisation approach determines configurations whose total utility is on average more than 20% higher than intuitively generated solutions. • The intuitive approach is very subjective and not reliable: different people determine different configurations. The best possible configurations are thus only determined by chance with the intuitive approach. • The use of the configuration system makes it easier and faster to determine new configurations, for example, due to changes in the general conditions, and thus provides suitable support. Based on the findings of previous research projects, the authors of this book recommend the use of the optimisation approach, as it brings the above-mentioned advantages and is comparatively hardly associated with any additional effort when the configuration system is used.

29 30

Kreß (2022). Kreß (2022).

6.1 Learning Factory Planning and Design

143

After selecting the factory elements and the product, in the checking and modularisation phase, the following steps should be conducted: • It should be critically examined whether the chosen concept is able to address the defined intended competences and whether it meets the underlying requirements and objectives. The configuration process described above ensures that only factory elements and products that fulfil the mandatory requirements can be selected. However, at the end of the macro level, it should still be checked whether the didactic-methodical concept and the infrastructure fulfil all requirements. • If the test comes to a positive result, the preparation and the monitoring of the learning factory realisation follow. If an addressing of defined targets is not possible adequately, an extension or adaptation of the learning factory concept follows, and the previously mentioned steps of the macro level should be repeated. • In the context of the modularisation, the higher-level learning objectives are grouped into modules. The sum of all modules forms the learning factory program, which is not to be regarded as rigid but continuously expandable. 6.1.2.2

Learning Factory Design on the Meso Level

At the meso level, learning modules are created based on the modularised learning targets, which structure the learning content. The first didactic transformation specifies what should be learned in each learning module, while the second didactic transformation defines the sequence and learning environment of the module. The procedure at the meso level shown in Fig. 6.9 is performed for every learning module. In the first step, the requirements and the learning module framework are analysed and defined. First, the learning module framework of each learning module is defined: General conditions and requirements are derived based on the previous steps, e.g., the design elements of the morphology. For the general definition of the framework conditions at the learning module level, a checklist for learning module framework definition presented in Fig. 6.10 can be used.

Fig. 6.9 Simplified learning factory design process on meso level according to Tisch (2018)

144

6 The Life Cycle of Learning Factories for Competence Development

Checklist for the learning module framework What is the title of the planned learning module? What is the content of the learning module?

(short description: two to three sentences)

Who are the planned trainers of the module to be developed?

(see also morphology 2.3)

How many coaches are available? Who is the target group of the learning module to be developed?

(see also morphology 2.3)

Is this target group homogeneous or heterogeneous? With regard to which features?

(if heterogeneous, see also morphology 2.4)

For how many participants should the training be designed?

(see also morphology 7.1)

What is the duration of the learning module to be developed?

(see also morphology 7.3)

Other special features (for the learning environment, setting, etc.) which must be specified after defining the framework conditions at the macro level.

(description in bullet-points)

Fig. 6.10 Checklist for the learning module framework definition (Tisch, 2018)

For example, in the learning module “shopfloor management expert,” shopfloor meetings and the shopfloor board as well as systematic problem-solving processes are designed by the participants. Trainers are the research associates of the institute that operates the learning factory. The target group is primarily the operational management of industrial companies, while other participants like the top management and consultants are allowed. Therefore, the target group is heterogeneous. Based on the size of the learning factory, up to twenty participants are possible. The duration of that learning module is two days. The socio-technical infrastructure should be prepared with predefined problems that can be solved during the learning module. In the next step, the roles addressed in the learning module are identified and described based on the specific content. Typical roles in production consist of operational staff, logisticians, maintenance, team management, customers, suppliers, and production planning. Only those roles are to be defined that are necessary for the corresponding learning module. Exemplarily, roles in the field of the shopfloor management topic as well as a role description for the “shopfloor management expert” are shown in Fig. 6.11. In this description, tasks, and activities are described that are connected to the learning content of the respective roles. For the operationalisation of learning targets, a competence transformation table can be used to operationalise identified competences based on the requirements identified in the previous step. Within the competence transformation, the

6.1 Learning Factory Planning and Design

Workers

Team leader

145

Manager

Supporting functions

Shopfloor management expert

Description The shopfloor management expert supports the introduction of shopfloor management. The expert is involved in designing the instruments within the shopfloor management-system. Activities in the context of shop floor management The shopfloor management expert…

… supports in the area and company-specific design and implementation assists team leaders and executives in the introduction phase … trains the persons acting in the shop floor management system … supports the change processes in the introduction of shop floor management Fig. 6.11 Roles in the shopfloor management; tasks and activities of a shopfloor management expert

overarching competences are divided into sub-competences to structure the learning targets for the learning module. Correspondingly, competences and sub-competences are formulated and defined in this step. Each sub-competence is divided into corresponding knowledge elements as well as competence-related performances and actions, see Fig. 6.12. Furthermore, corresponding performances of the learners as well as required knowledge elements are related to the listed learning targets. In general, knowledge elements can be divided into: • professional knowledge: knowledge that addresses mainly questions regarding the “what?” and the “how?” • conceptual knowledge: knowledge that entails answers to “why?” questions.

6 The Life Cycle of Learning Factories for Competence Development

Sub-competence 1.1

Corresponding performance / action

Knowledge elements (professional and conceptional knowledge)

Sub-competence 1.2

Corresponding performance / action

Knowledge elements

Sub-competence 1.3

Corresponding performance / action

Knowledge elements

…

…

…

…

…

…

...

Competence 1

146

Approach for the design of new learning modules

Approach for the analysis in course of the redesign of existing learning modules

Fig. 6.12 Structure of the competence transformation for the design and redesign of learning modules according to Tisch et al. (2013)

Both knowledge types must be considered. This instrument can be used for the design of learning modules as well as for the redesign of existing learning modules: • Based on the competences defined, the performances and actions are derived, that the participants conduct within the learning module. Subsequently required knowledge elements are determined that are necessary for the performances and actions. In the generation of the competence transformation, sub-performances and sub-actions are related to differentiated knowledge elements in order to describe the intended competences in detail. Competence → sub-competence →performances/actions → knowledge elements • For the redesign of existing learning modules, firstly, relevant content is identified through the analysis of learning materials by assigning the contents to the various knowledge aspects. Subsequently, specific performances are linked to the respective professional and conceptual knowledge elements. Based on those assignments, the intended sub-competences and competences are derived. The learning module can be improved.

6.1 Learning Factory Planning and Design

Knowledge elements →competence

→

performances/actions

147

→

sub-competence

Using the competence transformation for existing learning modules, various problems or improvement potentials of intuitively designed learning modules can be discovered and recorded.31 Problems and potentials of existing learning modules are mainly in the following four areas: 1. 2. 3. 4.

Learning targets are not defined or not competence-oriented. Knowledge elements are not defined for all competences. Intended actions are not defined for all competences. Unnecessary redundant content is used with no added value.

Figure 6.13 shows an extract of the competence transformation table of the learning module “Quality techniques of Lean Production” offered in the Process Learning Factory CiP.32 The main intention of this learning module is the ability of reflective application of Jidoka methods and tools, which is the part of the Toyota Production System that deals with prevention and elimination of rework and defects. The extract from the competence transformation table shows the sub-competence that addresses the ability of the learners to develop an andon-concept in production systems, which includes a stop of production processes, an alert, and conditions for escalation in case of abnormal conditions. The competence transformation table assigns two related actions to the sub-competence that can be performed in the factory environment of the Process Learning Factory CiP: • Designing the physical andon system: Here, a problem scenario with a missing andon concept in the assembly department of the Process Learning Factory CiP is used. • Planning the escalation process for problem escalation part of the andon concept: Here, the learners plan the escalation process for the beforehand designed physical andon system in the assembly department. The competence transformation table is the starting point for the second didactic transformation, specifically the technical-methodical design of the learning module. In this step, the performances/actions as well as the knowledge elements for each sub-competence are assigned and structured to specific learning sequences during the learning module. The corresponding actions are addressed in the practical exploration and testing activities of the learning module33 : • Exploration activities: Exploration activities are directly related to the learners’ professional challenges in the daily working routine. In this phase, the learners discover new content and problem situations in an acting manner. Exploration activities require a subsequent systematisation, see Fig. 6.14. 31

A use case for the use of the competency transformation for existing learning modules is given in Abele et al. (2015). 32 See Enke et al. (2016). 33 See Abele et al. (2015) and Tisch (2018).

148

6 The Life Cycle of Learning Factories for Competence Development

*Jidoka: Part of the Toyota Production System which deals with the elimination and prevention of defects and rework

The participants are able to explain the methods and tools for the implementation of Jidoka* and for the solution of problems and to apply selected methods and tools

Competence

Sub-competence

Performance/ action

Knowledge elements

…

…

…

The participants are able to develop an andon concept for production

Design of an andon system with the physical implementation

Knowledge, that visual and acoustical signals and an andon board are needed; knowledge of the examined workplaces; knowledge of the functionality of andon; knowledge of the color meanings

Planning of an escalation process for the problem escalation based on the andon system

Knowledge of the person in charge and the available time; knowledge of the theoretical sequence of an escalation process (point in time for information, order of notification)

…

…

…

Fig. 6.13 Extract from the competence transformation chart of the learning module “Quality Techniques of Lean Production” (Enke et al., 2016)

Systematization

Exploration

Testing-based strategy Introduction

Reflection

Examination

Exploration-based strategy Exploration

Systematization

Fig. 6.14 Possible sequences of activities, according to Abele et al. (2015)

• Testing activities: Testing activities are also directly related to the learners’ professional challenges. In this phase, learners assess already learned content and problems in an acting manner. In contrast to the exploration activity, no new areas are discovered but already known content is applied. This means that a problem or task is solved following given specifications, e.g., a specific method. Testing activities require systematisation beforehand. A lack of activity-oriented phases (exploration and testing activities) lead to a low contextualisation and impractical learning modules. Figure 6.14 contains the required professional and conceptual knowledge base that is mostly addressed in systematisation and reflection phases of the learning

6.1 Learning Factory Planning and Design

149

module.34 The knowledge elements are addressed in systematisation and reflection activities in the learning module35 : • Systematisation activities: The systematisation activity is closely related to the technical or scientific basics of the content. Existing knowledge elements of the learners are activated, checked, supplemented, extended, and corrected based on objectified knowledge. Systematisation concretises and stabilises the actionrelated knowledge gained. A lack of these activities in the learning module does not meet the requirement of professionalism and leads to actionist learning modules. • Reflection activities: Reflection is an important part of all sequences. The learner is in focus during the reflection phases. Here, the learners themselves determine if they succeeded or not as well as which content might not have been understood. Furthermore, the reflection phases provide important hints about the learning effects of the sequence. Figure 6.14 shows the most used sequences in learning modules of learning factories: those sequences are using either exploration-based or the testing-based strategy. After the reflection phase of both sequences, an examination of the trainer regarding the effectiveness of the learning phases is optionally conducted. In addition to these sequencing strategies, reflection-based strategies may also be used in some cases. Reflection-based strategies start with an introduction followed directly by a discussion and reflection making use of professional experience of the learners. After the introduction, the sequence goes on with systematisation, testing, the second reflection or exploration, systematisation, and the last reflection. Figure 6.15 gives an overview of the sequence and the application fields of the strategies mentioned. Figure 6.16 exemplarily shows the sequence of activities used for the subcompetence. “The participants are able to develop an andon concept for production” of the learning module “Quality Techniques of Lean Production.” The introduction takes the role of placing the andon concept in the overall concept of Jidoka. Based on this, the testing-based sequencing strategy is used, which is composed of theoretical input regarding the design of andon systems, a development of the andon concept in the Process Learning Factory CiP, and a reflection phase that contains the presentation of group results and a discussion about results and the learning content in general. Parallel to the illustrated sequencing of the learning modules, the socio-technical infrastructure (predefined at the macro level) is detailed and adapted as required. For this purpose, in analogy to the procedure on macro level, requirements are derived from the intended competences and transferred to concepts for adaptation. Likewise, a review of the addressability of competences at the different factory levels is performed.36 34

Further information on the creation of the competency transformation table for this learning module is given in Enke et al. (2015). 35 See Abele et al. (2015) and Tisch (2018). 36 See Tisch (2018).

150

6 The Life Cycle of Learning Factories for Competence Development

Fig. 6.15 Overview of sequence steps and application areas of sequencing strategies for learning factory modules (Tisch, 2018)

For example, in learning module “Quality Techniques of Lean Production,” the andon systems defined in the macro level (such as the andon lamps) are disassembled before the start of the learning module, as participants are expected to develop andon systems themselves.

6.1.2.3

Learning Factory Design on the Micro Level

At the micro level, the learning activities that were structured at the meso level are designed. Here, the design of the learning situations takes place, continuing the second didactic transformation initiated at the meso level (Fig. 6.17). The first step on the micro level is the framework and target definition. Framework conditions, requirements, and goals for the learning situations at the micro level have for the most part already been defined at the higher levels. At the micro level, it is now necessary to distribute the defined time capacities for the individual

6.1 Learning Factory Planning and Design Introduction Andon as part of Jidoka

Systematisation Theoretical Input on Andon (prerequisites, escalation process,…)

151 Experimentation Development of a andon concept for the factory environment

Reflection Presentation of results, discussion about different design options and the learning content

Fig. 6.16 Sequence of activities for the sub-competence “ability to develop an andon-concept for production” (Enke et al., 2016)

Fig. 6.17 Simplified learning factory design process on micro level according to Tisch (2018)

sequences to the learning situations and to specify the individual components of the sequence.37 For this purpose, the following principles can be applied38 : • Output orientation instead of input orientation: The learning situations are adapted to the predefined learning targets. In this context, the question arises as to how learning situations must be arranged to achieve the intended learning outcomes (output orientation). In contrast, the question of which topics from the contentrelated field could also be addressed should not be the leading question (input orientation). • Practical orientation instead of theoretical orientation: In particular, a main emphasis on systematisation activities leads to input orientation. Theoretical units 37 38

See Tisch (2018). Abele et al. (2015).

152

6 The Life Cycle of Learning Factories for Competence Development

should therefore be planned as long as necessary but also as compact as possible. Only relevant knowledge elements should be added that are relevant for the practical implementation and the learning targets. Testing, development, and reflection elements should outweigh the systematisation activities in terms of duration. Consequently, at the center of the learning factory approach are the practicerelated phases. Accordingly, regarding the design of individual learning situations of a sequence, first, exploration and testing activities are created, followed by the systematisation activities and reflection phases.39 Finally, the implemented learning modules can also be reviewed and improved with a competence-based evaluation approach. The evaluation approaches are shown in Sect. 6.4.2.

6.2 Learning Factory Built-Up, Sales, and Acquisition The content of the phases in the learning factory life cycle “learning factory built-up” and “sales–acquisition” are strongly dependent on different business models that are pursued in context of the learning factories. Four different types of business models are identified that can be seen in connection with the built-up, sales, and acquisition phases of learning factories. Those business models are40 : • • • •

the analysis and concept definition for the built-up of a learning factory, the design and the built-up of customised learning factories, the built-up of standardised turnkey learning factories, and the auditioning and certification of existing learning factories.

The fourth business model “the auditioning and certification of existing learning factories” is regarded in the next section “learning factory operation, evaluation, and improvement.” In the following, the first three mentioned business models are described in more detail regarding the learning factory built-up, sales, and acquisition. Figure 6.18 gives an overview of the four described business models.

6.2.1 Analysis and Concept Definition for the Built-Up of a Learning Factory Many companies and universities have difficulties in designing learning factories. The design approach for learning factories presented in Sect. 6.1 can therefore be offered as a consultancy service. In such a consulting project, the organisational framework conditions are first examined. For this purpose, surveys are conducted 39

A detailed description and guideline how those learning situations can be designed based on the competency transformation can be found in Abele et al. (2015) and Tisch (2018). 40 According to Abele et al. (2015).

6.2 Learning Factory Built-Up, Sales, and Acquisition

Analysis & Concept Definition

Turn-Key Customized Learning Factory Learning Factory

Initial analysis of the situation and discussion of potential concepts for the individual learning factory

Delivery and builtup of a predefined turn-key learning factory. Additionally, training of personell to run the learning factory

See section 6.2.1

See section 6.2.2

Developed and customized learning factory based on individual goals, challenges, and industry specialities

See section 6.2.3

153

Auditioning and Certification Conduction of audits for learning factories and provision of a structured list of possible improvements

See section 6.4

Fig. 6.18 Business models for the built-up of learning factories

that aim at the integration of the learning factory into the organisation. In addition, the morphology for learning factories (see Chap. 5) is applied to define the scope of the learning factory project. At this point, it also becomes clear whether a turnkey learning factory (see Sect. 6.2.2) or a customised learning factory (see Sect. 6.2.3) should be set up: • Turnkey learning factories are particularly worthwhile when the existing requirements fit with an existing learning factory concept. • If there are additional requirements that are not met by the existing learning factory concepts, customised learning factories should be designed specifically for the respective organisation. This is especially the case if the intended competences, the products, and processes used, or the operator model differs from the existing concepts.

6.2.2 Built-Up of Standardised Turnkey Learning Factories Standardised turnkey learning factories offer low costs and a short project time for the built-up. In this case, an existing learning factory or a part thereof is replicated one-to-one with the customer, whereby the client is not, or little included in the concept development phase. The correspondence between the replicated and the original learning factory must be high, for example, regarding the target group, the operator model as well as the learning factory targets and purposes. Supposedly, the greater the requirement deviations between original and replicate, the more the effective competence development is reduced.

154

6 The Life Cycle of Learning Factories for Competence Development

The Turnkey Learning Factory Business Model Only organisations with the necessary expertise, personnel, and the corresponding capacities can manage such projects. After agreement and order of the turnkey learning factory, the learning factory supplier is responsible for all orders of the needed factory equipment, assures the functionality of the learning factory concept, supports the setup, and instructs trainers and operators among others in the production processes, products, and training concepts. Advantages for the customer in this scenario are: • lower cost compared to completely individually developed learning factories, • no expertise and personnel regarding the establishment of learning factories in the customer organisation are needed, • the exchange between experienced providers and the operator of the learning factory, which guarantees a smooth technical operation as well as high-quality standards in the competence development, and • a learning factory concept, which is already geared to the desired topic and research results, which are already implemented as part of a continuous improvement process in the learning factory (especially for learning factories affiliated to research institutions). Potential customers can be industrial companies, operators of further education institutions as well as research institutions. Learning factory concepts can be offered as part of this business model, which have already been successfully implemented (possibly with small variations) elsewhere; otherwise, a fast and uncomplicated implementation of the learning factory concept cannot be ensured. As a rule, in this model, the customer will also be the operator of the learning factory; in other constellations, a corresponding high investment would be difficult to justify. On the part of the customer, appropriate employees must be organised, who can be trained by the learning factory provider to ensure the sustainable learning factory operation. As part of the turnkey learning factory project, coordination of offers, execution of the orders, and development of necessary aids for the operation as well as selection and configuration of the learning environment take place by the supplier of the learning factory. An individual configuration of the learning factory within certain limits is possible, which is presented to the customer as a complete offer, which includes the construction, the production of the functionality, and the training of the personnel. Usually, a complete offer is made to the customer, which includes the customising of the standardised learning factory, the construction, the production of the functionality, and the training of the personnel. The latter is particularly important because the expertise to operate the learning factory in this model is initially not available to the customer. However, as he must gradually internalise the expertise for the learning factory operation, corresponding consulting and coaching services must be planned in this regard. In this business model, on the one hand, of course, revenues are generated by the supplier. Furthermore, on the other hand, the customer and then operator of the learning factory can generate revenue offering trainings to (organisation-external) industrial participants (see also business model “offer of learning factory trainings

6.2 Learning Factory Built-Up, Sales, and Acquisition Fig. 6.19 Overview of operators and target groups of the turnkey learning factory projects mentioned (Enke et al., 2017b)

155

Operator

Students

University

SGP

RUS Employees

Target group

Industrial Company

ZAF

TUR

NLD

for industrial companies”). The presented model has already been successfully implemented several times with the Process Learning Factory CiP. The PTW (TU Darmstadt) was established in cooperation with McKinsey & Co. learning factories in the Netherlands, South Africa, Russia, Singapore, and Turkey. In those cases, mostly the assembly line including material supply for the Process Learning Factory CiP was exported and implemented on site, for impressions of the delivered turnkey learning factories, see Fig. 6.19. Furthermore, Fig. 6.20 gives an overview of operators and target groups of the turnkey learning factory projects. These learning factories are operated by research institutions as well as by private companies. Accordingly, the application of the turnkey learning factories varies. For example, the first capability and digital transformation center in Turkey, the ASO Model Factory, trains its own Lean Leaders from industrial companies of all sectors and sizes through Learn and Transform programs. Phases of a Typical Turnkey Learning Factory Project The project duration for the construction of turnkey learning factories comprises approximately 31 weeks from accepted offer to the final acceptance by the customer. In addition, a subsequent support in the operation of about 8 weeks via telephone calls and mail is guaranteed.41 Figure 6.21 shows the phases of a typical turnkey learning factory project. Negotiation and coordination: Expenses for materials and personnel are estimated in an offer together with the customer. Expenses depend on the specific design of 41 Enke et al. (2017b) describe the construction of such a turnkey learning factory from offer to final acceptance. This section is strongly based on the descriptions available there.

156

6 The Life Cycle of Learning Factories for Competence Development

NLD

The learning factory LeaRn is located at the university in Nyenrode, Netherlands and is used for training of office staff of a dutch bank, and a dutch insurance company as well as a consulting firm.

RUS

The learning factory in Russia is located at a university and was built for a big Russian heavy industry and manufacturing conglomerate. Students and industrial employees are trained together.

SGP

The learning factory at a university in Singapur provides trainings for students, especially with focus on systematic problem solving.

ZAF

In South Africa the learning factory is located at the training campus of one of the worldís largest mining companies in Johannesburg. It is used to train worldwide operators in lean methods.

TUR

In Turkey, the learning factory is run by a local chamber of industry and commerce to train companies in lean methods and principles.

Fig. 6.20 Examples for turnkey learning factories built up around the world after the model of the Process Learning Factory CiP

the learning environment at the request of the customer. Optionally, a site inspection can be provided at the customer, in which the conditions on site are checked and a detailed layout planning takes place. Order placement: A significant part of the personnel expenses of the project is based on the scheduled selection and ordering of all necessary components. For this purpose, a parts list of the original learning factory together with established providers is maintained continuously. For custom-made products (assembly devices etc.), which result above all from prior continuous improvement of the own learning factory, internal workshops and contract manufacturers are instructed. Pre-assembly: At an early stage, a team, which includes at least one expert with experience from previous construction projects, is compiled for the pre-assembly of

6.2 Learning Factory Built-Up, Sales, and Acquisition Negotiation & Coordination Calendar week

5

10

Order placement 15

157

Preassembly 20

25

Shipping

Installation & Training

30

Support

35

40

Duration project phase Support

Training Construction onsite

Shipping (e.g. by sea)

Preassembl of equipment

Orders

Commissioning

Site visit

Customer request

Milestones

Offer is placed

Project phase

Fig. 6.21 Phases of typical turnkey learning factory projects according to Enke et al. (2017b)

the workstations and material supply equipment. The focus here is a functional test of all customised constructions and the assembly and testing of all functional units. Shipping: For subsequent shipping, the learning factory equipment is partially dismantled, and a detailed packaging list is created. Packages consist of functional units to simplify on-site installation and to avoid searching activities. In particular, the shipping of the learning factory by sea requires a buffer of up to three weeks for regional customs and import regulations. Installation and training, service: Two experts usually conduct the installation and training on site. After unpacking the parts, the installation begins with the assembly of space-intensive infrastructure, such as material storages. Workplaces remain largely assembled and equipped after pre-assembly. If the complete infrastructure is ready, a test run follows. Here, the construction team operates the learning factory to make final adjustments and to be able to guarantee a smooth operation of the complete learning factory environment. Before dealing with the core training content, an introduction to technical processes, the product (pneumatic cylinder) and the basic didactical concept of the learning factory is necessary. The training of various learning factory states (predefined scenarios of the production system) is coordinated depending on number of trainees. For larger teams, subgroups can be created that rotate through the different tasks in turn. The training also addresses the transformation between different learning factory states. Here, a qualification matrix can help to keep track. Furthermore, daily performance test runs under real training conditions help to maintain the motivation of the participants and to ensure training progress. The training concludes with a test run, summary, and discussion of remaining questions. In the following, a technical briefing, the provision of spare parts, and maintenance

158

6 The Life Cycle of Learning Factories for Competence Development

Fig. 6.22 Success barriers and countermeasures regarding turnkey learning factory projects according to Enke et al. (2017b)

instructions for selected personnel of the learning factory operator are provided. After the end of the project, long-term collaborations are sought to facilitate and consolidate the stated benefits of learning factories. Success Barriers and Countermeasures for Turnkey Learning Factory Projects Due to the complexity and duration of the project, there are numerous barriers to success on a personal, organisational, communicative, and technical level. These barriers and proven countermeasures from the extensive project experience42 are described in detail in the following sections and summarised in Fig. 6.22. In practical application, an advance onsite inspection was helpful, especially regarding existing layout restrictions at the construction site. In this way, planning errors and necessary adjustments in setup and operation can already be avoided in advance. Early communication with the logistics company responsible for transport helps to shorten the process of pre-assembly and packaging. Although existing parts lists can be used in the ordering process, the large share of purchased parts and complex custom-made equipment is risky about necessary adjustments. Due to a changing product portfolio of the suppliers, seasonally fluctuating delivery times, and regularly rising prices, this phase is time-consuming but crucial for economic 42

Presented in Enke et al. (2017b).

6.2 Learning Factory Built-Up, Sales, and Acquisition

159

project success. The time required from assembly over functional testing to commissioning can be reduced through the deployment of customer service personnel for packaging disposal and the construction of simple standard parts to reduce project costs. The burden on the experts during the one-week on-site training should not be underestimated. Here, the composition of a training team of two experts and another experienced colleague for possible support can help. In particular, the project team should have sufficient language and cultural experience at the construction site. The use of an interpreter extends the training of customer personnel significantly, especially for the transfer of the conceptual approach of the learning factory. The formation of a project team involving fixed contact persons of the customer and a telephone conference every two weeks make a significant contribution to preventing the barriers to success presented.

6.2.3 Design and Built-Up of Customer-Individual Learning Factories High competence of personnel in different areas of production is a decisive competitive advantage. Learning factories are a suitable and technology-adequate form of competence development, but their intuitive design and built-up in course of pilot projects are complex and with only limited success. If the existing learning factory concepts do not meet all the additional requirements, it may be necessary to design customised learning factories tailored to the specific needs of the organisation. This is particularly relevant when the desired competences, products, processes, or operator models differ from the existing concepts. Learning factories have to be designed and built up geared to the specific requirements of individual companies. Here, the models and methods for the systematic design of learning factory systems are helpful.43 These learning factories are developed on the basis of current technical didactic findings and are systematically adapted to the individual requirements of the operators.44 The resulting learning factories are thus more effective regarding the achievement of learning objectives. Questions that have to be addressed during the company-specific learning factory design are described in Sect. 6.1 of this chapter. Figure 6.23 exemplarily summarises some of the most important issues related to the customer-specific design of learning factories along the dimensions of learning factory systems. Both universities and private companies45 could appear as developers and operators of these systematic designed learning factories. Learning factories, including technical, structural, and didactical questions, can be designed according to individual requirements and consequently, target groups can be trained according to 43

Those models and methods are presented in Sect. 6.1. For example: industry, university, vocational schools, etc. 45 Or in some cases also others like vocational schools. 44

160

6 The Life Cycle of Learning Factories for Competence Development

Design Dimension

Operating Model

Examplary questions How is the learning factory financed sustainably (built-up & operation)? How to find and develop staff to operate the learning factory? …

Purpose & Targets

What are the overarching business goals? What are the target groups of the learning factory? What should they be able to do? Content? Learning targets? …

Process & Setting

How is the factory environment of the learning factory look like? Which processes are needed inside a learning factory? What is the nature of the learning environment? (scaled-down/life-size,…) …

Product

Which product is produced inside the learning factory? What product complexity is needed? …

Didactics

What is the fundamental didactic concept of the learning factory? How can I evaluate regarding effectiveness? …

Fig. 6.23 Exemplary questions for the design of company-specific learning factories

their needs. However, developers and operators do not necessarily have to be the same organisation—in recent years, learning factories have been often planned and implemented by external learning factory experts and later used by other operating organisations. Here, the scope of the (external) developer’s performance can be individually adapted to the framework conditions of the project. Projects range from the use of design guidelines and methods over a competence-oriented revision of existing learning factories to a comprehensive development of the individual learning factories with preparation for the constructional implementation, see also Fig. 6.24. Both the industry and the public sector are showing great interest in learning factories. Potential clients belong accordingly to both sectors: • Industrial companies need technology-appropriate forms of learning to face the challenges of future production. First, learning factories will be of importance primarily to manufacturing industry. For example, in Germany alone, there are 205,028 manufacturing enterprises.46 The share of large companies,47 which should be of particular interest to learning factories, is 2.6%.48 Consequently, there are at least around 5300 manufacturing companies for which an individually adapted learning factory would be interesting and relevant—in Germany alone. 46

See Statistisches Bundesamt (2017). More than 249 employees or more than e50 million in annual turnover according to EU Recommendation 2003/361/EC. 48 See Statistisches Bundesamt (2017). 47

6.2 Learning Factory Built-Up, Sales, and Acquisition

Scope of the offered service

Use of guidelines and methods for systematic LF design

Creation of a rough concept for the learning factory

161

Individual development of single training modules

Comprehensive development and implementation of LF

Design effort client („operator“) Design effort LF expert („developer“)

Benefit of the service for the customers

Savings in connection with the planning and development phase of the learning factories

Benefits for companies and society through high-quality competency development

Fig. 6.24 Scope of support by external learning factory experts (Abele et al., 2015)

• Due to the great social relevance of learning factories, this form of learning is also interesting for the public sector. For universities and technical colleges, which are researching and educating close to production, a realistic form of learning could have positive effects on the learning success of the learners and the relevance of research results for industry. This would allow the students to make a greater contribution to strengthening the manufacturing sector immediately upon entering the labour market. Universities, technical colleges, and companies are involved as private or public clients in the development process. The architecture of the described value creation is shown in Fig. 6.25. The design of a customised learning factory helps clients avoid significant efforts and lengthy development periods for planning and construction, resulting in reduced development costs. External development of learning factories is particularly attractive for industrial companies as it eliminates the need to deploy own scarce personnel. Systematic development of learning factories through experts enhances expected economic benefits through effective competence development, leading to improved employee capacity, productivity, and flexibility in the industrial enterprise. Learning factories also enable efficient preparation of technology, product, and process innovations for employees, empowering production workers and carrying significant economic potential for qualification improvement of present and future production

162

6 The Life Cycle of Learning Factories for Competence Development

Client

Developer

Public / private client wants to use a learning factory

Specific requirements

Conceptional requirements

Constructional requirements

"Developer" selected with appropriate know-how

Systematic, individual development of a learning factory

Preparation of construction implementation

Tailored training of LF personnel

Fig. 6.25 Exemplary value-adding architecture for the design and construction of individualised learning factories (Abele et al., 2015)

personnel. In the context of the business model “design and built-up of customerindividual learning factories,” income can be generated for learning factory developers for the systematic design of learning factories49 as well as for the needs-based training of learning factory staff.50

6.3 Learning Factory Operation 6.3.1 Offer of Learning Factory Trainings for Industrial Companies As previously mentioned, universities and private training providers can utilise learning factories as authentic learning environments for hands-on, productionrelated training. In this business model, the training providers offer learning factory courses on various topics,51 which are accessible to the general market or to individual partner companies of the learning factory, see Fig. 6.26. To ensure short- and long-term success, the economic, contentual, personnel, and organisational quality need to be defined52 :

49

Advisory service for the set-up of the learning factory and the training. Training service for the operator of the learning factory. 51 For example, lean production, Industry 4.0, energy efficiency, etc. 52 See also Sect. 5.1.1. 50

6.3 Learning Factory Operation

163

Learning factory operator Development of competences of present and future specialists and executives

Industrial company a

with training offer

Industrial company b

Enabling the sustainable operation of the learning factory through financial payments, e.g., course fees

Industrial company n

Fig. 6.26 Offer of learning factory trainings for industry (Abele et al., 2015)

• Economic quality: Learning factory training providers can individually advertise trainings on the market53 or also offer training in the form of partnership models.54 Example: The partnership model of the Process Learning Factory CiP allows interested industrial companies to sign a partnership agreement. This contract grants the partners a certain number of training days, which can be freely distributed by the company to its employees. The CiP has such a partnership with approx. 14 industrial companies of different branches. The financial contribution of these companies is a substantial factor in the sustainable operation of the learning factory. For SMEs, additional compact training series are offered. Occasionally, events are also booked by German and foreign universities for students of certain degree programs. • Contentual quality: In addition, the training provider must ensure the quality of content shown in the learning factory. Only constant further development and incorporation of up-to-date research results into the training courses continuously ensures relevant and innovative trainings. Of course, also training providers from the private sector55 must ensure that their training offers also address the problems and challenges of customers in the longer term in an up-to-date manner. 53

See, for example, the Stuttgarter Produktionsakademie (2017). See, for example, the partnership model of the Process Learning Factory CiP (PTW, TU Darmstadt, 2017). 55 Such as management consultants. 54

164

6 The Life Cycle of Learning Factories for Competence Development

Example: Academic operators like to the Process Learning Factory CiP can keep training content up to date through insights from current research and industry projects—this prevents the training content from becoming obsolete; at present, for example, great efforts are being made to link climate-neutral production, artificial intelligence, and Industry 4.0 with existing offerings. • Personal and organisational quality: The quality of learning factory concepts is influenced by personal and organisational aspects. In addition to technical expertise, learning factory trainers need didactic and methodical skills for developing learning modules, moderating, and managing training, and coaching participants. Recruitment and training of suitable personnel for learning factories are crucial, and tools such as train-the-trainer workshops, training development guidelines, and qualification procedures for learning factory training can be helpful in this regard. Example: In a learning factory that focuses on lean manufacturing principles, trainers need to have a deep understanding of lean concepts and tools, as well as the ability to effectively convey these concepts to participants. They may also need expertise in facilitation and coaching to guide participants through handson learning activities and help them apply lean principles in a practical setting. Properly trained and qualified trainers can ensure the success of the learning factory by delivering high-quality training that aligns with the goals and objectives of the program.

6.3.2 Training Management for Learning Factories in Operation For the operation of learning factories, various different models exist, that vary depending on the various operators56 and target groups.57 Regarding the training management of a learning factory, several issues regarding the operation in the fields of training support and administration, training coordination and training delivery have to be addressed, see Fig. 6.27: • In the field training support and administration, several tasks regarding the direct administration of the trainings,58 support activities59 as well as organisational issues60 are fulfilled. • The training coordination includes the management of the learning factory program based on target group-specific learning targets, appropriate quality

56

For example: university, consulting, industry, vocational schools, etc. For example: students, industrial employees, etc. 58 For example: booking management. 59 For example: catering for the trainings. 60 For example: advertisement for trainings and administration of partnership agreements. 57

6.4 Learning Factory Evaluation and Improvement

165

Fig. 6.27 Issues for training management in learning factories

checks and evaluation, as well as a continuous improvement of the learning factory concept and the learning factory trainings. • The field of training delivery includes all tasks related to the actual learning processes; this includes, for example, the standardisation of learning factory equipment and the creation of offline and online learning materials. The operation phase of various learning factory use cases is described in the Best Practice Examples shown in Chap. 11.61

6.4 Learning Factory Evaluation and Improvement This chapter describes approaches and concepts connected to the phases evaluation and improvement of the learning factory life cycle.

61

Further use cases regarding the operation mode of learning factories can for example be found in Jorgensen et al. (1995), Abele et al. (2007), Jäger et al. (2012), Steffen et al. (2012), Rentzos et al. (2014), Balve and Albert (2015), Bender et al. (2015), Böhner et al. (2015), ElMaraghy and ElMaraghy (2015), Faller and Feldmüller (2015), Gebbe et al. (2015), Goerke et al. (2015), Hummel et al. (2015), Kaluza et al. (2015), Kreitlein et al. (2015), Krückhans et al. (2015), Lanza et al. (2015), Nöhring et al. (2015), Seitz and Nyhuis (2015), and many more.

166

6 The Life Cycle of Learning Factories for Competence Development

Challenges in the operation phase of a learning factory b) Continuously improving of existing learning systems

Potential / Maturity Level

Potential / Maturity Level

a) Preventing the deterioration of efficiency after the learning factory built-up

Built-up Operation

Quality Assurance

Time

Built-up Operation

Time

Further Development

Fig. 6.28 Challenges for a quality system for learning factories in the operation phase

6.4.1 Quality System for Learning Factories Based on a Maturity Model In the operation phase of the learning factory, it is important: (a) to prevent the deterioration of efficiency after the built-up and (b) to improve the existing learning system continuously, see Fig. 6.28. Quality systems for learning factories offer systematic approaches for analysing the current state, assessing the potential for improvement in relation to the target state and for deriving improvement measures. The aim of quality definitions and a quality system for learning factories is to fully exploit the great potential of the learning factory in the use phase and to prevent the learning factory processes and performance from decaying. Thus, learning factories can be further developed to higher maturity levels, the quality of the learning systems is sustainably secured, and the efficiency is increased. Such a quality system was developed in the project RQLes62 based on a maturity model. Within this framework, • quality standards for the learning factory are formulated, 62

“Reifegradbasierte, multidimensionale Qualitätsentwicklung von komplexen Lernsystemen am Beispiel der Lernfabriken für die Produktion,” literal translation: “Maturity-based, multidimensional quality development of complex learning systems using the example of learning factories for production”

6.4 Learning Factory Evaluation and Improvement

167

• a quality system for education and training in highly complex learning environments is available, and • within the validation the extent to which the implemented quality system has an impact on the learning outcomes of the participants is evaluated. Building on the established learning factory definition63 and the competenceoriented design approach of learning factories,64 a maturity-based quality system for learning factories was developed. The quality system enables quality assurance as well as further development of learning factories. The quality system was developed especially for learning factories but can also be applied to complex learning systems that integrate formal, non-formal, and informal learning. The structure for the maturity model is based on the EFQM65 model. Here, the EFQM model elements are connected to the design dimensions and design elements of the morphology as well as to the design levels, see Fig. 6.29. Based on this structure, the following terms are defined: • The maturity elements correspond to the retrieval of single aspects of the learning factory concept, e.g., “definition of standards for learning modules.” • The action fields summarise related maturity elements thematically, e.g., “establishing standards.” • The capability levels reflect the degree of fulfilment of the maturity elements and summarise the action fields after an audit is conducted. • The maturity level reflects the overall assessment of the learning factory after an audit is conducted. The action fields that have been identified consist of different statements that can be used to determine the capability level for a specific action field in the learning factory. By utilising these statements, it is possible to derive an overall maturity level for the learning factory, see Fig. 6.30. Each maturity level contains certain capability levels of various action fields. In the maturity model for learning factories, potentials for learning factories can be systematically discovered. For this purpose, learning factory operators are asked about relevant topics, which are called action fields. Each question corresponds to a so-called maturity element. Depending on how well a learning factory fulfils the requirements of this query, the corresponding maturity element receives a higher capability level. By means of capability levels, maturity elements can therefore be evaluated separately from each other. Each maturity element is assigned to both an action field and a maturity level. A maturity level is reached when all maturity elements of this maturity level have a correspondingly high capability level. As the maturity level of a learning factory increases, new action fields are added to raise expectations on the capability levels. Accordingly, more maturity elements are added to each maturity level. This is demonstrated by a target profile that requires 63

See Chap. 4. See Sect. 6.1. 65 European Foundation für Quality Management. 64

168

6 The Life Cycle of Learning Factories for Competence Development

Statements for maturity definition

Fig. 6.29 Structure of the maturity model (Enke et al., 2017a)

a capability level of at least 2 in all assigned action fields for a specific maturity level to be achieved. For instance, in Fig. 6.30, the assessed learning factory achieves an overall maturity level of 1 because of a low capability rating in action field 1. In order to reach an overall maturity level of 4, there are certain developments that the learning factory must undergo, as indicated in the example. The maturity model currently comprises 24 action fields and 245 maturity elements in total.66 In the maturity model, content of the action fields and capability levels of the action fields are specified. The assessment of action fields is enabled by the assignment of maturity elements to action fields. EFQM surveys for further education as well as the learning factory morphology elements and the learning factory design levels are used to identify and formulate relevant maturity elements. Furthermore, results of the learning factory stakeholder analysis67 as well as aspects of DIN EN ISO 9001 and DIN ISO 29990 are integrated. Figure 6.31 exemplarily shows defined maturity levels regarding the statement “schedule for periods of reflection in learning modules.” The CMMI includes five levels with rising quality: initial, managed, defined, quantitatively managed, and optimising. In the shown example, the description of different maturity levels ranges from no scheduled reflections to a use of reflections as a central step for the competence development. The validation of the described maturity model is executed using a multiperspective approach. In this approach, especially four perspectives will be addressed68 : 66

Enke et al. (2018) See Enke et al. (2016) 68 For further explanation see Enke et al. (2018). 67

6.4 Learning Factory Evaluation and Improvement

169

Fig. 6.30 Structure of maturity and capability level relations of the learning factory maturity model (Enke et al., 2018)

• Economic perspective: How are benefits and efforts of the development and application of the maturity model? • Deployment perspective: Are users capable and willing to apply the maturity model? • Engineering perspective: Is the maturity model accurately modelled and formally correct? • Epistemological perspective: Are the results of the maturity model objective, reliable, and valid? The maturity model was evaluated on the basis of seven learning factories. Based on previously defined requirements for the maturity model, an evaluation questionnaire was developed, which was completed by the respondents accompanying the audits. The results of the application and evaluation show69 : 69

See Enke (2020).

170

6 The Life Cycle of Learning Factories for Competence Development

Fig. 6.31 Definition of capability levels for all statements to enable a classification of particular learning factories (Enke et al., 2017a)

• The participants see a high benefit in regularly auditing their learning factory. • Due to the high degree of detail of the questioned contents and the implementation of the audit as an interview, specific recommendations for action can be derived individually for each learning factory. • The audited learning factories use the results for further improvement and integrate them into the instruments they use themselves. In this context, the communication of the maturity level as an external benchmark is important but must be put into perspective. • The requirements placed on the methodology are fulfilled to a large extent. Statistical evaluations, which are rarely conducted on maturity models, are possible. A simplified version of the maturity model can be found on the Homepage of the IALF (see also Chap. 10).70

6.4.2 Evaluation of the Success of Learning Factories The results and success of a learning factory should also be reviewed regularly in order to identify deviations and developments, to react appropriately, and to enable continuous improvement of the overall concept. There are different approaches to

70

See https://www.ialf-online.net/.

6.4 Learning Factory Evaluation and Improvement

Context Evaluation • Relation to other courses? • Is the time adequate? • Is there a need for the course? • …

171

Context

Input

Process

Product

Input Evaluation

Process Evaluation

Product Evaluation

• Entering competence level? • Appropriate targets? • Course structure? • …

• Active participation of learners? • Trainer-learner relations? • Learning methods? • …

• Final exam? • Overall experience? • Fulfilled learning target and achieved competence • …

Fig. 6.32 Evaluation in learning factories along the CIPP model according to Stufflebeam (1972)

measuring the output and success of learning factories, which will be discussed in the following.71 The CIPP model, which stands for Context, Input, Process, and Product, is a widely used framework for evaluating training programs and systems. This model involves assessing the context in which the program operates, the inputs and resources involved in delivering the program, the processes used to implement the program, and the resulting products or outcomes of the program72 : • context: analysis of the environment in relation to the needs of the target group addressed learning, didactic concepts, etc. • input: analysis of which resources and means are needed for the implementation of a measure or the provision of a service before the actual learning, e.g., preparation of training activities. • process: evaluation of the learning processes, which take place during actual learning, including the acceptance of the target group and stakeholders. • product/output: evaluation, whether the intended changes can be detected among the target groups and stakeholders, e.g., effects of the learning measure, which is located after the actual learning (Fig. 6.32). This section focuses on the different evaluation possibilities of products/output of learning processes in learning factories. The three quality criteria that any sciencebased learning success measurement forms have to meet are objectivity, validity, and reliability.73 Further relevant criteria coming from a practice-oriented point of view

71

See Solga (2011). See Stufflebeam (1972). 73 See Becker (2005). 72

172

6 The Life Cycle of Learning Factories for Competence Development

are, for example, economy, opportunity equality,74 transparency, manageability,75 sensitivity to change, or differentiability.76 There are various opportunities to measure the diverse effects of learning activities. A classification of these different instruments and methods, which is most recognised in practice,77 is provided by the Kirkpatrick four-level model.78 The four-level model classifies the levels of output evaluation: • Reaction: How satisfied are the participants with the training? Are they engaged in the learning activities? • Learning: What was learned well? And what was learned not so well? • Behaviour/transfer: Can the developed competences or skills be used in (and transferred to) the work environment? • Results: What is the economic effect of the trainings at the business level? What KPIs could be improved? Studies undertaken in this field were not able to confirm a (positive) effect from the level of the learner (e.g., learning success) to the level of an organisation (e.g., improved performance).79 This means that for example, learning effects cannot be estimated via participants’ satisfaction. However, the model gives an intuitive and pragmatic framework for the evaluation of learning results80 ; therefore, this section is structured and classified with the help of the presented CIPP-and Kirkpatrick fourlevel model as shown in Fig. 6.33. Furthermore, respective goals, evaluation questions, methods, and indicators of the different levels are given. In the next sections, the evaluation levels are described in detail.

6.4.2.1

Reaction of Participants

In learning factories, trainings are most often evaluated with structured and standardised satisfaction or feedback surveys,81 which participants fill out after completing single courses or the whole program.82 Such feedback sheets contain questions regarding: • the overall impression of the training, • the quality of moderation, • the quality of presentations, 74

See Elster et al. (2003). See Hertle et al. (2016). 76 See Clasen (2010) and Burlingame et al. (2006). 77 See Smith (2001). 78 See Kirkpatrick (1998). 79 See Alliger et al. (1997). 80 Nerdinger et al. (2014). 81 Sometimes, they are also referred to with a disparaging connotation as “Happy Sheets”. 82 See Becker et al. (2010). 75

Product / Output: Evaluation of the effects of training (after training)

Product

Measure the impact of training on the economic success of the company Does the training have an effect on earnings? Data analysis of financial indicators ROI, monetary benefits per training and participant, etc.

Process: Evaluation of learning processes (during training)

Process

Goal: Question: Method: Indicators:

Capture self-assessment and feedback from participants How satisfied were the participants? Questionnaire, interview Participant satisfaction with moderation, presentation, etc.

Four-level model of Kirkpatrick

Fig. 6.33 Evaluation possibilities for the effects of learning factories according to Tisch et al. (2014) based on Alliger et al. (1997), Becker et al. (2010), Gessler (2005) and Kirkpatrick (1998)

Reaction

Behaviour / Transfer

Goal: Measure the transfer of the learned into the own factory Question: Can the learned skills/competencies be ìappliedî within the company? Method: Interview, observation, 360£-feedback Indicators: Degree of implementation, training-induced projects, etc. Goal: Measure learning success Question: What was (not) learned well? Learning Method: Pre/Post knowledge test, observation, interview, simulation Indicators: Score or grade (knowledge test), degree of quality of action, etc.

Goal: Question: Method: Indicators:

Input: Evaluation of preparation of training (before training)

Input

CIPP model

Results

Context: Evaluation of the context of learning, didactic concepts, etc.

Context

6.4 Learning Factory Evaluation and Improvement 173

174

6 The Life Cycle of Learning Factories for Competence Development Structure of feedback sheet a) Subordinated view

b) Learning situation specific

1. How do you rate the overall workshop? very bad

--

-

0

+

+ + very good

Remarks:

2. How do you rate the lecture contents of the workshop?

How do you rate the individual parts of the training? 1. Prerequisites for the implementation of shopfloor management (SFM) very bad

very bad

--

-

0

+

+ + very good

--

-

0

+

+ + very good

Remarks:

Remarks:

2. Exercise: SFM Readiness Audit 3. How do you rate the content of the practical exercises? very bad

--

-

0

+

+ + very good

very bad

--

-

0

+

+ + very good

Remarks:

Remarks:

4. …

3. Performance indicators in SFM very bad

--

-

0

+

+ + very good

Remarks:

4. …

Fig. 6.34 Two variants of feedback sheets for the use in learning factory trainings

• • • • • •

the quality of practical exercises on the shopfloor, the quality of theoretical content, the adequacy of the mix regarding theory and practice, the exchange between participants, the quality of specific exercises or presentations, and free text fields for entering anything else. In general, the structure of these feedback sheets follows:

(a) a superordinate view, in which all quality-related requirements for the entire learning factory or entire learning modules are answered comprehensively,83 or (b) a learning situation-specific evaluation, in which individual learning situations (e.g., lectures and exercises) of the training are assessed individually by the participants.84 While option (a) allows a more detailed analysis of the components of the training arrangement, the procedure in (b) supports the improvement of individual learning phases (Fig. 6.34). Coordination of the feedback form design with the evaluation goals is crucial. This includes goals such as the improvement of trainings, quality assurance of training

83 84

See exemplary Fig. 6.34 on the left. See exemplary Fig. 6.34 on the right.

6.4 Learning Factory Evaluation and Improvement

175

components, and many others. However, it is important to note that participant feedback immediately after the training program has no correlation with higher evaluation levels, which measure factors such as learning success and successful transfer of concepts and methods to one’s own working environment.

6.4.2.2

Learning of Participants

Mainly, two key factors influence the learning success of learners: 1. The individual learner is the most influencing factor, including his or her attitude towards the learning situation, the trainer, and the learning content.85 2. The learning situation needs to be based on predefined learning targets.86 This comprises factors such as: • • • • •

the arrangement of the learning environment, the behaviour and the attitude of the trainer, the followed didactic principles, concepts as a whole, as well as the specific composition of the learning situation.

The measurement of certain characteristics, traits, and behaviours associated with the learner determines the extent of learning.87 To implement that kind of learning success evaluation, a lot of different measurement methods and forms exist. No specific form of measurement in this field can be fundamentally identified as better or worse than other forms, in general. The forms of measurement must always be considered in the context of the complete arrangement. Consequently, there are only more or less suitable measuring methods in different situations and arrangements.88 In literature, various classifications of methods for the evaluation of learning success are identified; the most important forms are described briefly in the following.89 Classification of Learning Evaluation In the following sections, forms of learning evaluation are classified distinguishing between: (a) (b) (c) (d) (e) 85

subjective and objective measurement, open and closed measurement, self and external measurement, summative and formative measurement, direct and indirect measurement, and

See Becker (2005). See Preussler and Baumgartner (2006). 87 See McMillan (1997). 88 Pietzner (2002). 89 The descriptions are based on Clasen (2010), for further information see also Clasen (2010). 86

176

6 The Life Cycle of Learning Factories for Competence Development

(f) quantitative and qualitative measurement. (a) Subjective and objective measurement A simple and fast way for learning evaluation is facilitated with subjective measuring forms. On the downside, the results of subjective measuring forms have limited validity since they are strongly dependent on the respective evaluator. For this reason, increasingly objective criteria form the basis for evaluations.90 In this context, it has to be mentioned that objective and subjective ratings on the same evaluation objects show only a low to medium correlation.91 Other meta-studies show similar results.92 Example for subjective learning evaluation in learning factories: Questions like “How do you rate the knowledge development by the learning factory training related to the topic ‘value stream analysis’?” are posed in a self or an external assessment Example for objective learning evaluation in learning factories: Questions (“What are the seven types of waste?”) or more complex tasks (“Please perform a value stream analysis for the displayed production environment”) related to the learning content are evaluated on the basis of predefined objective measuring points

(b) Open and closed measurement In open evaluations, learners are not constrained by predefined reactions or answers, while in closed evaluations, the evaluator predefines reactions and answers, and the learners can only select from those options.93 Example for open learning evaluation in learning factories: Tasks like “Please describe shortly the procedure for a value stream analysis” are answered (in written or orally) by the learner Example for closed learning evaluation in learning factories: Questions with predefined possible answers the learner has to select, like “What are existing symbols in the value stream analysis? • Supplier, process box, inventory, walking paths, information flows”

The first two systematisation sets (“objective and subjective measurement” and “open and closed measurement”) are used in combination in Table 6.1 to further systematise the different forms of learning success evaluation in learning factories and identify characteristics of those forms of evaluation.

90

See Clasen (2010). Bommer et al. (1995). 92 See for example Mabe and West (1982) or Harris and Schaubroeck (1988). 93 See Clasen (2010). 91

Low

Low

Assessment of knowledge, skills, characteristics

Possible

–

–

High

High

• Total

Processing time

Requirements to the participant

Bias tendency

Right solution due to guessing

At multiple measurement: substantial test effects

Objectivity of evaluation (incl. generation of criteria)

(Construct) validity

Requirements to evaluator skills on the target of evaluation

Low

Medium

Rather low

Aggregation (e.g., over a period or a field)

Low

High

–

–

Possible

Very low

Very low

Very low

Low

Very low

Subjective global (closed)

• For evaluation

Subjective aggregated (closed)

• For construction

Effort

Metering access

High

Given

Possible

–

Low (recognition)

Medium

Low–medium/high

Very high

Objective closed

Very high

Very high

Measurement of one/many detail(s) of the construct

Possibly limited

–

–

–

High (free reproduction)

High

High/very high

Very high

High

Objective open

Table 6.1 Characteristics of subjective and objective measuring approaches according to Clasen (2010) with orientation among others at Bommer et al. (1995) and Harris and Schaubroeck (1988)

6.4 Learning Factory Evaluation and Improvement 177

178

6 The Life Cycle of Learning Factories for Competence Development Exemplary scale for the self-evaluation

Competence class

Exemplary competences

Technical & methodological

Expertise for the control of machines

0 Non-existent

10 Optimally available

Technical & methodological

Structured problem solving

0 Non-existent

10 Optimally available

Personal

Enhance willingness to learn

0 Non-existent

10 Optimally available

Social & communicative

Communication of the capacity for teamwork

0 Non-existent

10 Optimally available

Fig. 6.35 Exemplary subjective self-evaluation sheet for different competence classes

(c) Self and external measurement Self and external evaluations are varieties of subjective evaluations.94 Self-evaluation techniques, which are also referred to as internal evaluation techniques, in general consist of the learners’ reflection on the bygone learning process. Figure 6.35 shows some exemplary questions for self-evaluation regarding different competence classes. In self (or internal) evaluation, learners reflect on the actual learning success.95 Self-evaluation is fraught with problems, and Harris and Schaubroeck (1988) discuss those potential problems. In contrast, external evaluations make use of persons other than the learner but are still subjective. Example for self-evaluation in learning factories: Participants answer questions like “How do you rate your knowledge after the learning factory training related to the topic ‘value stream analyses’?.”

Example for external evaluation in learning factories: Evaluators other than the learner answer questions like “How do you rate the knowledge development of the participants by the learning factory training related to the topic ‘value stream analysis’?”

(d) Summative and formative measurement Summative evaluations compare (final) results with intended goals at predefined times, e.g., after the learning module. Therefore, summative evaluation is linked to the accountability of a learning factory training. In contrast to this, formative evaluation is conducted during the learning process and therefore also allows immediate changes or improvements in current learning factory trainings.96

94

See Clasen (2010). See for example Mabe and West (1982). 96 See Dagley and Orso (1991). 95

6.4 Learning Factory Evaluation and Improvement

179

Example for summative evaluation in learning factories: Learning tests, tasks, or other forms of evaluation at the end of a learning factory training in order to assess learning results

Example for formative evaluation in learning factories: Accompanying observation through externals or participant-related evaluation during the learning process to find improvement potentials for the (current and next) learning factory training

(e) Direct and indirect measurement Indirect evaluations can be used with both objective and subjective measurement criteria, while direct evaluations are in general connected to subjective evaluations. If measurements are conducted at different time points in relation to the training, for example directly before training (pretest) and immediately after the training (posttest), and the change between the two measurements is seen as change or success indicator, this is called indirect evaluation. In contrast to this, direct evaluations look directly at the change or improvement, for example, using subjective and direct questions to the learning progress of participants in learning factory trainings.97 Example for direct evaluation in learning factories: Often subjective, direct questions at the learning progress, like “How do you rate the knowledge development by the learning factory training related to the topic ‘value stream analyses’?” are used

Example for indirect evaluation in learning factories: Equivalent to the example given above for the direct evaluation, an indirect question could look like this: • (before training, pretest) “How do you rate your knowledge related to the topic ‘value stream analyses’?” • (after training, post-test) “How do you rate your knowledge level now after the learning factory training related to the topic ‘value stream analyses’?”

Indirect evaluations are not limited to subjective measurement criteria. Furthermore, other evaluation designs are possible, for an overview of different evaluation designs please, see Fig. 6.36. In addition to the test designs mentioned in Fig. 6.36, an integration of follow-up tests (tests carried out after a few weeks or months after the treatment) can be used to determine the long-term success of the treatment.

(f) Quantitative and qualitative measurement

97

See Clasen (2010).

180

6 The Life Cycle of Learning Factories for Competence Development

Name

Group

Pretest

Treatment

Post-test

E

-

X

O

E

-

X

O

Post-test without control 2 Group, Post-test comparison 1 Group, Pre-test/post-test 2 Group, Pre-test/post-test

Solomon 4-Group Design

C

-

-

O

E

O

X

O

E

O

X

O

C

O

E w. PT

O

C w. PT

O

E \PT

O X X

C \PT

Legend

E C \PT

Experimental Group Control Group without pre-test

O O O O

w. PT X O

with pre-test Treatment Test

Fig. 6.36 General possible experimental designs for learning success evaluation

In general, evaluations using quantitative measures98 are focusing on the question what is learned. In contrast, qualitative evaluations explore and describe learning processes in a differentiated manner. Example for quantitative evaluation in learning factories: Often open or closed evaluation questions answered by the participants are rated and accordingly associated with points

Example for qualitative evaluation in learning factories: For example, answers to open evaluation questions can be evaluated in a qualitative way. Often qualitative measures are used in the evaluation of feedback, the learning context, or the learning process

General Approaches to Competence-Oriented Evaluation in Learning Factories Evaluating competence is difficult because it cannot be directly observed in an objective manner. Therefore, subjective methods such as self and external evaluations are used. Accordingly, evaluations that directly target the competence construct can only rely on subjective (self and external) measurements. Additionally in the approaches to competence-based evaluation, the following two statements are of importance: 98

Or translate qualitative measures in quantitative measures, e.g., with scoring models for answers to open questions.

6.4 Learning Factory Evaluation and Improvement

181

Fig. 6.37 Competence-oriented learning success evaluation approaches in learning factories

1. Competences manifest themselves in individual actions (or also: performances) in specific problem situations. These performances are observable.99 2. An important basis for the ability to act in unknown problem situations are knowledge elements corresponding to those competences (in particular for the technical and methodological competence class).100 Those knowledge elements can be evaluated directly in an objective manner. Those two statements result in a knowledge-based and a performance-based approach for competence-oriented evaluation in learning factories, i.e., the manifestations of competences (and actions) can be observed and the requirements can be queried. Furthermore, those two approaches can be combined to get a broader view on the competence concept. Figure 6.37 gives an overview of the possibilities for competence-oriented evaluation approaches. To gain access to the competence construct in this way, the intended competences are operationalised with assignable professional performances/actions as well as underlying knowledge elements (regarding professional and conceptual knowledge). The so-called competence transformation table is an instrument that can be used for this operationalisation of competences.101 Figure 6.38 demonstrates the operationalisation of the competence regarding the planning of flexible assembly systems in context of the learning module “flexible production systems.” 99

See Chomsky (1962). See for example Erpenbeck and Rosenstiel (2007) and Pittich (2014). 101 See Sect. 6.1 as well as Abele et al. (2015) and Tisch et al. (2015) for further information. 100

182

6 The Life Cycle of Learning Factories for Competence Development

Fig. 6.38 Exemplary operationalised competences of a learning module for “flexible production systems”

There are three methods to evaluate competences in learning factories: (a) knowledge-oriented evaluation approaches, (b) performance-oriented evaluation approaches, and (c) combination of knowledge- and performance-oriented evaluation approaches. (a) Knowledge-oriented evaluation approaches The effectiveness of competence development is often evaluated with the help of knowledge tests.102 The complex evaluation of the competence construct is in this case simplified by replacing it with a more accessible examination of transferred knowledge, see Fig. 6.39. This simplification comes with the cost of a less valid evaluation since knowledge is part of the competence concept, but only basis for selforganised professional ability to act.103 Furthermore, in this context, it is important what kind of knowledge is queried in the knowledge test. Studies show that conceptual knowledge in particular (which contains reasoning and referential knowledge) can be a good predictor for the professional ability to act.104 If knowledge tests are used in learning modules to determine the learning success, then either tests covering all knowledge types or tests covering especially higher forms of conceptual knowledge should be used. Knowledge can be assessed in open and closed as well as in oral or 102

See Lohaus and Habermann (2011). For the distinction between competency and knowledge see also Chap. 2. 104 See Pittich (2014). 103

6.4 Learning Factory Evaluation and Improvement

183

Fig. 6.39 Exemplary knowledge tests regarding different knowledge levels

written evaluations. Examples for the use of knowledge tests regarding the different knowledge levels are given in Fig. 6.39. (b) Performance-oriented evaluation approaches Besides the knowledge-oriented approaches, simulated problem scenarios can be used to check whether the learner is able to act in unknown and complex situations. In this case, the evaluation is simplified in the sense that not the competence construct itself, but its manifestations (the performances) are observed. This kind of evaluation emphasises the characteristic of technical and methodological competences to be effective in domain-specific application situations. Here, learning factory environments offer a great basis for this kind of learning success determination. In this case, conclusions about the developed competences are drawn from the observed actions of trainees in new problem situations that require the intended competences of the learning module. The procedure for the competence-oriented evaluation using problem scenarios can be described in the three phases105 : Phase (1) Preparation Development of the evaluation task based on the learning targets of the learning factory training. Phase (2) Implementation Perform evaluation task at the end of the training and observation, and Phase (3) Follow-up/evaluation

105

See Tisch et al. (2015b).

184

6 The Life Cycle of Learning Factories for Competence Development

Comparison of learning targets and developed competences, adjustment of training. The appropriate preparation (Phase 1) of a simulated problem scenario necessitates the use of a competence transformation table that identifies all actions aligned with the intended competences. This information is then utilised to construct a problem scenario that simulates, to the greatest extent possible, real-world challenges requiring professional skills from the trainees. For evaluation purposes, the environment utilised should differ from the one used in the rest of the learning module. Additionally, the complexity of the problem scenario should be high enough to meaningfully assess the trainee’s ability to apply their skills. Before commencing the simulated problem scenario, the trainees are informed about its underlying background and predefined objectives. However, it is crucial to ensure that the necessary information is communicated without divulging too much information about how to solve the problem or complete the task. To prevent the scenario from being too simplistic or optimised for the application of a particular method, ill-structured problems can be utilised. These types of problems are typically encountered more frequently in real-world situations.106 The evaluation exercise can be planned individually or in small groups of learners. On the one hand, the smaller the group, the more specific the results evaluation regarding the individual learning success. On the other hand, working and solving problems in teams resembles authentic work situations. To analyse the impact of learning modules, it is recommended to conduct a thorough observation (Phase 2) of practice performances, using pre-established observation guidelines to ensure the reliability of the observations and to minimise the observation effort required. By utilising such guidelines, participants’ practice performances (either individually or in groups) can be observed in complex, simulated situations. These observation guidelines are derived from the actions identified in the competence transformation table and are designed to capture self-organised actions exhibited by participants in novel problem situations, based on a set of comprehensive criteria and detailed observation indicators. A comprehensive list of relevant observation criteria and indicators can be found in Table 6.2. The observation can take place in form of a “live” accompanying observation or the observation of recorded videos of the performances.107 • In accompanying observations, observation sheets can be used in which the performances Pi of the learners can be logged regarding the observation criteria mentioned in Table 6.2. The semi-structured observation sheet supports the accompanying observer and allows the note-taking on key points. In addition, the semi-structured observations sheet further observations independent of the predefined criteria are recorded in a comment field which is provided for each defined performance.

106 107

Reiß (2012) proposes the so called resilience learning approach in order to deal with this problem. See e.g. Hambach et al. (2016).

6.4 Learning Factory Evaluation and Improvement

185

Table 6.2 Observation criteria and indicators for the performance evaluation in the simulated, complex problem situation (Tisch et al., 2015b) Dimension

Description

Possible observation indicators

Self-sufficiency

Are the participants able to tackle the problem autonomously?

Participants are able to perform the tasks: (a) Autonomously, (b) Hesitantly but on their own (discussion or look into script), (c) After guiding questions, (d) After a next step is pointed out, (e) They are not able to perform

Quality of approach Do the participants follow a structured approach to solve the problem?

(a) Methodologically or analytically structured, (b) Trial-and-error, (c) Unstructured

Result

(a) (b) (c) (d)

Are the results correct? (Not applicable for all actions)

Collaboration in the Are the participants working team together to tackle the problem?

Correct, Mainly correct, Mainly not correct, Not correct (at the end of the task the overall solution is assessed as well)

(a) Together as a team, (b) Individually

• The use of video recordings allows a more accurate and repeated analysis of the actions. It should be considered that a video recording in some cases may inhibit the conduct of actions. The quality of performances of the associated competences is rated based on observed indicators. Therefore, the importance of the performances for the competences as well as the importance of different observation indicators for the overall quality of the performance has to be weighted. This kind of evaluation includes subjective components due to subjective observation as well as a subjective weighing process by the training provider. • To reduce these subjective components, it is important that the observer and training provider have a clear understanding of learning targets, outcomes, and performance qualities. • The assessed performances finally give an indication of the competence development during the learning module. Participants or participant groups that solve the problem independently in a structured manner would be assessed at 100% quality of performances. Defined performance indicators, which are not observed, lead to a devaluation of performance quality. The resulting percentage values should be regarded as relative to the expectations and goals of the learning module. Furthermore, the results depend on individual

186

6 The Life Cycle of Learning Factories for Competence Development

Performance evaluation

Learning success evaluation in learning factories

Evaluating the trainees‘ ability to act in complex, unknown problem situations

Knowledge evaluation Evaluating the trainees‘ internalized, applicationrelevant, underlying (conceptual) knowledge

Combination of the evaluation results from performance and knowledge perspective to the comprehensive competence perspective

Fig. 6.40 Combination of knowledge and performance perspective to evaluate learning success in learning factories

valuation and weighting of specific indicators; thus, this form of learning success evaluation is not suiting for inter-training comparison.108 (c) Combination of knowledge- and performance-oriented evaluation approaches The last described option for competence-oriented evaluation of learning success in learning factories integrates the in the previous sections presented knowledge- and performance-oriented evaluation approaches, see Fig. 6.40. The disadvantage of the approaches to learning success evaluation is the sole focus on individual aspects of the competence concept: in the first case the underlying knowledge elements or in the second case the manifestations of competences in professional application situations. Therefore, a more reliable evaluation combines the knowledge- and the performance-oriented evaluation. More specifically, this combined approach tries to exclude positive evaluation results in the following two situations: i. That participants are only able to perform single actions (that may be only coincidentally right, for example, because they simply remember a sequence of work steps shown in an example) without internalising underlying knowledge that is crucial for the flexible handling of unknown situations. ii. That participants only remember single knowledge elements without bringing it into the professional context with the specific application of this knowledge. In this case, only inert knowledge was created that shows no effect for the ability to find solutions in unknown situations. For this purpose, after the observation of a performance task (see previous section), the observer questions the underlying knowledge by starting a discussion regarding the reasons for the procedure shown within the previously performance task. Answers 108

See also Tisch et al. (2015b).

6.4 Learning Factory Evaluation and Improvement

187

of the trainees can be recorded and analysed afterwards. Participants or participant groups solve the problem independently in a structured manner and have the competence underlying knowledge would be assessed at 100% competence level. Performance indicators that are not observed or underlying conceptual knowledge that is not present in the discussion lead to a devaluation of the competence level.109 Alternatively, a written knowledge test can be used for this purpose. In contrast to the performance task, which is perceived as an additional exercise as a part of the learning module, the written knowledge test is perceived as a mere evaluation tool. Glass (2021) developed a measurement methodology based on the multivariate, quantitative analysis approach of structural equation analysis (SEA) using the combined approach110 : A hypothesis-driven structural model is developed to understand the emergence and recognition of competences. Data is collected in courses of the Process Learning Factory CiP data which is generated using indicators and measuring instruments. The SEA is used for an explorative analysis, and only indicators and measuring instruments that produce significant results are incorporated into the final measurement procedure. This approach significantly reduces the effort required for competence measurement and makes it suitable for industrial use. The developed methodology can identify weaknesses in training courses and facilitate targeted improvements. Therefore, the methodology by Glass (2021) is a valuable contribution to enhancing further training measures in learning factories and industrial settings.111 The competence-oriented measuring of learning success conceptually already builds a bridge to the third evaluation level “transfer.” Learners that can manage unknown problem situations and have internalised the underlying conceptual knowledge are likely to be successful with the transfer of procedures to the own factory. However for the transfer, additional organisational obstacles must be overcome.

109

An example for such a combined performance- and knowledge-based evaluation is described in Tisch et al. (2015a, 2015b, 2018). 110 Glass (2021). 111 The detailed methodology can be found in Glass (2021).

188

6 The Life Cycle of Learning Factories for Competence Development

The competence measurement method by Glass (2021) enables the targeted transfer of the learned concepts into practice.112 The following formula shows the formal relationship between competence (Cnorm ), motivation (Mnorm ), knowledge (K norm ), and action (Anorm ). It can be seen that knowledge and action contribute approximately the same amount to the development of competences: Cnorm =

6.4.2.3

0.166Mnorm + 0.512K norm + 0.515Anorm . 1.193

Transfer (Use of Projects for Evaluation and Better Transfer)

An issue that can arise with learning factory training is that while they may effectively develop competences, transferring the skills learned in the learning factory to the actual work environment can be difficult. There are various reasons for this challenge, which may include factors such as: • • • •

a lack of ability to transfer the learnt to the own work environment, a lack of motivation to apply, a lack of permission to implement, and a lack of opportunities to implement.

To counteract these transfer barriers, it is advantageous for learning factories to incorporate elements of transfer into their training framework. The general dependencies between training and transfer are shown in Fig. 6.41. In this integrated learning factory training and transfer concept, not only are the specific learning needs of each person considered, but the training is also planned with how it can be applied in the industrial factory. This makes it easier to transfer what was learned in the training to the actual work environment. Additionally, this approach allows for practical evaluation of the success of the training and offers opportunities for further certification. The planning of both the training and the implementation in the company can be done at the same time. Figure 6.42 shows a proposed process of planning, implementation, and evaluation of learning modules and transfer projects in parallel. In the following, the three phases shown in Fig. 6.42 are explained more in detail. Alternatively, transfer projects can also be planned after the attendance of learning modules; here, however, there is the disadvantage that the selection of learning modules as a first step cannot be deliberately adjusted to the identification of possible transfer projects. In this way, it can happen that the learners visit unnecessary or inappropriate learning modules.

112

See Glass (2021).

6.4 Learning Factory Evaluation and Improvement

189

Fig. 6.41 Integrated learning factory training and transfer concept

Fig. 6.42 Process of planning, implementation, and evaluation using learning factory modules in combination with transfer projects

Planning In the planning phase of the integrated training and transfer concept, 1. suiting learning modules are selected and 2. an adequate transfer project is defined for the trainee. First, the selection of learning modules is of course dependent on the current and target competence profile of the trainee. In general, learning modules are selected in a way that they can close the gap between current and target competence profile.

190

6 The Life Cycle of Learning Factories for Competence Development

Trainee 1: Lean Machining Trainee 2: Lean Assembly

…

Autonomous Maintenance

SMED

Flexible Assembly Systems

Learning module selection Line Balancing

VSD

VSA

OEE

Standards, 5S

Analysis competence profiles

Trainee 1 Trainee 2

Trainee 3: Lean Basics

Trainee 3 …

Legend:

qualified

to be qualified

Project idea generation & assessment

Project definition

Project idea 1: Value stream analysis of product C and development of a future state

Trainee 1: Project idea 3, support from trainee 2, Start: 06/23 Trainee 2: Project idea 4, support from trainee 1, Start: 08/23 Trainee 3: Project idea 1, with support from trainees 1 or 2, Start: 07/23 …

Project idea 2: Find ways to stabilise processes and gain productivity in machining area of product C Project idea 3: Improvement of machining area of product B regarding flexibility and availability Project idea 4: Flexible adaptation of the assembly system from product B to the expected customer requirements over the next periods …

Fig. 6.43 Simplified example for the parallel planning of learning modules and transfer projects

Second, transfer projects are generated, which can be implemented in the trainee’s sphere of influence in the near future. The projects are then evaluated based on their compatibility with the trainee’s competence profile and their importance. It should be noted, however, that not all improvement projects need to be associated with learning factory training; for instance, urgent projects may not be suitable for this type of practice. The selection of this project must be done in cooperation with the respective manager, who at the same time must unequivocally promise his or her support in the implementation of the project. Without the intensive support of the manager, the use of transfer projects will not be successful. Figure 6.43 shows a simplified example for the parallel planning of learning module participation and transfer projects for three trainees in the field of lean production systems. Implementation In the implementation phase of the integrated training and transfer concept, 1. selected learning modules are attended by the trainee and 2. the defined transfer project is implemented in parallel.

6.4 Learning Factory Evaluation and Improvement

191

First, the trainee has to attend the selected learning modules. Once the trainee has completed the learning modules, the defined transfer project is then implemented in parallel. Second, the implementation of the transfer project can be supported by • a co-worker or an executive that is qualified in the defined field of action and • the trainer of the learning factory training that can function as a coach for the project. Additionally, it can be helpful for the success of the project that coach and trainee define a desired target state for the project that describes as precise as possible the targets and boundary conditions of the project. Those properly defined target states can be used in daily coaching routines to overcome obstacles and to improve processes and to learn.113 Target states are again oriented on a higher-level vision for production. Although training and transfer run in parallel, with regard to the details of the implementation phase, it has to be decided whether the training or the transfer project forms the starting point for the integrated concept, see Fig. 6.44. Both approaches are associated with advantages and disadvantages: • In the training-based approach, the (first) training module builds the starting point followed by the kick-off of the transfer project later. This approach is commonly considered intuitive because it aligns with the conventional approach followed in instructional situations. In this approach, trainees are first taught “what” and “how” to do, and later they apply their learning in real-life scenarios. However, this order is not obligatory because the real-world application not only follows the learning process but can also be a part of it. The primary drawback of the training-based approach is the lack of comprehension of the actual problem situation. Consequently, trainees may attend the learning module with minimal understanding of the real-world problem situation, which highlights the problem of “theory push” rather than “problem pull.”114 • In the project-based approach, the project kick-off builds the starting point and is followed by the (first) training module. This approach is considered to be more integrated as the application in the industrial factory is more interwoven into the entire learning process. The advantage of the project-based approach is analogous to the disadvantage of the training-based approach, which is the lack of problem sensitisation before the initial learning module is undertaken. The trainee is aware beforehand of the project or problem that will be particularly relevant to them and which individual topics they have questions on. The knowledge of the problem or project that will be worked on can be a source of motivation in the context of the learning process. A drawback of this approach is that the trainee only focuses on the contents in the learning module that are relevant for the transfer project and thus does not learn contents that are relevant for other projects. 113 114

Rother (2009) describes in detail how. See Tisch et al. (2013).

192

6 The Life Cycle of Learning Factories for Competence Development

1st learning module Trainingbased approach

2nd learning module

…

Kick-off transfer project

transfer project implementation

Coaching by co-worker, executive or learning factory trainer

1st learning module Projectbased approach

Kick-off transfer project

2nd learning module

…

transfer project implementation

Coaching by co-worker, executive or learning factory trainer

Fig. 6.44 Training- and project-based approach for the implementation phase of the integrated training and transfer concept

Evaluation In the evaluation phase of the integrated training and transfer concept, 1. results of parallel implementation of transfer project and learning modules are presented, 2. the results of the transfer project are evaluated, and 3. the trainee receives a certificate for the successful transfer project and the attendance of the learning module. First, in the presentation of the results, the project coach, executives, and involved colleagues can participate. In particular, the following subjects are presented: • the problem definition and the initial situation of the project, • the procedure within the transfer project as well as • the results of the project, which should also be able to be supported with key performance indicators (KPI) in a before-and-after-project comparison. Suiting KPIs vary depending on the project orientation, for example, they may be reject rate, rework cost, lead time, development time, volume or mix flexibility, productivity metrics or many more. Second, the coach and the executive can evaluate the results of the transfer project based on • these key performance indicators, • target states agreed in advance with the trainee as well as • the described trainee’s approach.

6.4 Learning Factory Evaluation and Improvement

193

Furthermore, it is possible that other improvement measures or events, such as the introduction of new machines, hiring of new employees, or implementation of new products or improvement projects, may have either positive or negative effects that overlap with the transfer project within a production area. Although it is not possible to completely prevent such overlap, it must be considered when evaluating the transfer project. Third, as a final optional step implementing transfer projects also presents an opportunity to recognise the trainee’s acquired qualifications through a corresponding certificate. To make the certificates meaningful for future use, they should be standardised in terms of level and content. This not only enhances the trainee’s motivation but also provides the company with insights into the trainee’s suitability for future improvement projects.

6.4.2.4

Economy of Learning Factories

Assessing the economic risks of implementing learning factories in industry and the public sector is an important issue for many learning factory operators. The high investment and costs of setting up and running a learning factory have to be weighed against the potential benefits. The return on investment must be assessed on a caseby-case basis. The following sections illustrate how to identify and weigh up the costs and benefits of learning factories. A distinction is made between: • direct monetary effects that can be attributed to the investment project without analysis or estimation (level I), and • indirect monetary effects, which are difficult or impossible to convert into monetary benefit equivalents without further estimation (level II). Level I: Direct Monetary Effects Direct monetary effects occur on both the benefit and cost sides in the form of cash inflows and outflows. Direct costs can be divided into non-recurring investment costs and recurring operating costs of the learning factory. Similarly, direct monetary benefits may be of a non-recurring or recurring nature. In order to anticipate as accurately as possible, the data required for the evaluation of the economic aspects, checklists can be used for the costs and benefits. Table 6.3 shows a checklist for the costs of setting up and running learning factories, divided into one-off and operating costs. A checklist for the direct monetary benefits of learning factories is shown in Table 6.3 that is also divided into non-recurring and operating monetary benefits (Table 6.4). If the costs and revenues mentioned in the checklists are known, the capital budgeting at level I can be conducted on the basis of this information. There are two methods of capital budgeting:

194

6 The Life Cycle of Learning Factories for Competence Development

Table 6.3 Checklist for non-recurring and operating direct costs of learning factories according to Abele et al. (2015) based on Zangemeister (1993) Direct costs of learning factories

Non-recurring costs

Operating costs

1. Planning of the learning system • Internal • External

1. Consumables • Raw materials and supplies • Energy costs

2. Acquisitions • Land, buildings • Machinery and equipment • Tools and other equipment

2. Personnel costs • Direct labour cost • Overhead costing • Incidental wage costs

3. Construction effort

3. External services (repairs, material)

4. Staff cost • Recruitment • Training

4. Allocations • Room costs (land, building) • General works service • IT etc.

5. Subsequent investment in 5. Debt service upstream and downstream sectors • Imputed amortisation • Imputed interest

Table 6.4 Checklist for direct monetary benefits of learning factories, non-recurring, and operating benefits (Abele et al., 2015) Direct benefits of learning factories

Non-recurring benefits

Operating benefits

1. Grants/subsidies • Public • Private

1. Trainings • Internal • External

2. Tax relief • Spendings

2. Product sales • Contract • On the market

3. Advertising revenue • Internal • External

3. Use as testbed • Internal • External

4. Investments • Sale of learning factory shares

4. Advertising revenue • Internal • External

• Static methods use only averages of incoming and outgoing payments for the calculation, which makes them simpler but less meaningful than dynamic methods. • Dynamic methods consider the time aspect of cash flows. The dynamic net present value (NPV) method is widely used because of its simplicity and power. In the method, the net present value of investment alternatives is calculated. The NPV is the sum of all discounted cash flows generated by the investment and can therefore

6.4 Learning Factory Evaluation and Improvement

195

Table 6.5 Detailed overview of expected non-recurring costs and direct monetary benefits of the Future Learning Factory Non-recurring costs calculated/estimated cost in million e

Non-recurring benefits calculated/estimated benefits in million e

1. Planning of the learning system

0.35

1. Grants/subsidies

1

2. Acquisitions

1.5

2. Tax relief

–

3. Construction effort

1.5

3. Advertising revenue

0.15

4. Staff cost (Recruitment, Training)

0.05

4. Investments

0.35

Sum of non-recurring direct monetary benefits (in million e)

1.4

5. Subsequent investment in upstream and – downstream sectors Sum of non-recurring costs (in million e)

3.4

Balance of non-recurring costs and direct monetary benefits (R0 )

− 2 Million e

be seen as the equivalent of a series of cash flows from an investment115 : NPV = NPV t T ci t cot Rt i

T T ∑ ∑ (ci t − cot ) (Rt ) = R + , 0 t + i + i )t (1 ) (1 t=0 t=1

Net present value Year of the cash flow Last time of relevant payments Cash inflow in the year t Cash outflow in the year t Resulting cash flow in the year t Discount rate.

An exemplary calculation of the Future Learning Factory at the Modern University is presented in the following. In the example, the difference is calculated in the first year between the estimated non-recurring incoming and outgoing cash flows R0 of a hypothetical learning factory of two million Euros (investments in machinery etc. minus non-recurring income). The detailed estimates of the initial costs and benefits of the Future Learning Factory on which the calculation is based are shown in Table 6.5.

115

See Busse von Colbe et al. (2015).

196

6 The Life Cycle of Learning Factories for Competence Development

Table 6.6 Detailed overview of expected operating costs and direct monetary benefits of the Future Learning Factory Operating cost calculated/estimated cost in million e

Operating benefits calculated/estimated benefits in million e

1. Consumables

0.1

1. Trainings

1

2. Personnel costs

0.3

2. Product sales

–

3. External services

0.05

3. Use as testbed

0.2

4. Allocations

0.1

4. Advertising revenue

–

5. Debt service

–

Sum of all operating cost

0.55

Sum of all operating direct monetary benefits

1.2

Balance of operating costs and direct monetary benefits (Rt )

650,000 e

Beyond the initial payments, an annual positive cash flow of 500,000 e is calculated based on the checklists in Tables 6.3 and 6.4 and a checklist for the direct monetary benefits of learning factories is shown in Table 6.3, that is also divided into non-recurring and operating monetary benefits Table 6.4. The detailed estimated operating costs and benefits of the Future Learning Factory are listed in Table 6.6.

Once the resulting cash flows Rt of a specific learning factory or of learning factory alternatives have been estimated, the NPV can be calculated by specifying the observation period (T ) and the interest rate i with which the investment is being compared. If the NPV of an investment is greater than zero, it is absolutely advantageous compared to the comparative investment with interest rate i. Moreover, the investment whose NPV. is greater than the NPV of all other alternatives is relatively advantageous.116 Table 6.7 shows the calculated NPV for the Future Learning Factory in relation to the number of periods considered in years and different interest rates i.

116

See Götze (2014).

6.4 Learning Factory Evaluation and Improvement

197

In our example for the Future Learning Factory, regardless of the interest rate i in the range from 1 to 10%, the payment will have amortised (paid off) after 4 years (see Table 6.7 in bold). Therefore, if the observation period is four years or longer, an investment in the Future Learning Factory can be considered to be beneficial if i < 10%. This is achieved in the example by, among other things, a relatively high utilisation of the learning factory’s capacity for industrial training: to reach one million e in training mode, the learning factory must be used for 100–150 days per year for industrial training. Economically, the payback can be calculated using the NPV formula and finding the period t for which the NPV is positive (or zero) for the first time: min{t}, for which NPV =

T T ∑ ∑ (ci t − cot ) (Rt ) = R + ≥ 0. 0 t (1 + i ) (1 + i)t t=0 t=1

In addition to the discount rate, the time in which the investment in a learning factory pays off changes with • deviating initial net investments R0 and • different annual cash flows Rt .

Table 6.7 Calculated NPV for the Future Learning Factory depending on interest rate i and the number of regarded periods Period t

Rt in million e

NPV in million e For i = 1%

For i = 2%

For i = 5%

For i = 10%

0

− 2.00

− 2.00

− 2.00

− 2.00

− 2.00

1

0.65

− 1.36

− 1.36

− 1.38

− 1.41

2

0.65

− 0.72

− 0.74

− 0.79

− 0.87

3

0.65

− 0.09

− 0.13

− 0.23

− 0.38

4

0.65

0.54

0.48

0.30

0.06

5

0.65

1.15

0.98

0.81

0.46

6

0.65

1.77

1.52

1.30

0.83

7

0.65

2.37

2.05

1.76

1.16

8

0.65

2.97

2.56

2.20

1.47

9

0.65

3.57

3.06

2.62

1.74

10

0.65

4.16

3.54

3.02

2.00

198

6 The Life Cycle of Learning Factories for Competence Development

Therefore, Table 6.8 gives an overview of the amortisation periods in years depending on R0 and Rt for a discount rate of i = 5%. Table 6.8 is visualised regarding the following rules: • Fast amortisation, low-risk investment Learning factory investments with a calculated amortisation period of five years or shorter are marked in green. • Medium amortisation, medium risk investment Learning factory investments with a calculated amortisation period from six to ten years are marked in yellow. • Slow amortisation, high risk investment Learning factory investments with a calculated amortisation period of 11 years or longer in red. Table 6.8 Amortisation periods in years for learning factory investments depending on R0 and Rt , based on a discount rate of 5%

Level II: Indirect and Non-monetary Effects of Learning Factories In the following discussion, level II of the economic analysis is introduced to examine the indirect and non-monetary effects of learning factories, in addition to the direct monetary effects that are considered in level I of the economic and financial analysis. It is not sufficient to evaluate the economic success of learning factories only in

6.4 Learning Factory Evaluation and Improvement

199

cost

benefit

non-monetary, negative effects

non-monetary benefit

indirect cost

indirect monetary benefit

direct cost

direct monetary benefit

Fig. 6.45 Monetary, indirect monetary, and non-monetary effects of learning factories on the cost and benefit side

terms of direct monetary effects. The analysis must also consider the effectiveness and impact of different learning outcomes. In general, impacts can be categorised into monetary, indirect monetary, and non-monetary impacts on both the cost and benefit sides, as shown in Fig. 6.45. This classification facilitates the application of comprehensive, multidimensional, and decision-oriented approaches. The aim of such approaches is to quantify and monetise as many effects as possible. Direct monetary, indirect monetary, and non-monetary impacts are defined in Table 6.9, which provides examples from the manufacturing sector and lists assessment tools that can be used for analysis. Table 6.9 Distinction between directly monetary, indirectly monetary and non-monetary effects based on Heinrich and Lehner (2005) and Hanssen (2010) Definition

Examples

Direct monetary effects

Direct monetary effects can be assessed directly in monetary terms

• Effective savings, • Capital budgeting, • Sales increase, • Traditional economic analysis • Increase in production costs etc.

Indirect monetary effects

Indirect monetary effects cannot be assessed directly in monetary terms. In order to quantify the monetary effects, other factors, for example, temporal quantities, must be consulted

• Increased employee productivity, • Higher customer satisfaction, • Lower absenteeism of employees etc.

• Cost accounting: analysis of the indirect monetary effects is usually done by capturing quantities

• Image gains, • Increased employee morale, • Increased versatility, • Increased expertise

• Utility analysis, • Work performance analysis, • Qualitative analysis

Non-monetary Non-monetary effects effects refer to the impacts that are not directly related to financial gains or losses. For non-monetary effects, there are no valuation standards available for quantification

Assessment tools

200

6 The Life Cycle of Learning Factories for Competence Development

Society ideas for future challenges

Academia attractiveness for industry and students better educational system

innovative technologies and concepts

industry-oriented research projects research possibilities

strengthening of production location quality-, cost-, delivery-, flexibility-KPIs

Learning Factory

better training system

collaboration & network opportunities

attractiveness of company

learning and transfer success

higher market value

Industry economy and competivenes performance of production system

Individual higher qualification motivation

attractiveness and autonomy of work

Fig. 6.46 Categorised indirect and non-monetary effects of learning factories on individual, academic, industrial, and societal level

In general, learning factories have a wide range of impacts that affect individuals, academical institutions, industry, and society as a whole, and these different levels are interdependent. Figure 6.46 categorises the indirect effects that exist at these different levels in relation to the implementation of learning factories in industry and academia. In order to include these effects in a cost–benefit analysis, it is necessary to specify which indirect monetary or non-monetary effects are to be analysed in detail. Each effect could be analysed in detail.

6.5 Remodelling Learning Factory Concepts The final phase of the learning factory life cycle involves the remodelling or, in some cases, the recycling of learning factory concepts. This section specifically addresses the remodelling of existing learning factory concepts. There are several reasons why learning factories need remodelling, including: • New trends and technologies: Learning factories have to react on new trends or innovative technologies that are coming up. Most recently, this can be recognised in the case of artificial intelligence and climate-neutral production. With these trends coming up, various learning factories are remodelled in order to open up

6.5 Remodelling Learning Factory Concepts

201

possibilities for the demonstration, experimentation, and testing of new ideas regarding these trends.117 • As a result of evaluation: Remodelling of learning factories and learning factory modules can be a result of the evaluation of learning processes, the learning outcome, or the learning concept.118 • Extension of the contentual or target group scope of the learning factory: When new topics or target groups are added to the learning factory program, an extension of the learning factory program as well as most likely an extension of the learning factory environment are needed. In order to remodel learning factory concepts, which include the learning environment as well as the learning factory program consisting of various learning factory modules, learning factory design approaches119 are used. Often those design approaches already contain remodelling aspects for existing learning factories.120 Figure 6.47 summarises121 the learning factory remodelling cycle that consists of the following phases: • The analysis of the existing learning factory reveals a comprehensive profile of its current state, including the environmental factors and the composition of its program. The learning factory’s profile is shaped by its physical infrastructure, technological resources, and human capital. The environment of the learning factory encompasses the physical layout of the facility, availability of equipment and tools, and the overall ambience for learning. Additionally, the technological resources, such as software applications, simulation tools, and data analytics, play a crucial role in shaping the learning experience. Furthermore, the composition of the learning factory program includes the curriculum, training modules, and pedagogical approaches employed to facilitate learning. This may encompass hands-on training, real-world simulations, interdisciplinary projects, and industry collaborations. A comprehensive analysis of these factors provides insights into the strengths, weaknesses, opportunities, and challenges of the existing learning factory, serving as a foundation for continuous improvement and enhancement. • The definition of planned extension of the learning factory involves deriving new learning targets, use cases, and technologies. The learning targets are the specific knowledge, skills, and competences that learners are expected to acquire through the extended program. These targets may be aligned with industry demands, emerging trends, or organisational goals. The use cases are practical scenarios or real-world situations that learners will engage with in order to apply their learning 117

See e.g. Faller and Feldmüller (2015), Wank et al. (2016), Prinz et al. (2016) and Erol et al. (2016). 118 Different types of those evaluation approaches are presented in this book with the evaluation of reaction, learning, transfer, and economy (see Sect. 6.4.2) or the evaluation of the complete learning factory concept based on a quality system and a maturity model for learning factories (see Sect. 6.3.2). 119 Presented in Sect. 6.1. 120 See for example Tisch et al. (2015a) or Plorin et al. (2015). 121 Derived from design approaches.

202

6 The Life Cycle of Learning Factories for Competence Development

in a meaningful way. These use cases could be simulations, case studies, or handson projects that allow learners to develop problem-solving skills and gain practical experience. The new technologies refer to the innovative tools, equipment, or digital platforms that will be integrated into the learning factory to enhance the learning experience. This may include technologies such as virtual reality, augmented reality, Internet of Things (IoT) devices, or advanced data analytics tools. The definition of these planned extensions provides a clear roadmap for the expansion of the learning factory, ensuring that it remains relevant, up-to-date, and aligned with the evolving needs of learners and industries. • The process of gap identification involves a comparison between the planned extensions and the possibilities offered by the existing learning environment. This involves a thorough assessment of the current state of the learning factory and a careful analysis of the intended expansions. By identifying gaps, it becomes possible to pinpoint areas where improvements or modifications are needed in order to bridge the differences between the existing learning environment and the planned extensions. This may include identifying gaps in technology infrastructure, curriculum, pedagogical approaches, or alignment with industry requirements. Through this comparison, the learning factory can determine areas where enhancements or changes are necessary to effectively implement the planned extensions and ensure a seamless integration of new learning targets, use cases, and technologies. This gap identification process serves as a crucial step in the continuous improvement and development of the learning factory, enabling it to adapt and evolve to meet the changing needs and demands of learners and industries. • The design of the extension entails a comprehensive approach to developing the learning factory to accommodate the planned expansions. This includes designing learning modules that align with the new learning targets and use cases defined earlier. These learning modules may include instructional content, learning activities, assessments, and resources that are tailored to meet the specific needs of the learners and the intended outcomes of the learning factory. Additionally, the design of the extension may also involve remodelling the existing learning environment to accommodate the changes. This may include reconfiguring the physical layout, upgrading the technological resources, and enhancing the overall ambience to create an optimal learning environment. The design of the extension is a crucial stage that requires careful consideration of various factors, including pedagogical approaches, curriculum design, and the integration of new technologies. It aims to create a cohesive and effective learning environment that facilitates the achievement of the learning targets and use cases, while also ensuring a seamless integration with the existing learning environment to promote continuity and consistency in the learning process. • Integration is a critical aspect of the overall process, involving the seamless incorporation of new components into the existing learning factory program. This includes the integration of learning modules, which are designed to align with the new learning targets and use cases, into the existing curriculum. These learning modules are carefully integrated into the program, ensuring that they

6.6 Wrap-Up of This Chapter

Integration Module integration in existing program Integration of environmental extensions Design of extension Design of learning modules Remodeling of existing learning environment

203 Analysis of existing learning factory Profile of existing learning factory environment Composition of existing learning factory program

Learning Factory Remodeling Cycle

Definition of planned extension New learning targets definition New use case definition New technologies definition … Gap identification Comparison of the planned extensions and possibilities of existing learning environment

Fig. 6.47 Learning factory remodelling cycle

complement and enhance the existing learning experiences, while also addressing the identified gaps. Furthermore, the integration process also involves incorporating the environmental extensions, such as upgraded technologies or remodelled learning environments, into the existing program. This may involve reconfiguring the physical layout, integrating new technologies into the existing infrastructure, and aligning the ambience with the planned extensions. The goal of the integration process is to create a cohesive and seamless learning experience for learners, where the new components are effectively integrated into the existing program to promote continuity, consistency, and effectiveness in achieving the desired learning outcomes.

6.6 Wrap-Up of This Chapter This chapter systematises the various models and methods for tasks planning, designing, evaluating, improving, and redesigning learning factories. These approaches are structured along the entire life cycle of a learning factory, starting with the definition of customer requirements and business objectives, and ending with the remodelling of existing learning factory concepts. In Sect. 6.1, different perspectives and approaches to the design and planning of learning factories are described. The concept of a learning factory can be viewed as either an idealised representation of a real production environment or a complex learning environment. Different authors propose linear, sequential approaches to designing learning factories that address different aspects of the design, such as the sociotechnical infrastructure, product development, or the use of new technologies. The IALF learning factory design approach is presented in detail and follows a competence-oriented design process. The approach is structured into three levels of design, namely macro, meso, and micro

204

6 The Life Cycle of Learning Factories for Competence Development

• The macro level focuses on developing a comprehensive curriculum that considers learning targets, target groups, and other stakeholders. At the meso level, specific learning modules are designed to teach workers how to improve various processes. At the micro level, specific learning situations and resources are created to teach learners. The approach is subdivided into two didactic transformations that derive intended competences and then implement them. The design steps at the macro level include the definition of the learning factory’s targets, evaluation criteria, and project plan. Furthermore, the configuration of a learning factory is presented. The configuration involves selecting appropriate factory elements and products and dividing the factory into different areas. The configuration process consists of four steps: identifying requirements, identifying possible configuration alternatives, evaluating alternatives, and selecting the appropriate configuration. An optimisation model can also be used to determine the best configuration. The text includes figures and explanations for each step in the configuration process. • Learning modules at the meso level are created based on modularised learning targets that structure the learning content. The first didactic transformation specifies what should be learned in each learning module, while the second didactic transformation defines the sequence and learning environment of the module. The design process involves analysing and defining the requirements and the learning module framework. The roles addressed in the learning module are identified and described based on the specific content. Competences are operationalised using a competence transformation table based on the requirements identified in the previous steps. The competence transformation table can be used for the design of learning modules as well as for the redesign of existing learning modules. Existing learning modules can be improved by consistently linking knowledge aspects and corresponding performances, defining missing knowledge elements for certain performances and actions, removing inert knowledge, and eliminating unnecessary redundant content. • The learning activities at the micro level are designed based on the structure and goals defined at the meso level. The first step at the micro level involves defining the framework and targets for the learning situations and then distributing time capacities for individual sequences. The design principles for the learning situations include output orientation, practical orientation, and a focus on practicerelated phases, with exploration and testing activities followed by systematisation activities and reflection phases. In Sect. 6.2, four different business models for built-up, sales, and acquisition learning factories are described. The first model involves consultancy services to help design learning factories, the second focuses on replicating an existing learning factory, and the third model creates customised learning factories based on specific needs. Each model caters to different needs and circumstances in the establishment, sales, and acquisition of learning factories. Section 6.3 discusses the operation of learning factories as authentic learning environments for hands-on, production-related training. Training providers can offer learning factory courses on various topics to the general market or individual partner

6.6 Wrap-Up of This Chapter

205

companies of the learning factory. To ensure economic, contentual, personnel, and organisational quality, training providers must constantly develop and incorporate upto-date research results into training courses. Learning factory trainers need didactic and methodical skills, technical expertise, facilitation, and coaching skills. Learning factory operation requires various models and training management addressing training support and administration, training coordination, and training delivery. Best Practice Examples of learning factory use cases in the operation phase are also provided. Section 6.4 discusses the evaluation and improvement phases of the learning factory life cycle. In the operation phase of the learning factory, it is essential to prevent efficiency deterioration and continuously improve the existing learning system. Quality systems for learning factories offer systematic approaches for analysing the current state, assessing the potential for improvement, and deriving improvement measures. Such a quality system is developed by Enke (2020) based on a maturity model. The development process and structure of the maturity model are described with the four simplified sequential phases: analysis, structure, design and implementation, and validation. The maturity model for learning factories enables quality assurance and further development of learning factories. The structure of the model includes maturity elements, action fields, capability levels, and maturity levels. Each maturity level contains certain capability levels of various action fields. As the maturity level of a learning factory increases, new action fields are added to raise expectations on the capability. The evaluation of learning factories is based on the Context, Input, Process, and Product (CIPP model) as a framework for training programs, which involves assessing the context, inputs, processes, and products of the program. Furthermore, the Kirkpatrick four-level model classifies the levels of output evaluation as reaction, learning, behaviour/transfer, and economic results. There are various opportunities and methods for measuring the effects of learning activities in learning factories. • The reaction of participants is often evaluated through structured and standardised satisfaction or feedback surveys. The surveys cover various aspects such as overall impression, quality of moderation and presentations, practical exercises, theoretical content, and more. • The learning of participants includes the factors that influence learning success, e.g., the individual learner and the learning situations. Different measurement methods and forms exist for evaluating learning success, A classification of these methods can be made between subjective versus objective measurement, open versus closed measurement, self versus external measurement, summative versus formative measurement, direct versus indirect measurement, and quantitative versus qualitative measurement. Furthermore, competence-oriented evaluation in learning factories is based on both knowledge and performance-based aspects of competences. • For the transfer of competences to the actual work environment, several barriers exist. To overcome these barriers, an integrated training and transfer concept for learning factories is suggested, which incorporates transfer elements into

206

6 The Life Cycle of Learning Factories for Competence Development

the training framework, considers individual learning needs, and plans for the application of training in the industrial factory. The concept consists of three phases: planning, implementation, and evaluation. In the planning phase, suitable learning modules are selected based on the trainee’s competence profile, and transfer projects are defined. In the implementation phase, the trainee attends the selected learning modules and works on the defined transfer project. The results of the parallel implementation of the transfer project and learning modules are evaluated in the evaluation phase, and the trainee receives a certificate for the successful transfer project and attendance of the learning module. This certification can be standardised to provide insights into the trainee’s suitability for future improvement projects. • The economic results of implementing learning factories are distinguished between direct monetary effects (level I) and indirect monetary effects (level II). Level I direct monetary effects include non-recurring investment costs and recurring operating costs of the learning factory, as well as non-recurring and recurring monetary benefits. To assess the economic risks, checklists for the costs and benefits of learning factories are used. The capital budgeting at level I can be conducted using static or dynamic methods. Dynamic methods, such as the net present value (NPV) method, consider the time aspect of cash flows and calculate the equivalent value of a series of cash flows from an investment. Level II includes indirect and non-monetary effects in evaluating the success of learning factories. The impacts of learning factories can be categorised as direct monetary effects, indirect monetary effects, and non-monetary effects, and each category requires a different approach for analysis. Learning factories have a wide range of impacts that affect individuals, academic institutions, industry, and society as a whole. Section 6.5 discusses the remodelling of existing learning factory concepts, which is the final phase of the learning factory life cycle. The remodelling is necessary due to new trends, evaluation results, and expansion of the learning factory content or target group. Learning factory design approaches are used to remodel the learning environment and the learning factory program. The process involves analysing the existing learning factory, defining planned extensions, identifying gaps, designing the extension, and integrating the new components. The remodelling process aims to ensure that the learning factory remains relevant, up-to-date, and aligned with the evolving needs of learners and industries.

