The Digitalization of Management Accounting: Use Cases from Theory and Practice 3658415231, 9783658415235

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Imke Keimer Ulrich Egle Editors

The Digitalization of Management Accounting Use Cases from Theory and Practice

The Digitalization of Management Accounting

Imke Keimer  •  Ulrich Egle Editors

The Digitalization of Management Accounting Use Cases from Theory and Practice

Editors Imke Keimer Institute of Financial Services Zug IFZ Lucerne University of Applied Sciences and Arts Business Rotkreuz, Switzerland

Ulrich Egle Institute of Financial Services Zug IFZ Lucerne University of Applied Sciences and Arts Business Rotkreuz, Switzerland

ISBN 978-3-658-41523-5    ISBN 978-3-658-41524-2 (eBook) https://doi.org/10.1007/978-3-658-41524-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 This book is a translation of the original German edition “Die Digitalisierung der Controlling-­Funktion” by Keimer, Imke, published by Springer Fachmedien Wiesbaden GmbH in 2020. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors. 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 Fachmedien Wiesbaden GmbH, part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Foreword

We all spend a lot of time on mundane tasks: Answering emails, collecting data, updating spreadsheets, preparing presentations, and creating reports. The digitization of controlling promises to reduce many of these repetitive tasks for controllers: Integrated digital reporting systems that combine internal and external, structured and unstructured data and provide users with real-time visualization of relevant information eliminate the need to manually enter or import data into individual spreadsheets. In addition, digital controlling systems provide all decision makers with comprehensive access to information, eliminating the need for a controller to provide reports or individual data points and act as a constant conduit between data and decision makers. While many of us are in the early stages of the shift to fully digital controlling, just a few steps in this direction will impact the skills, tasks, and roles required of controllers. As digital controlling systems become increasingly capable of capturing, integrating, and analyzing data, controllers can shift their attention to more complex, higher-level responsibilities. However, these responsibilities require a shift in controller skills to future-proof their roles and maximize business value. The trend of controllers becoming business partners to senior management has already begun in many companies and will continue. In addition, however, other skills will become more important. Here I would like to highlight critical analytical thinking and empathy. Critical analytical thinking is the necessary basis for logical and data-based decisions. The controller, as a business partner and as the person responsible for financial data, must be as familiar with critical analytical thinking as with the statistical methods required to analyze and use data correctly. Critical analytical thinking requires an understanding of the fundamentals of logical thinking in order to use and interpret data correctly (and to recognize the limitations of the data foundation). With critical analytical thinking, the controller can analyze complex problems to formulate coherent arguments and evaluate the data situation. Often data is good for showing correlations, but causal relationships are much more difficult to establish and may require well-designed A/B tests that generate new data. It’s easy to interpret higher sales after ad campaigns as a sign of ad effectiveness, even though the campaigns were run right before major holidays and may have had no impact on customer buying behavior. A competent controller will prevent such a v

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misinterpretation by not only providing data, but also highlighting assumptions and limitations of the analysis and visualizing the results in a way that is appropriate for the recipient so that decision makers can draw correct conclusions. With increasing data volumes and complex, multi-faceted data contexts, these tasks will become more important as part of the digitalization of controlling. Empathy may not be the first skill that comes to mind when we think of successful controllers. However, when data is ubiquitous, it is critical that controllers can empathize with others to ensure that the potential of the data is fully realized. Not all decision makers in an organization will be equally comfortable with a data-driven approach to decision making. In that case, the controller must be an empathetic translator and coach, tailoring the information provided to the end user. Controllers also better support data-savvy colleagues by being intentional about presentation and visualization. After all, decades of research in behavioral psychology tell us that decisions depend not only on the data itself, but also on how the data is presented. As we add predictive analytics and artificial intelligence methods to digital controlling, controllers need not only empathy, but sound ethical judgment. We’ve all heard of algorithms that reinforce bias: whether algorithms are used to determine the creditworthiness of applicants, cost-effective treatment of patients, or optimal workforce development, algorithms learn from existing reality and can cement bias. A controller who brings empathy and ethical judgment to anticipate these challenges, rather than hiding behind numbers, becomes all the more valuable to the organization. I am sure that reading this work, edited by Imke Keimer and Ulrich Egle, will provide you with numerous approaches and suggestions, regardless of the number of steps you have already taken towards digital controlling. I hope you enjoy reading it! Short Portrait of the Editor Anne Beyer  is an Associate Professor of Accounting at the Stanford Graduate School of Business. She teaches the introductory financial accounting course and received the MBA Distinguished Teaching Award in 2013 and the Walter J.  Gores Award, Stanford University’s highest award for excellence in teaching, in 2014. Her research examines the characteristics of financial analysts’ and management’s earnings forecasts, as well as investor reaction to earnings forecasts and other forms of corporate disclosure. Anne is from Germany and attended the University of Stuttgart and the University of Wales in Swansea before completing her PhD at Northwestern University’s Kellogg School of Management. Stanford, USA

