Industry 4.0 in Textile Production 3030625893, 9783030625894

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Yves-Simon Gloy

Industry 4.0 in Textile Production

Industry 4.0 in Textile Production

Yves-Simon Gloy

Industry 4.0 in Textile Production

123

Yves-Simon Gloy Berlin, Germany

ISBN 978-3-030-62589-4 ISBN 978-3-030-62590-0 https://doi.org/10.1007/978-3-030-62590-0

(eBook)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 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

Preamble

Industrie 4.0 continues to immensely change the textile industry in Germany, especially since the long-term digitisation is now on everyone’s lips. Projects mentioned in this book have started their work. The competence centre and the Vertex project are currently being implemented. The topic of Industrie 4.0, however, causes very different feedback. Just as there are companies that address the issue, there are also others that are still hesitating to act. The desired effects of Industrie 4.0 are not always directly visible. On the contrary, strategies regarding Industrie 4.0 initially require higher investments in capital goods and in the reorganisation of social processes. The fourth industrial revolution therefore does not run as a revolution, but unfortunately often as a tentative search process. As a result, the selected individual processes are then changed and digitised in order to increase productivity. It remains to be seen whether this single-step approach is effective. It is all the more exciting to see how the topic is dealt with abroad. Textile producers, for instance, in China or Turkey often seem to be further ahead when it comes to the implementation of Industrie 4.0 solutions. In Germany, large-scale concepts are being developed and implemented. We can only hope that the often-described willingness to innovate our country will not be slowed down by fear, arrogance and ignorance. Now and today, efforts must be made to ensure the future success of the German industrial sector. The key question is how a company’s management implements innovations and necessary changes. An environment in which change is seen as an opportunity and failure is possible seems to have a positive impact. The role of the manager in this environment is, of course, of particular importance. The Rhenish Basic Law, as always, helps with such thoughts: It goes like it goes, and it always ended up well!

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Preamble

Within this paper, all personal names equally refer to women and men alike, regardless of their grammatical form. Berlin, Germany

Yves-Simon Gloy

Note of Thanks

The author thanks the Federal Ministry of Education and Research (BMBF) for funding the junior research group “New Socio-Technical Systems in the Textile Industry (SozioTex)” and the VDI / VDE Innovation + Technik GmbH Berlin for project sponsorship. Further information can be found on the homepage of the BMBF http://goo.gl/txDQ6k and the project homepage www.soziotex.de.

The author thanks the Federal Ministry for Economic Affairs and Energy for the funding of the “Speedfactory” project and the DLR Projektträger Bonn for the project sponsorship. Further information can be found on the homepage http:// autonomik4.pt-dlr.de/SPEEDFACTORY.php.

The author thanks the Federal Ministry for Economic Affairs and Energy for the funding of the “Storefactory” project and the DLR Project Management Agency Bonn for the project management. Further information can be found on the homepage: http://www.digitale-technologies.de/DT/Redaktion/DE/Standardartikel/ SmartServiceWeltProjekte/smart-service-welt-rojekt_STOREFACTORY.html.

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Note of Thanks

The author thanks the German Research Foundation for funding the work described within the framework of the Cluster of Excellence “Integrative Production Technology for High-Wage Countries” and the project “Economic Spatial Implications of Industrie 4.0 in a Relational Perspective: Innovation, Evolution, Organisation and Interaction; examined using the example of the German textile industry”.

The author thanks the Exploratory Research Space at RWTH Aachen, which promoted the project “TEXTILE INDUSTRIE 4.0—assessing the co-evolutionary resilience dynamics of industries and their locational context” as part of the excellence initiative of the German federal and state government.

This book was created during my time as head of the “Textile Machines” Department at the Institute for Textile Technology (ITA) at RWTH Aachen University. I thank Mr. Univ.-Prof. Prof. h.c. Dr.-Ing. Thomas Gries for the many years of generous support in carrying out the habilitation. Furthermore, I like to thank Prof. Dr. rer. pol. Dipl.-Ing. Meike Tilebein, Deutsches Institut für Textilund Faserforschung Denkendorf, and Prof. Dr.-Ing. Gunther Reinhart, Institute for Machine Tools and Industrial Management, TU München, for the review of my habilitation thesis. Parts of this work are based on the results of the student work I supervised. A bibliographic listing can be found in the appendix. I would therefore particularly like to thank Ms. Tamara Gebert, Mr. Marco Saggiomo, Mr. Frederik Cloppenburg and Mr. Bernd Winkel. I also thank the companies • • • • •

Picanol N. V., Ypres, Belgium Balluff GmbH, Neuhausen Heusch GmbH & Co KG, Aachen iba AG, Fürth Schlafhorst branch office of Saurer Übach-Palenberg

Germany

GmbH

&

Co

KG,

Note of Thanks

ix

• adidas AG, Herzogenrath • DELCOTEX Delius Techtex GmbH & Co KG, Bielefeld • McKinsey & Company, Inc. for their support. Many thanks to the team members of the SozioTex project, specifically Dr. Jacqueline Lemm, Dr. Andrea Altepost, Mr. Daniel Kerpen, Mr. Mario Löhrer, Mr. Arash Rezaey and Dr. Bernhard Schmenk, as well as the employees of the “Textile Machines” Department at ITA. Many thanks also to Prof. Fromhold-Eisebit, Prof. Paul Thomes, Dr. Jens-Christian Winkler, Mr. Spaniol, Dr. Mühlbradt, Dr. Florian Neumann, Prof. Schlick and Prof. Klocke. I would like to thank Ms. Cremer and Ms. Spindler very much for taking over the correction of the habilitation thesis such as Mr. Krahl for the translation of the figures. I thank my family and especially my wife Katharina for their tireless support of my habilitation.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 3

2 State-of-the-Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Industrie 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Research Projects on Industrie 4.0 . . . . . . . . . . . . . . . . . . . . . 2.4 International Activities on Industrie 4.0 . . . . . . . . . . . . . . . . . 2.5 German Textile Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Textile Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Textile Machinery . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 SME Barometer Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Industrie 4.0 in the Textile Industry . . . . . . . . . . . . . . . . . . . . 2.7.1 Publicly Funded Projects on Industrie 4.0 Related to the Textile Industry . . . . . . . . . . . . . . . . . . . . . . . . 2.7.2 Industrie 4.0 Concepts and Solutions from the Industry 2.8 Conclusion and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3 Relevant Research Topics on Industrie 4.0 in the Textile 3.1 Industry Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 General Information from Respondents . . . . . . 3.1.2 Equipment with Hardware and Software . . . . . 3.1.3 Level of Knowledge on Industrie 4.0 . . . . . . . 3.1.4 Potential of the Company for Industrie 4.0 . . . 3.1.5 Opportunities, Challenges and Risks . . . . . . . . 3.1.6 Conclusion of the Survey . . . . . . . . . . . . . . . . 3.2 Supplementary Analysis of Research Projects . . . . . . . 3.3 Derivation of Relevant Research Topics . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contents

4 Networked Production Systems in the Textile Industry . . . . . . . . 4.1 Examples for the Networking of Process Steps in the Textile Process Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Methodology for Networking in Textile Production . . . . . . . . . 4.3 Actual State Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Target Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Solution Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Functional Elements of the RFID System . . . . . . . . . . . 4.5.2 Functional Elements of the Winding System . . . . . . . . . 4.5.3 Functional Elements of the Loom . . . . . . . . . . . . . . . . . 4.6 Realisation of the Networking Between Two Textile Machines . 4.6.1 RFID System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.2 Soft PLC and ibaLogic . . . . . . . . . . . . . . . . . . . . . . . . 4.6.3 Software Programming . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Validation of Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 Economic Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

..