References Abel, M., Czajkowski, S., Faatz, L., Metternich, J., & Tenberg, R. (2013). Kompetenzorientiertes curriculum für Lernfabriken. Werkstattstechnik Online: Wt, 103(3), 240–245. Abele, E., Eichhorn, N., & Kuhn, S. (2007). Increase of productivity based on capability building in a learning factory. In Computer Integrated Manufacturing and High Speed Machining: 11th International Conference on Production Engineering (pp. 37–41). Zagreb.

References

207

Abele, E., Metternich, J., Tenberg, R., Tisch, M., Abel, M., Hertle, C., Eißler, S., Enke, J., & Faatz, L. (2015). Innovative Lernmodule und -fabriken: Validierung und Weiterentwicklung einer neuartigen Wissensplattform für die Produktionsexzellenz von morgen. TU Prints. Abele, E., Tenberg, R., Wennemer, J., & Cachay, J. (2010). Kompetenzentwicklung in Lernfabriken für die Produktion. Zeitschrift für Wirtschaftlichen Fabrikbetrieb (ZWF), 105(10), 909–913. Alliger, G. M., Tamnenbaum, S. I., Bennett, W., JR, Traver, H., & Shotland, A. (1997). A metaanalysis of the relations among training criteria. Personnel Psychology, 50(2), 341–358. Anderson, L. W., Krathwohl, D. R., & Airasian, P. W. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives (Complete). Longman. Baena, F., Guarin, A., Mora, J., Sauza, J., & Retat, S. (2017). Learning factory—The path to industry 4.0. Procedia Manufacturing, 9, 73–80. Balve, P., & Albert, M. (2015). Project-based learning in production engineering at the Heilbronn learning factory. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 104–108). https://doi.org/10.1016/j.procir.2015.02.215 Becker, F. G., Wittke, I., & Friske, V. (2010). “Happy Sheets”: Empirische Befragung von Bildungsträgern. Retrieved from http://nbn-resolving.de/urn/resolver.pl?urn=urn:nbn:de:0070bipr-48208 Becker, M. (2005). Systematische Personalentwicklung: Planung, Steuerung und Kontrolle im Funktionszyklus. Schäffer-Poeschel. Bender, B., Kreimeier, D., Herzog, M., & Wienbruch, T. (2015). Learning factory 2.0—Integrated view of product development and production. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 98–103). https://doi.org/10.1016/j.procir.2015.02.226 Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Book I: Cognitive domain. McKay. Böhner, J., Weeber, M., Kuebler, F., & Steinhilper, R. (2015). Developing a learning factory to increase resource efficiency in composite manufacturing processes. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 64–69). https://doi.org/10. 1016/j.procir.2015.05.003 Bommer, W. H., Johnson, L. J., Rich, G. A., Podsakoff, P. M., & Mackenzie, S. B. (1995). On the interchangeability of objective and subjective measures of employee performance: A metaanalysis. Personnel Psychology, 48(1), 587–605. Burlingame, G. M., Seaman, S., Johnson, J. E., Whipple, J., Richardson, E., Rees, F., Earnshaw, D., Spencer, R., Payne, M., & O’Neil, B. (2006). Sensitivity to change of the brief psychiatric rating scale-extended (BPRS-E): An item and subscale analysis. Psychological Services, 3(2), 77–87. Busse von Colbe, W., Lassmann, G., & Witte, F. (2015). Investitionstheorie und Investitionsrechnung (4., vollständig überarbeitete Auflage). Springer. Cachay, J., Wennemer, J., Abele, E., & Tenberg, R. (2012). Study on action-oriented learning with a learning factory approach. Procedia—Social and Behavioral Sciences, 55, 1144–1153. Chomsky, N. (1962). Explanatory models in linguistics. In E. Nagel, P. Suppes, & A. Tarski (Eds.), Logic, methodology and philosophy of science (pp. 528–550). Stanford University Press. Clasen, H. (2010). Die Messung von Lernerfolg: Eine grundsätzliche Aufgabe der Evaluation von Lehr- bzw. Trainingsinterventionen [Dissertation]. Dresden. Dagley, D. L., & Orso, J. K. (1991). Integrating summative, formative modes of evaluation. NASSP Bulletin, 75(536), 72–82. https://doi.org/10.1177/019263659107553610 Dinkelmann, M. (2016). Methode zur Unterstützung der Mitarbeiterpartizipation im change management der variantenreichen Serienproduktion durch Lernfabriken [Dissertation]. Stuttgart.

208

6 The Life Cycle of Learning Factories for Competence Development

Doch, S., Merkler, S., Straube, F., & Roy, D. (2015). Aufbau und Umsetzung einer Lernfabrik. Produktionsnahe Lean-Weiterbildung in der Prozess- und Pharmaindustrie. [lit. trans.: Construction and implementation of a learning factory. Lean advanced training in the process and pharmaceutical industry.]. Industrie Management, 03, 26–30. ElMaraghy, H. A., & ElMaraghy, W. (2015). Learning integrated product and manufacturing systems. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 19–24). Elster, F., Dippl, Z., & Zimmer, G. (2003). Wer bestimmt den Lernerfolg? Leistungsbeurteilung in projektorientierten Lernarrangements. Bertelsmann. Enke, J. (2020). Methodik zur multidimensionalen, reifegradbasierten Entwicklung von Lernfabriken für die Produktion [Dissertation]. Shaker. Enke, J., Glass, R., & Metternich, J. (2017a). Introducing a maturity model for learning factories. In 7th CIRP-Sponsored Conference on Learning Factories. Procedia Manufacturing (Vol. 9, pp. 1–8). Enke, J., Kaiser, J., & Metternich, J. (2017b). Die Lernfabrik als Export-Erfolg. Zeitschrift für Wirtschaftlichen Fabrikbetrieb (ZWF), 112(10), 644–647. Enke, J., Kraft, K., & Metternich, J. (2015). Competence-oriented design of learning modules. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 7–12). https://doi.org/10.1016/j.procir.2015.02.211 Enke, J., Metternich, J., Bentz, D., & Klaes, P.-J. (2018). Systematic learning factory improvement based on maturity level assessment. In 8th CIRP Conference on Learning Factories—Advanced Engineering Education & Training for Manufacturing Innovation (Vol. 23, pp. 51–56). Enke, J., Tisch, M., & Metternich, J. (2016). Learning factory requirements analysis—Requirements of learning factory stakeholders on learning factories. In 5th CIRP Global Web Conference (CIRPe 2016). Procedia CIRP (Vol. 55, pp. 224–229). Erol, S., Jäger, A., Hold, P., Ott, K., & Sihn, W. (2016). Tangible industry 4.0: A scenariobased approach to learning for the future of production. In 6th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 54, pp. 13–18). Erpenbeck, J., & Rosenstiel, L. von. (2007). Handbuch Kompetenzmessung: Erkennen, verstehen und bewerten von Kompetenzen in der betrieblichen, pädagogischen und psychologischen Praxis (2., überarb. und erw. Aufl.). Schäffer-Poeschel. Faller, C., & Feldmüller, D. (2015). Industry 4.0 learning factory for regional SMEs. In 5th CIRPSponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 88–91). https://doi. org/10.1016/j.procir.2015.02.117 Gebbe, C., Hilmer, S., Götz, G., Lutter-Günther, M., Chen, Q., Unterberger, E., Glasschröder, J., Schmidt, V., Riss, F., Kamps, T., Tammer, C., Seidel, C., Braunreuther, S., & Reinhart, G. (2015). Concept of the green factory Bavaria in Augsburg. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 53–57). https://doi.org/10.1016/j.procir. 2015.02.214 Gessler, M. (2005). Gestaltungsorientierte Evaluation und der return on investment von Weiterbildungsprogrammen. bwp@, 9, 1–25. Glass, R. (2021). Methodik zur multivariaten Messung fachlich-methodischer Kompetenzen für die Produktion [Dissertation]. Shaker. Goerke, M., Schmidt, M., Busch, J., & Nyhuis, P. (2015). Holistic approach of lean thinking in learning factories. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 138–143). https://doi.org/10.1016/j.procir.2015.02.221 Götze, U. (2014). Investitionsrechnung: Modelle und Analysen zur Beurteilung von Investitionsvorhaben. Springer-Lehrbuch (7. Aufl.). Springer Gabler. Hambach, J., Diezemann, C., Tisch, M., & Metternich, J. (2016). Assessment of students’ lean competences with the help of behavior video analysis—Are good students better problem solvers? In 5th CIRP Global Web Conference (CIRPe 2016). Procedia CIRP (Vol. 55, pp. 230–235).

References

209

Hanssen, S.-C. (2010). Bestimmung und Bewertung der Wirkungen von Informationssystemen [Dissertation]. Harris, M. M., & Schaubroeck, J. (1988). A Metaanalysis of self-supervisor, self-peer, and peersupervisor ratings. Personnel Psychology, 41(1), 43–62. Hart, J. (2015). Modern workplace learning: A resource book for L&D. Centre for Learning & Performance Technologies. Heinrich, L. J., & Lehner, F. (2005). Informationsmanagement: Planung, Überwachung und Steuerung der Informationsinfrastruktur. Wirtschaftsinformatik (8., vollst. überarb. und erg. Aufl.). Oldenbourg. Hertle, C., Tisch, M., Kläs, H., & Metternich, J. (2016). Recording shopfloor management competences—A guideline for a systematic competence gap analysis. In 49th CIRP Conference on Manufacturing Systems. Procedia CIRP (Vol. 57, pp. 625–630). Hummel, V., Hyra, K., Ranz, F., & Schuhmacher, J. (2015). Competence development for the holistic design of collaborative work systems in the logistics learning factory. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 76–81). https://doi.org/10.1016/ j.procir.2015.02.111 Jäger, A., Mayrhofer, W., Kuhlang, P., Matyas, K., & Sihn, W. (2012). The “Learning factory”: An immersive learning environment for comprehensive and lasting education in industrial engineering. In 16th World Multi-Conference on Systemics, Cybernetics and Informatics (Vol. 16(2), pp. 237–242). Jäger, A., Mayrhofer, W., Kuhlang, P., Matyas, K., & Sihn, W. (2013). Total immersion: Hands and heads-on training in a learning factory for comprehensive industrial engineering education. International Journal of Engineering Education, 29(1), 23–32. Jorgensen, J. E., Lamancusa, J. S., Zayas-Castro, J. L., & Ratner, J. (1995). The learning factory: Curriculum integration of design and manufacturing. In 4th World Conference on Engineering Education (pp. 1–7). Kaluza, A., Juraschek, M., Neef, B., Pittschellis, R., Posselt, G., Thiede, S., & Herrmann, C. (2015). Designing learning environments for energy efficiency through model scale production processes. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 41–46). https://doi.org/10.1016/j.procir.2015.02.114 Karre, H., Hammer, M., Kleindienst, M., & Ramsauer, C. (2017). Transition towards an industry 4.0 state of the LeanLab at Graz University of Technology. Procedia Manufacturing, 9, 206–213. Kirkpatrick, D. L. (1998). Evaluating training programs: The four levels (2nd ed.). Berrett-Koehler Publishers. Kreitlein, S., Höft, A., Schwender, S., & Franke, J. (2015). Green factories Bavaria: A network of distributed learning factories for energy efficient production. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 58–63). https://doi.org/10.1016/j.procir. 2015.02.219 Kreß, A. (2022). Methodik zur Konfiguration von Lernfabriken für die schlanke Produktion [Dissertation]. Shaker. Kreß, A., Hummel, V., Ahmad, R., Hulla, M., Quadrini, W., Callupe, M., Gärt-ner, Q., Weyand, A., Barth, J., Riemann, T., Fumagalli, L., Ramsauer, C., & Metternich, J. (2023) Revision of the learning factory morphology. In Proceedings of the 13th Conference on Learning Factories (CLF 2023). Available at SSRN: https://ssrn.com/abstract=4458050 or https://doi.org/10.2139/ ssrn.4458050 Kreß, A., & Metternich, J. (2020). System development for the configuration of learning factories. Procedia Manufacturing, 45, 146–151. Krückhans, B., Wienbruch, T., Freith, S., Oberc, H., Kreimeier, D., & Kuhlenkötter, B. (2015). Learning factories and their enhancements—A comprehensive training concept to increase resource efficiency. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 47–52). https://doi.org/10.1016/j.procir.2015.02.224 Küsters, D. (2018). Methodik zum Aufbau und Betrieb einer Lernfabrik für die digitale Transformation der Produktion [Dissertation]. Shaker.

210

6 The Life Cycle of Learning Factories for Competence Development

Lanza, G., Moser, E., Stoll, J., & Haefner, B. (2015). Learning factory on global production. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 120–125). https://doi.org/10.1016/j.procir.2015.02.081 Lohaus, D., & Habermann, W. (2011). Weiterbildung im Mittelstand: Personalentwicklung und Bildungscontrolling in kleinen und mittleren Unternehmen. Oldenbourg. Mabe, P. A., & West, S. G. (1982). Validity of self-evaluation of ability: A review and meta-analysis. Journal of Applied Psychology, 67(3), 280. https://doi.org/10.1037/0021-9010.67.3.280 Matt, D. T., Rauch, E., & Dallasega, P. (2014). Mini-factory—A learning factory concept for students and small and medium sized enterprises. In 47th CIRP Conference on Manufacturing Systems. Procedia CIRP (Vol. 17, pp. 178–183). McMillan, J. H. (1997). Classroom assessment: Principles and practice for effective instruction. Allyn and Bacon. Metternich, J., Abele, E., & Tisch, M. (2013). Current activities and future challenges of the process learning factory CiP. In G. Reinhart, P. Schnellbach, C. Hilgert, & S. L. Frank (Eds.), 3rd Conference on Learning Factories, Munich: May 7th, 2013 (pp. 94–107). Augsburg. Nerdinger, F. W., Blickle, G., & Schaper, N. (2014). Arbeits- und Organisationspsychologie: Mit 51 Tabellen. Springer-Lehrbuch (3., vollst. überarb. Aufl. 2014). Springer. Nöhring, F., Rieger, M., Erohin, O., Deuse, J., & Kuhlenkötter, B. (2015). An interdisciplinary and hands-on learning approach for industrial assembly systems. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 109–114). https://doi.org/10.1016/j.procir. 2015.02.112 Pietzner, V. (2002). Lernkontrolle im “Vernetzten Studium Chemie”: Entwicklung und Evaluation eines Konzepts am Beispiel des Kapitels “Addition von Halogenen an Doppelbindungen” [Dissertation]. Braunschweig. Pittich, D. (2014). Diagnostik fachlich-methodischer Kompetenzen. Wissenschaft (Vol. 37). Fraunhofer IRB Verlag. Pittschellis, R. (2015). Multimedia support for learning factories. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 36–40). https://doi.org/10.1016/j.pro cir.2015.06.001 Plorin, D. (2016). Gestaltung und Evaluation eines Referenzmodells zur Realisierung von Lernfabriken im Objektbereich der Fabrikplanung und des Fabrikbetriebes. [Dissertation]. Chemnitz. Wissenschaftliche Schriftenreihe des Instituts für Betriebswissenschaften und Fabriksysteme: Heft 120. Chemnitz: Techn. Univ. Inst. für Betriebswiss. und Fabriksysteme. Plorin, D., Jentsch, D., Hopf, H., & Müller, E. (2015). Advanced learning factory (ALF)—Method, implementation and evaluation. In 5th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 13–18). https://doi.org/10.1016/j.procir.2015.02.115 Preussler, A., & Baumgartner, P. (2006). Qualitätssicherung in mediengestützten Lernprozessen— sind theoretische Konstrukte messbar? In A. Sindler, C. Bremer, U. Dittler, P. Hennecke, C. Sengstag, & J. Wedekind (Eds.), Qualitätssicherung im E-Learning (pp. 73–85). Waxmann. Prinz, C., Morlock, F., Freith, S., Kreggenfeld, N., Kreimeier, D., & Kuhlenkötter, B. (2016). Learning factory modules for smart factories in Industrie 4.0. In 6th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 54, pp. 113–118). https://doi.org/10.1016/j.procir. 2016.05.105 PTW, TU Darmstadt. (2017). Prozesslernfabrik CiP: Der Weg zur operativen Exzellenz. Retrieved from http://www.prozesslernfabrik.de/ Reiner, D. (2009). Methode der kompetenzorientierten Transformation zum nachhaltig schlanken Produktionssystem [Dissertation]. Darmstadt. Schriftenreihe des PTW: “Innovation Fertigungstechnik”. Shaker. Reiß, M. (2012). Change management: A balanced and blended approach. Books on Demand. Rentzos, L., Doukas, M., Mavrikios, D., Mourtzis, D., & Chryssolouris, G. (2014). Integrating manufacturing education with industrial practice using teaching factory paradigm: A construction equipment application. In 47th CIRP Conference on Manufacturing Systems. Procedia CIRP (Vol. 17, pp. 189–194).

References

211

Riffelmacher, P. (2013). Konzeption einer Lernfabrik für die variantenreiche Montage [Dissertation]. Stuttgart. Stuttgarter Beiträge zur Produktionsforschung (Vol. 15). Fraunhofer Verlag. Rother, M. (2009). Toyota Kata: Managing People for Improvement, Adaptiveness and Superior Results: McGraw-Hill Education. Sadaj, E. A., Hulla, M., & Ramsauer, C. (2020). Design approach for a learning factory to train services. Procedia Manufacturing, 45, 60–65. Schermutzki, M. (2007). Lernergebnisse—Begriffe, Zusammenhänge, Umsetzung und Erfolgsermittlung: Lernergebnisse und Kompetenzvermittlung als elementare Orientierungen des Bologna-Prozesses. Fachhochschule Aachen. Seitz, K.-F., & Nyhuis, P. (2015). Cyber-physical production systems combined with logistic models—A learning factory concept for an improved production planning and control. In 5th CIRP-sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 92–97). https:// doi.org/10.1016/j.procir.2015.02.220 Seufert, S., & Euler, D. (2005). Learning Design: Gestaltung eLearning-gestützter Lernumgebungen in Hochschulen und Unternehmen. SCIL-Arbeitsbericht (Vol. 5). SCIL. Smith, A. (2001). Return on investment in training: Research readings. NCVER. Solga, M. (2011). Evaluation der Personalentwicklung. Praxishandbuch Personalentwicklung: Instrumente, Konzepte, Beispiele (pp. 369–399). Gabler. Statistisches Bundesamt. (2017). Statistisches Jahrbuch 2017: Gewerbe und Dienstleistungen im Überblick. Retrieved from https://www.destatis.de/DE/Publikationen/StatistischesJahrbuch/Pro dGewerbeDienstleistungen.pdf?__blob=publicationFile Steffen, M., Frye, S., & Deuse, J. (2013a). The only source of knowledge is experience: Didaktische Konzeption und methodische Gestaltung von Lehr-Lern-Prozessen in Lernfabriken zur Ausund Weiterbildung im industrial engineering. In TeachING LearnING.EU. Innovationen für die Zukunft der Lehre in den Ingenieurwissenschaften (pp. 117–129). Steffen, M., Frye, S., & Deuse, J. (2013b). Vielfalt Lernfabrik—Morphologie zu Betreibern, Zielgruppen und Ausstattungen von Lernfabriken im industrial engineering. Werkstattstechnik Online: Wt, 103(3), 233–239. Steffen, M., May, D., & Deuse, J. (2012). The industrial engineering laboratory: Problem based learning in industrial engineering education at TU Dortmund University. In IEEE Global Engineering Education Conference (EDUCON)—Collaborative Learning & New Pedagogic Approaches in Engineering Education, Marrakesch, Marokko, April 17–20, 2012 (pp. 1–10). Stufflebeam, D. L. (1972). Educational evaluation decision making (3rd ed.). PDK National Study Committee on Evaluation. Stuttgarter Produktionsakademie. (2017). Produktion strategisch gestalten: vom Produkt bis zur Wertschöpfungsverteilung. Retrieved from http://www.stuttgarter-produktionsakademie.de/ Tisch, M. (2018). Modellbasierte Methodik zur kompetenzorientierten Gestaltung von Lernfabriken für die schlanke Produktion [Dissertation]. Darmstadt. Shaker. Tisch, M., Hertle, C., Abele, E., Metternich, J., & Tenberg, R. (2015a). Learning factory design: A competence-oriented approach integrating three design levels. International Journal of Computer Integrated Manufacturing, 29(12), 1355–1375. https://doi.org/10.1080/0951192X. 2015.1033017 Tisch, M., Hertle, C., Cachay, J., Abele, E., Metternich, J., & Tenberg, R. (2013). A systematic approach on developing action-oriented, competence-based learning factories. In 46th CIRP Conference on Manufacturing Systems. Procedia CIRP (Vol. 7, pp. 580–585). Tisch, M., Hertle, C., Metternich, J., & Abele, E. (2014). Lernerfolgsmessung in Lernfabriken: Kompetenzorientierte Weiterentwicklung praxisnaher Schulungen. Industrie Management, 30(3), 39–42. Tisch, M., Hertle, C., Metternich, J., & Abele, E. (2015b). Goal-oriented improvement of learning factory trainings. The Learning Factory, an Annual Edition from the Network of Innovative Learning Factories, 1(1), 7–12.

212

6 The Life Cycle of Learning Factories for Competence Development

Tisch, M., & Metternich, J. (2017). Potentials and limits of learning factories in research, innovation transfer, education, and training. In 7th CIRP-Sponsored Conference on Learning Factories. Procedia Manufacturing. Tisch, M., Ranz, F., Abele, E., Metternich, J., & Hummel, V. (2015c). Learning factory morphology: Study on form and structure of an innovative learning approach in the manufacturing domain. In TOJET, July 2015. Special Issue 2 for International Conference on New Horizons in Education 2015 (pp. 356–363). Tschandl, M., Mayer, B., & Sorko, S. R. (2020). An interdisciplinary digital learning and research factory—The smart production lab. Procedia Manufacturing, 45, 491–496. Tvenge, N., Martinsen, K., & Kolla, Sri Sudha Vijay Keshav. (2016). Combining learning factories and ICT-based situated learning. In 6th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 54, pp. 101–106). VDI. (1993). VDI 2221/Methodik zum Entwickeln und Konstruieren technischer Systeme und Produkte. VDI-Richtlinien. Beuth. Wagner, U., AlGeddawy, T., ElMaraghy, H. A., & Müller, E. (2014). Product family design for changeable learning factories. In 47th CIRP Conference on Manufacturing Systems. Procedia CIRP (Vol. 17, pp. 195–200). https://doi.org/10.1016/j.procir.2014.01.119 Wagner, U., AlGeddawy, T., ElMaraghy, H. A., & Müller, E. (2015). Developing products for changeable learning factories. CIRP Journal of Manufacturing Science and Technology, 9, 146– 158. Wank, A., Adolph, S., Anokhin, O., Arndt, A., Anderl, R., & Metternich, J. (2016). Using a learning factory approach to transfer Industrie 4.0 approaches to small- and medium-sized enterprises. In 6th CIRP-Sponsored Conference on Learning Factories, Procedia CIRP (Vol. 54, pp. 89–94). https://doi.org/10.1016/j.procir.2016.05.068 Zangemeister, C. (1993). Erweiterte Wirtschaftlichkeits-Analyse (EWA): Grundlagen und Leitfaden für ein “3-Stufen-Verfahren” zur Arbeitssystembewertung. Schriftenreihe der Bundesanstalt für Arbeitsschutz Forschung (Vol. 676). Wirtschaftsverlag N.W. Verlag für Neue Wissenschaft.

Chapter 7

Overview on Existing Learning Factory Concepts

As discussed in the previous chapters, the implementation of learning factories has become increasingly widespread throughout the world in recent years. These learning factories come in various sizes, serving different purposes, scopes, and levels of sophistication, all aimed at providing a comprehensive learning experience for learners from both industry and academia in the field of production. The upcoming three chapters aim to provide a comprehensive overview of the diverse range of existing learning factories. This includes examining their general concepts, the equipment utilised, and the specific targeted industries. The objective is to present and organise learning factories based on the most common concepts,1 the topics covered,2 and the different variations of the overall learning factory concept.3 Furthermore, this book presents 46 Best Practice Examples of learning factories.4 Many active operators of learning factories have contributed their valuable insights as guest authors, highlighting the significance of learning factories in both academia and industry. These contributions serve as essential examples of the relevance and impact of learning factories. Table 7.1 provides an overview of established learning factories worldwide, including those featured as Best Practice Examples.5 The last column offers crossreferences to the sections within the book where each respective learning factory is presented in detail. Additionally, the table offers important information such as the location of each learning factory, the key topics covered, and the operator responsible for its management. To provide a visual representation of the structure of the following chapters, Fig. 7.1 offers an overview of the structure of this chapter. 1

See this chapter. See Chap. 8. 3 See Chap. 9. 4 See Chap. 11. 5 The overview in Table 7.1 represents our current state of knowledge after thorough research. However, it is likely that more learning factories exist. 2

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 E. Abele et al., Learning Factories, https://doi.org/10.1007/978-3-031-46428-7_7

213

Technical University Darmstadt

Mechanical Engineering, University of Alberta

Hamilton, Canada

Nuremberg, Germany FAPS/KTmfK, FAU Erlangen-Nürnberg, McKinsey Anglo American

Darmstadt, Germany

Johannesburg, South Africa

Edmonton, Canada

Additive Manufacturing Center (AMC)

A Distributed Learning Factory with a Central Hub (SEPT LF)

AM model factory

Anglo American Training Center

Aquaponics 4.0 Learning Factory (All-Factory)

School of Engineering Practice and Technology (SEPT), McMaster University

DMP, Aalborg University

Aalto University

Espoo, Finland

Aalto Factory of the Future

AAU Smart Production Aalborg, Denmark Lab

MTC, Tongji University

Shanghai, China

5G Learning Factory

Operated by

Location

Name of learning factory

Aquaponics, Industrie 4.0, robotics, AI in vertical farming, data modelling

Business improvement, lean production

Additive manufacturing

Industry 4.0, IoT and IIoT, additive manufacturing, smart systems, cyber-physical systems

Additive manufacturing

Industrie 4.0, smart production

Flexible distributed automation, IEC 61499, Industrie 4.0

Industrie 4.0

Key topics

Table 7.1 Overview on existing learning factories around the globe (extract)

X

X

X

X

X

X

Research

X

X

X

X

X

X

Education

X

X

X

X

X

X

X

X

Training

(continued)

* Best Practice Example 5

Makumbe et al. (2018)

Yoo et al. (2016)

* Best Practice Example 4

* Best Practice Example 3

Madsen and Møller (2017)

* Best Practice Example 2

* Best Practice Example 1

References

214 7 Overview on Existing Learning Factory Concepts

Bremen Institute for Mechanical Engineering, University Bremen

Bremen, Germany

Ansbach, Germany

No fixed location

Aachen, Germany

Aachen, Germany

Vorarlberg, Austria

Braunschweig, Germany

Nuremberg, Germany FAPS, FAU Erlangen-Nürnberg

Chemnitz, Germany

BERTHA

CETPM Akademie

CubeFactory

Demonstration Factory Aachen DFA

Digital Capability Center Aachen

Digital Factory Vorarlberg

Die Lernfabrik

E|Drive-Center

E3 -Forschungsfabrik

Sustainability

Lean production

Manual assembly

Key topics

Fraunhofer IWU

IWF, TU Braunschweig

Digital Factory Vorarlberg GmbH

ITA Academy GmbH

Resource efficiency, Industrie 4.0

Production technology, machine learning

Sustainable production, cyber-physical production systems, urban production

Cloud-based production, data analysis, AI in production, 5G

AI, XR, digitisation

WZL, RWTH Aachen Lean production, Industrie 4.0, re-assembly/ re-manufacturing

IWF, TU Berlin

HS Ansbach

Operated by

Location

Name of learning factory

Table 7.1 (continued)

X

X

X

X

X

X

Research

X

X

X

X

X

X

Education

X

X

X

X

X

X

X

Training

(continued)

Putz (2013)

* Best Practice Example 9

* Best Practice Example 8

https://www.vac tory.at/

* Best Practice Example 7

* Best Practice Example 6

Muschard and Seliger (2015)

https://www.cet pm.de/

Schreiber et al. (2016)

References

7 Overview on Existing Learning Factory Concepts 215

FPL, TU Chemnitz

Chemnitz, Germany

EDF (Experimentierund Digitalfabrik)

Faculty of Industrial Management, Universiti Malaysia Pahang

Pahang, Malaysia

FIM Learning Factory

University of São Paulo (USP) Festo AG

São Paulo, Brazil

Fábrica do Futuro

PTW, TU Darmstadt

Festo Learning Factory Scharnhausen, Germany Scharnhausen

Darmstadt, Germany

ETA-Factory

Electronics Production Nuremberg, Germany FAPS, FAU Erlangen-Nürnberg

Operated by

Location

Name of learning factory

Table 7.1 (continued)

Supply chain and logistics, Industrie 4.0

Workplace-oriented trainings, Industry 4.0, lean production

Industrie 4.0, mass customisation

Energy efficiency, energy flexibility, resource efficiency

Electronics production

Factory planning and operation

Key topics

X

X

X

X

X

X

Research

X

X

X

X

X

Education

X

X

X

X

X

X

Training

(continued)

* Best Practice Example 12

https://www.pla ttform-i40.de/ IP/Redaktion/ EN/Use-Cases/ 395-festo-lea rning-factory/ festo-learningfactory-scharn hausen.html

* Best Practice Example 11

* Best Practice Example 10

https://www.lze. bayern/en/frontpage/

https://www.tuchemnitz.de/ mb/FabrPlan/ edf.php

References

216 7 Overview on Existing Learning Factory Concepts

Karlsruhe, Germany

Worldwide

Amberg; Ansbach; Augsburg; Bayreuth; Coburg; Erlangen; Fürth; Hof; Ingolstadt; Munich; Nuremberg; Schweinfurt, Germany

Globale Learning Factory

Global McKinsey Innovation & Learning Center Network (ILC)

Green Factory Bavaria

Twelve universities and Fraunhofer institutes in Bavaria

McKinsey & Company, Inc.

wbk, Karlsruhe Institute of Technology

Laboratory of Automation, digital twin Mechanical Engineering and Industrial Systems, Tampere University of Technology

Tampere, Finland

FMS Training Center

Resource and energy efficiency

Capital excellence, product development, procurement, supply chain, manufacturing, sales, customer service, corporate functions, lean, digital and analytics, sustainability and resilience

Lean production, Industrie 4.0, global production, data mining in quality assurance

Lean, digitised production of individual products

PTW, TU Darmstadt

Darmstadt, Germany

Key topics

FlowFactory

Operated by

Location

Name of learning factory

Table 7.1 (continued)

X

X

X

Research

X

X

X

X

Education

X

X

X

X

Training

(continued)

FAPS (2018)

* Best Practice Example 15

* Best Practice Example 14

Toivonen et al. (2018)

* Best Practice Example 13

References

7 Overview on Existing Learning Factory Concepts 217

Department of Industry 4.0, production X Management, planning and control, energy Economics, and efficiency Industrial Engineering (DIG), Politecnico di Milano University of Washington

Dortmund, Germany

Milano, Italy

Seattle, USA

Paderborn, Germany

Industrial Engineering Laboratory

Industry 4.0 Lab

Integrated Learning Factory

Laboratory for flexible industrial automation

Engineering design

Design of work systems, industrial engineering

Production planning and control (PPC), lean production, factory planning, Industrie 4.0

Heinz Nixdorf CPPS, holistic product Institute, University of creation Paderborn

IPS, TU Dortmund

IFA, LU Hannover

Hanover, Germany

X

X

X

IFA-Learning Factory

Industrie 4.0, lean production, remote assembly guidance

Laboratory for Manufacturing Systems and Automation, University of Patras

Patras, Greece

Research

Hybrid Teaching Factory for Personalised Education—Towards Teaching Factory 5.0

Key topics

Operated by

Location

Name of learning factory

Table 7.1 (continued)

X

X

X

X

X

X

Education

X

X

Training

(continued)

Gräßler et al. (2016a, 2016b)

University of Washington (2018)

* Best Practice Example 18

For example, Steffen et al. (2012)

* Best Practice Example 17

* Best Practice Example 16

References

218 7 Overview on Existing Learning Factory Concepts

Gjøvik, Norway

Berlin, Germany

Split. Croatia

Valladolid, Spain

Bochum, Germany

Lean Lab

LEAN-Factory

Lean Learning Factory

Lean School (LS)

Learning and Research Factory (LFF)

Lean production

Chair of Production Systems, Ruhr-University Bochum

Universidad de Valladolid

FESB, University of Split

Lean production, Industrie 4.0, robotics, AI

Lean production

Lean production, Industrie 4.0/5.0

Fraunhofer IPK, ITCL Lean management GmbH, pharmaceutical company

NTNU

Lean production, Industrie 4.0

Winnenden, Germany + other locations worldwide

Lean Academy Kärcher

Institute of Innovation Lean production, energy and Industrial efficiency, agility, Management (IIM), digitisation TU Graz

Graz, Austria

LEAD-Factory

Key topics

Operated by

Location

Name of learning factory

Table 7.1 (continued)

X

X

X

X

Research

X

X

X

X

X

X

Education

X

X

X

X

X

X

X

Training

(continued)

* Best Practice Example 23

* Best Practice Example 22

* Best Practice Example 21

* Best Practice Example 20

For example Tvenge et al. (2016)

For example Kärcher (2018) and Thomar (2015)

• Best Practice Example 19

References

7 Overview on Existing Learning Factory Concepts 219

Learning Factory of Stuttgart, Germany advanced Industrial Engineering (LF aIE)

Munich, Germany

Optimal machining

Automation, Industrie 4.0, energy efficiency

Integrated product and process planning, optimisation

Product creation process

Processes, logistics, IoT, digital twining, smart industry, lean, product design

Key topics

Institute of Industrial Lean production, quality Manufacturing and management Management (IFF), University of Stuttgart

Institute for Machine Tools and Industrial Management, TU Munich

IMW, TU Wien

Vienna, Austria

Learning and Innovation Factory

Learning Factory for Optimal Machining (LOZ)

Heilbronn University of Applied Sciences

Heilbronn, Germany

Learning Factory jumpING

University of Applied Sciences Bochum

Department of Design, Production and Management, University of Twente

Enschede, The Netherlands

Learning Factory CUBE

Learning Factory at the Heiligenhaus, Campus Velbert/ Germany Heiligenhaus

Operated by

Location

Name of learning factory

Table 7.1 (continued)

X

X

X

X

Research

X

X

X

X

X

X

Education

X

X

X

X

Training

(continued)

* Best Practice Example 26

https://www. mec.ed.tum.de/ en/iwb/lernfa brik-optimalezerspanung/ler nfabrik-fuer-opt imale-zerspa nung/

Faller and Feldmüller (2015)

* Best Practice Example 25

* Best Practice Example 24

References

220 7 Overview on Existing Learning Factory Concepts

Faculty of Mechanical Engineering, Computing and Electrical Engineering, University of Mostar Vocational Schools in Baden-Württemberg

Mostar, Bosnia and Herzegovina

Sixteen locations in Baden-Württemberg, Germany

Munich, Germany

Augsburg, Germany

Bruchsal, Germany

Windsor, Canada

Berlin, Germany

Stellenbosch, South Africa

Learning Factory SUM

Lernfabrik 4.0

Lernfabrik für Schlanke Produktion (LSP)

Lernfabrik für vernetzte Produktion

Live Training Center

Manufacturing Systems Learning Factory (iFactory)

MAN Learning Factory

MicroManu

IBi, Stellenbosch University

MAN Diesel and Turbo SE

Intelligent Manufacturing Systems (IMS) Center, University of Canada

SEW Eurodrive

Fraunhofer IGCV

iwb, TU München

Operated by

Location

Name of learning factory

Table 7.1 (continued) Research

Rapid prototyping, manufacturing, quality management

Assembly and maintenance of compressors

Integrated manufacturing systems and products design, system operation and control, Industrie 4.0

Lean production

Digitisation, paperless production

Lean production, Industrie 4.0

Industrie 4.0

X

X

Lean production, Industrie X 4.0, reverse engineering, 3D printing, metrology, energy efficiency, collaborative robots

Key topics

X

X

X

X

X

X

Education

X

X

X

X

X

X

X

Training

(continued)

* Best Practice Example 29

Reichert (2011)

https://www. lvp-bayern.de/

* Best Practice Example 28

See Sect. 7.1.9

* Best Practice Example 27

References

7 Overview on Existing Learning Factory Concepts 221

SIMTech, ARTC

Singapore

Mayagüez, Puerto Rico

Herzogenaurach, Germany

Sindelfingen, Germany

Luxembourg

Ostfalia, Germany

Vienna, Austria

Model Factory @ SIMTech

Model Factory

Move academy

MPS Lernplattform

Operational Excellence Learning Factory

Ostfalia Lern- und Innovationsfabrik (OLIF)

Pilotfabrik Industry 4.0

IFT, MIVP and IMW, TU Wien

Ostfalia Hochschule für angewandte Wissenschaften

Department of Engineering, University of Luxembourg

Daimler AG

Schaeffler

University of Puerto Rico

Operated by

Location

Name of learning factory

Table 7.1 (continued)

Industrie 4.0, CAD/CAM, digital twin, human–machine-interaction

Digital and real processes

Lean manufacturing, Industrie 4.0

Lean

Lean production

Manufacturing systems

Sense and response manufacturing, any-mix-any-volume production

Key topics

X

X

X

X

Research

X

X

X

X

X

X

Education

X

X

X

X

X

Training

(continued)

* Best Practice Example 33

https://www.ost falia.de/cms/de/ olif/

* Best Practice Example 32

* Best Practice Example 31

For example, Beauvais (2013) and Helleno et al. (2013)

UPRM (2018)

* Best Practice Example 30

References

222 7 Overview on Existing Learning Factory Concepts

PTW, TU Darmstadt

Darmstadt, Germany

Darmstadt, Germany

Augsburg, Germany

Bayreuth, Germany

Siegen, Germany

Process Learning Factory “Center for industrial Productivity” (CiP)

Railway Operation Research Center

Recycling Atelier Augsburg

RemanLab

SDFS Smart Demonstration Factory Siegen

Railway operation

Lean production, Industrie 4.0

Key topics

PROTECH—Institute for Production, University of Siegen

Product creation process, energy and resource efficiency, lean production, Industrie 4.0

Fraunhofer-Institut für Remanufacturing, circular Produktionstechnik economy und Automatisierung IPA

Institut für Mechanical recycling, AI in Textiltechnik production, design 4 Augsburg gGmbH & recycling, upcycling Hochschule Augsburg

Chair of Railway Engineering, TU Darmstadt

Operated by

Location

Name of learning factory

Table 7.1 (continued)

X

X

X

X

X

Research

X

X

X

X

X

Education

X

X

X

X

X

Training

(continued)

* Best Practice Example 36

https://www.ipa. fraunhofer.de/ de/veranstaltun gen-messen/ver anstaltungen/ 2023/remanlab. html

* Best Practice Example 35

Streitzig and Oetting (2016)

* Best Practice Example 34

References

7 Overview on Existing Learning Factory Concepts 223

Kaiserslautern, Germany

Bolzano, Italy

SmartFactory-KL

Smart Mini Factory

Free University of Bozen-Bolzano

German Research Center for Artificial Intelligence (DFKI)

Institute of production Cyber-physical production engineering, TU Graz system

Graz, Austria

Smartfactory @tugraz

X

X

X

Research

Industrie 4.0, smart X manufacturing systems, automation and robotics, VR and AR

Production level 4, Industrie X 4.0

Production planning, scheduling and execution in CPPS, mechatronics and automation in CPPS

Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control

Budapest, Hungary

Key topics

Smart Factory at SZTAKI

Operated by Department of Automatic production, Electrical Engineering Industrie 4.0, digital factory and Information Technology, University of Applied Sciences Darmstadt

Location

Smart factory AutFab Darmstadt, Germany

Name of learning factory

Table 7.1 (continued)

X

X

X

X

X

Education

X

X

X

X

Training

(continued)

* Best Practice Example 40

* Best Practice Example 39

https://www. smartfactory.tug raz.at/en

* Best Practice Example 38

* Best Practice Example 37

References

224 7 Overview on Existing Learning Factory Concepts

FH Joanneum Gesellschaft mbH

Kapfenberg, Austria

Stellenbosch, South Africa

Smart Production Lab

Stellenbosch Learning Factory

Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control LSWI, University of Potsdam

Penn State University

Purdue University

SZTAKI Industry 4.0 Gy˝or, Hungary Learning Factory

Potsdam, Germany

The Centre for Industry 4.0 at Chair of Business Informatics

The Learning Factory Pennsylvania, USA at Penn State University

The Purdue Learning Purdue, USA Factory

Department of Industrial Engineering, Stellenbosch University

Operated by

Location

Name of learning factory

Table 7.1 (continued)

X

X

Research

Cyber-physical production, Industrie 4.0

Industrie 4.0, DfM/X, mass customisation, design thinking

Concepts, trends, and technologies of Industrie 4.0, Work 4.0

X

X

X

Human–robot collaboration, X production planning, process planning and execution in CPPS

Process analysis and improvement

Future of work, digital shopfloor, ERP and MES, management and controlling, supply chain engineering

Key topics

X

X

X

X

X

X

Education

X

X

X

X

Training

(continued)

* Best Practice Example 45

* Best Practice Example 44

* Best Practice Example 43

* Best Practice Example 42

* Best Practice Example 41

https://www.fhjoanneum.at/en/ research/res earch-centres/ smart-produc tion-lab/

References

7 Overview on Existing Learning Factory Concepts 225

Michigan, USA

Stockholm, Sweden

World Class Manufacturing Academy

XPRES Lab

KTH Stockholm

Chrysler

Reutlingen, Germany ESB Business School, Smart factory, digital Reutlingen University engineering, Industrie 4.0, circular economy

Werk150

Production research

World class manufacturing

Lean production

BMW Group

Munich, Germany

Lean production

Lean production/lean logistics

VPS Center of the Production Academy

Competence Center for Production and Logistics, University of Applied Sciences Landshut

Key topics

Knorr Bremse

Landshut, Germany

The PuLL ® Learning Factory

Operated by

Value Stream Academy Several locations

Location

Name of learning factory

Table 7.1 (continued)

X

X

X

X

Research

X

X

X

Education

X

X

X

X

Training

Sivard and Lundholm (2013)

UAW-Chrysler National Training Center (2016)

* Best Practice Example 46

U-Quadrat (2018)

For example Blöchl and Schneider (2016) and Blöchl et al. (2017)

References

226 7 Overview on Existing Learning Factory Concepts

7 Overview on Existing Learning Factory Concepts

227

Fig. 7.1 Structure of the overview of existing learning factories in this chapter

In this chapter, the most common concepts of existing learning factories will be explored in more detail, with the primary targets of education, training, and research. The following sections provide a comprehensive description, explanation, and structure of the various learning factory concepts and their corresponding application scenarios. In addition, within each section, Best Practice Examples from academia and industry are given to illustrate the practical implementation of different learning factory concepts. Section 7.1 examines learning factories in the context of education, focusing on their role in enhancing the learning experience for students. Section 7.2 shifts the focus to learning factories dedicated to training or further education, exploring that how the development of practical skills and knowledge for professionals is supported. Section 7.3 explores the learning factory concepts in research, highlighting the ways in which learning factories facilitate experimental studies and innovation in the field. To provide a visual representation of the detailed structure of this chapter, Fig. 7.2 contains an overview illustrating the structure of the chapter.

228

7 Overview on Existing Learning Factory Concepts

Fig. 7.2 Detailed structure of the overview on existing learning factory concepts

7.1 Learning Factories in Education In the learning factory concept, educating students is one of three primary targets. An example is the early implementation of a learning factory at Penn State University.6 Overall, there are two main models that are commonly used for incorporating education into learning factories: • student projects and • steered and closed courses. Student Projects Student projects typically last several weeks or even months, during which students or groups of students work to find or design technical or organisational solutions to specific needs or problems. The process of finding solutions is open to creativity and not limited to a pre-determined path. These student projects are often linked to ongoing research and may involve partnerships with industry.7 In such cases, students are usually supervised, albeit in a less rigorous manner. It is important to note that these projects are not directly linked to specific courses, which allows for more flexibility. The use of learning factories for educational purposes can be visualised in both stand-alone student projects and industry-partnered student projects. Figure 7.3 illustrates these concepts. 6 7

See Jorgensen et al. (1995). See, e.g. Jorgensen et al. (1995).

7.1 Learning Factories in Education

229

Stand-alone student projects Trigger: industryrelevant problems New industryrelevant solution

Learning Factory

Supervisorstudent coaching

Industry-partnered student projects Trigger: specific predefined problem Industrial Factory

New solution for defined problem

Learning Factory

Supervisorstudent coaching

Fig. 7.3 Use of learning factories in education in connection with stand-alone and industrypartnered projects

Steered and Closed Courses In contrast, steered and closed courses typically take several hours or a few days at most. These courses can be conducted through regular sessions over a specific period, such as a lecture series. During these courses, students or groups of students work on pre-determined problem scenarios that align with relevant theoretical foundations. The process of finding solutions in these courses is guided or constrained in some way. A distinguishing feature of these courses is the coordination and preplanning of theoretical learning, practical exploration, and testing. The incorporation and alternation of theory and practice can be organised in short or long cycles: • Long-cycle courses in learning factories are extended programs that integrate theoretical learning and practical exercises. The theory sessions run over weeks to months before students apply the learning content in a practical exercise. Usually, there is only one alternation between theory and practice. • In contrast, short-cycle courses are short and concise, lasting some minutes to a few hours. The learning content is applied directly in a practical exercise. Compared to long-cycled courses, the theory input is much shorter with a higher number of alternations between theory and practice. The use of learning factories in educational settings is illustrated in Fig. 7.4, focusing on steered courses of varying lengths that integrate learning factories. While the red arrow displays the starting point for a learning course (need for competence development), the green arrows show the output (developed competences). Learning courses in learning factories are based on supervisor–student coaching, in which a supervisor (from a university or company) coaches a student and guides him or her through the theory sessions and practical exercises.

230

7 Overview on Existing Learning Factory Concepts

Fig. 7.4 Long-cycled and short-cycled steered courses in connection with learning factories in education

7.1.1 Active Learning in Learning Factories Active learning aims to move away from passive information transfer and instead focuses on actively involving learners in analysing situations, evaluating their own ideas, and evaluating results. It emphasises a comprehensive understanding of problem situations and concepts rather than mere information reproduction.8 Active learning encourages students to actively engage in tasks and reflect on their actions.9 In traditional educational settings, active learning can be stimulated through various

8 9

See Crawley et al. (2007). See Bonwell and Eison (1991).

7.1 Learning Factories in Education

231

Active learning Section 7.1.1

Actionoriented learning

Experiential learning

Gamebased learning

Problembased learning

Projectbased learning

Researchbased learning

Section 7.1.2

Section 7.1.3

Section 7.1.4

Section 7.1.5

Section 7.1.6

Section 7.1.7

Fig. 7.5 Most important active learning concepts in the field of engineering education in learning factories

methods such as writing, debates, cooperative learning, learning games, and roleplaying. In the field of engineering education, learning factories provide a highly complex form of active learning with numerous possibilities. Active learning serves as the foundational concept, which is further subdivided into various other concepts based on specific orientations. These concepts cater to different aspects of active learning. Examples of such concepts with specific orientations include: • research-based learning, which intends to integrate education and research activities for mutual benefits, • problem-based learning, which bases all learning processes on a comprehensive problem scenario, or • experiential learning, which creates knowledge based on own experiences. Figure 7.5 provides an overview of the key sub-concepts of active learning in the context of learning factories for engineering education. The structure of these concepts can be simplified into a three-level structure, where problem-based learning and project-based learning can be considered as sub-concepts of experiential learning, for example. However, to maintain clarity and avoid further complexity, all the concepts are presented at a single level as sub-concepts of active learning. It is important to note that learning factory approaches often encompass a combination of multiple forms rather than strictly adhering to a specific sub-concept. In the following sections, these sub-concepts are described in alphabetical order, highlighting their connections and similarities.

7.1.2 Action-Oriented Learning in Learning Factories Action-oriented learning is a specific type of active learning that emphasises the active involvement of learners through their own actions. Its primary goal is to enhance

232

7 Overview on Existing Learning Factory Concepts

conceptual knowledge, enabling learners to understand cause-and-effect relationships necessary for problem-solving.10 In action-oriented learning, learners independently tackle complex problems, while teachers take on the role of moderators in the background.11 This approach goes beyond simple actions and includes activities such as planning and reflection to enhance learning experiences.12 Action-oriented learning is closely linked to problem-based learning and experiential learning. However, it specifically focuses on the actions and observations of learners during the problem-solving process. A suitable learning environment for action-oriented learning is one that closely resembles real-world contexts. In addition to learning factories, this can be achieved through simulations, role plays, or the use of virtual reality.13 It is important to view this type of learning within the framework of activity theory, where learning emerges from activities rather than activities emerging from learning, as commonly believed.14

7.1.3 Experiential Learning and Learning Factories Experiential learning is widely recognised as one of the most effective concepts for the learning process.15 It involves learners assuming simulated roles of engineering professionals. This concept is frequently used in learning factory literature to describe how learning occurs within the learning factory approach.16 At its core, experiential learning defines learning as the “process of creating knowledge through the transformation of experience.”17 This means that traditional teaching methods fade into the background, and instead, the focus is on meaningful experiences for the learners. The experiential learning process involves several key stages: (a) Concrete experience18 : learners gain firsthand experience through actions in a specific field. (b) Reflective observation19 : experiences are reviewed and reflected upon. (c) Abstract conceptualisation20 : cognitive processes such as analysis, interpretation, and establishing cause-and-effect relationships take place, along with initial ideas for improvement. 10

See Abele et al. (2017). See Cachay et al. (2012). 12 See Jank and Meyer (2002). 13 See Lindemann (2002), Cachay et al. (2012). 14 See Jonassen and Rohrer-Murphy (1999). 15 Experiential learning according to Kolb (1984). 16 See, for example, Steffen et al. (2013) and Müller et al. (2017). 17 Kolb (1984). 18 See Fig. 7.6a. 19 See Fig. 7.6b. 20 See Fig. 7.6c. 11

7.1 Learning Factories in Education

233

(d) New active experimentation21 : concepts developed in previous phases are applied to new situations, leading to fresh concrete experiences, and starting the cycle again.22 The learning factory provides a realistic learning environment and the opportunity to create active learning experiences, making it well-suited for implementing experiential learning in manufacturing education. Figure 7.6 illustrates the learning process and a possible implementation of experiential learning within learning factories. Other forms of experiential learning in engineering education include simulations, project-based learning, and case studies. These approaches can be combined in a single course with additional active learning methods, further enhancing competence development.23

7.1.4 Game-Based Learning in Learning Factories and Gamification There are different forms of learning that incorporate game elements into the learning process. These can be categorised into two main types: • serious games and • gamification approaches.24 Serious games25 (also known as game-based learning) are structured playing situations designed to facilitate learning. They have clearly defined educational objectives and purposes.26 The primary focus of serious games is to enable learning rather than purely providing entertainment.27 By actively involving learners in gameplay, serious games can help them to understand specific topics, concepts, and improve certain skills.28 Gamification involves incorporating game design elements into non-game contexts.29 It aims to make non-game situations more motivating, enjoyable, and engaging.30 Gamification takes specific elements from games and applies them to enhance the learning experience. A commonly referred framework for gamification

21

See Fig. 7.6d. See Kolb (1984). 23 See Crawley et al. (2007). 24 See Deterding et al. (2011). 25 Or very similar used terms like educational games see also Freitas (2007). 26 See Abt (1987), Felicia (2014). 27 See Gredler (2004). 28 See Djaouti et al. (2011). 29 See Deterding et al. (2011). 30 Deterding et al. (2011). 22

234

7 Overview on Existing Learning Factory Concepts

Fig. 7.6 Experiential learning cycle in learning factories

7.1 Learning Factories in Education

Dynamics

Mechanics

Components

• • • • • •

235 Constraints: limitations and forced trade-offs Emotions: competition, curiosity, frustrations, happiness Narrative: Continuous, persistent storyline Progression: Player development in the learning situation Relationships: social interactions, status, altruism … • • • • • • •

Challenges that require the player‘s effort Chance: random elements in the learning situation Competition: Environments with winners and losers Cooperation: Teamwork among players Feedback on progress in learning situation Rewards on actions in learning situation … • • • • • •

Achievements or badges Combats during learning situation Leaderboards or high scores Levels inside a learning situation Teams in competition or cooperation …

Fig. 7.7 Framework and example of game elements used for gamification purposes according to Werbach and Hunter (2012)

elements is presented as a pyramid with different types of elements on the levels of “components,” “mechanics,” and “dynamics.”31 While serious games are complete games designed for educational purposes, gamification focuses on integrating game elements into existing learning contexts to enhance learner engagement and motivation. Figure 7.7 provides an overview of the framework and showcases some examples of game elements used in gamification. In the learning factory setting, both game-based learning and gamification can be implemented to enhance the learning experience. Here are examples of how these approaches are used: • In learning factories, one example of game-based learning involves creating a game where participants work together in teams to achieve specific objectives. For instance, they may be required to produce a certain number of products with pre-defined quality standards. An example of such a game is the paper plane game, where participants collaborate to meet the game’s targets. • Gamification is commonly used in learning factories by forming multiple teams within the participant group. These teams are given the same task and compete to accomplish it. An instance of gamification in action is seen in the Process Learning Factory CiP, where participants engage in a simulation game using the Single-Minute Exchange of Die (SMED32 ) method. They compete in performing changeover activities on an abstract machine model.

31 32

See Werbach and Hunter (2012). “Single-Minute Exchange of Dies” is a method to systematically reduce changeover times.

236

7 Overview on Existing Learning Factory Concepts

Game-based learning and serious games can be implemented in learning factory environments with the help of game-based team challenges, e.g., paper plane game

Gaming (Playing with structure)

Game-based learning (serious games)

Gameful design (gamification)

Completely

No application identified in a learning factory context

Gamification can be implemented with the use of the game elements in learning factory concepts, for example “competition”- and “challenge”-elements are used in the “RoboCup Logistics League” and the “Energy-bingo” Partly

Toys

Playful design

No application identified in a learning factory context

Playing (Gaming without structure)

Fig. 7.8 Game-based learning and gamification in learning factories. Classification according to Deterding et al. (2011)

Other examples of gamification in the learning factory context include the “RoboCup Logistics League”33 and the “energy-bingo”34 game. These initiatives incorporate elements of competition and game design to motivate and engage participants in the learning process. To summarise, game-based learning and gamification are illustrated in Fig. 7.8. In the upcoming paragraphs, three specific approaches are described: the “energybingo” game, the “RoboCup Logistics League,” and the paper plane game, which demonstrate the practical implementation of these concepts in the learning factory environment. In many learning factories, simple simulation games are used to facilitate learning. One such game is the paper plane game, which is implemented in the Process Learning Factory CiP to help learners understand material flow and pull systems in production.35 The paper plane game is an example of game-based learning. The game begins by establishing the goals, rules, and general course of the three simulation rounds with the participants. Two competing teams are formed. Each simulation round follows a specific sequence: 33

See Pittschellis (2015). See Böhner et al. (2015). 35 See Best Practice Example 34 in Chap. 11. 34

7.1 Learning Factories in Education

237

Fig. 7.9 Civil and military paper airplanes and respective process steps of the paper airplane game

• Preparation: participants prepare for the upcoming round. • Execution: the actual simulation takes place, where teams produce paper planes according to given work instructions. • Analysis: the performance of each team is measured in terms of quality, cost, and delivery. The results are analysed. • Improvement: based on the analysis, participants discuss possible ways to improve the manufacturing system. During the initial production round, teams produce paper planes for ten minutes. The round is then analysed based on performance measurements, and initial improvement opportunities are discussed within the teams. Over the course of the three simulation rounds, learners engage in a playful learning experience that covers topics such as waste reduction, the advantages of flow and takt time in a production system, and the utilisation of pull systems. Figure 7.9 illustrates the civil and military airplanes and the corresponding process steps involved in the simulation game.36 Moreover, there is a wide range of simulation games used for teaching lean manufacturing.37 These simple simulation games can be implemented independently of the learning factory concept. However, they are also conducted within learning factory environments, although it is preferable to start with less complex production systems during the initial sensitisation phase.38 In the case of the RoboCup Logistics League, a learning factory setting is created using multiple Festo MPS® stations that represent various stages of a production process. Additionally, mobile robots are employed to transport workpieces between these stations. The learning factory approach for the RoboCup Logistics League 36

An example of a similar simulation game is for example described in Stier (2003). See Badurdeen et al. (2010). 38 An example for the use of a simulation game in a learning factory environment can be found in Blöchl and Schneider (2016). 37

238

7 Overview on Existing Learning Factory Concepts

Fig. 7.10 RoboCup Logistics League 2016 in Leipzig, Germany. Screenshot taken from RoboCup (2016)

aligns with classical project-based learning (as discussed in Sect. 7.1.6). Participants in the course, typically university students, are tasked with developing and implementing transportation routes for the robots based on an unknown factory layout. In a second step, the transport routes need to be optimised to maximise the output of the production system within a specified timeframe. Moreover, the optimised robot routes must be capable of responding to express orders with higher priority that occur randomly.39 As mentioned earlier, the RoboCup differs from many other student projects conducted in learning factories due to an additional element: it is a competition between student groups from around the world. The championship locations have been chosen globally in recent years, including Leipzig, Germany (2016), Hefei, China (2015), João Pessoa, Brazil (2014), Eindhoven, the Netherlands (2013), and so on. This gamification element of global competition among different universities serves as a motivation for sending the most talented students, who can engage in intense and inspiring learning experiences (Fig. 7.10).40 Another gamification approach that combines the game element of “challenge” with a learning factory setting is called Energy-Bingo.41 This approach utilises the PDCA42 cycle as the learning method. In this method, the phases of the cycle are modified to Experience–Estimate–Measure–Transfer, as illustrated in Fig. 7.11. These phases outline the sequence of the learning course, with the “estimate” phase incorporating game elements. During this phase, participants are tasked with making educated guesses about specific parameters related to the energy consumption of a machine. 39

See Pittschellis (2015). See Pittschellis (2015). 41 Introduced by Böhner et al. (2015). 42 Plan–Do–Check–Act. 40

7.1 Learning Factories in Education

Transfer to other situations and to the company environment

239

Transfer

Experience specific operations hands-on Experience in the learning factory

Measure

Estimate

Measure the real value for the parameters and discussion and comparison with estimation

Estimate specific parameters based on underlying theory (educated guesses)

Fig. 7.11 Steps of a learning method using gamification elements, according to Böhner et al. (2015)

A short example of how game-based learning can be used to investigate energy consumption of a cutting machine and identify the most significant energy consumers is shown in the following43 : • Experience: the learning process begins with a supervisor demonstrating a typical cutting process used in composite manufacturing. They explain the functions of various machine components that affect energy consumption, such as a vacuum pump, conveyer motor, cabinet fan, servomotors for the cutter knife, and the machine control unit. • Estimation: based on their experiences from the previous phase, participants evaluate and estimate the expected energy consumption of each machine component. This estimation activity is also known as “energy-bingo.” Participants place adhesive tags on a prepared energy consumption chart to indicate their educated guesses for each component. • Measure: in this phase, suitable equipment is used to measure the electric power consumption of the mentioned machine components. Participants learn about measuring systems and concepts, and they receive feedback on their estimations. Comparing the estimations with the measured data leads to discussions and reflections. It may surprise learners to discover that the vacuum pump, in this example, consumes 95% of the total energy. 43

Böhner et al. (2015).

240

7 Overview on Existing Learning Factory Concepts

• Transfer: the goal of this learning module is to enable participants to apply their knowledge and motivation gained in the learning factory to other machines in their industry context. They should be capable and motivated to transfer what they have learned to optimise energy consumption in their own sphere of action in industry.

7.1.5 Problem-Based Learning in Learning Factories The problem-based learning approach has originated in medical education as a response to the limited clinical performance of students resulting from an overemphasis on memorising fragmented biomedical knowledge.44 In medical education, problem-based learning is defined as a learning method that occurs through the process of working towards understanding or resolving a problem.45 In this approach, the learning process begins with encountering the problem,46 which acts as a stimulus for active learning. The students are given the problem before receiving information about the theoretical background or principles related to the problem. In the 1990s, the problem-based learning approach gained popularity beyond medical education,47 including in engineering education.48 A commonly used method for the problembased learning process is the Maastricht Seven Jump method, which involves the following steps49 with an example for the topic lean production: 1. Clarify unfamiliar terms or concepts mentioned in the problem description. In a learning factory focusing on lean production, the participants encounter a problem related to reducing waste in a manufacturing process. Before diving into the problem, they clarify any unfamiliar terms or concepts associated with lean production, such as value stream mapping or Kanban. 2. Define the problem by listing the phenomena that need to be explained. The participants identify the specific phenomena they need to address, such as excessive inventory levels or inefficient material flow, which are causing waste in the production process. 3. Analyse the problem by brainstorming and generating multiple explanations for the phenomena using prior knowledge and common sense. Using their prior knowledge and common sense, the participants brainstorm and generate different explanations for the identified phenomena. They consider factors such as overproduction, unnecessary transportation, and waiting times that contribute to waste.

44

See Hung et al. (2008). Barrows and Tamblyn (1980). 46 See Barrows and Tamblyn (1980). 47 See Hung et al. (2008). 48 See Cawley (1989). 49 See, for example, Evensen and Hmelo-Silver (2000). 45

7.1 Learning Factories in Education

241

4. Criticise the proposed explanations and aim to create a coherent description of the underlying processes that explain the phenomena. The participants critically evaluate the proposed explanations and work towards developing a coherent understanding of the underlying processes that lead to waste. They discuss and refine their ideas, aiming to create a comprehensive explanation of the problem. 5. Formulate learning issues for self-directed learning. Based on their analysis and understanding of the problem, the participants formulate learning issues for self-directed learning. These may include questions like, “How can we optimise material flow to minimise transportation waste?” or “What strategies can be employed to reduce overproduction?” 6. Fill knowledge gaps through self-study. The participants engage in self-study to gather relevant information and deepen their understanding of lean production principles and techniques. They explore resources such as books, articles, case studies, and expert guidance to acquire the necessary knowledge to address the identified learning issues. 7. Share findings with the group and integrate the acquired knowledge into a comprehensive explanation for the phenomena. Assess whether enough knowledge has been acquired. The participants come together as a group to share their findings and insights from their self-directed learning. They collaborate to integrate their acquired knowledge into a comprehensive solution for the identified problem. They assess whether they have gained enough knowledge and understanding to propose effective strategies for waste reduction in the production process. This method encourages active engagement, critical thinking, and collaboration among learners as they work towards understanding and solving problems. In addition to the learning process sequence, there are seven characteristics of effective problem-based learning that can be summarised50 : 1. Learning objectives The learners define their own learning objectives after analysing the problem. These objectives should align with the intended objectives of the course. 2. Appropriate problem The presented problem should be suitable for the learners’ level of understanding and stage of the curriculum. 3. Interesting and relevant scenarios The problem scenarios should be interesting to the students and/or have relevance to their future practice. 4. Integration of fundamental theory Fundamental theory is presented in the context of the problem scenarios to motivate learners to integrate their knowledge. 5. Stimulating discussion

50

See Wood (2003).

242

7 Overview on Existing Learning Factory Concepts

Problem scenarios stimulate discussion among the learners and create motivation to seek explanations and solutions. 6. Openness of problem scenarios Problem scenarios are (sufficiently) open-ended, allowing room for discussion and exploration throughout the learning process. 7. Active participation Problem scenarios encourage active participation from learners, prompting them to find relevant information from various resources. By incorporating these characteristics, problem-based learning becomes more effective in engaging learners, promoting critical thinking, and facilitating the application of knowledge to real-world situations. Learning factories are highly beneficial for problem-based learning in engineering education. These simulated learning environments provide practical settings where problem situations can be defined and prescribed, offering a more contextualised approach compared to purely linguistic formulations. Additionally, learning factories allow for the testing and refinement of developed explanations that address previously analysed problems. The use of problem-based learning in learning factories is also discussed, although certain implementations may deviate slightly from the original approach.51 For example, instead of starting with a problem occurrence as a stimulus for active learning, some learning factories may begin with a self-directed literature research on a pre-defined topic.52 Furthermore, in the Pilotfabrik Industry 4.0 at TU Wien, a combination of problem-based learning with action-oriented53 and experiential54 learning approaches, as well as traditional lectures during the initial preparation phase of students, is employed.55 In Best Practice Example 33, the Pilotfabrik Industry 4.0 is described, highlighting its unique features and methodologies.

7.1.6 Project-Based Learning in Learning Factories Project-based learning is a type of learning where students work on real-world projects in groups. The purpose is to motivate students to engage with the learning content while they solve problems, find answers to questions, and complete the projects.56 Project-based learning is not a new concept and was already being implemented in the mid-nineteenth century at the Massachusetts Institute of Technology.57 51

See, for example, Jäger et al. (2012, 2013), and Tietze et al. (2013). See the problem-based learning approach described in Tietze et al. (2013). 53 See Sect. 7.1.2. 54 See Sect. 7.1.3. 55 See Jäger et al. (2012, 2013). 56 See Bender (2012), Wurdinger (2016). 57 See Knoll (1997). 52

7.1 Learning Factories in Education Team of students:

243 Challenge: Improvement of machining process in cellular manufacturing Quality • •

100% source inspection of critical specifications Failure proof design Autonomation

• •

Integration of simple intelligence into the machine (Jidoka) Simple and cost efficient Autonomation to reduce manual work

Project plan: 1 week

Analysis

1 week

Concept development

2 weeks

Implementation and documentation

1:50:00 1:40:00 1:30:00 1:20:00 1:10:00 1:00:00 0:50:00 0:40:00 0:30:00 0:20:00 0:10:00 0:00:00

Fig. 7.12 Advanced Design Project regarding the optimisation of a Lean Machining Line in the Process Learning Factory CiP

Learning factories provide an excellent environment for project-based learning in the field of production.58 In the Process Learning Factory CiP (see Best Practice Example 34), small student groups can conduct projects in an authentic production environment, known as Advanced Design Projects. Figure 7.12 provides an overview of one such project, which focuses on improving a Lean Machining Line in terms of quality and autonomation aspects.

58

Examples for the use of project-based learning with the help of learning factories are for example described in Balve and Albert (2015) and Jorgensen et al. (1995).

244

7 Overview on Existing Learning Factory Concepts

7.1.7 Research-Based Learning in Learning Factories If learning factories are operated by research institutions, they should also be used for research and the transfer of research results. This idea traces back to Wilhelm von Humboldt, a German philosopher, who believed that universities should primarily be research institutions where knowledge is passed on and critically discussed.59 Humboldt envisioned a unified approach to teaching and research, where learning in the university context involves engaging in research-like activities to solve unresolved problems. This fosters an open culture of discussion among teachers, learners, and across different disciplines.60 Traditional teacher-centered teaching and isolated research routines are not suitable for this type of university education.61 The concept of research-based learning is built upon this vision.62 In the literature, four different concepts that link research and education are described. One of these concepts is research-based learning.63 The classification and definition of each concept are described as follows64 : 1. Research-led learning: education focuses on subjects based on current research findings. Students play a passive role as an audience and are not actively involved in the research process. 2. Research-oriented learning: the emphasis is on understanding the research process, techniques, and instruments. Students are passive participants and not directly engaged in the research process. 3. Research-tutored learning: active discussions of research content take place between learners and teachers. Although students actively participate in the discussions, they are not directly involved in the research process. 4. Research-based learning: students learn by actively designing, experiencing, and reflecting on research projects. They actively contribute to generating results and new findings, being directly involved in the research process. Figure 7.13 visualises the classification, indicating whether students play an active or passive role and whether the focus is on research content or the research process and problems. It is recommended that well-designed educational programs integrate different concepts and strike a balance between them based on the goals of the educational activities.65 However, it is generally advised for university education to allocate more time to student-focused activities, prioritising concepts from the top half of Fig. 7.13.66 59

See Blume et al. (2015). See Blume et al. (2015), Euler (2005), Humboldt (1957). 61 See Humboldt (1957). 62 See Blume et al. (2015), Euler (2005), Humboldt (1957). 63 See Healey (2005). 64 Healey (2005). 65 Healey (2005). 66 See Healey (2005), Blume et al. (2015). 60

7.1 Learning Factories in Education

245

Student-focused (students as participants)

Emphasis on research content

Research-tutored

Research-based

Curriculum emphasizes learning focused on students writing and discussing papers or essays

Curriculum emphasizes students undertaking inquiry-based learning

Research-led

Research-oriented

Curriculum is structured around teaching subject content

Curriculum emphasizes teaching processes of knowledge construction in the subject

Emphasis on research processes and problems

Teacher focused (students as audience)

Fig. 7.13 Classification of forms of teaching linked with research—the research-teaching nexus (Healey, 2005)

Learning factories are well-suited for implementing research-based learning concepts related to factories and production processes because they offer students access to real-world, industry-like processes. Without this opportunity, data collection and experimentation would only be possible in less realistic laboratory conditions.67 Figure 7.14 illustrates the research process that guides research-based learning in learning factories.68

7.1.8 Best Practice Examples for Education The main goal of establishing learning factories is to enhance education and provide practical training in manufacturing. Learning factories have been widely used in manufacturing education, and numerous examples can be cited to illustrate their effectiveness. For specific case studies and successful implementations of learning factories, please refer to the Best Practice Examples in Chap. 11. These examples highlight the successful application of learning factories in various educational settings: • Best Practice Example 1: 5G Learning Factory 67

As two examples for research-based learning in learning factories, Blume et al. (2015) and Schreiber et al. (2016) can be named. 68 Research process according to Blume et al. (2015).

246

7 Overview on Existing Learning Factory Concepts

Report & Critical Review

Research Question

Literature Review

Interpretation Research process for research-based learning in learning factories

Hypothesis Derivation

Data Analysis

Data Collection

Research Design

Fig. 7.14 Research process for research-based learning in learning factories according to Blume et al. (2015), adapted from Creswell (2008)

• Best Practice Example 2: Aalto Factory of the Future • Best Practice Example 3: Additive Manufacturing Center (AMC) • Best Practice Example 4: A Distributed Learning Factory with a Central Hub (SEPT LF) • Best Practice Example 5: Aquaponics 4.0 Learning Factory (All-Factory) • Best Practice Example 6: Demonstration Factory Aachen DFA • Best Practice Example 8: Die Lernfabrik • Best Practice Example 9: E|Drive-Center • Best Practice Example 10: ETA-Factory • Best Practice Example 11: Fábrica do Futuro • Best Practice Example 12: FIM Learning Factory • Best Practice Example 13: FlowFactory • Best Practice Example 14: Globale Learning Factory • Best Practice Example 15: Global McKinsey Innovation & Learning Center Network (ILC) • Best Practice Example 16: Hybrid Teaching Factory for Personalised Education • Best Practice Example 17: IFA-Learning Factory • Best Practice Example 18: Industry 4.0 Lab • Best Practice Example 19: LEAD Factory at IIM, TU Graz, Austria • Best Practice Example 20: LEAN-Factory • Best Practice Example 21: Lean Learning Factory • Best Practice Example 22: Lean School • Best Practice Example 23: Learning and Research Factory (LFF)

7.1 Learning Factories in Education

247

• Best Practice Example 24: Learning Factory (CUBE) • Best Practice Example 25: Learning Factory jumpING • Best Practice Example 26: Learning Factory of advanced Industrial Engineering aIE (LF aIE) • Best Practice Example 27: Learning Factory SUM • Best Practice Example 28: Lernfabrik für schlanke Produktion (LSP) • Best Practice Example 29: Manufacturing Systems Learning Factory (iFactory) • Best Practice Example 30: Model Factory @ Singapore Institute of Manufacturing Technology • Best Practice Example 31: MPS Lernplattform • Best Practice Example 32: Operational Excellence • Best Practice Example 33: Pilotfabrik Industry 4.0 • Best Practice Example 34: Process Learning Factory CiP • Best Practice Example 35: Recycling Atelier Augsburg • Best Practice Example 36: SDFS Smart Demonstration Factory Siegen • Best Practice Example 37: Smart factory AutFab • Best Practice Example 38: Smart Factory • Best Practice Example 39: SmartFactory-KL • Best Practice Example 40: Smart Mini Factory • Best Practice Example 41: Stellenbosch Learning Factory (SLF) • Best Practice Example 42: SZTAKI Industry 4.0 Learning Factory • Best Practice Example 43: The Centre for Industry 4.0 • Best Practice Example 44: The Learning Factory • Best Practice Example 45: The Purdue Learning Factory Ecosystem • Best Practice Example 46: Werk150.

7.1.9 Example: Learning Factories for Industrie 4.0 Vocational Education in Baden-Württemberg Learning factories are not only used in university education but also in vocational education. In Germany, particularly in the state of Baden-Württemberg, learning factories are extensively utilised for vocational training. The Ministry of Economics, Labour, and Housing of Baden-Württemberg is providing substantial support of 6.8 million euros to establish 16 learning factories 4.0 in vocational schools. These learning factories aim to prepare professionals and junior staff for the challenges of digitalisation. The “Lernfabrik 4.0” serves as a laboratory where processes and equipment resemble industrial automation solutions. These learning factories enable the teaching of practical Industrie 4.0 processes. They integrate mechanical engineering and electrical engineering through professional production control systems.69 The distribution of the 16 learning factories across the state can be seen on the map as presented in Fig. 7.15. 69

See Ministerium für Wirtschaft, Arbeit und Wohnungsbau Baden-Württemberg (2017).

248

7 Overview on Existing Learning Factory Concepts

Fig. 7.15 Sixteen learning factories in Baden-Württemberg (Germany) on the map (Ministerium für Wirtschaft, Arbeit und Wohnungsbau Baden-Württemberg, 2017)

The Philipp-Matthäus-Hahn-Schule in Balingen, a vocational school, was equipped with a learning factory 4.0 in 2016.70 This learning factory in Balingen provides training for engineering students and vocational students to prepare them for digitalised production environments. Inside the learning factory, there are Festo Didactic learning factory modules that incorporate the latest industrial technology. These modules cover various areas such as CNC milling and turning processes, Industrie 4.0, hydraulics, and mechatronics (Fig. 7.16).71

70 71

See Festo Didactic (2017). See Festo Didactic (2017).

7.1 Learning Factories in Education

249

Fig. 7.16 Impression of learning factory 4.0 in Balingen as example for one of the 16 learning factories 4.0 in Baden-Württemberg, pictures taken from Festo Didactic (2017)

7.1.10 MecLab—A Learning Factory for Secondary Schools Author: Reinhard Pittschellisa a

Festo Didactics, Denkendorf, Germany

Initial Situation72 Learning Factories are used in universities, vocational training, and workforce development all around the world. But in all of these examples, learning factories are used to train professionals. Can the idea of learning factories also be used in secondary schools, where the learning goal is general knowledge, not training for professionals? The question arose in 2007 when Baden-Württemberg—one of the 16 federal states in 72

See Kultusministerium Baden-Württemberg (2023).

250

7 Overview on Existing Learning Factory Concepts

Germany—announced to implement a new subject Natural Science and Technology (Naturwissenschaft und Technik, NWT) in the Gymnasium. The Gymnasium is one of the three types of secondary schools in Germany, usually preparing the pupils to study after graduation. There are many differences between secondary schools on the one hand and universities and vocational schools on the other hand. While the latter seek to build professional competences and aim to train their students to become engineers or skilled workers, secondary schools aim on providing a general education. What does this mean for the subject technology in secondary schools? Rather than becoming an expert in a special field of technology, the pupils should achieve what we can call “Technological Literacy.” This means the pupils learn: • how technology is developed, manufactured, used, recycled, and communicated, • how technology influences society and our daily life, • what technology related jobs exist. The term “technology” is extremely broad, and different to universities and vocational training centers, there is no further specialisation. While engineering studies are split into mechanical engineering, civil engineering, electrical engineering, and software engineering, there is no such specialisation in a secondary school. Of course, it is impossible to cover the complete “Technology” with 3 h a week in a secondary school.73 Many curricula for secondary schools try to solve this problem by focusing on general principles which should be taught via examples (like energy, material, or information flow), while the field of application can be chosen freely by the teacher. Other curricula specify precisely what is to be done, e.g., renewable energy with solar cells. But, there are more differences which have to be considered: • Often technology is combined with other topics into one class (e.g., NWT in Baden-Württemberg, a mixture of natural science and technology), thus limiting even more the time which can be spend on technology. • Very often teachers are not trained in technology but teach physics or math or biology as their main subject. Therefore, everything used for technology training in secondary schools needs to be easy and robust to use. Training for teachers is of key importance. • Only a few secondary schools have labs designated for technology, so the classes usually took place in a normal classroom environment.74 • Most schools still work with a 45 min cycle. This means that the setup time for the equipment needs to be short, and ideally, the setup should be done by the pupils themselves. • Secondary schools have only limited budgets to spend for equipment; therefore, a cost-effective design of any learning equipment is of major importance. 73

See Pittschellis (2014). This has changed meanwhile since many schools started implementing Tech Labs or nowadays Maker Spaces.

74

7.1 Learning Factories in Education

251

Together with teachers from Baden-Württemberg, Festo Didactic developed the idea of a “Mini Learning Factory,” transferring the success factors of the learning factory approach to secondary schools. Manufacturing is an interesting field of application for technology in secondary schools since: • Manufacturing is interdisciplinary, merging mechanical and engineering and especially software engineering. • Manufacturing can be simple to start with, but also extremely complex for advanced pupils. • Manufacturing is a good example to demonstrate how developing, manufacturing, and using technology are merged. Furthermore, manufacturing is a basis to discuss the impact on society based on manufacturing (for example, the impact of automation on jobs). • In many curricula, making things and manufacturing are explicitly mentioned. The Concept of the Mini Learning Factory The basic concept used a small and simplified automated assembly line consisting of three stations: • a stack magazine to store and separate the workpieces, • a conveyor to transport and sort the workpieces, and • a handling system to move and assemble the product. Storing, separating, transporting, and managing are the most basic functions in every assembly line. All other more advanced functions typically found in an assembly line like glueing, welding, screwing, painting, etc., have been disregarded to keep the system simple (Fig. 7.17). Every station can be used individually and covers slightly different topics, e.g.: • pneumatic actuators and relay control in the stack magazine, • electrical actuators and sensors in the conveyor, and • logical programming in the handling station.

Fig. 7.17 Three stations: handling, conveyor, and stack magazine

252

7 Overview on Existing Learning Factory Concepts

Fig. 7.18 MecLab workpiece: top and bottom parts of a cylinder in three different colours

The workpiece is also extremely simple. It is just a cylinder with a top and a bottom parts and a diameter of 40 mm which form a container. The assembly process is also simple: just put both parts together regardless of the orientation of the parts. Top and bottom parts have the same dimension, therefore can be stored, and managed with the same equipment (Fig. 7.18). As the pictures of the stations indicate, industrial components have been used, e.g., sensors, pneumatic valves, actuators, etc. However, looking at the system makes clear that not all the components are used in a “proper” way from an engineering point of view. For example, the handling station consists of simple guided cylinders rather than pneumatical axis with ball bearings. Such “misuse” of components is justified by the fact that these handling systems shall demonstrate the working principle, but not achieve industrial grade accuracy. Nevertheless, the pupils feel encouraged because they are working with “real” components, not toys. The same principle has been applied to the control system. Rather than an industrial PLC, a simulation software combined with an USB interface is used to control the stations. The software allows to create electrical, pneumatical, and logical circuits to simulate the stations and—via a simple interface—control them as well. This measure saves a lot of costs, but—more important—allows the pupils to evaluate their control program with the simulation first before connecting with the real system. But, the most important component of the Learning Factory is the courseware. This is especially true since most of the teachers in secondary schools have no experience with automation or production technology. Didactical Concept The courseware consisted75 of three parts: • a textbook, explaining in a condensed manner all the components, their function and how to use them, but also concepts like technical documentation, relay, or logic programming, 75

It says “consisted” since when the system was launched, the courseware was printed books. Meanwhile, the courseware comes in a digital format.

7.1 Learning Factories in Education

253

• worksheets with exercises related to every station, with increasing level of difficulty, and • a teacher guideline explaining how to use the system in the classroom. The course can be divided in two parts. The first part is an introduction into the basics of automation and production technology. Depending on his personal teaching style and the time constraints, the teacher can start the class with a short introduction (e.g., how to use the simulation software or the function of a certain sensor) or let the pupils work on this information in self-study with the textbook. Then, the teacher hands out the worksheets, which are processed by the pupils in working groups of two, three, or four. Most of the exercises do not require immediate and continuous access to the stations, so usually one set of stations is sufficient for a class of up to 25 pupils. The setup of the stations can be done by the pupils themselves and takes only a few minutes: • take the station out of the box, • connect the mini compressor with the power supply and with the stations, and • connect the interface with the laptop computer and start the simulation program. Figure 7.19 shows the configuration. The time necessary to prepare a worksheet should not exceed one or two school hours. After working through all worksheets for one station, the pupils understand the function of every component and how to program and set up the station. Depending on the available time, the pupils can then switch to the next station or present their findings to the other pupils to make sure every pupil has a comparable knowledge. Since the level of difficulty is not the same for the three stations, this can be used for didactical differentiation. After this first part of the course, the students have achieved the basic competences enabling them to realise their own projects. Such projects form the second part of the course. The projects can be small and short and be done within a school hour (e.g., adding a sensor on the stack magazine to monitor the filling level) or more extensive like Fig. 7.19 Setup with a MecLab station and laptop

254

7 Overview on Existing Learning Factory Concepts

building an assembly line which produces a certain product, which usually require more changes on the stations. The teacher behaves like a customer asking for a certain product (which could be the production line or the product it is manufacturing), while students become engineers instead of becoming the engineering department and plan the project, design, and make the solution and present it to the customer. It is important that the teacher present the pupils a challenge and does not specify a certain technical solution to force the pupils to go through the complete engineering cycle with analysing the requirements, designing, and implement a solution and check the solution against the requirement. Solving such a task requires teamwork and therefore also fosters the communication and collaboration skills of the students. This second part of the course realises project-based and challenge-based learning, while the first part follows the classic guided text approach. Experiences Before market launch in 2008, the concept was evaluated with ten test schools, all located in Baden-Württemberg. The pupils were 7–10 graders. Per school, one teacher received a brief introductory training of half a day length. Although none of the involved teachers had prior experience with automation technology or production, the introduction was kept short by intention to evaluate whether inexperienced teachers could implement the concept. After several weeks of implementation in the schools, the teachers filled a questionnaire with these open questions: • • • •

Describe how you used MecLab in your class. How do you evaluate the learning outcomes? Did you use the courseware? What would you keep or change?

The overall results were very promising. Most of the teachers used the courseware and the didactical concept; however, only a few schools really reached the second level with projects due to time restrictions. Nevertheless, all teachers evaluated the learning achievements positively and mentioned especially the motivating factor of using industrial hardware and the immediate feedback the systems gave to the pupils. (They did not need to wait for the teacher, the systems showed whether their solution was working or not.) The simulation software allowed the pupils to continue working even at home, a possibility many pupils took advantage of (to the surprise of the teachers). Overall, the teachers were surprised by the level of the pupil’s motivation while working with MecLab.76 But there were also things to improve, e.g., the level of difficulty which was sometimes too low or too high. Some teachers wished for more options to assemble new solution out of the existing stations, thinking in the direction of an assembly kit. Other later evaluations have shown similar results.77

76

This effect could be observed whenever the pupils got the chance to work on their own in NWT classes with real hardware, which had been proven to be one success factor of the new subject NWT. 77 See Eisele (2010).

7.2 Learning Factories in Training

255

In 2008 happened another test of the new Mini Learning Factory, when the German company Thyssen Krupp organised the “Ideenpark”78 with the aim to make young people interested in science and technology. Festo Didactic participated in the show by organising a workshop with the then new MecLab. Groups of pupils with an age between 11 and 18 years were invited to take part in a three-day workshop with the following objectives: • Day 1: Learn the basics of automation with the MecLab system. • Day 2: Create an automated production line which produces Give Aways for the visitors at the end of the day with a partly automated process. • Day 3: Create an automated production line which produces Give Aways for the visitors at the end of the day with a completely automated process. The Give Away was simply the standard workpiece with some dice in it. The standard MecLab system provides no possibility to put the dice into the container— this part had to be invented by the pupils on their own. To do this, additional material like sensors, actuators and mechanical material was provided. Altogether, there were six teams of pupils during the Ideenpark, and all teams were able to achieve the goal and produce fully automatically the Give Aways at the end of day three. And each production line created by the six teams looked different, see Fig. 7.21. It should be mentioned that the pupils were supported by two students who made the introduction on day one but did not intervene in the design process during day 2 or 3—the solutions were entirely made by the pupils. Of course, the production lines were neither pretty nor reliable, but of all of them worked and produced the Give Aways which were handed to the visitors of the Ideenpark by the proud students! (Fig. 7.20). These examples demonstrate that the idea of learning factories, although initially created for professional education, can play a valuable role in secondary schools, too. It motivates the pupils, teaches them a lot about engineering, automation, and manufacturing, and finally, gives them a positive insight in the job of engineers in manufacturing, thus a valuable contribution to make young pupils interested in the field of engineering and manufacturing.

7.2 Learning Factories in Training Learning factories with the primary target training can have different roles or purposes. In general, three types of ways in which learning factories are used for training can be identified:

78

See also https://www.zukunft-technik-entdecken.de/aktivitaeten/ideenpark/.

256

7 Overview on Existing Learning Factory Concepts

Fig. 7.20 Samples for the production lines created by the pupils during Ideenpark 2008 Disseminated of necessary competences/ transformation/ project implementation With competence development in learning factories

Increased competence through learning factories

Without competence development in learning factories

Time

Fig. 7.21 Learning factories in training to speed up transformation and project implementation

7.2 Learning Factories in Training

257

• When learning factories are used for training, one main purpose is often to develop competences.79 Similar concepts to the primary target education can be applied, but the time available for training activities is generally limited.80 • Learning factories in training can also be used as part of change management approaches.81 In addition to technical topics, motivating participants and helping them to overcome internal barriers are crucial. • In recent years, especially in the context of digitalisation and Industrie 4.0, learning factories in training have been frequently employed to demonstrate new technologies or other innovations. Examples include scenario-based approaches82 and the use of elaborate case studies for Industrie 4.0.83

7.2.1 Developing Competences in Learning Factories Using learning factories to develop skills in training is similar to the primary target education in Sect. 7.1. The purpose of operating learning factories for training is to quickly spread knowledge and practical abilities within a company. This enables the successful implementation of improvement or transformation projects (Fig. 7.21). In general, there are two main types of learning processes observed in workoriented learning within learning factories during further education and training. These are known as the information assimilation approach84 and the experiential learning approach.85 • The information assimilation approach involves deriving and explaining theoretical learning content, which is then applied and evaluated. In the context of a learning factory course, this could mean that theoretical methods or experiences of others are first learned and understood, and then applied, evaluated, and observed within the learning factory environment. This approach is also known as theory push.86 • The experiential learning approach begins with the learner’s own actions and experiences, which serve as the foundation for understanding the learning content. In a learning factory course, this means that learners first gain hands-on experiences within the learning factory environment based on their own actions and observations, followed by an understanding of the underlying principles. Another term for this approach is problem pull.87 79

See Tisch et al. (2015a). See Sect. 7.1. 81 See Reiner (2009), Wagner et al. (2010), Dinkelmann et al. (2014), and Dinkelmann (2016). 82 See Erol et al. (2016). 83 See Wank et al. (2016). 84 According to Coleman (1982). 85 According to Kolb (1984). 86 See Tisch et al. (2013). 87 See Tisch et al. (2013). 80

258

7 Overview on Existing Learning Factory Concepts

Fig. 7.22 Role of learning factories in “information assimilation” and “experiential learning” (Tisch & Metternich, 2017)

The advantage of the learning factory concept regarding these two types of learning processes is that it actively involves learners for production-related topics. Without the learning factory concept, theory-based learning processes remain confined to theoretical understanding without the opportunity for practical implementation and reflection. Likewise, experiential learning processes for productionrelated topics would lack a formal yet realistic learning setup, leading to either the absence of initiation or the use of unrealistic simulations of production processes, which hinders the later transfer of knowledge to real-world production scenarios. The integration of the learning factory concept supports and enhances both opposing types of learning processes in production-related education and training courses, as illustrated in Fig. 7.22. The literature on learning processes extensively discusses the benefits and tradeoffs associated with different learning approaches.88 Table 7.2 provides a summary of the advantages and disadvantages associated with the two types of learning processes mentioned in the literature. Understanding the advantages and disadvantages of both learning process types is crucial to determine their suitability for training and courses in the workplace, depending on various factors such as objectives, time constraints, target audience, and trainer expertise. In general, the experiential learning sequence is particularly advantageous when learners have little or no prior experience of the specific problems being addressed and their motivation to engage with the content is relatively low. 88

See, e.g., Wurdinger (2005), Keeton et al. (2002), Coleman (1982).

7.2 Learning Factories in Training

259

Table 7.2 Advantages and disadvantages of the two opposed learning process sequences based on Coleman (1982), Keeton et al. (2002), Kolb (1984), Tisch (2018) Advantages

Information assimilation

Experiential learning

Great information density in a short amount of time is possible

Context-specific perception of knowledge is enabled automatically

Learning processes can be planned better in advance compared to experiential learning processes

Potential to develop leadership capabilities inside a learning group

Direct and systematic classification of Application-oriented learning information without uncertainty for the motivates and creates a link between learner learning and real life, facilitates the transfer, and generates motivation

Disadvantages

(Unexperienced) learners are brought to an appropriate level of knowledge

Knowledge transfer tends to be more sustainable than in information assimilation learning processes

Knowledge is presented mostly based on speech, and if concepts and principles behind words are not understood, problems are the result

The knowledge transfer is less efficient, and the preparation of courses are more complex

Transition to the application phase Systematisation of experiences and often does not take place—which can observations often does not take place be prevented with the use of the application phases in learning factories Often there is a time gap before the application phase—which can be prevented with the use of learning factories for application

Might not be helpful for unexperienced learners; negative experience may lead to demotivation

Straightforward learning processes may encourage experienced learners too little

Learning outcomes are less predictable; learning process tends to be less focused

However, it requires enough time for the learning module to be implemented effectively. On the other hand, the information assimilation sequence has an advantage even when there are stricter time constraints. This approach is advantageous when learners have already encountered the problems involved in their own company and the importance of the topic has been recognised. Ultimately, the choice between the two learning sequences should be made based on the specific context, the time available, the learners’ prior experience, and the importance of the topic at hand.89

89

See also Tisch (2018).

260

7 Overview on Existing Learning Factory Concepts

7.2.2 Best Practice Examples for Training Learning factories are commonly used to improve the skills and competences of employees in a variety of industries. The following examples of competence development through training in learning factories are presented in Chap. 11: • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

Best Practice Example 1: 5G Learning Factory Best Practice Example 2: Aalto Factory of the Future Best Practice Example 3: Additive Manufacturing Center (AMC) Best Practice Example 4: A Distributed Learning Factory with a Central Hub (SEPT LF) Best Practice Example 5: Aquaponics 4.0 Learning Factory (All-Factory) Best Practice Example 6: Demonstration Factory Aachen DFA Best Practice Example 7: Digital Capability Center Aachen Best Practice Example 8: Die Lernfabrik Best Practice Example 9: E|Drive-Center Best Practice Example 10: ETA-Factory Best Practice Example 11: Fábrica do Futuro Best Practice Example 12: FIM Learning Factory Best Practice Example 13: FlowFactory Best Practice Example 14: Globale Learning Factory Best Practice Example 15: Global McKinsey Innovation & Learning Center Network (ILC) Best Practice Example 16: Hybrid Teaching Factory for Personalised Education Best Practice Example 17: IFA-Learning Factory Best Practice Example 19: LEAD Factory at IIM, TU Graz, Austria Best Practice Example 20: LEAN-Factory Best Practice Example 21: Lean Learning Factory Best Practice Example 22: Lean School Best Practice Example 23: Learning and Research Factory (LFF) Best Practice Example 24: Learning Factory (CUBE) Best Practice Example 26: Learning Factory of advanced Industrial Engineering aIE (LF aIE) Best Practice Example 27: Learning Factory SUM Best Practice Example 28: Lernfabrik für schlanke Produktion (LSP) Best Practice Example 29: Manufacturing Systems Learning Factory (iFactory) Best Practice Example 30: Model Factory @ Singapore Institute of Manufacturing Technology Best Practice Example 31: MPS Lernplattform Best Practice Example 33: Pilotfabrik Industry 4.0 Best Practice Example 34: Process Learning Factory CiP Best Practice Example 35: Recycling Atelier Augsburg Best Practice Example 36: SDFS Smart Demonstration Factory Siegen Best Practice Example 39: SmartFactory-KL Best Practice Example 40: Smart Mini Factory

7.2 Learning Factories in Training

• • • •

261

Best Practice Example 43: The Centre for Industry 4.0 Best Practice Example 44: The Learning Factory Best Practice Example 45: The Purdue Learning Factory Ecosystem Best Practice Example 46: Werk150.

7.2.3 Success Factors for Learning Factories Conducive learning processes are often understood as feedback loops,90 in which learners modify the physical world (their work environment), which in turn undergoes specific changes. The specific changes in the environment provide the learner with information about the system they are interacting with. The learner integrates this information to update their understanding of the system, which then influences their future attempts to shape the environment based on their goals and intentions.91 In this process, behaviour and cognition are linked.92 While actions are guided by understanding, that understanding is itself refined by observations made on the basis of those actions.93 This feedback loop is shown in Fig. 7.23. In the organisational learning literature, the direct link between “information feedback” and “decision making” is referred to as “single loop learning,” while the indirect link through “corrected mental models of the world” and associated “decision rules” is referred to as “double loop learning.”94 In the context of competence development, it can be observed that only the double-loop learning path promotes the development of competences in the cognitive domain. Double-loop learning enhances the learner’s ability to cope with complex and unfamiliar situations by deepening their understanding of cause and effect. In contrast, in single-loop learning, the feedback of information is directly linked to a specific situation or decision and cannot be easily transferred to other situations. Even if the learner attempts are to do so, it is usually unsuccessful. In real factory environments, individuals experience single- and double-loop learning processes based on feedback. However, there are several challenges to the feedback process in this context, including: • False decisions in real factory environments can have significant economic and safety risks. • Factories are complex systems that are not easily understood in a short period of time. • Feedback on actions may be delayed, overshadowed by other developments, or not properly perceived.

90

See Sterman (1994), Forrester (1961), Argyris and Schön (1996). See Sterman (1994). 92 See Neisser (1976). 93 See Brown and Duguid (1991), Weick (1979), Crossan et al. (1999). 94 Argyris and Schön (1996). 91

262

7 Overview on Existing Learning Factory Concepts

affect

gives Real factory/ Real world

Decisions

Information feedback

influences

result in

Strategy, Structure, Decision Rules

shape

single loop learning

Mental models of the world (deeper understanding of cause and effects)

changes

double loop learning

Fig. 7.23 Learning as a feedback process according to Sterman (1994)

• Learning processes are difficult to control in real work environments, leading to learning outcomes that may not be in line with specific learning objectives (informal learning). To address these challenges of informal learning, learning factories offer a potential solution in the manufacturing sector. Learning factories can be thought of as “virtual worlds”95 that provide industry-relevant feedback and experiences in a highquality and realistic form. In this type of environment, the focus shifts from the mere production of goods to learning and gaining experience in relevant fields. By incorporating systematic phases within the learning environment, learners find it easier to contextualise their experiences within a broader framework (changing mental models of the world). This supports double-loop learning. Consequently, effective competence development in learning factories ensures that the feedback received not only influences individual decisions repeatedly, but also permanently changes the learners’ underlying understanding and approaches. The extended feedback cycle in the learning factory is illustrated in Fig. 7.24. In addition, various factors that contribute to effective learning processes and their modelling have been identified and discussed in different scientific fields.96 These include active learning,97 situated learning,98 problem-based learning,99 and constructivist learning in general.100 These approaches and models highlight success

95

See also Sterman (1994). Such as psychology, didactics, and learning design. 97 See Johnson et al. (1991). 98 See Lave and Wenger (1991). 99 See Boud and Feletti (1999). 100 See Jonassen (1999). 96

7.2 Learning Factories in Training

263

affect

gives Real factory/ real world

Unknown structure, dynamic complexity, inability to conduct controlled experiments

affect

gives Learning factory/ virtual world

Known structure, variable level of complexity, controlled experiments Decisions

Information feedback

• Real world:

Implementation failures, game playing, inconsistency, performance is goal • Virtual world: Perfect implementation; consistent incentives; consistent application of decision rules; Learning can be goal

• Real world:

Selective perception; missing feedback; delay, bias, distortion, error; ambiguity • Virtual world: Complete, accurate, immediate feedback

influences changes

result in shape Strategy, structure,decision Rules

Simulation used to infer dynamics of cognitive maps correctly

single loop learning

Mental models of the world (deeper understanding of cause and effects)

• Mapping of feedback structure • Disciplined application of scientific reasoning • Discussability of group process, defensive behavior double loop learning

Fig. 7.24 Extended feedback loop using the learning factory as virtual world, shown in Abele et al. (2017), inspired by Sterman (1994)

factors that should be considered in order to facilitate effective competence development. Here is an overview of the main success factors for methodological modelling of learning processes and how the learning factory concept reinforces these factors101 : • Contextualisation in a situated context 102 : the learning factory concept incorporates a partial model of a real factory, providing an authentic learning context in which situated learning approaches can be implemented.

101 102

According to Tisch and Metternich (2017). See Jonassen (1999), Lave and Wenger (1991).

264

7 Overview on Existing Learning Factory Concepts

• Learner activation103 : the learning factory concept emphasises the generation and application of knowledge through the active participation of learners during their time in the learning factory. • Problem-solving104 : learning factories provide opportunities for learners to engage in solving real-life problems, often as a basis for active learning phases. • Motivation105 : the realistic nature of the learning factory and the practical experience it offers create motivation for learning. Successful experiences in the learning factory also increase the motivation to apply what has been learned in real-life situations. • Collectivisation106 : learning in groups through self-organisation is an effective model for the phases of exploration, testing, systematisation, and reflection in learning factories. • Integration of thinking and doing107 : the learning factory concept fosters a controlled alternation and integration of practical phases in the simulated factory environment with systemisation phases, allowing a balanced combination of thinking and doing. • Self-regulation108 and self-direction109 : learning factories enable both externally and self-directed learning processes. The specific type of learning process used depends on the learner’s pre-requisites and course objectives. Through the integration of these success factors, the learning factory concept improves the effectiveness of skills development in several ways. Furthermore, it provides learners with a rich and immersive learning environment. Considering these success factors of effective learning processes, it becomes clear that the learning factory concept has the potential for high-quality competence development. However, to realise this potential, it is necessary to design, establish, use, and improve learning factories appropriately. Chapter 6 focuses on issues related to the entire life cycle of learning factories, from planning and design to modification or recycling. It covers various aspects such as the design of learning factories, starting from the initial conceptualisation phase, the design of the learning environment, the generation of learning modules, and the iterative improvement of the design through appropriate evaluation methods.

103

See Bonwell and Eison (1991), Johnson et al. (1991). See Boud and Feletti (1999). 105 See Deci et al. (1991). 106 See Greeno et al. (1996). 107 See Aebli (1994). 108 See Schunk (1990). 109 See Garrison (1997). 104

7.2 Learning Factories in Training

Learning factory

Problem in practice

Qualification Planning

Step 3 Abstract Communication solution

Communicated abstract solution Realisation

Step 1

Abstraction

Abstract problem

Step 2

265

Barriers of will, skill and knowledge

Step 4

Solution in practice

Real factory

Fig. 7.25 General process of integrating the learning factory concept in a change management approach according to Dinkelmann et al. (2014) and Dinkelmann (2016)

7.2.4 Learning Factory Trainings as a Part of Change Management Approaches Moreover, learning factories can be a useful tool for the change management process within an organisation.110 A survey of managers in Austria, Switzerland, and Germany identified employee resistance as the main reason for the failure of change projects.111 This resistance can stem from barriers related to willingness or barriers related to skills and knowledge.112 Learning factory concepts can help to overcome these barriers.113 The process for implementing change management using learning factories is shown in Fig. 7.25. It involves abstracting the problem within the learning factory, providing training and skills to staff, and involving staff in planning and finding solutions within the learning factory. Finally, the solutions developed by the employees are transferred and implemented in their respective factories.114

110

See Dinkelmann et al. (2014). See Hernstein/Hernstein International Management Institute (2003). 112 See Reiß (2012), Dinkelmann et al. (2014). 113 See Dinkelmann et al. (2014) and Dinkelmann (2016). 114 A detailed procedure for using the learning factory concept as part of a change management approach is described in Dinkelmann (2016). 111

266

7 Overview on Existing Learning Factory Concepts

Learning factories provide a safe and controlled environment where employees can experiment with new processes and technologies, allowing them to gain confidence and reduce the fear associated with change. By providing hands-on learning experiences, learning factories enable employees to understand the practical implications of change initiatives, fostering deeper commitment, and buy-in to organisational transformation. One notable success story is Kärcher, which implemented learning factories as part of their change management strategy. Through hands-on training and collaborative problem-solving activities, employees developed innovative solutions that not only improved operational efficiency but also fostered a culture of continuous learning and improvement.

7.2.5 Technology and Innovation Transfer in Course of Learning Factory Trainings A specific type of learning factory training has gained significant importance, especially with the rise of Industrie 4.0. This approach focuses on using the learning environment to present innovative technologies and processes. Rather than emphasising the active participation of learners, the main objective is to provide a real experience of the technology in a production environment. This demonstration-based approach aims primarily to inform and stimulate interest, rather than to develop highlevel competences. Often these demonstrations are combined with practical training sessions in the learning factory. Examples of such use of learning factories can be found in the literature.115 In addition, several best practice examples of learning factories serve as platforms for innovation transfer. These learning factories actively promote the exchange and implementation of innovative ideas and practices between different organisations. They facilitate the dissemination of knowledge and foster collaboration between industry partners, enabling the transfer of leading innovations into real-world applications. • • • • • • • • • • 115

Best Practice Example 1: 5G Learning Factory Best Practice Example 2: Aalto Factory of the Future Best Practice Example 3: Additive Manufacturing Center (AMC) Best Practice Example 4: A Distributed Learning Factory with a Central Hub (SEPT LF) Best Practice Example 5: Aquaponics 4.0 Learning Factory (All-Factory) Best Practice Example 6: Demonstration Factory Aachen DFA Best Practice Example 7: Digital Capability Center Aachen Best Practice Example 8: Die Lernfabrik Best Practice Example 9: E|Drive-Center Best Practice Example 10: ETA-Factory See, for example, Wank et al. (2016) or Erol et al. (2016).

7.3 Learning Factories in Research

• • • • • • • • • • • • • • • • • • • • • • • • • • • • •

267

Best Practice Example 11: Fábrica do Futuro Best Practice Example 12: FIM Learning Factory Best Practice Example 13: FlowFactory Best Practice Example 14: Globale Learning Factory Best Practice Example 15: Global McKinsey Innovation & Learning Center Network (ILC) Best Practice Example 16: Hybrid Teaching Factory for Personalised Education Best Practice Example 18: Industry 4.0 Lab Best Practice Example 19: LEAD Factory at IIM, TU Graz, Austria Best Practice Example 20: LEAN-Factory Best Practice Example 23: Learning and Research Factory (LFF) Best Practice Example 24: Learning Factory (CUBE) Best Practice Example 25: Learning Factory jumpING Best Practice Example 26: Learning Factory of advanced Industrial Engineering aIE (LF aIE) Best Practice Example 28: Lernfabrik für schlanke Produktion (LSP) Best Practice Example 29: Manufacturing Systems Learning Factory (iFactory) Best Practice Example 30: Model Factory @ Singapore Institute of Manufacturing Technology Best Practice Example 31: MPS Lernplattform Best Practice Example 33: Pilotfabrik Industry 4.0 Best Practice Example 34: Process Learning Factory CiP Best Practice Example 35: Recycling Atelier Augsburg Best Practice Example 36: SDFS Smart Demonstration Factory Siegen Best Practice Example 37: Smart factory AutFab Best Practice Example 38: Smart Factory Best Practice Example 39: SmartFactory-KL Best Practice Example 40: Smart Mini Factory Best Practice Example 42: SZTAKI Industry 4.0 Learning Factory Best Practice Example 43: The Centre for Industry 4.0 Best Practice Example 45: The Purdue Learning Factory Ecosystem Best Practice Example 46: Werk150.

7.3 Learning Factories in Research Learning Factories are linked to research in two ways. According to the learning factory morphology116 of the learning factory, this link is established as follows: • learning factories as research objects (Sect. 7.3.1) and • learning factories as research enablers (Sect. 7.3.2).

116

See Chap. 4 or Tisch et al. (2015c) and Kreß et al. (2023).

268

7 Overview on Existing Learning Factory Concepts

7.3.1 Learning Factories as Research Objects In recent years, research on learning factory systems has focused on questions how: • learning factories can be described and defined holistically,117 • learning factories can be classified according to their changeability,118 • learning factories, learning modules, and learning situations in learning factories can be designed,119 • products for changeable learning factories can be designed,120 • miniaturised factories can be used as learning environments in learning factories,121 • non-visible topics such as energy efficiency can be addressed in learning factories,122 • learning success can be measured in learning factories,123 and • learning factories can be improved holistically based on a maturity model and a quality system,124 to define essential requirements on learning factories for different stakeholders,125 and many more. Research on learning factories has evolved to a wide field considerably in recent years. Learning factory concepts are now well structured and described, and research on the learning factory system is regularly published.126 However, there are still many topics that should be considered and explored. Some of these topics include the scalability of learning factories, the effectiveness of learning factories, and the application of new content (e.g., circular economy) in learning factories, which has gained attention in recent years. By further exploring these areas, the possibilities and potential of learning factories can be improved and expanded. For further research topics related to the learning factory system, see Chap. 6.

117

See Tisch et al. (2015c), Abele (2016). See Wagner et al. (2012). 119 See Tisch et al. (2015a). 120 See Wagner et al. (2015). 121 See Kaluza et al. (2015). 122 See Abele et al. (2016). 123 See Tisch et al. (2015b). 124 See Enke et al. (2017). 125 See Enke et al. (2016). 126 For an overview, see also Abele et al. (2017). 118

7.3 Learning Factories in Research

269

7.3.2 Learning Factories as Platforms for Production-Oriented Research Production engineering, as an applied science, aims to discover new knowledge that can be applied in real-world scenarios.127 In production engineering, research questions often arise from industrial practice. The research process in applied sciences involves identifying problems relevant to industry and consulting with practical applications using newly created knowledge.128 Figure 7.26 illustrates this as a process that moves back and forth between theory and practice. It involves conducting desk research, analysing data, and applying empirical methods. However, there is a challenge in production-related research because the direct link between research and industrial practice can potentially disturb the basic functioning of factories. In addition, the direct transfer of research results into industrial production is complex and costly. This is a particular problem for small and medium-sized enterprises, which lack research infrastructure and expertise in various fields.129 To address this issue, learning factories can support the production engineering research process by providing a risk-free environment for integrating new concepts and gain practical experience at lower cost and with reduced complexity. Learning factories are highly suitable as laboratory environments for studying manufacturing systems in research. Figure 7.27 provides a concept for employing learning factories as research enablers.130 The concept begins with existing knowledge and progresses through the following steps: • • • •

Problem identification, Abstraction of the problem with real data, Solution finding based on theory, and Realisation of the solution into practice, and validation of new knowledge.131

Based on this concept, the Learning Factory acts as a research enabler, helping to identify research problems in a quasi-realistic environment. It allows solutions to be evaluated using a physical factory model, reducing complexity and cost compared to real-world testing.132 To illustrate this, the example of “artificial intelligence in manufacturing” is used. Step 1: Problem Identification133 In the context of artificial intelligence in manufacturing, an essential step is identifying the problems related to predictive maintenance in a real factory setting. By 127

See Ulrich et al. (1984). According to Ulrich et al. (1984). 129 See Abele et al. (2017). 130 See Seifermann et al. (2014b). 131 See Seifermann et al. (2014a). 132 See Abele et al. (2017). 133 Described in detail for example in Abele et al. (2011, 2012). 128

270

7 Overview on Existing Learning Factory Concepts

Identification and interpretation of problem-related theory and hypothesis of empirical science

2

Identification and specification of a problem-based approach to formal formal science

3

Derivation of evaluation criteria, design rules and models f(x)=

1

Identification & typology of practical problems

4

Identification & investigation of the relevant application context

6

Review of rules and models for the application context

7

Industry consultation

5

Basic empirical and formal sciences Integration of practical experience

Desk research

f(x)=

Analytics

Empiricism in industrial practice or learning factories

Fig. 7.26 Research process of applied sciences and the integration of practical experience through learning factories (Abele et al., 2017), according to Schuh and Warschat (2013) on the basis of Ulrich et al. (1984)

Theory Solution finding Abstract Model

Simulation Model

Abstraction

Realization

Physical Model

Reality

Problem identification

Existing knowledge

Verification

New knowledge

Fig. 7.27 Learning factories as research enablers according to Seifermann et al. (2014a)

7.3 Learning Factories in Research Step 1: Problem identification in the real factory Observations

Talks with industrial professionals Understanding of the problem

271 Step 2: Abstraction of the problem in the learning factory

Replicate the problem in a safe environment Adapt the factory elements (machines, assembly lines etc.)

in a simulation model f(x)=

Model the problem mathematically Collect data from the learning factory Adapt and run the algorithms

Fig. 7.28 Problem identification and abstraction of the problem in a learning factory

collaborating with an industrial factory, researchers can gain invaluable insights into the maintenance challenges faced by industry professionals. During this phase, researchers closely observe the factory’s equipment, processes, and maintenance operations. They aim to identify recurring issues such as unexpected equipment breakdowns, costly repairs, and sub-optimal maintenance scheduling. This comprehensive understanding of the problems enables researchers to develop more effective and targeted solutions for predictive maintenance in manufacturing systems. Step 2: Abstraction of the Problem with Real Data134 To address, e.g., the predictive maintenance challenge, researchers can utilise the learning factory as a physical model to simulate and replicate the maintenance issues observed in the real factory. By configuring the learning factory to resemble the real factory in terms of equipment and operational conditions, researchers can gather realtime data from various sensors, including temperature, vibration, and performance metrics. Furthermore, abstraction occurs in two stages. The first stage involves modelling the identified problem within the learning factory environment. The second stage involves developing a simulation model on a computer, typically utilising neural networks, to represent the algorithmic aspect of the predictive maintenance solution. This abstraction enables researchers to program and train the neural network using historical data, allowing it to learn patterns and make accurate predictions regarding maintenance needs (Fig. 7.28). Step 3: Solution Finding Based on Theory135 In Step 3, researchers focus on devising solutions grounded in theoretical principles within the chosen field of study. By utilising insights from prior stages, such as data collection and problem abstraction, researchers apply established concepts, often 134 135

Described in detail, for example, in Abele et al. (2012) and Seifermann et al. (2017). Described in detail, for example, in Abele et al. (2011, 2012), and Seifermann et al. (2017).

272

7 Overview on Existing Learning Factory Concepts

from fields like artificial intelligence and machine learning, to formulate effective solutions. Fine-tuning existing algorithms, adjusting parameters, and feature engineering are typical activities. This step serves as a critical link between theoretical knowledge and the practical implementation of solutions, facilitating the translation of abstract ideas into tangible outcomes. For example, researchers leverage theoretical concepts from the field of artificial intelligence in manufacturing to formulate effective solutions for predictive maintenance. Researchers start by utilising insights gained in the second step, where real data was collected and the problem was abstracted into a simulation model. This forms the groundwork for applying theoretical principles in crafting the predictive maintenance solution. Key to this phase is the use of machine learning (ML) algorithms, particularly artificial neural networks. These computational models, inspired by biological brains, excel at pattern recognition and prediction based on learned knowledge. Production researchers refine existing ML algorithms, like neural networks, by determining optimal hyperparameters to achieve the best model performance. The training process involves exposing the neural network to historical sensor data from the learning factory. Through iterative adjustments of weights and biases, the model learns patterns and correlations between input sensor data and maintenance requirements. Optimisation techniques, such as gradient descent, are employed to minimise the gap between predicted and actual maintenance needs. A significant element is feature extraction, where researchers identify informative features from sensor data to input into the neural network. These features, such as temperature variations and vibration patterns, serve as indicators of potential equipment failure. Feature engineering methods enhance the model’s predictive capabilities, often involving dimensionality reduction or data transformation techniques. Step 4: Solution Realisation into Practice, and Validation of New Knowledge Step 4 involves the practical implementation and validation of the predictive maintenance solution developed in Step 3 within the physical model of the learning factory. This step focuses on deploying the algorithm and assessing its effectiveness and reliability in real factories. During the implementation phase, researchers closely monitor the performance of the predictive maintenance solution within the learning factory. They assess its ability to accurately detect potential equipment failures, provide timely maintenance recommendations, and minimise downtime. The solution is evaluated based on predetermined metrics, such as the percentage of true positive predictions, false positives, or false negatives, to measure its predictive accuracy and efficiency. Researchers compare the predictions generated by the algorithm with the actual maintenance requirements observed in the learning factory. This verification process helps to determine the effectiveness of the solution in identifying maintenance needs and mitigating potential breakdowns. By analysing the correlation between predicted maintenance requirements and the actual outcomes, researchers can assess the reliability and performance of the algorithm. Iterative improvement is a crucial aspect of Step 4. Researchers utilise the feedback obtained from the implementation and verification process to refine and enhance the predictive maintenance solution. They

7.3 Learning Factories in Research

273

analyse any discrepancies between predicted and actual maintenance needs and identify areas where the algorithm can be further optimised. Adjustments to the algorithm’s parameters, fine-tuning of the neural network architecture, or incorporating additional data sources may be performed to improve its accuracy and effectiveness. Researchers also consider the practical implications of implementing the predictive maintenance solution in real manufacturing systems. They assess the scalability and adaptability of the solution to different factory environments and equipment types. Factors such as computational resource requirements, real-time data processing capabilities, and integration with existing maintenance workflows are considered to ensure that the solution can be seamlessly integrated into industrial settings. Throughout this iterative process, researchers collaborate closely with industry partners and stakeholders to gather feedback, address any operational challenges, and fine-tune the solution based on real-world constraints and requirements. This collaborative approach helps to validate the applicability and viability of the predictive maintenance solution in actual manufacturing contexts. By rigorously implementing and verifying the predictive maintenance solution within the learning factory, researchers can ensure its practical relevance and effectiveness. The iterative nature of this step allows for continuous improvement and refinement, leading to a robust and reliable solution that can be deployed in real manufacturing systems, optimising maintenance practices, and contributing to improved productivity and operational efficiency (Figs. 7.29 and 7.30). Additionally, apart from the research process discussed earlier, learning factories are increasingly being utilised as test beds to evaluate and confirm the effectiveness of research findings. Numerous examples from various research areas demonstrate the value of learning factories in this regard. Here are a few examples:

Step 3: Solution finding based on theory

Step 4: Realization into practice & validation in the learning factory

Applying theoretical concepts

Monitor the developed solution

Developing solution

Analyze the result

Fig. 7.29 Solution finding, realisation into practice, and verification

in the real factory

274

7 Overview on Existing Learning Factory Concepts

Fig. 7.30 Learning factories as a research enabler with the example of artificial intelligence in manufacturing

• Continuous improvement processes and systems136 : learning factories provide a practical setting to test and improve processes and systems aimed at enhancing productivity and efficiency. • The impact of goal setting in production137 : learning factories can be used to study the effects of setting specific goals on production outcomes and employee performance. • Online control of assembly processes138 : researchers can explore and optimise methods for real-time monitoring and control of assembly processes within the learning factory environment. • Video analysis of production-relevant competences139 : learning factories offer an opportunity to analyse and enhance the skills and competences of workers by using video analysis techniques. • Technologies for smart factories140 : learning factories serve as a platform to develop and evaluate technologies that enable the implementation of smart factory concepts, such as the Internet of Things and automation.

136

See Cachay and Abele (2012). See Asmus et al. (2015). 138 See Tracht et al. (2015). 139 See Hambach et al. (2015, 2016). 140 See Kemény et al. (2016). 137

7.3 Learning Factories in Research

275 Field experiment

Laboratory experiment

Social reality

Arranged

Influence

Low

High

Internal validity

Low

High

External validity

High

Low

Environment

Embedded experiments

Fig. 7.31 Combining the advantages of field and laboratory experiments for research in learning factories according to Schuh et al. (2015)

• Value stream mapping 4.0141 : researchers can investigate and refine the application of value stream mapping techniques, an approach used to identify and eliminate waste in manufacturing processes regarding data and information, within the context of Industry 4.0. • Defect prevention with optical object identification142 : learning factories provide a suitable environment to develop and evaluate optical identification systems for detecting and preventing defects in production processes. • Lean stress sensitisation143 : researchers can explore how to sensitise workers to lean principles and practices, aiming to improve their understanding and implementation of lean manufacturing concepts. Moreover, learning factories offer an opportunity for conducting embedded experiments, which combine the advantages of field experiments and laboratory experiments. This concept allows researchers to leverage the authentic social dynamics and high internal and external validity of learning factories, creating an ideal framework for conducting research.144 Figure 7.31 visually illustrates the characteristics of field and laboratory experiments and their connection to research possibilities in learning factories. This way, learning factories enable the execution of embedded experiments that closely resemble real-world conditions while maintaining rigorous scientific standards. Furthermore, learning factory environments are becoming more widely utilised to support companies use the newest technologies and innovative methods. Learning factories have great potential when it comes to showcasing and spreading innovations. In addition to serving as practical platforms for research and development, learning factories offer application-focused technology and innovation platforms that guide the development of production processes, production technologies, and products until they are ready for the market. Moreover, learning factories facilitate the subsequent dissemination of these innovations. The following Best Practice Examples provide illustrations of how learning factories are utilised for research, demonstration, and 141

See Meudt et al. (2017). See Wiech et al. (2017). 143 See Dombrowski et al. (2017). 144 See Schuh et al. (2015). 142

276

7 Overview on Existing Learning Factory Concepts

the dissemination of knowledge. Examples for research in learning factories can be found in various sections: • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

Best Practice Example 1: 5G Learning Factory Best Practice Example 2: Aalto Factory of the Future Best Practice Example 3: Additive Manufacturing Center (AMC) Best Practice Example 4: A Distributed Learning Factory with a Central Hub (SEPT LF) Best Practice Example 5: Aquaponics 4.0 Learning Factory (All-Factory) Best Practice Example 6: Demonstration Factory Aachen DFA Best Practice Example 7: Digital Capability Center Aachen Best Practice Example 8: Die Lernfabrik Best Practice Example 9: E|Drive-Center Best Practice Example 10: ETA-Factory Best Practice Example 11: Fábrica do Futuro Best Practice Example 12: FIM Learning Factory Best Practice Example 13: FlowFactory Best Practice Example 14: Globale Learning Factory Best Practice Example 16: Hybrid Teaching Factory for Personalised Education Best Practice Example 17: IFA-Learning Factory Best Practice Example 18: Industry 4.0 Lab Best Practice Example 19: LEAD Factory at IIM, TU Graz, Austria Best Practice Example 21: Lean Learning Factory Best Practice Example 22: Lean School Best Practice Example 23: Learning and Research Factory (LFF) Best Practice Example 24: Learning Factory (CUBE) Best Practice Example 25: Learning Factory jumpING Best Practice Example 26: Learning Factory of advanced Industrial Engineering aIE (LF aIE) Best Practice Example 27: Learning Factory SUM Best Practice Example 28: Lernfabrik für schlanke Produktion (LSP) Best Practice Example 29: Manufacturing Systems Learning Factory (iFactory) Best Practice Example 30: Model Factory @ Singapore Institute of Manufacturing Technology Best Practice Example 32: Operational Excellence Best Practice Example 33: Pilotfabrik Industry 4.0 Best Practice Example 34: Process Learning Factory CiP Best Practice Example 35: Recycling Atelier Augsburg Best Practice Example 36: SDFS Smart Demonstration Factory Siegen Best Practice Example 37: Smart factory AutFab Best Practice Example 38: Smart Factory Best Practice Example 39: SmartFactory-KL Best Practice Example 40: Smart Mini Factory Best Practice Example 41: Stellenbosch Learning Factory (SLF) Best Practice Example 42: SZTAKI Industry 4.0 Learning Factory

7.4 Wrap-Up of This Chapter

• • • •

277

Best Practice Example 43: The Centre for Industry 4.0 Best Practice Example 44: The Learning Factory Best Practice Example 45: The Purdue Learning Factory Ecosystem Best Practice Example 46: Werk150.

7.4 Wrap-Up of This Chapter This chapter provides an overview of the ways in which learning factories are used in teaching, training, and research. In this context, the role of the learning factory for the most diverse concepts in the context of the application areas is clarified. The chapter discusses the prevalence of learning factories worldwide, providing an extensive overview of existing concepts. Learning factories vary in size, purpose, and complexity, aiming to offer a comprehensive learning experience for individuals in production-related fields. The focus is primarily on education, training, and research in learning factories. The chapter offers detailed descriptions and structures of various learning factory concepts, supported by practical examples from academia and industry. Two main models of incorporating education into learning factories are discussed: open student projects and steered and closed courses. Open student projects foster creativity and problem-solving, while steered and closed courses provide guided problem scenarios aligned with theoretical foundations. Active learning is emphasised, including action-oriented learning, experiential learning, and problem-based learning. Action-oriented learning involves learners independently tackling complex problems, while experiential learning allows learners to assume professional roles and create knowledge through transformative experiences. Game-based learning and gamification are integrated into learning factories to enhance motivation and engagement. The chapter also explores project-based learning, research-based learning, and the connection between research and education. It highlights the use of learning factories in vocational schools in Baden-Württemberg, Germany, where students are trained for digitalised production in Industrie 4.0. These learning factories equipped with advanced technology prepare students for the challenges of digitalisation in practical production environments. Learning factories used for training purposes serve three main roles: developing competences, facilitating change management, and demonstrating new technologies. When used for developing competences, learning factories aim to quickly spread knowledge and practical abilities within a company to implement improvement or transformation projects. Two main types of learning processes observed within learning factories are information assimilation and experiential learning, each with their advantages and disadvantages. The integration of the learning factory concept supports and enhances both types of learning processes in production-related education and training courses. Success factors for effective learning processes in learning factories include contextualisation in a situated context, learner activation, problem-solving, motivation, collectivisation, integration of thinking and doing, and

278

7 Overview on Existing Learning Factory Concepts

self-regulation. By incorporating these success factors, learning factories provide a rich and immersive learning environment for high-quality competence development. However, it is crucial to appropriately design, establish, use, and improve learning factories to realise their full potential. Furthermore, learning factories are instrumental in change management processes, helping organisations overcome employee resistance. They provide a controlled environment for experimentation, building confidence and reducing fear of change. Through hands-on learning, employees grasp the practical implications of change, fostering commitment and facilitating innovation. Learning factory training also encompasses technology and innovation transfer, offering real-world experiences to inform and generate interest. Rather than focusing on high-level competences, this approach aims to provide a firsthand encounter with innovative technologies and processes. Practical training sessions and demonstrations in the learning factory further enhance the learning experience. Numerous literature examples highlight the use of learning factories for technology and innovation transfer. Learning factories play a twofold role in research: as research objects and as research enablers. Research on learning factories as objects focuses on holistic descriptions, classification based, design of learning environments, modules and situations, product design, miniaturised factories as learning environments, addressing non-visible topics like energy efficiency, measuring learning success, improving learning factories based on maturity models and quality systems, understanding stakeholder requirements, and more. While significant progress has been made, areas such as scalability, effectiveness, and the application of new content (e.g., circular economy) require further exploration. Learning factories also serve as research enablers by providing a risk-free environment for integrating practical experience at a lower cost and with reduced complexity. They enable problem identification, abstraction with real data, solution finding based on theory, realisation of solutions in practice, and verification of new knowledge. For example, in the context of artificial intelligence in manufacturing, researchers can identify maintenance problems, abstract them within the learning factory, develop simulation models, apply theoretical concepts, implement solutions, and validate their effectiveness. Learning factories can also be used to evaluate and validate research findings in various areas such as continuous improvement, goal setting, online control of assembly processes, video analysis of competences, technologies for smart factories, and more. They offer the advantages of both field and laboratory experiments, providing an ideal framework for research while maintaining scientific standards. Moreover, learning factories serve as platforms for showcasing and disseminating innovations, supporting companies in adopting new technologies and methods. They offer applicationfocused technology and innovation platforms, guiding the development of production processes, technologies, and products, and facilitating their dissemination. Learning factories are dynamic environments that contribute to the advancement of research, demonstration, and knowledge sharing.

References

279

References Abele, E. (2016). Learning factory. In CIRP encyclopedia of production engineering. Abele, E., Bauerdick, C., Strobel, N., & Panten, N. (2016). ETA learning factory: A holistic concept for teaching energy efficiency in production. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 83–88. Abele, E., Bechtloff, S., & Krause, F. (2011). Flexible Serienfertigung im Kundentakt. Werkstatt und Betrieb -Munchen-, 144(6), 24–27. Abele, E., Bechtloff, S., & Seifermann, S. (2012). Sequenzfertigung für flexible und schlanke Zerspanung. Productivity Management, 17(1), 45–48. Abele, E., Chryssolouris, G., Sihn, W., Metternich, J., ElMaraghy, H. A., Seliger, G., Sivard, G., ElMaraghy, W., Hummel, V., Tisch, M., & Seifermann, S. (2017). Learning factories for future oriented research and education in manufacturing. CIRP Annals—Manufacturing Technology, 66(2), 803–826. Abt, C. C. (1987). Serious games. University Press of America. Aebli, H. (1994). Denken: das Ordnen des Tuns (2. Aufl). Klett-Cotta. Argyris, C., & Schön, D. A. (1996). Organizational learning (Reprinted with corr). In Organization Development Series. Addison-Wesley Pub. Co. Asmus, S., Karl, F., Mohnen, A., & Reinhart, G. (2015). The impact of goal-setting on worker performance—Empirical evidence from a real-effort production experiment. In 12th Global Conference on Sustainable Manufacturing. Procedia CIRP, 26, 127–132. Badurdeen, F., Marksberry, P., Hall, A., & Gregory, B. (2010). Teaching lean manufacturing with simulations and games: A survey and future directions. Simulation & Gaming, 41(4), 465–486. Balve, P., & Albert, M. (2015). Project-based learning in production engineering at the Heilbronn Learning Factory. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 104–108. https://doi.org/10.1016/j.procir.2015.02.215 Barrows, H. S., & Tamblyn, R. M. (1980). Problem-based learning: An approach to medical education. Springer Publishing Company. Beauvais, W. (2013). Qualification as an effective tool to support the implementation of lean. In G. Reinhart, P. Schnellbach, C. Hilgert, & S. L. Frank (Eds.), 3rd Conference on Learning Factories, Munich, May 7, 2013 (pp. 108–117). Augsburg. Bender, W. N. (2012). Project-based learning: Differentiating instruction for the 21st century. Sage. Blöchl, S. J., Michalicki, M., & Schneider, M. (2017). Simulation game for lean leadership— Shopfloor management combined with accounting for lean. Procedia Manufacturing, 9, 97–105. https://doi.org/10.1016/j.promfg.2017.04.031 Blöchl, S. J., & Schneider, M. (2016). Simulation game for intelligent production logistics—The PuLL® learning factory. Procedia CIRP, 54, 130–135. https://doi.org/10.1016/j.procir.2016. 04.100 Blume, S., Madanchi, N., Böhme, S., Posselt, G., Thiede, S., & Herrmann, C. (2015). Die Lernfabrik—Research-based learning for sustainable production engineering. In 5th CIRPSponsored Conference on Learning Factories. Procedia CIRP, 32, 126–131. https://doi.org/ 10.1016/j.procir.2015.02.113 Böhner, J., Weeber, M., Kuebler, F., & Steinhilper, R. (2015). Developing a learning factory to increase resource efficiency in composite manufacturing processes. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 64–69. https://doi.org/10.1016/j.procir. 2015.05.003 Bonwell, C. C., & Eison, J. A. (1991). Active learning: Creating excitement in the classroom. ASHE-ERIC higher education report: 1, 1991. School of Education and Human Development, George Washington University. Boud, D., & Feletti, G. (Eds.). (1999). The challenge of problem-based learning (2nd ed., reprinted). Kogan Page. Brown, J. S., & Duguid, P. (1991). Organizational learning and communities-of-practice: Toward a unified view of working, learning and innovation. Organization Science, 2(1), 40–57.

280

7 Overview on Existing Learning Factory Concepts

Cachay, J., & Abele, E. (2012). Developing competencies for continuous improvement processes on the shopfloor through learning factories—Conceptual design and empirical validation. In 45th CIRP Conference on Manufacturing Systems. Procedia CIRP, 3(3), 638–643. Cachay, J., Wennemer, J., Abele, E., & Tenberg, R. (2012). Study on action-oriented learning with a learning factory approach. Procedia—Social and Behavioral Sciences, 55, 1144–1153. Cawley, P. (1989). The introduction of a problem-based option into a conventional engineering degree course. Studies in Higher Education, 14(1), 83–95. https://doi.org/10.1080/030750789 12331377632 Coleman, J. S. (1982). Experiential learning and information assimilation: Toward an appropriate mix. In D. Conrad & D. Hedin (Eds.), Child & youth services: Volume 4, Numbers 3/4. Youth participation and experimental education (pp. 13–20). The Haworth Press. Crawley, E., Malmqvist, J., Ostlund, S., & Brodeur, D. (2007). Rethinking engineering education: The CDIO approach (1st ed.). Springer. Creswell, J. W. (2008). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (3rd ed.). Pearson/Merrill Prentice Hall. Crossan, M. M., Lane, H. W., & White, R. E. (1999). An organizational learning framework: From intuition to institution. The Academy of Management Review, 24(3), 522–537. Deci, E. L., Vallerand, R. J. M., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3 & 4), 325–346. Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness: Defining “gamification”. In MindTrek’11, Tampere, Finland, September 28–30, 2011 (pp. 9–15). Dinkelmann, M. (2016). Methode zur Unterstützung der Mitarbeiterpartizipation im Change Management der variantenreichen Serienproduktion durch Lernfabriken [Dissertation]. Stuttgart. Dinkelmann, M., Siegert, J., & Bauernhansl, T. (2014). Change management through learning factories. In M. F. Zäh (Ed.), Enabling manufacturing competitiveness and economic sustainability (pp. 395–399). Springer. https://doi.org/10.1007/978-3-319-02054-9_67 Djaouti, D., Alvarez, J., Jessel, J. P., & Rampnoux, O. (2011). Origins of serious games. In M. Ma, A. Oikonomou, & L. Jain (Eds.), Serious games and edutainment applications (pp. 25–43). Springer. Dombrowski, U., Wullbrandt, J., & Reimer, A. (2017). Lean stress sensitization in learning factories. Procedia Manufacturing, 9, 339–346. https://doi.org/10.1016/j.promfg.2017.04.016 Eisele, G. (2010). Evaluation GuT 2009/10. Hochschule Esslingen. S, 26. Enke, J., Glass, R., & Metternich, J. (2017). Introducing a maturity model for learning factories. In 7th CIRP-Sponsored Conference on Learning Factories. Procedia Manufacturing, 9, 1–8. Enke, J., Tisch, M., & Metternich, J. (2016). Learning factory requirements analysis—Requirements of learning factory stakeholders on learning factories. In 5th CIRP Global Web Conference (CIRPe 2016). Procedia CIRP, 55, 224–229. Erol, S., Jäger, A., Hold, P., Ott, K., & Sihn, W. (2016). Tangible Industry 4.0: A scenariobased approach to learning for the future of production. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 13–18. Euler, D. (2005). Forschendes Lernen. In S. Spoun & W. Wunderlich (Eds.), Studienziel Persönlichkeit: Beiträge zum Bildungsauftrag der Universität heute (pp. 253–272). Campus-Verlag. Evensen, D. H., & Hmelo-Silver, C. E. (2000). Problem-based learning: A research perspective on learning interactions. Taylor & Francis. Faller, C., & Feldmüller, D. (2015). Industry 4.0 learning factory for regional SMEs. In 5th CIRPSponsored Conference on Learning Factories. Procedia CIRP, 32, 88–91. https://doi.org/10. 1016/j.procir.2015.02.117 FAPS. (2018). Green Factory Bavaria. Retrieved from http://www.greenfactorybavaria.de/ Felicia, P. (2014). Game-based learning: Challenges and opportunities. Cambridge Scholars Publisher.

References

281

Festo Didactic. (2017). Learning factory 4.0, Philipp-Matthäus-Hahn-Schule. Reference Project Global Project Solutions. Forrester, J. W. (1961). Industrial dynamics. MIT Press. Freitas, S. D. (2007). Learning in immersive worlds: A review of game based learning. Prepared for the JISC e-Learning Programme. Retrieved from http://researchrepository.murdoch.edu.au/ id/eprint/35774/1/gamingreport_v3.pdf Garrison, D. R. (1997). Self-directed learning: Toward a comprehensive model. Adult Education Quarterly, 48(1), 18–33. https://doi.org/10.1177/074171369704800103 Gräßler, I., Pöhler, A., & Pottebaum, J. (2016a). Creation of a learning factory for cyber physical production systems. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 107–112. https://doi.org/10.1016/j.procir.2016.05.063 Gräßler, I., Taplick, P., & Yang, X. (2016b). Educational learning factory of a holistic product creation process. Procedia CIRP, 54, 141–146. https://doi.org/10.1016/j.procir.2016.05.103 Gredler, M. E. (2004). Games and simulations and their relationships to learning. In D. Jonassen (Ed.), Handbook of research on educational communications and technology (2nd ed., pp. 571– 581). Lawrence Erlbaum Associates Publishers. Greeno, J., Collins, A., & Resnick, L. (1996). Cognition and learning. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 15–46). Macmillan, Prentice Hall. Hambach, J., Diezemann, C., Tisch, M., & Metternich, J. (2016). Assessment of students’ lean competencies with the help of behavior video analysis—Are good students better problem solvers? In 5th CIRP Global Web Conference (CIRPe 2016). Procedia CIRP, 55, 230–235. Hambach, J., Tenberg, R., & Metternich, J. (2015). Guideline-based video analysis of competencies for a target-oriented continuous improvement process. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 25–30. https://doi.org/10.1016/j.procir.2015.02.212 Healey, M. (2005). Linking research and teaching: Exploring disciplinary spaces and the role of inquiry-based learning. In R. Barnett (Ed.), Reshaping the university: New relationships between research, scholarship and teaching (pp. 67–78). McGraw-Hill/Open University Press. Helleno, A. L., Simon, A. T., Papa, M. C. O., Ceglio, W. E., Rossa Neto, A. S., & Mourad, R. B. A. (2013). Integration university-industry: Laboratory model for learning lean manufacturing concepts in the academic and industrial environments. International Journal of Engineering Education, 29(6), 1387–1399. Hernstein/Hernstein International Management Institute. (2003). Management Report: Befragung von Führungskräften in Österreich, Schweiz und Deutschland. Hernstein. Humboldt, W. V. (1957). Über die innere und äußere Organisation der höheren wissenschaftlichen Anstalten in Berlin. In H. Weinstock (Ed.) (pp. 229–241). Fischer Bücherei. Hung, W., Jonassen, D. H., & Liu, R. (2008). Problem-based learning. In M. Spector, D. Merrill, J. van Merrienböer, & M. Driscoll (Eds.), Handbook of research on educational communications and technology (pp. 485–506). Erlbaum. Jäger, A., Mayrhofer, W., Kuhlang, P., Matyas, K., & Sihn, W. (2012). The “learning factory”: An immersive learning environment for comprehensive and lasting education in industrial engineering. In 16th World Multi-Conference on Systemics, Cybernetics and Informatics (Vol. 16, No. 2, pp. 237–242). Jäger, A., Mayrhofer, W., Kuhlang, P., Matyas, K., & Sihn, W. (2013). Total immersion: Hands and heads-on training in a learning factory for comprehensive industrial engineering education. International Journal of Engineering Education, 29(1), 23–32. Jank, W., & Meyer, H. (2002). Didaktische Modelle (5., völlig überarb. Aufl.). Cornelsen-Scriptor. Johnson, D. W., Johnson, R. T., & Smith, K. A. (1991). Active learning: Cooperation in the college classroom. Interaction Book Co. Jonassen, D. (1999). Designing constructivist learning environments. In C. M. Reigeluth (Ed.), Instructional theories and models: A new paradigm of instructional theory (pp. 215–239). Lawrence Erlbaum Associates Publishers.

282

7 Overview on Existing Learning Factory Concepts

Jonassen, D. H., & Rohrer-Murphy, L. (1999). Activity theory as a framework for designing constructivist learning environments. Educational Technology Research and Development, 47(1), 61–79. https://doi.org/10.1007/BF02299477 Jorgensen, J. E., Lamancusa, J. S., Zayas-Castro, J. L., & Ratner, J. (1995). The learning factory: Curriculum integration of design and manufacturing. In 4th World Conference on Engineering Education (pp. 1–7). Kaluza, A., Juraschek, M., Neef, B., Pittschellis, R., Posselt, G., Thiede, S., & Herrmann, C. (2015). Designing learning environments for energy efficiency through model scale production processes. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 41–46. https://doi.org/10.1016/j.procir.2015.02.114 Kärcher. (2018). Kärcher Lean Consulting: Lean erleben und verstehen. Retrieved from https:// www.kaercher.com/de/services/professional/service-angebote/kaercher-lean-consulting.html Keeton, M. T., Sheckley, B. G., Griggs, J. K., & Council for Adult and Experiential Learning. (2002). Effectiveness and efficiency in higher education for adults: A guide for fostering learning. Kendall/Hunt Pub. Kemény, Z., Nacsa, J., Erd˝os, G., Glawar, R., Sihn, W., Monostori, L., & Ilie-Zudor, E. (2016). Complementary research and education opportunities—A comparison of learning factory facilities and methodologies at TU Wien and MTA SZTAKI. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 47–52. https://doi.org/10.1016/j.procir.2016.05.064 Knoll, M. (1997). The project method: Its vocational education origin and international development. Journal of Industrial Teacher Education, 34(3). Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice Hall. Kreß, A., Hummel, V., Ahmad, R., Hulla, M., Quadrini, W., Callupe, M., Gärtner, Q., Weyand, A., Barth, J., Riemann, T., Fumagalli, L., Ramsauer, C., & Metternich, J. (2023). Revision of the learning factory morphology. In Proceedings of the 13th Conference on Learning Factories (CLF 2023). Available at SSRN: https://ssrn.com/abstract=4458050 or https://doi.org/10.2139/ ssrn.4458050 Kultusministerium Baden-Württemberg. (2023). Bildungsstandars für Naturwissenschaft und Technik (Profilfach) 397 Gymnasum, Klasse 10. Stuttgart. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Learning in doing. Cambridge University Press. Lindemann, H.-J. (2002). The principle of action-oriented learning. Retrieved from http://www.hal inco.de/html/docde/HOL-prinzip02002.pdf Madsen, O., & Møller, C. (2017). The AAU smart production laboratory for teaching and research in emerging digital manufacturing technologies. Procedia Manufacturing, 9, 106–112. https:// doi.org/10.1016/j.promfg.2017.04.036 Makumbe, S., Hattingh, T., Plint, N., & Esterhuizen, D. (2018). Effectiveness of using learning factories to impart lean principles in mining employees. Procedia Manufacturing, 23, 69–74. https://doi.org/10.1016/j.promfg.2018.03.163 Meudt, T., Metternich, J., & Abele, E. (2017). Value stream mapping 4.0: Holistic examination of value stream and information logistics in production. CIRP Annals—Manufacturing Technology, 66(1), 413–416. https://doi.org/10.1016/j.cirp.2017.04.005 Ministerium für Wirtschaft, Arbeit und Wohnungsbau Baden-Württemberg. (2017). Lernfabriken 4.0 in Baden-Württemberg: Digitalisierung BW. Retrieved from https://wm.baden-wuerttemb erg.de/de/innovation/schluesseltechnologien/industrie-40/lernfabrik-40/ Müller, B. C., Menn, J. P., & Seliger, G. (2017). Procedure for experiential learning to conduct material flow simulation projects, enabled by learning factories. Procedia Manufacturing, 9, 283–290. https://doi.org/10.1016/j.promfg.2017.04.047 Muschard, B., & Seliger, G. (2015). Realization of a learning environment to promote sustainable value creation in areas with insufficient infrastructure. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 70–75. https://doi.org/10.1016/j.procir.2015.04.095

References

283

Neisser, U. (1976). Cognition and reality: Principles and implications of cognitive psychology. W.H. Freeman. Pittschellis, R. (2014). Der Beitrag von Festo für die didaktische Strukturierung der Technikbildung. In Wissenschafts-und Technikbildung auf dem Prüfstand (pp. 223–233). Nomos Verlagsgesellschaft mbH & Co. KG. Pittschellis, R. (2015). Multimedia support for learning factories. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 36–40. https://doi.org/10.1016/j.procir.2015. 06.001 Putz, M. (2013). The concept of the new research factory at Fraunhofer IWU—To objectify energy and resource efficiency R&D in the E3-Factory. In G. Reinhart, P. Schnellbach, C. Hilgert, & S. L. Frank (Eds.), 3rd Conference on Learning Factories, Munich, May 7, 2013 (pp. 62–77). Augsburg. Reiß, M. (2012). Change management: A balanced and blended approach. Books on Demand. Reichert, M. (2011). Qualification of employees in the development department with SEW Live Training Center. In E. Abele, J. Cachay, A. Heb, & S. Scheibner (Eds.), 1st Conference on Learning Factories, Darmstadt (pp. 100–117). Institute of Production Management, Technology and Machine Tools (PTW). Reiner, D. (2009). Methode der kompetenzorientierten Transformation zum nachhaltig schlanken Produktionssystem [Dissertation]. Schriftenreihe des PTW: “Innovation Fertigungstechnik”, Darmstadt. Shaker, Aachen. RoboCup. (2016). RoboCup logistics league 2016 final—Carologistics vs. solidus. Retrieved from https://www.youtube.com/watch?time_continue=300&v=od1oEeHl8k8 Schreiber, S., Funke, L., & Tracht, K. (2016). BERTHA—A flexible learning factory for manual assembly. Procedia CIRP, 54, 119–123. https://doi.org/10.1016/j.procir.2016.03.163 Schuh, G., Gartzen, T., Rodenhauser, T., & Marks, A. (2015, July). Promoting work-based learning through Industry 4.0. In 5th Conference on Learning Factories, Bochum, Germany. Schuh, G., & Warschat, J. (2013). Potenziale einer Forschungsdisziplin Wirtschaftsingenieurwesen. acatech DISKUSSION. Herbert Utz Verlag. Schunk, D. H. (1990). Goal setting and self-efficacy during self-regulated learning. Educational Psychologist, 25, 71–86. Seifermann, S., Böllhoff, J., Adolph, S., Abele, E., & Metternich, J. (2017). Flexible design of lean production systems in response to fluctuations due to logistics and traffic. In E. Abele, M. Boltze, & H.-C. Pfohl (Eds.), Dynamic and seamless integration of production, logistics and traffic: Fundamentals of interdisciplinary decision support (pp. 51–82). Springer International Publishing. https://doi.org/10.1007/978-3-319-41097-5_4 Seifermann, S., Böllhoff, J., Metternich, J., & Bellaghnach, A. (2014a). Evaluation of work measurement concepts for a cellular manufacturing reference line to enable low cost automation for lean machining. In 47th CIRP Conference on Manufacturing Systems. Procedia CIRP, 17, 588–593. Seifermann, S., Metternich, J., & Abele, E. (2014b, January). Learning factories—Benefits for research and exemplary results. CIRP, CIRP January Meeting, STC-O Technical Presentation, Paris, France. Sivard, G., & Lundholm, T. (2013). XPRES—A digital learning factory for adaptive and sustainable manufacturing of future products. In G. Reinhart, P. Schnellbach, C. Hilgert, & S. L. Frank (Eds.), 3rd Conference on Learning Factories, Munich, May 7, 2013 (pp. 132–154). Augsburg. Steffen, M., Frye, S., & Deuse, J. (2013). The only source of knowledge is experience: Didaktische Konzeption und methodische Gestaltung von Lehr-Lern-Prozessen in Lernfabriken zur Ausund Weiterbildung im Industrial Engineering. In TeachING LearnING.EU. Innovationen für die Zukunft der Lehre in den Ingenieurwissenschaften (pp. 117–129). Steffen, M., May, D., & Deuse, J. (2012). The industrial engineering laboratory: Problem based learning in industrial engineering education at TU Dortmund University. In Global Engineering Education Conference (EDUCON), IEEE, Collaborative Learning & New Pedagogic Approaches in Engineering Education, Marrakesch, Marokko, April 17–20, 2012 (pp. 1–10).

284

7 Overview on Existing Learning Factory Concepts

Sterman, J. D. (1994). Learning in and about complex systems. Working paper/Alfred P. Sloan School of Management: WP # 3660-94-MSA. Alfred P. Sloan School of Management, Massachusetts Institute of Technology. Stier, K. W. (2003). Teaching lean manufacturing concepts through project-based learning and simulation. Journal of Industrial Technology, 19(4), 1–6. Streitzig, C., & Oetting, A. (2016). Railway operation research centre—A learning factory for the railway sector. Procedia CIRP, 54, 25–30. https://doi.org/10.1016/j.procir.2016.05.071 Thomar, W. (2015, July, 8). Kaerchers global lean academy approach: Incentive talk (industry). In 5th Conference on Learning Factories, Bochum, Germany. Tietze, F., Czumanski, T., Braasch, M., & Lödding, H. (2013). Problembasiertes Lernen in Lernfabriken. Werkstattstechnik Online: wt, 103(3), 246–251. Tisch, M. (2018). Modellbasierte Methodik zur kompetenzorientierten Gestaltung von Lernfabriken für die schlanke Produktion [Dissertation]. Darmstadt. Shaker, Aachen. Tisch, M., Hertle, C., Abele, E., Metternich, J., & Tenberg, R. (2015a). Learning factory design: A competency-oriented approach integrating three design levels. International Journal of Computer Integrated Manufacturing, 29(12), 1355–1375. https://doi.org/10.1080/0951192X. 2015.1033017 Tisch, M., Hertle, C., Cachay, J., Abele, E., Metternich, J., & Tenberg, R. (2013). A systematic approach on developing action-oriented, competency-based learning factories. In 46th CIRP Conference on Manufacturing Systems. Procedia CIRP, 7, 580–585. Tisch, M., Hertle, C., Metternich, J., & Abele, E. (2015b). Goal-oriented improvement of learning factory trainings. The Learning Factory, an Annual Edition from the Network of Innovative Learning Factories, 1(1), 7–12. Tisch, M., & Metternich, J. (2017). Potentials and limits of learning factories in research, innovation transfer, education, and training. In 7th CIRP-Sponsored Conference on Learning Factories. Procedia Manufacturing. Tisch, M., Ranz, F., Abele, E., Metternich, J., & Hummel, V. (2015c). Learning factory morphology: Study on form and structure of an innovative learning approach in the manufacturing domain. In TOJET, July 2015 (Special Issue 2 for International Conference on New Horizons in Education 2015) (pp. 356–363). Toivonen, V., Lanz, M., Nylund, H., & Nieminen, H. (2018). The FMS Training Center—A versatile learning environment for engineering education. Procedia Manufacturing, 23, 135–140. https:// doi.org/10.1016/j.promfg.2018.04.006 Tracht, K., Funke, L., & Schottmayer, M. (2015). Online-control of assembly processes in paced production lines. CIRP Annals—Manufacturing Technology, 64, 395–398. Tvenge, N., Martinsen, K., & Kolla, S. S. V. K. (2016). Combining learning factories and ICT-based situated learning. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 101–106. UAW-Chrysler National Training Center. (2016). World Class Manufacturing Academy. Retrieved from http://www.uaw-chrysler.com/world-class-mfg-academy/ Ulrich, H., Dyllick, T., & Probst, G. (1984). Management. Schriftenreihe Unternehmung und Unternehmungsführung: Bd. 13. P. Haupt. University of Washington. (2018). Integrated learning factory. Retrieved from https://www.washin gton.edu/change/proposals/factory.html UPRM. (2018). Model factory. Retrieved from http://uprm.edu/p/model_factory/about U-Quadrat. (2018). Knorr-Bremse und U2 sind Partner. Retrieved from http://www.u-quadrat.de/ knorr-bremse-und-u%C2%B2-sind-partner/ Wagner, C., Heinen, T., Regber, H., & Nyhuis, P. (2010). Fit for change—Der Mensch als Wandlungsbefähiger. Zeitschrift für Wirtschaftlichen Fabrikbetrieb (ZWF), 100(9), 722–727. Wagner, U., AlGeddawy, T., ElMaraghy, H. A., & Müller, E. (2012). The state-of-the-art and prospects of learning factories. In 45th CIRP Conference on Manufacturing Systems. Procedia CIRP, 3, 109–114.

References

285

Wagner, U., AlGeddawy, T., ElMaraghy, H. A., & Müller, E. (2015). Developing products for changeable learning factories. CIRP Journal of Manufacturing Science and Technology, 9, 146– 158. Wank, A., Adolph, S., Anokhin, O., Arndt, A., Anderl, R., & Metternich, J. (2016). Using a learning factory approach to transfer Industrie 4.0 approaches to small- and medium-sized enterprises. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 89–94. https:// doi.org/10.1016/j.procir.2016.05.068 Weick, K. E. (1979). The social psychology of organizing (2nd ed.). In Topics in social psychology. McGraw-Hill. Werbach, K., & Hunter, D. (2012). For the win: How game thinking can revolutionize your business. Wharton Digital Press. Wiech, M., Böllhoff, J., & Metternich, J. (2017). Development of an optical object detection solution for defect prevention in a learning factory. Procedia Manufacturing, 9, 190–197. https://doi.org/ 10.1016/j.promfg.2017.04.037 Wood, D. F. (2003). Problem based learning. BMJ : British Medical Journal, 326(7384), 328–330. Wurdinger, S. D. (2005). Using experiential learning in the classroom: Practical ideas for all educators. Scarecrow Education. Wurdinger, S. D. (2016). The power of project-based learning: Helping students develop important life skills. Rowman & Littlefield Publishers. Yoo, I. S., Braun, T., Kaestle, C., Spahr, M., Franke, J., Kestel, P., Wartzack, S., Bromberger, J., & Feige, E. (2016). Model factory for additive manufacturing of mechatronic products: Interconnecting world-class technology partnerships with leading AM players. Procedia CIRP, 54, 210–214. https://doi.org/10.1016/j.procir.2016.03.113

Chapter 8

Overview on Learning Factory Topics

This chapter presents a comprehensive examination of the learning content in existing learning factories, focusing on their diverse concepts and approaches. The following sections provide an overview of various learning factory concepts and their contentrelated aspects, as depicted in Fig. 8.1. The topics most frequently addressed in learning factories are lean production (Sect. 8.1), Industry 4.0 (Sect. 8.2), resource and energy efficiency (Sect. 8.3), industrial engineering (Sect. 8.4), and product development (Sect. 8.5). Specific learning factories deal with other topics, e.g., additive manufacturing, automation, changeability, global production (Sect. 8.6). In addition, specific industries and products are modelled in learning factories (Sect. 8.7).

8.1 Learning Factories for Lean Production Learning factories for lean production or production process improvement empower learners to apply and further develop lean methods and principles. These learning environments provide an immersive setting where individuals can gain hands-on experience in implementing lean techniques such as line balancing, problem-solving, value stream analysis and design, just-in-time, and job optimisation. By actively engaging with these methodologies, learners enhance their understanding of lean principles and develop practical skills that can be applied in real-world manufacturing scenarios. Extensive literature reviews have shed light on the prevalence of learning factories that primarily focus on lean management or lean production topics.1 These learning factories serve as platforms for knowledge exchange and skill development, drawing upon a diverse range of approaches from industry and academia.

1

See Micheu and Kleindienst (2014), Plorin (2016).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 E. Abele et al., Learning Factories, https://doi.org/10.1007/978-3-031-46428-7_8

287

288

8 Overview on Learning Factory Topics

Fig. 8.1 Detailed structure of the overview on content of existing learning factories

• Industry-driven approaches bring real-world experiences and practical insights into the learning factory environment.2 Manufacturers actively contribute their expertise, sharing best practices, and case studies that highlight the successful application of lean principles in their production processes. These industryderived approaches offer valuable insights into the challenges faced by manufacturers and the strategies they employ to optimise efficiency, reduce waste, and improve overall productivity. • Academic approaches in learning factories contribute theoretical foundations and research-based methodologies to the lean production domain.3 Academic institutions collaborate with industry partners to develop innovative learning experiences that blend theory and practice. These approaches often integrate established lean frameworks with emerging concepts, encouraging learners to explore new avenues for process improvement and operational excellence. The following sections provide exemplary descriptions of selected Best Practice Examples implemented in learning factories.4 Lean Learning Factories in Academia The Process Learning Factory CiP (Center for Industrial Productivity) at the PTW, Technical University of Darmstadt, stands as one of the first implementation of learning factories for lean production. Established in 2007, this learning factory 2

See for example Block et al. (2011), Hammer (2014). See for example Abele et al. (2007), Goerke et al. (2015), Tietze et al. (2013). 4 All Best Practice Examples are described in Chap. 11. 3

8.1 Learning Factories for Lean Production

289

Fig. 8.2 Process Learning Factory CiP at PTW, TU Darmstadt

serves as a platform for training, education, and research in the realm of lean production and Industrie 4.0 methodologies. Within the Process Learning Factory CiP, a collection of nine machine tools and two assembly lines is employed, enabling learners to acquire hands-on experience and knowledge in the application of lean production techniques and exploration of Industrie 4.0 technologies. These resources provide a realistic factory environment that closely simulates industrial settings, facilitating a contextualised learning experience. A key feature of the learning factory is its holistic representation of a multistage value stream enabling the production of a pneumatic cylinder. This comprehensive approach allows learners to gain a deep understanding of the interrelationships and interdependencies between various stages of production within an authentic manufacturing context. Furthermore, the Process Learning Factory CiP serves as a valuable testbed for researchers to identify research gaps, explore new avenues, and implement research outcomes. Through practical experiments and investigations conducted within the learning factory, researchers can gain practical insights into the challenges and opportunities associated with lean manufacturing and Industrie 4.0. For further details regarding the Process Learning Factory CiP, comprehensive information can be found in the Best Practice Example 34 (Fig. 8.2). The IFA-Learning Factory, situated at the Leibniz University Hannover, offers an extensive array of lean trainings encompassing factory layout planning, lean thinking, workplace design, ergonomics, and production control.5 The primary focus revolves around optimising the operational organisation of manufacturing and assembly processes, aiming to achieve efficient and customer-oriented order processing. The training program provided by the IFA-Learning Factory comprises various modules that cover diverse aspects, such as the application of lean management methods, 5

See Wagner et al. (2010), Goerke et al. (2015), Seitz and Nyhuis (2015).

290

8 Overview on Learning Factory Topics

different approaches to production control, and methods of production controlling. Furthermore, participants can acquire competences related to the evaluation of ergonomic and age-appropriate workplaces, as well as utilising tools for factory planning. The IFA-Learning Factory serves as a realistic and innovative training environment for both students and industry professionals, including specialists and managers. It offers a unique blend of hands-on learning experiences, allowing individuals to gain practical insights into manufacturing operations while incorporating innovative approaches. Additionally, educational purposes are supported by the utilisation of a virtual representation of the factory environment, further enhancing the learning experience. Detailed information about the IFA-Learning Factory can be found in Best Practice Example 17. The Learning and Research Factory (LFF), located at Ruhr University of Bochum, operates at the interfaces of human beings, technology, and organisations. This learning factory serves as a platform to implement theoretical concepts, to facilitate technology demonstration and transfer to the industry. Notably, the LFF specialises in integrating the principles of lean production with a strong emphasis on workers’ participation.6 The key areas of focus within the LFF revolve around lean production, Industry 4.0, and resource efficiency. Its establishment dates to 2009, and it encompasses a production area of 1800 m2 dedicated to manufacturing bottle caps and bottle cap holders. The production environment is equipped with a diverse range of machine tools, load transports, manual assembly stations, and industrial robots. The LFF hosts approximately 900 students annually who participate in practical exercises conducted within its premises. Additionally, numerous research projects are conducted in collaboration with the learning factory. These projects span various domains, including the development of an Industry 4.0 maturity model, assistance and learning systems, cyber-physical production systems, and industrial robotics. Best Practice Example 23 provides more detailed information of the LFF. The LSP (Lernfabrik für die Schlanke Produktion) operated by the Institute for Machine Tools and Industrial Management (iwb, TU Munich) focuses also on streamlined production management and lean management. An adaptable and modular assembly system is employed to facilitate the implementation of diverse production structures, ranging from traditional workshop production to synchronised flow assembly, accompanied by an assortment of material supply strategies. Training participants engage in practical exercises where they apply and evaluate the principles and methods of lean manufacturing through the assembly of industrially utilised planetary gears. The modular training concept allows for the customisation of individual training courses, providing flexibility in addressing specific learning objectives and needs. The LSP is presented in Best Practice Example 28. The Lean Lab, situated at Norwegian University of Science and Technology (NTNU) in Gjøvik, Norway, functions as a learning factory primarily dedicated to the instruction of flow production, line balancing, and workplace design. The learning

6

See Prinz et al. (2014), Wagner et al. (2015).

8.1 Learning Factories for Lean Production

291

factory is established and operated in close collaboration with a nearby industrial park, which serves as the host location for several companies.7 Numerous additional Best Practice Examples from academia pertaining to the lean topic can be discerned throughout the entirety of the book: • • • • • • • • • • • • • • • • • • • • • • • • • •

Best Practice Example 6: Demonstration Factory Aachen DFA Best Practice Example 7: Digital Capability Center Aachen Best Practice Example 13: FlowFactory Best Practice Example 14: Globale Learning Factory Best Practice Example 15: Global McKinsey Innovation & Learning Center Network (ILC) Best Practice Example 16: Hybrid Teaching Factory for Personalised Education Best Practice Example 17: IFA-Learning Factory Best Practice Example 19: LEAD Factory at IIM, TU Graz, Austria Best Practice Example 20: LEAN-Factory Best Practice Example 21: Lean Learning Factory Best Practice Example 22: Lean School Best Practice Example 23: Learning and Research Factory (LFF) Best Practice Example 24: Learning Factory (CUBE) Best Practice Example 26: Learning Factory of advanced Industrial Engineering aIE (LF aIE) Best Practice Example 27: Learning Factory SUM Best Practice Example 28: Lernfabrik für schlanke Produktion (LSP) Best Practice Example 31: MPS Lernplattform Best Practice Example 32: Operational Excellence Best Practice Example 33: Pilotfabrik Industry 4.0 Best Practice Example 34: Process Learning Factory CiP Best Practice Example 36: SDFS Smart Demonstration Factory Siegen Best Practice Example 41: Stellenbosch Learning Factory (SLF) Best Practice Example 43: The Centre for Industry 4.0 Best Practice Example 44: The Learning Factory Best Practice Example 45: The Purdue Learning Factory Ecosystem Best Practice Example 46: Werk150.

Lean Learning Factories in Industry While academic learning factories, such as the Process Learning Factory CiP, often provide training opportunities for industrial employees, it is noteworthy that certain industrial companies have established their own learning factories, either independently or in collaboration with universities or consulting firms. These companyspecific learning factories are designed to concentrate on the most pertinent topics and technologies specific to their respective businesses. Consequently, these learning environments and products are tailored to address the unique requirements and objectives of the companies involved. 7

See Tvenge et al. (2016).

292

8 Overview on Learning Factory Topics

The Mercedes-Benz Production System (MPS) of Daimler AG recognised the significance of lean production processes for continuous improvement. In 2011, the MPS Lernplattform was established as a learning centre focused on lean principles. Its training programs, attended by over 10,000 Daimler employees, target executives, production planners, plant engineers, and improvement managers. The MPS Lernplattform offers diverse training programs tailored to employees’ needs and current trends, emphasising practical implementation, and working with real components. Its success led to expansion across multiple Daimler AG production sites worldwide. Training takes place in a production-oriented area, showcasing the complete production process, including shops like the press shop, body shop, paint shop, assembly, and logistics. The training courses are conducted by qualified in-house trainers with extensive production experience and didactic background knowledge. The didactic concept follows an 80% practical and 20% theoretical approach, enabling participants to directly apply insights to their work. The MPS Lernplattform provides both basic and advanced training courses, fostering a pleasant working atmosphere and team learning. To ensure lasting qualification effects, theory training is combined with practical application in participants’ daily work. This approach enhances learning and memory retention. The MPS Lernplattform is described in Best Practice Example 31. McKinsey & Company has established Global McKinsey Innovation & Learning Center Network (ILC) that provide immersive learning experiences for adult learners. These centres focus on lean methods, digital technologies, and the transformation of operations. Equipped with real machines and staffed by human operators, the centres simulate different operational states. Visitors can transition from legacy systems to lean operations or advance to digitally advanced states. The centres now cover the entire operations value chain and offer insights into sustainable and resilient operations. They support capability building across functions and provide technology solutions beyond manufacturing. The centres have evolved from stand-alone model factories to a comprehensive global model company, offering a range of programs and support for organisations.8 More information about the McKinsey ILC can be found in Best Practice Example 15. The LEAN-Factory in Berlin serves as a learning facility focused on process optimisation tailored to the requirements of the pharmaceutical industry. It incorporates social aspects such as health, safety in factory environments, and global participation in value creation. The initiative is a collaboration between a major pharmaceutical company, Fraunhofer IPK, and TU Berlin. The LEAN-Factory is presented in Best Practice Example 20. The Value-Oriented Production System (VPS) Center at BMW, located in Munich, Germany, is a learning factory that serves as the application of lean management principles, methods, and mindset within the company.9 Established in 2012, the VPS Center was created to provide a central hub for training and development in lean manufacturing for both BMW employees and suppliers. It focuses on the qualification 8 9

See Hammer (2014), McKinsey & Company (2017). See Herrmann and Stäudel (2014).

8.1 Learning Factories for Lean Production

293

of management and lean experts, offering a range of training modules and courses. The VPS learning factory, encompassing an area of 600 m2 , replicates the value stream of a heat protection plate production, from raw material receipt to pressing, welding, and assembly on engines. The factory operates as a practical training ground, allowing participants to experience and apply VPS principles, methods, and mindset in a realistic production environment. The goal is to impart sustainable lean management skills and foster continuous improvement within the BMW Group. The training modules offered at the VPS learning factory can be classified into three core fields: VPS standard training for employees and managers, VPS-specific training in the use of specific lean methods, and customised family trainings tailored to specific training groups. The modules are developed and conducted by internal BMW Group trainers, with collaboration from external training institutes for standard trainings. In addition to its role in internal lean qualification, the VPS learning factory also serves as a platform for production research. Current research projects include the integration of low-cost Intelligent Automation (LCIA) into production and the implementation of augmented reality (AR) tools in employee qualification. The LCIA research focuses on improving logistics and bottleneck tasks through simulations and the development of generalised solution catalogues. Physical demonstrators are used to showcase and validate the solutions. AR research aims to explore the use of headmounted displays, such as Microsoft HoloLens, for assembly training, evaluating its impact on assembly quality, time, and knowledge retention. The VPS learning factory at BMW demonstrates the company’s commitment to continuous improvement and the application of innovative technologies in lean management. Through its training programs and research initiatives, it contributes to the development of lean expertise and the exploration of emerging technologies like LCIA and AR within the automotive industry. The company Kärcher employs a similar approach to globally train its employees. In 2013, the first Kärcher Lean-Academy was launched in Winnenden, Germany, drawing inspiration from the Process Learning Factory CiP. The Kärcher LeanAcademy has since been implemented worldwide, with adaptations made to suit the specific products of each site.10 The Festo Learning Factory Scharnhausen was established in 2014 as part of the Festo Technology Plant, which focuses on producing pneumatic valves and valve terminals. The learning factory was created to address the need for small and demand-oriented learning units that could be integrated into the daily work of production departments. The factory uses real production equipment and a serious business game called ProBest to facilitate learning and skill development. It aims to familiarise employees with digitalisation, automation, and Industrie 4.0 concepts. The learning factory is funded by Festo and is located within the Festo Technology Factory in Scharnhausen, consisting of four rooms with different focuses. It offers various learning workplaces, topics, and modules related to valves and valve terminals. The learning factory is used exclusively for Festo employees, primarily for training new operators and providing advanced qualification for existing workers. 10

See Thomar (2015).

294

8 Overview on Learning Factory Topics

Trainers are team leaders or qualified specialists who undergo a “Train-the-Trainer” program. The learning factory serves as a platform for collaboration between plant managers and employees to optimise value streams and develop strategies to manage changes. The ProBest serious game has evolved into ProBest Advanced, allowing participants to evaluate strategies in a simulated value stream environment. The Chrysler World Class Manufacturing Academy,11 located in Michigan, USA, provides experiential learning opportunities for manufacturing competences through two life-size physical learning factories. In addition, the academy offers online learning courses that can be accessed remotely by employees as a supplementary support. The MOVE academy disseminates the lean philosophy of Schaeffler, a supplier of industrial, automotive, and aerospace components. The learning factory associated with the academy encompasses authentic drilling, deburring, assembly, logistics, and quality assurance processes.12

8.2 Learning Factories for Industrie 4.0 The industry currently faces significant challenges in transferring and implementing digital technologies and new business models proposed by the Industrie 4.0 concept.13 The literature widely acknowledges the need for highly skilled professionals and executives who can comprehend and adapt the intricate interdisciplinary aspects of cyber-physical production systems.14 Learning factories have emerged as a highly suitable approach for addressing this demand as they provide a unique platform for integrating theoretical knowledge and practical experience within a production context.15 This enables learners to actively engage in the design of systems that combine manufacturing, information, and communication technologies. As a result, there has been a noticeable increase in the development of various learning factories aimed at facilitating and promoting the adoption of Industrie 4.0 solutions in recent years.16 Mostly, those learning factory concepts are addressing the needs of small and medium sized companies (SMEs). For example, in Germany, the initiative “Mittelstand-Digital” provides comprehensive support to SMEs and skilled crafts in navigating the digital transformation. It offers valuable guidance by informing them

11

UAW-Chrysler National Training Center (2016). See Beauvais (2013), Helleno et al. (2013). 13 Monostori et al. (2016). 14 See, e.g., Acatech (2016a, 2016b), Wank et al. (2016), Thiede et al. (2016), Prinz et al. (2016). 15 See Prinz et al. (2016), Wank et al. (2016), Thiede et al. (2016), Gräßler et al. (2016a), Seitz and Nyhuis (2015). 16 Faller and Feldmüller (2015), Wank et al. (2016), Prinz et al. (2016), Schuh et al. (2015), Baena et al. (2017). 12

8.2 Learning Factories for Industrie 4.0

295

about the potential opportunities and challenges associated with digitisation. Additionally, Mittelstand-Digital provides financial assistance for digitisation projects undertaken by these organisations. It is worth noting that all these services are made available free of charge, financed by the German Federal Ministry for Economic Affairs and Climate. German companies are assisted by 28 Mittelstand-Digital competence centres throughout Germany as of 2023, see Fig. 8.3. The depicted numbers on the left side represent the involved institutions within the competence centres. With the latest expertise and learning factories used as demonstration and testing facilities, this nationwide comprehensive network for SMEs offers hands-on digitisation support. In addition, Mittelstand-Digital agencies are working on overarching digitisation topics such as cloud computing, communications, trade, and processes and are spreading them with the help of multipliers. The Industry 4.0 Pilot Factory (I40PF) is developed as a research platform and educational environment at the Vienna University of Technology, building upon the principles of the “Learning and Innovation Factory for Integrative Production Education” (LIF). It serves as a hub for teaching and training in the domain of human-centric cyber-physical production systems, virtualised production systems, and adaptive manufacturing. The I40PF offers valuable insights into Industrie 4.0 Mittelstand-Digital Competence Centres in Germany

Legend

8

Structure of the Mittelstand-Digital Competence Centre in Darmstadt

•

Events to raise awareness

•

Action-oriented trainings to train analytical and implementation abilities

•

On-site support & coaching

Number of institutions involved in the competence centre

Fig. 8.3 Overview over the regional Mittelstand-Digital competence centers (left) and the structure and impressions of the Hessen Digital Competence Center in Darmstadt

296

8 Overview on Learning Factory Topics

equipment and technologies, including intelligent assembly technologies, collaborative robotic systems, and augmented reality-based assistance systems.17 The I40PF is featured as Best Practice Example 33 in Chap. 11 of this book. The DFA Demonstration Factory Aachen is a compact production facility spanning an area of 1600 m2 . It operates with a high degree of vertical integration and manufactures market-ready products in collaboration with e.GO Mobile AG, serving as the lead customer. The production processes encompass various activities such as sheet metal forming, joining automotive body structures, and manual assembly. These processes mirror industrial production, exhibiting similar complexities and quality requirements.18 In addition to its focus on prototype construction and industrialisation, the Demonstration Factory Aachen serves as a research and training centre, providing an experimental platform for production-related studies. The DFA also supports the development of methodologies and tools for cost-effective production and design, contributing to the realisation of production-ready solutions. Its distinctive feature lies in its ability to produce marketable products, setting it apart from other similar facilities. Best Practice Example 6 presents the DFA Demonstration Factory. In 2012, the University of Bolzano in Italy established the Mini Factory as an educational tool to enhance practical learning experiences for engineers, initially focusing on lean production and later expanding to include Industrie 4.0 concepts. The Mini Factory uses a pneumatic cylinder and a camp stove oven as representative products, providing a realistic setting for learning and experimentation. By incorporating the Mini Factory into engineering education, the University of Bolzano aims to bridge the gap between theory and practice, equipping students with hands-on skills and knowledge in the fields of lean production and Industrie 4.0. The Mini Factory is featured in Best Practice Example 40. The Process Learning Factory CiP was since the inauguration in 2007 used for training, education, and research in the field of lean manufacturing. In recent years, lean methods are further developed using the new opportunities of digitalisation and Industrie 4.0. The Process Learning Factory CiP brings together the lean and the Industrie 4.0 world. The Process Learning Factory CiP is described in the Best Practice Example 34. Since its establishment in 2007, the Process Learning Factory CiP has served in the domain of lean production on 400 m2 . Over time, the advancements in digitalisation and Industrie 4.0 have provided new insights for the development of lean methodologies. Consequently, the Process Learning Factory CiP has been actively working towards integrating the principles of lean manufacturing with the world of Industrie 4.0.19 As described in Best Practice Example 34, the Process Learning Factory CiP showcases the convergence of lean manufacturing and Industrie 4.0. It highlights the synergies and opportunities that arise from combining these two

17

See Erol et al. (2016). Schuh et al. (2015). 19 See also Metternich et al. (2017). 18

8.2 Learning Factories for Industrie 4.0

297

domains in its trainings for industrial companies. By leveraging the potential of digitalisation and embracing the principles of Industrie 4.0, the Process Learning Factory CiP continues to evolve and explore innovative approaches to lean methodologies. The Institute for Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI), operates two learning factories located in Budapest and Gy˝or. The Smart Factory in Budapest serves as a scaled-down model of a factory, occupying an area of 30 m2 . It focuses on various aspects such as production planning and scheduling in Cyber-Physical Production Systems (CPPS), as well as mechatronics and automation technology. The learning environment within the Smart Factory includes a warehouse, four automated workstations, a collaborative robot cell, a loading/unloading station, a closed-path conveyor system, and space for two mobile robots. The Smart Factory in Budapest is presented in Best Practice Example 38. The MTA SZTAKI Learning Factory, primarily used for education, emphasises human–robot collaboration and production planning and scheduling in CPPS. This life-size factory environment offers flexibly configurable robotic workstations, automated guided vehicles (AGVs) for intralogistics, indoor positioning devices, a 3D printer, and reconfigurable human–machine interfaces. Additionally, the MTA SZTAKI Learning Factory located in Gy˝or is featured in Best Practice Example 42. It serves as an educational platform and encompasses elements of human–robot collaboration, production planning, and scheduling in CPPS. The learning factory provides a realistic factory setting with adaptable robotic workstations, AGVs, indoor positioning systems, a 3D printer, and reconfigurable human–machine interfaces. When establishing a learning factory, careful consideration must be given to the selection of equipment. Numerous learning factory concepts, particularly in the realm of Industrie 4.0, rely on the utilisation of Festo Didactic learning factory modules.20 This equipment, developed in 1989, was initially employed for educational and training purposes in the fields of factory automation and automation technology. Festo Didactic offers well-known learning factories such as MPS, iCIM, and CP Factory, which have gained prominence in the field of Industrie 4.0. These learning factory modules serve as valuable resources for hands-on learning and practical training in the context of advanced manufacturing technologies. Festo operates the independent Festo Learning Factory in Scharnhausen, located at the heart of the Scharnhausen plant, with a focus on factory automation and Industrie 4.0 topics. This learning factory serves as a dedicated facility for hands-on training, experimentation, and research, offering practical insights into the implementation of advanced automation technologies and the integration of Industrie 4.0 principles in industrial settings (Fig. 8.4). Throughout the whole book even more Best Practice Examples from academia addressing the topic Industrie 4.0 can be identified: • Best Practice Example 1: Best Practice Example 1: 5G Learning Factory • Best Practice Example 2: Aalto Factory of the Future • Best Practice Example 3: Additive Manufacturing Center (AMC) 20

See e.g. Madsen and Møller (2017), Mayer et al. (2017).

298

8 Overview on Learning Factory Topics

Fig. 8.4 Part of the Festo Learning Factory Scharnhausen

• Best Practice Example 4: A Distributed Learning Factory with a Central Hub (SEPT LF) • Best Practice Example 5: Aquaponics 4.0 Learning Factory (All-Factory) • Best Practice Example 6: Demonstration Factory Aachen DFA • Best Practice Example 7: Digital Capability Center Aachen • Best Practice Example 8: Die Lernfabrik • Best Practice Example 9: E|Drive-Center • Best Practice Example 11: Fábrica do Futuro • Best Practice Example 12: FIM Learning Factory • Best Practice Example 13: FlowFactory • Best Practice Example 14: Globale Learning Factory • Best Practice Example 15: Global McKinsey Innovation & Learning Center Network (ILC) • Best Practice Example 16: Hybrid Teaching Factory for Personalised Education • Best Practice Example 17: IFA-Learning Factory • Best Practice Example 18: Industry 4.0 Lab • Best Practice Example 19: LEAD Factory at IIM, TU Graz, Austria • Best Practice Example 21: Lean Learning Factory • Best Practice Example 23: Learning and Research Factory (LFF) • Best Practice Example 24: Learning Factory (CUBE) • Best Practice Example 25: Learning Factory jumpING • Best Practice Example 26: Learning Factory of advanced Industrial Engineering aIE (LF aIE) • Best Practice Example 27: Learning Factory SUM • Best Practice Example 28: Lernfabrik für schlanke Produktion (LSP) • Best Practice Example 29: Manufacturing Systems Learning Factory (iFactory) • Best Practice Example 30: Model Factory @ Singapore Institute of Manufacturing Technology • Best Practice Example 32: Operational Excellence

8.3 Learning Factories for Resource and Energy Efficiency

• • • • • • • • • • • •

299

Best Practice Example 33: Pilotfabrik Industry 4.0 Best Practice Example 34: Process Learning Factory CiP Best Practice Example 36: SDFS Smart Demonstration Factory Siegen Best Practice Example 37: Smart factory AutFab Best Practice Example 38: Smart Factory Best Practice Example 39: SmartFactory-KL Best Practice Example 40: Smart Mini Factory Best Practice Example 42: SZTAKI Industry 4.0 Learning Factory Best Practice Example 43: The Centre for Industry 4.0 Best Practice Example 44: The Learning Factory Best Practice Example 45: The Purdue Learning Factory Ecosystem Best Practice Example 46: Werk150. See also Sect. 8.6.2 on learning factories for automation.

8.3 Learning Factories for Resource and Energy Efficiency Another important topic in the field of learning factories is energy and resource efficiency,21 while also learning factory approaches in industry can be identified.22 Learning factories have recognised the importance of sustainable practices and the need to optimise energy consumption and resource utilisation. This recognition has led to the establishment of learning factories specifically dedicated to exploring and promoting energy and resource efficiency. These learning factories serve multiple purposes, including education, training, and research.23 They provide a platform for students, professionals, and researchers to gain practical knowledge and hands-on experience in the realm of energy and resource management within a manufacturing setting. By integrating real-world scenarios and challenges, these learning factories enable participants to develop a comprehensive understanding of sustainable production principles and techniques. One of the challenges faced by energyefficient learning factories is the inherent complexity of visualising and quantifying energy flows.24 Unlike tangible production processes or physical components, energy flows are often intangible and difficult to observe directly. This poses a unique hurdle in effectively teaching and training individuals in energy efficiency practices. To overcome this challenge, learning factories for resource and energy efficiency focus on the development and testing of strategies and measures. These include the implementation of key performance indicators (KPIs) to monitor energy consumption, as well as advanced metering techniques to accurately measure and analyse resource usage. Through these approaches, participants can gain insights into the 21

Abele et al. (2016), Plorin et al. (2013), Gebbe et al. (2015), Kreitlein et al. (2015). McKinsey & Company (2017). 23 See e.g. Plorin et al. (2013). 24 Abele et al. (2016). 22

300

8 Overview on Learning Factory Topics

optimisation of the production-output-to-energy-consumption ratio, enabling them to identify areas for improvement and implement targeted energy-saving measures. The following sections provide detailed descriptions and case studies of notable learning factories that prioritise energy and resource efficiency. These examples highlight the diverse methodologies, technologies, and approaches employed to promote sustainable manufacturing practices and drive continuous improvement in energy management within the context of learning factories. In 2008, the vision to expand the successful concept of the Process Learning Factory CiP to energy efficiency led to the creation of the ETA-Factory at the PTW, TU Darmstadt, Germany. The ETA-Factory, inaugurated in 2016, aims to enable climate-neutral production and has since evolved to include energy flexibility and resource efficiency. The ETA-Factory serves as both a research facility and a learning environment, transferring research insights to industry and education. It operates two complete value chains, producing market-ready products. The first value chain involves the manufacturing of a control plate for hydraulic pumps, utilising energyefficient machines and research demonstrators. The process chain includes cutting machines, cleaning machines, and a gas nitriding retort furnace, supported by additional thermal storage units and energy-saving building technologies. The brownfield value chain within the ETA-Factory is part of the learning factory for energy productivity (LEP). It represents discrete mechanical manufacturing and allows the conversion from an energy-inefficient to an energy-efficient system design within minutes. Trainees learn about energy flows, identify wastages, and implement optimisation measures. Interactive exercises enable the calculation of product carbon footprints using traceability systems. Training sessions in the ETA-Factory consist of interactive workshops, combining theoretical foundations with practical implementation. Targeting students and industry professionals, the workshops cover topics such as energy efficiency, product-specific carbon footprint accounting, and energy management based on artificial intelligence. The research group focuses on climate-neutral production strategies, efficient infrastructure planning, production machines, and cyber-physical systems, including energy monitoring. The ETA-Factory is described in the Best Practice Example 10 (Fig. 8.5).

Fig. 8.5 Learning factory ETA at TU Darmstadt (PTW, TU Darmstadt, 2016)

8.3 Learning Factories for Resource and Energy Efficiency

301

Fig. 8.6 12 Green Factory Bavaria locations for resource and energy efficient production (FAPS, 2018)

The Green Factory Bavaria is a network comprising 12 learning factories located in various regions of Bavaria, Germany. These learning factories play a crucial role in enhancing the industrial sector’s capacity to optimise resource consumption. The network’s objectives encompass both the dissemination of knowledge derived from applied research to industry and the facilitation of experience sharing among companies. The learning factories within the network are designed as multifunctional platforms for demonstration, learning, and research, specifically focusing on energyefficient production methods. They provide valuable insights, practical examples, and opportunities for experimentation in the realm of sustainable and resource-conscious manufacturing (Fig. 8.6).25 The Learning and Research Factory (LFF), located at the Ruhr-Universität Bochum, Germany, encompasses a wide range of areas including process optimisation, management and organisation, and resource efficiency. This learning factory serves as a valuable platform for research, education, and practical training in these domains. It offers comprehensive insights into strategies and methodologies for improving manufacturing processes, optimising resource utilisation, and enhancing overall operational efficiency.26 The LFF is presented in Best Practice Example 23. The E 3 -Factory located at Fraunhofer IWU in Chemnitz, Germany, is an advanced learning factory that simulates powertrain and car body production processes, with

25 26

See FAPS (2018). See Kreimeier et al. (2014).

302

8 Overview on Learning Factory Topics

a primary emphasis on energy research. This learning factory serves as a multifunctional platform for educational purposes, prototyping activities, technology transfer, and upscaling initiatives. It enables students to gain practical insights into the intricacies of powertrain and car body manufacturing while fostering innovation and facilitating the transfer of cutting-edge technologies to real-world applications. The E3 -Factory stands as a prime example of the integration of research and education in the pursuit of energy-efficient production.27 Die Lernfabrik is a platform that focuses on resource and energy efficiency in sustainable manufacturing. It was established as a platform at the Technical University of Braunschweig in 2012. The concept arose from the need to share research results with manufacturing SMEs. It consists of three laboratories: research, experience, and education. The research lab develops prototypes and tools, the experience lab teaches engineering students, and the education lab provides technical training. The research lab disseminates research results in a real production environment. The experience lab provides hands-on learning with a scaled-down factory system. The education lab provides technical and commercial training, including energy and resource efficiency topics. The organisational structure reflects different user groups and business models. The concept includes event-based education, such as hackathons and game jams, to enhance learning outcomes. Die Lernfabrik also operates internationally in Singapore and India to strengthen engineering education and contribute to sustainable development. The platform bridges research, education, and industry, fostering innovation in manufacturing technologies and practices and is shown in Best Practice Example 8. The topics resource and energy efficiency are addressed in several Best Practice Examples in Chap. 11 of this book: • Best Practice Example 2: Aalto Factory of the Future • Best Practice Example 3: Additive Manufacturing Center (AMC) • Best Practice Example 4: A Distributed Learning Factory with a Central Hub (SEPT LF) • Best Practice Example 5: Aquaponics 4.0 Learning Factory (All-Factory) • Best Practice Example 7: Digital Capability Center Aachen • Best Practice Example 8: Die Lernfabrik • Best Practice Example 9: E|Drive-Center • Best Practice Example 10: ETA-Factory • Best Practice Example 13: FlowFactory • Best Practice Example 14: Globale Learning Factory • Best Practice Example 18: Industry 4.0 Lab • Best Practice Example 19: LEAD Factory at IIM, TU Graz, Austria • Best Practice Example 22: Lean School • Best Practice Example 24: Learning Factory (CUBE) • Best Practice Example 25: Learning Factory jumpING

27

Stoldt et al. (2015), Putz (2013).

8.4 Learning Factories for Industrial Engineering

303

• Best Practice Example 26: Learning Factory of advanced Industrial Engineering aIE (LF aIE) • Best Practice Example 27: Learning Factory SUM • Best Practice Example 31: MPS Lernplattform • Best Practice Example 35: Recycling Atelier Augsburg • Best Practice Example 36: SDFS Smart Demonstration Factory Siegen • Best Practice Example 45: The Purdue Learning Factory Ecosystem • Best Practice Example 46: Werk150.

8.4 Learning Factories for Industrial Engineering The field of learning factories within the broad domain of industrial engineering includes several distinct approaches.28 A common feature of these learning factories dedicated to industrial engineering is their predominant use for educational purposes, aiming to provide students with a comprehensive understanding of industrial engineering challenges,29 alongside exposure to intricate, holistic product creation processes.30 A brief overview of selected learning factories that exemplify this educational paradigm is presented in this section. The Bernard M. Gordon Learning Factory, located within Penn State University, serves as an educational facility dedicated to industrial engineering. It plays a central role in capstone design courses, research projects, and various other academic endeavours. The Learning Factory is equipped with state-of-the-art design, prototyping, and manufacturing equipment to provide students with hands-on learning experiences. Central to its operational framework is a collaborative partnership between academia and industry, where students address industry-relevant problems through design projects. Over the course of its existence, the Learning Factory has hosted more than 1800 design projects, sponsored by over 500 companies, and involving the active participation of nearly 9000 engineering students.31 The Penn State Learning Factory is described in Best Practice Example 44. The Industrial Engineering Laboratory at TU Dortmund University is integrated into the curriculum of the Institute of Production Systems. Within these courses, students participate in the comprehensive planning of an entire assembly line, with specific workplace design considerations. The educational focus of these courses varies, with students delving into topics such as ergonomic workplace design and conducting time studies related to the assembly process. As a practical application, students assemble a gearbox from various components, including housings, gears, shift levers, drive mechanisms, and drive shafts.32 28

Among others they are described in Steffen et al. (2012), Jäger et al. (2012, 2013), Jorgensen et al. (1995). 29 See, e.g., Jäger et al. (2013) and Gräßler et al. (2016b). 30 See also Sect. 8.6 regarding “Complete Product Creation Processes”. 31 See Penn State University (2017) and Best Practice Example 44. 32 See Steffen et al. (2012).

304

8 Overview on Learning Factory Topics

Additionally, the Department of Industrial Engineering at Stellenbosch University launched a learning factory concept for undergraduate education in the field of industrial engineering in 2015, facilitated by collaboration with various partners associated with the National Institute for Laser, Plasma, and Radiation Physics (NIL). In particular, a learning module focusing on Methods-Time Measurement (MTM) was developed and implemented at the Stellenbosch Learning Factory. This module serves as a valuable resource for students, providing them with practical training and knowledge of MTM techniques, which are crucial in the field of industrial engineering.33 Moreover, in Chap. 11 of this book, readers can explore a number of additional illustrations that span the field of industrial engineering. These examples serve to broaden the scope of the discussion and provide additional insight into various aspects of the field: • • • • • • • • • • • • • • • • • • • • • • •

33

Best Practice Example 3: Additive Manufacturing Center (AMC) Best Practice Example 6: Demonstration Factory Aachen DFA Best Practice Example 7: Digital Capability Center Aachen Best Practice Example 9: E|Drive-Center Best Practice Example 11: Fábrica do Futuro Best Practice Example 12: FIM Learning Factory Best Practice Example 13: FlowFactory Best Practice Example 15: Global McKinsey Innovation & Learning Center Network (ILC) Best Practice Example 18: Industry 4.0 Lab Best Practice Example 19: LEAD Factory at IIM, TU Graz, Austria Best Practice Example 25: Learning Factory jumpING Best Practice Example 26: Learning Factory of advanced Industrial Engineering aIE (LF aIE) Best Practice Example 27: Learning Factory SUM Best Practice Example 29: Manufacturing Systems Learning Factory (iFactory) Best Practice Example 34: Process Learning Factory CiP Best Practice Example 38: Smart Factory Best Practice Example 39: SmartFactory-KL Best Practice Example 40: Smart Mini Factory Best Practice Example 41: Stellenbosch Learning Factory (SLF) Best Practice Example 42: SZTAKI Industry 4.0 Learning Factory Best Practice Example 43: The Centre for Industry 4.0 Best Practice Example 44: The Learning Factory Best Practice Example 46: Werk150.

See Morlock et al. (2017).

8.5 Learning Factories for Product Development

305

8.5 Learning Factories for Product Development Learning factories specifically dedicated to product development or located at the interface between product development and production remain relatively rare in the current learning factory landscape. Consequently, two main obstacles or difficulties for product development learning factories can be identified: 1. Integration of learners’ actions: The learning factory concept relies heavily on the active involvement of learners in practical applications within the respective domain. However, when it comes to product development, challenges can arise due to either the time-consuming nature of the required actions or the delayed response of the environment. In the former case, the compressed timeframe of training sessions makes it difficult to conduct product development actions effectively. In the latter case, learners may lack awareness of the impact of their product development actions on subsequent production processes. To address these challenges, innovative approaches are needed, such as fast-forward simulations that allow accelerated learning and immediate feedback. 2. Visibility and tangibility of actions: The essence of the learning factory concept lies in making actions visible and tangible in authentic environments. However, in the context of product development, many processes are software-based or exist in the mind of the product developer. This poses a particular challenge, as these intangible processes are often difficult to observe or understand from an external perspective. Efforts must therefore be made to find novel approaches to make these product development processes, which lack external visibility and tangibility, more experiential and accessible to learners. • Addressing these obstacles is crucial to foster the widespread implementation of learning factories for product development, enabling learners to acquire practical experience and proficiency in this critical facet of industrial engineering. Especially in the context of the transition to a circular economy, the interlinking of product development and production is becoming increasingly important. In order to be able to successfully implement the strategies of the circular economy (e.g., from Rethink to Recycle), an increasing share of design activities in learning factories is therefore to be expected. The Learning and Research Factory (LFF) in Bochum is a rare example of the integration of product development and production.34 This collaboration is the result of a partnership between the LPE (Chair of Product Development) and the LPS (Chair of Production Systems). The physical infrastructure used for the LFF is located within the existing LPS Learning Factory at RU Bochum, which focuses primarily on production-related topics such as lean production, resource efficiency, and Industry 4.0. The LFF serves as an educational platform to impart knowledge to students on topics and problem areas at the interface between product development and production. In this environment, students are divided into two groups: some take on the role 34

See Bender et al. (2015).

306

8 Overview on Learning Factory Topics

Fig. 8.7 Impression of the teaching course in the Learning and Research Factory at RU Bochum

of product developers, while others take on the role of production workers. The LFF is presented as Best Practice Example 23. Another learning factory approach focused on product development is the Fábrica do Futuro at the Universidade de São Paulo, Brazil.35 This learning environment is specifically designed to replicate a comprehensive product development process, with a particular focus on cross-axis. It serves as an educational resource for both undergraduate and postgraduate students. The Learning Factory infrastructure includes CAD/CAM/CAE systems seamlessly integrated into a Product Life cycle Management (PLM) environment. This integration facilitates a holistic approach to product development education, enabling students to gain hands-on experience of different stages of the development process, from design to manufacturing.36 More details on the Fábrica do Futuro can be found in the Best Practice Example 11 (Fig. 8.7). Within the industrial sector, various instances of the learning factory concept can be observed, particularly in the area of product development. A good example of this concept is the Daimler Trucks Process Learning Factory in Mannheim, Germany. This learning factory specialises in providing a specific learning module tailored to developers. This module is of great importance to Daimler Trucks, as the interplay between product development and production planning has a profound impact on subsequent manufacturing costs. The primary objective of this module is to convey the principles and philosophy of lean production in the areas of development and production planning. In doing so, it aims to instil a mindset and practices that are aligned with lean production principles in these critical areas.37

35

See Schützer et al. (2017). A use case description of the Learning Factory for Product Development Process can be found in Schützer et al. (2017). 37 See Abele et al. (2015). 36

8.6 Other Topics Addressed in Learning Factories

307

Furthermore, also parts of the product development are addressed in the following Best Practice Examples: • Best Practice Example 3: Additive Manufacturing Center (AMC) • Best Practice Example 4: A Distributed Learning Factory with a Central Hub (SEPT LF) • Best Practice Example 5: Aquaponics 4.0 Learning Factory (All-Factory) • Best Practice Example 6: Demonstration Factory Aachen DFA • Best Practice Example 11: Fábrica do Futuro • Best Practice Example 13: FlowFactory • Best Practice Example 15: Global McKinsey Innovation & Learning Center Network (ILC) • Best Practice Example 16: Hybrid Teaching Factory for Personalised Education • Best Practice Example 25: Learning Factory jumpING • Best Practice Example 29: Manufacturing Systems Learning Factory (iFactory) • Best Practice Example 44: The Learning Factory • Best Practice Example 46: Werk150.

8.6 Other Topics Addressed in Learning Factories Besides the five main topics addressed in learning factories, there are additional distinct topic clusters that are explored and engaged with by individual learning factories.38 These topics are organised below in alphabetical order: • • • • • • • •

Additive Manufacturing (Sect. 8.6.1), Automation (Sect. 8.6.2), Changeability (Sect. 8.6.3), Complete product creation processes (Sect. 8.6.4), Global production (Sect. 8.6.5), Intralogistics (Sect. 8.6.6), Sustainability in production (Sect. 8.6.7), and Worker’s participation (Sect. 8.6.8).

8.6.1 Learning Factories for Additive Manufacturing In the coming years, it is widely anticipated that the field of additive manufacturing will see remarkable advances, potentially leading to significant transformations in applied manufacturing technologies. As a result, proactive measures are being taken today to establish dedicated learning factories to conduct comprehensive research into additive manufacturing technology, organisational aspects, and

38

These topics are identical to the specific foci of competence identified in Chap. 2.

308

8 Overview on Learning Factory Topics

production processes.39 The primary objective of these learning factories is to bridge the gap between theoretical knowledge and practical implementation through extensive education and training initiatives. While many learning factories are using 3D printers as their equipment, the following examples of learning factories integrated additive manufacturing in their curriculum as a main topic. The Additive Manufacturing Center (AMC) at TU Darmstadt was established in May 2023 to promote knowledge and technology transfer in the field of additive manufacturing. It brings together expertise from thirteen institutes at the university and houses modern equipment covering the entire additive manufacturing process chain. The AMC offers workshops, collaboration opportunities, and consulting services for the manufacturing industry, especially small and medium-sized enterprises. The center focuses on research-based learning and aims to bridge the gap between academia and industry by providing comprehensive solutions and innovation in the field of additive manufacturing. The AMC is described in Best Practice Example 3. The SEPT Learning Factory at McMaster University’s W Booth School of Engineering Practice and Technology has a strong emphasis on additive manufacturing. The facility was established to provide experiential learning and research opportunities for students in the field of additive manufacturing. It offers specialised labs and equipment dedicated to additive manufacturing technologies. The learning factory includes 3D metal and plastic printers, which enable the production of complex designs and structures in a short time and at a lower cost compared to conventional manufacturing methods. The facility has two Laser Powder-Bed Fusion (LPBF) machines, also known as Selective Laser Melting (SLM) machines, for metal additive manufacturing. These machines use high-power lasers to process metal and composite powders and produce fully functional products. The SEPT Learning Factory serves as a hub for students, graduate researchers, and industry partners to engage in teaching, research, and development activities related to additive manufacturing. It supports undergraduate students in designing and testing automation systems and optimising production processes. Additionally, it facilitates capstone projects and real-life projects proposed by industry and community partners. The learning factory is equipped with various sensors and actuators that independently report errors and statuses to the control system. IO-Link communication systems are used for these devices, which provide information for Industry 4.0 applications. The SEPT Learning Factory attracts graduate students from manufacturing programs across Canada and worldwide and has facilitated research studies on process parameter selection, thermal properties’ effects on additive manufactured parts, and additive manufacturing of aerospace alloys. More information about the SEPT Learning Factory is given in Best Practice Example 4. The Faculty of Mechanical Engineering, Computing and Electrical Engineering (FSRE) initiated the development of the Learning Factory SUM Mostar in 2018, with the goal of providing education and training in various fields, including additive manufacturing. The FSRE collaborated with local metal and plastic companies, the 39

See Yoo et al. (2016).

8.6 Other Topics Addressed in Learning Factories

309

city of Široki Brijeg, and the municipality of Posušje to establish the learning factory. The FSRE Learning Factory integrates additive manufacturing into its undergraduate and graduate study curricula, particularly in the department of Industrial Engineering and Management. Students can engage in research, theses, and other professional work related to additive manufacturing. The learning factory is equipped with several 3D printers, including an industrial 3D printer (Stratasys F270), Makerbot Method X CF Edition printers, Zortrax M200 Plus printers, and Ultimaker 2+. These printers enable additive manufacturing using Fused Filament Fabrication (FFF) or Fused Deposition Modelling (FDM) technologies with a wide range of materials such as PLA, ABS, Nylon, carbon, and glass fibre-reinforced materials, PETG, and TPU. The FSRE Learning Factory is described in Best Practice Example 27. In addition, the following learning factories integrated additive manufacturing equipment: • Best Practice Example 3: Additive Manufacturing Center (AMC) • Best Practice Example 4: A Distributed Learning Factory with a Central Hub (SEPT LF) • Best Practice Example 6: Demonstration Factory Aachen DFA • Best Practice Example 8: Die Lernfabrik • Best Practice Example 9: E|Drive-Center • Best Practice Example 11: Fábrica do Futuro • Best Practice Example 13: FlowFactory • Best Practice Example 24: Learning Factory (CUBE) • Best Practice Example 27: Learning Factory SUM • Best Practice Example 33: Pilotfabrik Industry 4.0 • Best Practice Example 40: Smart Mini Factory • Best Practice Example 41: Stellenbosch Learning Factory (SLF) • Best Practice Example 44: The Learning Factory.

8.6.2 Learning Factories for Automation During the 1980s, Festo Didactic pioneered the use of learning factory modules in vocational education and training to teach students about automation basics, sensor utilisation, industrial networking, and PLC technology.40 These modules were primarily used in the vocational school sector. However, similar approaches have also been observed in universities, where academic learning factories focus not only on automation fundamentals but also on research areas related to automation. This includes topics such as scalable automation,41 low-cost automation,42 and

40

See Pittschellis (2015). See Buergin et al. (2017). 42 See Seifermann et al. (2014). 41

310

8 Overview on Learning Factory Topics

smart automation.43 Two representative examples of learning factories in the field of automation are briefly described below. The Smart factory AutoFab, located at Hochschule Darmstadt, serves as a learning factory dedicated to Industrie 4.0 and factory automation, emphasising education on automation technologies. AutoFab had a floor area of 50 m2 and featured an automated assembly of relay kits using a six-axis industrial robot and a pneumatic press. The AutoFab learning factory is introduced in Best Practice Example 37. The SmartFactoryKL , located at the Technical University of Kaiserslautern, is dedicated to advancing the development of the intelligent factory of the future. This vision encompasses flexibility, networking, self-organisation, and user-centricity. In addition to its Industrie 4.0 production plant, the SmartFactoryKL features a demo centre where experiences and tests are conducted using various demonstrators. These initiatives focus on important areas such as scalable automation, cyberphysical systems, and augmented reality. The SmartFactoryKL ’s achievements and contributions are showcased in Best Practice Example 39. More examples of how automation is integrated in learning factories can be found in the following examples: • Best Practice Example 2: Aalto Factory of the Future • Best Practice Example 4: A Distributed Learning Factory with a Central Hub (SEPT LF) • Best Practice Example 5: Aquaponics 4.0 Learning Factory (All-Factory) • Best Practice Example 7: Digital Capability Center Aachen • Best Practice Example 9: E|Drive-Center • Best Practice Example 14: Globale Learning Factory • Best Practice Example 23: Learning and Research Factory (LFF) • Best Practice Example 27: Learning Factory SUM • Best Practice Example 33: Pilotfabrik Industry 4.0 • Best Practice Example 37: Smart factory AutFab • Best Practice Example 38: Smart Factory • Best Practice Example 39: SmartFactory-KL • Best Practice Example 40: Smart Mini Factory • Best Practice Example 42: SZTAKI Industry 4.0 Learning Factory • Best Practice Example 43: The Centre for Industry 4.0.

8.6.3 Changeability Changeability, in the context of current challenges such as the pandemic and disrupted supply chains, plays a crucial role in adapting and responding effectively to dynamic situations. In today’s rapidly evolving world, the ability to change and implement timely adjustments is essential. The impact of disruptive events requires changeable and resilient production systems. Learning factories have a significant role 43

See Zuehlke (2008).

8.6 Other Topics Addressed in Learning Factories

311

in addressing the importance of changeability. Through practical experiences and hands-on training, individuals can develop the necessary skills and competences to respond to emerging challenges. Moreover, learning factories can foster interdisciplinary collaboration, promoting the exchange of diverse perspectives and knowledge. This interdisciplinary approach is essential for addressing complex challenges and generating comprehensive solutions. Considering the pandemic and conflicts, learning factories have an opportunity to emphasise the importance of changeability in crisis management and disaster response. By simulating and analysing scenarios, learners can develop strategies to ensure business continuity, optimise resource allocation, and contribute to the resilience of production. In this field, approaches can be identified that focus • on training of engineers,44 • on education of students,45 and also • on changeability-related research.46 The iFactory at the Intelligent Manufacturing Systems (IMS) Center in Windsor, Canada, serves as a changeable learning factory that promotes adaptability in manufacturing. Through its modular assembly system, incorporating robotic and manual assembly stations, computer vision inspection capabilities, and an ASRS, the iFactory offers valuable insights into the dynamic nature of modern production systems. The iFactory is described in Practice Example 28. The Experimental and Digital Factory (EDF) at the Technische Universität Chemnitz serves as a learning factory and research facility, encompassing a network of specialised laboratories focused on innovation, ergonomics, CAD, biometry, and usability. It addresses changeability challenges in manufacturing systems and provides a collaborative environment for research, teaching, and industry collaboration. Through its comprehensive approach, the EDF contributes to the development of adaptable and responsive manufacturing systems, while also facilitating knowledge exchange and practical testing for equipment manufacturers.47 Further learning factory concepts described in this book dealing with this topic are • • • •

Best Practice Example 6: Demonstration Factory Aachen DFA Best Practice Example 13: FlowFactory Best Practice Example 14: Globale Learning Factory Best Practice Example 26: Learning Factory of advanced Industrial Engineering aIE (LF aIE) • Best Practice Example 40: Smart Mini Factory.