5 Self-optimising Textile Machines . . . . . . . . . . . . . . . . . . . . . 5.1 State-of-the-Art and Research . . . . . . . . . . . . . . . . . . . . 5.1.1 Database-Based Setting Aids . . . . . . . . . . . . . . . 5.1.2 Optimisation of the Weaving Process Through “Auto-Warp” . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.3 One-Dimensional Model-Based Self-optimisation of the Web Process . . . . . . . . . . . . . . . . . . . . . . 5.2 Basics of Multidimensional Optimisation . . . . . . . . . . . . 5.2.1 A Priori Procedure . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 A Posteriori Procedure . . . . . . . . . . . . . . . . . . . . 5.3 Concept for the Extension of Self-optimisation . . . . . . . . 5.3.1 Measurement and Control Technology . . . . . . . . 5.3.2 Signal Processing and Control . . . . . . . . . . . . . . 5.4 Selection of Model Type and Experimental Design . . . . 5.4.1 Description of the Initial Situation . . . . . . . . . . . 5.4.2 Determine Target Values and Factors . . . . . . . . . 5.4.3 Determine Model Type and Set Up Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.4 Modelling of the Target Variables . . . . . . . . . . . 5.4.5 Test Performance and Evaluation of Results . . . . 5.5 Development of Desired Functions . . . . . . . . . . . . . . . . 5.6 Weighting of the Target Figures . . . . . . . . . . . . . . . . . . 5.7 Runtime Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.8 Cost-Effectiveness of Self-optimisation and Ways to Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 195 195 198 202 204 209 214 222 234 244

6 Assistance Systems in Textile Production . . . . . . . . . . . . . . . . . 6.1 Impact of Industrie 4.0 on Work . . . . . . . . . . . . . . . . . . . . . 6.2 Assistance Systems in Production . . . . . . . . . . . . . . . . . . . . 6.3 Definition of Assistance Systems in Production . . . . . . . . . . 6.4 Development of an Assistance System for the Weaving Mill . 6.4.1 Creation of a Catalogue of Requirements . . . . . . . . . 6.4.2 Concept and Development . . . . . . . . . . . . . . . . . . . . 6.4.3 Implementation in the Laboratory . . . . . . . . . . . . . . . 6.4.4 Validation in the Laboratory . . . . . . . . . . . . . . . . . . . 6.5 Effects of Industrie 4.0 on the Work in a Weaving Mill . . . . 6.6 Marketing of AR-Based Assistance Systems for Weaving Mills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 Outlook, Future Fields of Action and Transfer to the Industry 7.1 Speedfactory and Storefactory . . . . . . . . . . . . . . . . . . . . . . . 7.2 Future Fields of Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Future Fields of Action for Textile Production 4.0 . . . . . . . . 7.4 Competence Centre “Textil Digital” . . . . . . . . . . . . . . . . . . . 7.5 Competence Centre “Textil Digital” . . . . . . . . . . . . . . . . . . . 7.6 Learning Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302 Annex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317

Abbreviations

AR AS BITKOM BMBF BMWi CAD CAM CPS CRM DFKJ DLL EAN EAP EPC ERP EU FMEA IAS ICD ICT ISA IT ITA KFZK KIT SME LMS MV

Augmented reality Assistance system Federal Association for Information Technology, Telecommunications and New Media e. V. Federal Ministry of Education and Research Federal Ministry for Economic Affairs and Energy Computer-aided design Computer-aided manufacturing Cyber physical systems Customer relationship management German Research Center for Artificial Intelligence GmbH Dynamic link library European article number Extensible authentication protocol Electronic product code Enterprise resource planning European Union Failure mode and effects analysis Interactive assistance system Integrative cluster domain Information and communications technology Intelligent speed adaptation information processing sensor-actuator system Information technology Institute for Textile Technology at RWTH Aachen University Warp tension Karlsruhe Institute of Technology Small and medium-sized enterprises Learning management system Mecklenburg-Western Pomerania

xv

xvi

NFC NRW OPC PLM PPS QFD QM QA RFID SaaS SAP SCM SDK SOA PLC TCP/IP TID UK VDE VDI VDMA VPN XaaS ZVEI

Abbreviations

Near field communication North Rhine-Westphalia Object linking and embedding for process control Product life cycle management Production planning and control Quality function development Quality management Quality assurance Radio frequency identification Software as a service Systems, applications, products Supply chain management Software development kit Service-oriented architecture Programmable logic controller Transmission control protocol/Internet protocol Tag identification United Kingdom Association of Electrical Engineering Electronics Information Technology e. V. Association of German Engineers Association of German Mechanical and Plant Engineering e. V. Virtual private network Everything as a service Association Electrical Engineering and Electronics Industry e. V.

Chapter 1

Introduction

Since 2006, the government of the Federal Republic of Germany has been pooling its research and innovation activities across departments in the so-called “high-tech strategy”. In this high-tech strategy, the funding of research and innovations is specifically linked to the mechanisms of knowledge and technology transfer. Another goal is to secure a large base of skilled workers in Germany. In addition, an environment for innovative start-ups should be created. It is essential that the high-tech strategy does not focus on individual technologies or research topics, but rather addresses the entire value chain from basic research to application [Bun12]. Ten future projects are defined as part of the strategy. Every one of them is characterised by the fact that all participants in the innovation process in Germany work together, aligned with a specific goal. This should enable systematic solutions to be explored in a specific field of innovation. We are looking for answers to the big questions of our time. The intention is to improve our quality of life and secure a leading position for the German economy in the crucial markets. This requires an innovative network of companies and public research [Bun12]. One such future project is “Industrie 4.0”. It aims to make German production competitive in the future. More individualised products should be manufactured and customers and business partners should be involved in the business and value-creation processes. In addition to production, there will be high-quality services. The value chains should also be controlled and optimised in real time. Intelligent monitoring and decision-making processes should be used for this [Bun12]. It is a paradigm shift within the industry. New business models have become possible on the basis of cyber-physical systems. For this purpose, the promoter group Communication of the Research Union Economy—Science of the Federal Government proposed the future project Industrie 4.0 in its recommendations for action on January 25, 2011 [KLW11]. Internationally, Industrie 4.0 means the digitalisation of industry. The latter is essentially shaped by the Internet of Things. The Internet of Things is the merging of the real world with the virtual world of the Internet [Bun12]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. Gloy, Industry 4.0 in Textile Production, https://doi.org/10.1007/978-3-030-62590-0_1

1

2

1 Introduction

textile industry report

objective: to increase production efficiency

state of the art Industrie 4.0 derivation of research topics and textile production theory selfoptimisation

networking

assistance systems

transfer to the industry methods overview

technology selection

exemplary application

Fig. 1.1 Approach and content of the present work

Industrie 4.0 came into being in 2011. The Research Union in the Stifterverband für die Deutsche Wissenschaft e. V. presented it in 2012 [PDD+12]. The German Academy of Engineering Sciences e. V., Munich, or Acatech for short, developed a corresponding research agenda and implementation recommendations in 2013 [Pro13]. Industrie 4.0 is important for securing prosperity and jobs in Germany, since it will increase production. That is why Industrie 4.0 is particularly important for the exportoriented German textile industry, if it wants to survive in the highly competitive global market environment. Industrie 4.0 is already partially being implemented by the textile industry. The company Adidas AG, Herzogenaurach is using Industrie 4.0 to set up a “Speedfactory” in Ansbach. A total of 160 jobs are to be created and textiles to be produced in close proximity to the customer. This means that long transport routes, delivery times and complicated value chains can be reduced [Mro16]. This book shows ways and possibilities for the design of textile production within the future project Industrie 4.0. The procedure of the work is shown in Fig. 1.1. This work aims to identify and analyse research fields in textile production in regards to Industrie 4.0. It is followed by the conception and development of industry-relevant solutions and approaches for implementing Industrie 4.0 into companies.