44

See, e.g., Wagner et al. (2010), Bauernhansl et al. (2012). See, e.g., ElMaraghy et al. (2017). 46 See, e.g., Wagner et al. (2012). 47 See Abele et al. (2017). 45

312

8 Overview on Learning Factory Topics

8.6.4 Complete Product Creation Processes By addressing the complete product creation process, learning factories enable students to develop a system thinking mindset. They learn to consider the interconnectedness and interdependencies between different stages and departments involved in product development and production. This holistic perspective enhances their ability to identify potential bottlenecks, optimise processes, and make informed decisions to improve overall product quality and efficiency. In some learning factories, the focus is precisely on this comprehensive view on the complex and interdisciplinary product creation processes. The following examples below address this topic. The Fábrica do Futuro at the University of São Paulo (USP) in Brazil covers various phases of the product life cycle, including design, in-house production, assembly, product testing (including smart product/digital twin), and disassembly. Information technology infrastructure is employed, including Product life cycle Management (PLM), Enterprise Resource Planning (ERP), and Manufacturing Execution System (MES) solutions. These software packages manage the product life cycle, generate production orders, and support activities at the assembly station level. More information about this learning factory can be found in Best Practice Example 11. The Pilotfabrik Industrie 4.0 at TU Wien serves as an engaging and experiential hub for educational and research endeavours focused on advancing methods, processes, and innovations in product development, production, and logistics. Operated by three esteemed institutes—IMW, IFT, and MIVP—at TU Wien, the Pilotfabrik offers a dynamic environment where undergraduate students partake in an annual learning module designed to immerse them in the entire spectrum of activities involved in the emergence of a product. Through this immersive experience, students gain a comprehensive understanding of the product life cycle and its associated processes.48 This learning factory initiative is showcased as Best Practice Example 33 (Fig. 8.8). The Learning Factory jumpING at Heilbronn University, Germany, aims to provide students with a comprehensive understanding of the product creation process. It follows a problem-based and project-oriented learning approach, allowing students the freedom to gain new knowledge and tackle challenges. The Learning Factory replicates a real-world manufacturing company, providing students with authentic industrial equipment and workspace. Each semester, students are assigned a unique product engineering project, which they must develop and manufacture within a set timeframe and budget. Students work in self-organised teams, covering various functions in the complete product creation chain. The project timeline is standardised, with milestone dates and due dates for examinations and presentations. The course concludes with a public exhibition and a final oral examination. The Learning Factory jumpING is described in Best Practice Example 25. More examples of learning factories that address the product creation process can be found in the following: 48

See Sihn et al. (2012), Jäger et al. (2012).

8.6 Other Topics Addressed in Learning Factories

313

Fig. 8.8 Impressions from the learning module at the Learning and Innovation Factory, TU Wien

• Best Practice Example 3: Additive Manufacturing Center (AMC) • Best Practice Example 4: A Distributed Learning Factory with a Central Hub (SEPT LF) • Best Practice Example 5: Aquaponics 4.0 Learning Factory (All-Factory) • Best Practice Example 6: Demonstration Factory Aachen DFA • Best Practice Example 9: E|Drive-Center • Best Practice Example 11: Fábrica do Futuro • Best Practice Example 12: FIM Learning Factory • Best Practice Example 13: FlowFactory • Best Practice Example 15: Global McKinsey Innovation & Learning Center Network (ILC) • Best Practice Example 16: Hybrid Teaching Factory for Personalised Education • Best Practice Example 18: Industry 4.0 Lab • Best Practice Example 23: Learning and Research Factory (LFF) • Best Practice Example 24: Learning Factory (CUBE) • Best Practice Example 25: Learning Factory jumpING • Best Practice Example 29: Manufacturing Systems Learning Factory (iFactory) • Best Practice Example 30: Model Factory @ Singapore Institute of Manufacturing Technology • Best Practice Example 35: Recycling Atelier Augsburg • Best Practice Example 36: SDFS Smart Demonstration Factory Siegen • Best Practice Example 37: Smart factory AutFab • Best Practice Example 40: Smart Mini Factory • Best Practice Example 43: The Centre for Industry 4.0 • Best Practice Example 44: The Learning Factory • Best Practice Example 46: Werk150.

314

8 Overview on Learning Factory Topics

8.6.5 Global Production Networks Global production networks play a crucial role in driving economic integration, innovation, and growth. They enable companies to optimise resources and leverage global opportunities while also necessitating responsible practices to ensure social and environmental sustainability. The design and operation of the topic “global production networks” in learning factories is a very rare, complex, and difficult-to-implement. A learning factory approach focused on the field of global production can be observed at the Globale Learning Factory at wbk, KIT Karlsruhe, Germany.49 This learning factory examines the impact of site location on the implementation of production systems. Participants in this learning factory adapt various components of production systems and the fundamental principles of production to suit specific production locations. The concept of this learning factory for global production is detailed in Best Practice Example 14. The Global McKinsey Innovation & Learning Center Network (ILC) describes a network of learning centres established by McKinsey & Company across the world. Demonstrations, capability-building programs, and access to cutting-edge technology solutions are offered. The centres have evolved from independent learning factories to a network of integrated centres, providing comprehensive support for organisations’ transformation efforts and aim to facilitate experiential learning and enable organisations to navigate the complexities of global production effectively. More information can be found in Best Practice Example 15.

8.6.6 Intralogistics and Logistics Learning factories should address and focus on intralogistics because intralogistics plays a crucial role in optimising and improving the efficiency of internal logistics processes within a production environment. It includes the movement, storage and handling of materials, components, and finished products within a facility. By focusing on intralogistics, learning factories can help participants understand and implement best practices in material handling, inventory management, warehouse layout, automation, and transportation systems. This knowledge is essential for streamlining production processes, reducing lead times, minimising errors, and improving overall productivity and competitiveness in a global manufacturing environment. A few learning factories are focusing their activities on the topics logistics and intralogistics. In the following, exemplarily those learning factories are presented briefly. The Werk150 at ESB Business School, Reutlingen University in Germany, is a learning factory that focuses on production logistics, with a recent shift towards becoming a “circular factory.” It integrates digital and physical factory elements 49

See Lanza et al. (2015, 2016).

8.6 Other Topics Addressed in Learning Factories

315

and aims to explore the integration of circular processes, particularly remanufacturing, into existing production systems. The learning factory serves as an innovative platform for education, research, and application-oriented transfer in the context of Industrie 4.0 and Industrie 5.0. The Werk150 consists of a transformable cyberphysical production system, comprising a digital factory and a physical factory. It offers students the opportunity to simulate and optimise realistic production and logistics processes. The Werk150 is described in Best Practice Example 46. The Green Factories in Bavaria, specifically located in Bayreuth and Erlangen, focus on addressing the influence of intralogistics matters on the resource efficiency of factories through education and research. In Bayreuth, learners gain knowledge about the electrical energy consumption of transportation systems and explore measures aimed at enhancing energy efficiency. On the other hand, in Erlangen, the emphasis is placed on the design and implementation of diverse transportation systems that align with specific constraints and application areas.50 The PuLL ® Competence Centre at the University of Applied Sciences in Landshut operates a learning factory for practical education and research, particularly focusing on Lean Logistics. Recently, the learning factory has been upgraded to incorporate the advancements of Industrie 4.0, enabling the exploration of intelligent production logistics as a new area of study.51 Nearly all learning factory concepts described as Best Practice Examples in Chap. 11 address different facets of this field without focusing on them.52

8.6.7 Sustainability Sustainability is a subject of great interest in the field of manufacturing research in recent years.53 It is crucial for production due to several reasons: • Firstly, it helps in conserving natural resources and minimising environmental impacts such as pollution and depletion of ecosystems. By adopting sustainable practices, production processes can be designed to reduce waste, energy consumption, and carbon emissions, promoting a cleaner and healthier environment. • Secondly, sustainability enhances long-term economic viability. Implementing sustainable production methods can lead to cost savings through improved resource efficiency, waste reduction, and lower operational expenses. It also helps companies comply with regulations, access new markets, and meet the growing demand for eco-friendly products from environmentally conscious consumers. • Thirdly, sustainability is essential for social responsibility. Sustainable production practices promote fair and ethical treatment of workers, ensuring safe and healthy 50

See Scholz et al. (2016). See Blöchl and Schneider (2016). 52 Based on the definition of learning factories (see Chap. 4), at least two or more processes should be connected. As a result, at least a rudimentary logistics system is required. 53 See Sutherland et al. (2016). 51

316

8 Overview on Learning Factory Topics

working conditions, fair wages, and respect for human rights. It also contributes to the well-being of local communities by minimising negative impacts on their livelihoods and supporting sustainable development. Therefore, learning factories play a significant role in addressing sustainability issues. Within the realm of sustainable manufacturing, learning factories must tackle diverse topics that are associated with the relatively understudied social dimension. This is necessary to foster the appropriate behaviours and mindsets among individuals.54 The subsequent examples provide an overview of selected learning factory approaches addressing this topic. The ETA-Factory at PTW, TU Darmstadt, Germany, serves as a Best Practice Example of sustainable production. Its overall goal is to achieve economically optimised and environmentally friendly production. The research group “Sustainable Production” focuses on reducing energy consumption in production and improving energy efficiency. Workshops cover various topics such as energy efficiency, climateneutral production, and resource efficiency through digitisation. The research group addresses strategic aspects of climate-neutral production, infrastructure planning, efficient production machines, and cyber-physical systems. More information about the ETA-Factory is given in Best Practice Example 10. Die Lernfabrik at IWF, TU Braunschweig, Germany, focuses on sustainable manufacturing. Its overall goal is to develop new research questions and transfer knowledge from research projects into teaching, training, and industry to create a positive impact in sustainable manufacturing. Die Lernfabrik consists of three laboratories: the research lab, the experience lab, and the education lab.55 The research lab focuses on developing innovative research prototypes and tools in a real production environment. The experience lab provides a scaled-down factory system for teaching and training purposes, allowing learners to conduct experiments and deepen their theoretical knowledge in practice. The education lab offers technical and commercial training with a focus on metalworking, electrical circuits, and energy and resource efficiency. This approach is described in Best Practice Example 8. The Aquaponics 4.0 Learning Factory (AllFactory) at the University of Alberta, Canada serves as a transdisciplinary research and education platform to explore Aquaponics systems with a strong emphasis on sustainability. Aquaponics is an innovative farming technique that combines aquaculture and hydroponics to optimise plant yield while minimising resource consumption. By using stacked layers of plants in a vertical indoor farming setup, Aquaponics can achieve a significant reduction of up to 70% in water and fertiliser usage per plant compared to traditional farming methods. More details about this learning factory are given in Best Practice Example 5. In the realm of sustainable manufacturing, the adoption of the learning factory approach is enhanced through the utilisation of learning-supportive artefacts known 54

For the potential and role of manufacturing regarding the social dimension see also Sutherland et al. (2016). 55 See for example Herrmann (2013), Blume et al. (2015).

8.6 Other Topics Addressed in Learning Factories

317

as learnstruments.56 These learnstruments facilitate dynamic and automated learning processes, aiming to enhance the effectiveness and efficiency of learning while promoting awareness of sustainability across social, economic, and environmental dimensions.57 Additionally, several learning factories integrated aspects of sustainable production: • • • • • • • • • • •

Best Practice Example 3: Additive Manufacturing Center (AMC) Best Practice Example 5: Aquaponics 4.0 Learning Factory (All-Factory) Best Practice Example 7: Digital Capability Center Aachen Best Practice Example 8: Die Lernfabrik Best Practice Example 10: ETA-Factory Best Practice Example 13: FlowFactory Best Practice Example 15: Global McKinsey Innovation & Learning Center Network (ILC) Best Practice Example 17: IFA-Learning Factory Best Practice Example 20: LEAN-Factory Best Practice Example 34: Process Learning Factory CiP Best Practice Example 35: Recycling Atelier Augsburg.

8.6.8 Worker’s Participation Workers’ participation in production is important due to several key reasons. Firstly, it enhances productivity as engaged and motivated workers contribute their knowledge and creativity to streamline processes and identify areas for improvement. Secondly, workers’ participation leads to higher quality standards and fewer defects as they take ownership of the final outcome and actively contribute to identifying and resolving quality issues. Additionally, involving employees in decision-making fosters a culture of innovation, problem-solving, and continuous improvement. It also promotes job satisfaction, employee retention, and a positive work environment. Furthermore, workers’ participation is crucial for maintaining a safe and healthy working environment, as employees who actively participate in safety programs are more likely to identify hazards and propose preventive measures. The Learning and Research Factory (LFF), established by the Chair of Production Systems (LPS) at Ruhr-Universität Bochum, encompasses not only the conventional learning factory themes of lean production, energy efficiency, and industry 4.0, but also delves into matters of corporate co-determination, communication, and organisational processes between the shopfloor and middle management. This interdisciplinary program, titled “Management and Organisation of Labour,” is designed to

56

See Gausemeier et al. (2015). The learnstruments concept is implemented in several use cases, see Müller et al. (2016a, 2016b), Muschard and Seliger (2015), Heyer et al. (2014), Menn and Seliger (2016).

57

318

8 Overview on Learning Factory Topics

address these specific areas of study. The LPS Learning Factory is presented in the Best Practice Example 23. The Centre for Industry 4.0 at the University of Potsdam emphasises workers’ participation in production through its Process Learning Factory. This platform provides training and research opportunities in Industry 4.0, allowing workers to actively engage with automation systems and learn through immersive scenarios. The focus on participation empowers workers to adapt to the future of production and understand the implications of Industry 4.0 on work processes. This learning factory approach is summarised in Best Practice Example 43.

8.7 Learning Factories for Specific Industry Branches or Products In addition to the aforementioned topics, certain learning factories are designed to cater to specific industry branches or products. In the following, some examples are given for that kind of learning factories. For instance, the LEAN-Factory focuses on Lean Management principles tailored for the pharmaceutical industry, presented by in the Best Practice Example 20. The Digital Capability Center Aachen at the Institut für Textiltechnik der RWTH Aachen University is a capability building and testing center for digital solutions for the textile industry in Germany (see Best Practice Example 7).58 Furthermore, the Recycling Atelier Augsburg also focuses on the textile industry (in Best Practice Example 35). Further Best Practice Examples addressing specific industry branches needs can be found in this book: • A learning factory concept for E-Drives described in the Best Practice Example 9. • A learning factory concept for the fish and plant production in Best Practice Example 5. • Learning factory concepts for the automotive industry are described in the Best Practice Example 31 (“MPS Lernplattform at Daimler AG in Sindelfingen, Germany”) as well as in the Best Practice Example 22 (“Lean School at Faculty of Industrial Engineering, University of Valladolid, Spain”).

58

A detailed description of the Textile Learning Factory 4.0 including first learning modules can be found in Küsters et al. (2017).

8.8 Wrap-Up of This Chapter

319

8.8 Wrap-Up of This Chapter The chapter provides an overview on topics that are addressed in learning factories. Topics covered include lean production, Industry 4.0, resource and energy efficiency, industrial engineering, product creation processes, and specific industries and products. Learning factories offer hands-on experience in applying lean methods and principles, combining industry-driven approaches with academic foundations. Examples include the Process Learning Factory CiP at Technical University of Darmstadt, IFA-Learning Factory at Leibniz University Hannover, LPS Learning Factory at Ruhr University of Bochum, LSP at TU Munich, and Lean Lab at NTNU in Norway. Companies like Mercedes-Benz, McKinsey & Company, BMW, and Festo also establish their own learning factories. Learning factories have emerged as a solution to help industries adopt digital technologies and new business models associated with Industrie 4.0. They integrate theoretical knowledge and practical experience within a production context, allowing learners to actively participate in designing cyber-physical production systems. Many learning factories focus on supporting small and medium-sized companies, e.g., in the “Mittelstand-Digital” initiative in Germany, which offers comprehensive support for digital transformation. Best Practice Examples include the Industry 4.0 Pilot Factory (I40PF) at Vienna University of Technology, the DFA Demonstration Factory Aachen, the Mini Factory at the University of Bolzano, the Process Learning Factory CiP, the learning factories operated by the Institute for Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI), and Festo Didactic learning factory modules like MPS, iCIM, and CP Factory. These learning factories provide research platforms, training centers, and realistic environments for learning and experimentation in areas such as human-centric cyber-physical production systems, Industrie 4.0, and human–robot collaboration. Learning factories dedicated to energy and resource efficiency have emerged as important platforms for education, training, and research in sustainable manufacturing practices. These learning factories aim to optimise energy consumption and resource utilisation by integrating real-world scenarios and challenges. By optimising the production-output-to-energy-consumption ratio, participants can identify areas for improvement and implement energy-saving measures. Notable examples of learning factories prioritising energy and resource efficiency include the ETAFactory at TU Darmstadt, Germany, which focuses on climate-neutral production and offers interactive workshops on energy efficiency; the Green Factory Bavaria network comprising 12 learning factories in Bavaria, Germany, promoting resourceconscious manufacturing; the LPS Learning Factory at Ruhr-Universität Bochum, Germany, providing insights into process optimisation and resource efficiency; and the E3 -Factory at Fraunhofer IWU in Chemnitz, Germany, simulating powertrain and car body production processes with a focus on energy research.

320

8 Overview on Learning Factory Topics

Learning factories in the field of industrial engineering aim to provide students with comprehensive educational experiences and practical understanding of industrial engineering challenges. The Bernard M. Gordon Learning Factory at Penn State University offers hands-on learning through design projects that tackle realworld problems. The Industrial Engineering Laboratory at TU Dortmund University focuses on assembly line planning and workplace design, involving tasks like ergonomic design and time studies. The Stellenbosch Learning Factory, in collaboration with the National Institute for Laser, Plasma, and Radiation Physics, offers a module on Methods-Time Measurement (MTM) to provide practical training and knowledge in industrial engineering. Learning factories dedicated to product creation processes or situated at the interface between product development and production are relatively rare. Overcoming challenges such as integrating learners’ actions and making processes visible, and tangible requires innovative approaches like fast-forward simulations and experiential learning. Examples of successful learning factories in this domain include the Integrated Learning Factory in Bochum, Germany, which integrates product development and production within the context of lean production, resource efficiency, and Industry 4.0. The Fábrica do Futuro at the Universidade de São Paulo replicates a comprehensive product development process with a focus on cross-axis collaboration. The Daimler Trucks Process Learning Factory in Mannheim provides a specific learning module for developers, emphasising lean production principles in product development and production planning. Learning factories explore a range of additional topics beyond the main areas. These include Additive Manufacturing, Automation, Changeability, Complete Product Creation Processes, Global Production, Intralogistics, Sustainability in Production, and Worker’s Participation. Learning factories dedicated to Additive Manufacturing focus on researching and implementing 3D printing technologies. Examples include the SEPT Learning Factory at McMaster University and the Learning Factory SUM Mostar. Automation learning factories teach students about automation basics, sensor utilisation, industrial networking, and PLC technology. Examples include the SmartFactory AutoFab and the SmartFactoryKL. Changeability learning factories emphasise adapting to dynamic situations, such as the iFactory at the Intelligent Manufacturing Systems Center and the Experimental and Digital Factory. Learning factories also address complete product creation processes, systems thinking, global production networks, intralogistics, sustainability, and worker’s participation. Examples in these areas include the Fábrica do Futuro, LIF, Globale Learning Factory, Werk150, Green Factories, and the PuLL Competence Centre. These additional topics broaden the knowledge and practical implementation scope of learning factories across various fields. Certain learning factories are specialised to serve specific industries or products. Examples include the LEAN-Factory, which focuses on applying Lean Management principles to the pharmaceutical industry. The Digital Capability Center Aachen at the Institut für Textiltechnik der RWTH Aachen University is a center that facilitates the development and testing of digital solutions for the textile industry in Germany. Additionally, the Recycling Atelier Augsburg also caters to the textile industry.

References

321

References Abele, E., Bauerdick, C., Strobel, N., & Panten, N. (2016). ETA learning factory: A holistic concept for teaching energy efficiency in production. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 83–88. Abele, E., Chryssolouris, G., Sihn, W., Metternich, J., ElMaraghy, H. A., Seliger, G., Sivard, G., ElMaraghy, W., Hummel, V., Tisch, M., & Seifermann, S. (2017). Learning factories for future oriented research and education in manufacturing. CIRP Annals—Manufacturing Technology, 66(2), 803–826. Abele, E., Eichhorn, N., & Kuhn, S. (2007). Increase of productivity based on capability building in a learning factory. In Computer Integrated Manufacturing and High Speed Machining: 11th International Conference on Production Engineering, Zagreb (pp. 37–41). Abele, E., Metternich, J., Tenberg, R., Tisch, M., Abel, M., Hertle, C., Eißler, S., Enke, J., & Faatz, L. (2015). Innovative Lernmodule und -fabriken: Validierung und Weiterentwicklung einer neuartigen Wissensplattform für die Produktionsexzellenz von morgen. TU Prints. Acatech. (2016a). Industrie 4.0: International benchmark, options for the future and recommendations for manufacturing research. HNI, Paderborn University; WZL, RWTH Aachen University. Acatech. (2016b). Kompetenzen für Industrie 4.0. Qualifizierungsbedarfe und Lösungsansätze. acatech POSITION. Herbert Utz Verlag. Baena, F., Guarin, A., Mora, J., Sauza, J., & Retat, S. (2017). Learning factory: The path to industry 4.0. Procedia Manufacturing, 9, 73–80. https://doi.org/10.1016/j.promfg.2017.04.022 Bauernhansl, T., Dinkelmann, M., & Siegert, J. (2012). Lernfabrik advanced Industrial Engineering Teil 1: Lernkonzepte und Struktur. Werkstattstechnik Online: wt, 102(3), 80–83. Beauvais, W. (2013). Qualification as an effective tool to support the implementation of lean. In G. Reinhart, P. Schnellbach, C. Hilgert, & S. L. Frank (Eds.), 3rd Conference on Learning Factories, Munich, May 7, 2013 (pp. 108–117). Augsburg. Bender, B., Kreimeier, D., Herzog, M., & Wienbruch, T. (2015). Learning factory 2.0—Integrated view of product development and production. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 98–103. https://doi.org/10.1016/j.procir.2015.02.226 Blöchl, S. J., & Schneider, M. (2016). Simulation game for intelligent production logistics—The PuLL® learning factory. Procedia CIRP, 54, 130–135. https://doi.org/10.1016/j.procir.2016. 04.100 Block, M., Bertagnolli, F., & Herrmann, K. (2011). Lernplattform—Eine neue Dimension des Lernens von schlanken Abläufen/Learning platform—A new dimension to learn about lean processes. Productivity Management, 4, 52–55. Blume, S., Madanchi, N., Böhme, S., Posselt, G., Thiede, S., & Herrmann, C. (2015). Die Lernfabrik—Research-based learning for sustainable production engineering. In 5th CIRPSponsored Conference on Learning Factories. Procedia CIRP, 32, 126–131. https://doi.org/ 10.1016/j.procir.2015.02.113 Buergin, J., Minguillon, F. E., Wehrle, F., Haefner, B., & Lanza, G. (2017). Demonstration of a concept for scalable automation of assembly systems in a learning factory. Procedia Manufacturing, 9, 33–40. https://doi.org/10.1016/j.promfg.2017.04.026 ElMaraghy, H. A., Moussa, M., ElMaraghy, W., & Abbas, M. (2017). Integrated product/system design and planning for new product family in a changeable learning factory. In 7th CIRPSponsored Conference on Learning Factories. Procedia Manufacturing, 9, 65–72. https://doi. org/10.1016/j.promfg.2017.04.008 Erol, S., Jäger, A., Hold, P., Ott, K., & Sihn, W. (2016). Tangible industry 4.0: A scenariobased approach to learning for the future of production. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 13–18. Faller, C., & Feldmüller, D. (2015). Industry 4.0 learning factory for regional SMEs. In 5th CIRPSponsored Conference on Learning Factories. Procedia CIRP, 32, 88–91. https://doi.org/10. 1016/j.procir.2015.02.117

322

8 Overview on Learning Factory Topics

FAPS. (2018). Green Factory Bavaria. Retrieved from http://www.greenfactorybavaria.de/ Gausemeier, P., Seidel, J., Riedelsheimer, T., & Seliger, G. (2015). Pathways for sustainable technology development—The case of bicycle mobility in Berlin. In 12th Global Conference on Sustainable Manufacturing. Procedia CIRP, 26, 202–207. https://doi.org/10.1016/j.procir.2014. 07.164 Gebbe, C., Hilmer, S., Götz, G., Lutter-Günther, M., Chen, Q., Unterberger, E., Glasschröder, J., Schmidt, V., Riss, F., Kamps, T., Tammer, C., Seidel, C., Braunreuther, S., & Reinhart, G. (2015). Concept of the Green Factory Bavaria in Augsburg. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 53–57. https://doi.org/10.1016/j.procir.2015.02.214 Goerke, M., Schmidt, M., Busch, J., & Nyhuis, P. (2015). Holistic approach of lean thinking in learning factories. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 138–143. https://doi.org/10.1016/j.procir.2015.02.221 Gräßler, I., Pöhler, A., & Pottebaum, J. (2016a). Creation of a learning factory for cyber physical production systems. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 107–112. https://doi.org/10.1016/j.procir.2016.05.063 Gräßler, I., Taplick, P., & Yang, X. (2016b). Educational learning factory of a holistic product creation process. Procedia CIRP, 54, 141–146. https://doi.org/10.1016/j.procir.2016.05.103 Hammer, M. (2014, August). Making operational transformations successful with experiential learning. In CIRP Collaborative Working Group—Learning factories for future oriented research and education in manufacturing, CIRP General Assembly, Nantes, France. Helleno, A. L., Simon, A. T., Papa, M. C. O., Ceglio, W. E., Rossa Neto, A. S., & Mourad, R. B. A. (2013). Integration university-industry: Laboratory model for learning lean manufacturing concepts in the academic and industrial environments. International Journal of Engineering Education, 29(6), 1387–1399. Herrmann, C. (2013). Die Lernfabrik—Research and education for sustainability in manufacturing. In G. Reinhart, P. Schnellbach, C. Hilgert, & S. L. Frank (Eds.), 3rd Conference on Learning Factories, Munich, May 7, 2013 (pp. 48–61). Augsburg. Herrmann, S., & Stäudel, T. (2014). Learn and experience VPS in the BMW learning factory. In 4th Conference on Learning Factories, Stockholm, Sweden (pp. 1–18). Heyer, S., Nishino, N., Muschard, B., & Seliger, G. (2014). Enabling of local value creation via openness for emergent synthesis. International Journal of Precision Engineering and Manufacturing, 15(7), 1489–1493. https://doi.org/10.1007/s12541-014-0496-5 Jäger, A., Mayrhofer, W., Kuhlang, P., Matyas, K., & Sihn, W. (2012). The “learning factory”: An immersive learning environment for comprehensive and lasting education in industrial engineering. 16th World Multi-Conference on Systemics, Cybernetics and Informatics, 16(2), 237–242. Jäger, A., Mayrhofer, W., Kuhlang, P., Matyas, K., & Sihn, W. (2013). Total immersion: Hands and heads-on training in a learning factory for comprehensive industrial engineering education. International Journal of Engineering Education, 29(1), 23–32. Jorgensen, J. E., Lamancusa, J. S., Zayas-Castro, J. L., & Ratner, J. (1995). The learning factory: Curriculum integration of design and manufacturing. In 4th World Conference on Engineering Education (pp. 1–7). Kreimeier, D., Morlock, F., Prinz, C., Krückhans, B., Bakir, D. C., & Meier, H. (2014). Holistic learning factories—A concept to train lean management, resource efficiency as well as management and organization improvement skills. In 47th CIRP Conference on Manufacturing Systems. Procedia CIRP, 17, 184–188. Kreitlein, S., Höft, A., Schwender, S., & Franke, J. (2015). Green Factories Bavaria: A network of distributed learning factories for energy efficient production. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 58–63. https://doi.org/10.1016/j.procir.2015.02.219 Küsters, D., Praß, N., & Gloy, Y.-S. (2017). Textile learning factory 4.0—Preparing Germany’s textile industry for the digital future. Procedia Manufacturing, 9, 214–221. https://doi.org/10. 1016/j.promfg.2017.04.035

References

323

Lanza, G., Minges, S., Stoll, J., Moser, E., & Haefner, B. (2016). Integrated and modular didactic and methodological concept for a learning factory. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 136–140. https://doi.org/10.1016/j.procir.2016.06.107 Lanza, G., Moser, E., Stoll, J., & Haefner, B. (2015). Learning factory on global production. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 120–125. https://doi. org/10.1016/j.procir.2015.02.081 Madsen, O., & Møller, C. (2017). The AAU smart production laboratory for teaching and research in emerging digital manufacturing technologies. Procedia Manufacturing, 9, 106–112. https:// doi.org/10.1016/j.promfg.2017.04.036 Mayer, B., Rabel, B., & Sorko, S. R. (2017). Modular smart production lab. Procedia Manufacturing, 9, 361–368. https://doi.org/10.1016/j.promfg.2017.04.025 McKinsey & Company. (2017). Model factories and offices: Building operations excellence. Retrieved from https://capability-center.mckinsey.com/files/mccn/2017-03/emea_model_fact ories_brochure_1.pdf Menn, J. P., & Seliger, G. (2016). Increasing knowledge and skills for assembly processes through interactive 3D-PDFs. In The 23rd CIRP Conference on Life Cycle Engineering. Procedia CIRP, 48, 454–459. https://doi.org/10.1016/j.procir.2016.02.093 Metternich, J., Müller, M., Meudt, T., & Schaede, C. (2017). Lean 4.0—zwischen Widerspruch und Vision. Zeitschrift für Wirtschaftlichen Fabrikbetrieb (ZWF), 112(5), 346–348. https://doi.org/ 10.3139/104.111717 Micheu, H.-J., & Kleindienst, M. (2014). Lernfabrik zur praxisorientierten Wissensvermittlung: Moderne Ausbildung im Bereich Maschinenbau und Wirtschaftswissenschaften. Zeitschrift für Wirtschaftlichen Fabrikbetrieb (ZWF), 109(6), 403–407. Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., & Ueda, K. (2016). Cyber-physical systems in manufacturing. CIRP Annals— Manufacturing Technology, 65(2), 621–641. https://doi.org/10.1016/j.cirp.2016.06.005 Morlock, F., Kreggenfeld, N., Louw, L., Kreimeier, D., & Kuhlenkötter, B. (2017). Teaching methods-time measurement (MTM) for workplace design in learning factories. Procedia Manufacturing, 9, 369–375. https://doi.org/10.1016/j.promfg.2017.04.033 Müller, B. C., Nguyen, T. D., Dang, Q.-V., Duc, B. M., Seliger, G., Krüger, J., & Kohl, H. (2016a). Motion tracking applied in assembly for worker training in different locations. In The 23rd CIRP Conference on Life Cycle Engineering. Procedia CIRP, 48, 460–465. https://doi.org/10.1016/j. procir.2016.04.117 Müller, B. C., Reise, C., Duc, B. M., & Seliger, G. (2016b). Simulation-games for learning conducive workplaces: A case study for manual assembly. In 13th Global Conference on Sustainable Manufacturing. Procedia CIRP, 40, 353–358. https://doi.org/10.1016/j.procir.2016.01.063 Muschard, B., & Seliger, G. (2015). Realization of a learning environment to promote sustainable value creation in areas with insufficient infrastructure. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 70–75. https://doi.org/10.1016/j.procir.2015.04.095 Penn State University. (2017). Bernard M. Gordon Learning Factory: We bring the real world into the classroom. Retrieved from http://www.lf.psu.edu/ Pittschellis, R. (2015). Multimedia support for learning factories. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 36–40. https://doi.org/10.1016/j.procir.2015. 06.001 Plorin, D. (2016). Gestaltung und Evaluation eines Referenzmodells zur Realisierung von Lernfabriken im Objektbereich der Fabrikplanung und des Fabrikbetriebes [Dissertation], Chemnitz. Wissenschaftliche Schriftenreihe des Instituts für Betriebswissenschaften und Fabriksysteme: Heft 120. Technische Universität Chemnitz, Institut für Betriebswissenschaften und Fabriksysteme. Plorin, D., Poller, R., & Müller, E. (2013). Advanced Learning Factory (aLF): Integratives Lernfabrikkonzept zur praxisnahen Kompetenzentwicklung am Beispiel der Energieeffizienz. Werkstattstechnik Online: wt, 103(3), 226–232.

324

8 Overview on Learning Factory Topics

Prinz, C., Morlock, F., Freith, S., Kreggenfeld, N., Kreimeier, D., & Kuhlenkötter, B. (2016). Learning factory modules for smart factories in Industrie 4.0. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 113–118. https://doi.org/10.1016/j.procir.2016. 05.105 Prinz, C., Morlock, F., Wagner, P., Kreimeier, D., & Wannöffel, M. (2014). Lernfabrik zur Vermittlung berufsfeldrelevanter Handlungskompetenzen: Fragen der Gestaltung und des Managements von Arbeit theoretisch kennenlernen und in einer Lernfabrik realitätsnah erproben. Industrie Management, 30(3), 39–42. PTW, TU Darmstadt. (2016). Welcome to ETA-Factory: The energy efficient model factory of the future. Retrieved from http://www.eta-fabrik.tu-darmstadt.de/eta/index.en.jsp Putz, M. (2013). The concept of the new research factory at Fraunhofer IWU—To objectify energy and resource efficiency R&D in the E3-Factory. In G. Reinhart, P. Schnellbach, C. Hilgert, & S. L. Frank (Eds.), 3rd Conference on Learning Factories, Munich, May 7, 2013 (pp. 62–77). Augsburg. Scholz, M., Kreitlein, S., Lehmann, C., Böhner, J., Franke, J., & Steinhilper, R. (2016). Integrating intralogistics into resource efficiency oriented learning factories. Procedia CIRP, 54, 239–244. https://doi.org/10.1016/j.procir.2016.05.067 Schuh, G., Gartzen, T., Rodenhauser, T., & Marks, A. (2015). Promoting work-based learning through Industry 4.0. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 82–87. https://doi.org/10.1016/j.procir.2015.02.213 Schützer, K., Rodrigues, L. F., Bertazzi, J. A., Durão, L. F. C. S., & Zancul, E. (2017). Learning environment to support the product development process. Procedia Manufacturing, 9, 347–353. https://doi.org/10.1016/j.promfg.2017.04.018 Seifermann, S., Böllhoff, J., Metternich, J., & Bellaghnach, A. (2014). Evaluation of work measurement concepts for a cellular manufacturing reference line to enable low cost automation for lean machining. In 47th CIRP Conference on Manufacturing Systems. Procedia CIRP, 17, 588–593. Seitz, K.-F., & Nyhuis, P. (2015). Cyber-physical production systems combined with logistic models—A learning factory concept for an improved production planning and control. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 92–97. https://doi.org/ 10.1016/j.procir.2015.02.220 Sihn, W., Gerhard, D., & Bleicher, F. (2012). Vision and implementation of the learning and innovation factory of the Vienna University of Technology. In W. Sihn & A. Jäger (Eds.), 2nd Conference on Learning Factories—Competitive Production in Europe Through Education and Training (pp. 160–177). Steffen, M., May, D., & Deuse, J. (2012). The industrial engineering laboratory: Problem based learning in industrial engineering education at TU Dortmund University. In Global Engineering Education Conference (EDUCON), IEEE, Collaborative Learning & New Pedagogic Approaches in Engineering Education, Marrakesch, Marokko, April 17–20 (pp. 1–10). Stoldt, J., Franz, E., Schlegel, A., & Putz, M. (2015). Resource networks: Decentralised factory operation utilising renewable energy sources. In 12th Global Conference on Sustainable Manufacturing. Procedia CIRP, 26, 486–491. Sutherland, J. W., Richter, J. S., Hutchins, M. J., Dornfeld, D., Dzombak, R., Mangold, J., Robinson, S., Hauschild, M. Z., Bonou, A., Schönsleben, P., & Friemann, F. (2016). The role of manufacturing in affecting the social dimension of sustainability. CIRP Annals—Manufacturing Technology, 65(2), 689–712. https://doi.org/10.1016/j.cirp.2016.05.003 Thiede, S., Juraschek, M., & Herrmann, C. (2016). Implementing cyber-physical production systems in learning factories. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 7–12. https://doi.org/10.1016/j.procir.2016.04.098 Thomar, W. (2015). Kaerchers Global Lean Academy Approach: Incentive talk (industry). In 5th Conference on Learning Factories, Bochum, Germany. Tietze, F., Czumanski, T., Braasch, M., & Lödding, H. (2013). Problembasiertes Lernen in Lernfabriken. Werkstattstechnik Online: wt, 103(3), 246–251.

References

325

Tvenge, N., Martinsen, K., & Kolla, S. S. V. K. (2016). Combining learning factories and ICT-based situated learning. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 101–106. UAW-Chrysler National Training Center. (2016). World Class Manufacturing Academy. Retrieved from http://www.uaw-chrysler.com/world-class-mfg-academy/ Wagner, C., Heinen, T., Regber, H., & Nyhuis, P. (2010). Fit for change—Der Mensch als Wandlungsbefähiger. Zeitschrift für Wirtschaftlichen Fabrikbetrieb (ZWF), 100(9), 722–727. Wagner, P., Prinz, C., Wannöffel, M., & Kreimeier, D. (2015). Learning factory for management, organization and workers’ participation. In 5th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 32, 115–119. https://doi.org/10.1016/j.procir.2015.02.118 Wagner, U., AlGeddawy, T., ElMaraghy, H. A., & Müller, E. (2012). The state-of-the-art and prospects of learning factories. In 45th CIRP Conference on Manufacturing Systems. Procedia CIRP, 3, 109–114. Wank, A., Adolph, S., Anokhin, O., Arndt, A., Anderl, R., & Metternich, J. (2016). Using a learning factory approach to transfer Industrie 4.0 approaches to small- and medium-sized enterprises. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 89–94. https:// doi.org/10.1016/j.procir.2016.05.068 Yoo, I. S., Braun, T., Kaestle, C., Spahr, M., Franke, J., Kestel, P., Wartzack, S., Bromberger, J., & Feige, E. (2016). Model factory for additive manufacturing of mechatronic products: Interconnecting world-class technology partnerships with leading AM players. Procedia CIRP, 54, 210–214. https://doi.org/10.1016/j.procir.2016.03.113 Zuehlke, D. (2008). SmartFactory—From vision to reality in factory technologies. In 17th International Federation of Automatic Control (IFAC) World Congress (pp. 82–89).

Chapter 9

Overview on Potentials and Limitations of Existing Learning Factory Concept Variations

This chapter provides an overview of the potentials and limitations associated with the existing variations of learning factory concepts. In both practical implementation and academic literature, numerous variations can be identified. The various concepts are associated with the specific advantages and disadvantages. This chapter aims to address this ambiguity by presenting the characteristics, advantages, and disadvantages, as well as the potentials and limitations, of these distinguishable concepts (Fig. 9.1).

9.1 Potentials of Learning Factories The main objectives for the operation of learning factories are • the effective development and motivation of learners,1 • the facilitation of practical innovations,2 and • the transfer of competences and innovations to industry.3

9.2 Limitations of Learning Factories Several obstacles to establishing learning factories—or limitations of the learning factory concept itself—can be identified on the way to achieve these objectives:4 • needed resources for learning factories along the learning factory life cycle, 1

Regarding education and training in learning factories. Regarding research in learning factories. 3 Regarding innovation transfer in learning factories. 4 See Tisch and Metternich (2017). 2

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 E. Abele et al., Learning Factories, https://doi.org/10.1007/978-3-031-46428-7_9

327

328

9 Overview on Potentials and Limitations of Existing Learning Factory …

Fig. 9.1 Structure of the overview over concept variations of existing learning factories

• • • •

the mapping ability in learning factories, the scalability of learning factory concepts, the mobility of learning factory, and the effectiveness of learning factories.

Needed Resources for Learning Factories Along the Learning Factory Life Cycle A significant barrier to the establishment of more learning factories around the world lies in the considerable effort needed to plan, develop, build, and operate them. This effort includes: • • • • • •

the financial resources required, the availability of qualified personnel to initiate and run the learning factory, the appropriate educational content to be addressed within the learning factory, access to the necessary factory equipment, sufficient space, and a suitable location.

Insufficient resources, particularly in the early stages of the learning factory life cycle, can bring the whole project to a halt. Figure 9.2 provides an overview of the critical resources throughout the learning factory life cycle, highlighting their importance in ensuring successful implementation and operation. These challenges highlight the need for strategic resource allocation and effective management practices to overcome barriers and promote the widespread adoption of learning factories. The developed configuration system for learning factories,

9.2 Limitations of Learning Factories

329 Critical resources related to LF lifephases

LF lifephases Market / need / problem Business potentials / goals

Learning Factory development Learning Factory built-up Sales –Acquisition (depending on business model)

Use of Learning Factory / Trainings

Sufficient space / facility for the LF Requirements / Goals

Product tracking / monitoring

Learning Factory planning

Learning Factory Remodeling / Recycling

Personnel for the development of a LF Monetary resources for machines, etc.

Partners & personnel willing/able to participate in/run LF Operating model that ensures sustainability (monetary, personnel, content) Integration of latest research results in the LF environment

Fig. 9.2 Required resources along the learning factory life cycle (Tisch & Metternich, 2017), life cycle similar to general product life cycle according to VDI (1993)

presented in Sect. 6.1.2, is a way to maximise the use of the required financial and construction resources during factory planning. The Mapping Ability in Learning Factories A notable limit of current learning factory concepts is the fact that learning factories can only address limited sections and aspects of industrial production. A single learning factory, by focusing on specific areas or sectors, may not capture the full complexity and diversity of the broader industrial landscape. The emphasis on specific topics or environments within a learning factory is necessary to ensure indepth exploration and effective learning outcomes. However, this selectivity also means that certain aspects of industrial production may remain unaddressed or overlooked. The limited mapping ability may relate to specific industrial sectors, learning topics covered, individual production processes, company departments, or target groups. Consequently, a single learning factory can only map a small part of a complex industrial reality, as each facility has to focus on specific topics or environments; this is also referred to as content- and object-related mapping ability limitation.5 In theory, the challenges and problems of all factory levels can be considered within the learning factory concepts; from the process and station level to the factory network level. However, since the factory levels whose problems need to be studied in more detail need to be part of the tangible learning environment of the learning factory, there are space- and cost-related mapping limitations; in particular, higher factory levels are difficult to address within learning factory concepts. Nonetheless, there are approaches emerging that attempt to tackle the problems associated with 5

See Tisch and Metternich (2017), Abele et al. (2015).

330

9 Overview on Potentials and Limitations of Existing Learning Factory …

global production systems. These approaches aim to extend the scope of learning factories beyond individual factory levels and incorporate a broader perspective that considers the interconnectedness and dynamics of global manufacturing networks. By adopting a systems approach, these approaches strive to analyse and address the challenges arising from the integration of factories at a global scale, considering factors such as supply chain management, cross-site coordination, and international collaboration. While the mapping limitations of learning factories may pose challenges in examining higher factory levels, the exploration of global production systems showcases ongoing efforts to bridge this gap.6 By integrating knowledge from various disciplines and leveraging advanced technologies, these approaches aim to broaden the horizons of learning factories and enhance their ability to address complex real-world industrial scenarios. Another limitation of the learning factory concept pertains to the temporal relationship between the actions taken by learners and the subsequent feedback from the learning environment. When there is a significant time gap between the actions and the natural feedback received, it becomes challenging to effectively implement the learning factory concept for certain topics. To overcome this limitation, it becomes crucial to incorporate fast-forward mechanisms that can bridge the temporal gap and ensure timely feedback. The duration of learning modules also influences the extent to which these temporal limits are encountered. The shorter the duration of the learning modules, the more quickly these limitations become apparent.7 In cases where the time-related mapping ability within the learning factory concept reaches its temporal limits, simulations can be employed as a tool to expedite the feedback cycles. Simulations serve to compress the temporal distance between actions and feedback, enabling learners to receive timely and meaningful experiential feedback. By integrating simulations into the learning factory concept, the feedback cycles and, subsequently, the experiential component can be fully utilised even when temporal constraints hinder the direct mapping of actions and natural feedback. This enables learners to engage in a more dynamic and immersive learning experience, enhancing the effectiveness and efficiency of the learning process within the context of a learning factory. Figure 9.3 classifies various lean methods and topics according to the factory level addressed and the duration of the feedback cycle. Topics in the lower left corner, i.e., topics addressing the lower factory levels with immediate feedback cycles, are typical topics of today’s learning factories. Examples for topics with fast feedback cycles, and therefore well-suited to be addressed in learning factories, are basic lean

6

See Lanza et al. (2015) and Sect. 8.6.5. As examples for topics or learning content with long feedback loops product development, supplier development or planning of maintenance activities can be named.

7

9.2 Limitations of Learning Factories

System Cell Station

VSD

Production planning

Cellular manufac. OEE Autonomous SMED maintenance

Line balancing Poka yoke

5S

immediate

CIP and SFM

Segment

Process FMEA

Factory levels

Factory

Global production networks Supplier development Typical focus of current LF Design for Manufac. VSA, Root cause analysis

Network

331

late Feedback to actions

Fig. 9.3 Exemplary limits regarding space- and time-related mapping ability (Abele et al., 2017b; Tisch, 2018)

production and management topics such as 5S,8 SMED,9 line balancing,10 and poka yoke11 solutions. Another limitation of learning factories can be observed in the context of poka yoke, where learners actively engage in designing and proposing solutions to enhance the factory environment, specifically in the area of mistake-proofing. While these proposed solutions could potentially be implemented directly within the learning factory, there are often content-related mapping limitations associated with their ad-hoc implementation. The ability to map and integrate these solution-related improvements within the learning factory environment is contingent upon the flexibility and changeability of the learning factory itself across various dimensions. These dimensions may include the physical layout of the facility, the availability of resources and equipment, the adaptability of processes and workflows, and the readiness of the learning environment to accommodate and support the proposed solutions. When the learning factory environment lacks the necessary flexibility and changeability to incorporate these ad-hoc solutions, the potential mapping ability of the learning factory concept may be constrained. Therefore, addressing this limitation requires considering the dynamic nature of the learning factory and ensuring its adaptability to effectively implement solution-oriented improvements proposed by the learners. By fostering a more agile and responsive learning factory environment, the limitations associated with solution-related mapping can be mitigated. This 8

Method to design workplaces and their environment safely, cleanly, and transparently. Single-minute exchange of dies, a lean method to reduce changeover times. 10 Method for a balanced work content over several stations or workers for example in assembly lines. 11 Method to design mistake proof products and processes. 9

332

9 Overview on Potentials and Limitations of Existing Learning Factory …

enables a smoother integration of proposed solutions and enhances the overall effectiveness of the learning factory concept in promoting continuous improvement and innovation within industrial settings.12 To overcome these different mapping ability limitations, collaborations between multiple learning factories, industry partners and academic institutions can help provide a more comprehensive understanding of different topics and industrial processes. By sharing knowledge, expertise and resources between different learning factories, a wider range of topics and sectors can be covered, allowing for a more holistic approach to learning and research. In addition, advances in digital technologies and virtual learning environments offer opportunities to overcome the physical limitations of a single learning factory. Virtual learning platforms can simulate different industrial scenarios and provide learners with access to a wider range of production processes and sectors, overcoming the constraints of a physical facility. It is important to recognise and address this limitation of learning factory concepts to ensure a balanced and comprehensive approach to industrial education and training. By actively seeking collaboration, using digital tools, and promoting interdisciplinary cooperation, the potential of learning factories can be maximised to provide a more realistic and comprehensive learning experience for learners in the industrial sector. The Scalability of Learning Factory Concepts The scalability of learning courses within learning factories presents a significant limitation. This limitation becomes particularly apparent when compared to other forms of learning. For instance, traditional lectures can accommodate several hundred students without major issues, with only one teacher required—especially in online sessions. In contrast, learning factory courses typically involve a maximum of fifteen to twenty learners, necessitating the involvement of at least two trainers. Even when multiple courses are conducted simultaneously to accommodate more learners, the learning factory facility itself becomes a limiting factor in terms of capacity. In most cases, only one course can run at a time within the learning factory, and occasionally, with careful planning, two courses may be accommodated. The limited scalability highlights the need to explore alternative strategies to increase the capacity of learning courses within learning factories. One potential approach is the utilisation of virtual and remote learning technologies, which can extend the reach and participation capacity of learning factory experiences. By leveraging online platforms, augmented reality, and virtual simulations, a larger number of learners can engage in interactive learning activities and gain exposure to industrial processes and problem-solving scenarios. Furthermore, considerations should be given to the design and layout of learning factory facilities to optimise space utilisation and accommodate more learners simultaneously. This may involve the creation of flexible and modular learning environments that can adapt to varying course requirements and accommodate multiple learning groups concurrently. Addressing the scalability limitation of learning factory concepts is crucial for expanding their reach and 12

Among others the changeability dimensions process, product, organisation, and layout are mentioned by Wiendahl et al. (2007).

9.2 Limitations of Learning Factories

333

impact, enabling a broader audience of learners to benefit from hands-on industrial learning experiences. By embracing technological advancements and adopting innovative approaches to facility design, learning factories can enhance their capacity and provide transformative learning opportunities at a larger scale. The Mobility of Learning Factories Another significant limitation arises from the inherent nature of learning factories, which are typically established and operated within a specific physical location. As a result, the training and learning experiences offered by learning factories are confined to that particular place, limiting accessibility to learners within a specific region. Learning factories, in their traditional sense, lack mobility as they are predominantly immobile entities. To address this limitation and achieve mobility in the learning factory approach, three main approaches have been identified (Fig. 9.4):

Fig. 9.4 Virtual factories and learning factories from McKinsey&Company, Festo, and Siemens (Abele et al., 2017b)

334

9 Overview on Potentials and Limitations of Existing Learning Factory …

1. Mobile learning factory: A space-saving and transport friendly design of the entire learning factory equipment, which enables a transport to different locations without bigger efforts. In those cases, often only certain selected processes (e.g., assembly processes) can be mapped inside the learning factory.13 2. Virtual learning factory: The use of virtual learning factories, which accordingly do not include immobile, physical factory equipment.14 In those approaches, the challenge is to preserve the hands-on characteristic of physical learning factory concepts. 3. Teaching Factory: On-site learning in the factory environment is replaced by remote learning using new ICT equipment. In these cases, image and sound of the factory environment are transferred to another learning location.15 In those approaches, the challenge is to create an immersive learning environment and allow the students to get access to the factory environment. The Effectiveness of Learning Factory Approaches The effectiveness of learning factory approaches depends on their ability to facilitate high-quality competence development. However, in many instances, the integration of these objectives into the design of learning factories and learning modules, as well as the evaluation of their achievement, is lacking. To create truly effective learning factories, it is necessary to incorporate competence-oriented learning targets from the outset during the design phase and establish a target-oriented evaluation phase.16 By adopting a competence-oriented approach, learning factories can align their objectives with the specific skills, knowledge, and capabilities that learners need to develop. This entails identifying the desired competences and designing learning modules that explicitly address and nurture these competences. Furthermore, the evaluation phase should be structured to assess the attainment of these competence-based learning targets, ensuring that the desired outcomes are being achieved. The inclusion of competence-oriented learning targets in the design phase ensures a deliberate and purposeful approach to competence development within learning factories. This involves carefully selecting appropriate learning activities, resources, and instructional strategies that align with the identified competences. Moreover, a target-oriented evaluation phase allows for ongoing assessment and feedback, enabling continuous improvement and refinement of the learning factory concept. Ultimately, the integration of competence-oriented learning targets and a target-oriented evaluation phase enhances the effectiveness of learning factories. It ensures that learners are equipped with the necessary competences to thrive in realworld industrial settings and provides valuable insights into the success and impact of 13

A learning factory using this approach to gain mobility is described in the Best Practice Example 28 “Lernfabrik für schlanke Produktion (LSP) at the iwb, Technical University of Munich (TUM), Germany”. 14 Digital and virtual learning factory approaches are discussed in Sect. 9.4.1. 15 The Teaching Factory concept uses this approach to obtain mobility. Two approaches are presented in the Best Practice Example 16 “Hybrid Teaching Factory for Personalised Education—Towards Teaching Factory 5.0”. 16 For the success evaluation in learning factories, see Sect. 6.3.3.

9.3 Learning Factory Concept Variations of Learning Factories …

335

the learning factory approach. By embracing this comprehensive approach, learning factories can deliver meaningful and impactful learning experiences that empower learners to excel in their respective fields.

9.3 Learning Factory Concept Variations of Learning Factories in the Narrow Sense—Advantages and Disadvantages This section provides an overview of different variations of the learning factory concept aimed at addressing limitations identified in the previous section. It explores new concepts such as virtual learning factories and examines whether they offer superior advantages compared to the classic learning factory approach. It also acknowledges that while variations may excel in certain areas, they may have drawbacks in others. By understanding the characteristics and implications of these variations, stakeholders can make informed decisions about the most suitable approach for their specific learning objectives and target groups. The covered learning factory concept variations of learning factories in the narrow sense are namely. • • • • • •

the learning factory core concept (Sect. 9.3.1), model scale learning factories (Sect. 9.3.2), physical mobile learning factories (Sect. 9.3.3), low-cost learning factories (Sect. 9.3.4), digitally and virtually supported learning factories (Sect. 9.3.5), and producing learning factories (Sect. 9.3.6).

9.3.1 The Learning Factory Core Concept The learning factory core concept, as outlined in Sect. 4.3, encompasses the establishment of a realistic and full-scale physical factory environment in which a physical product is created and where learners can directly engage with the production process and experience the creation of a tangible product on site. This core concept emphasises the integration of practical, hands-on learning within an authentic industrial setting, providing learners with a first-hand understanding of real-world production operations. On the one hand, learning factories that follow this core concept provide best prerequisites for an effective competence development in the broad field of production technology and organisation, namely through. • • • • •

hands-on experience and own actions of the learners and connected feedback, a high contextualisation of the learning environment, activation of the learners in practical learning tasks, use of realistic problem situations, motivational benefits of the immersive learning environment,

336

9 Overview on Potentials and Limitations of Existing Learning Factory …

• possibilities for collectivisation of on-site learning processes, • possibilities for the integration of thinking and doing, and • self-regulation and self-direction of the learners can be used appropriately. On the other hand, this kind of learning factory approaches come especially with some of the described learning factory obstacles and limitations, namely. • a large resource requirement for construction and operation, • the mapping of only certain production processes and topics that represents a small part of complex manufacturing systems, • the challenge in mapping large factory structures, e.g., entire factories or factory networks, • the challenges with learning content that does not allow direct feedback from the learning environment to the learners’ actions, • the challenge with ad-hoc representation of participants’ ideas and solutions in the learning factory (changeability and flexibility of the factory environment), • the difficulties with scalability, e.g., in the use of learning factories for educational purposes in lectures with many participants, and • the lack of mobility of the learning factory facility and equipment.

9.3.2 Model Scale Learning Factories Contrary to the core concepts of learning factories discussed in the previous section, scaled-down or model scale learning factories use smaller equivalents of factory equipment that closely resemble their original counterparts, except for their reduced dimensions. However, achieving a high level of similarity between industrial and model scale equipment can be challenging, particularly when comparing load profiles. Model scale learning factories offer the advantage of requiring less space, such as a classroom, to replicate a factory in a scaled-down format. Moreover, they require fewer financial resources for setup. The design of the learning environment in model scale factories is specifically tailored to the learning objectives, ensuring that equipment is easily accessible to learners, albeit at the cost of authenticity. A comprehensive assessment of the advantages and disadvantages of using scaleddown learning factory environments, in comparison with the learning factory core concept discussed in Sect. 9.3.1, is presented in Fig. 9.5. This summary provides an overview of the key considerations when deciding between scaled-down learning factories and the traditional core concept (Fig. 9.6). The manufacturer Festo Didactic provides some equipment for these scaled-down learning factories; scaled-down learning factories from Festo Didactic factories are

9.3 Learning Factory Concept Variations of Learning Factories …

337

Fig. 9.5 Advantages and disadvantages of the learning factory core concept (learning factories in the narrow sense)

Fig. 9.6 Advantages and disadvantages of model scale learning environments compared to the learning factory core concept

338

9 Overview on Potentials and Limitations of Existing Learning Factory …

mainly used in universities17 and vocational schools,18 but also, for example, in Festo’s own company. The Learning Factory for advanced Industrial Engineering (aIE) at the Institute of Industrial Manufacturing and Management (IFF), Universität Stuttgart, Germany, contains scaled-down Festo Didactic factory equipment that addresses the link between the digital production planning and the implementation of physical models in the laboratory.19 The transformable production platform of the aIE learning factory consists of mobile plug-and-play-modules for assembly, inspection, coating, storage, and transportation. The re-configuration into different layouts of the learning factory is possible. Festo Didactic designed and implemented the aIE learning factory. In the Best Practice Example 26, the learning factory aIE is described. The iFactory is a similar learning factory concept with Festo Didactic equipment at the Intelligent Manufacturing Systems (IMS) Center, Windsor, Canada.20 The learning factory was set up as the first of its kind in North America; it is a modular and changeable assembly system that comprises robotic and manual assembly stations, computer vision inspection station, automated storage, and retrieval system (ASRS) and several material handling modules. The iFactory is extended by • • • •

the iDesign module, which is a design innovation studio, the iPlan module, which contains process and production planning tools, a 3D printing facility, and a coordinate measuring machine (CMM) facility.

In the Best Practice Example 29, the iFactory at the Intelligent Manufacturing Systems Center in Windsor, Canada, is described. The Festo Scharnhausen Learning Factory is placed in the middle of the Festo Technology Factory in Scharnhausen and is surrounded by production departments. The Scharnhausen Learning Factory entails four rooms (mechanical processing, valve and valve terminal assembly, automation, CPPS and process improvements, administration of the learning factory). The learning factory at the plant that uses Festo Didactic equipment is used to train employees exclusively; mainly new employees come to the learning factory, but also advanced qualification takes place. Furthermore, in the Best Practice Example Section further learning factories using model scale learning environments can be identified: • • • • 17

Best Practice Example 2: Aalto Factory of the Future Best Practice Example 8: Die Lernfabrik Best Practice Example 18: Industry 4.0 Lab Best Practice Example 20: LEAN-Factory

See for example Best Practice Example 29 “Manufacturing Systems Learning Factory (iFactory) at University of Windsor, Canada” and Best Practice Example 26 “Learning Factory of advanced Industrial Engineering aIE (LF aIE) at IFF, University of Stuttgart, Germany”. 18 See Sect. 7.1.9 “Example: Learning factories for Industrie 4.0 vocational education in BadenWürttemberg”. 19 Hummel and Westkämper (2007). 20 ElMaraghy and ElMaraghy (2015).

9.3 Learning Factory Concept Variations of Learning Factories …

• • • • •

339

Best Practice Example 31: MPS Lernplattform Best Practice Example 37: Smart factory AutFab Best Practice Example 38: Smart Factory Best Practice Example 39: SmartFactory-KL Best Practice Example 40: Smart Mini Factory.

9.3.3 Physical Mobile Learning Factories In general, learning factories are established at specific locations, resulting in their availability limited to certain regions. The immobility of physical learning factories, as discussed in Sect. 9.2, presents a significant constraint, making it challenging to conduct trainings at company sites and hindered by longer travel times for employees. However, alongside the utilisation of virtual learning factories, the implementation of mobile physical learning equipment offers a promising solution to overcome this limitation. This approach enables on-site trainings at company premises, providing direct access to the organisation’s value creation processes while minimising travel logistics for employees. The Lernfabrik für die Schlanke Produktion (LSP) at iwb, TU München, is a physical, yet completely mobile learning factory. This learning factory approach is described in the Best Practice Example 28. For future research on the mobility learning factory approaches, additionally to the physical approach described in this section, virtual21 and ICT-based remote22 learning factory concepts can provide added value regarding the mobility of the learning factory concept (Fig. 9.7).23

9.3.4 Low-Cost Learning Factories Low-cost learning factories offer a viable approach for establishing and operating learning factories with limited financial resources. These cost-effective learning factory concepts primarily focus on production processes that can be mapped in a more affordable manner. Assembly and logistics processes, in particular, are commonly utilised within these low-cost learning factory frameworks. One of the key challenges associated with low-cost learning factories revolves around achieving adequate contextualisation of the learning environment. Despite the constraints of limited representation, it is crucial to create a learning environment that is perceived by learners as an authentic factory setting. Striking the balance between cost efficiency and maintaining a realistic learning experience remains a critical aspect of low-cost learning factories. 21

See Sect. 9.4.1: Digital, virtual, and hybrid learning factories. See Sect. 9.4: Remotely accessible learning factories. 23 See also Tisch and Metternich (2017). 22

340

9 Overview on Potentials and Limitations of Existing Learning Factory …

Fig. 9.7 Advantages and disadvantages of physical mobile learning factories compared to the learning factory core concept

Learning factories at a lower cost can also be achieved with the integration of so called learnstruments24 into the learning factory concept.25 Figure 9.8 summarises advantages and disadvantages of low-cost learning factories compared to the learning factory core concept. Another alternative for very small budgets is simulation games. On the one hand, the activity of learners as an important component of the learning factory concept can be achieved with those games; while on the other hand, a realistic representation of production processes is not considered. Since the use of an authentic learning environment is an important and critical part of the learning factory concept, such highly abstract simulation games are not considered a learning factory.26 Furthermore, simulation games are often integrated into learning factory concepts, see also Sect. 7.1.4 on game-based learning in learning factories and gamification.

9.3.5 Digitally and Virtually Supported Learning Factories In the literature and in existing learning factory approaches, the digital and virtual extension of physical learning factories can be categorised into three different types of support: 24

The term learnstrument is introduced by Gausemeier et al. (2015). See Muschard and Seliger (2015). 26 Examples of the use of these simulation games in learning factories can be found in the literature, see, e.g., Stier (2003) and ElMaraghy and ElMaraghy (2014). 25

9.3 Learning Factory Concept Variations of Learning Factories …

341

Fig. 9.8 Advantages and disadvantages of low-cost learning factories compared to the learning factory core concept

• e-learning in learning factories,27 • multimedia and ICT support in learning factories,28 and • virtually extended physical learning factories.29 E-learning in Learning Factories To improve the utilisation of limited capacities in learning factories, the systematisation phases involving theoretical input can be separated from the actual on-site training. This can be achieved by integrating e-learning components into the learning factory concepts. Learners have the flexibility to access e-learning materials on their personal computers at their own convenience to prepare for the topics covered in the subsequent learning factory training. While this approach offers the advantage of freeing up learning factory capacity and enabling learners to engage with theoretical content beforehand, it introduces a potential drawback. The decoupling of theory and practice results in longer intervals between the theoretical input and the practical application, disrupting the short cycles of alternating between theory and practice commonly found in traditional learning approaches.30

27

See, e.g., Lanza et al. (2015), Lanza et al. (2016). See, e.g., Pittschellis (2015), Tvenge et al. (2016). 29 See, e.g., Thiede et al. (2017). 30 A use case with the integration of e-learning into the learning factory concept is presented by Lanza et al. (2016). 28

342

9 Overview on Potentials and Limitations of Existing Learning Factory …

Fig. 9.9 Tec2Screen® enables individual learning paths related to learning speed and preferred media, picture taken from Festo Didactic (2018)

Multimedia and ICT Support in Learning Factories Multimedia and ICT support play a crucial role in enhancing hands-on learning experiences within learning factories. These technologies offer various applications that facilitate systematisation and reflection phases by making relevant theoretical content more accessible to learners.31 To cater to individual learning speeds and preferences, specialised tools are required that accommodate different media formats. An example of such a tool is Festo Didactic’s Tec2Screen® learning device, which leverages the capabilities of an iPad to provide personalised learning paths for learners. The Tec2Screen system not only delivers information but can also be integrated with sensors and other hardware, allowing its utilisation in testing or exploratory learning phases. This device can be effectively combined with learning factory approaches, enabling learners to engage with augmented reality applications within the learning factory context (Fig. 9.9).32 In addition, there are various other multimedia and ICT tools that can be used to enhance learning experiences in learning factories. These tools can include interactive simulations, computer-based training modules, and online collaboration platforms. • Interactive simulations provide learners with a virtual environment where they can engage in realistic scenarios and practice their skills in a risk-free setting. 31

Two examples for extension of on-site learning factory trainings with multimedia support are given by Tvenge et al. (2016) and Pittschellis (2015). 32 For further information regarding the multimedia support in learning factories using Tec2Screen®, see also Pittschellis (2015).

9.3 Learning Factory Concept Variations of Learning Factories …

343

These simulations can mimic different production processes, allowing learners to understand the underlying principles and dynamics involved. • Computer-based training modules offer a flexible and self-paced learning approach, allowing learners to access learning materials and resources at their own convenience. These modules can incorporate interactive exercises, quizzes, and multimedia elements to enhance understanding and retention of the content. • Online collaboration platforms enable learners to connect and collaborate with peers and instructors, fostering knowledge sharing, discussions, and problemsolving. These platforms can facilitate remote learning, allowing learners from different locations to participate in collaborative projects and share their insights and experiences. While multimedia and ICT support offer significant benefits in learning factories, it is important to ensure that these tools are integrated effectively into the learning process. Proper training and guidance should be provided to learners and instructors to maximise the potential of these technologies and ensure they align with the learning objectives of the specific learning factory context. Virtually Extended Physical Learning Factories Learning factories with physical learning environments offer learners the opportunity to engage in realistic factory settings. However, the construction and maintenance of these physical learning factories can be costly.33 To address this limitation, one approach is to enhance the physical learning environment with a complementary virtual representation, especially when economic or technical constraints hinder complete physical replication.34 The virtual supplements not only provide cost-effective alternatives but also offer advantages in scenarios where feedback from the physical environment would be time-consuming.35 By incorporating virtual elements, learning experiences can be optimised, ensuring that learners receive timely feedback on their actions and can engage in productive learning activities within the available time constraints.36 The integration of virtual extensions enables the combination of physical and virtual learning phases. This approach becomes particularly valuable when feedback cycles in the physical environment would require an excessive amount of time compared to the duration of the learning module. By leveraging virtual components, learners can engage in interactive and dynamic learning experiences that bridge the gap between theory and practice, enhancing their overall learning outcomes. Furthermore, the virtual learning phase can be personalised to individual learners, e.g., to better match their production environment or to offer different difficulty levels in the Process Learning Factory CiP.37 33

See the described space and cost-related mapping limitations in Sect. 9.2. See also Thiede et al. (2017) and Tisch and Metternich (2017). 35 See the described time-related mapping ability limitation in Sect. 9.2. 36 See also Thiede et al. (2017) and Tisch and Metternich (2017). 37 See Riemann et al. (2021) and Best Practice Example 34. 34

344

9 Overview on Potentials and Limitations of Existing Learning Factory …

Fig. 9.10 Use of virtual extensions and simulation depending on the implementation effort for a physical environment and the feedback time to actions of the learners based on Thiede et al. (2017)

Figure 9.10 depicts a comprehensive framework that categorises various actions based on feedback duration and implementation effort, facilitating decision-making in the establishment of the physical learning environment. The framework helps determine: • Actions planned in the physical learning factory in real time: These actions are selected when the implementation effort is within acceptable limits, and the feedback time to action is shorter than the available time in the learning module. In such cases, a purely physical environment suffices for immediate engagement and feedback. • Actions addressed in the virtual extension of the learning factory: When the implementation efforts for certain actions exceed practical limits, a virtual extension becomes necessary. By incorporating virtual elements, learners can interact with simulated components, ensuring a comprehensive learning experience. • Actions requiring simulation support: Simulation becomes essential when the real-time feedback to participants’ actions takes an extended duration. By employing simulations, the feedback cycles can be significantly reduced, accelerating the learning process, and providing timely insights for learners. By considering this framework, learning factory practitioners can make informed decisions regarding the allocation of resources and the integration of physical, virtual, and simulation-based elements. It enables the creation of a diverse portfolio of learning experiences that optimises feedback time, implementation effort, and overall learning outcomes. The resulting portfolio is shown in Fig. 9.10.

9.3 Learning Factory Concept Variations of Learning Factories …

345

Fig. 9.11 Advantages and disadvantages of digitally and virtually supported learning factories compared to the learning factory core concept

Advantages and disadvantages of digitally and virtually supported learning factories using e-learning, ICT, multimedia, and virtual extensions are summarised in Fig. 9.11.

9.3.6 Producing Learning Factories A notable variation of the learning factory core concept involves the integration of a learning factory with a real production environment, where the goods manufactured are produced to order or made available in the market. This shift in concept brings about several implications: • Enhanced realism and authenticity: The utilisation of the factory environment for real production creates a more realistic setting and fosters authentic processes within the learning factory concept. Learners are exposed to genuine production conditions, providing them with a deeper understanding of real-world challenges and operations.

346

9 Overview on Potentials and Limitations of Existing Learning Factory …

Fig. 9.12 Advantages and disadvantages of producing learning factories compared to the learning factory core concept

• Constant quality assurance: With real production taking place, ensuring the consistent quality of the manufactured products becomes paramount. This necessity for quality control leaves limited room for learners’ free experimentation and exploration. In contrast, the traditional learning factory core concept allows learners to implement their ideas in an economically risk-free environment. • Competition between learning and production: Learning processes and production processes may occasionally clash, requiring decisions on priority. In cases of conflict, the decision must be made regarding whether the learning objectives or production requirements take precedence. Typically, the production aspect is given higher priority due to its economic significance. Integrating a learning factory with a real production environment presents opportunities for learners to experience the complexities and constraints of genuine manufacturing operations. However, it also introduces challenges in managing quality control and balancing the needs of learning and production within the same facility. A well-known example of such a producing learning factory is the DFA Demonstration Factory Aachen that contains a small-scale production with a high vertical range of manufacture on 1.600 sqm. The electric cars and go karts that are produced in the DFA are sold on the market with the e.GO Mobile AG as lead customer. In this learning factory concept, the production processes are not only similar to industrial production but also have the same quality and complexity requirements.38 The Demonstration Factory DFA is presented in the Best Practice Example 6 (Fig. 9.12).

38

Schuh et al. (2015).

9.4 Learning Factory Concept Variations of Learning Factories …

347

9.4 Learning Factory Concept Variations of Learning Factories in the Broader Sense—Advantages and Disadvantages In addition to the concept variations of learning factories in the narrower sense39 discussed earlier, this section provides an overview of concept variations in the broader sense of learning factories. These broader variations include the following: • digital, virtual, and hybrid learning factories40 as well as • remotely accessible learning factories and teaching factories.41 These variations extend the scope and applicability of the learning factory concept, encompassing diverse approaches and implementations that go beyond the traditional boundaries. By exploring these broader concept variations, a more comprehensive understanding of the potential and versatility of learning factories can be gained.

9.4.1 Digital, Virtual, and Hybrid Learning Factories With the increasing digitisation of production processes, there is a growing emphasis on digital, virtual, and hybrid learning factories in the context of production-related teaching and training.42 This trend allows for the continuous expansion of both the topics covered in learning factories and the application areas of the factories themselves. Digital and virtual learning factories can be employed in similar subject areas as traditional learning factories, but with the added capability of extending learner activities to various planning and simulation tasks.43 Examples of these tasks include factory planning, layout planning, concurrent engineering, front-loading, ergonomics evaluation, and virtual commissioning.44 By incorporating digital and virtual learning environments, learning factory concepts can provide additional value for production-related teaching and training. This can be achieved by utilising software tools specifically designed to facilitate digital and virtual learning factories. These software tools offer a range of functionalities that enable learners to engage in realistic simulations, virtual experiments, and collaborative problem-solving activities. They provide a platform for learners to explore different scenarios, analyse

39

See Sect. 9.3. See Sect. 9.4.1. 41 See Sect. 9.4.2. 42 See for example Weidig et al. (2014), Kesavadas (2013), Haghighi et al. (2014), Hammer (2014), Celar et al. (2016). 43 See Weidig et al. (2014). 44 See Abele et al. (2017a). 40

348

9 Overview on Potentials and Limitations of Existing Learning Factory …

production processes, and make informed decisions in a virtual environment. Additionally, digital and virtual learning factories offer the advantage of scalability, as they can accommodate a larger number of learners without being constrained by physical space limitations. Learning factory concepts can include a digital or virtual learning environment instead45 or alongside46 of a physical learning environment to create added value for production-related teaching and training. Furthermore, the integration of digital and virtual components in learning factories allows for the integration of real-time data and analytics, providing learners with immediate feedback and performance evaluation. This promotes a more dynamic and adaptive learning experience, enhancing learners’ understanding and competence in production-related concepts and skills. However, it is important to note that digital and virtual learning factories should complement, rather than replace, physical learning environments. The combination of physical and digital elements, known as hybrid learning factories, offers a balanced approach that leverages the benefits of both approaches. Hybrid learning factories provide hands-on experiences in a physical environment while leveraging the flexibility and scalability of digital and virtual tools. The integration of digital, virtual, and hybrid learning factories opens up new possibilities for more immersive, interactive, and comprehensive production-related teaching and training experiences. By embracing these advancements, educators and trainers can better prepare learners for the challenges and opportunities of the digitised manufacturing landscape.

9.4.1.1

Software Tools for Digital and Virtual Learning Factories

For the creation of digital and virtual learning factories, various existing software tools can be identified on the market. Those tools are crucial enablers for digital and virtual learning factories, although the didactic concept that is also an important part of each learning factory concept is no integral part of these software tools themselves. VisTable47 and TaraVRbuilder48 are examples of software tools that facilitate the visualisation, analysis, and optimisation of virtual production environments in 3D, without requiring advanced programming or CAD expertise. These tools offer a comprehensive object library that includes buildings, machinery, and other manufacturing equipment: • VisTable is particularly suitable for static factory planning, material flow analysis, and assembly line optimisation. It allows users to create virtual representations of production facilities and evaluate their layout and efficiency. By simulating different scenarios, users can identify potential bottlenecks, optimise workflows, 45

The learning factory concepts that use digital and virtual representations instead of a physical environment are named digital and virtual learning factories, see Sect. 9.4.1.2. 46 Learning factory concepts that use digital and virtual representations additional to physical environments are called hybrid learning factories, see Sect. 9.4.1.3. 47 See visTABLE (2017a). 48 See Tarakos (2017).

9.4 Learning Factory Concept Variations of Learning Factories …

349

Fig. 9.13 Virtual model of the FlowFactory at PTW; visTable®touch Software (visTABLE, 2017b)

and improve overall productivity. It should be noted that VisTable does not support the use of virtual reality (VR) and is therefore less interactive. Figure 9.13 shows a virtual model of the FlowFactory at PTW, TU Darmstadt, Germany, in VisTable. • TaraVRbuilder focuses on the simulation of manufacturing processes, enabling the creation of dynamic virtual production and logistics environments. Users can simulate the behaviour of machines, workers, and materials within the virtual environment to gain insights into production workflows and identify opportunities for improvement. Additionally, TaraVRbuilder can also be used for planning Industrie 4.0 use cases and applications, leveraging its capabilities to model and optimise advanced production systems.49 • 3DEXPERIENCE: To integrate the planning of products, processes, resources, and layouts formerly separated tools, like Delmia, Catia, Enovia, and Simula, are combined to the software pack 3DEXPERIENCE by Dassault Systèmes.50 In addition to virtual product development, the software package also supports the creation of a virtual factory environment in which different factory scenarios can be simulated directly. Advanced CAD knowledge is required to use the software package. For the integration of the software into learning factory approaches, an indepth familiarisation with the software components must be provided accordingly. • Unity: Another powerful platform for creating virtual learning factories is Unity. Unity is a popular game development engine that offers robust features for creating interactive and immersive experiences, making it suitable for programming virtual learning environments. Using Unity, developers can design and construct virtual factory environments with realistic graphics, physics simulations, and interactive 49 50

Abele et al. (2017a). Dassault Systèms (2017).

350

9 Overview on Potentials and Limitations of Existing Learning Factory …

elements. They can import 3D models of factory equipment, materials, and products and arrange them in a virtual space. Unity’s intuitive interface and scripting capabilities allow for the creation of interactive elements such as buttons, levers, and control panels that learners can interact with. One of the key advantages of using Unity for virtual learning factories is the ability to program dynamic behaviours and simulations. Developers can implement realistic physics simulations to model the movement and interactions of objects within the virtual environment. This enables learners to observe and understand the cause-andeffect relationships of various production processes and operations. Unity also supports the integration of additional technologies, such as virtual reality (VR) and augmented reality (AR). With VR headsets, learners can immerse themselves in a virtual factory environment and interact with objects and machines as if they were physically present. AR can overlay virtual information onto the real-world environment, allowing learners to view virtual representations of factory processes and equipment within their physical surroundings. Moreover, Unity provides a wide range of programming languages, including C# and JavaScript, to create custom functionalities and interactions within the virtual learning factory. This flexibility enables developers to tailor the learning experience to specific educational objectives and adapt the virtual environment based on learners’ needs. Furthermore, Unity offers a supportive community and extensive documentation, making it easier for developers and educators to learn and use its features for programming virtual learning factories. There are also pre-existing assets and plugins available in Unity’s asset store, which can be leveraged to accelerate the development process and enhance the visual quality and functionality of the virtual learning environment. These software tools provide valuable support in the development and analysis of digital and virtual learning factories. They empower learners to engage in realistic simulations, explore various production scenarios, and make informed decisions based on the outcomes. By using these tools, learners can develop a deeper understanding of production processes, evaluate different strategies, and enhance their problem-solving skills. Furthermore, Ureality has been developing virtual reality solutions for training and education since 2013. The company also offers a software solution with which anyone can create and conduct VR training themselves. No programming is necessary.51

9.4.1.2

Digital and Virtual Learning Factories

Digital learning factories are innovative systems that employ information technology and various IT tools to create digital models that map the processes, resources, and products within a learning factory environment. These digital models serve as comprehensive representations of the factory, enabling a deeper understanding and 51

See https://www.ureality.de/.

9.4 Learning Factory Concept Variations of Learning Factories …

351

analysis of its operations. In addition to digital models, virtual learning factories use appropriate infrastructure and visual software tools to provide a visual representation of these digital models. This is often achieved through the use of virtual or augmented reality technologies. By immersing users in a simulated factory environment, virtual learning factories facilitate the visualisation of operations and simulations at a factory level.52 One of the key advantages of virtual learning factories is their ability to support process and layout planning before actual production begins. Through virtual simulations, tasks can be simulated and evaluated, and alternative factory designs can be assessed.53 This empowers decision-makers to identify and address potential conflicts or issues in the implementation of factory planning solutions. By resolving these conflicts beforehand, virtual learning factories enable the direct implementation of verified and optimised factory planning solutions. Virtual learning environments play a pivotal role in enhancing the quality of educational activities within the manufacturing domain.54 They are widely recognised as crucial tools for delivering high-quality teaching experiences.55 As a result, numerous approaches utilising virtual environments have emerged in the field of manufacturing education and training.56 One notable approach is the utilisation of virtual factory environments in conjunction with physical environments. These innovative concepts, often referred to as hybrid learning factories, combine the advantages of both virtual and physical settings. They provide a comprehensive learning experience by integrating virtual simulations and real-world interactions. To learn more about hybrid learning factories, please refer to Sect. 9.4.1.3. In the following, some virtual learning factory examples are described. One such instance is the KTH XPRES Lab, which serves as a digital and virtual learning factory in the field of manufacturing systems design. Its primary objective is to facilitate cross-disciplinary organisational learning and decision-making processes. The XPRES Lab encompasses a digital and virtual representation of a manufacturing system, accompanied by an innovative concept vehicle. Its purpose is to showcase the visualisation of digital models pertaining to products, factories, and manufacturing processes. Through these visualisations, the XPRES Lab aims to identify dependencies and facilitate holistic decision-making. In addition to visualisations, the XPRES Lab supports learning processes by engaging active learners in simulations of “what-if” scenarios. These simulations enable learners to evaluate the effects of various changes across the entire manufacturing system. The virtual learning factory within the XPRES Lab incorporates machining simulations

52

See Chryssolouris et al. (2008), Abele et al. (2017a). See Hummel et al. (2015). 54 See Manesh and Schaefer (2010b). 55 See Manesh and Schaefer (2010a). 56 See for example Dessouky (1998), Dessouky and Verma (2001), Cassandras et al. (2004), Ong and Mannan (2004), Chi and Spedding (2006), Watanuki and Kojima (2007), Manesh and Schaefer (2010a), Gadre et al. (2011), Goeser et al. (2011), Abdul-Hadi et al. (2011). 53

352

9 Overview on Potentials and Limitations of Existing Learning Factory …

Fig. 9.14 Virtual learning factory XPRES at KTH

and flow simulations, providing learners with a comprehensive educational experience.57 Figure 9.14 provides an impression of the virtual representations within the learning factory of the XPRES Lab. McKinsey & Co. has developed an authentic virtual 3D learning factory, which serves as a training tool for participants worldwide on various topics.58 This virtual learning factory is equipped with a computer containing visualisation software specifically designed for virtual factories. The participants are provided with a beamer and VR glasses to enhance their immersive experience. While the virtual learning factory offers infinite mobility, the participants are physically located in one place, ensuring they can interact and collaborate with each other instead of being isolated in front of their individual computers. Collectively, learners can explore, discuss, and optimise the virtual execution of production processes within this learning environment. The virtual trainings adopt an experiential learning approach known as “go-see-do.” This approach aligns with the learning processes observed in traditional learning factories, allowing learners to actively engage in transforming the virtual factory environment from a suboptimal state to a best practice factory. Through self-directed identification of potentials and the implementation of improvement ideas, participants gain practical experience and hands-on learning opportunities. The virtual learning factory offers a range of training programs, including the application of classic lean methods, training on lean management principles, and fostering a lean mindset and behaviour. This comprehensive approach enables participants to holistically implement improvement approaches that are tailored to the specific needs of their respective companies. Reutlingen University houses the werk150, which serves as a multifunctional learning factory for education, training, research, and innovation transfer in the field of cyber-physical production and logistics systems design and optimisation. The learning factory incorporates a virtual learning environment, developed using the

57 58

See Sivard and Lundholm (2013). See Hammer (2014).

9.4 Learning Factory Concept Variations of Learning Factories …

353

“3DEXPERIENCE” software provided by Dassault Systèmes.59 This virtual environment mirrors the physical factory environment, ensuring a congruent representation. The virtual environment seamlessly integrates process, product, and resource design, along with corresponding simulations, all within a single platform. The virtual models created within this learning factory environment complement the physical setting, allowing for pre-implementation testing and validation of system changes and design modifications. By using the virtual environment, researchers and practitioners can assess the impact of proposed changes, optimise system performance, and mitigate potential risks before implementing them in the physical setting. The Werk150 is detailed in the Best Practice Example 46. The Festo Didactic Modular Production System (MPS) learning factory serves as a customisable production system suitable for academic, industrial, and vocational school settings. These learning factories are further enhanced by the implementation of CIROS VR,60 a virtual platform that enables virtual planning, simulation, and optimisation of factory components and interactions. The MPS learning factory provided by Festo Didactic is designed to cater to the specific needs and requirements of educational and industrial environments. It offers a hands-on learning experience that replicates real-world production systems, allowing learners to gain practical knowledge and skills in a controlled environment. To augment the learning experience, the implementation of CIROS VR plays a crucial role. CIROS VR is a virtual platform that enables users to plan, simulate, and optimise factory components and their interactions in a virtual environment. This virtual representation of the production system facilitates a comprehensive analysis of various scenarios and allows for efficient decision-making before implementing changes in the physical factory. By integrating the MPS learning factory with CIROS VR, learners can engage in immersive virtual simulations and explore the consequences of different decisions and actions (Fig. 9.15). To facilitate a learning factory concept that is not constrained by time or location, the IFA (Institute of Factory Automation and Production Systems) at Leibniz Universität Hannover has developed a digital learning game in addition to its physical learning factory.61 This digital learning game provides a virtual learning environment where participants can engage in various activities related to order processing across different company departments. The digital learning game encompasses all the departments involved in order processing, including storage, assembly, transport, and production controlling, which align with the processes conducted in the physical IFALearning Factory. Furthermore, the game includes external suppliers and customers to simulate a realistic business ecosystem. Each participant assumes a specific role within the game, with roles distributed among the participants, typically involving 12–15 individuals.62 The digital learning game serves as a platform for learners to actively participate in the virtual representation of company operations. They 59

See Brenner and Hummel (2016). See Ciros (2016). 61 The physical IFA-learning factory is presented in the Best Practice Example 17. 62 See Görke et al. (2017). 60

354

9 Overview on Potentials and Limitations of Existing Learning Factory …

Fig. 9.15 Real assembly environment and its virtual model of the ESB Logistics Learning Factory (LLF) (Abele et al., 2017a)

can experience and understand the complexities of order processing, supply chain dynamics, and collaboration between different stakeholders. By assuming specific roles, participants gain practical insights into the responsibilities and challenges faced by various departments within a company. On the one hand, the major advantage of these digital and virtual learning factories is that they address various limitations of today’s physical learning factories discussed in Sect. 9.2.

9.4 Learning Factory Concept Variations of Learning Factories …

355

• Low setup and operational costs: Virtual learning factories offer a cost-effective alternative to physical learning factories.63 The expenses associated with infrastructure, equipment, and maintenance are significantly reduced. • Space efficiency: Virtual learning factories require less physical space compared to traditional learning factories, making them suitable for educational institutions with limited resources.64 Still, using VR requires at least 2.5 m × 2.5 m per participant. Assuming 15 participants, an area of 93.75 m2 is needed. • Anytime, anywhere accessibility: Virtual learning factories provide the flexibility of accessing and using the learning environment at any time and from any location, enabling learners to engage in educational activities without geographical constraints.65 • High-speed analysis and simulation: Digital systems used in virtual learning factories allow for rapid analysis and simulation of various processes, enabling learners to explore multiple scenarios and evaluate long-term implications efficiently.66 • Comprehensive value/supply chain execution: CAD models used in virtual learning factories facilitate the execution of all processes along the value or supply chain, allowing learners to gain a holistic understanding of manufacturing operations. • Integration of simulation: Virtual learning factories seamlessly integrate simulation capabilities, enabling learners to simulate and analyse topics with long feedback cycles.67 This enhances the accuracy and effectiveness of the learning experience. • Increased number of turbulences and scenarios: Virtual learning factories provide the opportunity to simulate a wide range of turbulent situations and scenarios, exposing learners to diverse challenges and enhancing their problem-solving skills.68 • Easy implementation of participant ideas: Virtual learning factories offer a favourable environment for implementing participant ideas, as changes and improvements can be more easily implemented in the virtual realm compared to physical factory environments.69 • Integration into larger lectures: Virtual learning factories can be effectively integrated into larger lectures or educational programs, allowing for a more comprehensive and versatile learning experience.70 On the other hand, there are disadvantages and risks using virtual learning factories: 63

Limited resources issues. Space- and cost-related mapping ability issues. 65 Immobility of learning factory approaches. 66 Wiendahl et al. (2003). 67 Time-related mapping ability issues. 68 See Riffelmacher (2013). 69 Solution-related mapping ability issues. 70 Scalability issues of learning factories. 64

356

9 Overview on Potentials and Limitations of Existing Learning Factory …

• Neglecting success factors of classic learning factories: When using virtual learning factories, there is a risk of overlooking the key elements that contribute to the success of traditional learning factories. These include hands-on learning experiences, integration of learners’ active participation, contextualisation of the factory environment, and the opportunity for collaborative learning within groups. Care must be taken to ensure these critical aspects are not neglected in the virtual learning environment. • IT infrastructure investment: The establishment of a digital or virtual learning factory requires significant investment in IT infrastructure. This includes acquiring suitable hardware and software, as well as ensuring the availability of reliable Internet connectivity. The associated costs and efforts must be considered before implementing virtual learning factories. • Implementation and programming efforts: Creating a digital or virtual learning factory involves substantial efforts in terms of programming and integration. Designing and developing a virtual environment that accurately reflects the complexities of manufacturing processes requires meticulous planning and programming skills. Integration of various software tools and systems also demands significant time and resources. • Limitations in tactile and physical engagement: Virtual learning factories may not fully replicate the tactile and physical engagement offered by traditional learning factories. Learners may miss the authentic character of learning situations, which can limit their understanding of practical aspects of manufacturing operations. • Challenges in contextualisation: Virtual learning factories may face challenges in providing a high degree of contextualisation within the factory environment. Creating a realistic and immersive virtual setting that effectively represents the complexities of real-world production environments can be challenging. • Reduced opportunities for collective learning: Virtual learning factories may limit the opportunities for collective learning experiences within groups. The absence of physical proximity and face-to-face interactions may hinder collaborative learning and the exchange of ideas among learners. • Technical constraints and dependencies: Virtual learning factories are dependent on technology infrastructure and software systems. Technical issues such as software bugs, compatibility problems, or hardware limitations can disrupt the learning experience and require technical support, leading to potential delays and frustrations. • Reduced social interaction: Virtual learning factories may lack the same level of social interaction and networking opportunities that are present in physical learning environments. Learners may miss valuable peer-to-peer interactions, collaborative problem-solving, and the building of professional relationships— even in multi-player scenarios because the number of virtual interactions may be limited compared to physical learning factories. • Potential for cognitive overload: Virtual learning environments can present a high cognitive load, particularly for learners who are less comfortable or experienced

9.4 Learning Factory Concept Variations of Learning Factories …

357

with technology. Navigating virtual interfaces, software tools, and complex simulations simultaneously may overwhelm some individuals, affecting their learning outcomes. • Motion sickness: VR environments used in virtual learning factories have the potential to induce motion sickness in some individuals. The sensory disconnect between visual perception and physical movement can lead to feelings of nausea, dizziness, and discomfort, commonly known as motion sickness. This can be particularly problematic for learners who are more susceptible to these symptoms or who spend extended periods of time in virtual environments. An additional overview of the advantages and disadvantages of virtual learning factories compared to the learning factory core concept can be found in Fig. 9.16.

Fig. 9.16 Advantages and disadvantages of digital and virtual learning factories compared to the learning factory core concept

358

9 Overview on Potentials and Limitations of Existing Learning Factory …

Fig. 9.17 Infrastructure and interfaces of physical, digital, and virtual learning factories (Abele et al., 2017a)

9.4.1.3

Hybrid Learning Factories

Digital and virtual learning factories serve as valuable complements to learning factories with physical learning environments, extending the thematic and applicationrelated scope of the learning systems, as described previously. By incorporating digital factories, the perspective of the factory can be broadened to encompass a holistic view without strict limitations on experimentation.71 Physical and digital learning environments contribute to the enhancement and adaptability of the respective factory environment in different ways.72 Furthermore, when physical, digital, and virtual learning factories can be interconnected and their respective strengths leveraged, hybrid learning factories are formed. However, these hybrid learning factories face the challenge of effectively and reliably integrating various data sources to seamlessly overcome media breaks between the real and virtual worlds, resulting in the creation of a unified and merged learning environment. To illustrate the infrastructure and interfaces of the physical, digital, and virtual learning factory, refer to Fig. 9.17. This visual representation demonstrates the interconnectedness and interdependencies between these components. 71 72

Lu et al. (1999). See ElMaraghy et al. (2011), Müller and Horbach (2011).

9.4 Learning Factory Concept Variations of Learning Factories …

359

Fig. 9.18 Interrelation of digital, physical, and hybrid learning factories (Abele et al., 2017a)

Figure 9.18 illustrates the interrelation of the digital, virtual, and physical learning factory concepts, showcasing their integration and coexistence. Lastly, Fig. 9.19 provides a summary of the advantages and disadvantages of the holistic learning factory compared to the core learning factory concept. This overview highlights the benefits and drawbacks of adopting a comprehensive approach to learning factory implementation, considering the combined elements of the physical, digital, and virtual environments.

9.4.2 Remotely Accessible Learning Factories and Teaching Factories The Teaching Factory, as described in this book, is a sub-concept within the framework of the learning factory.73 It serves as a bridge between industrial research and the practical implementation of industrial processes and products.74 The concept of the Teaching Factory is comprehensive and encompasses various sub-learning forms. The underlying idea behind the Teaching Factory concept is to seamlessly integrate the three key components of the knowledge triangle: research, innovation, and education. As part of the KNOW FACT project, the Teaching Factory is implemented as a non-geographically bound space for learning. This concept leverages the latest digital technologies and high-quality industrial didactic equipment.75 In the Teaching Factory, teams consisting of engineers, researchers, and students have 73

See Mavrikios et al. (2013). Chryssolouris et al. (2006), Mavrikios et al. (2013). 75 Chryssolouris et al. (2016). 74

360

9 Overview on Potentials and Limitations of Existing Learning Factory …

Fig. 9.19 Advantages and disadvantages of hybrid learning factories compared to the learning factory core concept

the opportunity to collaborate and engage with industrial and academic sites. By using the available equipment, they can collectively work on real-life problems and seek practical solutions. This bidirectional knowledge communication channel facilitates the exchange of insights, bringing the real-world factories into the classroom and allowing academic laboratories to be integrated into the industrial setting, as depicted in Fig. 9.20.76 For a more detailed description of the Teaching Factory concept, refer to Best Practice Example 16, where its implementation and benefits are further elucidated. Cal Poly State University has implemented a research and Teaching Factory that encompasses a fully functional real factory, alongside a dedicated center for production planning and control. The Teaching Factory is equipped with cutting-edge communication networks to facilitate seamless connectivity. Within this factory, car parts for various original equipment manufacturers (OEMs) such as GM and Ford, as well as their suppliers, are manufactured using high-precision machining equipment. To enhance the learning experience, the Teaching Factory integrates modern learning technologies, including remote learning and online courses. These technologies enable students to participate in industrial projects conducted within the Teaching Factory, offering a highly contextualised learning environment. The emphasis, as with most learning factory approaches, is on the effective development and application of competences. By engaging with this advanced equipment and participating in real-world industrial projects, students acquire practical experience and skills that are directly applicable to their future careers. The research and 76

Rentzos et al. (2014, 2015).

9.4 Learning Factory Concept Variations of Learning Factories …

361

Fig. 9.20 Teaching Factory sessions for factory-to-classroom and lab-to-factory knowledge communication, shown in Abele et al. (2017a)

Teaching Factory at Cal Poly State University provides a comprehensive and immersive learning environment, fostering the acquisition of valuable competences within an industrial setting.77 Kansas State University’s Advanced Manufacturing Institute (AMI) operates a Teaching Factory in the form of a comprehensive service engineering and manufacturing center situated in an industrial park. Within this Teaching Factory, students enhance their traditional academic education by actively engaging in hands-on experiences focused on designing and developing innovative solutions for industrial partners. The primary objective of this Teaching Factory is to integrate research, education, and innovation into a unified activity, fostering strong collaboration between industry and academia. Students gain valuable practical knowledge and skills by collaborating directly with industrial partners, offering their expertise, and creating solutions to address real-world challenges. By immersing themselves in this industryacademia-partnered activity, students bridge the gap between theoretical learning and practical application. The Teaching Factory at the Advanced Manufacturing Institute empowers students to actively contribute to the development of innovative solutions while simultaneously expanding their knowledge and skill set in a real-world manufacturing environment.78

77 78

See Chryssolouris et al. (2013). See Chryssolouris et al. (2006), Tittagala et al. (2008).

362

9 Overview on Potentials and Limitations of Existing Learning Factory …

Fig. 9.21 Advantages and disadvantages of remotely accessible learning and teaching factories compared to the learning factory core concept

Due to the wide range of possible implementations79 within the Teaching Factory concept, it is difficult to identify specific advantages and disadvantages compared to the core concept of the learning factory. However, Fig. 9.21 provides an overview of the advantages and disadvantages of remote teaching and learning factories compared to the core concept of the learning factory.

9.5 Wrap-Up of This Chapter This chapter presents an overview of various concept variations of learning factories, both in a narrow and broader sense. It discusses the advantages and disadvantages of these concept variations compared to the learning factory core concept. Additionally, the chapter provides examples from existing learning factories to illustrate the different concept variations.

79

For example, remote/on-site learning, training/education/research, etc.

9.5 Wrap-Up of This Chapter

363

The operation of learning factories aims to achieve three main objectives: effectively developing and motivating learners, facilitating practical innovations, and transferring competences and innovations to the industry. Various learning concepts, such as problem-based learning, project-based learning, and researchbased learning, can be effectively implemented in learning factories. Learning processes within learning factories, such as information assimilation and experiential learning, contribute to effective knowledge acquisition. Success factors in learning factories include learner feedback cycles, contextualisation of the learning environment, learner activation, problem-solving processes, motivation, collectivisation of learning, integration of thinking and doing, and learner self-regulation and self-direction. Learning factories serve as research enablers, supporting the research process from problem identification to solution finding and realisation. Embedded experiments in learning factories offer valuable research opportunities. Learning factories provide a controlled environment for the development and validation of innovative solutions before implementation in industry. They facilitate the transfer of cutting-edge methods and technologies to industry. Establishing learning factories and implementing the learning factory concept face challenges in terms of needed resources, scalability, mapping ability, mobility, and effectiveness. Adequate financial resources, qualified personnel, educational content, equipment, space, and location are required throughout the learning factory life cycle. Insufficient resources can hinder progress. Strategic resource allocation and effective management practices are necessary to overcome these obstacles and promote widespread adoption. The configuration system for learning factories aids in maximising resource utilisation during planning. Addressing these challenges ensures successful implementation and operation of learning factories. The learning factory concept faces limitations in terms of mapping ability, temporal constraints, and solution-related implementation. Learning factories often focus on specific areas or sectors, limiting their ability to capture the complexity of the entire industrial landscape. Higher factory levels and global production systems are challenging to address within traditional learning factories. Temporal gaps between actions and feedback can hinder effective implementation, but simulations can be employed to bridge this gap. Solution-related improvements proposed by learners may face content-related mapping limitations if the learning factory lacks flexibility and adaptability. Collaboration between learning factories, industry partners, and academic institutions, as well as the use of digital technologies and virtual learning environments, can help overcome these limitations and provide a more comprehensive and realistic learning experience. By addressing these challenges, the potential of learning factories can be maximised in industrial education and training.