References

3

References [Bun12] Bundesministerium für Bildung und Forschung (BMBF), Referat Grundsatzfragen der Innovationspolitik (Hrsg.): Bericht der Bundesregierung: Zukunftsprojekte der Hightech-Strategie (HTS-Aktionsplan) Berlin; Bonn: BMBF (2012). https://www. bmbf.de/pub/HTS-Aktionsplan.pdf [KLW11] Kagermann, H., Lukas, W.-D., Wahlster, W.: Industrie 4.0: Mit dem Internet der Dinge auf dem Weg zur 4. industriellen Revolution VDI Nachrichten (2011), H. 13 vom 1. April 2011. https://www.vdi-nachrichten.com/Technik-Gesellschaft/Industrie-40-MitInternet-Dinge-Weg-4-industriellen-Revolution [Mro16] Mrosek, C.: Pilotprojekt in Ansbach: Adidas baut Speedfactory für Schuhe München: Bayerischer Rundfunk (2016). https://www.br.de/nachrichten/mittelfranken/inhalt/spe edfactory-adidas-oechsler-ansbach-schuhe-100.html [PDD+12] Promotorengruppe Kommunikation der Forschungsunion Wirtschaft – Wissenschaft; acatech – Deutsche Akademie der Technikwissenschaften e. V.; Deutsches Forschungszentrum für Künstliche Intelligenz GmbH; Deutsche Post AG (Hrsg.): Deutschlands Zukunft als Produktionsstandort sichern: Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0 Abschlussbericht des Arbeitskreises Industrie 4.0, Vorabversion, Berlin, 2. Oktober 2012 Berlin: Promotorengruppe Kommunikation der Forschungsunion Wirtschaft – Wissenschaft; acatech – Deutsche Akademie der Technikwissenschaften e. V.; Deutsches Forschungszentrum für Künstliche Intelligenz GmbH; Deutsche Post AG (2012). https://forschungsunion.de/pdf/industrie_4_0_ums etzungsempfehlungen.pdf [Pro13] Promotorengruppe Kommunikation der Forschungsunion Wirtschaft – Wissenschaft; acatech – Deutsche Akademie der Technikwissenschaften e. V. (Hrsg.): Deutschlands Zukunft als Produktionsstandort sichern: Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0 Abschlussbericht des Arbeitskreises Industrie 4.0 Frankfurt a. M.: Plattform Industrie 4.0. (2013). https://www.forschungsunion.de/pdf/industrie_ 4_0_abschlussbericht.pdf

Chapter 2

State-of-the-Art

First, the relevant terms of Industrie 4.0 are defined. Then the current state of Industrie 4.0 research, focused on the textile industry, is presented. It is followed by a description of the German textile industry. Finally, relevant research topics for the textile industry are introduced in the context of developments regarding Industrie 4.0.

2.1 Definitions To understand the development of Industrie 4.0, a historical overview of the past changes in production technology will be given. Acatech divides industrialisation into four phases, the so-called “revolutions” (see Fig. 2.1). The beginning of industrialisation was marked by the introduction of mechanical production plants that were operated with steam or water power. Mechanical looms were used for the first time at the end of the eighteenth century. The second industrial revolution at the turn of the twentieth century was marked by the introduction of mass production based on division of labour and the use of electrical energy. After the third industrial revolution, which has been in effect since approx. 1970, electronics and information technology (IT) are increasingly used in production, and the automation of production processes is further expanded [Pro13]. Industrie 4.0 is therefore the technical integration of cyber-physical systems “into production and logistics, as well as the use of the Internet of Things and Services in industrial processes—including the resulting consequences for value creation, business models and downstream services and work organisation” [Pro13]. The assumption of clearly definable, long waves of revolution in production technology must be regarded with scepticism, similar to the theory of the Kondratieff cycles. The latter divides economic development into five cycles. In general, however, the assumption of cycles of development can help explain the change in an abstract way [RF84, Kle90]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. Gloy, Industry 4.0 in Textile Production, https://doi.org/10.1007/978-3-030-62590-0_2

5

6

2 State-of-the-Art

1969: first programmable logic controller control system (SPS)

1870: first assembly line production 4th industrial revolution based on cyber-physical systems

1784: first mechanical loom

3rd industrial revolution

complexity

electronics and IT are used to achieve advanced automation

2nd industrial revolution follows the development of electrically driven mass production according to the principle of division of labour

1st industrial revolution follows the development of water and steam powered factories

late 19th century

early 20th century

1970s

time

present

Fig. 2.1 The four stages of the industrial revolution [Pro13]

future: Internet of Things, data and services e. g.: virtual exchange of information between textile factories, suppliers

cyber-physical systems

data

e. g.: in the Smart Factory

embedded systems e. g.: sensors, actuators in the knitting machine

Fig. 2.2 Evolution from the embedded system to the Internet of Things, Data and Services

2.1 Definitions

7

CPS means cyber-physical systems. They include embedded systems, production, logistics, engineering, coordination and management processes as well as Internet services. In addition, CPS can directly capture physical data about their environment using sensors and act on physical processes using actuators. Furthermore, CPS are interconnected by digital networks and can use data and services available worldwide. Another characteristic of CPS is that they have multimodal human–machine interfaces and are therefore described as open socio-technical systems. The use of CPS enables new functions, services and properties [Pro13]. Embedded systems are “information processing systems that are integrated into a larger product”. These systems are often connected to the environment via sensors or actuators. The embedded systems must be reliable because they are often used in critical applications such as power plants or trains. In this context, reliability is described by terms such as maintainability, availability, security and integrity. In addition, embedded systems are very efficient in terms of storage requirements or energy consumption. Embedded systems have mostly been developed for a specific application: a control unit in a car is not used for computer games [Mar07]. The Internet of Things is, in the context, “the connection of physical objects (things) with a virtual representation on the Internet or an Internet-like structure”. The use of technologies for automatic identification, such as radio frequency identification (RFID), is a possible part of the Internet of Things. In addition, sensors and actuators can be used to enable the detection of states and the execution of actions in the Internet of Things [Pro13]. The interaction of the Internet of Things and Services and the Smart Factory including CPS is shown in Fig. 2.3.

intelligent mobility

intelligent Logistics

intelligent power grid

CPS intelligent products

intelligent building

Fig. 2.3 Internet of Services and Internet of Things in interaction with the Smart Factory [Pro13]

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2 State-of-the-Art

The Internet of Services is in turn the part of the Internet that “maps services and functionalities as granular, web-based software components”. As a rule, these services are made available to the user by providers on the Internet. The individual services or their software modules can be combined with one another via Internet service technologies. As part of a service-oriented architecture, companies can merge the individual software components into complex and flexible solutions. In the future, a large number of market players should very easily be able to develop and offer Internet-enabled services using the so-called cloud-based development platforms. Service platforms will be created so that customers do not have to search for, evaluate and combine offers for individual solutions on the Internet of Services. Demand- and process-oriented complete offers can be made available on these service platforms [Pro13]. A smart factory means an individual or a group of companies using information and communication technology for “product development, engineering of the production system, production, logistics and coordination of the interfaces to the customers in order to be able to react more flexibly to inquiries”. The Smart Factory should thus cope with more complexity, be less susceptible to faults and increase production efficiency. In the Smart Factory, people, machines and resources communicate with each other, comparable to communication in a social network [Pro13]. Figure 2.4 shows an overview of automation strategies and the associated technologies. It can be seen that technologies and amounts of data increase over the course of automation up to Industrie 4.0.

technology

Industrie 4.0

integration platform intranet, RFID, cloud, CPPS, embedded systems

sensors, e.g. thermometers, motors, flowmeters

Technology

controller, e.g. PID, MPC

networking regulation closed loop system control system open loop

no automation manual

number of data Fig. 2.4 Overview of the development of automation

2.1 Definitions

9

A socio-technical system means the “interaction of employees, technologies (machines, plants, systems) and work organisation in order to carry out a work task” [Pro13].

2.2 Industrie 4.0 Industrie 4.0 should enable a “new level of organisation and control of a value chain that spans the entire life cycle of a product”. This new level of organisation and control is shaped by increasingly individualised customer requests. The entire product life cycle is considered. The entire cycle includes the order, the subsequent development and manufacture, as well as the delivery of the product to the customer. In addition, recycling and product-related services are also considered in the product life cycle [Bun16b]. Figure 2.5 shows a possible design of the networking or information flow in textile production 4.0. In order to achieve the new level of organisation, it is necessary to connect all the partners who are involved in the value-creation process. The aim of networking is to make all necessary information available in real time. From the information and data obtained, an optimal value-creation flow can be determined at any time in terms of production efficiency [Bun16b]. Networking creates value networks that are characterised by connections between objects and systems. The value-creation networks can then be designed to optimise textile production chain textile production

suppliers

customer

final consumer

spinning mill inherent machine thinking customer request Production quantity

operational process level transport Media (e.g. RFID chips)

execution level

process step 1

process steps n

process step 1

assistance system (e.g. data goggles) process step n

Internet of Things / communication platform material information flow through transport media

information flow through intelligent machine network

Information flow through intelligent assistance system

Fig. 2.5 Possible design of networking in textile production 4.0

Sources: nx.-id.com Freedigitalphotos.net (bluebay)

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2 State-of-the-Art

in real time, self-organised and cross-company. Optimisation can be based on criteria such as costs, resource consumption or availability [Bun16b]. The goal is to establish Germany as a lead market provider and lead user for the new level of organisation. In addition to that, Industrie 4.0 aims to shape Germany as the production location of the future [Bun16b]. Figure 2.6 shows a general overview of the projects and subsidies described below for the implementation of Industrie 4.0 solutions and offers in Germany. For further implementation of the whole project, the Industrie 4.0 platform was developed by the following trade associations: • Federal Association for Information Technology, Telecommunications and New Media e. V., Berlin (bitkom), • Central Association for the Electrical and Electronics Industry e. V., Frankfurt am Main (ZVEI) and • Association of German Mechanical and Plant Engineering e. V. Frankfurt am Main (VDMA). The cooperation of these three associations combines the key technologies for Industrie 4.0: Information and communication, as well as automation and production.

BMBF - Federal Ministry of Education and Research

BMWi - Federal Ministry of Economics and Energy

Technology program AUTONOMICS for Industrie 4.0

Plattform Industrie 4.0 implementation strategy for Industrie 4.0 5 fields of action reference architecture model for Industrie 4.0 RAMI 4.0

Other

German Research Center for Artificial Intelligence (DFKI): demonstration Platform for Industrie 4.0 SmartFactory KL e. V.

ZIM - Central Innovation Programme for MediumSized Enterprises

publicly funded collaborative projects of the ‘‘Research for the production of tomorrow ” programme

production research: KMU-innovative

medium-sized 4.0 competence centres

Cluster of Excellence ‘‘ Integrative Production Technology for High-Wage Countries” at the RWTH Aachen

NRW lead market competition

ICT research

Technology network: It's OWL - Intelligent Technical Systems EastWestphaliaLippe

Fig. 2.6 State of research in Industrie 4.0

acatech - German Academy of Engineering Sciences

2.2 Industrie 4.0

11

The Industrie 4.0 platform is now managed by the Federal Ministry of Education and Research and the Federal Ministry of Economics and Energy. Other partners from ministries, associations and industry are also involved. The launch of the platform was officially announced at the Hannover Messe 2013 [Bun16b]. In 2015, an Industrie 4.0 implementation strategy was presented by the Industrie 4.0 platform. This implementation strategy was developed by the bitkom, ZVEI and VDMA associations, in cooperation with companies from the German industry. The implementation strategy is decisive for the future work of the Industrie 4.0 platform. Essential research topics within these fields of action are: 1. Horizontal integration via the value-creation networks a. Methods for new business models b. Framework value networks c. Automation of value networks 2. Consistency of engineering across the entire life cycle a. Integration of real and virtual world b. Systems engineering 3. Vertical integration and networked production systems a. Sensor networks b. Intelligence—flexibility—changeability 4. New social infrastructures of work a. Multimodal assistance systems b. Technology acceptance and work design 5. Continuous development of cross-sectional technologies a. b. c. d. e.

Network communication for Industrie 4.0 scenarios Microelectronics Security and safety Data analysis Syntax and semantics for Industrie 4.0 [BVZ15].

With the help of the implementation strategy, a framework was created in which the reference architecture for Industrie 4.0 – RAMI 4.0—was established [BVZ15]. This solution-neutral reference architecture model combines the essential elements of Industrie 4.0 in a layered model with three dimensions (see Fig. 2.7). The model is based on the Smart Grid Architecture Model [VDI15]. In the vertical axis of the model, there are layers for the representation of the different perspectives such as. • • • • •

data image, functional description, communication behaviour, hardware/assets or, business processes.

12

2 State-of-the-Art Value Stream IEC 62890

Hierarchy Levels IEC 62264 / IEC 61512

Layers Business Functional Information Communication Integration Asset

Fig. 2.7 Reference architecture model 4.0 [VDI15]

The product life cycle and its value chains are shown on the horizontal axis. Thus, dependencies should be visible, such as consistent data acquisition over the entire life cycle. The third axis presents the location where functionalities and responsibilities arise within the factories or plants. It is a functional hierarchy and does not contain device classes or hierarchy levels of the classic automation pyramid. With the help of the model, technologies can be systematically classified and further developed in terms of Industrie 4.0 [VDI15]. Using the example of a servo-hydraulic axis (see Fig. 2.8), the individual layers of the reference architecture model 4.0 can be explained. The system consists of a cylinder, block with pump, servo motor, and so on. The “Integration” layer consists of the drive amplifier including the web server and the controls. Communication is realised via ethernet with object linking and embedding for process control-unified architecture (OPC-UA).

energy management as a business process business

functional

information

communication

integration

asset

P O S

4 E E

data: position, energy

position control linked to head control on Functional Layer position control with position assignment, Collecting and evaluating all energy data Ethernet with OPC-UA open core interface regulations: wait, speed... additional loadable functions: saftey, condition monitoring, control technology drive amplifier, web server conversion of all sensor information system: cylinder, block with pump, servo motor, valves...

Fig. 2.8 Reference architecture model 4.0 using the example of a servo-hydraulic axis [VDI15]

2.2 Industrie 4.0

13

Table 2.1 Theses of the scientific advisory board on the implementation strategy of the Industrie 4.0 platform on the subject of “people” [BVZ15] Human 1. Diverse opportunities for a human-oriented organisation of work will arise, also in terms of self-organisation and autonomy. In particular, there are opportunities for age and age-appropriate work design 2. Industrie 4.0 as a socio-technical system offers the opportunity to expand the range of tasks for employees, to increase their qualifications and scope for action and to significantly improve their access to knowledge 3. Learning aids and communicable forms of work (community of practice) increase teaching and learning productivity. New training content with an increasingly high proportion of IT skills is created 4. Learning tools—usable, learning artefacts—automatically convey their functionality to the user

OPC-UA is a communication protocol that transports machine data (controlled variables, measured values, parameters, etc.) and makes it machine-readable and semantically writable. In the example, data on position and energy are passed on as information. On the function layer, the position control is connected to the head control and the energy data is evaluated. Lastly, the business layer implements energy management. As part of the implementation strategy, the scientific advisory board published the central theses at the Hannover fair 2014. These theses are divided into the subject areas of people, technology and organisation (Tables 2.1, 2.2 and 2.3) [BVZ15]. The theses show that work in Industrie 4.0 could be made more human-oriented. Industrie 4.0 should also increase the qualifications of employees. However, this requires a higher proportion of IT skills among employees. In the field of technology, the theses testify to a very positive attitude towards technical developments. Despite the increasing complexity, technical systems in Industrie 4.0 are easy to understand. Solutions in these systems can be found more quickly, products are active and carry relevant information with them. System components interact with each other and there will also be a new type of security culture. This security culture leads to “trustworthy, resilient and socially accepted Industrie 4.0 systems”. The technology theses are unfortunately not elaborated or explained in the publication [BVZ15]. On the subject of area organisation, the theses are relatively vague compared to the other topic areas. The changes due to Industrie 4.0 could lead to new value-creation networks and new structures within organisations. There could also be growth opportunities for regional value production network. What these changes could look like, and what they are, is not further explained [BVZ15].