364

9 Overview on Potentials and Limitations of Existing Learning Factory …

The scalability of learning factory concepts is limited due to the smaller number of learners accommodated compared to traditional lectures. To overcome this limitation, virtual and remote learning technologies can be used, and the design of learning factory facilities can be optimised. Enhancing scalability is essential to reach a larger audience and provide transformative learning experiences. The mobility of learning factories is restricted as they are typically fixed to a specific physical location. To address this limitation, three approaches have been identified: mobile learning factories, virtual learning factories, and teaching factories. These approaches aim to increase flexibility and accessibility to different locations, preserving the hands-on characteristic of physical learning factories or creating an immersive learning environment. The effectiveness of learning factory approaches relies on incorporating competence-oriented learning targets and conducting target-oriented evaluations. By aligning objectives with specific competences, designing modules accordingly, and evaluating their achievement, learning factories can facilitate high-quality competence development. This comprehensive approach ensures learners acquire the necessary skills and knowledge for real-world industrial settings, enabling continuous improvement and refining the learning factory concept. An overview of different variations of the learning factory concept is given, including virtual learning factories, model scale learning factories, physical mobile learning factories, low-cost learning factories, digitally and virtually supported learning factories, and producing learning factories. The learning factory core concept emphasises the integration of practical, hands-on learning within an authentic industrial setting, allowing learners to directly engage with the production process and experience the creation of a tangible product. However, this approach has limitations such as high resource requirements, mapping only certain production processes, and lack of mobility. Model scale learning factories use smaller equivalents of factory equipment that closely resemble their original counterparts but in reduced dimensions. They require less space and financial resources for setup but may sacrifice authenticity. Physical mobile learning factories offer a solution to the immobility of traditional learning factories by providing on-site trainings at company premises. This minimises travel logistics for employees and allows direct access to the organisation’s value creation processes. Low-cost learning factories focus on cost-effective approaches, primarily targeting assembly and logistics processes. The challenge lies in achieving adequate contextualisation while maintaining a realistic learning experience. Digitally and virtually supported learning factories involve the integration of e-learning, multimedia, and ICT tools to enhance the learning experience. This includes separating theoretical input through e-learning, utilising multimedia and ICT support within the learning factory, and extending physical learning environments with virtual components. These variations offer flexibility, personalised learning paths, interactive simulations, computer-based training modules, and online collaboration platforms. Producing learning factories integrate a learning factory with a

9.5 Wrap-Up of This Chapter

365

real production environment, allowing learners to gain hands-on experience in an operational setting. This variation combines the benefits of practical learning with real-world production processes. By understanding the characteristics and implications of these variations, stakeholders can make informed decisions about the most suitable approach for their specific learning objectives and target groups. In addition to the concept variations of learning factories in the narrower sense discussed earlier, this section provides an overview of concept variations in the broader sense of learning factories. These broader variations include digital, virtual, and hybrid learning factories, as well as remotely accessible learning factories and teaching factories. These variations expand the scope and applicability of the learning factory concept, encompassing diverse approaches and implementations that go beyond traditional boundaries. By exploring these broader concept variations, a more comprehensive understanding of the potential and versatility of learning factories can be gained. With the increasing digitisation of production processes, there is a growing emphasis on digital, virtual, and hybrid learning factories in the context of production-related teaching and training. These trends allow for the continuous expansion of both the topics covered in learning factories and the application areas of the factories themselves. Digital and virtual learning factories can be employed in similar subject areas as traditional learning factories but with the added capability of extending learner activities to various planning and simulation tasks. Software tools specifically designed for digital and virtual learning factories offer a range of functionalities that enable learners to engage in realistic simulations, virtual experiments, and collaborative problem-solving activities. These tools provide a platform for learners to explore different scenarios, analyse production processes, and make informed decisions in a virtual environment. Digital and virtual learning factories also offer the advantage of scalability, accommodating a larger number of learners without physical space limitations. It is important to note that digital and virtual learning factories should complement, rather than replace, physical learning environments. The combination of physical and digital elements, known as hybrid learning factories, offers a balanced approach that leverages the benefits of both approaches. Hybrid learning factories provide hands-on experiences in a physical environment while using the flexibility and scalability of digital and virtual tools. Software tools such as VisTable, TaraVRbuilder, 3DEXPERIENCE, and Unity are examples of tools that facilitate the visualisation, analysis, and optimisation of virtual production environments in 3D. These tools empower learners to engage in realistic simulations, explore various production scenarios, and make informed decisions based on the outcomes. Unity, in particular, offers robust features for creating interactive and immersive experiences, making it suitable for programming virtual learning environments. Digital learning factories employ information technology and various IT tools to create digital models that map the processes, resources, and products within

366

9 Overview on Potentials and Limitations of Existing Learning Factory …

a learning factory environment. These digital models serve as comprehensive representations of the factory, enabling a deeper understanding and analysis of its operations. Virtual learning factories use appropriate infrastructure and visual software tools to provide a visual representation of these digital models, often using virtual or augmented reality technologies. Virtual learning factories enhance the quality of educational activities within the manufacturing domain by providing high-quality teaching experiences. Hybrid learning factories combine virtual and physical settings to provide a comprehensive learning experience through the integration of virtual simulations and real-world interactions. Various examples of virtual learning factories include the KTH XPRES Lab, McKinsey & Co.’s virtual 3D learning factory, Reutlingen University’s werk150, and the Festo Didactic Modular Production System (MPS) learning factory. These examples demonstrate the application of virtual environments in manufacturing education, training, research, and innovation transfer. Digital and virtual learning factories offer several advantages, including low setup and operational costs, space efficiency, anytime, anywhere accessibility, high-speed analysis and simulation, comprehensive value/supply chain execution, and integration of simulation capabilities. These advantages address limitations of physical learning factories and provide learners with more flexibility, cost-effectiveness, and immersive learning experiences. Overall, digital, virtual, and hybrid learning factories expand the possibilities for production-related teaching and training, enabling educators and trainers to better prepare learners for the challenges and opportunities of the digitised manufacturing landscape. Figure 9.22 summarises the effects on the current limitations of learning factories based on • concept variations of the learning factory in the narrow sense,80 • concept variations of the learning factory in the broader sense,81 and • additional methods and approaches along the learning factory life cycle.82

80

See Sect. 9.3. See Sect. 9.4. 82 See Chap. 6. 81

9.5 Wrap-Up of This Chapter Fig. 9.22 Effects of concept variations and methods and approaches along the learning factory life cycle on current limitations

367

368

9 Overview on Potentials and Limitations of Existing Learning Factory …

References Abdul-Hadi, G., Abulrub, A. N., Attridge, A., & Williams, M. A. (2011). Virtual reality in engineering education: The future of creative learning. In IEEE global engineering education conference (EDUCON): Learning environments and ecosystems in engineering education (pp. 751–757) Abele, E., Metternich, J., Tisch, M., Chryssolouris, G., Sihn, W., ElMaraghy, H. A., Hummel, V., & Ranz, F. (2015). Learning factories for research, education, and training. In Proceedings of the 5th CIRP-sponsored Conference on Learning Factories, Procedia CIRP (Vol. 32, pp. 1–6). https://doi.org/10.1016/j.procir.2015.02.187 Abele, E., Chryssolouris, G., Sihn, W., Metternich, J., ElMaraghy, H. A., Seliger, G., Sivard, G., ElMaraghy, W., Hummel, V., Tisch, M., & Seifermann, S. (2017a). Learning factories for future oriented research and education in manufacturing. CIRP Annals Manufacturing Technology, 66(2), 803–826. Abele, E., Chryssolouris, G., Sihn, W., Metternich, J., ElMaraghy, H. A., Seliger, G., Sivard, G., ElMaraghy, W., Hummel, V., Tisch, M., & Seifermann, S. (2017b). Learning factories for future oriented research and education in manufacturing. Presentation CIRP STC-O Keynote-Paper, GA 2017. CIRP General Assebly 2017, Lugano, Switzerland Brenner, B., & Hummel, V. (2016). A seamless convergence of the digital and physical factory aiming in personalized product emergence process (PPEP) for smart products within ESB logistics learning factory at Reutlingen University. Procedia CIRP, 54, 227–232. https://doi.org/10. 1016/j.procir.2016.06.108 Cassandras, C., Deng, M., Hu, J.-Q., Panayiotou, C., Vakili, P., & Zhao, C. (2004). Development of a discrete event dynamic systems curriculum using a web-based “real-time” simulated factory. In Proceeding of the 2004 American control conference, Boston, Massachusetts June 30–July 2, 2004 (p. 1307) Celar, S., Turic, M., Dragicevic, S., & Veza, I. (2016). Digital learning factory at FESB: University of split. In XXII nauˇcna i biznis konferencija YU INFO (pp. 1–6) Chi, X., & Spedding, T. A. (2006). A web-based intelligent virtual learning environment for industrial continuous improvement. In IEEE international conference on industrial informatics 2006, Singapore, August 2006 (pp. 1102–1107) Chryssolouris, G., Mavrikios, D., Papakostas, N., & Mourtzis D. (2006). Education in manufacturing technology and science: A view on future challenges and goals. In Proceedings of the International Conference on Manufacturing Science and Technology (ICOMAST 2006), Melaka, Malaysia (pp. 1–4) Chryssolouris, G., Mavrikios, D., & Mourtzis, D. (2013). Manufacturing systems: Skills and competencies for the future. In Proceedings of the 46th CIRP conference on manufacturing systems. Procedia CIRP (Vol. 7, pp. 17–24) Chryssolouris, G., Mavrikios, D., & Rentzos, L. (2016). The teaching factory: A manufacturing education paradigm. In Proceedings of the 49th CIRP conference on manufacturing systems. Procedia CIRP (Vol. 57, pp. 44–48) Chryssolouris, G., Mavrikios, D., Papakostas, N., Mourtzis, D., Michalos, M., & Georgoulias, K. (2008). Digital manufacturing: History, perspectives, and outlook. Journal of Engineering Manufacture, 222(5), 451–462. Ciros. (2016). Ciros: Virtual engineering, virtual learning, virtual reality, service. Retrieved from http://www.ciros-engineering.com/en/home/ Dassault Systèms. (2017). 3D experience platform. Retrieved from https://www.3ds.com Dessouky, M. M. (1998). A virtual factory teaching system in support of manufacturing education. Journal of Engineering Education, 87(4), 459–467. Dessouky, M. M., & Verma, S. (2001). A methodology for developing a web-based factory simulator for manufacturing education. IIE Transactions, 33(3), 167–180. ElMaraghy, H. A., AlGeddawy, T., Azab, A., & ElMaraghy, W. (2011). Change in manufacturing: Research and industrial challenges. In H. A. ElMaraghy (Ed.), Enabling manufacturing

References

369

competitiveness and economic sustainability: Proceedings of the 4th international conference on changeable, agile, reconfigurable and virtual production (CARV2011) (pp. 2–9). Springer ElMaraghy, H. A., & ElMaraghy, W. (2014). Learning factories for manufacturing systems. In Proceedings of the 4th conference on learning factories, Stockholm, Sweden ElMaraghy, H. A., & ElMaraghy, W. (2015). Learning integrated product and manufacturing systems. In Proceedings of the 5th CIRP-sponsored conference on learning factories, Procedia CIRP (Vol. 32, pp. 19–24) Festo Didactic. (2018). Kurse und Simulationen. Retrieved from http://www.festo-didactic.com/ de-de/highlights/connected-learning/tec2screen/kurse-und-simulationen/?fbid=ZGUuZGUuN TQ0LjEzLjEwLjc0MjAuNDMyMw Gadre, A., Cudney, E., & Corns, S. (2011). Model development of a virtual learning environment to enhance lean education. Procedia Computer Science, 6, 100–105. https://doi.org/10.1016/j. procs.2011.08.020 Gausemeier, P., Seidel, J., Riedelsheimer, T., & Seliger, G. (2015). Pathways for sustainable technology development: The case of bicycle mobility in Berlin. In Proceedings of the 12th Global Conference on Sustainable Manufacturing. Procedia CIRP (Vol. 26, pp. 202–207). https://doi. org/10.1016/j.procir.2014.07.164 Goeser, P. T., Johnson, W. M., Hamza-Lup, F. G., & Schaefer, D. (2011). VIEW-a virtual interactive webbased learning environment for engineering. Advances in Engineering Education, 2(3), 1–24. Görke, M., Bellmann, V., Busch, J., & Nyhuis, P. (2017). Employee qualification by digital learning games. Procedia Manufacturing, 9, 229–237. https://doi.org/10.1016/j.promfg.2017.04.040 Haghighi, A., Shariatzadeh, N., Sivard, G., Lundholm, T., & Eriksson, Y. (2014). Digital learning factories: Conceptualization, review and discussion. In The 6th Swedish production symposium (SPS14). Retrieved from http://conferences.chalmers.se/index.php/SPS/SPS14/paper/viewFile/ 1729/401 Hammer, M. (2014). Making operational transformations successful with experiential learning. CIRP collaborative working group: Learning factories for future oriented research and education in manufacturing, CIRP General Assembly, Nantes, France Hummel, V., & Westkämper, E. (2007). Learning factory for advanced industrial engineering: Integrated approach of the digital learning environment and the physical model factory. In Production engineering, Oficyna Wydawnicza Politechniki Wroctawskiej (needs translation): Krakow, Poland (pp. 215–227) Hummel, V., Hyra, K., Ranz, F., & Schuhmacher, J. (2015). Competence development for the holistic design of collaborative work systems in the logistics learning factory. In Proceedings of the 5th CIRP-sponsored conference on learning factories, procedia CIRP (Vol. 32, pp. 76–81). https://doi.org/10.1016/j.procir.2015.02.111 Kesavadas, T. (2013). V-learn-fact: A new approach for teaching manufacturing and design to mechanical engineering students. In ASME 2013 international mechanical engineering congress and exposition (Vol. 5, pp. 1–6) Lanza, G., Moser, E., Stoll, J., & Haefner, B. (2015). Learning factory on global production. In Proceedings of the 5th CIRP-sponsored conference on learning factories, procedia CIRP (Vol. 32, pp. 120–125). https://doi.org/10.1016/j.procir.2015.02.081 Lanza, G., Minges, S., Stoll, J., Moser, E., & Haefner, B. (2016). Integrated and modular didactic and methodological concept for a learning factory. In Proceedings of the 6th CIRP-sponsored conference on learning factories, procedia CIRP (Vol. 54, pp. 136–140). https://doi.org/10. 1016/j.procir.2016.06.107 Lu, S. Y., Shpitalni, M., & Gadh, R. (1999). Virtual and augmented reality technologies for product realization. CIRP Annals Manufacturing Technology, 48(2), 471–495. Manesh, H. F., & Schaefer, D. (2010a). Virtual learning environments for manufacturing education and training. Computers in Education Journal, 147, 77–89. Manesh, H. F., & Schaefer, D. (2010b). A virtual factory approach for design and implementation of agile manufacturing systems. American Society for Engineering Education, 15(111), 1–12.

370

9 Overview on Potentials and Limitations of Existing Learning Factory …

Mavrikios, D., Papakostas, N., Mourtzis, D., & Chryssolouris, G. (2013). On industrial learning and training for the factories of the future: A conceptual, cognitive and technology framework. Journal of Intelligent Manufacturing, 24(3), 473–485. https://doi.org/10.1007/s10845011-0590-9 Müller, E., & Horbach, S. (2011). Building blocks in an experimental and digital factory. In H. A. ElMaraghy (Ed.), Enabling manufacturing competitiveness and economic sustainability: Proceedings of the 4th international conference on changeable, agile, reconfigurable and virtual production (CARV2011) (pp. 592–597). Springer Muschard, B., & Seliger, G. (2015). Realization of a learning environment to promote sustainable value creation in areas with insufficient infrastructure. In Proceedings of the 5th CIRP-sponsored conference on learning factories, procedia CIRP (Vol. 32, pp. 70–75). https://doi.org/10.1016/ j.procir.2015.04.095 Ong, S. K., & Mannan, M. A. (2004). Virtual reality simulations and animations in a web-based interactive manufacturing engineering module. Computers and Education, 43(4), 361–382. https:// doi.org/10.1016/j.compedu.2003.12.001 Pittschellis, R. (2015). Multimedia support for learning factories. In Proceedings of the 5th CIRPsponsored conference on learning factories, procedia CIRP (Vol. 32, pp. 36–40). https://doi. org/10.1016/j.procir.2015.06.001 Rentzos, L., Doukas, M., Mavrikios, D., Mourtzis, D., & Chryssolouris, G. (2014). Integrating manufacturing education with industrial practice using teaching factory paradigm: A construction equipment application. In Proceedings of the 47th CIRP conference on manufacturing systems. Procedia CIRP (Vol. 17, pp. 189–194) Rentzos, L., Mavrikios, D., & Chryssolouris, G. (2015). A two-way knowledge interaction in manufacturing education: The teaching factory. In Proceedings of the 5th CIRP-sponsored conference on learning factories, Procedia CIRP (Vol. 32, pp. 31–35). https://doi.org/10.1016/j.procir.2015. 02.082 Riemann, T., Kreß, A., Roth, L., Metternich, J., & Grell, P. (2021). User-oriented design of virtual reality supported learning factory trainings: Methodology for the generation of suitable design elements. In Proceedings of the conference on learning factories (CLF) Riffelmacher, P. (2013). Konzeption einer Lernfabrik für die variantenreiche Montage. Dissertation, Stuttgart. Stuttgarter Beiträge zur Produktionsforschung: Vol. 15. Fraunhofer Verlag Schuh, G., Gartzen, T., Rodenhauser, T., & Marks, A. (2015). Promoting work-based learning through industry 4.0. In Proceedings of the 5th CIRP-sponsored conference on learning factories, Procedia CIRP (Vol. 32, pp. 82–87). https://doi.org/10.1016/j.procir.2015.02.213 Sivard, G., & Lundholm, T. (2013). XPRES: A digital learning factory for adaptive and sustainable manufacturing of future products. In G. Reinhart, P. Schnellbach, C. Hilgert, & S. L. Frank (Eds.), Proceedings of the 3rd conference on learning factories, Munich: May 7th, 2013 (pp. 132–154). Augsburg Stier, K. W. (2003). Teaching lean manufacturing concepts through project-based learning and simulation. Journal of Industrial Technology, 19(4), 1–6. Tarakos. (2017). S: Virtual made reality. Retrieved from http://www.tarakos.de/ Thiede, B., Posselt, G., Kauffeld, S., & Herrmann, C. (2017). Enhancing learning experience in physical action-orientated learning factories using a virtually extended environment and serious gaming approaches. Procedia Manufacturing, 9, 238–244. https://doi.org/10.1016/j.promfg. 2017.04.042 Tisch, M., & Metternich, J. (2017). Potentials and limits of learning factories in research, innovation transfer, education, and training. In Proceedings of the 7th CIRP-sponsored conference on learning factories. Procedia manufacturing Tisch, M. (2018). Modellbasierte Methodik zur kompetenzorientierten Gestaltung von Lernfabriken für die schlanke Produktion. Dissertation, Darmstadt. Shaker. Tittagala, R., Bramhall, M., & Pettigrew, M. (2008). Teaching engineering in a simulated industrial learning environment: A case study in manufacturing engineering. Engineering Education, Loughborough, England

References

371

Tvenge, N., Martinsen, K., & Kolla, S. V. K. (2016). Combining learning factories and ICT-based situated learning. In Proceedings of the 6th CIRP-sponsored conference on learning factories, Procedia CIRP (Vol. 54, pp. 101–106) VDI. (1993). VDI 2221/Methodik zum Entwickeln und Konstruieren technischer Systeme und Produkte. VDI-Richtlinien. Beuth VisTABLE. (2017a). visTABLE: Innovative Fabrikplanungswerkzeuge. Retrieved from http://www. vistable.de/ VisTABLE. (2017b). visTABLE®touch software. Retrieved from http://www.vistable.de/vistablet ouch-software Watanuki, K., & Kojima, K. (2007). Knowledge acquisition and job training for advanced technical skills using immersive virtual environment. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 1(1), 48–57. https://doi.org/10.1299/jamdsm.1.48 Weidig, C., Menck, N., Winkes, P. A., & Aurich, J. C. (2014). Virtual learning factory on VRsupported factory planning. In Collaborative systems for smart networked environments 15th IFIP WG 5.5 working conferencen virtual enterprises, Amsterdam, Netherlands (pp. 455–462) Wiendahl, H.-P., ElMaraghy, H. A., Nyhuis, P., Zäh, M. F., Wiendahl, H.-H., Duffie, N., & Brieke, M. (2007). Changeable manufacturing: Classification, design and operation. CIRP Annals Manufacturing Technology, 56(2), 783–809. https://doi.org/10.1016/j.cirp.2007.10.003 Wiendahl, H.-P., Harms, T., & Fiebig, C. (2003). Virtual factory design: A new tool for a cooperative planning approach. International Journal of Computer Integrated Manufacturing, 16(7–8), 535–540.

Chapter 10

International Association of Learning Factories

Learning factories are an important support for production-related teaching, research, and training. But which projects or groups are currently dealing with learning factories in different parts of the world? Who offers forums for exchange and mutual support in designing, setting up, and operating learning factories? This chapter provides an overview of past and ongoing projects and groups of the International Association of Learning Factories (IALF). As already described in Sect. 4.1 on the historical development of learning factories, the first isolated, local learning factory approaches have been developed in the 1980s. In the following years, however, only a few learning factories were built up and operated. After an accumulation of learning factories in Europe since 2007, these learning factory operators joined forces to form the first network: The Initiative on European Learning Factories1 and the Conference on Learning Factories2 were born. On this basis, another European wide Network was established with the Netzwerk innovativer Lernfabriken.3 Furthermore, with a CIRP Collaborative Working Group (CWG) on “Learning Factories for Future Oriented Research and Education in Manufacturing”4 the topic was elevated to a worldwide level including a scientific consideration of the topic. Finally, a CIRP Keynote Paper on Learning Factories was presented and published in August 2017 as a result of the intensive work in the CWG, summarising in 25 pages the basics, state of the art, learning factory definitions and variations, and future challenges of learning factories.5 In the year 2017, this worldwide working group dealing with learning factory related topics terminated. As a result, the members of the European Learning Factories Initiative decided to reach out to global partners to have a global platform to share, discuss, 1

See Sect. 10.1. See Sect. 10.2. 3 Network of innovative Learning Factories, see Sect. 10.3. 4 See Sect. 10.4. 5 See Abele et al. (2017a, 2017b). 2

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 E. Abele et al., Learning Factories, https://doi.org/10.1007/978-3-031-46428-7_10

373

374

10 International Association of Learning Factories

Fig. 10.1 Learning factory networks, starting from local efforts in 1980s to a worldwide association

and implement ideas in joint projects. In 2017, the Initiative on European Learning Factories was transformed to the International Association of Learning Factories.6 Since then, partners from all over the world have joined the Association, such as from the USA, Singapore, and South Africa. To promote the exchange of content within the network, 11 working groups on specific topics have been established such as energy and resource efficiency, work-based learning and learning factory design. The development from isolated learning factories to worldwide learning factory networks is visualised in Fig. 10.1. In the following sections, the mentioned groups, projects, and associations are presented, see Fig. 10.2.

6

See Sect. 10.1.

10.1 History of the IALF

375

Fig. 10.2 Structure of projects and groups related to learning factories

Fig. 10.3 Founding members of the Initiative on European Learning Factories (from left to right: Professor Laszlo Monostori, Professor Wilfried Sihn, Professor Friedrich Bleicher, Professorin Vera Hummel, Professor Kurt Matyas, Professor Eberhard Abele, Dr. Thomas Lundholm, Dr. Dimitris Mavrikios, Christian Morawetz, Professor Ivica Veza, Professor Toma Udiljak, Jan Cachay, Professor Bengt Lindberg. Not in the picture: Professor Gunther Reinhart, Professor Pedro Cunha)

10.1 History of the IALF On May 20, 2011, together with the “1st Conference on Learning Factories” that took place in Darmstadt7 the Initiative on European Learning Factories (IELF) was founded as union of several European learning factory operators with the goal to start joint projects as well as improve and disseminate the learning factory concept worldwide. The Initiative on European Learning Factories can be understood as a first European network of Learning Factory experts with a broad expertise in the field of education, training, and applied research (Fig. 10.3). First president of the Initiative in the years from 2011 to 2016 was Prof. Eberhard Abele from PTW, TU Darmstadt. The corresponding research institutes of the founding members of the Initiative for European Learning Factories are listed in Fig. 10.4. 7

See Abele et al. (2011).

376

10 International Association of Learning Factories

Fig. 10.4 Names of the founding members of the Initiative on European Learning Factories in 2011 and their institutes

From 2014 to 2017, the CIRP Collaborative Working Group on learning factories8 was used as a platform for worldwide exchange; in addition to the Europewide exchange inside the IELF. In summer 2016, Prof. Joachim Metternich, also PTW, TU Darmstadt, took over as president of the Initiative on European Learning Factories. With the ending of the CIRP CWG after three years, the members of the IELF decided in 2017 to open up the Initiative for worldwide members in order to establish an enduring worldwide learning factory community. The Initiative was following renamed to “International Association of Learning Factories” (IALF) 8

See Sect. 10.4.

10.2 Mission of the IALF

377

Fig. 10.5 Presidencies in the IALF

with the goal to integrate worldwide learning factory operators and enable a further internationalisation of the learning factory topic. In 2021, Prof. Christian Ramsauer (TU Graz) succeeded Prof. Joachim Metternich (TU Darmstadt) as president of the IALF. One year later in 2022, Prof. Vera Hummel (EBS Reutlingen) became Vice-President, replacing Prof. Wilfried Sihn (TU Vienna), who had held the position since the foundation. In addition, a Presidential Committee was established to advise the President and Vice-President on important issues, as well as a Scientific Committee on scientific matters within the organisation (Fig. 10.5). Table 10.1 summarises the important dates of the IALF including the General Assemblies and new members (Fig. 10.6). Further information on the current history of International Association of Learning Factories can be found online: http://ialf-online.net/.

10.2 Mission of the IALF The current formulation of IALF’s mission is as follows: Mission Statement of the IALF Success-oriented industrial companies have to offer unique products and service bundles to their customers. At the same time, they have to shape their value-adding processes to address actual challenges like digitalisation, global warming, sustainability, etc. To manage the necessary transition processes staff competence is the key enabler. Excellently prepared students, qualified engineers and workers must plan and implement the necessary steps. Qualification processes must be oriented towards these practical requirements. The appropriate learning systems for developing the competences for setting up and operating new production processes are the success factors for the factory of the future. Our mission is to design learning systems in such a way that stakeholders can grasp the complex technical and organisational interrelationships of today’s industrial environment and obtain the competences to systematically improve it. We are convinced that the key to competitiveness more than ever will be

378

10 International Association of Learning Factories

Table 10.1 Important dates of the IALF Date

Description

May 20, 2011

Founding of the Initiative on Learning Factories (IELF) in Darmstadt, Germany

May 9, 2012

General Assembly of the IELF in Vienna, Austria

May 8, 2013

General Assembly of the IELF in Munich, Germany

May 27, 2014

General Assembly of the IELF in Stockholm, Sweden

July 7, 2015

General Assembly of the IELF in Bochum, Germany

June 29, 2016

General Assembly of the IELF in Gjøvik, Norway

April 4, 2017

General Assembly of the IELF in Darmstadt, Germany

April 12, 2018

General Assembly of the IALF in Patras, Greece

March 26, 2019

General Assembly of the IALF in Brauschweig, Germany

April 15, 2020

General Assembly of the IALF in Graz, Austria

July 1, 2021

General Assembly of the IALF in Graz, Austria

New members: • Institute for Machine Tools and Production Technology (IWF), Technical University Braunschweig New members: • Institute of Production Science (wbk), Karlsruhe Institute of technology (KIT), • Norwegian University of Science and Technology (NTNU) New members: • Institut für Textiltechnik, RWTH Aachen New members: • Institut für Innovation und Industrie Management—IIM, Technical University Graz New members: • Purdue Polytechnic Institute, Purdue University, USA • Faculty of Industrial Management, University Malaysia Pahang, Malaysia • Faculty of Mechanical Engineering, Computing and Electrical Engineering, University of Mostar, Bosnia, and Herzegovina • University of Luxumbourg, Luxumbourg New members: • Singapore Institute of Manufacturing Technology, Singapore • McMaster University, Canada • Tongji University, China New members: • University of Alberta, Canada • Free University of Bozen-Bolzano, Italy • University Twente, Netherlands • Politecnico di Milano, Italy (continued)

10.2 Mission of the IALF

379

Table 10.1 (continued) Date

Description

April 13, 2022

General Assembly of the IALF in Singapore

May 9, 2023

General Assembly of the IALF in Reutlingen, Germany

New members: • Aalto University, Finland • University of São Paulo, Brazil

Fig. 10.6 Group picture of the members of the International Association of Learning Factories in Darmstadt, 2017

• the enhancement of competences for technical students and • efficient training and qualification “on the job in industry” with newest processes and developments. We are a group of research institutions which is driven by the idea that classical forms of classroom education in manufacturing, product development, and logistics are no longer appropriate. We believe that lessons and seminars should be supplemented by learning in a realistic environment we call learning factory. Here, course participants can analyse realistic processes for improvement opportunities and deepen experience in the use of methods for continuous improvement. In this way, they are prepared for an industrial or academic career, and they gain self-confidence and the enthusiasm to implement new technologies. We all work on actual challenges for manufacturing and product-developing and integrate newest results into our learning factories and our course programs. Our students and our partners from industrial practice benefit from this. At the same time, we create a link between theory and practice. This new approach to competence development for industrial production requires comprehensive expertise in the fields of manufacturing technology,

380

10 International Association of Learning Factories

production organisation, and digitalisation. This ties up extensive resources at our research institutions in terms of both staff and infrastructure. To be successful in the long term, our work must be based on • exchange of knowledge, modules, solutions in our group and • close cooperation with partners from industry. Only together we can contribute to a successful transforming of our manufacturing industry and our economies! Our members strive for • exchange of knowledge, good practices, and learning modules, • built up common research activities which may be supported by national or international funding agencies or industrial consortiums, • synergies in physical establishment of learning factories, and • leadership through anticipating new technologies and integration in these learning factories. Driven by a strong interdisciplinary and trusted community, we will add unique value to tomorrow’s manufacturing. Let us shape it together!

10.3 Working Groups of the IALF The IALF Working Groups were founded in 2020 to promote the exchange of content and projects within the members of the IALF. For this purpose, joint papers were published by the Working Groups and joint research proposals were submitted and approved. The ongoing working groups with their description is given in Table 10.2.9

10.4 Conferences on Learning Factories (CLF) The conferences on Learning Factories (CLF) are organised each year by a member of the IALF and so far it took place in Darmstadt (2011), Vienna (2012), Munich (2013), Stockholm (2014), Bochum (2015), Gjovik (2016), Darmstadt (2017), Patras (2018), Braunschweig (2019), Graz (2020, 2021), Singapore (2022), and Reutlingen (2023). The conference is growing in popularity and internationality. At the 2017 conference in Darmstadt 150 participants from 18 countries participated in the 2-day conference. Since 2015, the conference has been CIRP-sponsored, which can be seen as an indication of the growing importance of learning factories in manufacturing research. 9

More information and contact persons of the Working Groups can be found at www.ialf-online.net.

10.4 Conferences on Learning Factories (CLF)

381

Table 10.2 Ongoing working groups of the IALF Name of the working group

Description

Virtual and augmented reality

A lot of learning factories are using VR and AR to show new applications in production and product development and to extend their didactical concept. In this working group different concepts and ideas to use VR and AR can be discussed

Sustainability and circular economy in learning factories

This working group is dealing with the overarching goal of sustainability and how it is addressed in learning factories, taking different levels of the term into consideration. In this regard, the concept of circular economy is seen as a key strategy. Topics of the working group and the participating members furthermore include energy and material efficiency

Work-based learning

Industry 4.0’s enabling technologies, especially AI and collaborative robotics, increasingly create skill mismatches on the labour market. Hence, efficient training and learning opportunities for manufacturing workforce is essential to close skill gaps and enable workers to factor into manufacturing transformation effectively. Emerging technologies also introduce new ways of work-based learning such as reciprocal learning. The working group of work-based learning (WbL) aims to foster synergies towards bringing new ideas into reality in learning and smart factories

Human robot collaboration

Application of HRC, quick and easy integration of HRC to workstations, universal integration of one cobot in multiple workstations

AI for manufacturing systems/ • Development and adaptation of AI algorithms for artificial intelligence in manufacturing systems production processes • Process monitoring, control, and anomaly detection • Predictive quality and zero-defect-manufacturing • Digital twins, simulation, and process optimisation • Predictive maintenance and condition monitoring • Application of different AI methods for the manufacturing environment: predictive maintenance, assistance systems, production planning a control, quality control, shopfloor management, etc. Development of new KI-algorithms Digital assistance systems for manual and semi-automatic assembly

• Development and combination of methods for the identification of potentials, • Digital assistance and its combination with artificial intelligence: identifying use cases • Potential quantification (depending on use cases) • Analysis and improvement of the (automated) creation of information material • New methods for analysing workstation, to find a potential for assistance systems, development of new context sensitive and adaptive assistance systems • The assistance of workers in production with cognitive and physical systems is currently studied in various learning factories. In this working group, the concepts and ideas of worker assistance systems can be discussed (continued)

382

10 International Association of Learning Factories

Table 10.2 (continued) Name of the working group

Description

5G in learning factories

5G is changing industrial production and offers great potential for the manufacturing industry. A lot of learning factories are carrying out research and application of 5G. In this working group, different 5G application scenarios focusing on training in learning factory are discussed

Cross Learning Factory Product Production System (CLFPPS)

The aim of this working group is to develop a concept for a “Cross Learning Factory Product Production System (CLFPPS)” including Learning Factory teaching and training modules to foster the cross Learning Factory collaboration with a holistic consideration of the product design and creation processes in production networks

Learning in the digital transformation

Digitalisation is constantly increasing in the manufacturing industry. Besides technological knowledge also transformability and flexibility of workers are required. This working group deals with needed competences in the digital transformation and how they can be taught and measured

Learning factory design

An increasing number of companies and universities is establishing and using learning factories for training, teaching and in research. It is a challenge to design a learning factory in a new context because of the lack of common methodology. A common approach will facilitate the integration of different factors into a specific design. Existing design approaches available today do not consider the new technological development, human aspects, digital needs, or cultural aspects

In 2022, the conference was held for the first time outside Europe in Singapore—a sign of the international spread of learning factories. The dates and topics of the conferences on Learning Factories are listed in Table 10.3. Figure 10.7 gives some impressions of the conferences organised from 2011 to 2018 and Fig. 10.8 of the conferences from 2019 to 2023.

10.5 Past Activities of the IALF Netzwerk innovativer Lernfabriken (Network of Innovative Learning Factories) Initiated inside the Initiative on European Learning Factories (IELF), the project and network “Netzwerk innovativer Lernfabriken” (NIL, Network of innovative learning factories) was funded by the German Academic Exchange Service (DAAD) and the German Federal Ministry of Education and Research (BMBF). Under the lead of Prof. Vera Hummel from ESB Reutlingen national and international members mainly of the IELF participated in the project:

10.5 Past Activities of the IALF

383

Table 10.3 Dates and topics of the CLFs Date

Description

May 19, 2011

1st conference on learning factories in Darmstadt, Germany

May 10, 2012

2nd conference on learning factories in Vienna, Austria

May 7, 2013

3rd conference on learning factories in Munich, Germany

May 28, 2014

4th conference on learning factories in Stockholm, Sweden

July 7, 2015

5th CIRP-sponsored conference on learning factories in Bochum, Germany

June 29, 2016

6th CIRP-sponsored conference on learning factories in Gjøvik, Norway

April 4, 2017

7th CIRP-sponsored conference on learning factories in Darmstadt, Germany

April 12, 2018

8th CIRP-sponsored conference on learning factories in Patras, Greece

March 26, 2019

9th CIRP-sponsored conference on learning factories in Braunschweig, Germany

April 15, 2020

10th CIRP-sponsored online-conference on learning Factories (organised by TU Graz)

Main topics: Learning and competence-building as a competitive factor, learning factories in operational application, leaders as teachers Main topics: Universities, industry, learning and innovation factory of the Vienna University of Technology Main topics: Learning factories for optimisation of energy efficiency, sustainable efficiency in production and logistics through lean learning factories, creating the future with digital learning factories Main topics: Learning factories for optimisation of resource efficiency, Sustainability in production and logistics through lean learning factories, innovation through virtual production in digital learning factories Main topics: Idactical approaches, resource efficiency and sustainability, new learning factory concepts, Industry 4.0 and cyber-physical systems, productivity management and lean production Main topics: Learning in Industry 4.0/cyber-physical manufacturing systems, cooperation, flexibility, transparency in manufacturing education and learning, research-based innovation and learning Main topics: Learning factory concepts, ‘Industrie 4.0’ production systems, ‘Industrie 4.0’ use cases, integration of digital learning, Competence development Main topics: Advanced skills and Competences, learning factory for digitisation and Industrie 4.0, learning factories for training and education Main topics: Learning factories and Industry 4.0 Learning with cyber-physical manufacturing systems, sustainable manufacturing through learning factories, mixed reality, and immersive learning environments, learning approaches and evaluation, and interdisciplinary cooperation

Main topics: Learning factories across the value chain: from innovation to service, Mixed reality in learning factories, workforce agility in learning factories, interdisciplinary education in learning factories July 1, 2021

11th CIRP-sponsored online-conference on learning factories (organised by TU Graz) (continued)

384

10 International Association of Learning Factories

Table 10.3 (continued) Date

Description Main topics: Machine learning and artificial intelligence in learning factories, teaching global value chains in learning factories, sustainability and circular economy in learning factories, 5G and IoT in learning factories, mixed reality in learning factories, learning factory concepts

April 13, 2022

12th CIRP-sponsored conference on learning factories (organised by A*STAR)

May 9, 2023

13th CIRP-sponsored conference on learning factories

Main topics: Mixed reality and immersive learning, learning factory concepts, machine learning and AI in learning factories, cyber-physical production systems in learning factories, 5G and IIoT in learning factories, sustainability and circular economy in learning factories, building learning factories in actual production environment, harnessing synergy in learning factory ecosystem Main topics: Technology and approaches for cyber-physical production networks, digital twins, machines, humans, IoT and simulation, learning factory and Industry 5.0, learning factory concepts and business models, learning and didactics for future work

• ESB Business School, Reutlingen-University, • Institute of Production Management, Technology and Machine Tools (PTW), Technical University Darmstadt, • Royal Institute of Technology, Stockholm, • Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics, University of Patras, • Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, • Institute of Management Science, Vienna University of Technology, • Institute of Production Engineering and Laser Technology, Vienna University of Technology, • Institute for Machine Tools and Industrial Management, Technische Universität Munich, • Center for Integration and process Innovation, The Computer and Automation Research Institute, Hungarian Academy of Sciences, in cooperation with Budapest University of Technology and Economics, • Chair for Production Systems, Ruhr-Universität Bochum, and • Stellenbosch University, South Africa. To improve and use the learning factory concept for educational purposes, the network NIL organised the exchange of ideas as well as stays abroad of students and researchers among ten learning factories in the field of manufacturing education. Nevertheless, the network is not seen as a closed circle, but rather as an open platform that aims to develop collaborations and partnerships. During the four-year project, a total of about 400 scientists and students from seven countries took part in the workshops, exchanges, summer/winter schools and other activities. The stays abroad lasted from a few days to six months. Additionally,

10.5 Past Activities of the IALF

385

1st Conference on Learning Factories, Darmstadt, Germany, 2011

2nd Conference on Learning Factories, Vienna, Austria, 2012

3rd Conference on Learning Factories, Munich, Germany, 2013

4th Conference on Learning Factories, Stockholm, Sweden, 2014

5th CIRP-sponsored Conference on Learning Factories, Bochum, Germany, 2015

6th CIRP-sponsored Conference on Learning Factories, Gjovik, Norway, 2016

7th CIRP-sponsored Conference on Learning Factories, Darmstadt, Germany, 2017

8th CIRP-sponsored Conference on Learning Factories, Patras, Greece, 2018

Fig. 10.7 Impressions of the conferences on learning factories from 2011 to 2018

386

10 International Association of Learning Factories

9th Conference on Learning Factories, Bochum, Germany, 2019

11th Conference on Learning Factories, Graz, Austria, 2020

10th Conference on Learning Factories, Graz, Austria, 2020

12th Conference on Learning Factories, Singapore, 2022

Fig. 10.8 Impressions of the conferences on learning factories from 2019 to 2023

numerous workshops were organised in the learning factories and the participation in relevant conferences was facilitated. In addition, the project released several publications, including.

10.5 Past Activities of the IALF

387

Fig. 10.9 Students in the NIL winter school playing a logistics game (Bauer, 2017)

• three series of papers including articles about the partner learning factories10 • and two high-quality learning videos about line-balancing11 and Industry 4.0 in learning factories.12 The project was successfully concluded with the two-week winter school at the ESB Business School, where students from Greece, Croatia, and South Africa were able to compete in various challenges, expand their knowledge with practice-oriented educational games and gain insights into the automotive industry during company visits.13 Figure 10.9 shows students playing a logistics game during. CIRP Collaborative Working Group on Learning Factories Basically, from the members of the Initiative on European Learning Factories with additional partners of CIRP14 active in the field of learning factories, a CIRP Collaborative Working Group with the title “Learning factories for future-oriented research and education in manufacturing” was agreed on and consequently initiated in 2014. 10

See “The Learning Factory an Annual Edition from the Network of Innovative Learning Factories,” for example Network of innovative Learning Factories (2015) and Network of innovative Learning Factories (2016). 11 The video “Line Balancing: Practical example in a Learning Factory” was shot at the Process Learning Factory CiP in Darmstadt, the video can be found at: https://www.youtube.com/watch? v=PJg1DyZElvc&feature=youtu.be. 12 The video “Industrie 4.0 in Learning Factories” was shot in several learning factories in Reutlingen, Bochum, and Darmstadt, the video can be found at: https://www.youtube.com/watch?v= pg64P0laeTM. 13 See Bauer (2017). 14 The International Academy for Production Engineering.

388

10 International Association of Learning Factories

In connection with the establishment of the CIRP CWG scientific, educational, and industrial objectives have been formulated to organise and boost learning factory activities globally15 : • Scientific objectives – provide a comprehensive overview of the global state of the art of actionoriented learning in learning and teaching factories, – identify potentials and limits of learning and teaching factories, – secure the knowledge gathered in CIRP CWG on learning factories as a basis for future research, and – identify and name future research fields on the topic and potential (inter)national funding programs. • Educational objectives – provide a comprehensive overview of education in learning and teaching factories around the globe and – simplify the exchange of educational and didactical contents among CIRP members. • Industrial objectives – link CIRP closer with the industry in the learning and Teaching Factory area by including industrial efforts on this topic and – raise the visibility of the topic in the industry by providing scientifically sound data. All objectives were tackled in the three years from 2014 to 2017, including wellattended CWG meetings at all General Assemblies and at all winter meetings. Right from the start, the aim was to additionally summarise the results of the CIRP CWG on learning factories in a STC-O Keynote Paper. This Keynote Paper was presented at the General Assembly in Lugano, Switzerland in summer 2017: Abele, E.; Chryssolouris, G.; Sihn, W.; Metternich, J.; ElMaraghy, H.; Seliger, G; Sivard, G.; ElMaraghy, W.; Hummel, V.; Tisch, M.; Seifermann, S. (2017): Learning Factories for future oriented research and education in manufacturing. In: CIRP Annals—Manufacturing Technology 66 (2), 803–826. Beyond the contributors named above as authors of the keynote paper many persons contributed to successful CWG meetings in the three years. Among others, thanks go to the CIRP contributors (in alphabetical order) F. Bleicher, C. Herrmann, A. Jäger, G. Lanza, K. Martinsen, D. Mourtzis, G. Putnik, M. Putz, G. Schuh, K. Schützer, T. Tolio, and J. Vancza. As well as to further contributors: J. Bauer; B. Brenner, Y. Gloy, M. Hammer, D. Mavrikios, J. Menn, G. Michalos, E. Moser, B. Muschard, R. Pittschellis, F. Ranz, C. Reise, and K. Tracht. At the end of the CIRP CWG, it can be stated that there has been great progress and tremendous development in recent years, including learning factory networks and 15

See Abele et al. (2014).

References

389

joint research projects in industry and academia. Learning factories have established themselves as valuable tools for educating students, training workers, but also for production-related research and innovation. Even after these developments, a great potential for synergies in the field of learning factories is seen; in this respect, e.g., an exchange of learning factory modules and joint courses can be mentioned. For the coming years, the main challenges identified are to keep up with or even get ahead of industrial innovation—and—to combine digital and virtual learning factories with real learning factories. To address the problem of scarce resources related to building and operating learning factories, it will be critical in the future to work together on challenges in the topic of learning factory. The International Association on Learning Factories was founded as a platform for this.16

10.6 Wrap-Up of This Chapter In this chapter provides an overview of the various associations, working groups, projects, and initiatives that aim to promote, improve, and disseminate learning factories as a practical form of education, training, and research for manufacturing. The basic insights are • The International Association of Learning Factories (IALF) represents the world’s largest initiative for the dissemination and further development of learning factories. The topic-specific exchange takes place in Working Groups. • The Conference on Learning Factories (CLF) is the world’s largest scientific conference on new findings from and around learning factories and is organised each year by a member of IALF. • In the past, exchange formats of students and researchers were organised in the NIL network of learning factory-operating research institutes for four years. • The CIRP Collaborative Working Group purposefully wrote a summary keynote paper summarising the current state on learning factories up to 2017.

References Abele, E., Cachay, J., Heb, A., & Scheibner, S. (Eds.). (2011). 1st Conference on Learning Factories, Darmstadt. Institute of Production Management, Technology and Machine Tools (PTW). Abele, E., Chryssolouris, G., Sihn, W., Metternich, J., ElMaraghy, H. A., Seliger, G., Sivard, G., ElMaraghy, W., Hummel, V., Tisch, M., & Seifermann, S. (2017a). Learning factories for future oriented research and education in manufacturing. CIRP Annals—Manufacturing Technology, 66(2), 803–826.

16

See Sect. 10.1.

390

10 International Association of Learning Factories

Abele, E., Chryssolouris, G., Sihn, W., Metternich, J., ElMaraghy, H. A., Seliger, G., Sivard, G., ElMaraghy, W., Hummel, V., Tisch, M., & Seifermann, S. (2017b, August). Learning factories for future oriented research and education in manufacturing. Presentation CIRP STC-O Keynote-Paper, GA 2017. CIRP General Assebly 2017, Lugano, Switzerland. Abele, E., Chryssolouris, G., Sihn, W., & Seifermann, S. (2014, January). CIRP Collaborative Working Group—Learning Factories for Future Oriented Research and Education in Manufacturing. CIRP. CIRP Winter Meeting, Paris. Bauer, J. (2017). Netzwerk innovativer Lernfabriken (NIL): Erfolgreicher Projektabschluss mit NIL-Winter School. Retrieved from https://www.esb-business-school.de/fakultaet/aktuelles/det ail/artikel/netzwerk-innovativer-lernfabriken-nil/ Network of Innovative Learning Factories (Ed.). (2015). The learning factory 2015: An annual edition from the network of innovative learning factories. Network of Innovative Learning Factories (Ed.). (2016). The learning factory 2016: An annual edition from the network of innovative learning factories.

Chapter 11

Best Practice Examples

11.1 Overview Best Practice Examples

#

Learning Factory

1

5G Learning Factory at AMTC, Tongji University, China

2

Aalto Factory of the Future at Dept. of Electrical Engineering and Automation, Aalto University, Finland

3

Additive Manufacturing Center (AMC) at TU Darmstadt, Germany

4

A Distributed Learning Factory with a Central Hub (SEPT LF) at McMaster University, Hamilton, Canada

5

Aquaponics 4.0 Learning Factory (AllFactory) at University of Alberta, Canada

6

Demonstration Factory Aachen DFA at WZL & FIR, RWTH Aachen University, Germany

7

Digital Capability Center Aachen lead by ITA Academy GmbH Aachen, Germany

8

Die Lernfabrik at IWF, TU Braunschweig, Germany

9

E|Drive-Center at FAPS, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany

10

ETA-Factory at PTW, TU Darmstadt, Germany

11

Fábrica do Futuro at University of São Paulo (USP), Brazil

12

FIM Learning Factory at Faculty of Industrial Management, Universiti Malaysia Pahang, Malaysia

13

FlowFactory at PTW, TU Darmstadt, Germany

14

Globale Learning Factory at wbk, Karlsruhe Institute of Technology, Karlsruhe, Germany

15

Global McKinsey Innovation & Learning Center Network (ILC)

16

Hybrid Teaching Factory for Personalised Education – Towards Teaching Factory 5.0

17

IFA-Learning Factory, Leibniz University Hannover (LUH), Germany

18

Industry 4.0 Lab at the Politecnico di Milano, Italy

19

LEAD Factory at IIM, TU Graz, Austria (continued)

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 E. Abele et al., Learning Factories, https://doi.org/10.1007/978-3-031-46428-7_11

391

392

11 Best Practice Examples

(continued) #

Learning Factory

20

LEAN-Factory at Fraunhofer IPK, Germany

21

Lean Learning Factory at FESB, University of Split, Croatia

22

Lean School at Faculty of Industrial Engineering, University of Valladolid, Spain

23

Learning and Research Factory (LFF) at the Chair of Production Systems, Ruhr-University Bochum, Germany

24

Learning Factory (CUBE) at the Department of Design, Production and Management (Faculty of Engineering Technology), University of Twente, Enschede, The Netherlands

25

Learning Factory jumpING at Heilbronn University, Germany

26

Learning Factory of advanced Industrial Engineering aIE (LF aIE) at IFF, University of Stuttgart, Germany

27

Learning Factory SUM Mostar, Bosnia, and Herzegovina

28

Lernfabrik für schlanke Produktion (LSP) at the iwb, Technical University of Munich (TUM), Germany

29

Manufacturing Systems Learning Factory (iFactory) at University of Windsor, Canada

30

Model Factory @ Singapore Institute of Manufacturing Technology, Singapore

31

MPS Lernplattform at Mercedes-Benz AG in Sindelfingen, Germany

32

Operational Excellence at Department of Engineering, University of Luxembourg, Luxembourg

33

Pilotfabrik Industry 4.0 at TU Wien, Austria

34

Process Learning Factory CiP at PTW, TU Darmstadt, Germany

35

Recycling Atelier Augsburg at the Institut für Textiltechnik Augsburg and University Augsburg for applied sciences, Germany

36

SDFS Smart Demonstration Factory Siegen at PROTECH, University Siegen, Germany

37

Smart factory AutFab at h_da, University of Applied Sciences Darmstadt, Germany

38

Smart Factory at SZTAKI (Institute for Computer Science and Control), Budapest, Hungary

39

SmartFactory-KL at the German Research Center for Artificial Intelligence (DFKI), Germany

40

Smart Mini Factory, Free University of Bozen-Bolzano, Italy

41

Stellenbosch Learning Factory (SLF), Department of Industrial Engineering, Stellenbosch University, South Africa

42

SZTAKI Industry 4.0 Learning Factory, Gy˝or, Hungary

43

The Centre for Industry 4.0 at Chair of Business Informatics, esp. Processes and Systems, University of Potsdam, Germany

44

The Learning Factory at Penn State University, Pennsylvania, USA

45

The Purdue Learning Factory Ecosystem—Preparing Future Engineers, West Lafayette, USA

46

Werk150, ESB Business School, Reutlingen University, Germany

11.1 Overview Best Practice Examples

393

11.1.1 Best Practice Example 1: 5G Learning Factory at AMTC, Tongji University, China Authors: Ziwei Jiaa , Weimin Zhanga a School of Mechanical Engineering, Tongji University, Shanghai, China

5G Learning Factory Operator:

AMTC, Tongji University

Year of inauguration:

2019

Floor space:

300 m2

Manufactured product(s):

Hydraulic valve, Turbine blade

Main topics / learning content:

Industrie 4.0

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

Top

Semi-skilled workers

Unskilled workers

Management

…

Design

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

394

11 Best Practice Examples

Overall Goal 5G is changing industrial production and offers great potential for the manufacturing industry. Industrial practitioners should learn more about 5G technologies, close their knowledge gap between operation technology (OT) and 5G information technology (IT) and make better use of the advantages of 5G in industrial production. The 5G Learning Factory was established immediately after the 5G commercialisation in China in 2019. 5G full connectivity of the whole learning factory has been realised in 2020. In the 5G Learning Factory, the operators are committed to the research on the fusion of 5G and industrial communications. In recent years, different 5G application scenarios focusing on training in the 5G Learning Factory are implemented including but not limited to intelligent manufacturing, plug-and-play application, Industrial Internet of Things, and other applications related to Industry 4.0. Equipment and Products In the Tongji University Advanced Manufacturing Technology Center (AMTC), research is carried out related to engineering practice around machine tool equipment, production process automation, and industrial communication. The projects have received investments and established close relationships on co-research from manufacturing companies such as SYMG, Bosch Rexroth Group, Schaeffler Group, Siemens, and DMG. In AMTC, part of the production line and equipment is used to build a 5G Learning Factory. By 2022, the learning factory includes three machines (named M1.4, DMG five-axis machining center, and EMCO), an assembly line, three robots, a coordinate measuring machine (CMM), flexible manufacturing cells (i5Block), and other computing equipment (see Fig. 11.1). A production demonstration line consists of processing equipment such as DMG, ABB robot and a Bosch Rexroth automatic line. It is used for the research and display of multiple technologies such as intelligent manufacturing/assembling, wireless sensor networks, device data acquisition and digital twinning for Rexroth hydraulic valves. All devices are interconnected through a 5G network. Most device controllers have built-in OPC UA servers, which can be directly integrated into the 5G network with a gateway or adapter connected to the Ethernet port. Other protocols, such as NCLink (used by M1.4) or devices without communication capabilities (CMM, assembly line), require protocol stack conversion, or install external sensors to build a unified protocol platform. The monitoring center is developed based on the iSESOL Industrial Internet of Things platform. Various machine tool communication protocols are converged to the cloud, and students can subscribe and monitor the status of machine tools in real time through mobile apps or web. With the support of a telecom provider, end-to-end

11.1 Overview Best Practice Examples

395 5G Base Station

DMG

Monitor

EMCO Robots

M1.4 CMM Assembly line

Fig. 11.1 Value stream of the 5G Learning Factory

communication is opened up to ensure transmission of critical data, with which a non-public 5G local area network (LAN) is established. Students in the area of the learning factory have automatic access to factory resources through the 5G LAN. The one-way latency of 5G LAN end-to-end communication is at milliseconds level due to the advantages of dedicated network and license frequency bands. Beyond the 5G LAN, a server runs in the cloud for remote access and online education. Operational Concept In the past three years of learning factory training experience, a teaching model has been formed from theoretical learning to practical learning to case studies, as shown in Fig. 11.2, the architecture is divided into two parts, one is the construction of the 5G Learning Factory, and the other is the training methods for students. The fusion of 5G, artificial intelligence (AI) and plug-and-play (PnP) technology has been fully applied to the learning factory and has shown great results, the whole factory can accommodate 30 people in three groups at the same time, and as cloud factory goes live, more students can simultaneously take courses with on-offline interacting between the real factory and the virtual environment. Every student has the opportunity to gain hands-on practice and even realise their reasonable ideas.

396

11 Best Practice Examples

Construction of Learning Factory with 5G, AI and Plugand-Play technology 5G Industrial Communication

Artificial Intelligent

Student education program related to 5G application in manufacturing field

Plug-and-Play

Theoretical Learning

Practical Learning

Training Case study

2019 Concept design of integrated 5G and AI LF

2020

2021

2022 now

Identification of machine tools state with 5G & AI

AI-based machine vision assist to hydrogen fuel cells assembly

Visualization of machine vision design process

ABCD Training Principal

5G PnP Application in LF

Cloud-based 5G LF for multiple learning methods

VR and 5G in energy management

Teach students to develop 5G-based PnP using modular units

On-offline collaborate: Remote PLC & 5G MEC

Self-practice studentsí own idea in LF

Factory 3D modeling

Virtual reality interconnection

Fig. 11.2 Teaching model in the 5G Learning Factory

Theoretical learning: The primary purpose of theoretical learning is to provide trainees with a general understanding of how 5G powers manufacturing and the feasibility of 5G applications. In 2020, a minimal set concept is proposed to present students with a specific case about 5G and AI. Besides, The ABCD (AI, big data, cloud, and domain) principle is defined to illustrate that the learning factory has in a minimum set and role-play learning explains how to implement the learning factory program in a more interesting way. 5G-based PnP modular unit design is the evolution of the minimal set concept, and the purpose of the design is to cultivate the ability of process design for specific tasks through the practice of modular unit combination and has a cognition of the flexibility of 5G. In order for students to have a deeper understanding of the 5G technology application in industry, the 5G-based cloud learning factory was proposed in 2021. The factory mirror connected via 5G runs on the edge cloud located in 5G non-public network with ultra-low-latency endto-end communication. Part of the functions in the cloud server are open to students for AI training and testing. Practical learning: The primary purpose of practical learning is to provide students with a platform for hands-on practice and to deepen the understanding of 5G applications. After a series of practice, a group of at least three people is recommended and a maximum of ten people. At present, the learning factory is mainly for the training of undergraduate and graduate students in school. The i5Block modular production line was designed to implement the concept of PnP modular units. The i5Block is composed of lifting wheels, a 5G module, a control system, a supply and clamping interface, quick change fixtures and a device. The modular system consti-

11.2 Best Practice Example 2: Aalto Factory of the Future at Dept. …

397

tutes a series of flexible manufacturing units, which are arranged and combined to form a production line. Students can design the production process according to their own needs and verify their ideas by forming a production line with i5Block modules. 5G technologies are not only an object of study in this scenario, but the tool that interconnects the PnP unit in a modular way. With the wireless characteristics of the 5G network and by interacting in accordance with the positioning module, the movement and tasks of each module can be easily controlled remotely. All modular PnP units, machine tools’ controllers and computers (used as edge/cloud servers) are connected to external 5G modules driven by a Raspberry Pi. Each device is bound to a static 5G LAN IP address, which can be used for device domain name resolution (device mapped to IP). Students can develop their own applications of OPC UA, MQTT, or other IoT protocols on this platform. Cases study training: The primary purpose of practical learning is to cultivate the design ability of students for 5G applications by reconstructing, updating, and improving previous 5G application scenarios. Giving students maximum amount of freedom under reasonable restrictions is the key to learning and comprehension. The modular positioning function, protocol conversion function, AI-based cloud computing, and the real-time data monitoring function in VR and energy management are all completed by students in different groups, and the underlying data exchange is based on 5G transmission.

11.2 Best Practice Example 2: Aalto Factory of the Future at Dept. of Electrical Engineering and Automation, Aalto University, Finland Authors: Udayanto Dwi Atmojoa , Valeriy Vyatkina a Department of Electrical Engineering and Automation, Aalto University

398

11 Best Practice Examples

Aalto Factory of the Future (AFoF) Operator: Year of inauguration:

Dept. Electrical Engineering and Automation, Aalto University, Finland 2017

Floor space:

80 m2

Manufactured product(s):

Battery module and mobile phone replica, cylindrical piece, edible plants (salads, herbs) Flexible Distributed Automation, IEC 61499, Industrie 4.0

Main topics / learning content: Morphology excerpt

Open models

Target industries

Flexible Production System Open public

Job-seeking

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal 2015 was the year when an idea came up on setting up an industrial testbed facility at the Department of Electrical Engineering and Automation, Aalto University, Finland. The main driver for this idea was the appointment of the new professor in Information Technology in Industrial Automation (ITiA), who presented a research view on (hyper)-flexibility in the shopfloor including its automation system, and this industrial testbed will be used as a vehicle for research in this domain. Then, in 2017, the first inception of the testbed facility named Aalto Factory of the Future (AFoF) was inaugurated. The facility was set up with seed funding from the School of Electrical

11.2 Best Practice Example 2: Aalto Factory of the Future at Dept. …

399

Engineering as one of the strategic research investments to attract industrial stakeholders and facilitate state-of-the-art research in Industry 4.0 domain. The facility was initially given a space of approximately 20m2 located in the TUAS building, Maarintie 8, Espoo, Finland. The space initially was able to house the Festo EnAS demonstrator (https://www. energieautark.com/). As the space was quite small, the group lobbied the higher management and in 2019, the facility is moved to the Konemiehentie 2 building across the street from Maarintie 8, and the facility has been there ever since. In 2021, the facility received a sizable industry donation from Schneider Electric valued over 1 million euros in the form of software licenses, control hardware, and educational training. The AFoF facility has been used mainly for postgraduate students and researchers to carry out their research activity, and for teaching and education within a postgraduate project work course framework. Currently, the facility is not yet offering trainings to industry but options are being explored, e.g., through participation in EIT Manufacturing educational projects and explore the possibility to establish an outcome (post-projects) where educational trainings for non-Aalto learners could become available in the future. Equipment and Products After the facility moved to Konemiehentie 2 in 2019, the facility had a bigger space and thus the facility was able to procure and house more equipments. Now, the facility houses various demonstrators including two Festo didactic demonstrators (Festo EnAS and Festo CP Lab), two autonomous guided vehicles (AGVs) with mounted robot arm (MIR 100 with a mounted UR3 robot, and SEIT 100 with a mounted ABB Yumi robot), a vertical farming demonstrator, a video wall, and smaller equipments such as HTC Vive Virtual Reality (VR) goggle. In this facility, three products are produced: cylindrical pieces, mobile phone replica, and also small edible plants (salads, herbs). The Festo EnAS and CP Lab are connected to energy storage modules (battery), which allows additional capabilities to investigate additional degrees of flexibility, in particular related to energy associated with the use of Festo demonstrators and its automation systems. A snapshot showing the space also with highlighted equipments is shown in Fig. 11.3. The AFoF is used usually to replicate some properties of manufacturing processes, e.g., assembly and disassembly. Cylindrical pieces (see Fig. 11.4) are assembled (and disassembled) mostly within the EnAS demonstrator production island, and mobile phone replica is assembled or disassembled mostly within the Festo CP Lab production island, while edible plants are produced within the vertical farming demonstrator production island. The MIR100 or SEIT100 AGV may carry out logistical functions to take the product from the production island and move it elsewhere.

400

11 Best Practice Examples

Fig. 11.3 Aalto Factory of the Future

Fig. 11.4 Cylindrical piece

The cylindrical piece consists of two parts, one is the round-shaped container, and second is a small cylinder piece. The round-shaped container sits on the bottom while the cylinder piece is mounted on top of it. The phone and battery replica (see Fig. 11.5) consists of several components: the base plate, phone or battery module, and top cover. When put on the conveyor, it will put on a black-coloured palette. Edible plants considered are green leafy plants grown in the vertical farming demonstrator. Most often the requirements of the product can be met within one production island; however, flow of materials and pieces and simple processes such as pick and place can be carried out by AGV with robot arm demonstrators. The products which are produced in the facility are not sold; rather they are reused as much as possible for research and education purposes.

11.2 Best Practice Example 2: Aalto Factory of the Future at Dept. …

401

Fig. 11.5 Mobile phone or battery module replica

Operational Concept The underlying concept of Aalto Factory of the Future facility assumes that the shopfloor is highly flexible, from the physical layout, connectivity and the automation system level. The equipment at the facility is arranged in “production islands,” which are distributed across different physical positions in the space. As the physical layout is highly flexible and the production islands are distributed, so is the location of equipments changing from time to time and thus the facility has no fixed physical layout. This flexibility sets the research and educational topics addressed in the facility, which includes but not limited to distributed automation, wireless connectivity for manufacturing and its automation system, mechanisms for on-thefly integration of production islands into specific stream (e.g., plug and play). The facility has been used as a mean to facilitate existing educational courses offered by the university. In particular: 1. The facility offers a playground for a course on project work. This course is held once a year, which teaches students on some methods on how to plan and execute a project and provides opportunities for them to do them in practice. 2. The facility considers the distributed automation technology standard IEC 61499, and this setup has been used as a motivating example, use case to explain and illustrate the concept of distributed automation in the manufacturing sector in a course on industrial automation software system, and also the concept on using formal methods applied to industrial automation software system.

402

11 Best Practice Examples

Fig. 11.6 QR code for the Aalto Factory of the Future

AFoF, in particular the university, does not offer trainings for external stakeholders; however, the group is currently exploring possible options. An option is being explored through funded collaborative projects, in particular, EIT Manufacturing educational projects. As Aalto University is a founding member of EIT Manufacturing, the AFoF facility has been used to support various funded EIT Manufacturing educational projects which helped in keeping up a momentum for sizable educational activities for stakeholders outside of the university (non-Aalto students and professionals). So far, the AFoF facility has been used to produce educational training courses in the area of Industry 4.0, which are or will become available under the EIT Manufacturing learning platform. One example is to develop training courses for companies within the “Self-Made: Self-management and device digitalisation in manufacturing” project, an activity funded by EIT Manufacturing, which later may produce educational courses that may become available for longer term after the project ends. The educational courses that have been designed and are being developed are based on the topics on distributed automation IEC 61499 and ongoing research on human centric production systems. A video that shows the learning factory is available in Fig. 11.6.1 Finally, to conclude the article, the author would like to thank for the support of the “Self-Made: Self-management and device digitalisation in manufacturing” project that received funding from the European Institute of Innovation and Technology Manufacturing (EIT Manufacturing), and the European Union’s Horizon Europe research and innovation programme under grant agreement no. 101057083 (project Zero-SWARM).

1

See also https://www.youtube.com/watch?v=X2fbIzfYZ0Y.

11.3 Best Practice Example 3: Additive Manufacturing Center (AMC) at TU …

403

11.3 Best Practice Example 3: Additive Manufacturing Center (AMC) at TU Darmstadt, Germany Authors: Holger Merschrotha,c , Michael Krämerb,c , Matthias Weigolda,c , Matthias Oechsnerb,c a Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt b Institute for Materials Technology (IfW), TU Darmstadt c Additive Manufacturing Center, TU Darmstadt

Additive Manufacturing Center (AMC) Operator:

Additive Manufacturing Center, TU Darmstadt

Year of inauguration:

2023

Floor space:

1030 m2

Manufactured product(s):

Individual

Main topics / learning content:

Additive manufacturing, Digital process chains

Morphology excerpt

Open models

Target industries

Additve manufacturing Open public

Top

Semi-skilled workers

Unskilled workers

Management

Job-seeking

Design

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Lower

Researcher

Profit-oriented operator

Industrie 4.0

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

404

11 Best Practice Examples

Overall Goal Since 2017 the idea of a learning factory in the field of Additive Manufacturing was discussed between the Institute of Materials Technology (IfW) and the Institute for Production Management, Technology and Machine Tools (PTW). The industry needed new methods, new ways of thinking and new training concepts for their employees. In parallel, an already existing expertise at the TU Darmstadt in the field of Additive Manufacturing and in novel teaching concepts for AM is seen. However, the knowledge was spread all over the university and a central contact point to bundle the competencies was needed. For this reason, a center for technology and knowledge transfer “Additive Manufacturing Center—AMC” was established at the TU Darmstadt, starting its activities in May 2023. Within the AMC, the existing expertise of thirteen institutes (Fig. 11.7) across the TU Darmstadt in the field of digital transformation of mechanical engineering and additive manufacturing is bundled and the existing machines and equipment along the entire process chain—from conception phase at the beginning of the design process to material selection, construction, manufacturing, post-processing, quality control, reliability assessment, and end of life—are brought together in one building. This creates a unique center that forms a bridge between science and industry and will enable in particular medium-sized companies to gain access to the scientific and technological potential of the TU Darmstadt. In this context, the AMC offers professional training courses and hands-on workshops as well as collaborations on project level. It takes up the approach of a learning factory, which has been developed and firmly established in Darmstadt to promote knowledge and technology transfer in the field of AM.

Fig. 11.7 Additive Manufacturing Center and the connected institutes

11.3 Best Practice Example 3: Additive Manufacturing Center (AMC) at TU …

405

Fig. 11.8 Value stream of the Additive Manufacturing Center

Equipment and Products The Additive Manufacturing Center has four laboratories as well as two floors for workshops, interdisciplinary working groups and design thinking meetings. A new building with over 1000 m2 of floor space of which 560 m2 are laboratories was erected to house the different machines and equipment from partner groups for joint use. The building project was co-funded by the Hessian Ministry for Economics through the European Regional Development Fund (ERDF). The laboratories are arranged along the process chain as shown in Fig. 11.8, starting with the powder production, followed by the additive manufacturing laboratory to build up parts. The as-build parts then pass through thermal and mechanical post-processing and are in the last step analysed in the quality control laboratory. As metal powder contamination is safety critical, a special safety concept was developed. To produce application-individual materials, e.g., functional materials or new alloys, a gas atomiser as well as an ultrasonic powder production system are available. The produced powder then can be characterised by the particle size distribution, the chemical composition, and flowability. Both self-produced and industrial materials can be processed using powder bed fusion or directed energy deposition as additive manufacturing principles. Powder bed fusion using a laser beam can be conducted on industrial systems to qualify process and material, but also investigated using a high-temperature system (up to 1200 °C) or a system with a laser wavelength of 530 nm (“green laser”). Besides classical process control approaches, new concepts of part individual process scanning and parameter strategies are available. These new approaches are necessary to unlock the full potential of layer-wise additive manufacturing of complex geometries. Remaining powder is then removed from the parts followed by a thermal and mechanical post-processing. The heat treatment is used to remove thermal-induced stress or to adapt the microstructure. Mechanical processing, e.g., milling, is used to achieve functional surfaces or threads and to improve the surface quality.

406

11 Best Practice Examples

Finally, the manufactured parts can be evaluated by destructive (e.g., tensile testing, hardness measurements, high-temperature creep/fatigue) and non-destructive testing (e.g., computer tomography X-ray diffraction). Gathered data along the value chain allows insights into relations between design, manufacturing, and part quality and can be used for component individual process optimisation. Besides the physical value chain, the digital value chain is addressed. Methodical approaches enable identifying parts or assembly groups with high potential for additive manufacturing. Following, part (re-)design using methods to Design for Additive Manufacturing (DfAM) considers process-induced restrictions and reveals chances to utilise full AM-potentials. Data-driven topology optimisation, local property grading, and lattice structures optimise weight and functionality. Digital pre-processing transfers the design into digital process information, considering the available build volume, necessary support structures, and resulting costs. Process monitoring systems gather information of each layer and allow a parallel process evaluation. Regarding the manufacturing industry, a cooperation with the AMC offers a high added value, especially for small- and medium-sized enterprises. These companies gain access to cutting-edge technology through the AMC in terms of available plant and machine technology as well as accumulated research expertise, which is often not available to small- and medium-sized enterprises for economic and capacity reasons. The AMC addresses equally developers and manufacturers of AM systems, manufacturers of additively manufactured components, and users of these components. An explicit target group also consists of manufacturers of raw materials (e.g., powder manufacturers, compounders) as well as equipment and processes for the pre- and post-treatment of additively manufactured components. In the area of service providers, the AMC will address engineering firms and developers of software solutions along the digital process chain. The individual nature of additive manufacturing processes cannot be represented in one specific application type. Therefore, the focus lies on use cases and multiple examples direct from the participants. Operational Concept The underlying concept is based on academic education: research-based learning. Approaches and methods developed at the university are transferred to industrial applications in basic workshops appropriate for the different target groups as shown in Fig. 11.9. Individual expert teams developed the workshops considering crosscorrelations and knowledge along the value chain.

11.3 Best Practice Example 3: Additive Manufacturing Center (AMC) at TU …

407

PROCESS CHAIN MATERIALS

CONSULTING

SAFETY

Introduction to Certification and Standards in AM

Low-Cost 3D Printing

Occupational Safety in AM

Introduction to DED

AM Post-Processing Technologies

Individual Consulting

Integrating Cellular Structures

AM Process Monitoring using Digital Twins

Metallurgy of AM Components

Topology Optimization

Data-driven PBF-LB/M Process Adaptation

Structural Properties in AM

Design

Processing

Materials in AM

Introduction to AM Design

Introduction to PBF-LB/M

3D-Bioprinting

Methodical AM Product Development

PostProcessing

Management Operatives - Engineering

Online Seminars AM Crash Course

Data Processing in AM AM Management Workshop

Operatives - Technology

Fig. 11.9 Workshop program for qualification along the process chain

Our partners learn how to use and apply these methods onto their individual needs. This way the industry is enabled to think ahead, think new, and solve problems in a methodical manner. Therefore, the offered workshops are considered as basic information and qualification training. Further consulting, collaboration, and co-innovation benefit from this standardised qualification as a starting point. The basic workshops are scheduled once in each quarter. Individual consulting, prototyping, machine renting, and transfer projects are on request. Consulting and bilateral projects can be executed in the industry environment as well as in the AMC. Modern facilities, high safety standards, and innovative concepts, such as design thinking, enable efficient and cooperative work. Today, the Additive Manufacturing Center is an innovative education, training, and research center that connects academia and industry. The capability to address all questions along the additive process chain in one technical center delivers holistic answers to open research topics in this field.

408

11 Best Practice Examples

11.4 Best Practice Example 4: A Distributed Learning Factory with a Central Hub (SEPT LF) at McMaster University, Hamilton, Canada Authors: Ishwar Singha , Dan Centeaa , Mo Elbestawia , Tom Wanyamaa a School of Engineering Practice and Technology (SEPT), McMaster University, Canada

A Distributed Learning Factory with Central Hub (SEPT LF) Operator:

SEPT, McMaster University, Canada

Year of inauguration:

2015

Floor space:

590 m2

Manufactured product(s):

Electronic screwdriver, solenoid valve, MaxIoT training board Industry 4.0, IoT & IIoT, additive manufacturing, smart systems, cyber-physical systems

Main topics / learning content: Morphology excerpt

Open models

Target industries

Additive Manufact. Open public

Job-seeking

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

SME

11.4 Best Practice Example 4: A Distributed Learning Factory with a Central …

409

Overall Goal The idea of establishing a learning factory (LF) at the W Booth School of Engineering Practice and Technology (SEPT), an educational unit in the Faculty of Engineering at McMaster University, came up in 2015 with the purpose of combining four goals: expanding the use of an existing manufacturing lab; developing an infrastructure to study additive manufacturing technologies; testing and implementing digital technologies such as Internet of Things (IoT), Industrial Internet of Things (IIoT), and Industry 4.0; and establishing academic programs related to smart systems. The concept was developed for educational purposes to provide experiential learning for students enrolled in seven undergraduate programs and to allow graduate students to conduct research activities. The LF was also expected to address the upcoming workforce needs of the industry by supplying graduates with knowledge and skills related to digital technologies and by training current semi-skilled and skilled workers and their managers to implement at their workplace Industry 4.0 and smart system concepts. The development of the SEPT LF was funded by a partnership between McMaster University and Mohawk College, two academic institutions located in Hamilton, Ontario, Canada. The funding currently comes from the McMasterMohawk partnership and from national research grants and will come from training industrial employees. Equipment and Products The development of the SEPT LF was carried out in two stages. The first step was establishing the SEPT cyber-physical systems (CPS) learning center with the aim to complement students’ knowledge and abilities by providing technical skills that emphasise the inherent multidisciplinary nature of smart systems and advanced manufacturing. The centre includes a series of specialised learning labs that allow the development of various technical skills needed for production from the concept phase to the final product. The centre is also a modern training facility for specialists from industry who are interested in the advantages of implementing in their facilities digital technologies such as Industry 4.0. The second step, more practical in nature, was the establishment of a learning factory for Industry 4.0 education and applied research. The LF was designed to include machine tools and specialised stations with focus on Industry 4.0, IoT and IIoT that were expected to address the educational, research, and training components of the SEPT CPS Learning Center. Currently, the LF uses several edge-to-cloud applications with integrated hardware and software, messaging software platforms, and manufacturing execution systems (MES). The students and industry employees use the components of the SEPT LF based on the purpose of the educational component, training, or applied research. The focus of the SEPT LF is educating students and training employees in implementing Industry 4.0 elements in production and using IoT/IIoT technologies, the facility needs a solid networking infrastructure. This infrastructure includes access to the standard university-wide wired network and to two wireless networks, one local and one campus-wide. There are two local area networks (LAN) that specifically set up for the LF access. The data center is located in the LF and is also used as the control centre for initiating manufacturing processes using the manufacturing

410

11 Best Practice Examples

execution system (MES) software. Blade servers mounted on a server rack are used for creating 64 virtual machines (VM). Two 24-port managed switches are installed inside LF and inside server room. Two types of hypervisors are available in the LF to create, manage, and monitor virtual machines: These hypervisors run directly on the blade servers and hosts to control the hardware and manage guest operating systems such as Windows and Linux. Several cyber-physical systems (CPS) stations with IIoT Implementation have been designed and built in-house for manufacturing, assembly and testing processes and post-processing activities: manufacturing stations include: 3D metal and plastic printers, 5-axis CNC machine, laser cutter and injection moulding and an electronics station; and post-processing stations include a marking station, assembly stations (one for mechanical and a second one for electronics components), a packaging station, and testing stations for mechanical and for electronic components (Fig. 11.10). The CPS stations are generally used to manufacture, assembly, and test mechatronics systems with mechanical and electronic components. Each of these stations is equipped with a programmable logic controller (PLC) with standard inputs and outputs, communication modules (an IO-Link module for smart sensors, and industrial Ethernet communication interface, a managed switch for secure integration with the enterprise network), electric drives (an AC drive for motor control, a servo drive,

Fig. 11.10 Equipment installed in the SEPT LF

11.4 Best Practice Example 4: A Distributed Learning Factory with a Central …

411

and a servomotor for motion control application), and smart sensors (photoswitch, laser distance, and object detection sensor). Each CPS station is designed to be equipped with a smart camera for inspection, correct orientation, and placement, and a 2D bar code reader with industrial Ethernet capabilities that ensure integration with the PLC. Each CPS station is also equipped with an RFID reader/writer connected to a software system that allows writing and retrieving data directly to or from a database. Furthermore, each manufacturing item, component, or assembly, is expected to be equipped with RFID chips holding all the data relevant to the production of this item. The item itself is carried through different CPS stations and processes. The plan for the near future is to deploy RFID scanners on machines or branches of a conveyor system that will read the data and issue corresponding commands to robots and logistics systems. The SEPT Learning Factory also includes auxiliary stations: a Kanban station, a Parts station, and a Design and system management station. All stations and machine tools included in the SEPT Learning Factory are designed to include various sensors that can independently report errors and statuses to the control system. Some stations also include actuators that receive and process signals. The communication systems used by these sensors and actuators is IO-Link. Each IO-Link device includes a sensor, an actuator, or a combination of both and provides information for Industry 4.0 applications. The SEPT Learning Factory also includes four collaborative and mobile-intelligent robots. One of the robots, mounted on a programmable mobile-intelligent robot system that includes laser scanners and ultrasonic sensors, is designed for pick-and-place services in an autonomous fashion across the entire learning factory space. A similar robot is used on an automated guided vehicle (AGV) for automatic loading and retrieval of parts from the 5-axis CNC machine. A third robot is mounted on a platform for collaborative assembly of parts. The fourth robot is used for assembling electronic components. The first products designed and manufactured using the operation stations described above included an electronics screwdriver, a solenoid valve and MacIoT training board (Fig. 11.11). The design, production, assembly, marking, and testing of these products using the technologies and concepts described above were all carried out by the undergraduate students working in the SEPT LF.

Fig. 11.11 Electronic screwdriver and solenoid valve

412

11 Best Practice Examples

Fig. 11.12 SEPT LF Architecture and SEPT Learning Factory Hub

Operational Concept The SEPT LF, acting as central hub of the CPS Learning Center, includes several specialised labs available to students (Fig. 11.12). Undergraduate and graduate students and industry partners use the facility to learn about Industry 4.0 within an experiential learning environment using manufacturing and assembly line modules and specialised stations for quality testing, marking, and packaging (Fig. 11.12). The LF provides its users an implementation of IoT and IIoT technologies to demonstrate the main characteristics of Industry 4.0 that include vertical networking of production systems; horizontal integration of global value chain networks; end-toend engineering of overall value chain; using high-impact disruptive technologies; and web-based access for all operations from any device and from anywhere. The technology approaches implemented in the SEPT LF are the use of cyberphysical systems, the digitisation of the production line using Industry 4.0 concepts, the development and use of IoT applications, and the implementation of IIoT applications, as shown in the corners of Fig. 11.12. The specialised laboratory facilities connected to the LF hub are related to the Department’s fields of study. The Automation labs include devices related to process and manufacturing automation. The power and energy lab demonstrate modern approaches like the smart grid. The design, prototyping and simulation lab includes a product life cycle management (PLM) software, major CAD/CAM/CAE software packages, hardware machines for prototyping, and cluster of computers that run various simulation software packages for different fields of specialisation. The robotics lab complements the classical pick-and-place application with collaborative robots and automated guided vehicles (AGV) that carry robots and create automated transfer of parts between machining, assembly, and storage stations. The smart systems lab allows the design, manufacture, assembly, and testing of smart devices, as well as the demonstration and development of various smart systems applications. The automotive lab allows the development

11.4 Best Practice Example 4: A Distributed Learning Factory with a Central …

413

and testing of electrical and hybrid electrical vehicles with different autonomous levels. The controls and mechatronic lab provide a place for the implementation and testing of the electrical, electronic, and control components such as microcontrollers and PLCs used in the applications developed in the other labs. The Additive Manufacturing lab includes several 3D printing devices for metal and plastic used to manufacture components for many types of applications and for conducting research activities in additive manufacturing (AM). The SEPT LF is distributed over several rooms and covers a surface of 590 m2 . The central hub shown in Fig. 11.12 has a surface of 210 m2 , and the associated labs have a surface of 380 m2 . The CPS Learning Center, which is distributed over several rooms and includes the LF, computer clusters and labs where the students can prepare the work that will be accomplished in the LF, has a total surface of 1430 m2 . The SEPT LF is a cutting-edge facility that attracts most of graduate students from the manufacturing programs around Canada and worldwide to foster the development of novel research in the area of modern manufacturing. Compared to conventional manufacturing methods, AM is an advanced modern manufacturing technology that enables the production of complex designs and structures in a very short time with less cost. Several AM stations installed in the LF give opportunities to undergraduate students, graduate students, and industrial partners to do teaching and research activities. The SEPT LF has had two Laser Powder-Bed Fusion (L-PBF), known as Selective Laser Melting (SLM), machines for metal AM. L-PBF is an AM process for processing metal and/or composite powders to produce fully functional products using high-power laser. The LF has been used by experts of AM, including faculty members who taught manufacturing for the last 4 decades and highly qualified graduate students in Canada. The LF created the opportunity to conduct several research studies such as the selection of process parameters in additive manufacturing of aerospace alloys and the effect of thermal properties on residual stresses and mechanical properties of AM parts. Application of SEPT Learning Factory in Teaching The SEPT Learning Factory provides four main learning channels. The first channel is based on two modes. The first mode involves undergraduate students designing and testing automation systems, including PLC/robot programming, workstation tooling, as well as plant-floor and business systems integration. For the second model, graduate students design and optimise the whole production process or design new parts/products and processes. The second learning channel is based on capstone and maters projects. Under this channel, the learning factory supports universityindustry/community partnerships with students designing and implementing reallife projects proposed and supported by industry/community partners. The fourth

414

11 Best Practice Examples

learning channel supported by SEPT Learning Factory is graduate research. But it is the fourth learning channel that is designed to ensure that all students in the School of Engineering Practice and Technology at McMaster University get a chance to use the learning factory. Under this channel, the learning factory facilities are integrated with the regular laboratory facilities. Integration of SEPT LF with Traditional Teaching Laboratories Establishing a learning factory is an expensive venture. Therefore, if usage of a learning factory is low, its return on investment is low as well. SEPT Learning Factory is designed to be mass education facility where students from all faculties at McMaster University can train in areas such as supply chain integration and management, quality control, plant adaptability, predictive maintenance, industrial systems integration, IoT devices and networks, and energy management and optimisation and all with respect to the Industry 4.0 and smart manufacturing paradigm. To achieve this design objective, the following has been done (Fig. 11.13): • Integrated the LF facilities with the traditional laboratory facilities. • Implemented infrastructure for flexible manufacturing. • Developed a real-life demonstration product which is created from design to production.

Fig. 11.13 Focusing on products assembly reduced the cost of running the W. Booth Learning Factory

11.4 Best Practice Example 4: A Distributed Learning Factory with a Central …

415

Integrating the factory with teaching laboratories increased usage. Having a reallife product of a walking cane created hardware, software and networking infrastructure that supports effective delivery of Industry 4.0 technologies competence-based learning modules. Integrating the learning factory and laboratory facilities has allowed us to deploy either the learning factory or the laboratory facilities where they are most effective. For example, the competence-based learning modules on industrial systems integration begin with students working on horizontal industrial integration using laboratory equipment fitted with PLCs, remote I/Os, and HMIs. After student wire and program, the equipment to create workstations, and group control systems, they use the learning factory facilities to implement a plant vertical integration that integrates the production floor equipment with the manufacturing execution system (MES). Other example is to optimise the use of the learning factory, and laboratory facilities are in the vibrations-based machine condition monitoring modules. Here, student place smart vibration sensors on various SEPT Learning Factory machines and program the sensors to send the data to remote computers in a laboratory, or to cloud servers (Fig. 11.14). From the laboratory, implement the machine condition monitoring applications is utilising data from the smart sensors in real time.

Fig. 11.14 Laboratory and learning factory infrastructure for teaching vibrations-based machine condition monitoring competence-based learning modules

416

11 Best Practice Examples

11.5 Best Practice Example 5: Aquaponics 4.0 Learning Factory (AllFactory) at University of Alberta, Canada Author: Rafiq Ahmada a Aquaponics 4.0 Learning Factory (AllFactory), Department of Mechanical Engineering

Aquaponics 4.0 Learning Factory (AllFactory), University of Alberta Operator:

AllFactory, University of Alberta, Canada

Year of inauguration:

2016

Floor space:

500 m2

Manufactured product(s):

Fish and Plants Production, Production Systems

Main topics / learning content:

Aquaponics, Industry 4.0, Robotics, AI in vertical farming, Data modeling

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

Agriculture

11.5 Best Practice Example 5: Aquaponics 4.0 Learning Factory (AllFactory) …

417

Overall Goal The Aquaponics 4.0 Learning Factory (AllFactory), started in 2016 by Prof. Rafiq Ahmad at the University of Alberta, provides a unique Factory-in-a-lab transdisciplinary research, education/training, and learning environment to investigate Aquaponics systems. Aquaponics is a vertical indoor farming method and a new sustainable farming technique that uses stacked layers of plants to improve plant yield per unit area while reducing required resources. Compared to conventional farming, vertical farming can result in a 70% reduction in water and fertiliser consumption per plant produced. Aquaponics combines aquaculture (farming of fish) and hydroponics (growing plants without soil), a technique to grow plants with aquaculture effluent. This technique claims to have a high water efficiency, is pesticide-free, and reduces the use of fertilisers. All in all, this technology is considered green and sustainable. The Aquaponics learning factory revolves around the production systems focusing on vertical farming and, more specifically, sustainable, environmentally friendly Aquaponics 4.0 systems, process engineering, and education. The target systems provide effective designs, tools, and methods for the growth of various selected crops, i.e., lettuce and/or basil, in an automated Aquaponic system that is distributed between two locations, i.e., dry and wet labs. The Aquaponics Learning Factory enables high-end research in engineering, science, and education, as well as automation, IT, production, and systems in the Aquaponics domain. The research focuses on the area of novel automation, and Industry 4.0 applications to the Aquaponics concept are enabled using the learning factory concept proposed. Technologies, such as digital twins, IoT networks, intelligent decision-making, sustainability, robotics, knowledge modelling, software, and autonomous systems, can be developed, tested, and validated in this environment. Systems are developed using image-processing techniques, deep learning, and regression analysis to estimate the size of the crops as they grow using image segmentation. Different models are such as for the correlation between the size of the crops and their fresh weight estimation. Also, a framework is designed that involves the creation of a wireless sensing module that uses a pH, electroconductivity, water temperature, light resistor, air humidity, and air temperature sensor. Furthermore, ontology models are developed to connect the database of storing and linking the information to a quality assessment tool that can be used for future smart applications towards the feasibility of Aquaponics at a commercial scale.

418

11 Best Practice Examples

The AllFactory is primarily used for educating graduate and undergraduate students. Graduate students get their education and training through the technology discussed above through various experiential and gamification methods. Graduate students, schoolteachers, and industry personnel training are planned in full or halfday workshops. The undergraduate students are involved through an undergraduate permaculture group that operates from this laboratory. The permaculture group provides hands-on training and experience to the group members and dissemination for high school students to motivate them to make engineering a profession. The operators of the learning factory are working on full-semester modules for this education and training. Equipment and Products The AllFactory at the University of Alberta (U of A) involves four different processes: seeding, transportation, growing, and harvesting. The seeding and harvesting processes occur in the dry lab (location #1), whereas the growth stage happens in a wet laboratory (location #2). A schema of the laboratories and processes can be found in Fig. 11.15. This provides an exciting opportunity for education and training in two distinct setups working for the same production process in both real and virtual environments. These four processes represent the entirety of any industrial Aquaponics system connected through an autonomous robotic transportation system and autonomous engineering systems. The Aquaponics 4.0 learning factory is used to produce fish and plants within the same symbiotic environment, as shown in Fig. 11.16. The initial focus is on leafy green, Talapia, and Goldfish. It is planned to integrate further plants and fish within the same environment. Operational Concept The transdisciplinary approach to Aquaponics system design and education prepared for this learning factory based on Bloom’s taxonomy allows for a comparative analysis between serious games (gamification), experiential learning, and cognitive sciences. These tasks will be performed by professors, industry professionals, schoolteachers, and groups of students alike. The proposed learning factory has several principal didactical components: (1) students gain firsthand experience regarding automation, robotics, autonomous systems and SLAM, intelligent logistics, monitoring, and IoT technologies, and applied Industry 4.0 principles; and (2) students experience applying lean principles, such as just-in-time (JIT), value stream mapping (VSM), or Kaizen, among others, to analyse the various and diverse (automated, semi-automated, and manual) processes available.

11.5 Best Practice Example 5: Aquaponics 4.0 Learning Factory (AllFactory) …

419

Fig. 11.15 Production systems, factory design, and virtual models in AllFactory, University of Alberta

The Aquaponics Learning Factory, AllFactory, is also meant to be used as a training facility for the Aquaponics industry. For a manual, labor-intensive industry, this learning factory allows professionals to train on different Industry 4.0 technologies such as automation, digital twins, connectivity, robotics, and new management approaches to the Aquaponics concept. The proposed system shall train and educate personnel to develop low-cost (conventional) and highly productive (novel, Aquaponics 4.0) systems.

420

11 Best Practice Examples

Fig. 11.16 Fish and leafy green plants production at AllFactory

The Aquaponics 4.0 Learning Factory is the westernmost learning factory in North America, centred around unique transdisciplinary research and education topics such as connecting science, engineering, IT, and society, e.g., related to fishing, vertical farming, engineering, or agriculture and nature. This non-conventional concept of a learning factory hopes to open this teaching and research concepts beyond the manufacturing environment.

11.6 Best Practice Example 6: Demonstration Factory Aachen DFA at WZL & FIR, RWTH Aachen University, Germany Authors: Günther Schuha,b , Seth Schmitza , Jan Maetschkea , Tim Hommena , Sebastian Junglasb a Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University b Institute for Industrial Management (FIR), RWTH Aachen University

11.6 Best Practice Example 6: Demonstration Factory Aachen DFA …

421

Demonstration Factory Aachen DFA Demonstrationsfabrik Aachen GmbH Operator:

WZL & FIR, RWTH Aachen University

Year of inauguration:

2012

Floor space:

1,600 m2

Manufactured product(s):

Sheet metal assemblies, welded assemblies

Main topics / learning content:

Lean production, Industrie 4.0, Re-assembly/Re-manufacturing

Morphology excerpt

Open models

Target industries

Circular Production Open public

Job-seeking

Design Self-employed

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research Lean production

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Middle

Partnership

Technical expert

Industrie 4.0

Business model

Student assistant

Profit-oriented operator

Lower

Researcher

Skilled workers

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal Since 2012, the Demonstration Factory Aachen (DFA) allows researchers, industry, and visitors to experience and investigate topics of Industry 4.0, Lean Production and Re-Manufacturing in real life. As part of the Smart Logistic Cluster, the Demonstration Factory Aachen is affiliated to the RWTH Aachen Campus and connected to its infrastructure. The Smart Logistic Cluster focuses on interdisciplinary research at the intersection of production, logistics, and services. The DFA provides the infrastructure to enable scientists and companies from different disciplines to collaborate on new topics in piloting use cases. Thus, the network of companies enrolled on RWTH

422

11 Best Practice Examples

Demonstration Environment • Environment for I4.0, Lean Production,

I4.0 and Re-Manufacturing solutions • Tours, seminars, trainings, ...

Contract Manufacturing • Make-to-order & small batch production • Laser cutting, welding, assembly

3D-printing, ...

Piloting Partnership • Piloting of company-specific I4.0, Lean &

Re-Manufacturing solutions • Experience exchange, infrastructure, ...

Fig. 11.17 Service portfolio

Aachen Campus can participate in the latest developments in production technology and other research disciplines by accessing the DFA. In terms of research, the DFA works closely with the neighbouring institutes, the Laboratory for Machine Tools, and Production Engineering (WZL) of RWTH Aachen University and the Institute for Industrial Management (FIR) at RWTH Aachen University. In this way, topics from research can be transferred to an industrial environment at an early stage. For example, the DFA is investigating how Circular Economy and Re-Manufacturing can be implemented successfully by piloting and further developing a concrete physical Re-Assembly line. At the same time, the DFA is deliberately organised as a GmbH (limited liability company), so that it operates as an independent company and must therefore be profitable. This constellation allows the DFA to offer a unique portfolio of services, which is composed of the three pillars: demonstration environment, contract manufacturing, and piloting partnership (see Fig. 11.17). The demonstration environment enables the experience and learning of Lean Production, Industry 4.0, and Re-Manufacturing use cases in a real production environment. It offers different formats such as guided tours, trainings, seminars, and certificate courses. The contract manufacturing carried out on the same shopfloor ensures a strong practical relevance for the participants and avoids an isolated learning environment. In manufacturing, the DFA has focused on the area of prototype production and industrialisation and has built up competencies in this area. Finally, as a piloting partner, the DFA provides the infrastructure for companies to pilot company-specific Industry 4.0 solutions by using the ecosystem and the enrolled companies of the RWTH Aachen Campus. Thus, the DFA is constantly enriched with the latest industry-driven use cases developed by these companies and acquires relevant knowledge and skills, which again are transferred into the demonstration formats.

11.6 Best Practice Example 6: Demonstration Factory Aachen DFA …

423

In this way, the DFA represents a unique environment on around 1,600 m2 to make production technology experienceable and learnable. The symbiosis of research and industry ensures practical relevance. Equipment and Products The DFA provides the complete value chain for sheet metal and profile processing, including material handling, manufacturing, and assembly. In the manufacturing area, the DFA is equipped with a laser cutting line as well as other manufacturing technologies, e.g., bending technologies. In addition, the DFA is a certified welding shop according to DIN EN ISO 3834. The DFA has competences in metal inert gas (MIG) and metal active gas (MAG) welding and owns an industrial welding robot, which enables automated welding processes. The digital infrastructure includes various state of the art but also innovative technologies such as real-time locating systems (RTLS), 5G, Wi-Fi 6. In addition, there is a future-proof IT system landscape consisting of enterprise resource planning (ERP), manufacturing execution system (MES), and middleware for testing the integration of various (Internet of Things)assets and processes. The infrastructure of the DFA allows the manufacturing of even complex products. With the increasing importance of changeability and thus mobility, modularity, and scalability, all dimensions—layout, product, technologies, and quantities—can be adapted in the factory. This rapid changeability is reflected in the newly created Re-Manufacturing line. Utilising the accumulated assembly process knowledge, the pilot Re-Assembly line was implemented in a short period of time. In a Re-Assembly process, the products are first disassembled before they are reassembled with replaced or reworked parts. In addition, the Re-Assembly line offers the opportunity to investigate the extent to which digital assembly tools, which are already established in the DFA, can support disassembly processes, and thus contribute to the industrialisation of circular production. Starting in material management, Pick-by-Voice and Pick-by-Light systems help the employee to find required parts with the support of visual and acoustic instructions. RTLS tags enable to monitor the commissioned parts along the value chain. Location and status of all parts are constantly tracked by this indoor-positioning system and transferred to a process mining visualisation. This enables monitoring of the material flows and subsequent analysis. The ERP system uses the current positioning data for generating feedback data (process data, transport, and waiting times), enabling production planning and control in real time. The material is transported with an automated guided vehicle (AGV), which is connected to the local network via 5G. This low-latency technology enables research into safety relevant use cases in the field of driverless transport systems. Further, down the value chain, each assembly station is equipped with a touch screen enabling support by threedimensional assembly instructions. Furthermore, various digital tools record the condition of the component, partly by means of image recognition, and thus, for example, automatically execute a screwdriving operation. To efficiently steer and control the production, a live shopfloor KPI visualisation board is positioned in the

424

11 Best Practice Examples Warehouse

1

Manufacturing 12

Pick-by-Voice

2 Sensor-Based C-Part Mgmt. 3

2 Pick-by-Light

5

AR for AGV

9 10 11

8

9

16 18 20 16

Trolley Tracking Digital Shadow Process Mining Cockpit

14 13

15

Retrofitting

17

Business Application Systems 15 Internet of Production Assembly

19 16

Zone Tracking 5G vs. Wi-Fi 6 Benchmark

12

14 10

11

AR Welding Assistant Infopoint

8

4

6

7 Welding Area

7

3 5

4

6

13 Work Environment Sensing

1

AGV

Energy Monitoring

17 18 19 20

Re-Assembly Line Realtime Location System Smart Assembly Tools AI-based Quality Control Collaborative Robots

Fig. 11.18 Factory layout

factory and supported by data collection. Figure 11.18 provides an overview of the various use cases implemented in the DFA. All the Industry 4.0 technologies in the factory are updated regularly as new findings and developments emerge. The value chain as depicted above shows the current configuration in the learning factory. Operational Concept The DFA stands open for both external firms and student classes. As a hybrid learning factory, it serves the dual purpose of training and research parallel to real production. Around 15.000 participants visit the DFA per year. Five to nine full-time equivalent workers are employed in the learning factory. Visitors of the learning factory may choose from more than ten different standardised and individualised trainings, which are continuously adapted and extended. An example of an educational game offered in the DFA is the so-called Lead time game. In this sensor-based game, players learn the effect of release strategies such as constant work in process (ConWIP) on lead times. Through a simulation based on real feedback data, the players receive digital support and thus experience the benefits of Industry 4.0 technologies. Depending on the type of training, the number of participants varies between 10 and 30 and the duration reaches from one to five days. Researchers or consultants of the RWTH Aachen University lead the practical laboratory courses providing practical and theoretical guidance. Additionally, cognitive, and psychomotoric skills are practised throughout activities. For this purpose, the trainings include various use cases using demonstration and do-it-yourself approaches to transfer knowledge. While the trainings aim at building up technological and methodological competences, research in the factory revolves around the core topics Industry 4.0, prototype construction, Re-Assembly, and industrialisation. Hence, the DFA demonstration factory unites up-to-date research, real production, and training at one place.

11.7 Best Practice Example 7: Digital Capability Center Aachen Led by ITA …

425

11.7 Best Practice Example 7: Digital Capability Center Aachen Led by ITA Academy GmbH Aachen, Germany Authors: Gesine Köppea , Thomas Griesb a ITA Academy GmbH b RWTH Aachen University

Textile Model Factory “Digital Capability Center Aachen” (DCC) Operator:

ITA Academy GmbH & McKinsey&Company

Year of inauguration:

2017

Floor space:

500 m2

Manufactured product(s):

Smart wristband (human machine interaction)

Main topics / learning content:

AI, XR, Digitalization, Lean production

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

426

11 Best Practice Examples

Overall Goal The Digital Capability Center Aachen (DCC) has been at the forefront of digital innovation for the last six years. It was founded by ITA Academy GmbH and McKinsey & Company in 2017 with the objective of bringing together discrete industry, academia, and tech-start-ups to drive digital transformation in diverse manufacturing processes. The DCC is a collaborative centre that enables members to exchange ideas, share knowledge, and collaborate on the development of the latest digital solutions. In the past six years, the DCC has succeeded in drawing in a varied range of members, including both small and large companies, as well as young professionals. These members have worked together on a range of projects aimed at harnessing the power of digital technology to solve complex business and social challenges. The DCC provides a platform for its community to access cutting-edge research and development, as well as collaborate on projects that drive digital innovation. Machine connectivity is a particularly important problem for SMEs in the context of digital transformation. The digital collection, storage, analysis, and subsequent application of process data are frequently uncharted areas. Older equipment, sometimes from different manufacturers, and varied technical conditions provide extra difficulties. Retrofitting appears to be a workable approach in this situation. ITA Academy GmbH is an innovative provider of training courses and development projects in the field of artificial intelligence and machine learning. The company offers a wide range of courses, including AI Parameter Optimisation, Digital Assistance and Adaptive Workstations, Data Handling for Condition Monitoring and Digital Performance Management, and XR Assistance for Logistics, Workflow, and Maintenance. These courses are designed to help professionals in the textile industry stay up to date with the latest advancements in technology and gain the necessary skills to succeed in this rapidly evolving field. Equipment and Products The learning factory demonstrates the production of a textile product: a smart wristband (see Fig. 11.19). The wristband itself is an example for digitisation as it includes an RFID chip. Fig. 11.19 Smart wristband for human–machine interaction. Operators personal log in for smart assistance system and ergonomic workspaces

11.7 Best Practice Example 7: Digital Capability Center Aachen Led by ITA …

427

It includes also various functions regarding the human–machine communication. Based on the employee identification, machines or working stations are automatically adjusted to everyone’s setup when logging in. For example, table heights, orders, working instructions as well as machine rights can be adjusted as they are saved on the wristbands RFID chip. The shopfloor of the centre covers the entire textile value chain to produce a smart wristband. Thereby all stages of the supply chain from order intake towards product development and wristband production are covered. The production of the wristband includes six stages: weaving preparation (warping), additive manufacturing for fabric production (weaving), finishing (heat setting, surface treatment, and digital printing), assembly (cutting, sewing, and packaging), and quality control. At each stage, several digital solutions are demonstrated. To teach clients about digital transformation, the centre covers the most important topics in digital manufacturing, such as AI and sustainability (see Fig. 11.20). This serves industry sectors like textile manufacturing companies, which are facing the paradigm shift towards sustainable and more ecological production. At processes with high energy consumption, and where carbon footprint is relevant, ITA Academy and the DCC community have developed an AI Parameter Optimisation strategy. Based on machine learning algorithms and real-time manufacturing data, a yield, energy, and throughput (YET) analysis and optimisation are applied to reduce the carbon footprint by 13% at DCC Aachen. The potential of digital assistance and adaptive workstations, data handling for condition monitoring, and digital performance management is significant and farreaching. These technologies have the power to revolutionise the way businesses operate, by automating manual processes, optimising performance, and efficiency, and enabling real-time data analysis and decision-making. Furthermore, the use of extended reality (XR) as assistance for logistics, workflow, and maintenance can

IoT-enabled product lifecycle management (closed loop)

Digital operator assistance system Digital factory twin

Real-time tracking and tracing of products, operators, tools and movable assets

Innovation Area Digital Performance Engine Paperless instructions, documentation and reporting

Collaborative robotics

Hands-free pick-by-vision Automated intralogistics

Condition monitoring

Autonomous asset optimization

Predictive and condition based maintenance

Solution categories

Production-integrated product configuration

Digital-enabled end-toend maintenance process

Machine vision based quality inspection Assembly workflow and operator ergonomics monitoring and analysis Real-time work-in-process (WIP) analysis

Digital enabled management (used by e.g. plant manager ) Real-time cycle time analysis

Advanced analytics (used by e.g. quality engineer)

Digital ways of working (used by e.g. operator)

Integrated by an IT/OT architecture

Personalized assembly station

Advanced automation (used by e.g. logistician)

Fig. 11.20 Overview on digital solutions along the textile value chain

Digital product shadow Digital performance management

428

11 Best Practice Examples

greatly improve the accuracy and speed of operations, leading to increased productivity and (travel) cost savings. At the DCC shopfloor, these and further technologies are demonstrated in several production scenarios. Operational Concept ITA Academy offers trainings and seminars for digitisation engineers, application engineers, and future-oriented managing directors from various companies who want to take the next step towards digital production (Fig. 11.21). In the so-called FUTURE WALK participants experience digital production within a shopfloor tour. The “LEAN TRANSFORMATION” workshop aims to teach the most important methods and impact of lean management (e.g., Yamazumi and line balancing, Gemba—Root cause problem-solving and Performance board and KPIs to track targets and enhance performance). To learn about “DIGITAL TRANSFORMATION” ITA Academy offers a 1-day workshop in the topics of. • • • •

Digitalisation classroom training Discovering deficits in analogue production (Lean training) Experience digital production (Shopfloor tour) Flexible module selection.