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2 State-of-the-Art

Table 2.2 Theses of the scientific advisory board on the implementation strategy of the Industrie 4.0 platform on the subject of “technology” [BVZ15] Technology 1. Industrie 4.0 systems are easy for the user to understand and intuitive to use. They are conducive to learning and react reliably 2. Generally accessible solution patterns allow many actors to design, implement and operate Industrie 4.0 systems (Industrie 4.0 by design) 3. The networking and individualisation of products and business processes creates complexity that is, for example, managed through modelling, simulation and self-organisation. A larger solution space can be analysed faster and solutions can be found more quickly 4. Resource effectiveness and efficiency can be continuously planned, implemented, monitored and autonomously optimised 5. Intelligent products are active carriers of information and can be addressed and identified across all life cycle phases 6. System components can also be addressed and identified within production facilities. They support the virtual planning of production systems and processes 7. New system components have at least the capabilities of the ones to be replaced and can perform their functions in a compatible manner 8. System components offer their functionalities as services that others can access 9. A new security culture leads to trustworthy, resilient and socially accepted Industrie 4.0 systems

Table 2.3 Theses of the scientific advisory board on the implementation strategy of the Industrie 4.0 platform on the topic of “organisation” [BVZ15] Organisation 1. New and established value-creation networks with added value integrate product, production, and service, and enable the dynamic variation of the division of labour 2. Cooperation and competition leads to new business and legal structures 3. System structures and business processes can be mapped on to the applicable legal framework; new legal solutions enable new contract models 4. Opportunities arise for conveying regional added value—also in developing markets

2.3 Research Projects on Industrie 4.0 In addition to privately financed research projects, there are numerous publicly funded projects for the implementation of Industrie 4.0 solutions and offers. The Federal Ministry of Education and Research is currently funding 33 collaborative projects for Industrie 4.0 as part of the “Research for tomorrow’s production” programme. An overview of the projects can be found in the appendix. Three central, publicly funded Industrie 4.0 joint projects by the Federal Ministry of Education and Research are presented below. The project CyProS, cyber-physical production systems, stands for an increase in productivity and flexibility through the networking of intelligent systems within

2.3 Research Projects on Industrie 4.0

15

the factory. The goal of the project is to create methods and tools for the development and operation of cyber-physical production systems (CPS). These CPS should lead to a better mastery of the complexity in production and logistics. The intelligent networking in production should enable a company’s production and logistics systems, as well as planning and control systems, to easily adapt to new conditions, and thus increase the ability to react to market changes. The collaborative project includes, for example, “the development of new types of assistance systems that provide employees with information on products, processes and systems in real time on a tablet PC in the context of their tasks. In addition, production systems for effortless retrofitting are being researched” [Bun14]. The KapaflexCy project, self-organised capacity planning in cyber-physical systems, is themed “Capacity planning 2.0 through Industrie 4.0”. This project is also based on increasing the versatility and flexibility of companies, but focuses on short-term and flexible personnel deployment planning. The goal is to develop a self-organised capacity control system “that allows companies to manage their production capacities together with the executing staff in a highly flexible, shortterm and cross-company manner”. The consistent use of mobile devices and CPS in production should avoid unproductive times and reduce the effort for capacity control. The CPS provide “real-time information about the production environment, learn typical capacity profiles and combine them with communication functions for the employees”. With a developed CPS tool, employees will be able to independently adapt their capacity use to the required needs on a platform for capacity determination [Bun14]. The piCASSO project, an industrial cloud-based control platform for production with cyber-physical systems, stands for scalable control technology for networked production. The goal is to provide a scalable, server-compatible and cloud-based industrial control platform for cyber-physical systems that calculates control functions independently of the hardware. The control platform thus offers scalable computing power and improved versatility [Bun14]. The BMWi has initiated the “AUTONOMICS for Industrie 4.0” technology programme to implement and promote research projects for the application of Industrie 4.0. In the targeted research projects, solutions are to be developed that. • • • •

are intelligent autonomous systems (including robot systems), operate in intelligent environments (rooms, buildings, regions), communicate via intelligent networks with each other (M2M) and with the users. with the users.

An overview of the funded projects can be found in the appendix. Two autonomous 4.0 joint projects funded by the Federal Ministry for Economic Affairs and Energy are presented below as examples. The CoCoS project stands for plug-and-play networking in production. For this purpose, an intelligent information and communication platform is being developed that recognises and links different components of the production line. To achieve this, all components of the cyber-physical production system, such as machines, means of transport and workpieces, are connected with one another via sensors,

16

2 State-of-the-Art

actuators, software and communication technology, and combined to form an intelligent production system. For the user, the production system thus presents itself as a collection of information objects in a service platform, the virtual services of which can be used via apps and combined with one another to create new production processes. With CoCoS, the user receives a production control system with which the production can be installed, monitored, serviced, controlled and configured. This enables quick and easy adaptation to new production processes. The intelligent information and communication infrastructure should also form the communication bridge between production, logistics and other management systems and enable the networking of independent production systems at different locations. In this way, manufacturing at suppliers and producers, as well as logistics service providers, are integrated [Bun16d]. [MBG+16] et al. offer an overview and classification of existing studies on Industrie 4.0, and are shown in Fig. 2.9. The goal of the Speedfactory project is the industrial small batch production up to the automatic one-off production of sports shoes and textiles, where humans and robots fabricate goods in a common working environment. The products can be manufactured inexpensively and flexibly from design to final product within a short time. Using innovative production processes, without sacrificing added value and optimal human–robot interactions, very short cycle times should be achieved with the greatest flexibility. This requires intelligent factory concepts with a high degree of automation and cognitive skills of the workers employed. Only the application of innovative sensors, environmental intelligence and cognition, as well as the use of augmented reality, enables an economically sensible interaction between humans and robots. In order to design the processes efficiently, individual production steps are modularised and largely automated, and decentralised coordination and control of the individual modules are set in place. In line with the trend towards individualisation, quick model changes and cost-effective small series production can be achieved through shorter logistics routes (physically and informally) [Bun16d]. In the projects of the Autonomik 4.0 technology programme, systems are to be developed that. • • • • • •

make decisions easier (if necessary, make appropriate decisions themselves), give assistance, learn and pass on what they have learned, initiate cooperation or actions, obtain the required information independently and propose options for action [Bun16d].

The systems should also enable more flexibility, quality and efficiency, as well as sustainable business models. The overarching goal is to support the German industry on its path to digitising production processes [Bun16d]. In addition, numerous collaborative and individual projects on Industrie 4.0 are supported with the help of instruments such as the programme “KMU-innovativ:

2.3 Research Projects on Industrie 4.0

17

Present study ‘‘Current barriers and concrete needs’’

Industry 4.0 - Economic and business factors for Germany as a business location (Wischmann et al. 2015)

Guideline Industrie 4.0 (VDMA 2015)

Future prospects for digitisation (Radic et al. 2015)

Analytics as a competitive factor (Gronau et al. 2015)

Industry 4.0 readiness (Light blue et al. 2015)

Cracking The Digital Code (Bughin et al. 2015)

Implementation Strategy Industry 4.0 (Dorst et al. 2015)

Industry 4.0 and Digital Economy (BMWi 2015)

Manufacturing's Next Act (Baur et al. 2015)

Tapping the potential of applications of Industry 4.0 in small and medium-sized enterprises (Bischoff et al. 2015)

Reference Architecture Model Industry 4.0 (RAMI 4.0) (Adolphs et al. 2015)

Opportunities and challenges of the fourth industrial revolution (Koch et al. 2015)

Industry 4.0 - Whitepaper R&D issues (Bauernhansl et al. 2014)

Industry 4.0 - Economic potential for Germany (Bauer et al. 2014)

Production work of the future - industry 4.0 (Ganschar et al. 2013)

Implementation recommendations for the Future Project Industry 4.0 (Kagermann et al. 2013)

Industry 4.0 vision of the future (BMBF 2013)

considered studies

potential for increasing efficiency through intelligent, networked solutions

subject areas

risks and barriers opportunities and potential infrastructure and other requirements standards; standardization

evaluation criteria

data security legal framework creation of additional benefits through new business models economic impact of industry 4.0

viewing method

more effective work design for people in production resource efficiency current implementation status of industry 4.0 prioritized development areas for demonstrators technical economic socio-technical core of the study

one of several priority topics

key is partially considered

not considered

Fig. 2.9 Overview and classification of existing studies on Industrie 4.0 [MBG+16]

Produktionforschung” (funded by the BMBF) or the ZIM—Zentrales Innovationsprogramm Mittelstand (funded by the BMWi) [PTK16, AiF16]. There are also research activities within the framework of the BMBF’s ICT research. These include, for example, projects such as “New tools for software development of embedded systems” or the development of cyber-physical IT systems to master the complexity of a new generation of multiadaptive factories (SmartF-IT) [Bun14]. In the technology network intelligent technical systems OstWestfalenLippe (in short OWL), 180 partners have come together to develop intelligent technical systems for Industrie 4.0. The goal of the top cluster is to secure a leading position for the OstWestfalenLippe region within the global competition for intelligent technical systems. To this end, companies in mechanical engineering, electrical, electronics and automotive supplier industries, as well as research institutions, cooperate.

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2 State-of-the-Art

A total of 47 research projects between business and science, with a volume of 100 million Euros, are to be realised. The project is funded as part of the top cluster competition of the Federal Ministry of Education and Research with 40 million Euros. The technology initiative SmartFactory KL e. V. serves as a demonstration platform for Industrie 4.0 of the German Research Center for Artificial Intelligence GmbH (DFKI). The SmartFactory is advertised as the first worldwide Industrie 4.0 demonstrator. The SmartFactory production line is characterised by. 1. a modular system structure for flexible and quick configuration of the production line, 2. universal plug connections for electricity, compressed air, industrial ethernet and emergency stop, 3. control of the production process by intelligent products with the help of RFID identification of each individual workpiece in a standardised data format, 4. plug-and-produce during operation, 5. a uniform infrastructure that enables uniform connection of all production modules to the comprehensive IT services, and 6. a uniform mechanical, electromechanical and information technology interface to ensure cross-manufacturer compatibility [Tec16]. There are also research projects with German participation that are funded by the European Union as part of the “Factories of the Future” programme or the ERANETMANUNET funding programme [Bun14]. Relevant preparatory work for Industrie 4.0 is also provided by the Cluster of Excellence “Integrative Production Technology for High-Wage Countries” at RWTH Aachen University, funded by the Deutsche Forschungsgemeinschaft e. V. Central research topics of the cluster are. 1. 2. 3. 4.

individualised production, virtual, hybrid and self-optimising production systems [Bre11].

The map of the Industrie 4.0 platform provides an overview of application examples for Industrie 4.0. In addition to 237 application examples, 32 test environments and 33 consulting and information offers for Industrie 4.0 are compiled on this map [Bun16b]. The publication “Industrie 4.0 research at German research institutes—an overview” of the VDMA provides a further comprehensive overview of research in the field of Industrie 4.0. In this overview, more than 70 German universities and institutes present their current Industrie 4.0 projects [VDM16]. Acatech, the German Academy of Engineering Sciences e. V., Munich, led and carries out fundamental projects on the topic of Industrie 4.0. Figure 2.4 [aca17b] provides an overview. Industrie 4.0 activities are also promoted and implemented at the level of the Federal Republic of Germany. To this end, key programmes and projects of the State of North Rhine-Westphalia are shown as examples. Furthermore, as part of the so-called “lead market competitions”, 640 million Euros are being invested in the implementation of projects on the topics of.

2.3 Research Projects on Industrie 4.0

19

• networking of production sites and simulation of process chains, • software engineering and cloud computing and • smart grids and cyber-physical security [Lan16] (Table 2.4). There are two major lead market competitions: • Production.NRW: Digitisation as a future topic for North Rhine-Westphalian machine and plant construction, • IKT.NRW: Strengthening and networking the ICT industry for the digital change to Industrie 4.0 [Lan16]. For the NRW state’s digitisation strategy, CPS.HUB NRW is an important project that is being promoted as part of the lead market competitions. It is intended to serve as an “innovation engine for digital transformation”, which “drives the further development of the technological basis for intelligent networked systems”. The CPS.HUB NRW conducts studies with more than 500 actors, as well as market and potential analyses, on the topic of “Cyber Physical Systems”. The project is supervised by the Bergische University of Wuppertal, the Technical University of Dortmund, the University of Duisburg-Essen and the University of Paderborn. In the CPS.HUB NRW there are working groups on the following topics: • • • • •

software, communication networks, cybernetics and robotics, ICT and energy networks, CPS in production practice,

Table 2.4 Industrie 4.0 projects of Acatech, German Academy of Science e. V., Munich Name

Running time

Industrie 4.0

11/2012 to 05/2013 Spaecific implementation recommendations

Industrie 4.0 in a global context—strategies for working with global partners

07/2015 to 11/2016 Empirical statements on opportunities (e.g. expansion of the market by establishing interoperability) and challenges (e.g. protection of intellectual property) of international cooperation between companies and institutions in the area of research and development, as well as norms and standards

Industrie 4.0—international 11/2013 to 06/2016 benchmark, future options and recommendations for action in production research (INBENZHAP)

Goal

Develop options for the design of industrial production in Germany and uncover areas with opportunities for a German pioneering role (continued)

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2 State-of-the-Art

Table 2.4 (continued) Name

Running time

Scientific Advisory Board Industrie 4.0

09/2013 to 12/2015 The Scientific Advisory Board advises the Platform Industrie 4.0 in close exchange with accompanying research on all scientific and programmatic research questions

Goal

Scientific Advisory Board Industrie 4.0—Phase II

03/2016 to 02/2019 The Scientific Advisory Board advises the Platform Industrie 4.0 in close exchange with accompanying research on all scientific and programmatic research questions

Collaboration as the key to the successful transfer of innovations using the example of automotive logistics 4.0 (InnoKey 4.0)

01/2016 to 06/2017 Methodologically develop sound recommendations for action for politics and companies that should support the rapid transfer of the results from the R&D area into business practice

Hands-on: Industrie 4.0—a massive 06/2015 to 11/2016 Use a new knowledge transfer tool open online course for the fourth to increase awareness of the industrial revolution Industrie 4.0 concept, especially in SMEs Industrie 4.0 Maturity Index

04/2016 to 04/2017 Record the status quo of Industrie 4.0 within companies in order to develop individual roadmaps for the successful introduction of Industrie 4.0 solutions

Industrie 4.0—future of industrial work

12/2014 to 12/2017 Cover information requirements for the introduction of Industrie 4.0 (six workshops over a period of two years)

Competence development study Industrie 4.0

06/2015 to 11/2016 Identify and analyse the qualification needs of companies, especially in small and medium-sized companies

• cyber-physical devices, • cloud computing and • cyber-physical security [Lan16, CPS16]. The Federal Ministry for Economic Affairs and Energy also set up the socalled “Mittelstand 4.0 competence centres” (see Table 2.5). They are intended to create knowledge about digitisation, the use of Industrie 4.0 and the networking of operational processes accessible to medium-sized companies.