DIGITAL LEADER

DIGITAL TRANSFORMATION

LEAN TRANSFORMATION

FUTURE WALK

Fig. 11.21 Modular system for workshops at the DCC Aachen

11.7 Best Practice Example 7: Digital Capability Center Aachen Led by ITA …

429

Yield energy throughput optimization

Extended reality experience

Digital business models & change management

Data acquisition & artificial intelligence

Sensor technologies for the textile industry

Smart textiles

Value stream mapping

Open innovation & project management

Automation & robotics

Fig. 11.22 Excerpt from training portfolio: clients can choose their individual training modules

For extended training needs, the “DIGITAL LEADER” workshop gives a comprehensive insight into various topics (Fig. 11.22): ITA Academy supports and consults clients during their digital transformation process from the first training to the successful implementation in your processes. According to the client’s digitalisation level and needs, the consultants provide several approaches and methods to enhance the capability building of the client’s expertise. For clients with little prior knowledge of digitalisation, the consultancy starts with basic training in digital manufacturing and a workshop to train clients on the digital culture in their company. For companies with existing digitalisation strategies, the Digital Capability Centre offers a more advanced approach that focuses on supporting the optimisation of manufacturing processes and systems. Therefore, the ITA Academy offers a so-called Potential Analysis 4.0 to identify the needs of customers and the best possible digitalisation strategy (Fig. 11.23). The didactic consulting approach accelerates the strategic engagement with cutting-edge technologies such as machine learning, artificial intelligence, and augmented reality and helps companies to integrate them into their existing digitalisation strategy. ITA Academy’s team of textile engineers, developers and data scientists work with each client to identify their specific needs and develop tailored training programmes that maximise the impact of digital transformation.

Online remote or on-site in Aachen for up to 20 participants.

DIGITAL TRANSFORMATION WORKSHOP

With 9 advanced modules - online remote or on-site in Aachen for up to 20 participants.

DIGITAL LEADER WORKSHOP

Analysis of your processes and equipment in your plant with regard to digital maturity and digitization potential. The results are project proposals.

POTENTIAL ANALYSIS

Basics of digitization

In the basic workshop, participants learn what potential digitization offers for their own company.

Digital culture | Organization

Using lean methods, engineers from DCC Aachen uncover concrete digitization potential in your processes.

Identifying benefits

The first digital solutions are being implemented in the company with a pilot project.

Pilot project

In order to successfully shape the digital transformation, the results of the analysis are implemented in the company on a sustainable basis.

Implementation at the customer

In a 3-hour training course, participants learn the basics of digitization.

Around the topics of supply chain management, life cycle assessment and reduction of the carbon footprint. Implementation of state of the art solutions around the topics of machine connectivity, artificial intelligence and work 4.0, as well as development of customized solutions in the areas of predictive maintenance, virtual and augmented reality (VR/AR), machine learning and much more.

RESEARCH AND DEVELOPMENT PROJECTS

430 11 Best Practice Examples

Fig. 11.23 Didactic approach of ITA Academy’s consulting model: identify clients digitisation level and create customised project approaches

11.8 Best Practice Example 8: Die Lernfabrik at IWF, TU Braunschweig …

431

11.8 Best Practice Example 8: Die Lernfabrik at IWF, TU Braunschweig, Germany Authors: Gerrit Posselta , Max Jurascheka , Christoph Herrmanna a Institute of Machine Tools and Production Technology (IWF), Technische Universität Braunschweig

Die Lernfabrik Operator:

IWF, TU Braunschweig

Year of inauguration:

2012

Floor space:

450 m2

Manufactured product(s):

Diverse

Main topics / learning content:

Sustainable production, Cyber-physical production system, Urban production

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

432

11 Best Practice Examples

Overall Goal The development of new research questions within current research works and the transfer of knowledge from methods and tools into teaching, training and industry is of particular importance for creating positive impact in the context of sustainable manufacturing. For this purpose, “Die Lernfabrik” was established as an appropriate platform in 2012 at the Chair of Sustainable Manufacturing and Life Cycle engineering of Technische Universität Braunschweig. The original motivation for the concept of the learning factory arose in 2011 from the question posed by the Ministry of Education and Research as to how the results of research projects can be made accessible to a broad public, in particular manufacturing SMEs, beyond the common duration of these projects. Subsequently, the first thematic focus and as well the focus on research and further education emerged from a joint research project “EnHiPro-Energy- and Auxiliary Material-Optimised Production.” This focus was further expanded by embedding the learning factory in student teaching, which led to the development of innovative teaching concepts for engineering education integrating the infrastructure of the learning factory. For example, future engineers in production and systems engineering are trained in the learning factory in the subject areas “energy efficiency in production engineering,” “sustainable cyber-physical production systems,” and “future production systems.” A scaled production system was developed especially for this purpose in cooperation with Festo Didactic SE, which permits safe, practice-oriented learning and testing. At this point, it was clear that the learning factory had to be given a broader organisational structure. Target group-specific and purpose-specific laboratories were defined and established as visualised in Fig. 11.24 The first is the “research lab” with its focus on research activities and the purpose of serving as a test and pilot environment for new technologies. The second laboratory is the “experience lab,” which core is a scaled production environment, especially for student teaching and the further training of specialists and managers. The third laboratory focuses on technical-industrial training and skilled workers and is called the “education lab.” Equipment and Products The research laboratory focuses on the dissemination of research results and the continuous derivation of new research questions. On an area of more than 400 m2 , innovative research prototypes and tools are developed with partners from industry in a real production environment as an infrastructure close to industry. This means that tests can be carried out both at machine and equipment level and at the factory level, considering all technical building services. In this way, interactions between machines and the technical building services of the factory and the building shell can be made measurable and evaluable.

11.8 Best Practice Example 8: Die Lernfabrik at IWF, TU Braunschweig …

433

Fig. 11.24 Organisational structure and core topics of Die Lernfabrik and its three laboratories

The experience laboratory focuses on the transfer of research methods and tools into the teaching of engineering students and the training of experts. For this purpose, a scaled-down factory system was built in the real-scale factory environment. The model factory consists of a real working modular production system in a process chain from additive manufacturing to product assembly up to scrap recycling. In a safe environment without high electrical voltages and high mechanical forces, learners can define their own research goals and conduct their own experiments to test and deepen their theoretical knowledge in practice. It could be evaluated that through the approach of research-oriented learning, learners reach their admired competences and related knowledge much faster than with conventional teaching methods. As a central institution of the Technical University of Braunschweig, the education laboratory provides technical and commercial training on more than 50 m2 . The trainees learn the basics of metalworking and electrical, pneumatic, and hydraulic circuit construction in an energetically renovated and technologically reequipped workshop. As a predicate of the training, the curricular prescribed contents are extended by the topics of energy and resource efficiency as well as Industrie 4.0. Energy-efficient circuits, mineral oil-free cooling lubricants, and the effective use of compressed air are part of the education lab’s daily business.

434

11 Best Practice Examples

Operational Concept The organisational structure shown in Fig. 11.24 also reflects the diversity of user groups, business models and personnel structure. The research focus is mainly represented by research assistants and doctoral candidates in cooperation with colleagues from collaborative projects from industry. The training focus, on the other hand, calls for more robust structures in personnel as well as in the thematic orientation. In most cases, permanent trainers and scientists work here in the field of further education. The didactical approach is built around the concept of research-oriented learning. In addition to the thematic priorities already mentioned above, “Die Lernfabrik” is also an expert factory for energy transparency and mixed reality in production within the Network SME-Digital. With information events, company talks, workshops and digitisation projects, companies are empowered to implement innovative technologies strategically. Furthermore, “Die Lernfabrik” is active at two international locations. In Singapore, a virtual twin concept with the SIMTech from A*STAR is developed. Joint Master Classes for industry in Southeast Asia and Europe are organised there, and certified specialists are trained. The second anchor point lies in India, at BITS in Pilani. The stated goal here is to strengthen engineering education through practice-oriented training and to make a major contribution to sustainable development. Additionally, the concept of event-based education was developed at Die Lernfabrik. As learning factories can offer an appealing location and useful assets for conducting such technology-oriented and education-based events, learning success and competence development can be achieved by topic-specific event types. At Die Lernfabrik, hackathons and a game jam were successfully organised. The HoloHack hackathon, for instance, aimed at lowering the entry barriers to new mixed reality technologies by enabling learners to experience novel devices they usually do not have access to in a factory environment. In a three-day event, participants were challenged to develop applications within the scope of sustainable urban production with mixed reality technology. Another example is a two-day GameJam hosted at Die Lernfabrik within the scope of German Science Year 2018 “Working Life of the Future,” in which the implications of digitalisation and new manufacturing technology on workplaces and their required skills were set as central theme. Participants were challenged to develop concepts and prototypes of games to convey potentials, challenges, or future required competencies with access to the learning factory infrastructure. The results of the self-evaluation of the participants indicate a positive reception and knowledge improvement by event-based education (Fig. 11.25), which now complements the operational concept of Die Lernfabrik.

11.9 Best Practice Example 9: E|Drive-Center at FAPS …

435

Fig. 11.25 Impressions and results of survey among the participants of the events HoloHack and GameJam at Die Lernfabrik2

11.9 Best Practice Example 9: E|Drive-Center at FAPS, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany Authors: Alexander Kühla a Institute for Factory Automation and Production Systems (FAPS), FriedrichAlexander-Universität Erlangen-Nürnberg (FAU)

2

See Juraschek et al. (2020).

436

11 Best Practice Examples

E|Drive Center Operator:

FAPS, Friedrich-Alexander-Universität

Year of inauguration:

2011

Floor space:

900 m2

Manufactured product(s):

Electric motors and inductive charging pads

Main topics / learning content:

Production technology, Machine learning

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal Since the mid-1990s, the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) at its chair for Factory Automation and Production Systems (FAPS) is developing production technologies for the assembly of electric machines. Within this framework, various winding applications and magnet mounting technologies were initially developed. The continuous expansion of research activities necessitated an expansion of laboratory capacities. As part of a major research project funded by the Bavarian Ministry of Economic Affairs, the E|Drive-Center moved into a former factory building of AEG/Electrolux in Nuremberg in 2011. Since then, the learning factory

11.9 Best Practice Example 9: E|Drive-Center at FAPS …

437

has been filled and expanded with various current technologies for the production of electric drives. Today, this research area develops innovative drive concepts and associated production technologies, intending to transfer the knowledge to industrial applications. In addition, production, and testing processes for contactless power transmission components in electric vehicles are addressed. The following three objectives are pursued with the E|Drive research and learning factory: • Firstly, the processes are investigated in greater depth in numerous publicly funded projects and are also being industrialised in close cooperation with innovative regional, national, and international companies. • Secondly, the production environment and current research results are used to educate FAU students in the production of electric drives within the framework of lectures, exercises, theses, and internships. • Thirdly, new knowledge is transferred to industrial users in a practice-oriented way, particularly in the context of seminars. Equipment and Products In the E|Drive-Center learning factory, the entire value chain of electrical machines is represented, right through to recycling. More than 25 sophisticated production facilities and a great quantity of equipment are available within the 900 sq m factory (Fig. 11.26).

Ferrite assembly

Magnetizing, magnet processing and recycling

Winding technology

Additive manufacturing

Prefabrication and lamination

Fig. 11.26 Electric machine learning factory

Insulation and IPT Systems

Test laboratory

Contacting technology

438

11 Best Practice Examples

Basis

Flat wire, Polyesterimid coated

Straightening wire

Twist ends

Rectangular semi-open groove

Strip ends

Bend flat wire in u-shape

Laser welding of the ends

Insert groove insulation

Electrical insulation test

Insert hairpins in

Impregnation

Fig. 11.27 Process chain of hairpin stators

The process chain of electric motors begins with the production and packaging of electrical steel sheets, e.g., by the use of a laser cutting system. The sheet stack of the rotor has to be equipped with magnets. Therefore, assembly and recycling technologies for magnetised and non-magnetised magnets are available. For further processing as a stator, the packages receive their windings. For this process step, various flexible and partly robot-assisted production lines are available, which offer a broad portfolio of different winding topologies and geometries, e.g., single teethes, inner or outer stators in different sizes. Currently, a particular focus product is the hairpin stator for traction drives of electric cars. The process chain is pictured in Fig. 11.27 and differs from traditional winding. Worth mentioning here is on the one hand the use of rectangular wire and on the other hand the challenge of forming, assembling, and joining individual hairpin coils. Various technologies in the field of stripping enamelled copper wires and the subsequent contacting process are available, including an 8 kW infrared laser system or ultrasonic and crimp machines. For the subsequent insulation of stators, various impregnation and powder coating processes are accessible, such as different machines for the heating of the stators (e.g., thermal ovens or inductors). In addition to electric motors, the automation of the production of inductive power transfer systems is addressed (see Fig. 11.28). Since the process chain is rather similar, it was and is possible to develop and integrate the available production technologies while expanding the existing plant only in certain areas, especially regarding testing equipment. Another focus is on digital production planning and process control using datadriven models. A wide variety of processes along the process chain have already been instrumented and optimised using applied machine learning algorithms tailored to the respective manufacturing process. The research and training spectrum is decisively

11.9 Best Practice Example 9: E|Drive-Center at FAPS … Soft- and hard magnetic materials for flux guidance

439

Conductor materials for flux generation

Dielectric and insulating materials for encapsulation, insulation and heat transport

Production technologies (e.g. assembly, joining and forming processes) Recycling

Measurement technologies and quality assurance Digital production planning and process control also through data-driven models Process simulation (e.g. process-, FEM, CFD and process flow simulation)

Electric Motors

Inductive Powertransfer Systems

Fig. 11.28 Manufactured products in the Process Learning Factory E|Drive-Center

supplemented by the laboratory for quality assurance. The main topics are optical inspection and analysis, environmental simulations, electrical measurement methods (also high frequency), and quality assurance of hard and soft magnetic materials. Operational Concept The aim of the teaching and training activities is the sustainable development of crucial competencies in the field of the production of electric drives. The offered courses and seminars can be subdivided according to the process chains for rotors, stators, and final assembly. Special emphasis is placed on winding, contacting, insulation, magnetisation, and magnet assembly as well as quality assurance technologies. A team of more than 15 scientific staff members teaches the theoretical content. The practical in-depth training then takes place at various plants of national and international partner companies. The two-day seminars with up to 40 participants are therefore divided into small groups for optimal supervision and knowledge transfer. In addition, various laboratory tours are possible, which should lead to a deeper understanding of the process through a detailed technical discussion directly with the process demonstrator. Various technology experts are available as discussion partners and provide detailed insights into current process research. The training activities on the topic of artificial intelligence and machine learning will also be carried out at the Demonstration and Transfer Center for Intelligent and Human-Centred Joining Processes (Pro-KI-Nürnberg), which is integrated with the E|Drive-Center. In the framework of hands-on demonstrations and guided tours, the interdisciplinary contents, such as software engineering, sensor technology, automation technology, and machine learning, are conveyed by means of process-specific demonstrators (among others in the field of laser welding).

440

11 Best Practice Examples

11.10 Best Practice Example 10: ETA-Factory at PTW, TU Darmstadt, Germany Authors: Matthias Weigolda , Astrid Weyanda a Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt

ETA-Factory Operator:

PTW, TU Darmstadt

Year of inauguration:

2016

Floor space:

810 m2

Manufactured product(s):

Control plate for hydraulic pump, metal ball maze, gear-shaft combination Energy efficiency, Energy flexibility, Resource efficiency

Main topics / learning content: Morphology excerpt

Open models

Target industries

Climateneutral production Open public

Job-seeking

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

11.10 Best Practice Example 10: ETA-Factory at PTW, TU Darmstadt, Germany

441

Overall Goal A sustainable production that is economically optimised and environmentally friendly is a success factor for manufacturing companies. In this context, the research group “Sustainable Production” was founded in 1996 at the PTW. Starting point of research activities is the question of how energy consumption in production can be reduced, and thus, the energy efficiency of an entire production system can be improved. Against this background, in the year 2008, the vision was born to transfer the already successful concept of the Process Learning Factory CiP to the field of energy efficiency. An energy efficiency factory was to be built on the campus, where students can get to know industrial processes and, in particular, PhD students are able to conduct practice-oriented research. After the elaboration of the comprehensive concept, construction work of the ETA-Factory started in 2013; just-in-time for the 120th anniversary of PTW. A picture of the building is shown in Fig. 11.29. Through the high level of commitment from all stakeholders and with the support of the BMWi, the state of Hesse and the Technical University of Darmstadt, the ETAFactory inauguration was achieved only three years later. Since then, the concept of the ETA-Factory was developed further, also including the topics of energy flexibility and resource efficiency nowadays. The ETA-Factory aims at enabling the industry to achieve climate-neutral production.

Fig. 11.29 Building of the ETA-Factory

442

11 Best Practice Examples

Equipment and Products In addition to the excellent opportunities for research, the ETA-Factory also serves as a learning environment in which gained research insights are transferred to industry and education. On the shopfloor of the ETA-Factory, two complete value chains are operated in which market-ready products are produced. Greenfield Process Chain The first value chain of the ETA-Factory is shown in Fig. 11.30. This value chain was created in a greenfield approach in the context of the project “ETA-Factor.“ A control plate for hydraulic pumps is manufactured here. The hydraulic pump is originally manufactured and distributed by Bosch Rexroth. The production machines integrated in the process chain comply with the latest energy efficiency standards—in course of the project the machines have been modified as research demonstrators. The process chain essentially comprises: two cutting machine tools made by EMAG, two wet cleaning machines from MAFAC, a gas nitriding retort furnace from IVA. In addition, the research building contains further machines in the factory hall, the technical building equipment of the ETA-Factory as well as various thermal storage units, an absorption chiller and the thermally activatable building façade. The machine park and the entire technical building services are supplemented by an interactive learning course. Here, not only the basics of energy and energy efficiency are explained, but also detailed descriptions of the innovations in the ETA-Factory process chain as well as in the rest of the factory building.

Fig. 11.30 ETA-Factory shopfloor—Greenfield process chain

11.10 Best Practice Example 10: ETA-Factory at PTW, TU Darmstadt, Germany Joining shaft and gear wheel

443

Assembly

Transportation

End product

Roboter cycle: oil bath cooling and cleaning

Turned shaft

Material supply: Shaft Heat treatment: Gear

Material supply: Gear

Fig. 11.31 Learning factory for energy productivity (LEP)—Brownfield process chain

Brownfield Process Chain The brownfield value chain of the ETA-Factory is shown in Fig. 11.31. This is part of the learning factory for energy productivity (LEP), which was installed on the shopfloor of the ETA-Factory as part of a former cooperation between PTW and McKinsey & Company. In the process chain of the LEP different gear shaft combinations are manufactured for different gear types. At the end of the process chain, after final assembly with externally purchased housing parts, ready-to-ship gearboxes for various purposes are available. The LEP process chain is representative of discrete mechanical manufacturing operations and consists of a turning machine, a cleaning machine, two robots, a continuous curing oven, a shrink oven, two decentralised air compressors, and several assembly processes. A special feature of the LEP is that the process chain can be converted from an energy-inefficient to an energy-efficient system design (and vice versa) within a few minutes. This conversion demonstrates the energetic optimisation of an existing brownfield system. Based on the inefficient system state, training participants can learn to understand the energy flows in the processes, identify existing energy wastages, and derive improvement measures. In addition, trainees can immediately implement optimisation measures and analyse in detail the potential of individual measures. Furthermore, the calculation of a product carbon footprint with the help of traceability systems can be taught in interactive exercises.

Workshops

444

11 Best Practice Examples

Energy Efficiency

Energy Management & Monitoring

Climate-Neutral Production

ETA-Basics

Implementation of Energy Management Systems

Strategy Development for Climate-Neutral Production

Energy Efficiency in Heating, Cooling and Ventilation (HVAC)

Measurement Techniques

Resource Efficiency through Digitization

Energy Efficiency in Pumps, Motors and Compressed air systems

AI in Energy Management

PCF Accounting

Fig. 11.32 Overview of existing workshop modules in the ETA-Factory

Operational Concept The training sessions in the ETA-Factory are conducted as interactive workshops in which theoretical foundations are linked with practical implementation. Target groups are students as well as company representatives such as energy managers or production managers. While theoretical sessions usually take place in the seminar room, the practical parts use the learning environment of the ETA-Factory. A lively exchange of knowledge and experience between the moderators and participants is intended to create a fruitful learning atmosphere. The current training offer consists of a basic module on energy efficiency in the industry (ETA-Basics) and several modules addressing specific key topics, such as product-specific carbon footprint (PCF) accounting or energy management based on artificial intelligence (AI). An overview of the existing workshops is pictured in Fig. 11.32. The research group itself addresses several topics: from a strategic view on climate-neutral production and the competencies needed in the industry, over the planning and operation of climate-neutral infrastructure and efficient production machines towards the topic of cyber-physical systems and energy monitoring.

11.11 Best Practice Example 11: Fábrica do Futuro at University of São Paulo (USP), Brazil Authors: Eduardo Zancula , Klaus Schützera , Gabriel Rodrigues Santosa a Department of Production Engineering, Escola Politécnica, University of São Paulo

11.11 Best Practice Example 11: Fábrica do Futuro at University of São Paulo …

445

Fábrica do Futuro at the University of São Paulo Operator:

Escola Politécnica, University of São Paulo

Year of inauguration:

2016

Floor space:

400 m2

Manufactured product(s):

Customizable skateboard

Main topics / learning content:

Industrie 4.0, Mass customization

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Bachelor

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal Fábrica do Futuro implementation at the University of São Paulo in Brazil was motivated by the need to enhance undergraduate and graduate education related to Industry 4.0 technologies and to support Industry 4.0 adoption by Brazilian companies, especially SMEs.3 The implementation activities started in 2015, strongly grounded on the Learning Factory’s theoretical and practical developments from the International Association of Learning Factory (IALF) members and researchers. 3

See Leal et al. (2020).

446

11 Best Practice Examples

Moreover, Fábrica do Futuro foundation significantly derived from the results of the project Smart Components within Smart Production Processes and Environments (SCoPE) conducted between 2014 and 2020 in partnership with the Technical University Darmstadt within the German-Brazilian manufacturing research collaboration framework.4 Fábrica do Futuro implementation followed a phased approach in three main stages—Pilot (2015–2017), Development (2018–2019), and Consolidation (2020). This phased approach was necessary considering the environment with limited resources and funding. It allowed the Fábrica do Futuro team to achieve short-term milestones while obtaining increasing support from both the University and external industry partners for the subsequent implementation phase.5 The Pilot phase was funded by a University’s internal seed grant and supported by start-ups from the University ecosystem. The first Fábrica do Futuro implementation was inaugurated in 2016. Despite the limited space, the Pilot validated the value of a Learning Factory on campus. The Pilot also enabled demonstrations calling the attention of novel company partners to support the project.6 As a result, the University management decided to allocate more space to host Fábrica do Futuro in a novel multipurpose innovation center building. The second phase—Development—was funded by a grant from a national-level agency for industrial development. It enabled the deployment of a flexible assembly line and other demonstrators of specific Industry 4.0 technologies. In this stage, Fábrica do Futuro was transferred to its definitive location of more than 400m2 . The Consolidation phase started in 2020 and allowed expanding the Learning Factory application for education and research purposes. Fábrica do Futuro main goal is to support education—undergraduate and graduate—as well as professional training. Therefore, it mainly relies on a projectbased learning approach. Moreover, it is also used as an infrastructure for graduate students’ research projects and to develop Industry 4.0 proofs of concept (PoCs) for the industry. The main topics covered include flexible production-oriented to mass customisation7 and specific Industry 4.0 technologies such as additive manufacturing, computational vision applied to quality control,8 and digital twin.9 As such, Fábrica do Futuro stimulates applied engineering and design research, industry-university partnerships, alongside education and training. Equipment and Products Fábrica do Futuro production environment was conceived considering a customisable skateboard as a sample product. The production environment was established to cover relevant product life cycle phases, from product design, over the in-house 4

See Durão et al. (2017). See Leal et al. (2021). 6 See Zancul et al. (2022). 7 See Romeral et al. (2021). 8 See Zancul et al. (2020). 9 See Durão et al. (2020). 5

11.11 Best Practice Example 11: Fábrica do Futuro at University of São Paulo … Embedded training space

Additive manufacturing

Assembly line and stations

Assembly station

Smart product / Digital Twin

Embedded training space

Design

Smart product / Digital Twin

Disassembly and automatic sorting

Assembly line

447

Production planning

Fig. 11.33 Layout and equipment at Fábrica do Futuro

production of selected parts and complete product assembly, to the use phase and product disassembly at the end of life. Each life cycle phase demands respective infrastructure, summarised in the floor layout in Fig. 11.33. The space is organised into shopfloor areas (assembly line and stations and additive manufacturing), office spaces (design and production planning), product testing space (smart product/digital twin), and training area (embedded teaching space). Regarding production, the focus is on the flexible assembly of a customisable skateboard based on individual product orders resulting in a product-specific bill of materials (BOM). Parts are mainly outsourced and delivered to the assembly line except for some optional accessories, which are produced at Fábrica do Futuro employing additive manufacturing—these are low volume and can be high-variety parts enhancing product customisation. The assembly operations are conducted on four workbenches. An automatic torque wrench machine and computational vision are employed along the assembly to apply and control customisation features. Beyond the assembly line, there is a specific work area dedicated to research and developments regarding smart products and digital twin, in which Internet of Things (IoT) and connectivity technologies are tested on a specially designed prototype skateboard. Finally, Fábrica do Futuro is equipped with a disassembly workbench and an automatic sorting belt based on computational vision, which supports the part sorting in containers to be resupplied to the assembly line. All products assembled in education and testing activities are disassembled for reuse in further didactic activities. The product was selected according to some major criteria. The product should have low unit cost, be simple to assemble by untrained students, enable an interesting degree of customisation, support connectivity in a useful way, and be meaningful to students. Based on these criteria, a customisable skateboard was defined as the sample product (Fig. 11.34). The predefined configuration alternatives include different wheel colours (a discrete variable), shock absorber adjustment (soft to hard on a continuous scale), and optional parts (accessories). A product configurator was developed to support configuration choices (Fig. 11.34, left part).

448

11 Best Practice Examples Sample product

Product configurator Configuration • Wheel colors

(front and back) • Shock absorber

(soft – hard) • Optional 1 –

accessory • Optional 2 – IoT

Fig. 11.34 Designed and assembled product at Fábrica do Futuro

Lifecycle stage

Product design

IT support

PLM (Windchill) CAD (Creo)

Sales / product order ERP (TOTVS) ERP Configurator

Production planning ERP to MES integration

Assembly MES (PPIMultitask)

Product use Proprietary solution

Disassembly ERP (planned)

Strong focus on data integration Mainly cloud based software

Fig. 11.35 Information technology environment and support software at Fábrica do Futuro

In order to support flexible production of the configurable skateboard, a comprehensive information technology (IT) environment was implemented. Commercially available off-the-shelf software packages were installed in the university cloud infrastructure and specially configured for their application at Fábrica do Futuro. The IT environment includes Product Life cycle Management (PLM), Enterprise Resource Planning (ERP), and manufacturing execution system (MES) solutions (Fig. 11.35), supplied by industry-leading partners. The ERP manages the BOM released for production. The product configurator is integrated with the ERP to generate new production orders. The MES receives information from the ERP and manages activities at the assembly station level. The ERP and MES serve as the information backbone for other software.10 Finally, a specific internally developed solution is used as an interface for the smart product.

10

See Durão et al. (2022).

11.12 Best Practice Example 12: FIM Learning Factory at Faculty of Industrial …

449

Thus, the IT infrastructure plays a major role in providing a production environment at Fábrica do Futuro that approximates a real Industry 4.0 production line. Operational Concept The Fábrica do Futuro concept allows for high flexibility, including its physical layout, to be applied in various training situations. Hence, it has already been used to support undergraduate courses in production planning, ergonomics, and electrical engineering (e.g., by the development of low-cost automation devices). One major focus of Fábrica do Futuro is on project-based courses in which students work in real-world Industry 4.0 demands from partner companies. The demands are developed as solutions to actual problems devised by partners, up to a proof-of-concept level, leveraging Fábrica do Futuro as a prototyping, testing, and demonstration environment. Regarding professional training, Fábrica do Futuro was already employed in a closed model training program for a specific company. However, the focus is on the open model with a standard one-day Industry 4.0 immersion workshop. This workshop was offered online with virtual classes when in-person classes were not possible during the COVID-19 pandemic. More training and Industry 4.0 workshops are expected to continue in the near future. Besides education, supporting research has also been relevant at Fábrica do Futuro. The current research focuses on applying selected Industry 4.0 technologies, mainly additive manufacturing, digital twin, and computational vision. Research projects have been primarily developed by graduate students. Finally, Fábrica do Futuro also focuses on technology demonstrations and events for practitioners as part of its commitment to Industry 4.0 dissemination to the industry.

11.12 Best Practice Example 12: FIM Learning Factory at Faculty of Industrial Management, Universiti Malaysia Pahang, Malaysia Authors: Mohd Ridzuan Daruna , Mohd Ghazali Maarofa a FIM Learning Factory, Faculty of Industrial Management, Universiti Malaysia Pahang, Malaysia

450

11 Best Practice Examples

Supply Chain Learning Factory “FIM Learning Factory” (FLP) Operator:

FIM Learning Factory, UMP, Malaysia

Year of inauguration:

2018

Floor space:

586 m2

Manufactured product(s):

Air purifier & Mini Portable Fan

Main topics / learning content:

Supply chain and logistics, Industrie 4.0

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

Top

Semi-skilled workers

Unskilled workers

Management

…

Design

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal Learning Factory is designed to simulate learning experience in a manufacturing environment that replicates natural production systems and value chains to allow the participants to learn through hands-on learning experiences. Many universities and industries have increasingly used it to transfer research work output to the industry and bring industry to the classroom.11 Furthermore, Learning Factory has the capability and capacity to convert abstract learning ideas into simulated learning and 11

See Centea et al. (2020).

11.12 Best Practice Example 12: FIM Learning Factory at Faculty of Industrial …

451

convert them into concrete learning through assimilation to enhance teaching environments.12 As manufacturing itself is facing rapid advancement in production technologies, tools, and techniques, practical training, and educational programs are needed to address the emerging challenges in the manufacturing industry.13 Often issues of mismatch between the academic qualification and industry requirements were discussed, which call for universities to align their teaching programs with the requirements of the labour market.14 Students are expected to apply the knowledge they have gained in the university to solve crucial industrial issues. However, many of the traditional methods applied in the teaching–learning process at universities and institutions of higher learning lack imparting knowledge of competencies, especially in manufacturing education.15 Therefore, students should be equipped with comprehensive competencies such as professional competencies, socio-communicative competencies, and personal competencies so that they are able to act in complex situations.16 Furthermore, such competencies are needed as current, and future businesses tend to be more dynamic in nature. Hence, the call for a competent workforce is needed to respond flexibly to market volatility and changing customers requirement.17 Past research has found that students lack the soft skills needed by the industry.18 The soft skills include problem-solving, decision-making, and work planning skills19 and the ability to apply technical knowledge.20 This lack of soft skills among university graduates has been identified as a critical factor affecting graduate employability in Malaysia. Therefore, this article will explain how the FIM learning factory was set up to teach operational and supply chain management through experiential learning. Setting up a Learning Factory: Realising the importance of a learning factory in teaching and learning, the Faculty of Industrial Management has taken a step forward to set up a learning factory in the year 2016. A group of lecturers initiated the project concept and design from the faculty of Industrial Management together with some industry experts. The learning factory was later named the FIM learning factory, located in the Universiti Malaysia Pahang, Gambang campus. It started its operations in October 2017 after a year of planning, designing, and constructing the facility. To begin, a 588-m square size learning factory was built, which consists of a receiving warehouse, manual assembly line, quality control section, outgoing warehouse, and packing/logistic section. Currently, the FIM learning factory consists of two sections: a manually operated supply chain learning factory and a newly built automated learning factory. 12

See Lindvig & Mathiasen (2020). See Abele et al. (2015). 14 See Khalid et al. (2014). 15 See Cachay et al. (2012) and Tether et al. (2005). 16 See Abele (2019). 17 See Abele et al. (2015). 18 See Nazron et al. (2017). 19 See Agus (2011). 20 See Khalid et al. (2014). 13

452

11 Best Practice Examples

Fig. 11.36 Process flow

Manual-operated supply chain FIM learning factory: The production flow in the manually operated supply chain learning factory is provided in Fig. 11.36. A continuous flow of an air hand dryer machine assembly processes and a mini portable USB fan were used to resemble a manufacturing process. Figure 11.37 shows the product used in the learning factory. The process starts with product design, product planning, and control, part receiving, production and inspection, quality control, and end at the outbound logistics. The manually operated supply chain learning factory evolved a didactic concept to expose undergraduate and postgraduate students of supply chain and operation management to a natural working environment. Two courses are conducted in this learning factory: Lean Management and supply chain management. Students are practically trained to use specialised machines, follow specific assembly procedures, and conduct product inspections through various assigned projects. One essential aspect of the FIM learning factory is that the processes consist of the characteristic of a changeable multilink value chain configuration. This is to allow a direct approach to different phases of a product life cycle. Most of the Fig. 11.37 Products in the Learning Factory

11.12 Best Practice Example 12: FIM Learning Factory at Faculty of Industrial …

453

equipments are designed and built to allow flexibility towards a self-organising form of production line with freedom in organising the shopfloor. As shown in Fig. 11.38, all workstations were built using a strong pipe racking system, a perfect basis for building individual workplaces, racks, material trolleys, and other lean solutions. The round pipe steel racking profile and steel pipe connectors joints are used to quickly join the components to build an array of simple, quick, and economically lean workstations structures and easy to dismantle later. In the electrical power distribution, an electrical busbar trunking system is used to provide a modular approach to the electrical wiring. Instead of using a standard cable wiring to every electrical device, the electrical devices are mounted onto an adapter directly fitted to a current-carrying busbar. The busbar is shown in Fig. 11.39. In this way, flexibility in designing the factory layout can be achieved. Additive manufacturing is also used in this learning factory. Three units of 3D printer, as shown in Fig. 11.40, and design software (Solid work) were provided in the learning factor to help students with the design activities.

Fig. 11.38 Workstation

Fig. 11.39 Busbar

454

11 Best Practice Examples

Fig. 11.40 3D printer

The framework used in the teaching and learning is shown in Fig. 11.41. Students are given the necessary theoretical concept during the class lecture. Later they are given a chance to practice what they have learned during class lectures in the learning factory. To test their understanding, the students were assigned group projects that required them to solve some scenarios. To help them with the project, students are allowed to benchmark by visiting the industry. At the end of the semester, students are asked to share and present their group projects. Smart Manufacturing FIM Learning Factory: Realising the importance of preparing the students for Industry 4.0, the Faculty of Industrial Management has taken another step to introduce the smart learning factory in 2018. This new learning factory has an additional 250 square-metre area, designed based on the Intelligent operated “smart bins system,” as shown in Fig. 11.42.

Fig. 11.41 Teaching framework (class lecture, practical learning, benchmarking, project presentation)

11.12 Best Practice Example 12: FIM Learning Factory at Faculty of Industrial …

455

Fig. 11.42 Production concept

In this system, all the workstations are connected to the automated storage and retrieval system (ASRS) with control by the manufacturing execution system (MES) and the supervisory control and data acquisition (SCADA) control system, as shown in Fig. 11.43. These two-monitoring information systems are used to connect, monitor, and control the data flows on the factory floor to ensure the effective execution of the manufacturing operations.

Fig. 11.43 MES architecture

456

11 Best Practice Examples

Fig. 11.44 System in the Learning Factory

Material is sent to the workstations using the automated intelligent vehicle (AIV), which controls the material through the integrated MES and PLC system for production planning, monitoring, and control. A real-time track and trace system of the material and parts is done by using Radio-Frequency Identification (RFID). An automated workstation with a robotic system (collaborative and SCARA robots) is used to train students on how robots and humans can work together in executing the process. Some manual workstations are also installed in the system, but the operation is assisted with the help of auto-guided LED-assisted lights. The system is shown in Fig. 11.44. Currently, FIM Learning Factory provides students with a hands-on learning experience that can expose them to industrial work experience in a complete value stream based on a real industrial site. The action-oriented learning process enables students to discover, experiment, and test what they have learned in a real industry setup. Apart from making the learning process more enjoyable, the learning factory also helps students expand their soft skills, such as problem-solving, teamwork, and creative thinking. The industry’s interest in helping the faculty that develops this learning factory has also increased. Industrial visits and discussions by some faculty lecturers with some industry players in Malaysia have started to show some results.

11.13 Best Practice Example 13: FlowFactory at PTW, TU Darmstadt, Germany

457

11.13 Best Practice Example 13: FlowFactory at PTW, TU Darmstadt, Germany Authors: Joachim Metternicha , Eberhard Abelea , Antonio Kreßa , Maximilian Steinmeyera , Jonas Bartha a Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt

FlowFactory Operator:

PTW, TU Darmstadt

Year of inauguration:

2023

Floor space:

500 m2

Manufactured product(s):

Smart office station

Main topics / learning content:

High-performance value stream, Circular economy, AI

Morphology excerpt

Open models

Target industries

Circular economy Open public

Job-seeking

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

458

11 Best Practice Examples

Overall Goal The intensive planning of the FlowFactory already started in 2020. After the general conditions for the construction of the learning factory could be clarified, the building and production concepts were planned. In 2023, the opening of the FlowFactory took place. The FlowFactory aims to address the long-term trend towards customised production by using digital technologies to make the entire order process faster, more sustainable, and more customised. The market segment of individualised or customised products is playing an increasingly important role in many industries—whether in the capital goods or end consumer business. As a learning factory, the FlowFactory uses a unique combination of selected machines and systems to demonstrate how industrial production can serve this market segment quickly, sustainably, with low waste, and in line of a circular economy. The FlowFactory aims to be a lighthouse in the realm of lean production, guiding the way towards the efficient and individualised production of products through a holistic process chain. Thus, the FlowFactory will be unique research and learning factory for the production of tomorrow. This vision stems from the understanding that a key factor in Germany’s competitiveness in production, despite its high wages, will be the ability to satisfy individual customer needs more quickly than competitors in other locations. With this objective in mind, material provisioning, machines, manual assembly workstations, and assistance systems are digitally upgraded in a targeted manner in the FlowFactory, linked with each other. If appropriate human decision-makers and problem-solvers are supported by AI-based systems. In addition, the value stream design also enables research in the area of circular economy in a production context. With the FlowFactory, PTW operates three learning factories. This enables the PTW to address the topic of production networks within its own infrastructure and conduct research, e.g., in the increasingly important field of resilient production networks. Equipment and Products The FlowFactory aims to represent a holistic value stream with all logistical processes. The value stream consists of the process steps sawing, milling, laser cutting, cleaning, including drying, checking, commissioning, powder coating, oven, 3D printing, assembly, testing, and packing with state-of-the-art machines. For the FlowFactory a special product was developed by the PTW that best serves the research topics: a smart office station. The smart office station is a small device for the office desk that supports efficient and economical work with many assistance functions. It consists of a base plate and up to four different modular units. The functions are located on these modular units that can be added by “plug and play.” In some units, sensors are integrated, e.g., for temperature or humidity. Mobile devices can also be connected to the smart office station via Wi-Fi and additionally charged. The accumulated data from the sensors is processed by a microcontroller and displayed via an HTML-based web interface on, e.g., the smartphone. The modular unit “smart organiser” is manufactured individually according to the customer´s specifications. The modular units “smart organiser”

11.13 Best Practice Example 13: FlowFactory at PTW, TU Darmstadt, Germany

459

Modular units

base plate smart organizer

note holder

Fig. 11.45 Smart office station

and “note holder” are produced in the value stream. The other modules consist of purchased parts which are commissioned, powder-coated, and assembled in the FlowFactory. Due to the modularity and compatibility, further modules for any relevant future research topic can be developed and easily added in future (Fig. 11.45). Regarding the connectivity of the infrastructure, the machines for sawing, milling and laser cutting have sensors and networking to record process data and feed it back to IT systems (in particular a manufacturing execution system). Numerous current research questions address data-based improvement (e.g., machine learning, digital twin, etc.). This requires a large amount of high-quality data, which can only be collected using modern equipment. Until now, the institute has not had the innovative laser cutting technology, so this new acquisition adds value in terms of experience and research opportunities for PTW researchers, students, and industry partners. Using digital and networked measuring equipment (including a coordinate measuring machine), data on product quality can be collected by the operators themselves and automatically transferred to the evaluating systems. This creates new opportunities for research into predictive quality issues. A powder coating system is used to model the surface treatment commonly used in industry, addressing not only the discrete manufacturing industry but also the process industry. Here, too, process data can be recorded and evaluated. Powder coating requires upstream component cleaning and downstream heating in an oven, which are also represented in the FlowFactory. In assembly, digital factory assistance systems are used, and their effects are researched. Here, in addition to an assembly of purely mechanical components, an assembly of electronic components is also applied.

460

11 Best Practice Examples

Intralogistics are of particular importance in the FlowFactory. The material flow between the individual workstations is carried out by means of autonomous mobile robots (AMRs). The interaction between the AMRs with the logistics infrastructure as well as the production and order control can be demonstrated and further researched. AMRs allow to collect transport data which can be used for comprehensive material and product traceability as well as data-based improvements of the logistical processes. For transition to more sustainability in industry, circular economy plays a prominent role. In the FlowFactory, the technical feasibility of disassembly and reintroduction into the production value stream is researched. Promising concepts for an organisational and economic implementation of a return of products to the manufacturer and thus a circularity in the production environment are to be developed and evaluated. Furthermore, the value stream is equipped with several digital use cases like a digital team board to support digital shopfloor management. In addition to the value stream a makerspace as an area for innovation is part of the FlowFactory (Fig. 11.46). Operational Concept The operation of the learning factory has just started (status 2023). For the primary target of research, the FlowFactory is operated regularly to generate data that can subsequently be analysed and evaluated. Regarding the primary target of training education, guided tours are conducted for students and industry partners. Through this, participants are made aware of current technologies and are shown the potential future development of industrial production, particularly in small- and medium-sized companies. In the makerspace of the FlowFactory, researchers and students develop and build innovations for production. Subsequently, the FlowFactory value stream enables practical implementation and testing of the innovations in an industry-like environment. The makerspace systematically uses existing machines and equipment of the FlowFactory value stream and provides an innovation environment for the development and realisation of mechatronic products. Various workbenches equipped with the necessary tools will be used to work on metal, plastics, and electronics. Industrial 3D printers enable the additive manufacturing of complex and individual components and prototypes. In a get-together area, an exchange between researchers, students and industry partners is made possible. In addition, smaller events are conceivable in this area. The makerspace thus improves the opportunities to research and work practically on innovations. For the students, an excellent environment is created to practically apply theoretically learned contents and, moreover, to develop and implement their own ideas.

11.14 Best Practice Example 14: Globale Learning Factory at wbk, Karlsruhe …

461

Fig. 11.46 Impressions of the FlowFactory

11.14 Best Practice Example 14: Globale Learning Factory at wbk, Karlsruhe Institute of Technology, Karlsruhe, Germany Authors: Gisela Lanzaa , Constantin Hofmanna , Rainer Silbernagela , Marvin Maya , Florian Stamera

462

11 Best Practice Examples

a

wbk Institute of Production Science, Karlsruhe Institute of Technology, Karlsruhe (KIT)

Learning Factory Global Production Operator:

wbk Institute of Production Science, KIT

Year of inauguration:

2015

Floor space:

200 m2

Manufactured product(s):

Electric gear drive

Main topics / learning content:

Lean production, Industrie 4.0, Global production, Data mining in quality assurance

Morphology excerpt

Open models

Target industries

Quality assurance Open public

Job-seeking

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal The learning factory global production of the wbk Institute of Production Science at the Karlsruhe Institute of Technology (KIT) has been started in 2011. In accordance with the research focus of Prof. Gisela Lanza, the learning factory aimed to create an environment to make the effects of global production palpable. Thus, the

11.14 Best Practice Example 14: Globale Learning Factory at wbk, Karlsruhe …

463

learning factory in Germany was designed to have a twin facility in Suzhou, China, at the KIT branch. The development of the learning factory followed several guiding principles: firstly, the effects of global production on quality and factory design and organisation should become visible. Secondly, the demonstration product should be a real industrial product, complex enough to encounter the same real-world problems the participants from industry face in their daily life in production. Thirdly, the production system itself should embrace the principles of changeability to enable participants to alter the production system freely and in short time. To live-up to the own expectations, strong cooperations have been created with industry companies. Especially Bosch has played a significant role in the development of the learning factory by contributing multiple variants of their electrical gear drive designed for the automotive market. This active product family consists of a multitude of electrical drives varying in shape, gearing, and dimension. To enable a circular use of the product, slight changes to enable disassembly had to be made to components. Unlike the production system at Bosch, the learning factory provides manual, semi-automatic, and automatic workstations. The development of these stations has been done at the institute over the course of two years. Due to strong and long-lasting partnerships not only with Bosch but also with companies such as BALLUFF, PILZ, among others, these workstations have been designed as changeable and flexible entities that can be arranged in any order to form an adaptive production system. Equipment and Products The assembly of the electrical gear drive, see Fig. 11.47, consists of ten assembly steps in addition to a functional testing at the end of the process. Supporting processes such as storage and logistics are also part of the learning factory. Each production step can be carried out by a manual, semi-automatised, or automatised workstation. These workstations can be combined to form production systems of different automation degrees. The layout of the production system is also flexible. To accommodate this extraordinary degree of changeability and flexibility on the system level, the workstations communicate either wired or wireless via industrial Ethernet, Wi-Fi6, 5G or industrial protocols such as Profinet or IO-Link. Various digital tools can be deployed to support the assembly process. These include assistance systems, visual quality inspection systems, and analytical tools such as spaghetti diagrams and dashboards to gain insights on the production process itself. Regardless of the automation degree, each product is traced through the entire production and test process and data on times and quality is recorded. Depending on the learning objective of the workshops, e.g., quality control, scalable automation, agile production networks or Lean & Industry 4.0, the production system can be reconfigured to ideally support the learning goals.

464

11 Best Practice Examples

Fig. 11.47 Different variants of the electrical gear drive assembled in the learning factory

The twin learning factory at GAMI in Suzhou, China, manufactures a different industrial product, a valve block. The production line focuses on seamless digitisation of the production flow and on material flow optimisation. The used production equipment is based on a FESTO assembly line as the outpost in China does not have the same possibilities to manufacture equipment in-house (Fig. 11.48).

Fig. 11.48 Exemplary configuration of the learning factory in Germany

11.15 Best Practice Example 15: Global McKinsey Innovation & Learning …

465

Operational Concept The learning factory aims to transmit theoretical but foremost practical knowledge from research to practice in workshops. The main audience is practitioners from industry and students during their master’s degree. The groups of 12–20 persons analyse and improve the production system in the course of mostly two-day workshops. The groups are taught methods and are coached in applying them to the production system in the learning factory. Nonetheless, each group has the possibility to come up with new and individual solutions. The production system is flexible enough to accommodate these changes. Trainings are offered on the topics of Lean and Industry 4.0 focusing on material and information flow optimisation by deploying lean methods and digital tools, on quality assurance based on data mining demonstrating how Six Sigma can be leveraged using data mining approaches, on scalable automation teaching how to plan and adapt a production system to adapt to changeability drivers by adapting the degree of automation. Further trainings focus on collaboration between independent sites and the use of information sharing and traceability to improve the performance of production network. Leadership, circular economy, digital twins, and shopfloor management are also topics addressed in dedicated workshops. Trainings are offered as open workshops, where participants from different companies and backgrounds meet or as individually booked workshops for specific companies. The twin learning factory in China focuses on shorter trainings in the area of lean management, quality and supplier development and addresses only practitioners. Besides the use of the learning factory as workshop center, it serves also in various research projects as testbed for innovation. The learning factory is also home to an annual hackathon event for students during which the participants work on innovative solutions for production problems. Most of the continuous development of both hardware and software in Germany as well as in China is done in student projects.

11.15 Best Practice Example 15: Global McKinsey Innovation & Learning Center Network (ILC) Authors: Fabian Alexander Müllera , Sara Loewenthalb , Cinzia Lacopetac , Amy Radermacherd a McKinsey & Company, Inc., Stuttgart, Germany b McKinsey & Company, Inc., Atlanta, USA c McKinsey & Company, Inc., Milan, Italy d McKinsey & Company, Inc., Minneapolis, USA

466

11 Best Practice Examples

Global McKinsey Innovation & Learning Center Network (ILC) Operator:

McKinsey & Company, Inc. And local partners

Year of inauguration:

2007

Floor space:

>10,000 m2

Manufactured product(s):

Fridge compressor, gearbox, woven wristband with RFID chip, bottled lemonade and ice tea, cell and gene therapy

Main topics / learning content:

Capital excellence, product development, procurement, supply chain manufacturing, sales, customer service, corporate functions, lean, digital & analytics, sustainability & resilience

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal Experiential learning is known to be a particularly effective way for building capabilities of adult learners. To provide them with a forum for hands-on learning and capability building, McKinsey & Company—a global management consulting firm serving clients across industries on a range of challenges and functional topics—has established several full-size model companies around the world over the past two decades either independently or together with local partners. Focusing on lean and

11.15 Best Practice Example 15: Global McKinsey Innovation & Learning …

467

digital manufacturing at the beginning, they developed into advanced, technologyenabled innovation and learning engines that can guide the vision and execution of an organisation’s digital, sustainable, and resilient future today. The centers have been continuously upgraded and extended and eventually merged into the global Innovation & Learning Center network. It represents an ecosystem with currently 12 locations across Europe, North America, Latin America, and Asia (Fig. 11.49). These are immersive learning environments where visitors can develop new skills by experimenting with lean methods and digital technologies, explore new ways of working that will be critical to success, and envision and plan the transformation of their own operations outside their own facilities. Bringing together a technology-agnostic ecosystem with more than 150 leading technology companies and innovative start-ups, the model factories enable organisations to experience the latest digital and analytics solutions and build the capabilities to leverage them for holistic, sustainable impact. With more than 250 involved McKinsey experts, the network of Innovation & Learning Centers supports more than 450 organisations each year, with an average of 13,000 visitors per year. Equipment and Products In the past, the Innovation & Learning Centers were independent learning factories running their own model shopfloors. They offered demonstrations of selected technologies and related capability building activities both for interested clients and for internal learning programs (e.g., for McKinsey’s Operations practice). A typical focus of such skill-building programs was efficient and effective production systems with the application of lean principles to manufacturing-related activities, first taught in an interactive classroom and then applied in the model shopfloor through hands-on exercises.

Atlanta Monterrey

Aachen New Jersey

Beijing Istanbul Gurgaon

Venice Singapore Salvador

Sao Paulo

Fig. 11.49 McKinsey Innovation & Learning Center network

Jakarta

468

11 Best Practice Examples Sustainable & resilient Digital & analytics

Pre-lean Legacy state of operations

Lean Lean state of operations

Technologically advanced state of operations

Green and resilient state of operations

Fig. 11.50 “From-to” journey covering lean, digital, and sustainability

The shopfloors—all equipped with real machines and human operators—were gradually upgraded with the latest technology solutions, so that today they can be set up in three different states: a pre-lean state, a lean state, and a digitally advanced state. In this way, the Innovation & Learning Centers can take visitors on from-to journey, regardless of their organisation’s maturity level and production environment. Depending on the prerequisites of an organisation, visitors can either transition from a legacy state to a lean state, where the center team and subject matter experts help them to develop the culture and capabilities they need to run lean, agile, and innovative operations. Alternatively, more advanced visitors can be taken on a journey from an already lean state to a digitally advanced state of operations, giving them the chance to master their digital transformation by experiencing the power of data and artificial intelligence, cutting-edge IT infrastructure, streamlining and automating operations, and building digital talent. Furthermore, visitors are offered insights into sustainable and resilient operations across the entire value chain, such as showcasing the latest cleantech solutions and supply chain safeguarding approaches (Fig. 11.50). Recent market disruptions, such as the COVID-19 pandemic or the war in Ukraine, have shown that manufacturing can no longer be viewed as a silo function. Market trends, such as ongoing supply chain disruptions or the increasing demand for resilience, demonstrate the need for an integrated view of operations that also considers other functions, such as product development, procurement, or supply chain. These are closely intertwined with both manufacturing and each other, and a shock in one of these areas can quickly impact the others, with effects of different magnitudes and directions. As a result, learning factories should no longer focus solely on manufacturing, but need to consider the entire operations value chain—from product development to after-sales customer service—as well as corporate business functions in order to provide meaningful insights and learning experiences for their visitors. For this reason, the Innovation & Learning Centers have been progressively expanded to cover integrated technology solutions and offer related learning modules for all functions along the entire operations value chain, including capital excellence and resource productivity, smart product development, digital procurement, lean and digital supply chain management and warehousing, lean and digital manufacturing,

11.15 Best Practice Example 15: Global McKinsey Innovation & Learning …

469

digital quality management, sales, customer service, and corporate business functions. They established true to original model offices for each function in which corresponding technology solutions are deployed. These solutions are linked to the shopfloors and provide an end-to-end perspective on the respective operations. Visitors to the Innovation & Learning Centers can now experience the fictional “Industrial Co.” company with three business units for industrial goods, consumerpackaged goods, and textile products across the 12 center locations, where each represents a subsidiary of the global model company. Hence, depending on their location, workshop attendees can experience the production of fridge compressors, gearboxes, woven wristbands, bottled lemonade, or iced tea. Furthermore, there is one center fully dedicated to cell and gene therapy. For sustainability reasons, products are disassembled into their original components after each workshop and reused again. Dummy data for Industrial Co.’s company facts and figures complement the storyline during inspiration and capability building sessions and enable visitors to analyse a realistic company before drawing concrete conclusions for their own organisation. Operational Concept The transition of the operational concept and business model from scattered, independent learning factories to an integrated, state-of-the-art Innovation & Learning Center network can be described along three horizons: (i) from stand-alone model factories focusing on manufacturing to a global model company covering the entire operations value chain, (ii) from center-based learning activities to a full portfolio of capability building programs, including remote and on-site delivery, and (iii) from scattered days of workshops for visitors to end-to-end subject matter expert support throughout the entire transformation process. While early visitors could mainly experience selected technology solutions and participate in related learning programs at the centers, today’s Innovation & Learning Centers have significantly expanded their offering. It now ranges from immersive capability building to hands-on inspiration and co-creation along the entire transformation journey, in operations and beyond (Fig. 11.51). Today’s visitors can experience the latest technologies through envisioning walk-throughs or participate in related capability building programs, which are often complemented by reflective mindset and behaviour sessions facilitated by senior leadership coaches. The expanded offering does not only include technology solutions beyond manufacturing, a portable “Model Factory in a Box” (Fig. 11.52) and a “Model Warehouse in a Box” that can be shipped and set up anywhere, and professional directories that enable livestreaming from all centers. The ILC team also enables client organisations with the expertise to develop in the centers by supporting them during their entire transformation efforts and in setting up their own learning centers, so client organisations become able to build capabilities internally for entire workforces. Furthermore, a newly established technology platform helps search, compare, and find the most suitable technology solutions, partners, and experts from a portfolio of several thousand technologies.

470

11 Best Practice Examples Supporting discussions with short demos or virtual walk-throughs

15-30 minutes

Building fundamental capabilities in a hands-on bootcamp

1 week

0,5-1 day 2-3 days

Envisioning a digital transformation in operations

Running a roadshow to experience the technology and entrepreneurial network

Scanning domains with the Technology Search Accelerator platform

1+ weeks

Building capabilities at scale at the ILCs

1+ months

Designing and building client capability centers and learning academies

Tailored to 3+ months request

Enabling transformations and building capabilities through subject matter expert support

Building capabilities at scale at the client site: Model Factory in a Box (MFIB)

Fig. 11.51 Selected support formats Fig. 11.52 Model Factory in a Box (MFIB) concept

Day -X

Day -3

Day 0

Day 1-6

Day 7

Contact MFIB Team

Factory shipped to client site with MFIB engineer

Equipment set up at client facility by MFIB engineer

Series of 3x back-to-back 2-day workshops, up to 18 clients per session Equipment breakdown by MFIB engineer and factory shipped back again

11.16 Best Practice Example 16: Hybrid Teaching Factory for Personalised Education—Towards Teaching Factory 5.0 Authors: Dimitris Mourtzisa , John Angelopoulosa , Nikos Panopoulosa a Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece

11.16 Best Practice Example 16: Hybrid Teaching Factory for Personalised …

471

Hybrid Teaching Factory for Personalized Education – Towards Teaching Factory 5.0 Operator:

LMS, University of Patras

Year of inauguration:

2019

Floor space:

Non-geographically anchored learning space

Manufactured product(s):

Radio controlled (RC) car

Main topics / learning content:

Industrie 4.0, Lean production, Remote assembly guidance

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

CNC Manufact.

Overall Goal Laboratory for Manufacturing Systems & Automation (LMS) of the University of Patras has established a number of Teaching Factories (TF) over the years after realising the potential for transforming theoretical knowledge, research, and innovation into practical application.21 The motivation behind the TF is that manufacturing professionals “teach” engineering school students about manufacturing issues, 21

See Chryssolouris et al. (2016), Mourtzis et al. (2018a, 2018b, 2018c, 2019a, 2019b, 2020a, 2020b), Stavropoulos et al. (2018).

472

11 Best Practice Examples

manufacturing practices, and real industrial problems in order to help them gain experience and develop skills and competencies that are suitable for the needs of the industry, towards their gradual integration in the industrial field.22 The concept is based on the bidirectional information flow between the engineering classroom and the factory, where students and faculty “teach” manufacturing practitioners about advances made in manufacturing technology, new trends, and results of research and development work. The TF is a learning “space” that is not geographically anchored; rather, it is realised online and supported by cutting-edge Information Communication Technologies (ICT) tools and high-grade industrial didactic equipment, acting as a bidirectional knowledge communication channel, “bringing” the real factory to the classroom and the academic laboratory to the factory. Additionally, it is a continuous long-term process that involves regular sessions and constant communication between the factory and the classroom. Context and content modular configurations enable training and education on a variety of study topics while utilising various manufacturing facilities, engineering activities, delivery methods, and academic practices. However, the disruption of conventional educational systems brought on by the pandemic inspired pedagogy and has the potential to increase the resiliency of educational systems in an effort to meet the challenges of future digital education in an attempt to overcome future digital education challenges.23 The adaptability of the universities to the skills and competencies of the twentyfirst century will be a significant factor of the future workforce. Such abilities are critical thinking, collaboration, and communication, among others.24 Thus, the aim of this new hybrid TF model is to provide continuous lectures, laboratory courses, examinations, and educational webinars, following the official curriculum of the Department of Mechanical Engineering and Aeronautics, enhancing its resiliency. The hybrid TF is based on an adaptable architecture that can be applied using the current administrative and management system. The Hybrid Teaching–Learning Model has been established in 2019, just a few months after the lockdown due to COVID-19 pandemic.25 Although lectures, based on the curriculum are allocated to a classroom, all lectures are also delivered both online (Students) and in a physical space (Teaching Staff and a group of students). The applicability of the proposed framework has been validated in four real-life TF case studies, namely a) assembly assisted by augmented reality, b) Maintenance of CNC Work Centers, c) CNC Training and, d) Computer-Aided Manufacturing (CAM) with Software tool for three (3)-Axis Part.26 Equipment and Products The infrastructure of the Hybrid TF consists of clients (i.e., Factory shopfloor technicians, Expert Engineers from the Factory Conference Room, Academics, and 22

See Rentzos et al. (2014). See Rentzos et al. (2015). 24 See Mourtzis (2018). 25 See Mourtzis et al. (2021). 26 See Mourtzis et al. (2022a). 23

11.16 Best Practice Example 16: Hybrid Teaching Factory for Personalised …

473

Fig. 11.53 Cloud-based education model27

students) who connect to the platform via a variety of mobile devices, on-demand or live, and a Cloud platform that manages all necessary data and serves as a server (See Fig. 11.53). The novelty of the Hybrid TF lies in the fact that a group of student engineers can collaborate in real time to design a new product, share critical design information, and use cutting-edge AR technology to get a vivid visualisation of the idea under discussion/development. The Cloud platform is the most important component of the proposed solution because it is responsible for most of the actions carried out during the collaborative TF project. The Cloud platform is primarily used for managing and sharing the data needed for product design projects, specifically the 3D CAD geometry and text-based collaboration between participating engineers that may or may not be structured. As a result, each new product has its own unique directory created in the domain. This helps to keep all the project-related information organised. A group of protocols known as Transmission Control Protocol/Internet Protocol (TCP/IP) is used in order to connect network devices on the internet. With end-toend communications offered by TCP/IP, it is possible to specify how data should be broken up into packets, transferred, routed, and received at the destination. The hybrid TF pilot involved a “real-life” engineering challenge to be elaborated by engineering students under the supervision of the University Professors and technicians. Indicatively, the hybrid TF pilot was carried out in different sessions for each team. During these sessions, the student teams communicated with the expert engineers via video conference tools like Skype for Business and received guidance on how to assemble their unique remote-control car. Figure 11.54a depicts a representation of the Real-Time Assembly of the Hybrid TF pilot application. The field of view of the technician during the live assembly session is presented in Fig. 11.54b (Real-time Remote-Control Car Assembly) and Fig. 11.54c (Real-time Technician’s Field of View). 27

Adapted from Mourtzis et al. (2022a).

474

11 Best Practice Examples

Fig. 11.54 a AR assembly GUI, b Real-time Remote-Control Car Assembly, c Real-time Field of View of Technician

Fig. 11.55 Teaching Factory concept as a closed-loop control system30

Operational Concept Education was already undergoing a digital transformation prior to the pandemic. However, there is no doubt that the global health crisis has sped up the transformation and forced educational institutions to seek solutions that can ensure learning continuity for all students.28 One of the most prevalent forms of personalisation in digital learning environments is the adaptation of instructional materials to the learner’s “learning style.”29 The personalised TF model based on control system modelling principles is presented in Fig. 11.55. Next, the “Personalised Perception” educational model can be characterised as a new paradigm that enables educators and students to assess the distinctive learning characteristics and, as a result, to develop flexible teaching and learning models that consider the abilities, competencies, and interests of the students. The primary objectives of the Personalised Perception paradigm can be summed up as follows: 1) Creation of an educational plan that considers the specific needs, interests, skills, and strengths of each student; 2) provision of a learning strategy based on existing knowledge and areas for improvement. Phygital Learning Concept “Phygital” is a term for the integration of digital elements in the physical environment, for enhancing the learner’s experience, and hence the two keywords, PHYsical diGITAL is merged. Even before the pandemic, the practices of education were being transformed by the digital revolution. The pandemic has caused a 28

See Anderson & Mattsson. See Kumar and Ahuja (2020). 30 See Mourtzis et al. (2022b). 29

11.16 Best Practice Example 16: Hybrid Teaching Factory for Personalised …

475

Fig. 11.56 Architecture of the Cloud-based Personalised Learning Platform33

significant shift in education towards a hybrid or digital mode. Campus-based experiential learning should therefore be considered as top priority for higher education transformation. In contrast to the conventional Teaching Factory (TF) Model and the Hybrid/Digital TF,31 which dematerialises everything, the proposed Phygital Factory (PF) will enable contributors to observe, learn, and, most importantly, touch by materialising innovation with virtual and physical applications that have great potential for higher education. Digital learning can be referred to as a part of smart learning ecosystems. A high-level representation of the system architecture is shown in Fig. 11.56. The framework is based on the implementation of four different user groups, particularly Students, Academics, Research Organisations, and Industry, in an effort to ensure a better user experience. In essence, the ability to create, delete, and view content on the platform is enabled by the division of users into groups, which also provides access to the necessary materials and services. “Digital personalised education” is a broad term used to describe online teaching and learning based on digital services and platforms. Indicatively, the key novelties of the proposed framework can be comprised of the provision of synchronous online services like online meetings, webinars, tutorials, consultation, and chat about frequently asked questions. Additionally, it provides asynchronous Online or Offline services for learning or project work, including a library of instructional materials, test or lesson recordings, online team report preparation, and the ability to connect and train virtual infrastructure (e.g., Virtual Machine Shop with 5G Tactile Internet Applications, CAD similarity services, and so on).32

31

See Mourtzis et al. (2021, 2022a) See Mourtzis et al., (2020a, 2020b) and Mourtzis et al. (2022c). 33 See Mourtzis et al. (2022b). 32

476

11 Best Practice Examples

Table 11.1 Evaluation of Hybrid TF Case Studies No. Case study

Traditional tools

Hybrid method tools

Evaluation of hybrid method

1

Computer-aided Physical seminars manufacturing with NX Siemens for 3-Axis Part

Live or on-demand • Evaluated both by webinars via Cloud Academia and Expert Platform Technicians • Certification of level of Task assignment proficiency of the software per group of students • Increased grades by 15%

2

CNC training

Typical CNC Lecture

Live or on-demand • Virtual Wallet with webinars via Cloud academic credits (i.e., Platform ECTS, bonus for grades, subscriptions for webinars) Provision of a stand-alone • Digital certification application, compatible with Smart Devices and PCs Blockchain platform Graphical User Interfaces

3

Virtual Assembly assisted by augmented reality

Typical Assembly Process (Laboratory based) Evaluation method: (a) Successful assembly (b) shortest process time

Collaborative Cloud Platform through smart devices Services via Cloud platform: (a) Virtual Reality application, (b) Augmented Reality application, (c) CAD comparison tool

• Possibility to train and assemble the final product via VR and AR applications • Reduced assembly time approximately 33% (i.e., from 3 to 2 h) due to less errors that were avoided due to the virtual training

Evaluation of Hybrid Teaching Method The comparison between the performance of the students in each of the TF projects and the overall positive outcome of each case study are summarised in Table 11.1. With the proposed Cloud Platform, the students are able to increase their final grades by 10–15% in comparison with the traditional methods, increase their digital skills as well as to enrich their curriculum vitae with certifications and ECTS in their virtual wallets.34

34

See Mourtzis et al. (2022a).

11.17 Best Practice Example 17: IFA-Learning Factory, Leibniz University …

477

11.17 Best Practice Example 17: IFA-Learning Factory, Leibniz University Hannover (LUH), Germany Authors: Alexander Wenzela , Alexander Mützea , Peter Nyhuisa a Institute of Production Systems and Logistics (IFA), Leibniz University Hannover

IFA –Learning Factory at the Centre for Production Technology Operator:

IFA, Leibnitz University, Hannover

Year of inauguration:

2013

Floor space:

150 m2

Manufactured product(s):

Model helicopter and ist components

Main topics / learning content:

Production planning and control (PPC), Lean Production, Factory Planning, Industrie 4.0

Morphology excerpt

Open models

Target industries

PPC

Open public

Job-seeking

Factory planning

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

478

11 Best Practice Examples

Overall Goal Since 2000, the Institute of Production Systems and Logistics (IFA) of the Leibniz University Hannover has been offering professional training courses in which students, specialists, and managers develop new skills in designing, planning, and controlling production systems. While the initial focus of the training activities was on teaching the principles of lean production and effective assembly organisation using the training concept called IFA-Production Trainer, the institute’s training programme and training facility were fundamentally revised in 2013. A multifunctional and interactive training, teaching and research environment was created by setting up the IFA-Learning Factory, linking topics of production organisation with those of targeted digitalisation. The overall goal was to create an environment where training participants could face reality-like challenges of production organisation while having a high degree of freedom to master them and finally gain the necessary knowledge for everyday practice and develop essential key competencies. To achieve this goal in the best possible way, a completely new infrastructure was created at the Centre for Production Technology (PZH) of the Leibniz University Hannover (LUH), which, in addition to the interactive training environment of the IFA-Learning Factory, also includes a modern training room for theoretical knowledge transfer, discussions and group work. Furthermore, the training environment was developed based on the concept of changeability, so it is easily expandable and adaptable to the constantly changing (industrial and academic) requirements, allowing the creation of a multitude of training scenarios. In terms of knowledge transfer, the IFA-Learning Factory creates an elementary link between theoretical content and the practical testing of this content using planning games and simulations. Hence, it is an essential element of workshops, seminars and training courses focusing on factory planning, production management, production design and work organisation. Furthermore, the learning factory is integrated into several academic courses of the LUH, e.g., on Production Analytics and Lean Production. The technologies used in the learning factory environment are intended to demonstrate how target-oriented digitalisation, in combination with organisational measures, can meet the challenges of future production organisations. Equipment and Products All activities within the learning factory have one commonality–the product. The various training courses are mainly concerned with the production of components or the assembly of a model helicopter (see Fig. 11.57). Depending on selected scenarios

11.17 Best Practice Example 17: IFA-Learning Factory, Leibniz University …

479

Fig. 11.57 Product(s) within the IFA-Learning Factory

within the learning factory, either individual parts for the helicopter can be produced or the helicopter can be assembled in different variants. To illustrate the challenges of customer-oriented production with a high number of variants and customised products, technologies such as 3D printers or a laser marking system are used in the learning factory. Various workstations have been implemented in the IFA-Learning Factory, which can be used and organised in several different ways. Among them are programmable CNC milling stations and mobile, highly flexible workstations. All stations are equipped with a computer, including a touchscreen, corresponding signal technology, associated LED strips and height-adjustable workplaces. A continuous media grid supports the flexible arrangement and repositioning of all workstations by providing supply units with power, compressed air, and network interfaces at fixed (short) distances along the ceiling. IT infrastructure has been set up in the learning factory, enabling a high level of data availability using technologies like RFID, Real-time localisation, barcodes, and machine data acquisition to allow workshop participants to make decisions concerning the configuration of the production planning and control (PPC) system or the production organisation based on operational feedback data. This changeable and technology-supported infrastructure makes it possible to map different scenarios of production systems (e.g., flow-shop assembly, job-shop production, one-piece flow). The resulting different layout variants can be recorded and documented with a ceiling-mounted camera remotely controlled via a network-compatible camera interface. The production and assembly stations are supplemented by a small parts warehouse, automated guided vehicles (AGVs) for material supply and one administrative

480

11 Best Practice Examples

workstation for PPC. The raw materials, auxiliary materials and operating resources required to replicate the production process are stored in small load carriers (SLC). The individual compartments are labelled with electronic shelf labels (ESL), which can be controlled and updated via a central database. The IFA-Learning Factory environment also offers a wide range of worker assistance systems like a pick-by-vision solution in the warehouse or an augmented reality environment supporting production planning and control and production monitoring demonstrating the potential of technologically assisted processes (Fig. 11.58). Current development projects address the following technical improvements: • Real-time locating system (RTLS), • Automated guided vehicles (AGV), • 3D laser scanner,

Fig. 11.58 Impressions of the IFA-Learning Factory

11.17 Best Practice Example 17: IFA-Learning Factory, Leibniz University …

481

• Human–robot collaboration (HRC), • Learning behaviour in flexible working environments. The RTLS in the IFA-Learning Factory uses UWB technology based on the Time of Arrival (ToA) principle. The position of a trackable tag is thus determined by the distance between the tag’s location and at least three anchors. Six anchors were attached to the ceiling grid. A server writes the collected data to an SQL database and provides an open interface for data analytics. Up to now, RTLS has made it possible to locate products or equipment in the IFA-Learning Factory and thus reduces search efforts. In addition, it is possible to record movement profiles and, after evaluation, plan an efficient design of the work systems. It can also identify problems and their effects on the process, e.g., if individual analysed orders deviate from the usual material flow. The participants can select a heat map or a spaghetti diagram to visualise the recorded data. As a new research field within the learning factory, IFA is investigating how RTLS technology can assist intralogistics. Specifically for the learning factory environment, this means the development of a fleet control logic for AGVs based on real-time position data and order confirmation data to realise efficient intralogistics in a highly changeable learning environment. A concept consisting of a 3D laser scanner and virtual reality headsets was developed to support the early planning phase of production system design using the potential of digital factory planning within the IFA-Learning Factory. To exploit the advantages of both technologies, a workflow was developed to communicate the resulting potential to the end-users in a training program. The concept developed embeds the technologies into the training to facilitate the participants’ access to a possible introduction of digital tools into the factory planning process. Workshop participants can use a motion capture system to explore issues of physical workload as an essential component of social sustainability. The system can be used, on the one hand, to analyse the execution of the work task and derive measures to improve ergonomics and, on the other hand, to validate implemented measures. This evaluation’s results allow identifying potential areas of application for human–robot collaboration (HRC), as it can be used to identify unergonomic work tasks and set up an appropriate HRC workstation. This IFA-Learning Factory asset was designed, developed, and implemented in the learning environment within the research project SafeMate. The IFA-Learning Factory is part of a preliminary study within a current research project to investigate the learning behaviour and the competence development and reduction of employees over time. The aim is to collect data on training losses and to acquire experience in planning and conducting experimental studies in learning factories.

482

11 Best Practice Examples

Operational Concept The starting point for today’s training concept was to show workshop participants how changes due to organisational initiatives can affect a production system with its employees and resources. The focus was on teaching the tools and methods of lean production and illustrating their potential. Based on this, a modular training concept was developed and combined with the different training scenarios within the IFA-Learning Factory, promoting a holistic understanding of efficient production systems’ design, planning and control. Therefore, different levels of consideration in the design of production systems are mapped in the learning factory: Factory planning, production management and production and workplace design. The overall objective is to provide participants with the knowledge and methodological skills to identify, evaluate, and solve production logistics problems and to achieve a higher logistical performance in their companies. In contrast to conventional planning games, the IFA-Learning Factory’s learning environment is less abstract. The modular training offering is designed to contain basic modules for the major topics of production organisation. Next to the basic modules, various core modules can be selected according to the participants’ wishes, which examine a topic in more detail, e.g., lean tools or digital production monitoring. The flexible infrastructure of the learning factory makes it possible to adapt the planning games accordingly to illustrate the different topics. The participants can thus make implementation decisions consciously and experience consequences and errors practically (e.g., rush orders for the customer caused by misproduction). Moreover, the IFA-Learning Factory is part of the Mittelstand-Digital Zentrum Hannover, funded by the German Federal Ministry for Economic Affairs and Climate Action. As part of the centre, training on the topic of Industry 4.0 is developed and offered free for companies—especially SMEs—to promote digitised production. The latest developments regarding the shortage of skilled workers are causing companies to rethink their human resource strategies. Thus, topics such as professional training and multiple qualifications within the existing staff are becoming increasingly important.

11.18 Best Practice Example 18: Industry 4.0 Lab at the Politecnico di Milano, Italy Authors: Maira Callupea , Walter Quadrinia , Marco Taischa a Department of Management, Economics, and Industrial Engineering (DIG), Politecnico di Milano

11.18 Best Practice Example 18: Industry 4.0 Lab at the Politecnico di Milano …

483

Industry 4.0 Lab Operator:

Politecnico di Milano

Year of inauguration:

2017

Floor space:

200 m2

Manufactured product(s):

Mobile phone with fused PCB

Main topics / learning content:

Industrie 4.0, PP&C, Energy efficiency

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal The Industry4.0 Lab35 is a Learning Factory operated by the Politecnico di Milano. It was founded in 2017, the year after the introduction of the so-called Piano Industria 4.0, a government initiative aimed to norm and sustain the digitalisation of the Italian manufacturing tissue. The early adoption of this manufacturing perspective highlighted indeed how the main issue in embodying “4.0” practices sat in the lack of industrial personnel skilled in the required disciplines (e.g., ICT protocols, data 35

See Fumagalli et al. (2016).

484

11 Best Practice Examples

management, and data-driven algorithms). In order to train students from mechanical and management engineering in these areas, the Manufacturing Group of Politecnico di Milano decided indeed to create a dedicated environment, where students were allowed to improve their skills in solving real industrial problems in a real-like manufacturing environment. Given the traditionally large classes of Politecnico di Milano and the experimental attitude of the initiative, the laboratory has been designed to be reserved to Master of Science thesis students. This choice allowed the university not only to closely evaluate the progresses by the students, but also to involve the students in the research activities carried on by the laboratory personnel: given the “real-like” shopfloor (down-sized machines equipped with industrial PLCs) the laboratory has been indeed exploited as a sandbox where to implement and deploy in safe environment algorithms and analyses designed for third parties. Summarising, the laboratory has been accomplishing three main roles: education and training of students (mainly thesis students), research activities (mainly as a testbed), and communication (when it has been used as a showroom to disseminate the results achieved throughout the research) (Fig. 11.59). Equipment and Products Industry 4.0 Lab hosts three conceptual departments: (i) a fully automated assembly line, (ii) a computer vision-assisted automated robotic station designed to disassemble printed circuit boards (PCBs), and (iii) a robotic station which accomplishes the same task, but in a human–robot collaboration perspective.

Fig. 11.59 Industry 4.0 Lab at the Politecnico di Milano

11.18 Best Practice Example 18: Industry 4.0 Lab at the Politecnico di Milano …

485

The assembly line represents the core of the Industry 4.0 Lab and constitutes the first installation of the environment. From a hardware point of view, it is composed of seven stations which concur to assemble a small (dummy) electronic device (e.g., a remote control). Station 1 is a pneumatically actuated machine which drops the first part of a plastic cover on a hosting metal pocket fixed to a pallet-like carrier. Station 2 is a dual-spindle drill which drills holes on the cover part. Station 3 is a robotic cell (a 6-axes Mitsubishi Melfa RV FR series) which is in charge of mounting PCBs and fuses inside the cover. Station 4 is a computer vision-based quality control station, which acknowledges if the components have been properly mounted. Station 5 is a twin of Station 1 and is in charge to deploy the other half of the cover. Station 6 is a pneumatic press, which closes the cover. Station 7 is a manual station, where the operator can unload finished products and eventually re-instruct the line if the product is not compliant with the work order. All the stations are served by conveyor belts, which are juxtaposed but physically decoupled from each other, meaning that exiting a station, the pallet is “pushed” by its conveyor belt onto the one of the next stations. This solution has been chosen in order to guarantee a major flexibility in terms of layout and in terms of responsiveness to failures. Another feature implemented in the line is the one-product-batch policy, implemented thanks to the usage of Radio-Frequency Identification (RFID) tags on the pallets: as soon as the pallet approaches the working position at each station, the RFID is read and the required machine recipe is applied on the semifinished product. From a software architectural point of view, all the machines of the assembly line are equipped with PLCs hosting OPC UA servers, which are continuously polled by a central software embodying MES and SCADA functionalities: in this way, the software continuously controls the statuses of machines, also detecting when a product is in working position; as soon as the machine is ready to operate on the product, the software enables the correct recipe, sending a REST message which triggers the proper action by the machine. Every machine is indeed completely agnostic with respect to each other. The computer vision-assisted robotic department is composed by a 7-axes collaborative robot (namely a Franka Emika Panda) assisted by a stereoscopic camera (Intel D435i). The purpose of this department is to recognise the circuit elements installed on a PCB and to check the correctness of the acquisition procedure. The station is installed in a controlled light environment to limit the light disturbances in the object-recognition phase. The human–robot collaborative station deploys another 7-axes collaborative robot (Universal Robotics UR5e) coupled with a Robotiq Hand-e end effector and assists an operator in desoldering the components from a PCB providing support in handling the work in progress from/to a heated table, where the soldering alloy is supposed to melt.

486

11 Best Practice Examples

All the departments of Industry 4.0 Lab share their data (with various protocols) in an internal network, where a specifically designed software architecture (namely SHIELD) operates this allows students and researchers to interact with a unique interface in gathering the operational data they are interested in despite of the specific protocol. Operational Concept The Industry 4.0 Lab embodies its mission of training students in several ways including education and research. The course of Smart Manufacturing Lab sees indeed students facing real manufacturing problems recreated in the laboratory environment. Exercises such as extracting data from machines, using the data to derive data-driven models, to then find bottlenecks or solutions to simulated problems. The course has an average attendance of about 120 people per year and the students’ evaluation involves both a written exam measuring the acquired knowledge and a report summarising the methodologies and the evidence gained through the work deployed in the lab. From a research point of view, the laboratory has hosted several activities which related to the implementation of the digital shadow and digital twin of the learning factory, as well as the integration of AGV for internal logistics. One notable mention goes to the FENIX project (GA 760792) which employed the core assembly line to demonstrate the capability of a modern assembly plant to be converted into a disassembly one under a sustainability perspective.36 The demonstration was carried out through two steps: the first step involved the simulation of the production in a digital model developed in a MATLAB/Simulink development environment. During the second step, the assembly line underwent a process of reconfiguration according to the findings of the aforementioned simulation. This research has been included in a journal publication and was built upon the work developed in one of the twenty-four master theses developed in five years of activity.37

11.19 Best Practice Example 19: LEAD Factory at IIM, TU Graz, Austria Authors: Christian Ramsauera , Maria Hullaa , Matthias Wolfa , Kai Rüdelea a Institute of Innovation and Industrial Management (IIM), TU Graz

36 37

See Quadrini and Fumagalli (2022). See Rocca et al. (2020)

11.19 Best Practice Example 19: LEAD Factory at IIM, TU Graz, Austria

487

LEAD Factory Operator:

IIM, TU Graz

Year of inauguration:

2014

Floor space:

80 m2

Manufactured product(s):

Scooter

Main topics / learning content:

Lean production, Energy efficiency, Agility, Digitization

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal The name LEAD Factory is focusing on four main topics of teaching and research: Lean Production, Energy Efficiency, Agility, and Digitalisation. It is operated by the Institute of Innovation and Industrial Management (IIM) at Graz University of Technology (TU Graz). Inspired by learning factories for teaching at the Technical University of Darmstadt and the Technical University of Munich, the initiative for a comparable training environment at the IIM was started and supported by McKinsey & Company. After three years of development, the first learning factory course with 16 students was thought in 2014 with Dr. Markus Hammer and Dr.

488

11 Best Practice Examples

Mario Kleindienst; at that time, the LEAD Factory was called LeanLab. Since then, the range of learning content has been extended as well as adapted and the equipment has been continuously updated. In 2022, the available space in the LEAD Factory has increased from 50 m2 to almost 80 m2 by moving to another facility. The training of students as well as for industrial companies (management and operators) such as ÖBB Technical Services-GmbH, AVL GmbH List, or Knapp AG follow the same principle: After short theoretical sessions where the basics of industrial engineering, logistics management, lean production and industrial energy efficiency are taught, participants directly return to the LEAD Factory and implement what they have learned. Until now more than 800 students (Bachelor, Master, and PhD) and around 300 representatives from industry have been trained in the LEAD Factory. Besides teaching and training, the LEAD Factory is a research platform and more than 25 Bachelor’s theses/projects, 5 Master’s Theses and 2 PhD theses have been completed in the LEAD Factory. Further, about 25 publications have been presented at the yearly held Conference on Learning Factories. In 2018, the LEAD Factory became a member of the International Association of Learning Factories. Since then, members of the IIM contribute to research projects and led working groups initiated by the IALF. In 2021, Prof. Christian Ramsauer became the president of the IALF (see Chap. 10). Equipment and Products The LEAD Factory demonstrates an assembly line for a fully functional, marketavailable scooter in an IIM-adapted design. Fictional production orders distinguish between three variants of this IIM Graz scooter which differ by the wheel color and the attachment of a bell or customised plate. These variants are mainly used to demonstrate production balancing according to lean (e.g., Heijunka board). One assembled scooter consists of 60 parts (Fig. 11.60 left). Depending on the specific training, up to three components can produced by the course participants themselves. A cap is made with a 3D printer and the CNC milling machine is used for engraving the customised plates. The most complex is the manufacturing process of the connection plate between the footboard and the handlebar: a steel sheet is cut to size, drilled, and protected against corrosion (Fig. 11.60 right). For the latter, a small-scale electroplating machine is used. The assembly area (for the end product) is separated from the manufacturing line (for the plate) to be able to use them together as well as separately for training and research purposes. Manufactured connection plates can either be handed over directly to assembly or stored (see Fig. 11.61). In any case, the plates go through a quality check beforehand. An image recognition system is able to check the position and diameter of the three boreholes as well as the quality of the coating. Plates outside of the tolerance are sorted out. Historical data about the quality check is saved in a database for further analysis.

11.19 Best Practice Example 19: LEAD Factory at IIM, TU Graz, Austria

489

Fig. 11.60 Exploded view of the IIM scooter; Connection plate after cutting, drilling, and electroplating

Fig. 11.61 Layout and selected technologies of the LEAD Factory

State-of-the-art technologies also support the assembly process: each workplace is equipped with touchscreens to display instructions or real-time data, a RFID-based control system and smart Andon lights. As an alternative to displays on the workstation, head-mounted displays for augmented reality (HoloLens) are used to guide participants. Other implemented technologies include gesture and mimic control at some workplaces, a real-time locating system, e.g., for parts, products and workers, and augmented reality device (tablet) to monitor operating parameters of the 3D printer. A local server on the factory’s main computer is used for storing process data that can be visualised in real time on a digital shopfloor management board.

490

11 Best Practice Examples

A central energy management tool collects data from both, the assembly, and the manufacturing line. Via a web interface, the data of single devices and workstations, as well as the overall electricity consumption of the LEAD Factory, can be observed in real time. Through stored parameters, operating costs and emissions can also be determined directly via the power consumption. Moreover, participants can use plugand-play-based smart metres to access, process, and use energy data of single devices or groups of interest. To address topics such as ergonomics and workplace design in training and research, the LEAD factory is also equipped with numerous appropriate measuring instruments. A computing unit called Factory Cube acts is used for competence development in the areas of machine-to-machine communication and the Internet of Things. By using an open-source software the principle of the distribution of messages and their content via a publish-subscribe protocol can be showcased. Combined with sensors, monitoring of the machinery, especially the 3D printer is possible. Besides the physical learning factory, the LEAD Factory is also available with extensions in a virtual form for virtual reality. Operational Concept The LEAD Factory is used in six different courses at TU Graz. The curriculum aims to increase the industrial management skills and qualifications of students and industrial personnel. The focus is on industrial engineering aspects such as resource efficiency, logistics management, or factory planning. All trainings at the LEAD Factory follow the same concept: a course consists of one or more learning modules and each module assigned to at least one of the main topics provides different learning situations. Nearly all learning modules of the LEAD Factory start with theoretical input followed by experiencing the actual process. Next, solutions are developed, evaluated, and implemented. Finally, the whole group and the moderator discuss the effects, they have experienced. The benefits of the implemented improvements are also quantified through a comparison of predefined key performance indicators such as throughput time, number of defects, or energy consumption. As in most other learning factories, the didactic and methodological approach of the LEAD Factory is based on the continuous improvement philosophy facilitated by interactive involvement and actions of the participants. Core element is the variable setup with different states which are used during a course. In the early days of the LEAD Factory, the assembly process of the TU Graz scooter was mainly intended for competence development on the topic of lean principles. Starting point is the so-called current state (Fig. 11.62 left) characterised by sub-optimal conditions in terms of workspace, material and information flows, and equipment. Here eight participants need about 13 min to assemble one scooter. The participants analyse the assembly process and identify first improvements. The aim is to actually build an optimised production line and immediately experience the impact of the learned and directly applied methods. Selected solutions are implemented directly, and the improved setup is iterated towards the “future state” (Fig. 11.62 right). For example, working steps are aligned with the takt time. The result is a U-shaped and lean assembly line characterised by a one-piece-flow and a milk run

11.20 Best Practice Example 20: LEAN-Factory at Fraunhofer IPK, Germany

491

Fig. 11.62 Comparison of “current state” and “digital state” of the LEAD Factory

where only four workers (plus one logistician) are taking 3.5 min for assembling a scooter. The final state is the digital state which is also the focus of trainings dealing with digitalisation or energy efficiency. In order to the get to the digital state, participants need to create a digital roadmap based on a business case (case study) and select and implement digital technologies such as smart sensors. In more specific university courses, the students learn to use simulation models to analyse the energy consumption and to evaluate energy optimisation potentials using discrete event simulations of the LEAD Factory. Further, training modules on the concept of agility with a focus on the identification of uncertainties, the development of an agility playbook and the implementation of agility levers, including a simulation game have been developed. In 2023, the course Factory Planning & Design was enhanced to include practical exercises within the learning factory. In addition to the scooter, hand trucks were integrated as a second product. The objective of the exercise is to reorganise an existing layout to optimise disassembly, maintenance including the exchange of parts, and subsequent reassembly of both products. Besides digitalisation and energy efficiency, the infrastructure is used in particular for research on Industry 5.0 including ergonomics (e.g., with exoskeletons) and human-centred operations. Another focus of future research and training will be on CO2 footprints of products and production.

11.20 Best Practice Example 20: LEAN-Factory at Fraunhofer IPK, Germany Authors: Holger Kohla,b , Roland Jochemb , Felix Sieckmannc , Christoffer Rybskic , Natalie Petruscha a Institute for Production Systems and Design Technology IPK b TU Berlin c form. Fraunhofer IPK

492

11 Best Practice Examples

LEAN Factory Operator: Year of inauguration:

Fraunhofer IPK, ITCL GmbH, pharmaceutical company 2014

Floor space:

400 m2

Manufactured product(s):

Pharmaceutical tablets (bottled, blistered)

Main topics / learning content:

Lean management

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal According to a “Pharma Operations Benchmarking Study” of McKinsey, the pharmaceutical industry faces three major challenges: increase the performance of facilities and plants; interconnect and configure facilities and plants; increase quality and compliance. To meet these challenges, a leading German pharmaceutical company established a learning factory called “LEAN-Factory” together with its partners Fraunhofer IPK TU Berlin and ITCL (International Transfer Center for Logistics) in 2014. A main purpose of this learning factory is to function as a competence center for an integrated operational excellence system based on lean management tools and

11.20 Best Practice Example 20: LEAN-Factory at Fraunhofer IPK, Germany

493

the lean philosophy within the whole company. Internally, it has become the number one training facility to qualify employees from the shopfloor level through to the management level from Germany but also from international sites. The focus is on lean methods and tools, standardisation, mindset and behaviour as well as performance culture.38 Since there is mostly a mixture of participants from different sites and functions, the trainings also serve as a knowledge exchange platform. In addition to the employee trainings, there is special training for students from universities. Equipment and Products The core element of the LEAN-Factory is a realistically replicated pharmaceutical production of tablets. Raw materials are first weighed and subsequently mixed, granulated, dried, sieved, and compressed into tablets. An additional coating is possible if a high variant diversity needs to be demonstrated. Afterwards, the tablets are manually packaged into blisters or bottles and then into boxes. Several quality tests are performed in between, e.g., testing of tablet hardness or residual moistness. The raw materials are substances that are commonly used as excipients in real tablets, with the difference being that food colouring is used instead of an active pharmaceutical ingredient. The production and testing equipment consists of small machines that are normally used for research and development purposes, so that the machine layout can be rearranged easily. To provide a realistic environment of the heavily regulated pharmaceutical production, various activities necessary for the compliance with rules of the “Good Manufacturing Practice” (GMP) are integrated. For each product, the production process is documented in a detailed batch record. All production, testing and changeover activities are accompanied by thorough cleaning processes (Fig. 11.63).39 Operational Concept When it comes to the conduction of trainings, always two trainers, one internal and one external (from the research partners) are part of the team. The main topics of the first trainings were: seven types of waste, 5S, standard work, key performance indicators (KPIs), performance culture, problem-solving and in some special courses single-minute exchange of die (SMED). So far over 100 trainings with more than 2000 trainees from different hierarchy levels (management and shopfloor) were realised. Additionally, round about 250 students attended the special university trainings. The main topics for the management and shopfloor trainings are the same but differ regarding the learning objectives. The management trainings focus more on how to lead and support employees to realise the lean philosophy while the shopfloor train-

38 39

See Rybski and Jochem (2016). See Seliger et al. (2015).

494

11 Best Practice Examples

Fig. 11.63 Layout, process, equipment, and products of the production environment

ings focus on the actual implementation, execution and sustaining of lean elements. The university trainings combine both trainings and include additional exercises to implement lean elements with a focus on sustainability.40 For all target groups, a systematic approach (problem pull) is used, as shown in Fig. 11.64. It shows the general procedure to teach the different topics. Always starting with an observation of a semi-optimal situation, getting a short theoretical input to realise a better status afterwards, which will be measured in the end. During this process, the trainers first serve as a typical trainer to get more and more passive in the role of a coach later. To portray a realistic pharmaceutical production, students are acting in the role of operators demonstrating problems and improvements. This approach gives the trainees the chance to observe and reflect on the situation from a more passive point first. Since the LEAN-Factory acts as a training facility of the company-specific operational excellence production system, it has to adapt to changes. After the successful introduction of the production system, further development stages were defined and required a corresponding offer for training purposes and also for new functions in the company in the LEAN-Factory. For this reason, the portfolio of the learning factory has now been expanded by the partners to include aspects of Total Productive Maintenance, technically oriented problem-solving and change management. Another point of change concerns the scope. Triggered by the pandemic situation and access restrictions, individual topics were additionally digitised according to the 40

See Stock and Kohl (2018).

11.21 Best Practice Example 21: Lean Learning Factory at FESB, University …

495

Fig. 11.64 Didactical design of the LEAN-Factory41

concept described above. In further course, it is the aim of the partners to promote this digitisation in order to be able to offer support for the participants after the training on the one hand, and on the other hand, to be able to provide access to the LEAN-Factory trainings to an even larger audience. With this in mind and since the learning factory is not only a training facility but also a competence center for different topics, the whole concept is part of continuous improvement. Therefore, all the taught methods and tools are used to control and live the processes to manage the learning factory itself.

11.21 Best Practice Example 21: Lean Learning Factory at FESB, University of Split, Croatia Authors: Nikola Gjelduma , Ivica Vežaa , Marko Mladineoa , Marina Crnjac Žiži´ca , Amanda Aljinovi´ca , Andrej Baši´ca a Department of Industrial Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB), University of Split

41

See Rybski and Jochem (2016).

496

11 Best Practice Examples

Lean Learning Factory, FESB, University of Split Operator:

FESB, University of Split

Year of inauguration:

2011

Floor space:

300 m2

Manufactured product(s):

Karet (children‘s vehicle), Gearbox

Main topics / learning content:

Lean production, Industrie 4.0 / 5.0

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Lower

Researcher

Profit-oriented operator

Industrie 4.0

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal The development of the Lean Learning Factory at the Laboratory for Industrial Engineering of Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB) has started after the workshop and the establishment of the International Association of Learning Factory at PTW in Darmstadt 2011. The main idea of Lean Learning Factory at FESB was to base it on a didactical concept emphasising experimental and problem-based learning using tools and methods from Lean management. The learning of the continuous improvement philosophy was facilitated by the interactive involvement of the participants (students or industrial employees).

11.21 Best Practice Example 21: Lean Learning Factory at FESB, University …

497

From the very beginning, Lean Learning Factory at FESB was a part of “Network of Innovative Learning Factories.” Vision of Lean Learning Factory at FESB is to be a place where University, Industry and Government meet each other, share needs and expectations, and work on collaborative projects. Mission of Lean Learning Factory at FESB is to help bring the real world into the classroom by providing practical experience for engineering students, to help transfer the latest scientific research to industry through collaborative projects and LLL, and to help government identify the needs of industrial enterprises. The Lean Learning Factory at FESB was designed to be a “living lab” for research, development, demonstration, and transfer of knowledge to Croatian economy. It was used to develop a Croatian model of Innovative Smart Enterprise (HR-ISE model) during the “Innovative Smart Enterprise (INSENT)” project (CSF grant). Nevertheless, it was used as a research base for other projects, as well: “Logistics personnel excellence by continuous self-assessment (LOPEC)” (EU-LdV grant), “Network of Innovative Learning Factories (NIL)” (DAAD grant), “Know-how Exchange on the Consequences and Challenges of the Integration of Key Enabling Technologies in European Manufacturing for the Danube Region (DanKETwork)” (Fraunhofer ISI grant), and “Development of integrative procedure for management of production and service improvement process (DEPROCIM)” (UKF/CSF grant). The purpose of the Lean Learning Factory Split is education of the students, workshops for foreign student groups and professors, implementation of Lean and green concept in economy through seminars and workshops, scientific research activities, and innovative product development. Equipment and Products The layout of the Lean Learning Factory together with the associated equipment is shown in Fig. 11.65. Two parallel assembly lines were installed: one according to conventional organisation layout found in most of assembly working places within Croatian manufacturers, and a second which is improved by using Lean tools and methods, together with some smart components. These two lines are used to demonstrate major differences in the effectiveness of assembly systems to the learner, from organisational, technical and ergonomics point of interest. By deeper analysis of both assembly lines, hybrid assembly lines could be designed, to balance the assembly tact time according to customer demand on one side, and total cost of installation and running on the other side. In effort to make hands-on learning process more familiar to mechanical and industrial engineers and industry employers, assembly stations and conveyer lines for gearbox assembly have been developed (Fig. 11.66). Complete gearboxes, originally from car FIAT 128 and its succeeding models, are used to show learners the effect of numerous tools and methods for improvement and resolving assembly, warehousing, and inbound logistics problems. Real assembly stations and tools for complex product assembly, together with real products for assembly, give the opportunity to learners for further development of balanced assembly lines, assembly documentation and

498

11 Best Practice Examples

Fig. 11.65 Layout of Lean Learning Factory at FESB, University of Split

procedures, conveyor system or other transport system, clamping tools, measurement procedures and quality assurance tests. Since the integration of information and communication technology into manufacturing systems is generally considered as the next step of the industrial evolution (Industry 4.0), this new industrial platform was introduced in the Lean Learning Factory at FESB. First of all, gearbox assembly line was equipped with Industry 4.0 elements. Secondly, an assembly line for product called Karet (traditional children

Fig. 11.66 Assembly line for gearbox in accordance with Industry 4.0 trends

11.21 Best Practice Example 21: Lean Learning Factory at FESB, University …

499

vehicle in Split, Croatia) with elements of Industry 4.0 was developed. The idea was to implement a system of RFID sensors, create a manufacturing execution system (MES), and connect it with enterprise resource planning (ERP). Product Karet was designed in “Siemens NX” software with modular construction, which enables the selection of desired model through configurator software. Different modules which can be selected are: • wheels are ball bearings or wheels for in line skates or from transportation boxes on wheels (ball bearings are used in traditional construction), • brake system or no brakes at all, • extendable chassis for legs support or fixed chassis, • parts of different materials for achieving different weights and ergonomics. In the assembly line of Karet that contains four working units with Lean assembly stations, the following Industry 4.0 elements have been installed: RFID readers for product tracking through assembly line, interconnected tablet computers at every working unit, connected altogether on online server with central ERP and MES database (Fig. 11.67). Combination of these elements creates one simple MES, which enables production monitoring in real time. The complete MES is connected with the ERP; i.e., the production is initiated by working order created in the ERP. Operational Concept Lean Learning Factory at FESB has been integrated into the education of students and employees on all levels: • • • • • •

undergraduate lectures. bachelor thesis. graduate lectures. master thesis. postgraduate study lectures. doctoral thesis.

Fig. 11.67 Assembly line of Karet with vertical integration of ERP and MES

500

11 Best Practice Examples

• professional study lectures. • professional study thesis. Lectures are supplemented with exercises which are held in laboratory where Learning Factory is located. Students can experience practical effects of various tools and methods implementation in environment which is simulated and simplified production plant. Laboratory setup, besides computers with PLM software, includes didactic games specially developed for the simulation of production and logistic systems. On the other side, real assembly tables and tools for complex product assembly, together with real products for assembly, gives opportunity to students for further development of balanced assembly lines, assembly documentation and procedures, conveyor system or other transport system, clamping tools, measurement procedures and quality assurance tests. Furthermore, the establishment of new Master Study program, Product Life cycle Management, together with introduction of new study courses (Project Lean Management, Lean Management, etc.) gives interested students possibility to reach a certain level of specialisation in the field of emerging operational management methodologies. Lean and green concept is implemented in large and small-and-medium-sized enterprises in Croatia and Bosnia and Herzegovina (manufacturing industry, services, banks, etc.). Implementation starts with the selection of implementation team consisting of enterprise’s employees and external experts from university. Team consists of 15– 30 employees, depending on the size of enterprise, from all departments (design, administration, production, etc.) and all hierarchical levels (from top management to machine operators). The implementation program is divided into three steps. It all starts with learning basics of Lean, i.e., philosophy of Toyota Production System as the first step. The second step is learning of Lean tools and methods (5S, Kaizen, VSM, etc.) which employees try to apply on their workplaces and in their work tasks. To achieve sustainability of Lean implementation, the third step is to help top management to acquire Lean thinking. At the end of the training, each employee gets its project task for his/her workplace and work tasks. After consulting with experts from university, each employee presents his/her results.