2.3 Research Projects on Industrie 4.0

21

Table 2.5 Overview of the SME 4.0 competence centres and their focus [Bun16c] Location

Management of the competence centre Region and focus

Augsburg

Fraunhofer Institute for Machine Tools Learning factory, increasing resource and Forming Technology, Project efficiency in production Group Resource Efficient Mechatronic Processing Machine

Berlin

Federal Association of Small and Medium-sized Businesses, Business Association Germany e. V

Berlin-Brandenburg region, digitisation in companies, digital transformation processes

Chemnitz

Chemnitz University of Technology, Institute for Business Sciences and Factory Systems

Saxony region, digital, networked factory and production systems, product creation processes and virtual business processes, ergonomics and usability of Industrie 4.0 solutions, legal aspects of digitisation

Darmstadt

Technical University of Darmstadt, Institute for Production Management, Technology and Machine Tools

Rhine-Main region, learning factory, human work and their support through flexible automation

Dortmund

Fraunhofer Institute for Material Flow and Logistics

North Rhine-Westphalia, intelligent automation of products and production systems, efficient, autonomous and changeable logistics systems and related services, production technology

Hamburg

Chamber of Commerce Hamburg Service GmbH

Hamburg, Schleswig–Holstein, digitisation, Internet of Things, 3D printing, Industrie 4.0 and new business models

Hannover

Heinz Piest Institute for Handicraft Technology at Leibniz University Hannover

Competence Center Digital Crafts nationwide support for handicraft companies in the use of digital technologies and in the optimisation of internal processes, imparting of practice-relevant knowledge, experience reports, tailor-made “help for self-help”, knowledge and technology transfer – North: use of new information and communication technologies in your own company – East: expansion of offerings in IT-based business models - South: use of new production and automation technologies in your own company – West: process management for the professionalisation of business processes (continued)

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2 State-of-the-Art

Table 2.5 (continued) Location

Management of the competence centre Region and focus

Hannover

Leibnitz University, production technology centre

Lower Saxony and Bremen, digitisation of individual production and logistics processes, law, economy, work 4.0

Ilmenau

University of Ilmenau, Department of Manufacturing Technology in the Thuringian Center for Mechanical Engineering

Thuringia region, closer networking of mostly small-scale structured companies, networking of machines and production processes, 3D printing and individualised production, migration, process data generation and transfer, production control, control systems

Kaiserslautern Technology initiative SmartFactoryKL Rhineland-Palatinate, networking of e. V production from mechanics to IT systems, people in the environment 4.0, development of new product and business models, opening up new business areas Stuttgart

Head of the Fraunhofer Institute for Industrial Engineering and Organisation

Digitisation, networking of production, introduction of new applications, exchange and demonstration of digital solutions, testing and developing applications, training, opportunities and risks of digitally networked technologies

2.4 International Activities on Industrie 4.0 Internationally, the topic of digitisation has now played an important role in production research. The most important regions or countries in regards to digitisation in industrial production are Germany, the USA, the United Kingdom and Asia. These regions or countries have different requirements and therefore also different objectives of digitisation in production. Table 2.6 provides an overview of the global situation and orientations of future production technology in Germany, North America and Asia. The BMBF initiated the INBENZHAP project to get a precise picture of the global situation in production research. As part of this international benchmark project, future options of Industrie 4.0 for production research and corresponding recommendations for action are to be shown. In general, it was found that Industrie 4.0 is developing into a global brand [GKD+16]. According to the study, Germany has the best prerequisites for being a leading market and leading provider worldwide. This results from the high importance of industrial production and the worldwide top level of skilled worker and engineer

2.4 International Activities on Industrie 4.0

23

Table 2.6 Situation and directions of future production technology [Sch16b] Approach

Situation

Textile industry

Goals

Growing competition

Technical Maintaining a textiles, strong industrial textile base machine construction, international orientation, innovation leadership

Means

Industrie 4.0

Germany

Industrial internet

USA, UK Service-oriented End of mass Re-industrialisation Adding economy production, production technical technology to textiles, information focus on and own markets communication technology

Full East Asia Laboratory automation shortage

Mass production, increasing competition

Cost reduction, speed, less laboratory

Integration of information and communication technology and production

Expansion of robot use

training. However, in Germany there is a lack of an extensive competence base in the areas of internet technologies and innovative business models [GKD+16]. The goals of the European initiatives on Industrie 4.0 lie in the digitalisation of industrial added value, taking into account the requirements of a human-centred working world. The main focus is on increasing productivity and sustainability. For this, Europe can benefit from a very good infrastructure, cultural affinity and welldeveloped skills in industrial IT and production [GKD+16]. Figure 2.10 summarises priorities of selected countries and regions in the context of Industrie 4.0. Comparable activities in the USA aim to create added value for the customer. Innovative services play an important role in this. The Internet of Things is regarded as the most important technology. In production, new solutions seem to develop from the approach of data-driven services [GKD+16]. In Japan and South Korea, the productivity of the mechanical engineering and electronics industry is to be increased, primarily with the help of networked and intelligent production systems. In Japan in particular, this is intended to counteract the consequences of demographic change. Networked and intelligent production systems are also intended to strengthen small and medium-sized companies. In Japan, digital Kanban systems or smart devices are often used in intralogistics. In general, the industry in Japan is characterised by a high degree of automation [GKD+16]. In China, the digitalisation of the economy represents an essential and consistently driven field of action. In the area of “advanced manufacturing”, it should achieve a leading position worldwide. This position is primarily to be reached by increasing

24

2 State-of-the-Art

USA "Radical Innovation"

Europe, especially Germany "Engineering Excellence"

bringing digital innovation to the physical world

bringing excellent engineering to the digital world

start-ups for the Internet of Things and a renaissance of production

visionary concepts that integrate technology, society and economy

Japan and South Korea "Ability to Scale"

China "Speed" pragmatic application of quick wins and long-term strategy use of mature technologies, strategic key technology development

innovation through application massive build-up of smart factories and very large manufacturers who strengthen their products through internal demand

Fig. 2.10 Priority areas of selected countries and regions in the context of Industrie 4.0 [GKD+16]

automation, especially in small and medium-sized companies. China is characterised by the ability to set up supply chains for new productions very quickly [GKD+16]. The most important global “motors” for Industrie 4.0 are: • sustainability, • ease of use and • collaboration. The four global challenges for Industrie 4.0 are: 1. 2. 3. 4.

security, standards, migration and interoperability, business models and meeting expectations [GKD+16].

2.4 International Activities on Industrie 4.0

25

Another important initiative is the Industrial Internet Consortium, founded in March 2014 in the USA. It is a non-profit organisation in which companies that are not based in the USA can also become members. The aim is to jointly develop new Internet technologies, for the industry and other purposes. In mid-June 2015, the Industrial Internet Consortium presented its own architecture model with the Industrial Internet Reference Architecture. Table 2.7 shows a comparison of the Industrial Internet Reference Architecture with the reference architecture model 4.0. Gartner’s hype cycle is a helpful model for describing the status of technology launches. It describes how innovations and technologies are received on the market. The hype cycle is divided into five phases: 1. 2. 3. 4. 5.

innovation trigger, peak of expectations, valley of disillusionment, increasing education and plateau of productivity [SM13].

Figure 2.11 shows the Hype Cycle for Emerging Technologies 2016. Relevant Industrie 4.0 technologies such as “Machine Learning”, “Augmented Reality” or “IoT Platform” can be found on this cycle. Interestingly, these technologies are all in different phases of the hype cycle and will therefore reach the productivity plateau at different times. Table 2.7 Comparison of the Industrial Internet Reference Architecture with the Reference Architecture Model 4.0 [HH16] Aspekt

Industrial Internet consortium

Alignment

Internet of things

Connected industry/Industrie 4.0

Members

Mainly information technology

Cross-section of the electrical industry, mechanical engineering, information technology, science

Product life cycle

Platform Industrie 4.0

Complete life cycle including type and instance

Value stream

Production

Complete value streams: Order, development, production and service, suppliers, machine builders, end customers

Functional

Its structures

Expansion of the functional hierarchies: Connected world, products, field device

Hierarchy

Big data and analytics

Virtual world: Unique id, simulation, digital engineering

Information layer

Enumeration, everything permitted

Data: Models, semantics, …

Communication

Control for process

Focusing, preferential communication for i4.0

Integration

Physics

Connection to the digital world and functions in the functional layer

2 State-of-the-Art

expectations

26

innovation trigger

peak of expectations

valley of disillusionment

increasing appreciation

plateau of the productivity

Time years until adaptation by the mainstream less than 2 to 5 years 5 to 10 years 2 years

more than 10 years

obsolete before plateau

Fig. 2.11 Hype Cycle for Emerging Technologies 2016 [Riv15]

From 1980 to 1995 there was a great hype in the textile industry about the use of information and communication technologies. Fully automated textile companies emerged, which, however, could not be operated successfully. Therefore, from around 1995, an era began that can be described as a computer integrated manufacturing (CIM) crisis. A major deficit in the use of information and communication technologies in the textile industry is that no systematic derivation of fields of action and research topics has yet been developed. The approaches are often copied from other industries without paying attention to the specific features of the textile industry. Therefore, corresponding measures, methods and their implementation for the textile industry will be derived in the further course of this work. The textile industry will be analysed and the ongoing research projects and studies are being evaluated.

2.5 German Textile Industry The German textile and clothing industry has approx. 132,000 employees and generates sales of approx. 32 billion Euros, thus equalling approx. 1% of the gross domestic product of the Federal Republic of Germany in 2016. Approx. 25,000 employees are

2.5 German Textile Industry

27

working in 997 companies in the textile industry in North Rhine-Westphalia. The textile trade generated approx. 64 billion Euros in sales in 2016, thus equalling approx. 2% of the gross domestic product of the Federal Republic of Germany. In the field of technical textiles, Germany is the turnover-related world market leader and global technology leader. The turnover of German producers with technical textiles is approx. 13 billion Euros (approx. 0.4% of the gross domestic product of the Federal Republic of Germany in 2016) [Ges16]. The VDMA Textile Machinery Association represents one of the most powerful branches of German mechanical engineering. In 2013, the industry exported textile machines and accessories worth 3.1 billion Euros. Roughly 120 member companies from all branches of the textile machinery industry largely belong to the middle class. Many of these companies are world market leaders [VDM16]. In the following, the two areas of textile industry and textile machine construction are examined in more detail.

2.5.1 Textile Industry The statistical processing of the individual production stages of the textile industry is difficult. The reason for this is the frequently changed classification of economic sectors in the context of international economic statistics. The last modification was carried out in 2008. The following areas, according to the classification of the economic sectors, belong to the textile industry: Textile processing and spinning, weaving, finishing of textiles and clothing, as well as production of ready-made textile goods except clothing [Sta08]. In the past, individual stages were supplemented, regrouped or completely removed from the system [Sch03]. These different systems are accordingly taken into account in the statistical analysis below. In order to achieve a holistic view of the lead market and lead provider strategy, textile machine construction was also included in the analysis. Figure 2.12 shows that the German textile industry mainly focuses on the following federal states: • • • •

North Rhine-Westphalia, Baden-Wuerttemberg, Bavaria and Saxony.

The location of the textile industry is mostly based on the historical development of the textile industry. In the middle of the nineteenth century, textile manufacturers decided to build their factories in former structurally weak regions because those had comparatively low wage costs, but still had a good infrastructure with regard to raw material supply and a sufficient level of labour and demand potential [Bre83, BG12]. After World War II, the division of Germany led to a structural imbalance in the textile industry as the locations were geographically spread over the entire country.

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2 State-of-the-Art

map of the German textile industry

focus areas textile clusters West Saxony West Münsterland Lower Rhine

textile workers by federal states (subject to social security contributions) / Sep. 2016

associations & institutions

exhibitors from the leading trade fair Techtextil 2017 in Frankfurt, Germany are mapped employment data: Federal Employment Agency

companies research centers

kilometre

Fig. 2.12 The textile and clothing as well as leather and shoe industry in Germany (source Philip Marschall, 2017: DFG WiSoTex 4.0 project—focus on textile cluster)

In addition, there was a partly general process of relocating the textile industry to the west. The most important textile regions include. • • • •

Westphalia, South Baden, Upper Franconia, and Swabia.

2.5 German Textile Industry

29

A certain “textile” monostructure emerged because there was no settlement of companies that came from the so-called “key industries”. As a result, there was no way to transfer the workers that were freed by the structural change to other sectors [Bre83]. Eventually, this monostructure developed into regional textile clusters with similar demands regarding their suppliers and workers. “Clusters are understood as regional agglomerations of companies in a value chain and their supporting industries as well as the associated infrastructure, which are interdependently intertwined in many ways” [BG12]. The limits of a cluster are determined by an expansion of links between companies and institutions [Por98]. Textile clusters can consist not only of textile manufacturers but also of other industries, such as the clothing, leather and shoe industries, as well as of companies in the textile machinery industry. In addition to the textile industry, the clothing, leather and shoe industries were included in Fig. 2.12. Due to the inadequate data availability, the textile machine construction and the chemical fibre industry could not be considered. A regional representation of the textile clusters also had to be avoided, since the data is not available on the regional level. Furthermore, the Lower Rhine (linen and cotton industry), the Aachen area (wool industry) as well as the areas around Krefeld (silk and velvet industry) and Wuppertal (narrow textiles) were once important textile regions. Today, the regional textile clusters can be found in the Swabian Jura, South Baden, Upper Franconia, Swabia, Lower Rhine, East Westphalia, Münsterland, Wuppertal, Vogtland, Upper Lusatian Textile District as well as Chemnitz and Zwickau. Today’s textile industry in Germany has globalised. Nowadays, high-quality textile products are fabricated for customers all over the world. This is linked to the globalisation of the production sites. The German textile company van Laak GmbH, Mönchengladbach, for example, has production plants in Vietnam and Tunisia, in addition to the German plants, in order to reach the Asian and non-European market. Kümpers GmbH, Rheine, produces high-performance textiles at production sites in Eastern Europe and has plants in the Czech and Slovak Republic. According to the classification of the European Commission [Bor14], companies can be divided into four groups of employees (see Table 2.8). In order to simplify the statistical processing, the following analysis omits the parameter sales per year, especially since the Federal Statistical Office does not fully publish the required data for data protection reasons. Accordingly, the demarcation is based on the employment size class. Table 2.8 Classification of companies according to employee groups

Employee group

Employees

Turnover

Micro-enterprise

0,

(5.33)

where ki (i = 0, 1, …, 6) correspond to the coefficients of the quadratic regression models. If the null hypothesis for a coefficient cannot be rejected, the considered model parameter has no significant influence on the target variable. The so-called “p-value” serves as one of the decision criteria whether the null hypothesis can be rejected at a given significance level. Minitab specifies the p-value for each of the calculated model coefficients. The p-value is a probability value, and ranges between zero and one. A p-value smaller than the chosen significance level leads to the rejection of the null hypothesis. For the significance analysis of the model coefficients of multidimensional selfoptimisation, a significance level of 0.05 is defined. A significance level of 0.05 leads to a moderate statistical basis for the hypothesis test [GB05]. The decision rule for assessing the significance of a model coefficient is p