11.22 Best Practice Example 22: Lean School at Faculty of Industrial …

501

11.22 Best Practice Example 22: Lean School at Faculty of Industrial Engineering, University of Valladolid, Spain Authors: Angel Gentoa , José Pascuala , Ignacio Hoyuelosb , Jessica Pinoa a Escuela de Ingenierías Industriales, Universidad de Valladolid b Independent consultant

Lean School (LS) Operator:

Lean School, U Valladolid

Year of inauguration:

2014

Floor space:

305 m2

Manufactured product(s):

L34N car, cubition

Main topics / learning content:

Lean production

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

Healthcare

502

11 Best Practice Examples

Overall Goal In 2013, the Department of Business Organisation and CIM of the University of Valladolid decided to give a boost to the training of our students in the Faculty of Industrial Engineering. At the same time, the Renault Nissan group consulting firm (RN Consulting—rnconsulting.es) was considering the creation of a “learning factory” for the training of its middle managers and executives similar to the ones used in other European countries in the automotive sector (Abele et al., 2019; Cachay et al., 2012; Tisch et al., 2013). Given the long relationship of cooperation that the authors of this article have always maintained with the director at that time, PhD. Antonio Fernández, and his strong commitment to the University of Valladolid, the first learning factory in Spain was inaugurated in January 2014 (Fig. 11.68), in the presence of the Rector of the University of Valladolid. Through the collaboration between the employees of the consulting firm and the professors of the Faculty of Industrial Engineering, different training courses have been developed, introducing new products in the manufacturing lines (with different shelves and workstations), favouring the continuous learning and recycling of products and training elements (Pascual et al., 2019). The main goal has always been teaching and learning the five principles of lean manufacturing (Specify the Value, Identify the Value Stream, Establish the Flow, Establish the Pull and Continuous Improvement), the 7 + 2 wastes (Transportation, Inventory, Motion, Waiting, Overproduction, OverProcessing, Defects, Talent, and Resistance to Change) without forgetting Respect for People and Jidoka, in an environment similar to the real factory, so that, the tools used would be quickly reflected in the daily work. But continuous improvement is not only explained but also applied on a day-to-day basis by introducing new products and new concepts such as the circular economy and, more recently, the integration of digital technologies and new e-learning scenarios in collaboration with consulting firm (ARN Consulting—arnconsulting.es). The participants in the Lean School training courses come from two main sources: students from the University of Valladolid (undergraduate and master’s degree) and workers from surrounding companies (mainly from Renault, but also from other industrial and service companies). Equipment and Products Since Renault’s factories around Valladolid are mainly focused on vehicle manufacturing, the first product the training started with was an educational toy car (L34N) weighing about 25 kg and with more than 100 parts. There are two main versions (minivan and pickup) but they can be manufactured in more than 1500 different versions depending on the options of colour, wheels, roof, seats, board panel, interior equipment, lights, or accessories (Fig. 11.69).

11.22 Best Practice Example 22: Lean School at Faculty of Industrial …

503

Fig. 11.68 General view of the Lean School

Fig. 11.69 Two basic versions of the educational toy car (minivan and pickup)

In the Lean School’s production environment for the manufacture of the car, two main areas can be distinguished (Fig. 11.70): a machining and stamping area and an assembly area. In the first area, the front and rear bumpers are machined, passing through four workstations where quality and maintenance problems are analysed and the roofs of the vehicles are manufactured by stamping where concepts such as SMED, poka yoke, and standardisation of operations are explained. In the assembly area, vehicle production passes through three isolated work cells (with intermediate products to be transported internally) until it reaches the finished product warehouse after passing through the last quality control station. Each of these work cells must be supplied with raw materials and intermediate products in different handling elements (containers that are stored on the floor, boxes of different sizes to be placed on shelves) by the logistics workers. In addition, there is a team leader who must manage the different orders of the factory, assisted by indirect personnel who control the manufacturing times of each workstation. In the assembly area, the exercise usually starts with the production in batches of four versions (minivan-pickup, blue-green) and after analysing the problems that have arisen, considering the training objectives,

504

11 Best Practice Examples

Raw material warehouse WIP

Machining and stamping

Lean corner

Assembly area Finished products warehouse

Fig. 11.70 Initial layout of the Lean School to manufacture cars (L34N)

the participants evolve the factory by applying different tools: 5S, levelling of work stations, Kanban, visual controls, standard operation procedures, quick changeover, and even design of new plant layout to reduce movements and after the elimination of work in process. In this way, participants can better assimilate the concepts that have been briefly explained to them in the previous theory sessions by applying learning-by-doing, based on their own experiences. In any case, depending on the number of people participating in the training, as well as their initial level and the objectives to be achieved, the configurations can be varied to adapt to different circumstances, reaching more than 1500 different versions (normal or led), interior equipment (air-conditioning or climate control, navigator, multimedia, dash cam) seats (normal, colour, leather) or accessories (tow bar, roof rack). At the end of the training, to ensure the sustainability of the learning factory, all vehicles are disassembled, and the main parts are reused, but not the connecting elements (plastic rivets and screws).

Analysis

Training

505

Round 4

Re-configuration

Analysis

Training

Round 3

Re-configuration

Analysis

Round 2

Training

Re-configuration

Analysis

Training

Round 1

Evaluation

Information

11.22 Best Practice Example 22: Lean School at Faculty of Industrial …

Fig. 11.71 Basic training process42

In addition, new products have been designed applying the same concepts focused on other sectors (healthcare, industry in general or FMCG products) (Pascual et al., 2019) and processes have been improved by introducing the concepts of recycling and circular economy. Operational Concept The training courses are designed considering a set of sequential evolutionary stages in which, as the stages progress, new concepts are introduced, and different lean tools are practised and applied (Tisch et al., 2016). The first point in each training course is an initial assessment followed by a description of the initial Lean School configuration, typical in many factories, characterised by isolated cells, unbalanced workloads between workstations, poor definition of operations at workstations, many intermediate stocks on shelves and work in progress at workstations. Each stage ends with a quick analysis and a detailed analysis using different lean tools (VSM, spaghetti charts, flow charts, analysis of areas, Heijunka panels, 5W, A3) to determine the improvements to be implemented in the next stage. Additionally, the most important KPIs (Quality, Cycle Time, Takt Time, Lead Time, Overall Equipment Effectiveness (OEE), Total Effective Equipment Performance (TEEP), Direct and Indirect Labor, Overall Labor Effectiveness (OLE), Warehouse and Production Area, and Costs) are collected in a Scorecard to see the evolution along the production. To move on to a new improved stage, one or two 4–5 h sessions at the Lean School are usually necessary, depending on the experience and previous background of the participants and the tools applied (Gento et al., 2021). Thus, each training course is composed of a series of stages (Fig. 11.71), whose number depends on the available time and the objectives to be achieved, from two stages for the specific training courses to eight or nine stages for the most complete master-level training courses. Trainers are usually 2 or 3 people (university professors and consultants/workers working in a coordinated way) to provide academic rigour and practical experience. In this way, the group of participants (15–20) can be subdivided into smaller subgroups to practice and discuss the different tools to be taught.

42

Adapted from Gento et al. (2021).

506

11 Best Practice Examples

Takt-time 100 Area

75

Quality

50 Round 1

25 Movements

0

Quantity

Round 2 Round 3 Round 4

Labour costs

Lot size Investment costs

Fig. 11.72 Evolution of the main learning KPIs

Although all results are progressively improved depending on the objective of the training, the first is to ensure the quality and safety KPIs of the operations. Secondly, participants focus on the elimination of tasks that do not add value to the process and on the levelling of workstations. Thirdly, improvements in logistics flows allow for a very significant reduction in floor space. Finally, once the customer’s target delivery time has been reached, many more product variants are introduced, increasing the complexity of the manufacturing process from a push production with disorganised and unbalanced flow, with low standardisation to a one-piece flow pull production with balanced flow using kits (Fig. 11.72). At present, the factory continues to be improved by digitising some workstations to obtain information automatically and display it in real time on panels with the aim of evolving to a virtual factory in collaboration with a specialised company (Arsoft— arsoft-company.com) based on one of the principles of the Lean School which is to promote university and company collaboration.

11.23 Best Practice Example 23: Learning and Research Factory (LFF) at the Chair of Production Systems, Ruhr-University Bochum, Germany Authors: Christopher Prinza , Marius Knotta , Bernd Kuhlenköttera a Chair of Production Systems, Ruhr-University Bochum

11.23 Best Practice Example 23: Learning and Research Factory (LFF) …

507

Learning and Research Factory (LFF) Operator:

LPS, Ruhr-University Bochum

Year of inauguration:

2009

Floor space:

1,800 m2

Manufactured product(s):

UniLokk (bottlecap), switch box

Main topics / learning content:

Lean production, Industrie 4.0, Robotics, AI

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal In 2008 during a visit of the CiP in Darmstadt the first idea of a learning factory at the Ruhr-University Bochum came to life. The Chair of Production System (LPS, German “Lehrstuhl für Produktionssysteme”) already operated a Pilot Factory, where application-oriented research projects were carried out. Thus, the idea of action-based and problem-solving-oriented learning was initiated in 2009 and the first training was implemented and performed with students. During the first few years, a lot of experience was gained concerning the learning success of newly developed concepts thus improving these concepts and implementing more scenarios for teaching methods

508

11 Best Practice Examples

on process optimisation. In 2011, a cooperation with the consulting company LMX was initiated to develop training for business customers. In close cooperation, a curriculum was developed, and the first trainings were held. The goal of teaching and training activities is the sustainable development of competencies in the fields of lean management and the digitalisation of manufacturing processes. Since the first start of the learning factory in Bochum, the LPS has continuously increased the training quality and number of topics. The dedicated work of the whole Learning and Research Factory (LFF, German “Lern- und Forschungsfabrik”) team has been the driving factor of its success. Starting with teaching lean methods, nowadays the application of artificial intelligence methods (machine learning), different approaches for worker assistance systems (WAS), human–robot collaboration (HRC), worker’s participation and co-determination, application of maturity models for digitalisation and Industry 4.0 projects, the use of virtual reality (VR) for further education purposes and much more can be experienced in the LFF in Bochum. Additionally, visitors and participants receive the latest insights from current research projects and their goals through numerous research cells and developed prototypes. Equipment and Products The Learning and Research Factory (LFF) is fully equipped with different production machines (OPC ready, retrofitted, conventional), assembly lines (manual and hybrid), industrial robots, cobots, software systems, worker assistance systems, measuring equipment tools, high-speed cameras, 3D printers, 5G network and Wi-Fi. With its state-of-the-art equipment, the LFF is a place for research, teaching, qualification, and industrial cooperation. Figure 11.73 shows the lower floor of the two-story building. The two products which are used in training scenarios (see Fig. 11.74) are the photovoltaic switch box and the UniLokk (bottlecap holder). Both products have their unique possibilities for trainings. With the assembly of the switch box, it is possible to experience an automated hybrid assembly line with intense interaction between robots and workers from simple coexistence to extensive collaboration. Besides the demonstration of existing cooperation stages and collaboration types, this hybrid assembly line provides the necessary learning environment for designing and

Fig. 11.73 Learning and Research Factory (LFF) of the Chair of Production Systems (LPS)

11.23 Best Practice Example 23: Learning and Research Factory (LFF) …

509

balancing work cycles between robots and humans for the best output. The assembly line is also fully digitally simulated. A digital twin, which integrates different types of PLCs, robots, etc., can be used to commission the assembly line before start-up. The classical manual assembly line for the UniLokk is still in use and represents a very flexible production system that can be used to demonstrate as well as experience different stages of Industry 4.0 applications and lean stages. During lean training, this assembly line offers the opportunity to apply knowledge of different methods, such as 5S, value stream mapping, value stream design, chalk circle, cardboard engineering, shopfloor management, spaghetti diagrams, one-piece flow, etc. With the complementation of different cognitive worker assistance systems, RFID tracking systems for production data acquisition, digital data continuity and AI application for quality control, this assembly line offers state-of-the-art technologies for industrial and student participants. Furthermore, it also allows the application of value stream mapping 4.0, due to a holistic production process that includes production machines that produce the steel parts for the UniLook. This means the LFF also contains a digital data structure to collect data from the machines, assembly lines, etc. With the collected data, different easy-to-implement visualisations and state-ofthe-art applications from industrial partners can be experienced (e.g., andon boards, production dashboards, digital shopfloor management) and the fully implemented manufacturing execution systems (MES) Hydra by MPDV is supplied with data. The LFF represents a typical SME with its software and hardware infrastructure and provides an optimal opportunity for future engineers to learn about state-of-the-art production systems on the one hand, and it provides companies with the means to experience and test Industry 4.0 technologies on the other hand. Besides the training side of the LFF it has always been a place for research on automation, worker assistance, resource efficiency, industrial robotics, and production management topics, see Fig. 11.75. Operational Concept The concept of the learning and research factory (LFF) is predicated on the possibility of using the same infrastructure for both research and learning. A key component of this dual use is the quick low-effort conversion from research to learning purpose, the competence of our trainers to teach in this environment, and the ability to abstract the methods and technologies from this existing infrastructure to the participant’s infrastructure. Thus, besides technological availability in learning factories, another core element is organisational adaptability as well as human competence.

510

11 Best Practice Examples

Fig. 11.74 Manufactured products in the Learning and Research Factory (LFF)

Fig. 11.75 Examples of digitisation solutions in the LFF

For a while now the LPS had the opportunity to be a main partner in transfer projects which mainly focus on the training of SMEs in the transformation towards Industry 4.0 as well as projects focusing on further education. The LFF was part of the “Mittelstand 4.0-Kompetenzzentrum Siegen” and a main location for technology

11.24 Best Practice Example 24: Learning Factory (CUBE) at the Department …

511

Fig. 11.76 Chronological development of the Learning and Research Factory, LFF

tours as well as application-oriented workshops. The follow-up project “MittelstandDigital Zentrum Ländliche Regionen” funded by the Federal Ministry for Economic Affairs and Climate Action, is also focusing on transfer activities for SMEs, and thus the LFF is also a competence center where information events, training, and workshops are being organised free of charge for companies. Figure 11.76 shows an overview of the chronological development of LFF since 2009. Today, the LFF of the Chair of Production Systems (LPS) is a place for innovative education, training, fundamental, and industrial application-oriented research. The continuous development of the LFF is rooted on the one hand in the ongoing research which leads to new demonstrators and the possibility to evaluate new methodologies or technologies in a real-world environment. On the other hand, transfer projects, such as the “SME-Digital Center,” provide the opportunity for the LPS to further evolve the infrastructure of the LFF as well as gain practical experience through the integration of more industrial participants in free-of-charge training and seminars.

11.24 Best Practice Example 24: Learning Factory (CUBE) at the Department of Design, Production and Management (Faculty of Engineering Technology), University of Twente, Enschede, The Netherlands Authors: Eric Luttersa , Sebastian Thiedea a Department of Design, Production and Management, Faculty of Engineering Technology, University of Twente, Enschede, The Netherlands

512

11 Best Practice Examples

Learning Factory ‘Cube’ Operator:

ET/DPM, University of Twente

Year of inauguration:

2024

Floor space:

1,500 m2

Manufactured product(s):

Wide variety of products, e.g. small drone

Main topics / learning content:

Processes, Logistics, IoT, Digital twinning, Smart industry, Lean, Production design

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

Top

Semi-skilled workers

Unskilled workers

Management

…

Design

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal Over the past decades, the University of Twente, and in particular the educational programmes for Mechanical Engineering and Industrial Design Engineering, has adopted a project-oriented approach to education. One of the essential components of this project education is that academic students (B.Sc., M.Sc., and Ph.D.) are immediately challenged to apply the theoretical knowledge acquired in practical/ research projects. In this way, acquired knowledge takes root better; moreover, students are immediately confronted with the consequences and repercussions of their reasoning and design. Courses and projects related to manufacturing processes

11.24 Best Practice Example 24: Learning Factory (CUBE) at the Department …

513

and environments have become increasingly interrelated. Firstly, projects and theoretical courses subsequently build on the expertise and experience gained. Secondly, different courses/projects belabour diverse aspects of manufacturing engineering, thus providing different perspectives on a similar environment. At the University of Twente, several workshops are available for students, researchers, and technical staff to build prototypes, but also to research production processes, logistics, quality control etc. Over time, with an increasing number of students and research projects, the need and opportunity arose to establish a larger scale, unfragmented, manufacturing environment. From the outset, the design of this environment focuses on the integration of education and research. Various existing workshops will be consolidated and expanded into a new environment (the “CUBE”), currently under development and due to open in 2024. The “CUBE” is a separate building housing generic workshops for metal and woodworking, model making and assembly. These workshops can be used to build prototypes for educational projects, while at the same time allowing researchers to focus on production processes, design, planning, logistics, factory layout/planning, etc. On a separate floor, the “CUBE” houses a dedicated production/assembly line with a more well-defined scope, but with extensive (modular) flexibility and configurability. This line also allows for the integration of education and research; moreover, it extends into the Virtual Reality lab, which allows for virtual testing or commissioning of factory layouts, plannings, operator support etc. This VRLab also allows for what-if scenarios, comparing alternative factory layouts, schedules, disruptions, and the like. In this approach, the overall goal is to integrate research and education, for the benefit of both the learning experience for the students and the realistic (dynamic) environment for the researchers. In this context, serious gaming is an essential way to immerse learners in increasingly complex and scaled-up environments—ultimately contributing to daydreaming factories. Equipment and Products The more generic metalworking workshops mimic realistic job-shop-oriented industrial facilities and provide a wide range of machine tools for prototyping. These range from laser cutting and bending via milling and turning to additive manufacturing and injection moulding. The equipment itself is relevant here, but so too are certainly also the connectivity, sensoring, and IoT approaches that will be applied in this environment, alongside, for example, location and presence detection and user-dependent machine accessibility. This illustrates the multifaceted approach between education and research. The separate woodworking and modelmaking workshops have similar setups, with an additional focus on hand tools. Figure 11.77 gives the envisaged layout for a part of this workshop. The detached production/assembly line will house multiple modular workstations or setups, each providing specific assets or equipment for a production or an assembly process. For example, the modules will provide laser cutting/ engraving, CNC milling, 3D printing, and collaborative robots, as well as also traditional manual assembly stations. As an immobile asset in the line, a temperature/humidity-controlled measuring chamber with, for example, a 3D coordinate

514

11 Best Practice Examples

Fig. 11.77 Envisaged layout for part of the generic workshop in the “CUBE”

measuring machine. With the modular approach, the line will be flexible in nature, so that the (re)configuration of the line for changing/different products or product portfolios is also a topic of study and research in this production/assembly line. The line will be used in teaching/research for the production/assembly of a product portfolio that could be characterised as “shoebox”-sized product. Products will vary in complexity and batch/lot size, while ranging from purely mechanical to mechatronic products. One of the first products will be a small drone, with different geometries/designs, processes and assembly strategies/steps defining the portfolio. Product types, variety and production/assembly equipment will be added and modified on an ongoing basis, if only to reflect the complexity of realistic environments. In preparation for the production/assembly line in the “CUBE” several production/assembly modules have already been built and are currently being tested in smaller, distributed, educational setups to gain experience and to iterate on the technical, logistic, and educational capabilities of the modules and the resulting production/assembly line. At the same time, systems are being developed, implemented, and evaluated that will drive the operations of the overall facility, from PLM/ERP/MES, through RTLS to inventory/stock management and traceability. The Virtual Reality “extension” of the production/assembly line obviously allows for the creation of a synthetic environment, in which the physical line can be embedded in a much larger and more complex virtual environment. This not only makes activities more realistic, but it also allows for better contextualisation of decision-making, reflection on processes and results, and on quality management, to name but a few. Operational Concept The concept of the learning factory is based on the many different perspectives that characterise a production environment, and the evolvement of capabilities that learners go through during their studies and beyond. Thus, the learning factory, and in particular the production/assembly line will target academic learners and researchers from different (educational) backgrounds, with the explicit intention

11.25 Best Practice Example 25: Learning Factory jumpING at Heilbronn …

515

Fig. 11.78 Students of different educational programmes interacting with the learning factory— and with each other—at different levels of aggregation

to focus on multi-disciplinarity—both in education and research. This means that students from the target group (different academic programmes at the University of Twente) will work together—both as peers, but also in roles that have different levels of aggregation. For example, beginning learners who study, for example, takt times or stock management provide input to more advanced learners who study, for example, line balancing or optimisation. They can learn from each other, where, for example, the advanced learners internalise knowledge by being challenged to guide or tutor the beginning learners. This approach leads to a contemporary recursive master-apprentice approach, where different educational programmes and learners at different levels/maturities can work together. Figure 11.78 illustrates this approach, where students, under the supervision of teaching/research staff, progress from “experiencing” the environment to conducting their own purposeful research in the same environment. Moreover, with the envisaged embedding in industrial systems (such as PLM/ERP), digital twinning approaches and extended synthetic environments, the complexity of the tasks/assignments in the production/assembly line can be tailored to the maturity level of the students. The paradigm applied is that there is no real distinction between learners and researchers; advanced learners and researchers simply have a head start. Since most teaching staff also have research responsibilities, they can also employ the learning factory as a living laboratory, where they can actively participate in the teaching and learning community, together with all levels of learners, with only a head start over beginning learners.

11.25 Best Practice Example 25: Learning Factory jumpING at Heilbronn University, Germany Authors: Patrick Balvea , Markus Grafa , Juliane König-Birka

516

11 Best Practice Examples

a

Heilbronn University of Applied Sciences, Faculty of Industrial and Process Engineering

Learning Factory “jumpING” Operator:

Heilbronn University of Applied Science

Year of inauguration:

2011

Floor space:

800 m2

Manufactured product(s):

Mostly small mechatronical devices, each semester a new product challenge Experience the entire product creation process from idea generation to small series production

Main topics / learning content: Morphology excerpt

Open models

Target industries

Digitalisation Open public

Job-seeking

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal After almost three years of planning among the lecturers’ team of the bachelor’s programme “Manufacturing and Operations Management” (MOM), the first learning factory course at Heilbronn University started in the winter semester of 2011. The aim was to build up a learning arena that exposes students to the entire product creation

11.25 Best Practice Example 25: Learning Factory jumpING at Heilbronn …

517

process in just three months—from idea generation to small series production—and to apply competences acquired in previous semesters in an environment comparable to industrial reality. The underlying didactical concept is a problem-based and project-oriented learning approach, thus giving students individual freedom in gaining new knowledge and solving arising challenges. Since the course is positioned in the penultimate semester of the study programme, and to emphasise its purpose of best preparing future engineering graduate students for their professional careers, it has been brand named “jumpING” (ING is the German abbreviation for an engineer). Against this backdrop, our learning factory is aimed at the development of the students’ socio-communicative, methodological, and personal competences combined with the deepening of their engineering knowledge. Equipment and Products The jumpING concept allows students to fully emulate a real-world manufacturing company in an authentic industrial environment (see Fig. 11.79). The factory comprises 570 m2 of undivided shopfloor space with technical equipment (e.g., CNC machine centres, conventional tooling area, automated and manual storage areas, manual and semi-automated assembly workstations) and additional 230 m2 of student workspace and computer rooms providing dedicated software. Counting a total of 800 m2 , our learning factory is one of the largest single facilities dedicated to student education at Heilbronn University. The product engineering assignment is unique to each semester. However, there are strong similarities concerning the size (i.e., fitting into a shoebox) and technical complexity (i.e., the number of parts, the manufacturing challenges, and the mechatronic features). Product examples are displayed in Fig. 11.80: a model of a Mendocino motor, a dispenser for chocolate-coated peanuts with counting function (‘Choc Mate’), a device which periodically pushes a ball over a rail by magnetic force (‘Magic Ball’), and a timer with a whistle sound whose setting can be modified via a smartphone app (‘Whistle Blower’). Although it is important to provide technically

Fig. 11.79 Floor layout and arrangement of equipment

518

11 Best Practice Examples

Fig. 11.80 Examples of typical learning factory products with QR code to video

demanding details as part of the product assignment, the unfolding problem-solving process and the organisational challenges that come along with it are valued as high as the technical solution itself. Another distinctive feature of the jumpING course is its commitment to series production. For that reason, students are required to develop instructions and documents for parts manufacturing, product assembly, and quality assurance for their specific product. Depending on the number of participants in the project, the students will have manufactured up to 40 functioning units of their product by the end of the semester to take home as a souvenir. Operational Concept Primarily dedicated to hands-on student training, the learning factory course is held twice a year, lasts 15 project weeks, and has a high workload of 15 European credit points (ECTS). The main task for students is to cooperatively engineer and manufacture a functioning and fully documented product, based on a unique project assignment consisting of technical requirements, budget, and time constraints. Similar to industry, students work in small self-organised teams, each performing a different function in the value-added chain. The typical organisational team setup comprises functions and their respective combinations such as product development and software engineering, process engineering, logistics, parts manufacturing, assembly, purchasing, quality management, project management, and IT. Supervision is provided by a team of up to seven professors and additional staff members, thus assuring appropriate guidance for the various functions that need to be covered by students along the product creation process.

11.25 Best Practice Example 25: Learning Factory jumpING at Heilbronn …

519

Fig. 11.81 Project timeline with due dates and milestones

Although the product assignment and students’ composition vary from semester to semester, the project timeline with due dates and milestones is standardised and requires only minor modifications based on public holidays and alike (see Fig. 11.81). On the first two project days, the students are handed over the assignment and subsequently they are fully concerned with team building and elaborating their specific team responsibilities. To allow for the best possible project kick-off, these two days are supported by group facilitation and experiential education. In project week two, students brainstorm on technical solutions for their product assignment followed by three intensive weeks during which competing concept teams develop alternative prototypes. At the end of the prototyping phase, there is another workshop event involving all supervising professors and staff members to assess which of the technical concepts and concept elements will be the most feasible ones given the time and budget constraints. Based on the project timeline, students then take their first examination, i.e., the graded intermediate presentations. A milestone date that students find particularly challenging takes place by the end of week nine: the design freeze. For depending on processes (e.g., parts purchasing and industrial engineering) to be executed properly, it is required that beyond that milestone no changes to the technical drawings and bills of material are allowed any more. From that point on, there are only five weeks left for some remaining planning activities but—above all—for executing the manufacturing, assembly, logistics, and quality assurance activities that eventually lead to the end of production in week 14. The official closing event in week 15 is organised as a public exhibition with up to 100 visitors ranging from potential future students to representatives of regional industrial companies. Once the general lecture period is over, a final oral examination takes place. Until the end of the semester, each student is graded on three levels, thus considering individual, team (‘department’) and semester (‘company’) performance.

520

11 Best Practice Examples

Fig. 11.82 Development of competences in jumpING students

In 2017, a publicly funded project is launched to examine the development of competences in students based on their participation in our learning factory course. That study was carried out as a quantitative ex-post evaluation among graduates who had finished their study programme at least three months ago but no longer than two and a half years earlier. Based on a model consisting of 42 competences, graduates from the years 2015 through 2017 were able to choose a maximum of 6 items to answer the question of which specific competences were fostered by participating in the jumpING learning factory course. Survey results are displayed in Fig. 11.82. They show that the original qualification goals of the course (see above) are met. In particular, organisational, interdisciplinary, problem-solving, and general methodological engineering skills, which are very hard to address all at once in more traditional course formats, can successfully be promoted by a problem-based and project-oriented learning factory concept like ours.

11.26 Best Practice Example 26: Learning Factory of Advanced Industrial Engineering aIE (LF aIE) at IFF, University of Stuttgart, Germany Authors: Thomas Bauernhansla,b , Erwin Grossb ; Jörg Siegerta,b Thilo Schlegela , Marco Maiera a Institute of Industrial Manufacturing and Management (IFF), University of Stuttgart b Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Stuttgart

11.26 Best Practice Example 26: Learning Factory of Advanced Industrial …

521

Learning Factory of advanced Industrial Engineering aIE (LF aIE) Operator:

IFF, University of Stuttgart

Year of inauguration:

2007

Floor space:

350 m2

Manufactured product(s):

Desk tool set

Main topics / learning content:

Lean production, Quality management

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal Opened in 2007, the Learning Factory of advanced Industrial Engineering aIE (LF aIE) in Stuttgart provides education and training for managers, planners, and designers of production processes. For scientists, the LF aIE provides a research facility where new production and organisation concepts can be explored. One research focus is competence development during value creation and how it should be designed in future. Located at the IFF of the University of Stuttgart, the LF aIE drives training and further education of industrial engineers in practice, technical managers, planners, and designers of all branches in curricula that are virtual and

522

11 Best Practice Examples

at the same time practical and real. In the Learning Factory for advanced Industrial Engineering aIE engineers, planners and managers find a unique and innovative combination through a physical model factory, digital learning islands and theoretical modules. The learning factory of the IFF is also the basic building block of excellent post-university education and training in this field. Methods and tools are applied in a digital learning environment and then tested in reality, the physical model factory. The digital and virtual tools improve the effectiveness, efficiency, safety, and reproducibility of planning. Once recorded or—as a result of a planning step—determined data can be continuously forwarded and reused. The foundations have been laid by scientists from Stuttgart in several sub-projects of the Collaborative Research Center “Transformable Corporate Structures in Multi-Variant Serial Production;” involved were institutes of business administration, computer science and mechanical engineering. Project partners of the learning factory are the MTM-Bundesvereinigung, the REFA Bundesverband and the Festo Didactic GmbH & Co. KG. Other cooperation partners such as Delmia, PTC Parametric Technology GmbH, PSI AG, and Siemens AG support the learning factory by providing systems and products for special conditions. Equipment and Products The Learning Factory aIE (iFactory) covers 350 m2 and includes the modules presented in Fig. 11.83.

robot assembly cell manual workplace cell

linear-link cell

branch cell

AS/RS cell

quality inspection cell

90-degree-link cell

line termination cell

Fig. 11.83 Modules of the Learning Factory aIE, picture from Festo Didactic

11.26 Best Practice Example 26: Learning Factory of Advanced Industrial …

523

Depending on the customer’s order, different desk sets (Fig. 11.84) are produced in the LF aIE. More than 10.000. RFID technology enables the complete tracking of the individual production steps. The initial layout of the factory which is being optimised during the training by the participants in a simulation game is presented in Fig. 11.85.

different colors of cup insert 4 variants of additional parts clock thermometer hygrometer magnet

• • • •

3 variants of large cups

3 variants of small cups 2 variants of a cover

2 variants of base plate

Fig. 11.84 Product of the Learning Factory aIE

AL

customer

RS 3

ML2

FM FM

ML1

direction of material flow

FM FM

RS 1

direction of material flow

VM

Manufacturing Stations • FM: milling machine • VM: pre-assembly • AL: automatic store

• • •

work places • 2 x milling machine ML: Manual assembly • 2 x manual pre-assembly RS: Robot station (assembly) • 1 x pre-assembly Customer: customer • 1 x logistics expert • 1 x customer

Fig. 11.85 Layout of the initial situation in the Learning Factory aIE

524

11 Best Practice Examples

Fig. 11.86 Learning by doing simulation game in the Learning Factory aIE43

Operational Concept The trainers are scientists from the University of Stuttgart and Fraunhofer IPA, working on research projects and industrial projects in the field of production optimisation. This ensures a comprehensive expertise and the coaches can respond to specific questions of the participants. The Learning Factory aIE enhances the knowledge transfer from basic research to the application of the methods for industrial engineering. The training modules with a high degree of interaction and application of methods (see Fig. 11.86) are always ranked higher by the participants than theoretically oriented parts of the curriculum. This proves the effectiveness of the approach to teach these topics in a learning factory where they can be applied. The relatively wide scope of the curriculum is found helpful by most participants even if they apply only parts of it themselves. The reason for this is that a broad knowledge makes them understand the overall processes in the enterprise and the tasks of their colleagues. The focus of the existing learning concept is the assembly,

43

Concept according to Bonz (2009).

11.27 Best Practice Example 27: Learning Factory SUM Mostar, Bosnia …

525

because this is where most variants are created and the influence from the customers is high. Manufacturing processes are simulated so that they can be included into the value stream. Manufacturing technologies such as injection moulding or milling have been integrated during the last years. The training group usually includes 12 participants, who are supervised by two trainers. A training workshop lasts for two days, covering various topics in the area of lean management and Industrie 4.0. The focus is put on logistics design and kanban calculation. Other topics include production measurement technology and quality management. Also, short training courses are given, in which the most important topics to Lean Management and Industry 4.0 are mediated. In total, several hundred participants from industry and research are trained each year, but also pupils and students undergo these training courses as a part of their qualification. More than 50% of the participants are international and come mainly from Asia. Learning factories should always be a mirror of research to provide optimal and credible support for transfer. The pure operation of a learning factory is often not economical. The conversion of a learning factory to an application or application center is very promising. In particular, the use of Industry on Campus concepts opens up new potential in transfer or training and further education. The production of real products is still a challenge, but using the resources to support start-ups can make sense.

11.27 Best Practice Example 27: Learning Factory SUM Mostar, Bosnia, and Herzegovina Authors: Željko Stojki´ca , Igor Bošnjaka , Luka Šaravanjaa a University of Mostar, Faculty of Mechanical Engineering, Computing and Electrical Engineering

526

11 Best Practice Examples

Learning Factory SUM, University of Mostar Operator:

Learning Factory, SUM

Year of inauguration:

2018

Floor space:

500 m2

Manufactured product(s):

Lifting platform

Main topics / learning content:

Lean production, Industrie 4.0, Reverse engineering, 3D printing, Metrology, Energy efficiency, Collaborative robots

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal Faculty of Mechanical Engineering, Computing and Electrical Engineering (FSRE) initiated the development of the Learning Factory (LF) in January 2018. The initiative was based on the positive experiences of partner faculties/universities. The FSRE applied for the project co-financed by the EU and implemented by the GIZ (Deutsche Gesellschaft für Internationale Zusammenarbeit) and received a grant for the project Increasing Competitiveness of Small and Medium Enterprises through Creating Business Associations and Establishing a Learning Factory. The FSRE lead this project and is partnered by nine local metal and plastic companies, the city of Široki Brijeg

11.27 Best Practice Example 27: Learning Factory SUM Mostar, Bosnia …

527

and the municipality of Posušje. The role of the Faculty in the project was to develop the concept of the LF in collaboration with the enterprises and to develop the curricula and support for enterprises in the field of education and research. The role of the enterprise in the project was to contribute to the development of lifelong learning curricula and the transfer of knowledge in the field of practical training in the LF. The role of the local community is to support the construction of infrastructure, fostering better cooperation between the academic community and SMEs, as well as raising awareness of innovation among citizens through activities and workshops on the project. Based on all the above-mentioned, the FSRE has successfully established a Learning Factory. The vision of the FSRE LF is to provide education for its students and company employees in different fields. It will be integrated into the undergraduate and graduate study curricula in mechanical engineering, especially in the Department of Industrial Engineering and Management. This means that the LF will be available for bachelor, master and doctoral theses, professional study theses, and other research or professional work. Through different analyses, the FSRE has defined the relevant learning targets and contents for involved stakeholders. The FSRE currently offers education in Lean manufacturing, Shopfloor management, Collaborative robots, Additive manufacturing, Reverse engineering and metrology, CAD tools and Welding education. Equipment and Products Image below presents the current look of the FSRE Learning Factory (Fig. 11.87).

Fig. 11.87 FSRE Learning Factory

528

11 Best Practice Examples

Fig. 11.88 FSRE Learning Factory structure

Figure 11.88 presents the FSRE’s concept of the LF. It covers the whole supply chain: obtaining raw material from suppliers, the production process (product development, production planning, manufacturing, assembly, warehouse management), and product services. It is all supported by the information systems, logistics, automation, and lean tools. Information system. The FSRE has the knowledge, experience, and the required infrastructure for ERP and CRM business information systems to support the entire LF concept. Figure 11.88 shows the diagram of the information system at the FSRE Learning Factory. The configurator placed on the cloud server is used for creating proposals based on customer demand. The configurator is supported by the PDM system which helps generate custom product variation. The ERP system, PDM system (Solidworks PDM), Shopfloor management system (CAS Valuestreamer), IoT data from machines, CRM system (CAS Genesis) and digital signage system are all connected into one system by Connector (com:con solutions Connector). Product development. The FSRE has a long tradition, experienced staff, and the necessary knowledge (CAD software, 3D printers, 3D scanner and other necessary tools/methods) for product design and development. Production preparation. The FSRE has educated staff and infrastructure for production preparation. Also, FSRE is using modern ERP, MES, and CAM software that supports production planning and scheduling. Machining. The FSRE has educated personnel and some of the equipment for machining and welding, but the machines are old and the FSRE needs to acquire newer equipment. Also, FSRE has several modern 3D printers. There are seven 3D printers: Industrial 3D printer- Stratasys F270, 2x Makerbot Method X CF Edition, 3x Zortrax M200 Plus, and Ultimaker 2+. FSRE Learning Factory provides product

11.27 Best Practice Example 27: Learning Factory SUM Mostar, Bosnia …

529

design and manufacturing services using additive technologies (FFF / FDM technologies). The possibility of 3D printing with a wide range of materials, such as PLA, ABS, Nylon, materials reinforced with carbon and glass fibres, PETG, TPU, and many others. Assembly. The FSRE currently has assembly workstations that are also equipped with the touch screens to help workers as a digital assistant. Warehouse management. The FSRE uses a “pick by light system” that automatises warehouse management and an RFID tracking system. Product services. This stands for product distribution and sales activities. The FSRE has a CRM system and experienced staff for this element. Also, with the optical scanner and software, FSRE LF team can perform a wide range of measurements such as determining the deviation results of the digitised measured part concerning the CAD model or workshop documentation, fulfilment control of size, shape and position tolerances and product shape comparison with prototype or CAD sample. Logistics. The FSRE is in the process of defining factory layout and will use the chosen product (lifting platform) and logistic “supermarket” in the intralogistics of the LF. The FSRE plans to use a robot for logistics automation. Automation and robotics. As mentioned above, the FSRE has a collaborative robot UR5e that helps in intralogistics automation. It uses a camera for space orientation, and it could pull the supermarket or actual product-lifting platform. Lean tools and methods. Within the Learning Factory, there are a wide range of teaching materials and games for holding training and seminars (Games for SMED, 5S, Kaizen, Value Stream Mapping). Lean education is carried out through three steps: introduction to Lean basics, education from individual elements and finally Lean philosophy/way of thinking. Based on that, seminars and training for individual levels were conceived. Also, FSRE LF team offer consulting services from Lean Management. Within the FSRE Learning Factory, a lifting platform as its product was developed and produced, which would serve for many purposes (transport table, lifting table, assembly table, etc.). Also, FSRE LF team plan to expand the range of variants of lifting platforms based on lifting mechanism (single, double, and multiple), type of drive device (manual, electric, pneumatic, and hydraulic drive), type of mobility (fixed and mobile), type of material (steel, aluminium, wood) and dimensions.

530

11 Best Practice Examples

Operational Concept FSRE Learning Factory has been integrated into the education of students and employees on all levels: • undergraduate lectures: study of work and time, organisation of production systems, • bachelor thesis, • graduate lectures: reverse engineering, 3D printing, Collaborative robots in manufacturing, LEAN methodology, • master thesis, • doctoral thesis. Presentation lectures are supplemented with exercises which are held in the Learning Factory. Students can experience practical effects of various tools and methods implementation in an environment which is a simulated and simplified production plant. Learning Factory setup includes didactic games specially developed for the simulation of production and logistic systems. Also, one of the goals of the LF is the “Knowledge triangle,” which refers to the interaction between research, education, and innovation, which are key drivers of a knowledge-based society. Also, through the implementation of joint projects with companies, the participation of students who have the opportunity to feel the real conditions in production, and thus the possibility of further advancement and improvement, is enabled. Companies often have different challenges for which they do not have enough capacities in terms of people, equipment, and time. Figure 11.89 presents a developed concept of collaboration between industry and the Learning factory. First, the request for some specific project comes from the industry. Within the FSRE Learning Factory, such as project is developed, implemented, and tested within the existing infrastructure. After its implementation within the learning factory, it is then being implemented in the specific company. Together with the staff from the learning factory, the project can include employees of the specific company or some external experts, depending on the complexity of the specific project.

Fig. 11.89 Lifting platform-3D model and real product

11.28 Best Practice Example 28: Lernfabrik für schlanke Produktion (LSP) …

531

11.28 Best Practice Example 28: Lernfabrik für schlanke Produktion (LSP) at the iwb, Technical University of Munich (TUM), Germany Authors: Michael Zäha , Fabian Sippla , Quirin Gärtnera , Marc Wegmanna a Institute for Machine Tools and Industrial Management (iwb), TUM

Learning Factory for Lean Production (LSP) Operator:

iwb, TUM

Year of inauguration:

2009

Floor space:

250 m2

Manufactured product(s):

Planetary gearbox

Main topics / learning content:

Lean production, Industrie 4.0

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

532

11 Best Practice Examples

Overall Goal The Technical University of Munich’s (TUM) Institute for Machine Tools and Industrial Management (iwb) has long advocated Lean production in its lectures. In 2009, a significant step was taken towards realising this philosophy in practice by establishing a learning factory. A manufacturer of gear technology, the company Zeitlauf, collaborated with the iwb to create the Learning Factory for Lean Production (LSP). Zeitlauf provided the products and assembly processes while the iwb built the facilities. On this basis, a training program that emphasised the practical application of Lean production principles in the context of Zeitlauf’s planetary gearbox units was developed. The training in the LSP focuses on ensuring a deep understanding of the Lean philosophy, including customer focus, constant improvement, waste elimination, and teamwork. The methods of Lean production are introduced with the help of a problem-oriented learning approach. The goal of every workshop is to achieve a sustainable and lean assembly line in the LSP and to provide participants with the basic methodological expertise of the Lean philosophy. Alongside the training for industrial companies, the iwb also integrated the LSP as a two-week intensive course into the TUM’s course offer. The practical course has been very successful and popular among students. In the present day, the LSP is used for industrial training, research, and educational purposes. Additionally, the LSP actively invites industrial guest speakers for the student course to allow companies to interact directly with and recruit talents interested in the methods. Furthermore, a regular exchange with industrial experts and consultants enables the LSP to enhance its content. Equipment and Products The LSP at the iwb offers all the necessary resources to learn about and implement the principles of the Lean philosophy and continuous improvement within a planetary gearbox assembly line. The facility is divided into three main areas: the assembly area, the Kaizen (continuous improvement) workshop area, and the theoretical teaching area (see Fig. 11.90). The assembly area includes logistics, assembly, quality control, and packaging. A dashboard tracks performance indicators and improvements, such as delivery reliability. The theoretical teaching area serves to present the Lean philosophy and methods to provide participants and students with the necessary knowledge for practical implementation. It also serves as a space for guest speakers to share their practical insights on the Lean philosophy in the manufacturing industry. Participants work on improving the gearbox assembly line in the Kaizen workshop area. Digital whiteboards and rapid prototyping materials such as cardboard and 3D printers are available to assist this process.

11.28 Best Practice Example 28: Lernfabrik für schlanke Produktion (LSP) …

533

Kaizen workshop area Supermarket Value stream analysis

Assembly stations

Teaching area

Legend Assembly area Kaizen workshop area Theoretical teaching area

Dashboard

Lean philosophy

Fig. 11.90 Overview of the main areas and the equipment of the LSP

The produced planetary gearbox is a gearbox from the manufacturing company Zeitlauf that is used, for example, in train doors (see Fig. 11.91). The product aims to demonstrate a high-mix assembly with different processing steps and processing times. This way, the participants are provided with a realistic assembly environment from which numerous conclusions can be drawn about individual challenges. The planetary gearbox consists of 18 components and is produced in 24 variants with two or three gear stages. The assembly process involves over 20 steps divided among four assembly stations. All the assembled products can be disassembled and reused after every workshop. Planetary gearbox

High-mix assembly product from the company Zeitlauf 18 components 24 possible product variants All components can be disassembled and reused.

Fig. 11.91 Manufactured products in the LSP

Components

534

11 Best Practice Examples

Fig. 11.92 Overview of research and development projects in the LSP

Numerous research projects and internal initiatives helped to develop the LSP’s practical course and theoretical modules towards a digitally enhanced Learning Factory. The LSP can now address topics in the research fields of Industrie 4.0, human-centred manufacturing, and artificial intelligence (see Fig. 11.92). Operational Concept The LSP is staffed with experienced internal research associates of the iwb who have a wide range of expertise in Lean consulting and coaching. The facility’s training concept is kept up to date through close collaboration with industrial partners and clients. The training course has evolved while keeping the core idea that Lean is a comprehensive management philosophy that cannot be learned solely with the help of teaching methods. Instead, the course simulates a typical Lean journey by starting with a suboptimal situation, requiring the participants to understand and analyse the issues.

11.29 Best Practice Example 29: Manufacturing Systems Learning Factory …

535

The course includes theoretical instruction on topics such as a brief history of the Toyota Production System, the seven types of waste, and the value stream analysis method to provide a foundation for creating a lean assembly line. Furthermore, the theoretical modules present and demonstrate the basics and potentials of current topics such as Industry 4.0, artificial intelligence, and human-centred production. Participants are divided into groups within the practical modules. Throughout the modules, these groups compete in a playful way to minimise production costs based on used resources, delivery quality, and reliability. Participants take on the role of assembly operators and Lean experts within the three improvement steps. Every improvement step is planned on digital whiteboards, implemented on the assembly line, tested during an assembly run, and evaluated using key performance indicators that measure quality, costs, and time. This approach is based on the Plan-Do-CheckAct (PDCA) cycle and focuses on making minor continuous improvements (Kaizen) and eliminating waste. After the assembly line is redesigned according to the Lean principles, additional digitalisation measures can be implemented by the participants enhancing the Lean philosophy with Industrie 4.0 elements. Methods and tools to aid each improvement step are provided in theoretical instructions. The iwb offers 6 to 8 trainings per year with different options, including industrial and educational (summer and winter course) as well as internal trainings. The LSP usually involves 12 to 21 participants and two to three trainers. Industrial partners can also inquire about individual training with modified contents that can be run anywhere, as the equipment is completely mobile.

11.29 Best Practice Example 29: Manufacturing Systems Learning Factory (iFactory) at University of Windsor, Canada Authors: Hoda ElMaraghya , Waguih ElMaraghya a Intelligent Manufacturing Systems (IMS) Center, University of Windsor, Canada

536

11 Best Practice Examples

Manufacturing Systems Learning Factory (iFactory) Operator:

Intelligent Manufacturing Center (IMS)

Year of inauguration:

2011

Floor space:

450 m2

Manufactured product(s):

Family of desk sets & family of belt tensioners

Main topics / learning content:

Integrated manufacturing systems & products desgn, System operation & control, Industrie 4.0

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal An integrated products/systems Learning Factory, the first of its kind in North America, was set up in 2011 at the Intelligent Manufacturing Systems (IMS) Centre at the University of Windsor in Canada (Fig. 11.93) and is co-directed by Professor Hoda ElMaraghy and Professor Waguih ElMaraghy. It was initially funded by research awards, including an infrastructure grant from the Canada Foundation for Innovation (CFI) and the Ontario Ministry of Research and Innovation (MRI), along with industrial contributions. Ongoing operation and research in the Learning Factory is supported by the Canada Research Chairs (CRC), the Natural Sciences

11.29 Best Practice Example 29: Manufacturing Systems Learning Factory …

537

Fig. 11.93 Modular and reconfigurable iFactory at the IMS center

and Engineering Research Council (NSERC) of Canada, and industrial grants and contracts. The objective is “Systems Learning,” which integrates products design, customisation, and personalisation, through the iDesign and iOrder modules, with the design, planning, and control of changeable manufacturing systems and development of innovative physical and logical enablers of change on the shopfloor, e.g., variant-oriented reconfigurable process and production plans through the iPlan module (Fig. 11.94). This state-of-the-art Learning Factory provides an experiential design, planning, and realisation environment conducive to innovation in products, processes, and systems. It is used primarily for research but also for teaching and demonstrations for students and industry. Equipment and Products The integrated Learning Factory is a complete experiential learning environment from product design in the Design Innovation Studio (iDesign) and design prototyping and metrology equipment to process and production planning tools (iPlan) and its manufacture (iFactory) supported by systems design synthesis and configuration algorithms and methodologies. The iFactory (by FESTO Didactic) is a truly modular and reconfigurable assembly system with the ability to change its configuration and layout by modules relocation, addition and/or removal. This “Factory-in-a-Lab” contains modular Plug’n Produce robotic and manual assembly, computer vision inspection, automated storage, and retrieval system (ASRS), material handling modules, RFID communication sensors, and Siemens SCADA control system. Its intelligent control with neighbour awareness capability and modular, standardised interfaces do not require re-programming or change of set up after physical reconfiguration, which is easily done in a couple of hours, dramatically reducing ramp-up efforts and time.

538

11 Best Practice Examples

Fig. 11.94 Integrated products and systems design, planning, and control demonstrated within the learning Factory environment at the IMS center

Innovation Design Studio (iDesign) with state-of-the-art equipment to foster innovative design and group interactions includes interactive 3D graphics tablets and displays supported by powerful PC-based applications and a computing environment for design synthesis, configuration, modelling, and analysis that integrates products, processes, and manufacturing systems development. The iOrder (for customised orders entry) module, complemented by the iDesign, iPlan, and iFactory environment, 3D printing, and coordinate measuring machine facilities, constitute the “Learning Factory” (Fig. 11.94) which provides a unique experiential learning, training, and research experience for undergraduate and graduate students, researchers, and professional trainees. Knowledge elements covered include product design, prototyping, customisation, and personalisation; variantbased process planning; order processing for mixed-model production; dynamic production planning and scheduling; and principles and enablers of a flexible, reconfigurable, and changeable Intelligent Manufacturing System. The current products, assembled in the iFactory, are: (i) a family of office desk sets and (ii) a family of automobile engine belt tensioners (Fig. 11.95). The carrier base plate, workpiece pallet, and product base plate are linked by positioning pins and corresponding holes and can hold members of both product families. Additionally, the workpiece pallet is equipped with RFID tags, which allow the tracking of processes, production operations planning and scheduling, and principles and enablers of flexible, reconfigurable, and changeable Intelligent Manufacturing Systems.

11.29 Best Practice Example 29: Manufacturing Systems Learning Factory …

539

Fig. 11.95 Assembled products families in the Integrated Systems Learning Factory (automobile belt tensioners family variants and desk set family variants)

Operational Concept Since 2011, the integrated learning factory at IMS center has provided exceptional experiential learning, training, and research experience to undergraduate and graduate students, researchers, and professional trainees. Senior IMS center researchers carry out training. Knowledge elements covered include product design, prototyping, customisation, and personalisation; variant-based process planning; order processing for mixed-model production; dynamic production planning and scheduling; and principles and enablers of flexible, reconfigurable, and changeable Intelligent Manufacturing Systems. The Learning Factory facilitates the co-design and co-development of products and manufacturing systems for the whole life cycle and develops the enabling technologies needed to increase manufacturing competitiveness, agility, and flexibility. This environment is also conducive to innovative research such as (i) product variety management; (ii) manufacturing systems complexity; (iii) co-evolution and co-development of products and their manufacturing systems inspired by biological evolution; (iv) design synthesis of assembly systems and their optimum granularity; (v) manufacturing systems layout complexity modelling and metrics; (vi) products and systems configuration co-platforming; (vii) development of offline and online digital twins of the iFactory to support design and operation decisions and use for online adaptive control respectively; and (viii) applications of Industry 4.0 such as monitoring and collecting information about the operation of its physical modules and manufacturing/assembly processes, and fault detection and recovery using data analytics and artificial intelligence.

540

11 Best Practice Examples

11.30 Best Practice Example 30: Model Factory @ Singapore Institute of Manufacturing Technology, Singapore Authors: Joel Taya , Puay-Siew Tana , Keng-Soon Woona a Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research

Model Factory @ SIMTech Operator: Year of inauguration:

Singapore Institute of Manufacturing Technology (SIMTech) 2017

Floor space:

620 m2

Manufactured product(s):

Plastic small form-factor modules, Free-form metal parts and components Sense & response manufacturing, Any-mix-any-volume production

Main topics / learning content: Morphology excerpt

Open models

Target industries

Open public

Digital transformation Job-seeking

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

Precision eng.

11.30 Best Practice Example 30: Model Factory @ Singapore Institute …

541

Overall Goal In the first decade after the term “Industry 4.0” was introduced in 2011 by the Industrial Consortium members at Hannover Messe, there were limited implementations in Singapore of cyber-physical production systems (CPPS) integrating traditional manufacturing and industrial practices with computational, networking, and physical processes, despite the potential for companies to improve quality and reliability while decreasing operational costs. It became apparent that from an industry perspective, the concept of CPPS was relatively unknown and thus industry practitioners needed demonstrations of more mature technology to appreciate the potential impact of Industry 4.0 and to understand how this differed from piecewise adoptions of individual technologies. From a research perspective, there was also a lack of a platform that could align the research and development efforts among the local Institutes of Higher Learning (IHLs) and research agencies, hindering the synergistic development of key supporting digitalisation technologies to enable CPPS. To address these challenges, Singapore’s Agency for Science, Technology and Research (A*STAR), Singapore Institute for Manufacturing Technology (SIMTech) launched the Model Factory@SIMTech in October 2017 to allow local companies, particularly small- and mid-sized enterprises (SMEs), to learn and experiment with state-of-the-art digital technologies in an operational production environment without disruption to their existing business operations. As Singapore’s first model factory, the Model Factory@SIMTech showcases the potentials of CPPS to local practitioners and serves as a platform to educate the local workforce about Industry 4.0, inspire local companies to kick-start digital transformation, and enable the cocreation and test-bedding of new advanced manufacturing technologies through a public–private research partnership model with local companies. Equipment and Products The Model Factory@SIMTech features a fully connected any-mix-any-volume (AMAV) production line that encapsulates and mimics real-world disruptions to the manufacturing of actual products. The production line is sensorised to create an IoTenabled environment where data generated on the seamlessly connected shopfloor can be captured for sense-and-response manufacturing. To demonstrate the configuration of the production line to cater to AMAV production, where orders can have varying requirements and batch sizes, SIMTech showcases the interchangeable production of two small form-factor customisable plastic products: (1) eScentz, which is a scent-emitting USB drive that allows customers to customise the scents in the purifier, colour, and messages printed and (2) mfConnect, a digital USB namecard. This flexible production line was designed with a decou-

542

11 Best Practice Examples

Fig. 11.96 Manufactured products in the Model Factory@SIMTech

pling point, to allow for postponement strategies, by breaking the production line into two segments: Make-To-Stock (MTS) for generic stock production based on forecasts and Make-To-Order (MTO) for production based on personalised orders, see Fig. 11.96. The important considerations in designing the production line were: 1. for product and process, to consider the possibility of automation in Design for Manufacturing and Assembly (DFMA); 2. for manufacturing strategy, to consider the physical flow of Make-To-Order vs Make-To-Stock, the physical constraints of the location, and to increase throughput by minimising distance and bottlenecks as the production moves from High-Mix-Low-Volume to Any-Mix-Any-Volume, down to a lot size of 1; 3. for machines, to consider the numbers and types needed for each equipment, cycle-time specifications, and safety considerations; 4. for material handling, how to enable automated handling with sufficient accuracy, temperature constraints, and configuration for a lot size of 1. The Model Factory@SIMTech incorporates next-generation network and communication technologies, in addition to production line technologies, to address both vertical and horizontal integration considerations. These are layered on top of the Manufacturing Control Tower™ (MCT™) platform, which serves as a digital backbone for the Model Factory@SIMTech and provides total visibility through enabling a common digital thread running across all layers from Shopfloor to Enterprise and supply chain. Finally, MCT™ Demonstrators showcase the Model Factory@SIMTech digital technologies across all the layers of manufacturing systems. The MCT™ Demonstrators have been developed to ensure the successful technology transfer of SIMTech research outputs to the industry end-users. This allows novel research outcomes from

11.30 Best Practice Example 30: Model Factory @ Singapore Institute …

543

Fig. 11.97 Smart Engineering System (SES)

ongoing research tracks to be harnessed into new modular technologies for integrated and immersive demonstration, which is particularly useful for small- and mid-sized enterprises that may not have the capacity to implement many I4.0 technologies simultaneously and thus require a modular approach to technology adoption. More recently, the Model Factory@SIMTech houses the Smart Engineering System (SES) (see Fig. 11.97), a fully equipped metal processing platform for a wide range of size, shape and form factor, and adaptive in-process coordinate measurement machine (CMM), consisting of: i. ii. iii. iv. v.

Makino V61 Vertical Machining Center Makino EDAF2 Sinker EDM Hurco VMX30Udi 5-Axis Machining Center Hurco TM8Mi Turnmill Hexagon Tigo-SF CMM.

The system is integrated with SMT technologies with proprietary software such as manufacturing execution system (MES), Computer-Aided Process Planning (CAPP), and hardware solutions including Robot Handling System, Carrier, and Cleaning Stations. This comprehensive setup is fully compatible with OpenMind’s Hypermill that enables flexible design, plan and execution capabilities with in-process measurements and quality control, ensuring high accuracy and efficient production processes. Designed specifically for metal production with batch size of one, the fully automated smart machining line within the SES incorporates robotics, artificial intelligence (AI), machine learning, and Internet of Things (IoT) connectivity. These advanced technologies enable adaptive and responsive manufacturing, optimising productivity, and quality for a wide variety of applications ranging from aerospace, precision engineering, automotive and MedTech. See Fig. 11.98.

544

11 Best Practice Examples

Fig. 11.98 Smart Engineering System (SES) product range and applications

One of the key strengths of the SES lies in its rapid first article production and new product introduction (NPI) capabilities. This enables manufacturers to quickly prototype and validate new designs, significantly reducing time-to-market and fostering innovation. For instance, the system can swiftly produce mould inserts for plastic injection moulding, serving as a critical testbed to optimise their quality, performance, and manufacturability. In addition, the SES seamlessly integrates materials processing with other workshops within the building. This is facilitated by a complete digital thread, ensuring the smooth transfer of information and materials. Autonomous mobile robots play a pivotal role in physical materials handling, efficiently navigating the manufacturing floor and transporting components between workstations. Operational Concept To help companies to embark and navigate the complexity of digital transformation, SIMTech developed the Digital Transformation & Innovation™ (DTI™) Methodology. The DTI™ Methodology is a process methodology that guides companies to develop a holistic roadmap and action plans for digital transformation, which includes (1) business model transformation, (2) value stream redesign, as well as (3) the development of new system architecture to support new operational requirements.

11.31 Best Practice Example 31: MPS Lernplattform at Mercedes-Benz AG …

545

The DTI™ Methodology was developed to break down the individual planning processes of the three intertwined and cross-functional transformation areas into bite-sized activities, connect them logically, and reorganise these activities into a systematic and serial process. To customise an actionable blueprint for digital transformation, companies can follow the following 5-stage methodology. Firstly, in Stage 1 a company can define its business strategies, map its business models, and brainstorm technology use cases based on a prescribed list of digital value drivers, to clearly determine a set of business objectives. With scope set by the objectives, the company can map out its value streams and system architecture in Stage 2. In Stage 3, the focus is to identify hotspots and transformation areas. Subsequently, in Stage 4 the company can develop clear initiatives for digital transformation, map out future value streams, and develop a smart system architecture. Finally in Stage 5, the company can generate a clear roadmap with detailed action plans, as well as a budget and return of investment (ROI) plan. With a systematic approach to align considerations across all three business levels—strategic, tactical, and operational, cascading the analyses in a top-down linear approach, this methodology can cover and align all the areas of business model transformation, value stream redesign and system architecture development. As of December 2022, the Model Factory@SIMTech has hosted 10,785 visitors from 2147 organisations for in-person visits. Foreign visitors have come from regional countries in ASEAN (Indonesia, Malaysia, Myanmar, Philippines, Laos, Thailand, and Vietnam), other countries and territories in Asia (China, Hong Kong, India, Japan, Korea, and Taiwan), and from beyond (Australia, Austria, Denmark, France, Germany, the Netherlands, New Zealand, Russia, Spain, Sweden, Switzerland, the UK, and the USA). Since June 2018, 25 companies, ranging from SMEs to MNCs from industries such as the logistics and supply chain industry and chemicals industry, have applied the DTI™ Methodology and begun their digital transformation journeys.

11.31 Best Practice Example 31: MPS Lernplattform at Mercedes-Benz AG in Sindelfingen, Germany Author: Michael Schwarza a MPS Lernplattform Sindelfingen, Daimler AG.

546

11 Best Practice Examples

MPS Lernplattform at Mercedes Benz AG Operator:

Mercedes-Benz AG

Year of inauguration:

2011

Floor space:

3,000 m2

Manufactured product(s):

Different products

Main topics / learning content:

Lean production

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal In the middle of the twentieth century, methods for lean management were emerged in Japan for the first time. Over the years, “lean” has become a trend word, and so the Mercedes-Benz Production System (MPS) of the Daimler AG considered how lean production processes could be used to contribute to the continuous improvement process (CIP) of the company. For this purpose, lean specialists are required, who teach this increasingly important topic in a practical way to all other employees of the company. In 2011, the decision was made to create a learning center around the topic “lean”– the birth of the MPS Lernplattform. For this purpose, specialists from

11.31 Best Practice Example 31: MPS Lernplattform at Mercedes-Benz AG …

547

various fields were brought together, who were trained to become qualified business trainers. Over the years, the team of trainers has grown to over ten employees. Up to now, almost 10.000 Daimler employees have been trained at various seminars on the MPS Lernplattform. These are in particular executives, production planners, plant engineers and improvement managers. The training offer is diversified and covers various content of all areas of the entire production process. The training contents are specially adapted to the customer needs of the respective target group as well as to current trends in the production area. Thereby, the didactic focus is always on the imparting of lean knowledge as practically as possible, which is why the training courses are strongly geared towards implementation in the individual areas and work with real components. Therefore, a better understanding and ideal solutions should be developed. The successful concept of the MPS Lernplattform has meanwhile been extended to more than four worldwide production sites of Daimler AG. Equipment and Products The MPS Lernplattform offer the participants the advantage that the training takes place in a production-oriented teaching area. For this, original components of the production as well as additional 1:10 models and various simulations are used on an area of almost 3,000 square metres. During the various training courses and simulation rounds, the complete production area with all shops (press shop, body shop, paint shop, assembly, and logistics) is shown intensively. In particular, the focus is not only the value-added process, but also the planning and understanding of lean management. For the important practice units, various products are used which can be reused after the training. These are, for example, real components such as roof control units, sun visors, covers, floor mats, room tears, or the assembly of smaller model cars (Fig. 11.99). Moreover, e-learning courses are being offered in addition to face-to-face trainings. The digital learning content and media are provided and managed by the groupwide accessible, cloud-based learning environment system. Increasing digitalisation of the training offers enables flexible and hybrid learning opportunities in the form of blended learning. To maintain the high practical part in the training courses, further development and adaptation of the learning content are fundamental because of the changed training concept. The physical simulations provide a high experience factor because the participants experience the learning content together in practical scenarios. For that reason, a flexible and hybrid simulation environment has been set up, to enable a high experience factor in digital scenarios. The simulation environment includes hardware and software components that enable collaborative and interactive learning. Part of the flexible simulation environment is a multi-touch table. The entire system represents an output medium for the learning content of

548

11 Best Practice Examples

MPSlernplattform

Fig. 11.99 Layout and impressions of the MPS Lernplattform in Sindelfingen at Daimler AG

the learning environment system. In this way, the participants experience complex training content also in digital form. The flexible, hybrid simulation environment implements an exploratory and gamified approach in training courses. Furthermore, the simulation environment permits a synchronous link from face-to-face to online training (Fig. 11.100). Operational Concept The training courses are carried out by in-house employees from MPS. They were qualified as trainers by means of special training and, through the successful combination of didactic background knowledge and their own long-term experience in the production area, they can provide the participants with an optimal understanding of the content of course. The MPS Lernplattform increasingly relies on cooperation with external partners such as the TU Darmstadt. The didactic concept of the training consists of 20% theory and 80% practice. This division is the secret of success of the MPS Lernplattform because the participants can take important insights from the training for their own daily work. In addition to lean basic training, the MPS Lernplattform offers more than ten different advanced training courses. Small groups of 10–20 people allow for a pleasant working atmosphere as well as the possibility of joint learning in a team. This interaction between trainers and participants from different areas is very important to open new ways of thinking and to develop a better understanding of different topics. In order to achieve a long-lasting effect

11.32 Best Practice Example 32: Operational Excellence at Department …

549

MPSfactory

Fig. 11.100 MPSfactory at Daimler AG in Sindelfingen

in the qualification, theory training is combined with practical training as well as with the implementation in the daily work of the participants, since the learning and memory effect through the personal experience and comprehension of the contents is significantly higher. Consequently, the MPS Lernplattform not only creates a lasting added value for the participants, but also contributes to the continuous improvement process (CIP) of the Daimler AG.

11.32 Best Practice Example 32: Operational Excellence at Department of Engineering, University of Luxembourg, Luxembourg Authors: Sri S.V.K. Kollaa , Peter Plappera , Antonio Kreßb , Ferdinand Blaséb a Department of Engineering, Université du Luxembourg b Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt

550

11 Best Practice Examples

Operational Excellence Learning Factory Operator: Year of inauguration:

Department of Engineering, University of Luxembourg 2012

Floor space:

100 m2

Manufactured product(s):

Hole puncher

Main topics / learning content:

Lean production, Industrie 4.0

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

11.32 Best Practice Example 32: Operational Excellence at Department …

551

Overall Goal The establishment of the Operational Excellence Learning Factory is a direct response to the growing demand for highly customised products in the manufacturing industry. This demand necessitates the implementation of flexible and efficient production systems to meet the challenges of this development. The Learning Factory serves as a valuable platform for optimising production systems and achieving operational excellence. The primary objective of the Learning Factory is to enhance the understanding of lean manufacturing and tools by providing hands-on experience with actual products. Additionally, the Learning Factory provides a simulated production environment and contributes to research efforts focused on optimising manufacturing processes through the application of lean management tools. One significant research area in the Learning Factory is Value Stream Management, which lacked a standardised approach when the facility was founded. By addressing this issue, the Learning Factory can make significant contributions to the field of manufacturing research. The Learning Factory also provides a unique opportunity for Master students to gain practical experience in Lean Manufacturing and Industry 4.0 applications. By experiencing waste recognition and avoidance firsthand, students can develop a deeper understanding of these concepts. Equipment and Products The Lean Manufacturing Laboratory is a complete manual assembly line that has been specifically designed to assemble and disassemble a hole puncher provided by one of our industry partners. The Value Stream for this assembly line has been developed around the product, and the production flow has been optimised by reducing inventories. The assembly process is divided into seven subsequent process steps, as depicted in Fig. 11.101, ranging from the initial assembly stage to quality control and disassembly. During the initial production stages, the top and chassis of the hole puncher are assembled in parallel production steps. Once these two main product components have been assembled, the final product undergoes a thorough quality control check before it can be shipped to the customer. To promote sustainable product life cycles, all working parts of the final product can be extracted after the customer’s use phase. These recycled parts can then be used as initial components for the subsequent production cycle. The Learning Factory integrates several advanced technologies to streamline workers’ tasks. Traditional lean manufacturing approaches are augmented with Internet of Things (IoT) elements, such as replacing paper manuals with digital ones to decrease paper waste, using RFID sensors to monitor and gather data, and utilising augmented reality for mixed reality experiences. This approach is especially crucial for companies with older machines that are not Industry 4.0-compliant, enabling them to take advantage of new technologies without the need to replace their entire machinery. Augmented reality (AR) technology offers numerous benefits, including improved reliability, decreased error rates, enhanced intuitive learning, and greater traceability. In general, the learning process with AR assembly instructions

552

11 Best Practice Examples

Fig. 11.101 Process sequences for (dis)assembly of hole puncher

is more efficient than relying on paper-based instructions, with learning times significantly shortened for products with complicated assembly processes. Figure 11.102 shows the application of AR. Overall, the integration of advanced technologies in the Learning Factory enhances worker productivity and learning efficiency, while also promoting sustainability by reducing paper waste. Operational Concept The concept of the Operational Excellence Learning Factory was to offer the master’s students an opportunity to directly apply the knowledge they had learned, in a real production environment. Instead of just hearing the information in conventional frontal teaching, students increasingly have the opportunity to learn the material themselves in student-centred learning groups under expert guidance. Students in the

Fig. 11.102 Augmented reality Manual Work Instructions

11.33 Best Practice Example 33: Pilotfabrik Industry 4.0 at TU Wien, Austria

553

Fig. 11.103 QR code to a video of the Operational Excellence Factory

Master of Sustainable Product Creation are taught different lean management tools like the continuous improvement process, 5S, kanban and value stream method. The linked research conducted in the learning factory eliminates waste along the intracompany supply chain based on lean VSM. Our main research topic at the beginning of our Operational Excellence learning factory was the integration of a standardised VSM within the research Project “StreaM.” Over the years, the focus of our research has evolved and today a particular emphasis is placed on newer technologies ranging from Computerisation and Connectivity solutions over retrofitting legacy machines to using augmented reality in assembly. Figure 11.103 contains a link to a video of the Operational Excellence Learning Factory.

11.33 Best Practice Example 33: Pilotfabrik Industry 4.0 at TU Wien, Austria Authors: Claudia Schicklinga , Sebastian Schlunda , Friedrich Bleichera , Manfred Grafingera a TU Wien

554

11 Best Practice Examples

Pilotfabrik Industrie 4.0 @ TU Wien Operator:

TU Wien (institutes: IFT, MIVP and IMW)

Year of inauguration:

2016

Floor space:

800 m2

Manufactured product(s):

FDM 3D-printer and respective metal parts

Main topics / learning content:

Industrie 4.0, CAD/CAM, Digital twin, Humanmachine-interaction

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal By the initiative of the TU Wien institutes of i) Production Engineering, ii) Management Science, and iii) Engineering Design and more than 20 industrial companies the Austrian Ministry of Innovation supported the setup of a close-to-industry facility to research and showcase the principles of Industry 4.0.

11.33 Best Practice Example 33: Pilotfabrik Industry 4.0 at TU Wien, Austria

555

The overall goal of the Pilot Factory (Pilotfabrik) at TU Wien is to conduct research and education and to transfer knowledge within the area of intelligent production. The Pilot Factory follows the vision that individual work steps will be interconnected through information technology and coordinated with one another—which is related to “Industry 4.0” or “Smart Production,” based on the “Internet of Things” or cyber-physical systems. Neither idling, e.g., caused by the lack of required components, nor inventory costs, caused by overproduction at one step of the process, are supposed to occur; outages and failures are expected to be handled discreetly by the system. Strategy will not be managed manually at a control center but will essentially be supported by the communication of machines. Auxiliary industries as well as distribution will be incorporated by the integral system. That comes with many advantages: production is going to be faster, cheaper, and increasingly energy efficient. Furthermore, it will be easier to meet individual customer requests. Individually fitted products are harder to manufacture than mass products, which is why the TU Wien is working on strategies for manufacturing personalised products in very small batch sizes efficiently. Continuous digital images of products and the production system are crucial. Equipment and Products In order to be able to develop, test and improve such new strategies for the industry, one needs a realistic test environment—real machines, real production chains, and also a real product. In the Pilot Factory at TU Wien, the decision was made to use components from 3D (FDM) printers, because these are relatively complex objects that can be produced in a large number of variants. The production is therefore sufficiently challenging to be scientifically interesting. The manufacturing process of the FDM printer covers the machining of metal parts for the frame and the support structure of the printing head/s, five assembly steps with an integrated quality control as well as the supporting intralogistics processes. The machining and the assembly/logistics areas thereby follow different concepts of intelligent interconnection. In the machining area machine tending, handling and transport follow an optimisation path towards complete automation whereas in the assembly and logistics area due to the small batch sizes (down to lot size one) human– machine interaction and the use of manufacturing assistance systems are the guiding concept (Figs. 11.104 and 11.105). Overarching topic of the Pilot Factory is the transformation of manufacturing to cyber-physical production systems. Within this setting, the following research topics are covered: • distributed and collaborating services interacting with the actual manufacturing environment, • agile manufacturing,

556

Fig. 11.104 Machining area in TU Wien Pilot Factory

Fig. 11.105 TU Wien Pilot Factory and its simulation

• • • •

interoperability, skill-based engineering and control, plug and produce, assistance systems for assembly,

11 Best Practice Examples

11.33 Best Practice Example 33: Pilotfabrik Industry 4.0 at TU Wien, Austria

557

Fig. 11.106 Manufactured products in the TU Wien Pilotfabrik

• • • • • • • • •

human-centred design of production systems, human–robot collaboration, AI-based maintenance, tracking and tracing, PLM in Industry 4.0, virtual product development, edge computing for production systems, applied machine learning in manufacturing, computer vision to assist, automate, and/or improve manufacturing processes (Fig. 11.106).

Besides state-of-the-art machining, assembly and logistics equipment and mobile transport systems the Pilot Factory focuses on the interoperability of intelligent systems. Therefore, the following communication, software, and semantic tools are used: • communication: OPC UA, MQTT, HTTP, MTConnect, ModbusTCP, Profinet, Profibus, Ethercat, Ethernet, OpenProtocol, Wi-Fi, TCP, UDP, OSI, AMQP, NFC, RFID • semantics: OWL, RDF, OPC UA, RPC, SysML

558

11 Best Practice Examples

• software: RobotStudio, ROS, 4diac, TIA Portal, NX CAD/CAM, NodeRed, TUSequenzer, ELAM, Sarissa QA, Automated Content Generator, DS 3DExperience & Catia V5/6, Siemens Teamcenter & NX, CIM Database, Ansys, Tensorflow, OpenCV, ROS (Fig. 11.107).

Fig. 11.107 Exemplary use cases in the TU Wien Pilotfabrik

11.34 Best Practice Example 34: Process Learning Factory CiP at PTW, TU …

559

Operational Concept The Pilot Factory develops scientific expertise about optimal techniques for production system integration and plays a decisive role in teaching at the TU Wien. The students can get to know the entire supply chain and help to develop it—from construction to production and assembly to quality assurance and logistics. Furthermore, the Pilot Factory is used for add-on and company training—specialists in production should get to know new ideas, which they can then implement in their own companies. At the TU Wien Pilot Factory Industry 4.0, guided tours take place every Thursday. A partner model allows joint, interdisciplinary research and development such as: • infrastructure offers research and test environment for prototype development, • execution of test and trial series within individually configurable demonstration scenarios, • prototype implementation and evaluation of production, assembly, and logistics technologies, • use of existing hardware and software without investment risk for horizontal and vertical integration scenarios, • access to experts from different disciplines, • validation and evaluation of the developed solutions for transfer into the partner companies’ industrial applications. Today the initial state of Pilot Factory is well developed and was experienced by more than 10.000 visitors. Several lectures at TU Wien and other educational institutions cover content and use cases of the Pilot Factory and contribute to a further development. Another significant step is to be expected with the beginning of 2023 and the start of the PilotLin-X and ResearchLin-X initiatives to further combine the activities of the Austrian Pilot Factories at the example of generalised manufacturing use cases.

11.34 Best Practice Example 34: Process Learning Factory CiP at PTW, TU Darmstadt, Germany Authors: Joachim Metternicha , Eberhard Abelea , Michael Tischa , Jens Hambacha , Antonio Kreßa a Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt

560

11 Best Practice Examples

Process Learning Factory “Center for industrial Productivity“ (CiP) Operator:

PTW, TU Darmstadt

Year of inauguration:

2007

Floor space:

500 m2

Manufactured product(s):

Pneumatic cylinder, electric gear drive

Main topics / learning content:

Lean production, Industrie 4.0, Artifical intelligence

Morphology excerpt

Open models

Target industries

Artificial intelligence Open public

Job-seeking

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

11.34 Best Practice Example 34: Process Learning Factory CiP at PTW, TU …

561

Overall Goal In 2004, the idea of establishing a realistic factory on the campus of the Technical University Darmstadt came up at the Institute for Production Management, Technology and Machine Tools (PTW). As decision-makers at TU Darmstadt hesitated to provide CiP with a separate building in 2006, it was housed in a building already planned for another research project (Fig. 11.108). Visionary business leaders, for example from Bosch or SEW, recognised the benefits of this learning factory idea and signed a cooperation agreement that provided the initial foundation of the Process Learning Factory Center for Industrial Productivity (CiP). Beneficial at this point was the long-standing cooperation with McKinsey, especially with the former head of McKinsey Germany, Professor Jürgen Kluge. In close cooperation, a curriculum was developed, and first pilot trainings were held. The goal of teaching and training activities is the sustainable development of competencies in the fields of lean and digital production. Through continuous and dedicated work of the team, the learning factory has been continuously expanded since then—for example regarding the integration of digital technologies such as artificial intelligence for predictive maintenance. In addition, a virtual reality learning scenario was developed that allows participants to personalise their learning—for example, by selecting the production environment and the difficulty level.

Fig. 11.108 Building of the Process Learning Factory CiP

562 Milling Machine DMC 50H

11 Best Practice Examples Supermarket

Assembly line pneumatic cylinder

Purchased parts storage

Turning Machine Index C65

Individual machining

Sawing machine Kasto SBA2

AGV

Measuring

Fig. 11.109 Value stream of the Process Learning Factory CiP

Equipment and Products In the simulated, but authentic production environment of the Process Learning Factory CiP, two products are manufactured. The complete production of a compact pneumatic cylinder (as the first product) is mapped in a multi-stage manufacturing process from the delivery of raw material, machining, quality control, assembly, and packaging, see Fig. 11.109. In addition, indirect processes are mapped around the internal logistics and the management of the factory. The pneumatic cylinder consists of the cylinder itself, the cylinder base and top, piston, piston rod and mounting elements. The cylinder base is manufactured in two variants, the piston rod in eight variants; the remaining parts are purchased. Moreover, customer-individual requirements can be met in the individual machining area (see Fig. 11.109). In general, the manufactured products are not sold, but dismantled and returned to the materials cycle. To demonstrate the impact of a large number of variants in assembly, an additional flexible assembly cell which produces electric gear drives (as the second product) in more than 8.000 variants is integrated with an assembly assistance system. Both products are depicted in Fig. 11.110. The modular design of the assembly cells allows fast changes in organisation and line design by the participants. Learners can understand concepts based on own experiences, solve problems, and apply lean production methods on about 500 m2 . Since 2014, numerous research projects helped to build up a digital enhanced value stream in the Process Learning Factory CiP, which can now be modified to address topics in the field of Industrie 4.0, customer-individualised production, smart logistics, and artificial intelligence as well, see Fig. 11.111.

11.34 Best Practice Example 34: Process Learning Factory CiP at PTW, TU … Compact pneumatic cylinder

Gear drive

High volume production

High mix assembly

•

Complete value stream including machining, assembly and indirect areas

•

Product is not sold, but disassembled

• • • •

•

Customized production is possible

563

More than 8000 product variants Simplified product model Use of a digital assistance systeme All parts can be disassembled and reused

Fig. 11.110 Manufactured products in the Process Learning Factory CiP

Product steers process Digital Backbone

Milkrun 4.0

Energy Monitoring

Digital Shopfloor Management

Components as information carriers

Predictive Maintenance

Intelligent worker assistance systems

Fig. 11.111 Alternative value stream for trainings in the field of Industrie 4.0

Operational Concept The underlying concept is based on the improvement journey in a company: starting where a factory and its employees currently are to making step-by-step improvements, e.g., in reducing waste, integrating new technologies or increasing quality through systematic problem-solving. Therefore, on the one hand, some learning modules start in a wasteful and unbalanced production environment in which the basics of lean production are not integrated. On the other hand, other learning modules start in a lean but not yet digitalised shopfloor, where the introduction of new technologies, e.g., digital shopfloor management, is planned and executed. The lean production learning modules can be divided into the fields lean understanding (basics), lean core elements (material flow, quality, machining), and lean thinking

564

11 Best Practice Examples

Initial situation One vision

SME 4.0-Competence center Darmstadt

Extension Indirect area

Openin g

Factory extension FlowFactory

Many questions

Building concept

1st extension machining

Start of training

2nd extension machining

Learning parcours Lean 4.0 Industrie 4.0 extension

Reality (content and complexity)

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Fig. 11.112 Extensions of the Process Learning Factory CiP

(culture and leadership) and are offered to the approx. 20 partner companies, who have a particular contingent on training days. All learning modules are led by two research assistants supported by a technician and students in the role of production employees. Within a learning module, practical and theoretical phases alternate so that the learned knowledge can be put into practice interactively. Additionally, the Process Learning Factory CiP is operated as a competency center in Germany funded by the Federal Ministry for Economic Affairs and Climate Action. Within the center, trainings in the topic of Industrie 4.0 are developed and offered free for companies—especially SME—to promote the concept of digitally enhanced production environments. Figure 11.112 gives an overview over the extensions of the Process Learning Factory CiP since its start. Today, the Process Learning Factory CiP is an innovative education, training, and research center that enables a comparison of different states in production. Regarding research, such a possibility of direct comparison is particularly valuable, because improvements become measurable. The learning factory is therefore constantly utilised in the evaluation of newly developed approaches. Furthermore, the PTW won the Hessian Excellence Award for higher education for the establishment and operation of the learning factory in student education. Additionally, the industry’s interest in training activities has grown steadily in 15 years of operation (status 2022), with standardised trainings of over 4.000 experts, production planners, but also plant managers, and managing directors in lean topics. The latest development of the learning factory concept at the PTW is the establishment of the FlowFactory, see Best Practice Example 13.

11.35 Best Practice Example 35: Recycling Atelier Augsburg at the Institut …

565

11.35 Best Practice Example 35: Recycling Atelier Augsburg at the Institut für Textiltechnik Augsburg and University Augsburg for Applied Sciences, Germany Authors: Georg Stegschustera , Stefan Schlichtera , Simone Kubowitschb ; Sarah Melanie Hatfieldb a Institut für Textiltechnik Augsburg gGmbH b Hochschule Augsburg

Recycling Atelier Augsburg Operator: Year of inauguration:

Hochschule Augsburg & Institut für Textiltechnik Augsburg 2022

Floor space:

821 m2

Manufactured product(s):

Prototypes made of recycled textiles

Main topics / learning content:

Mechanical recycling, Artificial intelligence, Design 4 recycling, Upcycling

Morphology excerpt

Open models

Target industries

Artificial intelligence

Recycling

Open public

Job-seeking

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Lower

Researcher

Profit-oriented operator

Industrie 4.0

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

566

11 Best Practice Examples

Overall Goal The fast fashion trend outsourced corporate responsibility, and a general decline in raw material quality are key challenges on the way to circularity in the textile industry. Currently, one in a hundred textiles are recycled in a closed loop. The Recycling Atelier was founded in June of 2022 to start a trend reversal. After a six-month planning and build-up phase ITA Augsburg gGmbH—as part of the ITA Group—together with its main partner Augsburg University of Applied Sciences and twelve initial partners from the industry opened the Recycling Atelier Augsburg. The Recycling Atelier is a centre for research and development along the entire textile production chain for textile recycling. It combines all individual process steps of mechanical textile recycling in the setting of a model factory. The bundling of the most important processes enables holistic and comprehensive research along the textile recycling value chain, which has not existed in this form before. The focus of the Atelier is on upcycling and Design 4 Recycling. Upcycling refers to high-quality recycling in which old jumpers are turned into new jumpers again and do not end up as cleaning rags. Design 4 Recycling is the creation of a cycle-oriented product design. The reusability of a product plays a central role during its design process. From 2025 at the latest, recyclability must be considered, whether in design, training, or studies—(re)thinking and (re)designing in cycles is in demand and will be taught and practised in an interdisciplinary way. The Recycling Atelier offers educational trainings to companies to put their products to the test and to develop new concepts for a sustainable future. The findings are also taught directly to the students at the University of Applied Science, and the Recycling Atelier can be visited by anyone interested in the topic. Equipment and Products The Atelier includes all process steps from material analysis to sorting, preparation and textile processing—from spinning preparation to spinning or nonwoven production—and product design, as shown in Fig. 11.113. Station 1 Material analysis—Textiles are produced in a multitude of processes and stages of the value chain. Material analysis is used to determine the composition and quality of used textiles. The fibre length is the most important criteria among others to determine the further use of recycling textiles. Modern fibre length measurement systems like USTER AFIS of the company Uster Technologies AG, Uster, Switzerland, allow a detailed analysis of the textile materials. Station 2 Sorting—The sorting process today is purely manual and at the same time requires a high degree of expertise in the material, composition, and quality of the textiles. A testing rig has been set up that is able to automatically recognise the type of textile, the type of fabric and areas of particular interest such as buttons, zips, and seams using artificial intelligence. This is the first step to the development of an automated sorting process involving new sensor concepts, robotics, and artificial intelligence. Station 3 Processing—The sorted textiles are freed from impurities and broken down to the individual fibre by a patented “defibreing” process developed by the company Ommi Srl., Prato, Italy. This innovative process will be installed at the

11.35 Best Practice Example 35: Recycling Atelier Augsburg at the Institut …

567

Fig. 11.113 Seven stations of the Recycling Atelier Augsburg

Recycling Atelier in 2023 and is the evolution of the more traditional tearing process allowing for a gentler separation of fibres. The decisive quality criterion in mechanical recycling is the length of the individual fibres. Fine-tuning of the machines and AIsupported recording and evaluation of the production data are the key to long recycled fibres. Station 4 Textile processing—The recycled fibres are turned into textile (intermediate) products either by producing card slivers (Fig. 11.114, left) or nonwovens (Fig. 11.114, right). A carding machine TC11 from company Trützschler Group SE, Mönchengladbach, Germany, is installed and will be updated soon to specifically handle recycling materials. For nonwoven production, a complete industrial line containing bale opener, card willow, dosing opener, chute feeder, carding machine, cross-lapper, and needling loom has been lent to ITA by the company Dilo Systems GmbH, Eberbach, Germany. The processing of the shorter recycled fibres requires a high degree of

Fig. 11.114 Card sliver made from recycled post-consumer jeans (left) and pre-consolidated nonwoven made from recycled carbon fibres (right)

568

11 Best Practice Examples

process understanding and control to achieve a high-quality product. The use of artificial intelligence for process modelling and analysis is essential for a stable process. In addition, the targeted mixing of different recycling qualities and virgin fibres is being investigated. Station 5 Spinning mill—In the spinning mill, the card sliver is either drawn and twisted (ring spinning, for high quality) or unravelled and twisted (rotor spinning) to obtain a fine and strong yarn. The ITA group has access to both technologies at the institute in Aachen. Rotor spinning is possible with the Autocoro 10 and ring spinning with a modified version of the ZI 72 XL, both provided by Saurer Spinning Solutions GmbH & Co. KG, Übach-Palenberg, Germany. The Recycling Atelier will be equipped with a flyer machine and ring spinner machine in 2023. The aim is to achieve the highest possible recycling rate while maintaining a high yarn quality. The research focus ranges from the control of process parameters by AI systems to constructive adjustments of the machines for optimisation as well as online process monitoring for the detection of weak points in the yarn. Station 6 Product design—Fig. 11.115 envisions our goal for the future, an infinite loop. The challenges for cycle-oriented product development include a lack of economic perspectives for all process participants along the value chain, as well as a lack of coordination between the individual process steps. For future products, Design 4 Recycling concepts are developed which aid a closed-loop recycling economy. The Recycling Atelier accompanies the path of textile secondary raw materials back into high-quality products. In addition, indirect processes are mapped around the internal logistics and the management of the factory. Station 7 Learning & Innovation lab—This lab creates a physical and virtual space to transport Circular Economy and Design 4 Recycling skills to producing

Fig. 11.115 Textile cycle envisioned by the Recycling Atelier

11.35 Best Practice Example 35: Recycling Atelier Augsburg at the Institut …

569

companies, students and all parties interested in sustainable strategies. Using virtual reality as a gateway to grasp the textile recycling process without having to visit the Recycling Atelier in person is a global asset. The workshops and trainings are customised to the needs of the target groups, whether they aim at evaluating learnings or innovating, for example by new job profiles to increase the attractiveness of the textile as well as the recycling industry. The research approaches are diverse, ranging from experimental to action research. Operational Concept The challenges of creating a circular economy include knowledge transfer, fostering a new way of thinking, and designing products that are future-proof. This is where the Recycling Atelier comes into build and share knowledge of textile recycling processes, Design for Recycling and Upcycling methods. The Recycling Atelier is the first model factory dedicated to sustainable material cycles along the entire textile production chain in research and development together with partners from the industry. At each step of the process, a company from the industry supports the research with its industrial perspective and expertise. At the same time, a high degree of digitalisation within the chain through modern data collection, processing and evaluation enables the use of artificial intelligence-machine learning and neural networks. The collected knowledge is processed and conveyed in different formats and depths, depending on the target group. Starting with guided tours of the Recycling Atelier for schools, the training of students through teaching and research work on practical topics, as well as specially tailored workshops for companies and their employees from beginners to CEOs (Fig. 11.116). The range starts with basic training on recycling in general, continues with the development of concepts, then the realisation of prototype products and finally support with the industrial implementation.

Fig. 11.116 Trainings conducted at the Recycling Atelier

570

11 Best Practice Examples

11.36 Best Practice Example 36: SDFS Smart Demonstration Factory Siegen at PROTECH, University Siegen, Germany Authors: Peter Burggräfa , Fabian Steinberga , Philipp Nettesheima , Markus Schultea a PROTECH-Institute for Production, University of Siegen

SDFS Smart Demonstration Factory Siegen Operator: Year of inauguration:

SDFS Smarte Demonstrationsfabrik Siegen GmbH 2017

Floor space:

1,000 m2

Manufactured product(s):

Custom welding groups, Milling products, Bended tubes Product creation process, Energy & resource efficiency, Lean production, Industrie 4.0

Main topics / learning content: Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

11.36 Best Practice Example 36: SDFS Smart Demonstration Factory Siegen …

571

Overall Goal Since 2017, the SDFS Smarte Demonstrations Fabrik Siegen GmbH presents the newest trends in artificial intelligence and production technologies inside of a real factory in the southwestern region of North Rhine Westphalia. The SDFS was founded out of the chair for International Production Engineering and Management (IPEM) of the University of Siegen. The aim of the SDFS as an independent organisation is the manufacturing of products. The SDFS manufactures prototypes and small series at competitive prices, delivery times, and qualities. With an area of 1000 m2 , the SDFS is the heart of the Campus Buschhütten in Kreuztal as a real laboratory for production, creating a perfect environment for the development of future production facilities. The SDFS gives companies the possibility of a partnership that enables the partners to network and participate in networking events. Furthermore, the environment of the SDFS allows partners to demonstrate their technologies in a real production setting and to further develop their technologies in collaboration with the SDFS and other partners. Subsequently, these generated developments can be transferred effectively in a brief amount of time into the manufacturing industry. Now the SDFS’ network consists of more than fifty regional market-leading partner companies from different industry branches. Next to the production and demonstration, the environment of the SDFS enables teaching, training, and further education to be conducted on real production processes in the fields of artificial intelligence and lean production. The concept of the SDFS is rounded off by its expert circle for industrial companies on the topics of best practices and digital business models. Equipment and Products The SDFS utilises cutting-edge tools and technologies to produce products in a real production setting. For the manufacturing of prototypes and small series as well as the application of artificial intelligence to manufacturing processes a robot welding cell, a tube bending machine, a 5-axis CNC machining center, a 3D metal printer, and a coordinate measuring machine are used. Artificial intelligence applications and technologies from its partners are applied to the real production of the SDFS. These artificial intelligence applications are focused on the topics of production management, predictive quality, circular economy, and manufacturing process optimisation (Fig. 11.117). These artificial intelligence applications are for example an AI-based control of machine parameters, predictive quality, and predictive maintenance. The demonstrated partner technologies are an indoor localisation system, a condition monitoring system with innovative sensor technology, a worker assistant system, a coordinate measuring machine, an automated guided vehicle, and a digital twin.

572

11 Best Practice Examples

Fig. 11.117 Production facility of the SDFS Smart Demonstration Factory Siegen

In the following, a brief overview of the different applications and technologies is given: • An AI-based control of machine parameters is applied to the whole production facility of the SDFS with different alignments. For example, in the robot welding cell, the automatic adaption of the welding parameters is achieved with computer vision and auditory sensors in combination with artificial intelligence to react to the varying gap between the to-be-welded parts. The collected data from the sensors and the camera is transferred to the cloud via 5G communication where the artificial intelligence processes the data and sends the adjustments of the parameters back to the robot. • In the field of quality prediction, the machine parameters and sensor data are combined with information about the quality of the corresponding product to predict the impact of different parameters on the quality of the produced products. The predictive quality algorithms are applied on the tube bending machine, the 5-axis CNC machining center, and the 3D metal printer to increase the quality of the products. • An indoor localisation system tracks the position of the products which are placed

11.36 Best Practice Example 36: SDFS Smart Demonstration Factory Siegen …

•

• •

• •

573

in euro containers in the Campus Buschhütten and identifies the product’s actual step of production. This information about the product is transferred to the ERP system to provide real-time updates on the status of the goods in the production process. This recorded data is used for value stream analysis and optimisation. A condition monitoring system is applied to the production machines like the tube bending machine and the 5-axis CNC machining center to observe the state of the machines with different innovative sensors. The collected data is used for predictive maintenance to enhance the availability of the machines and the automatic generation of boundaries for the sensor data to detect malfunctions in the machines. A worker assistant system uses augmented reality in the parts placement of a welding group. The assistant system displays the information through the augmented reality glasses so that an unskilled worker can operate the machine. A coordinate measuring machine is used for the measurement and analysis of form and position tolerances, surface and contour measurement, gear measurement, and the measurement of regular geometries and free-form surfaces. This measurement data is then transferred back into the production process for continuous improvement. An automated guided vehicle is used for the automation of the logistic in the production of the SDFS. The automated guided vehicle connects the different machines and storage facilities for an optimised logistic process. A digital twin of the Campus Buschhütten is the result of a laser scan and is used for the layout planning of the facility, the simulation of all machines to optimise the value stream inside the SDFS, and the virtual tour of the Campus Buschhütten. Further, the data from technologies like condition monitoring and indoor localisation is transferred into the digital twin of the Campus Buschhütten and are used for the simulation of all machines to create a digital shadow of the real production.

Operational Concept The operational concept of the SDFS consists of various networking formats. These formats are the yearly partner meeting, the so-called Productive Afterhour as well as the expert circles such as the Best Practice Circle Production and the Innovation Circle: Digital Business Models. The yearly partner meeting brings all SDFS partners together to review the past year’s developments of the SDFS and its partner network with different impulse speeches. Afterwards, the orientation for the coming years is jointly set together with all partners.

574

11 Best Practice Examples

The Productive Afterhour takes place three times a year and consists of an expert presentation on a cutting-edge focus topic which is prepared and hosted by a partner. These events are the platform for partners to network and get an overview of existing best practices from the SDFS network. The Best Practice Circle Production is composed of leading, internationally operating, manufacturing companies and is focused on the exchange and discussion of inventions, trends, and solutions for the design of the value stream of manufacturing companies in Germany. The core topics of the Best Practice Circle Production are Industrie 4.0, lean management, the use of IT infrastructure, and complexity control in production. The schedule foresees three two-day meetings a year that are organised and held by alternating Best Practice Circle Production partners. Every meeting contains a variety of presentations, examples, and case studies along with guided tours around the partner’s facilities and benchmarking analyses. The Innovation Circle: Digital Business Models gets machinery manufacturers and IT companies together to discuss a variety of topics such as the identification of new business models, the collection and processing of relevant data, and possible sales concepts for digitisation solutions. The core topics are the identification of new business models and the collection and processing of relevant data or sales concepts for digitisation solutions. In the expert circle meetings, methodological tools, for example for business model development, suitable organisational structures, or necessary agile methods are taught via practice-oriented learning formats such as case studies. Three events are held each year at one of the partner companies. Each event has a specific thematic focus. This is deepened with keynote speeches on the practical experiences of the partners, studies, and benchmarking investigations, as well as case studies and moderated discussions. Unlike a seminar, our expert circles are a closed circle of leading companies in the manufacturing industry, which enables a continuous and intensive exchange (Fig. 11.118). Next to the networking format, the SDFS holds a cooperation with the chair for International Production Engineering and Management of the University of Siegen and provides on-the-job training for the industry. The cooperation with the University of Siegen gives students the possibility to work on a real technical investment planning project for a company, in which the students analyse the current situation in a company and develop a concept for new production facilities or processes. Afterwards, the students contact potential suppliers and evaluate the supplier’s offers to choose the optimal economic and technical solution. For the further enhancement of the connection between the students and the industry, the Talent Factory was created. Selected students receive a scholarship that is financed by companies and

11.37 Best Practice Example 37: Smart Factory AutFab at h_da, University …

575

Fig. 11.118 Grand opening of the Campus Buschhütten with the partners of the SDFS

the Federal Ministry of Education and Research. The Talent Factory extends this scholarship with mentors from the financing companies and organises two events for networking between the companies and the students per year. Furthermore, there is the possibility to visit the companies and take up work student activities in the company. On-the-job training of the SDFS is conducted on the topics of artificial intelligence in production and lean production. These further trainings are open to all companies and are targeted at skilled workers and employees of the management.

11.37 Best Practice Example 37: Smart Factory AutFab at h_da, University of Applied Sciences Darmstadt, Germany Authors: Stephan Simonsa , Stephan Neserb , Heiko Weberta a Department of Electrical Engineering and Information Technology, University of Applied Sciences Darmstadt b Department of Mathematics and Natural Sciences, University of Applied Sciences Darmstadt

576

11 Best Practice Examples

Smart factory AutFab Operator:

Department EIT, Hochschule Darmstadt (h_da)

Year of inauguration:

2012

Floor space:

50 m2

Manufactured product(s):

Relay kits

Main topics / learning content:

Automatic production, Industrie 4.0, Digital factory

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal In 2009, Prof. Dr. Stephan Simons came up with the idea to install a smart factory as a learning factory in the laboratories of the Department of Electrical Engineering and Information Technology (DEIT) of the University of Applied Sciences Darmstadt (h_da). Since the plant was to be used as a learning factory in the automation technology major of the bachelor’s and master’s degree courses, it was planned as a fully automated assembly line. From the very beginning, there was a desire to expand the factory on an interdisciplinary basis. Fortunately, Prof. Dr. Stephan Neser of the Department of Mathematics and Natural Sciences (DMN), decided to

11.37 Best Practice Example 37: Smart Factory AutFab at h_da, University …

577

take care of the machine vision parts of the assembly line. The line was initially funded solely by a special fund for improving the quality of teaching of the State of Hessen, Germany. Interestingly these funds are essentially approved by students, who directly recognised the value of the facility for education. The line, whose basic design and structure were supplied by Köster Systemtechnik GmbH, was inaugurated in February 2012. The first student project started in 2010 and focused on an overall 3D simulation of the line for virtual commissioning. In 2011, the first thesis dealt with the implementation of control software for the workstations of the plant. In 2012, the first students performed laboratory tasks with the plant, regarding visualisation techniques. Thus, starting in 2010 the system was developed from a purely automated to a full smart factory in a series of projects by students from the Departments of Electrical Engineering, Mechanical Engineering, and Mathematics and Natural Science. The primary topic of these projects is Industrie 4.0, the German high-tech strategy initiative for future production. In 2011, the authors were convinced, that in order to realise the full potential of Industrie 4.0, small- and medium-size companies need to experience implemented solutions for use cases firsthand. At the same time, these companies have a great need for graduates with the necessary skills to implement these new technologies in their facilities. For this purpose, students implement appropriate use cases with new technologies in projects over an entire academic semester in the learning factory of the university and thus learn the desired skills. These skills include a holistic understanding of systems, independent problem-solving skills, interdisciplinary expertise, knowledge of production processes and Industrie 4.0 technologies, as well as experience with business cases, project management and communication skills. In addition to its use as a learning factory, the smart factory is a valuable research platform, a perfect demonstrator of the implemented technologies for companies and for attracting potential students, as well as a good infrastructure for cooperation with companies in the development of new Industrie 4.0 technologies. The factory is continuously expanded and equipped with new technology, financed by the special fund for improving the quality of teaching of the State of Hessen, Germany, and a large number of component donations from well-known manufacturers. Equipment and Products The Learning Factory AutFab has a size of 50 m2 and automatically assembles different variants of relay kits. The components of the kits are stored in a high-bay storage. A six-axis industrial robot and a pneumatic press assemble the relay, which is afterwards inspected with an automated optical and a weight inspection, as well as with an automated functional test (see Fig. 11.119). The working stations of the smart factory AutFab are controlled by a network of modern programmable logic controllers (PLCs), which are interconnected with sensors and actuators, right down to pushbuttons and LEDs, via Industrial Ethernet and classic fieldbus communication. The products are assembled with batch size 1. RFID tags on the shuttles, which transport the components between the workstations, assure that the assembled relay kit variant corresponds to the customer order.

578

11 Best Practice Examples

Fig. 11.119 Working stations of the Learning Factory AutFab of h_da

Additionally, data matrix codes (DMC) are used for the adaption of the inspections. All functional safety components are fully integrated into the network. Energy consumption is monitored and measures for energy efficiency have been implemented. Human–machine interfaces are realised on fixed and mobile touch panels, but also on consumer devices like smartphones and tablets. Integrated bus analysers enable diagnosis of the more than 100 networked decentralised cyber-physical systems. The 3D CAD data of all components, all circuit diagrams and all automation programs for the plant are available in corresponding software systems with multi-user capability and can thus be updated or expanded. The vertical communication to higher control levels is realised via OPC UA, MQTT and TCP/IP including the use of ReSTful web services. Digital twins have been created in 3D for line planning, material flow simulation, energy consumption simulation and virtual commissioning. A collaborative electrical and automation engineering in a software system using data from a self-created reuse library stored in a PLM/PDM system was successfully tested. An assistance of the rework process was implemented using mixed reality glasses. In addition, the first steps for a use case for training operators using virtual reality were successfully implemented. The AutFab has been connected to a manufacturing system on premise at the h_da and additionally to an enterprise resource planning (ERP) and manufacturing execution system (MES) running in the cloud. In addition, a web store has been set up, allowing customer to place orders by selecting possible relay variants pre-determined by the ERP system. The customer orders are automatically transferred to the ERP system and converted in the MES into production orders with batch size 1. After release, the production orders

11.37 Best Practice Example 37: Smart Factory AutFab at h_da, University …

579

are transferred to the AutFab and manufactured there. During production, status and quality information is reported from the AutFab to the MES/ERP system, some of which can be displayed with the Internet application. Edge systems with associated management systems were integrated into the plant to enable fast data transfer with the PLCs and complex processing of the data on-site in the private network of the smart factory. In addition, the plant was connected to two cloud systems via various IoT gateways. Several applications for analysing and visualising the smart factory were implemented in the cloud systems. Remote control of the plant is possible using VPN technology for security reasons. Machine learning algorithms have also been integrated into the smart factory for several years, testing potential applications, which can be beneficial for a real production environment. Some examples are the use of different data sources, which may improve the overall functioning, or the transfer of more complex algorithmic to edge devices. An overview of the technologies, integrated in the smart factory to date, is shown in Fig. 11.120. Operational Concept Currently, the plant is operated by Prof. Dr. Simons and H. Webert, M. Sc. and Prof. Dr. Neser, as supervisors for semester-long student projects running as projectbased learning. The supervisors present possible projects at the beginning of the

Fig. 11.120 Industrie 4.0-techologies in the AutFab

580

11 Best Practice Examples

semester. The students choose a project, form teams, and work through the project in an independent manner. They begin by planning the project, analysing the problem, and researching the literature for expertise. Based on the acquired knowledge, they create a concept for a solution, work their way into the necessary technology, implement their concept, verify, and validate it. Finally, they document their work, create videos about it, and present their project with the results to an audience of the supervisors, other students, and invited guests. The supervisors act as the stakeholders, but at the same time as guides by side at regular project meetings and grade the project at the end. Up to now, more than 400 students have already worked in more than 130 student projects on the AutFab. More than 100 Videos, partly showing the results of the projects and partly as tutorials, were created and uploaded to the YouTube channel CRAatHDA.44 These videos have already been viewed in total more than 250.000 times, showing the enormous interest in the technologies implemented in the learning factory. In addition, the projects have a strong influence on many lectures, e.g., on “Industry 4.0 and the Digital Factory,” where the various technologies are demonstrated by means of the use cases in the smart factory. Several students from these projects are currently working on PhD topics in different institutions. In addition, the smart factory with the implemented technologies and the resulting data is used for PhD projects at the h_da. Furthermore, the gained knowledge about machine learning could be successfully applied in a large research project on the use of artificial intelligence in industry at an industrial partner. Student projects and PhD projects were conducted in interdisciplinary collaborations with the Department of Computer Science and the Department of Mathematics and Natural Science. The smart factory AutFab with its implemented technologies was demonstrated to a large number of company representatives, pupils, and students. Ten pilot projects were carried out at the plant, in collaboration with companies on newly developed tools. A six-part workshop for training on digital twins was also developed for one company. This workshop has already been translated into six languages. The vision for future development of the learning factory is a continuous evolution of the AutFab implementing further use cases using new tools and technologies. This is an integrative process, in which teaching and research are combined and in which students acquire key qualifications for a successful career start in the automation industry.

44

See: https://www.youtube.com/user/CRAatHDA.

11.38 Best Practice Example 38: Smart Factory at SZTAKI (Institute …

581

11.38 Best Practice Example 38: Smart Factory at SZTAKI (Institute for Computer Science and Control), Budapest, Hungary Authors: Richárd Beregia,b , Zsolt Keménya , Emma Takácsa , Kristóf Abaia , László Monostoria a Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control, Budapest, Hungary b Vehicle Industry Research Center, Széchenyi University, Gy˝or, Hungary

SZTAKI Smart Factory Operator:

SZTAKI (dept.: EMI), Budapest

Year of inauguration:

2013

Floor space:

30 m2

Manufactured product(s):

Reusable dummy workpieces

Main topics / learning content:

Production planning, scheduling and execution in CPPS; Mechatronics and automation in CPPS

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

582

11 Best Practice Examples

Overall Goal The Research Laboratory on Engineering and Management Intelligence (EMI) at the Institute for Computer Science and Control (SZTAKI) in Budapest, Hungary, is focusing on research and development in industrial production. While communicating scientific advances, it has often been found challenging to present innovative production-related approaches in a convincing way, especially to industrial representatives, but also to future engineers and the general public. Recurrent experience of scepticism towards simulation-only demonstration has highlighted the necessity of a physical plant with a simplified but tangible representation of selected production processes, and the possibility of demonstrating the advantages of new methods and solutions in a comprehensible, clearly presented way. A facility responding to these demands also provides a platform leveraging a “secondary” activity of EMI: involvement in higher education via lectures, exercises, and student projects as part of a production engineering curriculum. With initial funds from national sources and internal budget of the home institute, design, and construction of the SZTAKI Smart Factory started in 2011. In its current form, the facility operates since 2013 and keeps receiving regular hardware and control infrastructure updates as outcomes of research assignments, scheduled development, and student projects. The fundamental setting of the facility is a simplified and scaled-down model of production and intralogistics, retaining sufficient functionalities to examine production planning and control, process transparency, and the handling of disturbances, inaccurate information, and changes of external origin. Since its beginnings, the Smart Factory has maintained its primary use as a demonstration and piloting platform, and foreseeable extensions still do not intend to meet the criteria of a learning factory in the strict sense. Nevertheless, laboratory exercises (as part of a university course), and individual student projects (in support of a complete project-oriented mechatronics course, or in preparation for a BSc or MSc thesis) are hosted by the Smart Factory every year. Equipment and Products The Smart Factory is a compact (30 m2 ), high-level representation of a manufacturing facility with four automated workstations, a warehouse, a loading/unloading station, a sled-mounted collaborative robot, a closed-path conveyor system, and floor space for mobile robots. Workpieces are cylinders of identical geometry, carrying the cardboard inserts processed at the workstations. Each workpiece has a 1K NFC tag with a unique identifier and storage for additional data travelling with the product. Figure 11.121 shows the general layout of the Smart Factory. In the full production scenario, the workpieces are initialised with blank cardboard inserts and loaded into the warehouse. Upon receiving production orders, the prepared workpieces are removed from the warehouse and brought to the processing stations where the cardboard inserts undergo a stamping and drilling sequence in accordance with the production order, along with an optional deposition of production data in the NFC memory of the workpieces. Transfer of workpieces during production relies on both the conveyor and mobile robots. Depending on the production scenario, finished products are returned to the warehouse or removed from the intralogistic stream at

11.38 Best Practice Example 38: Smart Factory at SZTAKI (Institute …

583

Fig. 11.121 General view of the SZTAKI Smart Factory

the loading/unloading station. After production, the outcomes can be tested by visual inspection and by reading the NFC tags embedded in the workpieces. The workpieces can then be returned to their initial state by removing the cardboard inserts and erasing the contents of the embedded NFC tags. In recent years, the Smart Factory received a new fleet of custom-designed mobile robots (Fig. 11.122 left) with a ceiling-mounted vision system for fleet surveillance, and a modular embedded computing framework for a “smart retrofit” of the processing stations and warehouse control (Fig. 11.122 right), also in preparation for a comprehensive control architecture refurbishment in accordance with CPPS principles. The embedded computing framework is also responding to the experience that smart retrofit solutions with low-cost components can be set back by overheads of physical/mechanical interfacing, especially if demands change frequently. Standardisation of interfaces and physical dimensions within the framework is expected to reduce interfacing efforts and to leverage the more consistent connection of individual student projects for outcomes transcending the limits of a single, one-shot development. While most of the initial plans for collaborative support have been reassigned to another—specifically collaboration-oriented—facility, human–machine interfaces with direct relevance to the processes in the Smart Factory are still being developed locally. A major project in this field is the development of augmented reality (AR) support for issuing commands and pre-assessing operation sequences for the sledmounted collaborative robot employed for material handling (Fig. 11.123). The latter addition to the infrastructure will further extend distance learning capacities, as it allows the sharing of an AR-enhanced viewpoint of an on-site instructor with students attending online.

584

11 Best Practice Examples

Fig. 11.122 Recent additions to the Smart Factory infrastructure. Left: mobile robot with workpiece rack; right: embedded computing framework on a test stand

Fig. 11.123 AR extension for the material handling robot added recently to the Smart Factory infrastructure45

Operational Concept In the research and demonstration context, the Smart Factory is a tangible test bed for process planning, scheduling, and execution in a cyber-physical environment, as well as human–machine interaction at various decision points of the production hierarchy. Connection to resources maintained by other departments of the hosting

45

Left: photo of the physical robot with QR code shield for pose matching; right: virtual model of the robot imposed on the camera view, as displayed by AR glasses.

11.39 Best Practice Example 39: SmartFactory-KL at the German Research …

585

institute (immersive virtual reality, cloud services) is also involved in a considerable part of the research and demonstration activities in the Smart Factory. The Smart Factory has its share in higher education taking place at the Budapest University of Technology and Economics (Department of Manufacturing Science and Engineering), in several forms: • Laboratory exercises in scheduling and execution in cyber-physical production systems are hosted by the Smart Factory. On average, ca. 50 students are involved in the spring semester of each year, forming several groups of ideally 5–6 students. In response to the lockdowns and attendance restrictions mandated during the recent pandemic, laboratory exercises have been made suitable for remote attendance. To this end, a planning and simulation environment has been implemented, using a virtual representation of the workstations. The environment provides the same discrete scheduling decisions which are available to the students on the physical platform. • The Smart Factory provides a design and test environment for selected student groups in the “Mechatronics Project” course. In the one-semester course, groups of 3–4 students collaborate as a team in the design and construction of automation solutions for an existing deployment environment. While the specific development assignments change from project to project, the course does follow a definite set of milestones and acceptance criteria. • The Smart Factory hosts individual student projects leading up to BSc and MSc theses and research contest entries. This is independent of the “Mechatronics Project” course and can form the first step to further research as a Ph.D. student, supervised by a member of EMI staff. The outcomes of project-based student work are hardware or software components. If they are deemed fit for reliable live operation, they remain part of the equipment of the Smart Factory until replacement by subsequent solutions, similar to incremental changes in live production environments.

11.39 Best Practice Example 39: SmartFactory-KL at the German Research Center for Artificial Intelligence (DFKI), Germany Author: Ingo Herbsta a SmartFactory-KL

586

11 Best Practice Examples

SmartFactory-KL Operator: Year of inauguration:

German Research Center for Artifical Intelligence (DFKI) 2004

Floor space:

400 m2

Manufactured product(s):

Production of the future / dynamic suppy chains

Main topics / learning content:

ProductionLevel 4, Industrie 4.0

Morphology excerpt

Open models

Target industries

Production Level 4 Open public

Job-seeking

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Global production

Energy & resource efficiency

Industrial eng.

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

Any type

Overall Goal The formulation of the Industrie 4.0 (I40) concept back in 2011 led to re-thinking production. The idea of networking machinery, automating processes, and being able to manufacture individual products led to the increased use of digitalised operations in many companies. Parallel to this, society in general accepted the growing hype of a digital world and “developed” opportunities. Terms like Labor Law 4.0 and Vacation 4.0 appeared, although ultimately watering down the original idea of Industrie 4.0.

11.39 Best Practice Example 39: SmartFactory-KL at the German Research …

587

In 2019, Prof. Martin Ruskowski formed an interdisciplinary team to evaluate the experiences made in the context of Industrie 4.0 up to that time. Various aspects were considered: • What parts of I40 were retained, ignored, or distorted by industry during implementation? • What scientific and technical experience with I40 implementation has SmartFactory-KL had since 2014? • What potential technical innovations were missing in the 2011 formulation of I40? After much discussion, the researchers came to the following conclusions: • Digitalisation in itself is not enough to implement Industrie 4.0. It requires a complete change in mindset and this has not been the case at many companies. • New technologies like AI tools and Cloud technologies have greatly expanded the available processing power and must be considered in an updated vision of the production of the future. • The purpose of I40 is not to replace people in the production process through the use of automation. Yet, this is the prevailing mindset at most companies. • The role of people in production needs to be highlighted and defined. The idea of a “dark factory” with deserted factory floors is now recognised as misleading. The team structured the stages of development in Industrie 4.0 implemented to date in an effort to define a new approach. Digitalisation was seen as the first stage, in which cyber-physical production systems (CPPS) emerged and machines were networked to communicate with each other. The second stage witnessed the rise of cognition in the machines, i.e., interaction between humans and machines became possible, for example, by means of voice and gestures. This is the stage of development at the present time. Scientists borrowed a term from social philosophy to describe the stage ahead: Subsidiarity. By this, they mean the ability of a production module to act independently (to the maximum extent possible), while recognising that it is part of a larger system that can provide support if help is needed. The concept includes humans, who act as a decisive element at many points because of their unique abilities that machines or software are not currently able to offer a human can recognise systemic errors, creatively develop new processes, or optimise production sequences. This development stage aims to achieve uninterrupted production with the flexibility to react to external disturbances.

588

11 Best Practice Examples

The next task to emerge is aligning Industry 4.0 in the direction of future challenges for industry. Supplementing the familiar I40 elements of flexibility, modularity, plug and produce, Internet of Things, predictive maintenance, scientists are now mapping out new key data components like resilience, sustainability, and the role of people. Experts have coined the name Production Level 4 (PL4) and introduced the upgrade to the press at the end of 2019. PL4 is structured as a kind of learning process. The term Production Level 4 reflects its own development history. The number 4 underscores its orientation on I40. The number 4 also indicates the level of autonomy that is desirable in production. Level 4 describes the possibility of human intervention at any time in any ongoing production process. Correspondingly, these processes must be organised with respect to transparency. Equipment and Products After coordinating with the industrial partners, the team of SmartFactory-KL began implementing the first technical step in the new PL4 vision—construction of the production cell_JAVA. As expected, new questions arose during the work and the learning process from theory to practice quickly got underway. Two examples of learning are presented below to help explain: Example 1 Production Level 4 starts with the assumption that production-related considerations stem from the end product. More specifically, this means a finished product is placed in a data room and the services available in the data room configure themselves independently to form a dynamic supply chain and then implement the production of that product. The concept envisions matching platforms where matching services are recognised and, in turn, meaningfully assembled by production bots. They then organise, control, and monitor the production process. But how does this work? What exactly is required? Let us assume a product requires a round recess in a plastic block. This can be made either by drilling or milling. In the end, it doesn’t matter to the product or the customer how the result was achieved, the main thing is that all of the technical requirements are met. Over time, the colleagues concluded that it is sufficient to know the capability of the machine so that it may be selected via a digital matching platform. In other words, the machine must be able to communicate its capability. However, how that service is technically implemented does not matter for the overall production process. In theory, even manual labour could be used just as any other technical means that are available at various companies. The developers concluded that it makes sense to encapsulate the “how.” This logic can also be applied to software: it should be able to perform a certain task, but how it was programmed or which algorithm is running in the background is unimportant. Parallel to this, the dynamic supply chains created in this way were set up and subjected to practical testing in the skill-based shared production environment in Kaiserslautern. The learning approach reads: “Think. Do. Test. Analyse. Improve. Repeat (Fig. 11.124).”

11.39 Best Practice Example 39: SmartFactory-KL at the German Research …

589

Fig. 11.124 Factory concept of the SmartFactory-KL

Example 2 PL4 envisions the goal of sustainable production in ecological terms. The aim, for example, is to reduce the CO2 footprint and energy consumption and also, to know about the materials used in the product in order to facilitate its recycling. This data must be accessible at all times and it requires a dynamic storage as it can change through post-processing. Along the lines of this observation, a group of researchers developed the so-called life cycle file. Other colleagues began work on implementing shared production considering the issues mentioned above. They relied on the asset administration shell (AAS), which serves as a communication element in the data center of the modules and could, for example, contain the capability on offer. Through regular comparisons of the respective research results, joint discussions, and workshops the team proposed the idea of extending the AAS with a dynamic sub-model, namely, the life cycle file (Fig. 11.125). Operational Concept Cooperation as dynamic learning process—What makes SmartFactory-KL unique? The unique structure and work methods used at SmartFactory-KL (SF-KL) are the reason that it is able to combine theoretical concepts with practical implementation. SF-KL combines the work of three research organisations: the Innovative Factory Systems department at the German Research Center for Artificial Intelligence (DFKI), the Department of Machine Tools and Control Systems (WSKL) at

590

11 Best Practice Examples

Fig. 11.125 Asset administration shell

the RPTU Kaiserslautern campus, and the nonprofit association Technology Initiative SmartFactory-KL. The association also consists of 40 industrial companies organised to study the issues of everyday production. This is where AI researchers from DFKI, experts in mechanical engineering from the Technical University, and engineers from industry all come together. The work is organised in such a way that research takes place at RPTU and DFKI, supported by public project funding from the federal and state levels. Joint working groups led by scientific staff are organised to seek practical answers and test them in the production ecosystem of the PL4 testbed. Partners meet at eye level in the joint, pre-competitive working approach, which is a prerequisite just as is a concentration on results that benefit all. Transparency is a top priority and any thoughts of competition do not exist. The results are evaluated and fed back into both the research projects and the working groups. The staff members from non-technical areas like business administration or the humanities also participate in regular workshops, constructively adding to the joint discussions and bringing in new perspectives to broaden the view on more general visionary concepts such as the development of Production Level 4. Interdisciplinary research methods and getting away from academic “thought silos” enables disruptive impulses and a willingness to think “outside of the box.“ SmartFactory-KL actively seeks federal and state funding to further develop the production of tomorrow. The integration of science and industry plays an important role in the effort to demonstrate each advance made. This is shown in the two examples below: Project smartMA-X is funded by the German Federal Ministry of Economic Affairs and Climate Action (BMWK) since January 1, 2020. The defined goal is the development of a secure data center within the Gaia-X framework for production, which is a prerequisite for PL4. SmartFactory-KL set up shared production ecosystem in Kaiserslautern to explore how all assets can communicate with each other through a secure data center. The development team has made consistent progress: One production cell has expanded to three now as the test product, a “data loaded USB-stick”

11.40 Best Practice Example 40: Smart Mini Factory, Free University …

591

Fig. 11.126 Logo of the project “smartMA-X”

Fig. 11.127 Logo of the project TWIN4TRUCKS

produced on _JAVA, became a manufacturing process for a sophisticated model truck built on three production islands, each separated by up to 500 m (Fig. 11.126). TWIN4TRUCKS Another BMWK sponsored project TWIN4TRUCKS (T4T) (German only) was launched on September 1, 2022. The goal is the optimisation of commercial vehicle production at Daimler Trucks. SF-KL identifies several connections between smartMA-X and T4T. Not only is their work on model trucks transferable to the production of semitrailer trucks weighing several tonnes, the scientific results from smartMA-X concerning the “Industrial Edge Cloud” can be applied in T4T for the development of a “digital red thread” to be used on the shopfloor (Fig. 11.127). The practical sharing of findings is one of the outstanding outcomes produced by the work methods employed by SF-KL and its partners. The interdisciplinary nature of the association’s cooperation provides fertile ground for pre-competitive results. Eliminating competition between all participants leads to a better work focus and encourages teamwork. Working together and not competing against each other conserves physical resources and promotes innovation.

11.40 Best Practice Example 40: Smart Mini Factory, Free University of Bozen-Bolzano, Italy Authors: Dominik Matta , Erwin Raucha , Renato Vidonia a Free University of Bozen-Bolzano, Faculty of Science and Technology, Research Area “Industrial Engineering and Automation (IEA)”

592

11 Best Practice Examples

Smart Mini Factory (SMF) Operator:

Free University of Bozen-Bolzano

Year of inauguration:

2017

Floor space:

250 m2

Manufactured product(s):

Pneumatic cylinder, Pneumatic impact wrench

Main topics / learning content:

Industrie 4.0, Smart manufacturing systems, Automation and robotics, VR and AR

Morphology excerpt

Open models

Target industries

Industrial robotics Open public

Job-seeking

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal The Learning Factory Laboratory at the Free University of Bolzano was founded in 2012 as a “Mini Factory” with start-up funds from the Chair of Production Systems and Technologies in the research area Industrial Engineering and Automation (IEA). The name “Mini Factory” was chosen as name for the learning factory lab because it should reflect the principles of lean and agile production in a small and realistic scale.

11.40 Best Practice Example 40: Smart Mini Factory, Free University …

593

Furthermore, the concept of small and distributed manufacturing systems (“minifactories”) pursues the goal of producing mass-customised products on demand and in close proximity to the customer. In 2015, the Autonomous Province of Bolzano allocated a budget of 2.3 million euros to the Free University of Bolzano due to IEA’s efforts to establish research and teaching competence in the field of Industry 4.0 in Italy. These funds are planned for capacity building in the sense of suitable lab space, research personnel and investments in machinery and equipment. Due to the tight spatial situation in Bolzano, it took until 2017 to find a temporary solution to accommodate the learning factory in the city center. In 2024, the learning factory together with a part of the university campus is to be relocated to a new building in the brand-new NOI Technology Park in the south of Bolzano. The Smart Mini Factory lab is a learning factory laboratory used for applied research and for teaching. It aims to study and simulate different modern and advanced concepts of production technologies and methods in the context of Industry 4.0. The main focus is given to the requirements of SMEs regarding hybrid and human-centred production and assembly systems as well as robotics and mechatronics for industrial automation. An aim of the laboratory is to create a platform where researchers, students and industry meet to enable the transfer of knowledge from research to industry. By working together with companies, industrial requirements can be taken up directly and transferred into application-oriented research. In addition, results and know-how from research are passed on to future engineers in the form of student projects and student jobs. The Smart Mini Factory Lab is also used in many courses for practical training of students by carrying out exercises and simulations in the laboratory. Students can carry out their study projects and final theses in the Learning Factory Lab and thus gain valuable practical experience with state-of-the-art equipment in the field of Industry 4.0 and automation. Finally, the Smart Mini Factory Laboratory serves companies from industry as a contact point for collaborations in research and supports companies in the implementation of Industry 4.0 In addition to the possibility of contract research, companies can qualify their employees via an industry-oriented seminar offer and prepare them for the challenges of Industry 4.0. Equipment and Products The learning factory is divided into two different areas. One area comprises 3D printers for additive manufacturing and several numerically controlled machine tools in the form of a CNC machining workshop. This allows prototypes and parts to be manufactured and then brought into the second area for assembly. In the laboratory, most of the key enabling Industry 4.0 technologies are available and grouped as follows (see Fig. 11.128 for examples):

594

11 Best Practice Examples

Fig. 11.128 Exemplary pictures of the lab

Advanced Robotic Systems: this area includes traditional industrial robots with anthropomorphic and SCARA open-loop kinematics (e.g., ABB IRB 120, Adept Cobra i600), as well as parallel, closed-chain planar high-performance systems (e.g., Adept Quattro s650H). The integration of an intelligent transfer system (e.g., Montrac), as well as mobile and redundant robots (e.g., Kuka KMR iiwa), increases the capability of dealing with industrial scenarios as well as implementing solutions for high-complex test cases. Hybrid Assembly and Human–Robot Interaction: in this scenario, human and robots can safely and ergonomically cooperate or collaborate hand-by-hand in a hybrid and shared workspace, where humans are the key elements of the production system (“human-centred design” approach). In the Smart Mini Factory Laboratory, in addition to the Kuka KMR iiwa mobile robotic system, two models of collaborative anthropomorphic robots are available: Universal Robot UR3 and UR10 equipped vision systems (e.g., Robotiq, Realsense). These robots can be equipped with different electromechanical (e.g., Robotiq, Shunk) or pneumatic (e.g., Onrobot) grippers and integrated with other advanced manufacturing systems such as lean assembly workstations. Furthermore, they can be integrated with the available Smart Robots

11.40 Best Practice Example 40: Smart Mini Factory, Free University …

595

device, an AI-based vision system for enhancing human–robot communication and safety. Industrial Internet in Cyber-Physical Production Systems: in this area, the challenge of Internet of Things as the enabler of a common interoperability layer for coordination and orchestration of our Cyber-Physical Systems is addressed. Such an implementation spans from the physical communication layer where different fieldbus technologies must coexist (e.g., in the real manufacturing plants different communication technologies could be present), to the application layer where the representation of the information must be compatible with each software participating in an industrial application. Proprietary, standard, and open-source communication protocols are considered, implemented, and exploited for the research and teaching purposes. Mixed Reality for Factory Planning and Construction 4.0: in this area, Building Information Modelling (BIM)-based simulation tools and wearable devices for Virtual reality (e.g., Oculus Rift), as well as Augmented/Mixed reality (e.g., Microsoft HoloLens 2), are used for immersive and interactive digital factory planning and for supporting workers on the construction site. Furthermore, different software for production management and simulation (e.g., Flexim, Tecnomatix Process Simulate, VisTable, ThingsBoard), as well as for the creation of digital twin of production processes and systems (e.g., I-Physics, RoboDK) are available in the lab. Currently, two different products are produced on the assembly line (see Fig. 11.129). On the one hand a pneumatic cylinder and on the other hand a pneumatic impact wrench. A product analysis is carried out at the beginning defining the process steps with an assembly precedence graph. After preparation of the inventory of individual parts, the practical implementation in the line, the balancing of the assembly tasks as well as several loops to increase efficiency in the production system are carried out.

Fig. 11.129 Pneumatic cylinder from Kuhnke

596

11 Best Practice Examples

Operational Concept The use of the learning factory laboratory is divided into three parts. On the one hand, it is used for applied research and therefore serves for building up individual test setups. Secondly, it is used as part of the bachelor’s and master’s program for industrial and mechanical engineering (mainly the lectures “Production Systems and Industrial Logistics,” as well as “Industrial Automation and Mechatronics” and “Industrial Plants”). Thirdly, seminars on all aspects of Industry 4.0 for industrial companies are offered (e.g., on-demand courses or training financed by the European Social Fund). The exercises and seminars are designed and conducted by researchers, postdocs, and PhD students. The industrial seminars allow a close contact with local industry and thus facilitate the transfer of knowledge from research to industry. Furthermore, a summer camp for middle-school students is proposed every year to raise awareness of Industry 4.0 topics among the younger generation. Considering research, the interdisciplinary team focuses on the following topics: Automation and Robotics: Studies on this topic are carried out in terms of both the basic and applied research. In the first case, methods and technologies are designed and developed for the performance enhancement (e.g., time reduction, energy saving, vibration suppression, weight reduction, safety) of automatic machines as well as mechanisms and robots. In the second case, applied research topics are developed as answer companies to inputs such as soft gripping, quality inspection, automation of non-standard and complex production processes. Mechatronics and Electric Drives: This topic includes the design and implementation of mechatronic devices for the Industry 4.0 developments, e.g., machine automatic control, predictive maintenance, smart sensing, and actuation. Further, it includes the analysis, design, and control of electric drives for modern applications, which provide, besides pure motion control, the sensing, processing, and communication infrastructure, which enables the development and implementation of highlevel automation and human–machine interaction strategies (e.g., self-identification of machine and load parameters, automatic tuning of controllers, machinery data collection, and predictive maintenance). Simulation and Digital Twin: The focus of this research is on the application of simulation systems to create a digital twin of physical production systems and factories. Therefore, different systems of simulations are applied in various levels. In the level of material flow, simulation systems are used for discrete event simulations and layout planning. In the layer of machinery and systems, process simulations are used for manual, hybrid and automised systems as well as for ergonomic analysis. Human–Machine Collaboration: One focus of this research area is collaborative robotics and how collaborative workplaces can be designed ergonomically and safely. Intuitive and simple methods are also being developed to control robots and machines and thus facilitate their application in industry.

11.41 Best Practice Example 41: Stellenbosch Learning Factory (SLF) …

597

Hybrid Assembly Systems: This focus includes the use of a combination of manual workstations and automated or semi-automated stations in assembly. One focus is the design of human-centred (anthropocentric) production systems to enhance human capabilities in the best possible way. Assistance Systems for Production: This focus includes the investigation of (smart) assistance systems to facilitate work for people, also considering workers with disabilities. Assistance systems can reduce physical workload, can also minimise the mental workload through sensor-based or cognitive abilities, and thus lead to error reduction and increased efficiency. Virtual/Augmented Reality: The use of virtual and augmented reality is manifold and is studied in the laboratory in the manufacturing and assembly environment, as well as for applications in the building industry and in maintenance. This includes the combination with simulation systems for digital factory and process planning as well as with building information modelling systems. I4.0 Learning Factories: Learning factories have become an integral part of engineering education and enable practical application of what has been learned in a realistic factory environment. This argument focuses on how learning factories should be designed in future to support the transfer of knowledge in the field of Industry 4.0 methods and technologies. Construction 4.0: The research focus includes the optimisation and digitisation of processes and procedures in construction management. This includes above all realtime planning and monitoring of the construction site as well as the synchronisation of the fabrication shop to the site. A special focus is given to the engineerto-order environment. Furthermore, the focus is on topics for intelligent connectivity of construction processes, data management in construction projects (Building Information Modelling), and the use of modern assistance tools such as Virtual and augmented reality.

11.41 Best Practice Example 41: Stellenbosch Learning Factory (SLF), Department of Industrial Engineering, Stellenbosch University, South Africa Authors: Louis Louwa a Department of Industrial Engineering, Stellenbosch University, South Africa

598

11 Best Practice Examples

Stellenbosch Learning Factory (SLF) Operator: Year of inauguration:

Department of Industrial Engineering, Stellenbosch University 2016

Floor space:

150 m2

Manufactured product(s):

Model train

Main topics / learning content:

Process analysis and improvement

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal The idea for a learning factory at the Department of Industrial Engineering (IE), Stellenbosch University originated around 2014 through collaboration with Prof. Vera Hummel from the ESB Business School at Reutlingen University, Germany. Prof Hummel was responsible for setting up a learning factory at ESB, and through her shared experience and instigation a concept for a learning factory was born at Stellenbosch. Before any official documentation for a learning factory was drafted, an investigation into how others built their learning factories to achieve their envisioned

11.41 Best Practice Example 41: Stellenbosch Learning Factory (SLF) …

599

outcomes was explored. This was accomplished by visiting a few learning factories in Germany, including the one at ESB Business School. The focus of the IE department at Stellenbosch is more on undergraduate teaching and postgraduate research. A decision was therefore made to focus the learning factory on teaching and research, and not industry workshops and training. The curriculum at the IE department was a main driver, and it was decided to focus on learning outcomes related to production management, as well as manufacturing process and systems design undergraduate modules. In 2015, a product was conceptualised and custom built, based on curriculum alignment, complexity, size, cost, and assembly/disassembly criteria. This was followed by a process and facility design that will support the product being manufactured. One of the existing laboratory areas inside the IE department (150 m2 ) was made available to house the Stellenbosch Learning Factory (SLF). No external funding was available to initiate the SLF, and limited available internal funding was sourced to support the initiation. Initially, the focus was only on assembly and disassembly of the product consisting of two variants, with some parts manufactured with additive manufacturing. Teaching topics focus on production planning, assembly line design, process capacity and flow analysis and improvement using Lean and Theory-of-Constraints concepts, as well as factory layout design. Currently, the SLF facility collaborates with a second facility at the IE department at Stellenbosch, which is the Stellenbosch Technology Centre (STC). This second facility houses advanced CNC manufacturing equipment to expand the teaching to manufacturing-related concepts as well. Equipment and Products The product used in the SLF is an O-scale model train, consisting of two variants: a passenger coach and a driver coach (refer to Fig. 11.130). These model trains are inspired in colour and make-up by the South African Metrorail and were selected based on a Passenger-Railway Association of South Africa (PRASA) research chair at the IE department. The average amount of components to assemble a train is 61 individual parts, which increases the logistical and material handling complexity of assembly. Only three part types are manufactured using 3D printing. The other parts are “sourced” from suppliers under simulated conditions. The process chain includes order processing, production planning, sourcing of parts, manufacturing of selected parts, assembly, quality control, packaging, and dispatching. After dispatching, the products are disassembled and the parts returned to the parts and materials storage area (warehouse). A new product is currently being designed at the SLF that will include parts being manufactured in the subtractive manufacturing facility of the STC (refer to Fig. 11.132). The assembly area (refer to Fig. 11.131) includes assembly workstations with flow racks for storing part bins at the workstations. Assembly is manual, but some assembly operations can be assisted with a collaborative robot (cobot). The cobot is also used for machine tending and pick and place. It will unload printed parts from the 3D printer and place the printed parts in bins which are then placed by the cobot on the assembly workstation’s flow rack (refer to Fig. 11.131). The STC subtractive manufacturing area (refer to Fig. 11.132) is equipped with CNC turning and milling machines, as well as electrical discharge machining (EDM) equipment.

600

11 Best Practice Examples

Fig. 11.130 Products in the SLF

Fig. 11.131 Layout and equipment of the SLF assembly facility

Various final-year student projects are used to further digitalise and automate the SLF and STC facilities. These include projects developing a RFID material tracking system, a machine vision-based quality inspection station using machine learning, as well as a digital twin simulation model of the facilities and processes.

11.41 Best Practice Example 41: Stellenbosch Learning Factory (SLF) …

601

Fig. 11.132 Layout and equipment of the STC manufacturing facility

Operational Concept Teaching in the SLF is focused on production management concepts for undergraduate industrial engineering students. The didactic concept is an improvement project where students need to analyse and improve the current SLF production operations by utilising the lecture theory related to production and operations management. The project runs over three weeks, and students are arranged in groups of ten (due to the large size of the undergraduate cohort participating in the module, which can range from 120 to 140 students per year). Inefficiencies are built into the current SLF setup, and students then need to follow a process analysis approach to identify inefficiencies and ineffectiveness. This includes methods such as value stream mapping, demand forecasting, capacity and bottleneck analysis, waste identification, layout analysis, as well as calculating various performance measures related to time, quality, throughput, inventory, and cost. Improvement solutions are then conceptualised and the impact on operational performance indicators is determined. At the end of the project an example improvement solution is implemented and demonstrated to the students. The project is led by the lecturer for the module, and postgraduate students assist as “employees” of the production facility. In addition to undergraduate teaching, the SLF is used by final-year students who need to execute a project over the duration of their final year. These projects are related to the development, implementation, and evaluation of digital or artificial intelligence-related technologies.

602

11 Best Practice Examples

11.42 Best Practice Example 42: SZTAKI Industry 4.0 Learning Factory, Gy˝or, Hungary Authors: Zsolt Keménya , János Nacsaa,b , Mátyás Hajósa , József Vánczaa a Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control, Budapest, Hungary b Vehicle Industry Research Center, Széchenyi University, Gy˝or, Hungary

SZTAKI Industry 4.0 Learning Factory Operator:

SZTAKI (dept.: EMI), Győr

Year of inauguration:

2017

Floor space:

150 m2

Manufactured product(s):

Ball velve (assembly only)

Main topics / learning content:

Human-robot collaboration, Production planning, process planning and execution in PPS

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

11.42 Best Practice Example 42: SZTAKI Industry 4.0 Learning Factory, Gy˝or …

603

Overall Goal Design and construction of the Industry 4.0 Learning Factory in Gy˝or, Hungary, commenced as part of an R&D&I project supported by national funds, and was among the contributed assets in the follow-up of the EU-wide EPIC CoE (Centre of Excellence in Production Informatics and Control, transformed later into a separate legal entity). Most of the infrastructure and equipment in the facility was built up during 2017–2018. While full functionality and capacity for higher education were available by the 2018–19 academic year, admission of the didactic offering into university curricula is still in progress and likely to require several more years of targeted effort to succeed. The facility itself is located at the premises of Széchenyi University in Gy˝or; however, design and construction of the learning factory were, for most parts, conducted by the Research Laboratory on Engineering and Management Intelligence (EMI), a department of the Institute for Computer Science and Control (SZTAKI). EMI also maintains and operates most of the facility, with some of EMI staff members also involved in other education and research activities at Széchenyi University. The SZTAKI Industry 4.0 Learning Factory in Gy˝or is the second of such facilities constructed by EMI. As opposed to the primary research- and demonstration-oriented “Smart Factory” in Budapest, the site in Gy˝or has more emphasis on education, being able to support one-shot laboratory exercises as well as complete courses where student groups can gain hands-on experience in various life cycle stages of production assets and products in a production environment, emphasising aspects of cyber-physical production systems (CPPS) and human–robot collaboration (HRC). To this end, the facility is laid out as a shopfloor featuring reconfigurable workstations with collaborative robots, surrounded by open floor space shared by humans and autonomous mobile robots. Equipment and Products The SZTAKI Industry 4.0 Learning Factory in Gy˝or has a shopfloor area of ca. 150 m2 which is shared in part with conventional automation equipment owned and operated by Széchenyi University. The Industry 4.0 Learning Factory consists of several flexibly configurable robotised workstations equipped with UR5/10 collaborative robots. Each station is organised around a central pillar for a robot arm, while the surrounding space is configurable using prefabricated frames and work surfaces (Fig. 11.133). Also, part of the infrastructure is indoor positioning, devices for acquiring optical images and 3D point clouds, and reconfigurable human–machine interface components. Intralogistics is aided by mobile robots serving as automated guided vehicles (AGVs), one of them equipped with a UR5 robot for loading/unloading operations performed at the workstations. Augmenting the facility is a separate room of 75 m2 for lectures, product design and planning in virtual environments.

604

11 Best Practice Examples

Fig. 11.133 One of several configurable collaborative assembly stations installed in the SZTAKI Industry 4.0 Learning Factory

Material passing through the production equipment consists of components of ball valves, emphasising mechanical/geometrical aspects of assembly. The parts to be assembled are fed to the workstations via palletised kits (i.e., structured delivery). Assembly is aided by fixture sets which are reconfigurable within discretised degrees of freedom in design (via placement on raster pallets), each set providing complete support for one of several distinct routings of assembly operations. The workstations, delivery pallets and fixtures are laid out to allow handling by humans and robots, likewise, thereby allowing variable assignment of production resources to the individual tasks. The assembly process is fully reversible, i.e., the workpieces can be disassembled and returned to their initial state after a production run. Operational Concept The SZTAKI Industry 4.0 Learning Factory in Gy˝or is suited for supporting longer, possibly connected, courses organised around selected topics in various life cycle stages of production assets and products, also highlighting the contrast to—and potentials of synthesis with—conventional automation. Experience of past years has shown that acceptance through educational hierarchy is a long and laborious process which has, to date, not yet yielded a stable place for learning factory courses in a production engineering curriculum. Nevertheless, the development of one course, focusing on layout and process planning for collaborative assembly, has already made it to a trial run in a compact version as a summer school program, held in collaboration with Fraunhofer Austria.

11.42 Best Practice Example 42: SZTAKI Industry 4.0 Learning Factory, Gy˝or …

605

In the course, groups of 2–4 students receive the task of designing an assembly station layout, human/robot resource assignment and corresponding worker instructions for the assembly of ball valves. Each group is given a different set of prefabricated assembly fixtures and a corresponding consistent routing of assembly operations as part of the design constraints. The designs are tested in an iterative verification process (benchmarking virtual designs, test rounds on the physical system), and finalised solutions are run and benchmarked for cycle time, productivity, and resource usage. Results of the individual groups are evaluated and compared in a final discussion. The summer 2021 roll-out coincided with times of unpredictable travel and attendance restrictions during the recent pandemic, therefore, preparations were made to accommodate hybrid participation: (1) lectures and consulting time slots were made accessible via remote connection, (2) sample workpieces, and a lightweight replica set of fixtures, a simplified robot gripper, and assembly pallets were mailed to students attending online (Figs. 11.134 and 11.135), (3) a virtual design and benchmarking environment was made available to all attendees, and (4) a fully functional implementation of each workstation design was built up by expert staff in Gy˝or who also conducted assembly test runs with the physical equipment and relayed the results back to students attending online. Figure 11.136 shows a screenshot taken during the final benchmarking session involving physical equipment in Gy˝or, run in parallel with the simulations and worker instruction sequences designed by the student groups. The hybrid attendance extensions have proved successful and will remain part of the technological and didactic offering for future runs of the course. Aside from complete courses, stand-alone laboratory exercises and demonstrations can also be hosted by the facility, as well as independent work in preparation of BSc and MSc theses, or research activities of PhD students. SZTAKI also conducts some of its research activities at the site, partly under the involvement of remotely connected resources residing at the institute’s Budapest headquarters. Distributed work across the sites is also aided by further workstations being available in Budapest, identical to the equipment of the Industry 4.0 Learning Factory in Gy˝or.

Fig. 11.134 Left: replica of the robot gripper sent to students attending remotely. Right: ball valve subject to assembly, with parts pre-packaged for supporting remote attendance

606

11 Best Practice Examples

Fig. 11.135 Left: elements used in the assembly fixtures—different colours are marking their function. Right: lightweight assembly pallet for manual tests during remote attendance

Fig. 11.136 Screenshot captured during a hybrid session of the summer school course46

The strong presence of automotive industry in the area of Gy˝or makes it a natural choice to offer the facility for training, demonstration and performing tests of automation solutions by contract with industrial enterprises. Since its inauguration, the facility has already hosted R&D&I assignments of industrial partners contributing their own equipment to the projects. Also, a version of the layout and process planning course reworked specifically for industrial training or knowledge transfer is currently under consideration.

46

Top left: layout designed in the virtual environment, bottom left: physical process operated on-site, and right: worker instruction.

11.43 Best Practice Example 43: The Centre for Industry 4.0 at Chair …

607

11.43 Best Practice Example 43: The Centre for Industry 4.0 at Chair of Business Informatics, esp. Processes and Systems, University of Potsdam, Germany Authors: Norbert Gronaua,b , Malte Teichmanna,b , Sander Lassa a Chair of Business Informatics, esp. Processes and Systems, University of Potsdam b The Weizenbaum Institute for the Networked Society—The German Internet Institute, Research Group “Education for the Digital World”

Centre for Industry 4.0 Operator:

LSWI, University of Potsdam

Year of inauguration:

2012

Floor space:

120 m2

Manufactured product(s):

Knee joints, chocolate bars, optical lenses

Main topics / learning content:

Concepts, Trends and technologies of Industry 4.0, Work 4.0

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

Top

Semi-skilled workers

Unskilled workers

Management

…

Design

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

608

11 Best Practice Examples

Overall Goal The hybrid simulation environment is an artefact of a research project (called LUPO). The intention was to develop a tool for testing and evaluating decentralised technologies and organisational concepts in the factory context. The basic idea was to combine real and virtual objects to achieve low-effort modelling on the one hand and high immersion for the users (test persons and audience) on the other. Furthermore, the fast and demand-oriented configuration of the experimental setup with minimised dependencies on technical restrictions allows for concentrating on the experimental design and the knowledge-providing scenarios. Based on these potentials, the increasing use within projects focusing on learning resp. Knowledge transfer (among others in cooperation with the IG-Metall) took place, which implied the development of a suitable working method and resulted in a learning concept within the systematic work. From a technical point of view, the learning environment consists of loosely coupled elements that communicate using modern techniques (including OPC-UA and web-based UIs) and thus realise production processes. They also provide interaction points for learners in the role of workers or represent essential factory floor components relevant to the learners’ tasks. Thus, both elements of the automation level (sensors, PLCs) and IT systems are to be orchestrated into learning scenarios that provide an application context, for example, to test new learning ideas or to develop advanced assistance systems. Figure 11.137 shows parts of the environment. The ZIP4.0 is a learning factory for training and qualification, demonstration and application, research and evaluation, and a praxis example for the future factory 4.0. The training programs in ZIP4.0 offer training courses, workshops, and further education measures over one or more days on a variety of topics for competence development and education of employees regarding Industrial IoT and Industry 4.0 for companies, trade unions, and schools and the second educational path. The training

Fig. 11.137 Environment of the ZIP4.0

11.43 Best Practice Example 43: The Centre for Industry 4.0 at Chair …

609

Fig. 11.138 Principle of the hybrid simulation

courses aim to make the effects of Industrial 4.0 on work and company organisation theoretically and practically tangible. This understanding is essential for the estimation of implications for the future organisation of work. Equipment and Products The simulation environment of ZIP4.0 uses a hybrid approach for factory modelling. This approach combines a physical model factory with computer-aided simulation. As a result, the most suitable model implementation for each simulation component can be selected in each case. In this way, necessary production objects (machines, workpiece carriers, etc.) can be configured, which—regardless of whether realised as a physical original, as a physical model, or in virtual form—are integrated into the necessary variant of the production process and provide the desired scenario in the model factory. Figure 11.138 illustrates the basic principles of the hybrid simulation. Our practical implementation of this hybrid concept consists of physical and computer models that form the main elements, the so-called demonstrators. The interaction of demonstrators enables the construction and simulation of entire production processes. A demonstrator consists of a box configured with the parameters of a specific production object. Interface and communication modules enable the interaction with other components and allow various additions by, e.g., further modules (like additional sensors or actuators). The visualisation of the machining process takes place on both sides of the demonstrator. The user interface for human–machine interaction is located on the top of the demonstrator and displays relevant product, process, and job information. The demonstrators exist in a stationary form as well as in mobile design. A real material flow in different layouts is possible using transport devices (e.g., roller conveyors). Experience shows that transport by roller conveyor is perceived as very typical for a factory and positively affects immersion. Figure 11.139 illustrates an example of the basic structure. Furthermore, numerous components from the Industry 4.0 toolkit are available as elements for modelling. For example, assistance

610

11 Best Practice Examples

systems that use mobile technologists, such as tablets or AR glasses, can easily be configured and allow the actors to interactively experience the functions and advantages of modern and future production systems in the middle of the production process or train them how to use them efficently. Figure 11.140 shows the implementation of AR glasses in a maintenance-oriented learning scenario. Operational Concept The ZIP 4.0 realises vocational training projects at clearly defined didactic levels (macro, meso, and micro). For example, while questions about participation in continuing education are located at the macro-didactic level, the meso-didactic level describes the organisation and realisation of the training program itself. Specifically, this includes goal-oriented program planning (e.g., the survey of learning interests and organisational competence needs in advance), conception (e.g., selection of suitable topic areas, addressing the identified needs), and realisation (e.g., preparation of the contents in a suitable form) for vocational training projects. On the micro level, on the other hand, the micro-didactic design of concrete teaching methods (e.g., selection of suitable presentation media, depending on the situation) and learning

Fig. 11.139 Example of a basic structure in the ZIP4.0

Fig. 11.140 AR glasses in a learning scenario

11.43 Best Practice Example 43: The Centre for Industry 4.0 at Chair …

611

Fig. 11.141 Didactical concept of the ZIP4.0

situations (e.g., selection of specific contents, addressing suitable topic areas), as well as the role of the teachers (e.g., self-image as learning facilitators) are defined. Figure 11.141 provides an overview of the didactic concept of ZIP 4.0. Vocational training projects are carried out in the learning factory in threephase projects on the meso-didactical level: As a starting point, the development of the teaching–learning agreement takes place, followed by the planning and implementation of the training program. Finally, the training project is validated. The ZIP4.0 designs concrete teaching and learning situations on the microdidactical level with the help of learning scenarios. From a didactic perspective, these scenarios reduce the risk of a theory–practice divide due to their high application relevance. Likewise, the system’s high flexibility makes it possible to address action problems precisely. Finally, from a conceptual perspective, learning scenarios can be understood as versatile connecting points between individual action problems and the technical elements of the model factory. The scenarios are designed in a first step based on the existing configurations of the machine demonstrators and workpiece carriers and implemented within the technical environment. The ZIP4.0 systematise the scenarios in a scenario catalog, which includes various criteria (e.g., degree of digitisation of the company). Within a vocational training project, a suitable scenario is selected with recourse to the teaching–learning agreements. If special (learning) interests and problems of the employees contained therein make it necessary to modify a learning scenario, this is easy to implement thanks to the system’s adaptability. For example, this could be the integration of IoT technology (e.g., augmented reality glasses) or the simulation of an additional company-specific machine (e.g., a CNC milling machine) using a machine demonstrator.

612

11 Best Practice Examples

11.44 Best Practice Example 44: The Learning Factory at Penn State University, Pennsylvania, USA Author: Matthew B. Parkinsona a Director of the Learning Factory, Professor of Engineering Design and Innovation, Penn State University, USA

Penn State Learning Factory Operator:

Penn State University

Year of inauguration:

1994

Floor space:

3,500 m2

Manufactured product(s):

Varies by year depending on project sponsors

Main topics / learning content:

Industrie 4.0, DfM/X; Mass customization, Design thinking

Morphology excerpt

Open models

Target industries

Additive manufacturing Open public

Job-seeking

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

11.44 Best Practice Example 44: The Learning Factory at Penn State …

613

Overall Goal The objective of the Penn State Learning Factory (lf.psu.edu) is to provide hands-on learning opportunities for students from across the university. Facilities like ours have been shown to improve student outcomes including grades, postgraduate employment, and experience while at the university. This is particularly true for women and other groups that are under-represented in engineering. Consequently, the operators of the learning factory are committed to providing engagement opportunities with as many students as possible. Although the learning factory is part of an engineering facility, our large undergraduate engineering population (~ 8000 students) means that the learning factory is able to open our doors to students from other disciplines (e.g., business, psychology, architecture, etc.) without materially changing operational needs. These efforts are producing results: in this academic year, more than 3000 students received training in the space. The Learning Factory (LF) was established in 1994 to support coursework in Mechanical Engineering and Industrial Engineering at Penn State University. Courses included manufacturing instruction as well as the culminating client-sponsored “capstone” course required of all engineering students at accredited programs in the United States. A single full-time staff member and several TAs provided support to users and instruction came from faculty in the two departments. Today the Learning Factory supports manufacturing instruction for several departments, is the makerspace for the College of Engineering, and coordinates the largest multidisciplinary client-sponsored capstone program in the world. Formal participation has expanded to include more than a dozen engineering departments and multiple colleges. As a “service unit” to the College of Engineering, our objective is to support any activity that any instructor wants to incorporate into their course. This open posture has resulted in a dramatic increase in usage of the space—and motivated our expansion. In 2023, the learning factory moved into our first new facility since over 25 years ago. Our new building is almost 10,000 m2 , of which 3500 m2 are fabrication spaces. The remaining 6500 m2 are design studios, associated faculty offices, collaboration spaces for the students, and support. Figure 11.142 shows how these are distributed throughout the building. Equipment and Products The Learning Factory functionality can be decoupled into two primary categories: equipment and pedagogy (Table 11.2). Equipment is the tools and materials that students use to produce artefacts and learn about design and fabrication processes. Broadly, the LF has metal (Fig. 11.143) and wood shops, both of which contain equipment supporting Industry 4.0 instruction. Due to the relatively high risk associated with these machines, they require supervision from Penn State staff. The additive manufacturing space, Internet of Things shop, build spaces, and makerspaces (e.g., Fig. 11.144) all feature lower-risk equipment and are supported by teaching assistants.

614

11 Best Practice Examples

Fig. 11.142 A cross-section rendering of our new Engineering Design and Innovation building showing the layout of the Learning Factory Table 11.2 Equipment and pedagogical capabilities of the new facility

equipment Metal machining

Pedagogy Electronics / IoT

Studios

Composites

Additive mfg

Collaboration

Woodworking

AR / VR

Faculty

Computational design

Fig. 11.143 Metal shop has five CNC mills and four CNC lathes, each with a digital twin

11.44 Best Practice Example 44: The Learning Factory at Penn State …

615

Fig. 11.144 24-h access makerspace features 120 butcher block tables, 3D printers, and low-risk hand and power tools

Since several thousand students are supported working on myriad projects each year, there is tremendous diversity in the machines and equipment the learning factory makes available to the students. The learning factory currently provides service and training on a wide variety of tools and activities including laser cutters, sewing machines, dye sublimation printer, vacuum former, heat press, blacksmithing forge, and tools, and many more. Additive manufacturing is an area of strength for the LF with the ability to complete projects in metal, carbon fibre, and a number of other materials. Some of our most popular equipment is not what one might expect; for example, students use our CNC embroidery machine so much that a second machine had to be purchased. Although the Learning Factory supports student “passion projects” that they complete of their own initiative, our primary purpose it to support instruction across the College of Engineering. To that end 11 design studios, classrooms, are adjacent to the makerspaces in the building (Fig. 11.145). The proximity of the studio to a shop provides opportunities to seamlessly move from one mode of instruction (e.g., discussion) to another (hands-on). Rather than rows of tables all facing the front of the classroom, the studios feature moveable furniture with tables supporting groups of four students (our typical team size). The integration of teaching spaces with fabrication spaces is an important and distinguishing feature of the Penn State Learning Factory facility. Operational Concept The Learning Factory is funded primarily through project fees and donations from corporate and individual donors. The university provides faculty to teach related courses and a few full-time staff. Since a small fee is charged for capstone and other client-sponsored projects the learning factory is able to use that to offset the costs

616

11 Best Practice Examples

Fig. 11.145 A typical design studio in the Penn State Learning Factory. There is no “front” to the room and students sit with their team. Studios open into adjacent makerspaces for hands-on instruction

of materials, equipment, and the large number of part-time teaching assistants that help to supervise the spaces, provide training, and ensure that everything is operating smoothly. Because the LF story is very compelling, the learning factory is also able to secure donations from corporate donors and university alumni. Combined, these are sufficient for us to provide all of our training, equipment, and materials at no cost to the students. Scalability is vital to our success since the learning factory has so many users. For example, the learning factory needs to provide basic safety training to almost 2000 students in the first few weeks of the semester. Similarly, any opportunity or piece of equipment that is available to one student has the potential to be used by hundreds of students—or more! This means that every requirement, every training, every piece of new equipment needs to be considered in the larger context of how that opportunity is available to everyone. The underlying principle of accessibility motivates every decision and provides a constant pressure to innovate in all aspects of our operation.

11.45 Best Practice Example 45: The Purdue Learning Factory Ecosystem—Preparing Future Engineers, West Lafayette, USA Authors: Kibbey Claytona , Rakita Milana , Nanda Gaurava , Xingyu Lia , Jose Garciaa , Brittany Newella , Richards Granta , Athinarayanan Ragua a School of Engineering Technology, Purdue University, USA

11.45 Best Practice Example 45: The Purdue Learning Factory …

617

The Purdue Learning Factory Operator:

Purdue University

Year of inauguration:

2023

Floor space:

1,180 m2

Manufactured product(s):

Skateboard, Scooter

Main topics / learning content:

Cyber-physical production, Industrie 4.0

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal In March 2019, a group of academic and industry leaders met on Purdue University campus to develop strategy for education, research, and training for addressing the emerging Industry 4.0 needs in manufacturing. Purdue Board of Trustees in 2019 approved construction for this project providing 1180 m2 for a modern Learning Factory (LF) ecosystem consisting of a Smart Factory, Smart Foundry, Intelligent Process laboratory, and Industrial IoT laboratory. In 2021 the US Dept of Energy

618

11 Best Practice Examples

Fig. 11.146 Hybrid Skateboard/Scooter Parts and Smart Factory Production System Layout

provided funding to develop a new B.S. degree in Smart Manufacturing Industrial Informatics (SMII), and to design the LF ecosystem to serve as a platform for SMII students to build critical competencies relevant to the Industry 4.0 practice. Design of the facilities is also suitable for industry training. Equipment and Products In the LF ecosystem, the Smart Factory is designed to highlight Industry 4.0 cyberphysical production technologies with an interconnected network of machines and processes for manufacturing a skateboard/scooter. To emulate real-world operations, the Smart Factory is integrated into a value chain network sourcing designs and fabricated subassemblies from other facilities in the LF ecosystem. Figure 11.146 is the Smart Factory layout organised as a production value stream for manufacturing the skateboard/scooter, starting from warehousing, component fabrication, automated assembly, to the connected worker assembly operations. In the component manufacturing section of the Smart Factory, fabrications are performed using a 90-ton Milacron Q90 injection moulding machine, a CNC machining center with additive and subtractive capabilities using the Haas VF-4SS and a Phillips Meltio laser metal deposition 3D Printer. Subassemblies to be fabricated and used in the Smart Factory are shown in Fig. 11.147a–g, with the final manufactured product shown in Fig. 11.147h. The handlebar retainer (Fig. 11.147d), the wheel (Fig. 11.147e), the handlebar mount (Fig. 11.147f), and the truck bushings and bushing caps (Fig. 11.147g) are manufactured using the Milacron Q90 injection moulding machine, and the Haas VF-4SS CNC machine. Finished products are not sold, rather disassembled, and returned to inventory.

11.45 Best Practice Example 45: The Purdue Learning Factory …

619

Fig. 11.147 Prototype subassemblies (a–g); and product (h) manufactured in the Smart Factory

Figure 11.148 demonstrates warehouse operation in the Smart Factory, which includes receiving inventory from the LF ecosystem (currently only the Smart Foundry) and external suppliers, recording, storage, and retrieval of the inventory, followed by delivery/pickup of inventory/finished goods from assembly workstations. The smart warehouse physical assets include the storage racks, bins, and two types of robots for automated operations, (a) Veloce, a mobile robot that can perform multi-level storage and retrieval of cases and cartons, and (b) Dynamo, a mobile robot that can autonomously navigate the factory environment using advanced sensors to support material movement functions such as picking, tunneling, and tugging.

Fig. 11.148 Smart Warehouse region of the Smart Factory and the operation path of the Veloce and Dynamo autonomous robots for delivery and pickup across the production line

620

11 Best Practice Examples

Fig. 11.149 Layout of the automated and connected worker assembly stations

Movect, Addverb’s Fleet Management System (FMS) and Concinity, Addverb’s Warehouse Management System (WMS) are used for managing the fleet, for task allocation, inventory control, resource management and for outbound logistics. The FMS and WMS are integrated with the Flexware manufacturing execution systems (MES) and dynamics 365 enterprise resource planning (ERP) software. The automated and connected worker assembly stations in Fig. 11.149 are designed to feature robotic assembly operations at Station 2000. Collaboration between humans, machines, products, and processes is features at Stations 3010– 3060 using technologies such as IoT, mixed reality (MR), AI, and digital twin. These stations are also used for optimising human–robot collaboration by collecting realtime data from the collaborative processes, analysing the system performance, identifying areas for improvement, and making adaptations to enhance productivity and efficiency of the assembly operations. The smart production stations also feature applications using mobile MR technology such as the Microsoft HoloLens 2 to generate manufacturing assembly instructions, visualisation of parts fitment for assembly, and for visualisation of metadata of components. Also, a more advanced application requiring high luminosity AR projections, LightGuide DesignAR is being used to create instructions to guide assembly operations in the Smart Factory. This technology also finds applications in warehousing, organising inventory, kitting, among many others. The Smart Foundry in the LF ecosystem is used to manufacture the base and truck hanger subassemblies in Fig. 11.147a–c. Figure 11.150 is a rendering of the Smart Foundry showing the Simpson 1F mixer/muller, Sinto FDNX-1moulding machine, Inductotherm induction furnace, Thermtronix dipout furnace, conveyor system, a shakeout station, and a return sand conveyor system with a 6-ton sand capacity.

11.45 Best Practice Example 45: The Purdue Learning Factory …

(f)

621

(a)

(b)

(d) (e) (c)

Fig. 11.150 Smart Foundry Production Layout: a Sinto FDNX-1 moulding machine; b shakeout station; c Thermtronix dipout furnace, d Inductotherm induction furnace; e conveyor system; and f return sand conveyor system with a 6-ton sand capacity

This green sand gravity casting production grade foundry has capabilities for real-time monitoring, control, with full operational traceability across all processing stages of the foundry. Predictive AI/ML models are used for intelligent control of foundry processes to improve casting quality, system reliability, and maintenance activities in the foundry. This Smart Foundry was designed with a cadre of capabilities to serve academic, industrial training, and research functions at Purdue. The intelligent process manufacturing laboratory in the LF ecosystem feature technologies for automated control of continuous and batch manufacturing operations. This is demonstrated using both a large-scale and modular process manufacturing systems equipped with instrumentation, control systems, computing surfaces, and communications devices as shown in Fig. 11.151. Control capabilities for process optimisation in these systems include mixing, levelling, thermal management, loop regulation, pH control, among others. Data generated from this system will be used to develop AI-based digital twin models for real-time control and optimisation across different types of processes.

Fig. 11.151 A large-scale and modular industrial process manufacturing system

622

11 Best Practice Examples

Fig. 11.152 Student curricular progression through Industry 4.0 themes

Operational Concept The LF ecosystem was designed with equipment and processes to work synergistically with twelve courses developed for the SMII program. The underlying didactic concepts in these courses are to develop competencies for systematic utilisation of data and technology to optimise production, improve productivity, quality, and efficiency of manufacturing operations. Figure 11.152 shows how student interaction with the LF ecosystem grows as they progress through the program. In their first year, students begin with the Industrial IoT lab to understand sensor and computing technologies, connectivity, AI/ML, and security in local/edge/cloud and hybrid architectures. Activities in this laboratory are also designed to develop expertise in OT/IT convergence, exposure to the various communications protocols, including OPC-UA, MQTT, and MTConnect, edge gateways, cloud systems and services, MES, and ERP systems. Students then continue through the program and integrate components into processes in the process manufacturing laboratory and the Smart Foundry. After developing expertise across a variety of IoT components, devices, systems, and processes, they will begin to understand cyber-physical production and the role of data, informatics, predictive maintenance, AI/ML, and AR applications on the factory floor. Students graduating with this degree will have developed skills critical for the future needs of the Industry 4.0 workforce. This LF ecosystem can also be adapted to operate as a center for demonstrating Industry 4.0 technologies and as a platform for training incumbent workforce in industry.

11.46 Best Practice Example 46: Werk150, ESB Business School, Reutlingen University, Germany Authors: Vera Hummela , Jan Schuhmachera , Alexander Chukomina a ESB Business School, Reutlingen University

11.46 Best Practice Example 46: Werk150, ESB Business School, Reutlingen …

623

Werk150 Operator:

Werk150, ESB Business School

Year of inauguration:

2014

Floor space:

895 m2

Manufactured product(s):

Scooter, Mobility box

Main topics / learning content:

Smart factory, Digital engineering, Industrie 4.0, Circular economy

Morphology excerpt

Open models

Target industries

Open public

Job-seeking

…

Design

Management Top

Semi-skilled workers

Unskilled workers

Employees Apprentices

PhD

Master

Research

Self-employed

Industrial eng.

Energy & resource efficiency

Global production

Training

Students Bachelor

Schoolchildren

Target groups for education and training

Educationalist

Consultant

Closed model (training program for single company)

Course fee

Education

Product creation process

Main purpose

Manager

Lean production

Partnership

Technical expert

Middle

Business model

Student assistant

Industrie 4.0

Researcher

Profit-oriented operator

Lower

Trainer

Subject-related learning content

Non-academic institution

Academic institution

Skilled workers

Operator

Mechanical & plant eng.

Automotive

Logistics

Transportation

FMCG

Aerospace

Chemical industry

Electronics

Construction

Insurance / banking

Textile industry

…

Overall Goal In 2011, initial ideas for a learning factory focusing on aspects of production logistics were drafted. From the beginning, the integration of digital and physical factories took place. While the ESB Logistics Learning Factory itself was built and commissioned as a versatile production system, partners along the supply chain can be fully integrated and managed virtually. The comprehensive digital engineering is carried out with the innovative 3DEXPERIENCE Platform from Dassault Systèmes. In line with the motto “From the region for the future,” primarly local suppliers and manufacturers were selected as suppliers. In 2015, a new building for the ESB Logistics Learning

624

11 Best Practice Examples

Fig. 11.153 Way to the Werk150

Factory was approved by the Ministry of Education and Science of the State of Baden-Württemberg. In 2017, through the financial support of the Federal Ministry of Science and Federal Ministry of Education and Research, the ESB Logistics Learning Factory was extensively upgraded to an “Industry 4.0—Cyber-physical Production System.” In 2019, the move into the new building, first construction phase took place and was taken as an opportunity to rename the ESB Logistics Learning Factory to “Werk150.” With the financial support of the Ministry of Economics BadenWürttemberg, a 5G campus network was installed in 2020. Thus, the Werk150 provides an innovative platform for future-oriented education, research, and application-oriented transfer. Today, six professors and twenty-five scientists work and research at Werk150 in the context of Industrie 4.0 and Industrie 5.0 (Fig. 11.153). In 2022, as the further development of the Werk150, the shift into a “circular factory” began. The integration of circular processes, in particular remanufacturing, into existing production systems is to be investigated in order to create the basis for collaborative projects with industry. The research activities focus on the study of the interdependencies between existing production systems and circular valueadded structures envisaged for the future. This would allow better utilisation of existing facilities, identify needed changes, and decouple economic growth from resource consumption. Viewed in an application context, this means that companies can reconcile potential economic and ecological benefits by establishing circular value creation structures. Digital methods and approaches from the fields of industrial engineering and predictive analytics support integration and help with operations. It will also be investigated how digital and simultaneously circular ecosystems can bring about an acceleration of regenerative forms of economy. All findings will be immediately integrated into education and training. The necessary new upgrade of the

11.46 Best Practice Example 46: Werk150, ESB Business School, Reutlingen …

625

Werk150 infrastructure is to be made possible by means of a large-scale equipment application in 2023. Equipment and Products Werk150, the ESB Business School’s factory, is an innovative learning and research environment that offers industrial engineering students a wide range of opportunities to put their theoretical knowledge into practice. The learning factory is a transformable cyber-physical production system that comprises two areas of the “digital factory” and the “physical factory” and is equipped with extensive infrastructure. It enables students to simulate and optimise realistic production and logistics processes. Two products are manufactured at Werk150: the City Scooter (1) and Mobile Working Hub (2). The City Scooter is produced in different colours and designs and is suitable for children and adults alike. The assembly of the scooter includes various work steps in which different technologies and methods can be used. To further develop the Werk150 into a “circular factory,” a product suitable for this purpose was developed in 2022. The Mobile Working Hub is an innovative workstation system designed for flexible use in different environments. It consists of a mobile work surface that can be used with various devices as well as a flexible storage space for work materials and personal items. The assembly of the Mobile Working Hub involves various steps, such as assembling the frame, attaching the work surface, and installing the electronic components. The integration of a digital product memory was part of this product design. The goal is to develop dynamic forward controls by combining suitable sensor technology with simulation calculations combined with artificial intelligence technologies. A digital twin is used to plan and control the hybrid production, service, and remanufacturing system. The digital twin maps all aspects of the system and thus ensures efficient control of the bidirectionally designed closedloop processes. The further development from a linear to a circular value creation will enable the Werk50 through the dual use of production and remanufacturing which will set new standards for further research, teaching and also for the industry (Fig. 11.154). The Digital Factory The design, implementation and optimisation of various production processes can be carried out both physically and digitally at Werk150 using the 3DEXPERIENCE

Fig. 11.154 City Scooter (left) and mobile Working Hub (center and left)

626

11 Best Practice Examples

Fig. 11.155 Digital Factory Werk150

platform from Dassault Systèmes. The digital representation of the factory enables students and participants to follow the production processes in real time. In the complex scenarios in teaching, this platform is also used for collaborative product and process engineering (Fig. 11.155). In addition to this, other systems complement the digital image of the physical world. An important role is played by the Self-Execution System (SES), an automated system capable of executing production processes autonomously without the need for human intervention. The SES is programmed to perform a wide range of tasks, such as monitoring and controlling machines and production processes, planning production steps, collecting data, and analysing production data. At Werk150, the SES controls scooter production. In this process, a large part of the data is also provided by the ERP system, which at Werk150 is SAP/4 HANA. Both before and after the actual production process, the Visual Paradigm program supports process mapping, analysis, and key figures. For very large data volumes, Microsoft Azure is used to process the data package. The networking of the components of such a production system is supported by a 5G stand-alone campus network. It offers the opportunity to optimise production processes through the use of real-time communication and networking. The high bandwidth and low latency of 5G allow machines and sensors to communicate with each other quickly and precisely, enabling efficient control of production processes. This allows Werk150 not only to teach theoretical concepts, but also to train practical skills that prepare for the working world of tomorrow. The Physical Factory For the production of components, nine different 3D printers (Stratasys Obejt3000 connex3™, Ultimaker, Raise3D Pro3) as well as a CNC milling machine (emco

11.46 Best Practice Example 46: Werk150, ESB Business School, Reutlingen …

627

group Concept mill 260) and a laser cutter (Speedy300™ flexx-trotec) are available. Here, individual components are produced in order to experience the possibilities and limits of personalisation as well as the handling of small batch sizes. A central element of the infrastructure is the FlexConveyor from GEBHARDT Fördertechnik GmbH, a flexible conveyor system that makes it possible to simulate and optimise various logistics processes in real time. In doing so, the FlexConveyor can be adapted to different processes to ensure that logistics processes are mapped as realistically as possible. In combination with the adaptable assembly system from BeeWaTec AG, a store floor worthy of Industry 4.0 is created. In combination with the ART Motion Capture System, the flexible assembly workstation is set up according to current ergonomic guidelines. In addition, various collaborative robots such as the UR5 or UR10e are available to students in the learning factory to help automate logistics and assembly processes. In doing so, the robots work directly with the students to create an optimal learning environment. Another important element of the logistical infrastructure is free-navigating and lane-bound driverless transport systems, which enable (partially) automatic transportation of goods or containers. The fleet of vehicles consists of two HERBIE carrybots, two Nebobotix platforms and a BeeWaTec transport system. In the process, students can simulate and optimise various logistics processes and implement them in the Werk150 production system to gain a deep understanding of logistics operations. Likewise, the latest findings from research on decentralised, autonomous decision-making and process execution based on intelligent logistic objects (transport resources and goods) are applied according to the approach of self-control of logistic processes in Werk150. Self-control of intralogistics enables the flexibility corridors of flexible production systems to be tapped in a targeted manner, e.g., through the short-term rerouting of orders to alternative workstations in the event of disruptions in the assembly system (routing flexibility). In addition, the Werk150 provides two MetaQuest 2 glasses, two Hololens 2, and one Hololens 1 glasses for augmented and virtual reality, which enable participants to understand and optimise real logistics processes with a virtual environment (Fig. 11.156). Operational Concept Based on the taxonomy according to U. Hanke and W. Sühl-Strohmenger, lectures and seminars are held in different didactic formats. In the basic studies of the bachelor programs of the ESB Business School, the Werk150 is used for demonstrations and topic-specific exercises in the lectures (Operations Management Fundamentals, Procurement- and Productions Logistics, Process Management, and Industrial Engineering). Here, the students are first taught theoretical basics that are then deepened by means of hand-on exercises. These practice days last on average one to three block days. In the main study period, an interdisciplinary cross-module with a complex scenario takes place before the Bachelor thesis is written. The aim is to intensify professional action competence. All theoretical contents of the course of studies are applied once again on the basis of several tasks. Starting from a product idea to be generated by the students themselves, they will go through a number of product life cycle phases and conclude the seminar with a prototype production. The concept is

628

11 Best Practice Examples

Fig. 11.156 Physical Factory Werk150

scalable and can be applied to cohort sizes up to 48 students. The module goes for a total of eight block days. In the master’s program, the Werk150 is used as a hands-on training environment as well as a research, development, and implementation environment. In Smart Factory and Logistics, students can intensively apply all technologies in hands-on exercises after the safety briefings and teaching of the theoretical basics in order to experience the opportunities and limitations of innovative technologies. At the end of the course, students receive a comprehensive assignment in the context of Industrie 4.0. In total, this type of event for master’s students comprises a total of 4 block days. In addition, innovative research and development projects are carried out at Werk150 over the course of a single semester. The students work on innovative topics of Industrie 4.0 and Industrie 5.0. The group size is on average 7–9 students and duration 2–3 days per week and 15 weeks per semester. Here, the Werk150 serves as a research, development and validation environment. Additionally, Werk150 is the research and validation environment for students of the 4-semester double degree research master program “Digital Industrial Management and Engineering (DIME)” in cooperation with Stellenbosch University (South Africa) and Purdue University (USA). After the Werk150 has been in operation for more than 10 years, six professors and twenty-five scientists are working on numerous projects of proposal and contract research with national and international partners.47 More than 1800 students have experienced the added value of the learning factory during their studies. The team has carried out over 70 projects in the last 10 years in which the Werk150 was used as a development or validation environment. On average, four networking events are held per year with about 20–30 representatives from industry for knowledge transfer. On the occasion of the annual Open Day of Reutlingen University, many interested people flock to Werk150 to get an idea of the 47

See https://www.esb-business-school.de/forschung/wertschoepfungs-und-logistiksysteme/wer k150.

11.47 List of Contributors

629

existing training and research infrastructure. As a result, the Werk150 plays a role that should not be underestimated when deciding on a location for future studies. Since 2022, four professors of the Werk150 have the right to award doctorates with the Baden-Württemberg doctoral association. In future, the Werk150 will also be used here as a research and experimentation environment.

11.47 List of Contributors We Would like to Thank All Contributors for Their Time and Effort! • Kristóf Abai, Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control • Eberhard Abele, Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt • Rafiq Ahmad, Aquaponics 4.0 Learning Factory (AllFactory), Department of Mechanical Engineering • Amanda Aljinovi´c, Department of Industrial Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB), University of Split • John Angelopoulos, Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras • Udayanto Dwi Atmojo, Department of Electrical Engineering and Automation, Aalto University • Patrick Balve, Heilbronn University of Applied Sciences, Faculty of Industrial and Process Engineering • Jonas Barth, Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt • Andrej Baši´c, Department of Industrial Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB), University of Split • Thomas Bauernhansl, Institute of Industrial Manufacturing and Management (IFF), University of Stuttgart • Richárd Beregi, Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control, Budapest, Hungary, Vehicle Industry Research Center, Széchenyi University • Ferdinand Blasé, Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt • Friedrich Bleicher, TU Wien • Igor Bošnjak, University of Mostar, Faculty of Mechanical Engineering, Computing and Electrical Engineering • Peter Burggräf, PROTECH-Institute for Production, University of Siegen • Maira Callupe, Department of Management, Economics, and Industrial Engineering (DIG), Politecnico di Milano

630

11 Best Practice Examples

• Dan Cente, School of Engineering Practice and Technology (SEPT), McMaster University • Alexander Chukomin, ESB Business School, Reutlingen University • Kibbey Clayton, School of Engineering Technology, Purdue University • Marina Crnjac Žiži´c, Department of Industrial Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB), University of Split • Mohd Ridzuan Darun, FIM Learning Factory, Faculty of Industrial Management, Universiti Malaysia Pahang • Mo Elbestawi, School of Engineering Practice and Technology (SEPT), McMaster University • Hoda ElMaraghy, Intelligent Manufacturing Systems (IMS) Center, University of Windsor • Waguih ElMaraghy, Intelligent Manufacturing Systems (IMS) Center, University of Windsor • Jose Garcia, School of Engineering Technology, Purdue University • Quirin Gärtner, Institute for Machine Tools and Industrial Management (iwb), TUM • Nanda Gaurav, School of Engineering Technology, Purdue University • Angel Gento, Escuela de Ingenierías Industriales, Universidad de Valladolid • Nikola Gjeldum, Department of Industrial Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB), University of Split • Markus Graf, Heilbronn University of Applied Sciences, Faculty of Industrial and Process Engineering • Manfred Grafinger, TU Wien • Thomas Gries, RWTH Aachen University • Norbert Gronau, Chair of Business Informatics, esp. Processes and Systems, University of Potsdam, The Weizenbaum Institute for the Networked Society – The German Internet Institute, Research Group “Education for the Digital World” • Erwin Gross, Fraunhofer Institute for Manufacturing Engineering and Automation IPA • Mátyás Hajós, Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control • Jens Hambach, Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt • Sarah Melanie Hatfield, Hochschule Augsburg • Ingo Herbst, SmartFactory-KL • Christoph Herrmann, Institute of Machine Tools and Production Technology (IWF), Technische Universität Braunschweig • Constantin Hofmann, wbk Institute of Production Science, Karlsruhe Institute of Technology, Karlsruhe (KIT) • Tim Hommen, Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University

11.47 List of Contributors

• • • • • • • • • • • • • • • • • • • • • • • • • • • • •

631

Ignacio Hoyuelos, Independent consultant Maria Hulla, Institute of Innovation and Industrial Management (IIM), TU Graz Vera Hummel, ESB Business School, Reutlingen University Ziwei Jia, School of Mechanical Engineering, Tongji University Roland Jochem, TU Berlin Sebastian Junglas, Institute for Industrial Management (FIR), RWTH Aachen University Max Juraschek, Institute of Machine Tools and Production Technology (IWF), Technische Universität Braunschweig Zsolt Kemény, Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control, Budapest Marius Knott, Chair of Production Systems, Ruhr-University Bochum Holger Kohl, Institute for Production Systems and Design Technology IPK, TU Berlin Sri S.V.K. Kolla, Department of Engineering, Université du Luxembourg Juliane König-Birk, Heilbronn University of Applied Sciences, Faculty of Industrial and Process Engineering Gesine Köppe, ITA Academy GmbH Michael Krämer, Institute for Materials Technology (IfW), TU Darmstadt & Additive Manufacturing Center, TU Darmstadt Antonio Kreß, Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt Simone Kubowitsch, Hochschule Augsburg Alexander Kühl, Institute for Factory Automation and Production Systems (FAPS), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Bernd Kuhlenkötter, Chair of Production Systems, Ruhr-University Bochum Cinzia Lacopeta, McKinsey & Company, Inc. Gisela Lanza, wbk Institute of Production Science, Karlsruhe Institute of Technology, Karlsruhe (KIT) Sander Lass, Chair of Business Informatics, esp. Processes and Systems, University of Potsdam Xingyu Li, School of Engineering Technology, Purdue University Sara Loewenthal, McKinsey & Company, Inc., Atlanta Louis Louw, Department of Industrial Engineering, Stellenbosch University, South Africa Eric Lutters, Department of Design, Production and Management, Faculty of Engineering Technology, University of Twente Mohd Ghazali Maarof, FIM Learning Factory, Faculty of Industrial Management, Universiti Malaysia Pahang Jan Maetschke, Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University Marco Maier, Institute of Industrial Manufacturing and Management (IFF), University of Stuttgart Dominik Matt, Free University of Bozen-Bolzano, Faculty of Science and Technology, Research Area “Industrial Engineering and Automation (IEA)”

632

11 Best Practice Examples

• Marvin May, wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT) • Holger Merschroth, Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt & Additive Manufacturing Center, TU Darmstadt • Joachim Metternich, Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt • Rakita Milan, School of Engineering Technology, Purdue University • Marko Mladineo, Department of Industrial Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB), University of Split • László Monostori, Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control • Dimitris Mourtzis, Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras • Fabian Alexander Müller, McKinsey & Company, Inc. • Alexander Mütze, Institute of Production Systems and Logistics (IFA), Leibniz University Hannover • János Nacsa, Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control, Budapest, Hungary, Vehicle Industry Research Center, Széchenyi University • Stephan Neser, Department of Mathematics and Natural Sciences, University of Applied Sciences Darmstadt • Philipp Nettesheim, PROTECH-Institute for Production, University of Siegen • Brittany Newell, School of Engineering Technology, Purdue University • Peter Nyhuis, Institute of Production Systems and Logistics (IFA), Leibniz University Hannover • Matthias Oechsner, Institute for Materials Technology (IfW), TU Darmstadt & Additive Manufacturing Center, TU Darmstadt • Nikos Panopoulos, Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras • Matthew B. Parkinson, Director of the Learning Factory, Professor of Engineering Design and Innovation, Penn State University • José Pascual, Escuela de Ingenierías Industriales, Universidad de Valladolid • Natalie Petrusch, Institute for Production Systems and Design Technology IPK • Jessica Pino, Escuela de Ingenierías Industriales, Universidad de Valladolid • Peter Plapper, Department of Engineering, Université du Luxembourg • Gerrit Posselt, Institute of Machine Tools and Production Technology (IWF), Technische Universität Braunschweig • Christopher Prinz, Chair of Production Systems, Ruhr-University Bochum • Walter Quadrini, Department of Management, Economics and Industrial Engineering (DIG), Politecnico di Milano • Amy Radermacher, McKinsey & Company, Inc., Minneapolis • Athinarayanan Ragu, School of Engineering Technology, Purdue University

11.47 List of Contributors

633

• Christian Ramsauer, Institute of Innovation and Industrial Management (IIM), TU Graz • Erwin Rauch, Free University of Bozen-Bolzano, Faculty of Science and Technology, Research Area “Industrial Engineering and Automation (IEA)” • Grant Richards, School of Engineering Technology, Purdue University • Kai Rüdele, Institute of Innovation and Industrial Management (IIM), TU Graz • Christoffer Rybski, form. Fraunhofer IPK • Gabriel Rodrigues Santosa, Department of Production Engineering, Escola Politécnica, University of São Paulo • Luka Šaravanja, University of Mostar, Faculty of Mechanical Engineering, Computing and Electrical Engineering • Claudia Schickling, TU Wien • Thilo Schlegel, Institute of Industrial Manufacturing and Management (IFF), University of Stuttgart • Stefan Schlichter, Institut für Textiltechnik Augsburg gGmbH • Sebastian Schlund, TU Wien • Seth Schmitz, Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University • Günther Schuh, Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University; Institute for Industrial Management (FIR), RWTH Aachen University • Jan Schuhmacher, ESB Business School, Reutlingen University • Markus Schulte, PROTECH-Institute for Production, University of Siegen • Klaus Schützer, Department of Production Engineering, Escola Politécnica, University of São Paulo • Michael Schwarz, MPS Lernplattform Sindelfingen, Daimler AG • Felix Sieckmann, form. Fraunhofer IPK • Jörg Siegert, Fraunhofer Institute for Manufacturing Engineering and Automation IPA • Rainer Silbernagel, wbk Institute of Production Science, Karlsruhe Institute of Technology, Karlsruhe (KIT) • Stephan Simons, Department of Electrical Engineering and Information Technology, University of Applied Sciences Darmstadt • Ishwar Singh, School of Engineering Practice and Technology (SEPT), McMaster University • Fabian Sippl, Institute for Machine Tools and Industrial Management (iwb), TUM • Florian Stamer, wbk Institute of Production Science, Karlsruhe Institute of Technology, Karlsruhe (KIT) • Georg Stegschuster, Institut für Textiltechnik Augsburg gGmbH • Fabian Steinberg, PROTECH-Institute for Production, University of Siegen • Maximilian Steinmeyer, Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt • Željko Stojki´c, University of Mostar, Faculty of Mechanical Engineering, Computing and Electrical Engineering

634

11 Best Practice Examples

• Marco Taisch, Department of Management, Economics and Industrial Engineering (DIG), Politecnico di Milano • Emma Takács, Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control • Puay-Siew Tan, Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research • Joel Tay, Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research • Malte Teichmann, Chair of Business Informatics, esp. Processes and Systems, University of Potsdam, The Weizenbaum Institute for the Networked Society – The German Internet Institute, Research Group “Education for the Digital World” • Sebastian Thiede, Department of Design, Production and Management, Faculty of Engineering Technology, University of Twente, Enschede • Michael Tisch, Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt • József Váncza, Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control • Ivica Veža, Department of Industrial Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB), University of Split • Renato Vidoni, Free University of Bozen-Bolzano, Faculty of Science and Technology, Research Area “Industrial Engineering and Automation (IEA)” • Valeriy Vyatkin, Department of Electrical Engineering and Automation, Aalto University • Tom Wanyam, School of Engineering Practice and Technology (SEPT), McMaster University • Heiko Webert, Department of Electrical Engineering and Information Technology, University of Applied Sciences Darmstadt • Marc Wegmann, Institute for Machine Tools and Industrial Management (iwb), TUM • Matthias Weigold, Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt • Alexander Wenzel, Institute of Production Systems and Logistics (IFA), Leibniz University Hannover • Astrid Weyand, Institute for Production Management, Technology and Machine Tools (PTW), TU Darmstadt • Matthias Wolf, Institute of Innovation and Industrial Management (IIM), TU Graz • Keng-Soon Woon, Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research • Michael Zäh, Institute for Machine Tools and Industrial Management (iwb), TUM • Eduardo Zancul, Department of Production Engineering, Escola Politécnica, University of São Paulo • Weimin Zhang, School of Mechanical Engineering, Tongji University, Shanghai, China.

References

635

References Abele, E. (2019). Learning Factory. CIRP Encyclopedia of Production Engineering. https://doi.org/ 10.1007/978-3-662-53120-4_16828 Abele, E., Metternich, J., Tisch, M., Chryssolouris, G., Sihn, W., ElMaraghy, H., Hummel, V., & Ranz, F. (2015). Learning factories for research, education, and training. Procedia CIRP, 32(Clf), 1–6. https://doi.org/10.1016/j.procir.2015.02.187 Abele, E., Metternich, J., & Tisch, M. (2019). Learning factories: Concepts, guidelines, bestpractice examples. Springer International Publishing. https://doi.org/10.1007/978-3-319-922 61-4 Agus, A. (2011). Supply chain management, supply chain flexibility and business performance. Journal of Global Strategic Management, 9, 134–145. Andersson, P., & Mattsson, L. G. Future digitalisation of education after COVID-19. Available at: https://www.hhs.se/en/research/swedenthrough-the-crisis/future-digitalisation-of-edu cation-after-covid-19/ Cachay, J., Wennemer, J., Abele, E., & Tenberg, R. (2012). Study on action-oriented learning with a Learning Factory Approach. Procedia—Social and Behavioral Sciences, 55, 1144–1153. https:// doi.org/10.1016/j.sbspro.2012.09.608 Centea, D., Singh, I., Wanyama, T., Magolon, M., Boer, J., & Elbestawi, M. (2020). Using the SEPT learning factory for the implementation of industry 4.0: Case of SMEs. Procedia Manufacturing, 45(2019), 102–107. https://doi.org/10.1016/j.promfg.2020.04.079 Chryssolouris, G., Mavrikios, D., & Rentzos, L. (2016). The teaching factory: A manufacturing ed-ucation paradigm. Procedia Cirp, 57, 44–48. Durão, L. F. C. S, Christ, A., Zancul, E., Anderl, R., & Schützer, K. (2017). Additive manufacturing scenarios for distributed production of spare parts. International Journal of Advanced Manufacturing Technology, 93(2017), 869–880. https://doi.org/10.1007/s00170-017-0555-z. Durão, L. F. C. S., McMullin, H., Kelly, K., & Zancul, E. (2022). Manufacturing execution system as an integration backbone for Industry 4.0. In O. Canciglieri Junior, F. Noël, L. Rivest, & A. Bouras (Eds.), Product Lifecycle Management. Green and Blue Technologies to Support Smart and Sustainable Organizations (pp 461–473). Springer. https://doi.org/10.1007/978-3030-94335-6_33. Durão, L. F. C. S., Morgado, M., de Deus Lopes, R., & Zancul, E. (2020). Middle of life digital twin: Implementation at a Learning Factory. In F. Nyffenegger, J. Ríos, L. Rivest, & A. Bouras (Eds.), Product Lifecycle Management. Enabling Smart X. PLM 2020. IFIP Advances in Information and Communication Technology (pp. 116–127). Springer. https://doi.org/10.1007/978-3-03062807-9_10. Fumagalli, L., Macchi, M., Pozzetti, A., Taisch, M., Tavola, G., & Terzi, S. (2016). New methodology for smart manufacturing research and education: The lab approach (pp. 42–47). Proceedings of the Summer School Francesco Turco. Gento, A. M., Pimentel, C., & Pascual, J. A. (2021). Lean school: An example of industry-university collaboration. Production Planning & Control, 32(6), 473–488. https://doi.org/10.1080/095 37287.2020.1742373 Juraschek, M., Büth, L., Martin, N., Pulst, S., Thiede, S., & Herrmann, C. (2020). Event-based education and innovation in Learning Factories—concept and evaluation from Hackathon to GameJam. Procedia Manufacturing, 45, 43–48. https://doi.org/10.1016/j.promfg.2020.04.057 Khalid, N., Abd Hamid, N., & Sailin, R. (2014). Importance of soft skills for industrial training program: Employers’ perspective. Asian Journal of Social Sciences & Humanities, 3(4), 10–18. Kumar, A., & Ahuja, N. J. (2020). An adaptive framework of learner model using learner characteristics for intelligent tutoring systems. In Intelligent communication, control and devices (pp. 425–433). Springer. Leal, L. F., Fleury, A., & Zancul, E. (2020). Starting up a Learning Factory focused on Industry 4.0. Procedia Manufacturing, 45, 436–441. https://doi.org/10.1016/j.promfg.2020.04.049

636

11 Best Practice Examples

Leal, L. F., Zancul, E., & Schützer, K. (2021). Industry 4.0 Learning Factory phased development. In Proceedings of Conference on Learning Factories 2021, Graz. https://doi.org/10.2139/ssrn. 3858117 Lindvig, K., & Mathiasen, H. (2020). Translating the learning factory model to a Danish vocational education setting. Procedia Manufacturing, 45(2019), 90–95. https://doi.org/10.1016/j.promfg. 2020.04.077 Mourtzis, D. (2018, June). Development of skills and competences in manufacturing towards Education 4.0: A teaching factory approach. In International Conference on the Industry 4.0 model for Advanced Manufacturing (pp. 194–210). Springer. Mourtzis, D., Angelopoulos, J., & Dimitrakopoulos, G. (2020a). Design and development of a flex-ible manufacturing cell in the concept of learning factory paradigm for the education of generation 4.0 engineers. Procedia Manufacturing, 45, 361–366. Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022b). A Teaching Factory paradigm for personalised perception of education based on extended reality (XR). Available at SSRN 4071876. Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022c). A virtual collaborative platform for education in the design and simulation of aeronautics equipment: The Teaching Factory 5.0 Paradigm. Available at SSRN 4071869. Mourtzis, D., Boli, N., Dimitrakopoulos, G., Zygomalas, S., & Koutoupes, A. (2018a). Enabling small medium enterprises (SMEs) to improve their potential through the Teaching Factory paradigm. Procedia Manufacturing, 23, 183–188. Mourtzis, D., Panopoulos, N., & Angelopoulos, J. (2022a). A hybrid teaching factory model towards personalised education 4.0. International Journal of Computer Integrated Manufacturing, 1–21. Mourtzis, D., Panopoulos, N., Angelopoulos, J., Zygomalas, S., Dimitrakopoulos, G., & Stavropoulos, P. (2021). A hybrid teaching factory model for supporting the educational process in COVID-19 era. Procedia CIRP, 104, 1626–1631. Mourtzis, D., Siatras, V., Angelopoulos, J., & Panopoulos, N. (2020b). An augmented reality collaborative product design cloud-based platform in the context of learning factory. Procedia Manu-Facturing, 45, 546–551. Mourtzis, D., Tsakalos, D., Xanthi, F., & Zogopoulos, V. (2019a). Optimisation of highly automated production line: An advanced engineering educational approach. Procedia Manufacturing, 31, 45–51. Mourtzis, D., Vasilakopoulos, A., Zervas, E., & Boli, N. (2019b). Manufacturing system design using simulation in metal industry towards education 4.0. Procedia Manufacturing, 31, 155–161. Mourtzis, D., Vlachou, E., Dimitrakopoulos, G., & Zogopoulos, V. (2018b). Cyber-physical systems and education 4.0–the teaching factory 4.0 concept. Procedia Manufacturing, 23, 129–134. Mourtzis, D., Zogopoulos, V., & Vlachou, E. (2018c). Augmented reality supported product design towards industry 4.0: A teaching factory paradigm. Procedia Manufacturing, 23, 207–212. Nazron, M., Lim, B., & Nga, J. L. H. (2017). Soft skills attributes and graduate employability: A case in Universiti Malaysia Sabah Muhammad Ariff Nazron. Malaysian Journal of Business and Economics, 4(2), 65–76. Pascual Ruano, J., Hoyuelos, I., Mateo, M., & Gento, A. M. (2019). Lean school: A learning factory for training lean manufacturing in a physical simulation environment. Management and Production Engineering Review, 10(1), 4–13. https://doi.org/10.24425/mper.2019.128239 Quadrini, W., & Fumagalli, L. (2022). Impact of learning factories over sustainable production. In Proceedings of the Summer School Francesco Turco. Rentzos, L., Doukas, M., Mavrikios, D., Mourtzis, D., & Chryssolouris, G. (2014). Integrating manufacturing education with industrial practice using teaching factory paradigm: A construction equipment application. Procedia CIRP, 17, 189–194. Rentzos, L., Mavrikios, D., & Chryssolouris, G. (2015). A two-way knowledge interaction in manufacturing education: The teaching factory. Procedia CIRP, 32, 31–35.

References

637

Rocca, R., Rosa, P., Sassanelli, C., Fumagalli, L., & Terzi, S. (2020). Integrating virtual reality and digital twin in circular economy practices: A laboratory application case. Sustainability, 12(6), 2286. https://doi.org/10.3390/su12062286 Romeral, P. A. A. F., Leal, L. F., & Zancul, E. (2021). Mass customisation demonstrator at an Industry 4.0 Learning Factory. In Proceedings of Conference on Learning Factories 2021. https://doi.org/ 10.2139/ssrn.3858123 Rybski, C., & Jochem, R. (2016) Benefits of a learning factory for the pharmaceutical industry. In 6th CIRP Conference on Learning Factories. Procedia CIRP, 54, 31–34 Seliger, G., Jochem, R., Straube, F., & Kohl, H. (2015). Die Lernfabrik - Interdisziplinäre Zusammenarbeit von Praxis und Wissenschaft in der Prozessindustrie. Management Und Qualität, 4, 14–16. Stavropoulos, P., Bikas, H., & Mourtzis, D. (2018). Collaborative machine tool design: The teaching factory paradigm. Procedia Manufacturing, 23, 123–128. Stock, T., & Kohl, H. (2018). Perspectives for international engineering education: sustainableoriented and transnational teaching and learning; Keynote at the 15th global conference on sustainable manufacturing, Haifa, Israel. Procedia CIRP (2018) Tether, B., Mina, A., Consoli, D., & Gagliardi, D. (2005). A literature review on skills and innovation. How does successful innovation impact on the demand for skills and how do skills drive innovation ? A CRIC Report for the Department of Trade and Industry. Policy, September Tisch, M., Hertle, C., Cachay, J., Abele, E., Metternich, J., & Tenberg, R. (2013). A systematic approach on developing action-oriented, competency-based Learning Factories. Procedia CIRP, 7, 580–585. https://doi.org/10.1016/j.procir.2013.06.036 Tisch, M., Hertle, C., Abele, E., Metternich, J., & Tenberg, R. (2016). Learning Factory design: A competency-oriented approach integrating three design levels. International Journal of Computer Integrated Manufacturing, 29(12), 1355–1375. https://doi.org/10.1080/0951192X. 2015.1033017 Zancul, E., Martins, H. O., Lopes, F. P., & da Silva Neto, F. A. T. V. (2020). Machine vision applications in a Learning Factory. Procedia Manufacturing, 45, 516–521. https://doi.org/10. 1016/j.promfg.2020.04.069 Zancul, E., Romeral P. A. A. F., & Schützer, K. (2022) Learning Factory as an innovation ecosystem. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4074151

Chapter 12

Conclusion and Outlook

The book shows how learning factories have been established in recent years as a platform for production-oriented education, training, research and innovation transfer in academia, industry, and also vocational schools. This book gives a comprehensive overview of • the challenges to be addressed for future production supported by learning factories,1 • the most important fields of competence connected to learning factories,2 • the historical development, the forms, and types of work-related learning for and in production,3 • the historical development, the terminology, and the definitions around the topic learning factory,4 • the broad variety of existing learning factory concepts in the learning factory morphology,5 • the methods, tools, and guidelines along the learning factory life cycle,6 • a multitude of learning factory concepts and practical examples regarding different fields of application,7 contents8 and concept variations,9 • the projects, groups, and associations connected with the learning factory topic,10 as well as 1

See Chap. 1. See Chap. 2. 3 See Chap. 3. 4 See Chap. 4. 5 See Chap. 5. 6 See Chap. 6. 7 See Chap. 7. 8 See Chap. 8. 9 See Chap. 9. 10 See Chap. 10. 2

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 E. Abele et al., Learning Factories, https://doi.org/10.1007/978-3-031-46428-7_12

639

640

12 Conclusion and Outlook

• Best Practice Examples covering the complete range of the learning factory concept.11 In many respects, it has been difficult to provide this comprehensive overview of learning factories. Partly this is because the subject is comparatively new, and partly because many different scientific disciplines have to be considered in the field of the learning factory. The concept of the learning factory itself can be used in a variety of ways, for teaching and training, or for research and innovation transfer, and for different challenges, for training employees to be more competitive or to bring the latest ideas into industry, as well as for stimulating innovation activities. Nevertheless, there are a number of significant challenges that need to be addressed in the coming years regarding the operation of learning factories. One of the biggest challenges that remains is the technological and organisational development of real factories. The technologies involved in manufacturing, the industrial environments and processes, and the engineering problems associated with them are changing rapidly in industrial practice. With the speed at which production systems change, for example through the introduction of new technologies (e.g., artificial intelligence) and approaches (e.g., circular economy), learning factories must be able to keep up or even develop faster in order to be able to access current or even future production environments within existing learning factory concepts even after a few years. Additionally, it has been shown that production environments are very different from each other. Individual learning factories usually map only a small part of the industrial reality in terms of technologies, process chains, products to be manufactured and so on. In order to be able to represent the complete industrial reality in learning factories, or at least as much of it as possible, different approaches are being pursued: • the use of networked learning factories on different contents and foci in order to gain synergies for the single facilities,12 • the integration of ICT-equipment, simulation, and virtual environments into the learning factory concept in order to extend ranges of single learning factories,13 • non-geographically anchored leaning factories through the use of advanced ICTtechnology and industrial didactic equipment, e.g., virtual reality.14 Although these digital and virtual learning factories are considered very interesting in terms of extending the scope of learning factories, they somehow lack the hands-on learning characteristics, sometimes the teamwork qualities as well as the physical interaction that are facilitated in physical learning factories. Different types of learning factories have different advantages. In digital and virtual approaches, external factors that are not interesting for the current learning situation, such as machine noise or high temperatures, can be hidden in order to get a clearer view of the 11

See Chap. 11. See Weeber et al. (2016). 13 See Sects. 9.3.5 and 9.4.1. 14 See Sect. 9.4.2. 12

12 Conclusion and Outlook

641

currently interesting facets of the factory environment. In physical learning factories, this is not possible, or only possible to a very limited extent. However, the physical approaches provide an authentic experience that includes factory-specific uncertainties such as human or machine error. Finally, the physical and virtual learning factory approaches are complementary. Given the individual advantages of each approach, there is still great potential for integration and convergence into a hybrid learning factory environments.15 In order to fully establish these hybrid learning factories in education and training as well as in research, further research is needed on the interfaces and the general cooperation between the physical, digital, and virtual environments. Further, innovation is desirable in terms of individualised learning pathways for all participants. In addition to a worldwide exchange and systematisation of the learning factory concept, it is also interesting to network the range of learning factory training courses offered worldwide. This requires new business models and platforms aimed at creating learning factory networks. This would have many advantages: • First and foremost, the global exchange of learning factory modules or environments could significantly reduce the resource intensity of developing, building, and operating learning factories and designing learning factory modules. Many different themes and challenges could be addressed by individual learning factories. Key issues to be addressed are how to create incentives to make learning factory concepts and individual training courses accessible to other operators, and to what extent learning factory environments and training courses can be shared in a standardised form. • Second, a network of learning factories could ensure good regional availability of practical training on various production-related topics. A platform as a central contact point for people and companies interested in learning factory training could also channel demand to the individual regional learning factories; the platform as a central contact point for the network could efficiently link interregional supply and demand. Establishing a platform as a central contact point for learning factory training facilitates easy access for individuals and companies interested in this type of training, streamlining the process, and connecting them with suitable regional learning factories. • Third, the network of learning factories creates an extensive and diverse portfolio of training options, enabling individuals and organisations to choose from a wide array of specialised programs and courses. This ensures that learners have access to a comprehensive range of production-related topics, enhancing their ability to acquire specific skills and knowledge tailored to their needs and interests. Moreover, learning factories typically incur significant costs, particularly in the initial setup and ongoing operation phases. A future challenge is to make learning factories more accessible to smaller budgets, using simpler but industry-orientated equipment and making intelligent use of software and virtual environments to extend training and teaching capabilities. An efficient option to configure learning factories 15

See Sect. 9.4.1.3.

642

12 Conclusion and Outlook

is presented in Chap. 6 of this book with the help of a configuration system.16 Gamebased learning is an interesting approach that has the potential to facilitate simple yet effective learning factory concepts. The availability of low-cost learning factories could significantly contribute to a wider dissemination of this learning system.17 In order to fully leverage the opportunities offered by learning factories in the coming years, it is crucial to promote the exchange of ideas and best practices in the implementation of the learning factory concept through networks and conferences. With academic and industrial learning factory operators serving as experts in different fields, collaborative exchanges and coordinated efforts can pave the way for the development of an international learning factory curriculum that comprehensively addresses the pressing challenges of current and future production.

References Abele, E., Chryssolouris, G., Sihn, W., Metternich, J., ElMaraghy, H., Seliger, G., Sivard, G., ElMaraghy, W., Hummel, V., Tisch, M., & Seifermann, S. (2017). Learning factories for future oriented research and education in manufacturing. CIRP Annals—Manufacturing Technology, 66, 803–826. Kreß, A. (2022). Methodik zur Konfiguration von Lernfabriken für die schlanke Produktion [Dissertation]. Shaker, Düren. Weeber, M., Gebbe, C., Lutter-Günther, M., Böhner, J., Glasschröder, J., Steinhilper, R., & Reinhart, G. (2016). Extending the scope of future learning factories by using synergies through an interconnection of sites and process chains. In 6th CIRP-Sponsored Conference on Learning Factories. Procedia CIRP, 54, 124–129. https://doi.org/10.1016/j.procir.2016.04.102

16 17

See also Kreß (2022). See Abele et al. (2017).