Anne Beyer

Preface

It is undisputed that the digital transformation has reached the management accounting. Nevertheless, the question remains as to how exactly the digital transformation can be successfully implemented in management accounting. For years, we have been discussing the digitalization of management accounting with CFOs, accountants, researchers and students and have been intensively dealing with this topic. This also resulted in the idea for this edited work. With a mix of theoretical and practical examples, we would like to provide those responsible and employees in management accounting with trend-setting approaches, impulses and orientation. All contributions in our book deal with the digitalization of the controlling function. The German-language term Controlling is associated in the English-speaking world with management accounting tasks. Controlling is a generic term for a company’s planning, coordination and control. As the following contributions originate from German-speaking countries, the word controlling is used throughout instead of management accounting. This edited work shows concrete examples of how individual controlling functions are embracing the digital transformation. Large and small companies from the DACH region present their digitalization projects. They share their personal experiences by presenting their initial situation as well as their solution approach including learnings and give recommendations for action. When reading the contributions, you will notice that the digital transformation is not equally advanced in all controlling functions. Some controlling functions are still at the very beginning and are starting with the basics: introducing an ERP system, creating a uniform database (single source of truth) or standardizing simple and repetitive processes. Other controlling functions, on the other hand, are already taking a closer look at the possibilities of innovative technologies and predictive analytics methods. The theoretical contributions provide an in-depth examination of the topic area and highlight current developments. The contributions are supplemented by two interviews in which we discuss the digital transformation in controlling with experts. The diversity of the contributions in this edited work provides the reader with a broad view of the digital transformation in controlling. Our special thanks go to the authors for their contribution to our edited work. They share their theoretical and practical knowledge and let the readers participate in their vii

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experiences. Without their dedication and commitment, this edited work would not have come into being. We have greatly appreciated the open exchange and it has been a pleasure to work with them. We would also like to take this opportunity to thank Ms. Anna Pietras and Ms. Catarina Gomes de Almeida from Springer Gabler Verlag for their excellent and professional support. Finally, we would also like to thank our colleagues from the Institute of Financial Services Zug IFZ at the Lucerne University of Applied Sciences and Arts for their professional exchange and support. We wish all readers that you will be able to absorb and implement many digital impulses. Rotkreuz, Switzerland May 2020

Imke Keimer Ulrich Egle

Contents

1 Digital  Controlling: Basics for the Successful Digital Transformation in Controlling  1 Imke Keimer and Ulrich Egle 1.1 Introduction��������������������������������������������������������������������������������������������������   1 1.2 Controlling Digitalization ����������������������������������������������������������������������������   4 1.3 Digitalization in Controlling ������������������������������������������������������������������������   5 1.3.1 Digital Controlling����������������������������������������������������������������������������   6 1.4 Conclusion����������������������������������������������������������������������������������������������������  13 References��������������������������������������������������������������������������������������������������������������  14 2 Current  Trends in Digital Transformation in the Financial Sector  17 Imke Keimer and Markus Zorn 2.1 An Interview by Imke Keimer with Markus Zorn����������������������������������������  17 3 Further  Development of the Financial Forecast in the Context of the Digital Transformation Using the Example of SAP SE  23 Simone Raschig and Mike Schulze 3.1 Initial Situation���������������������������������������������������������������������������������������������  24 3.2 The New Forecast Process����������������������������������������������������������������������������  25 3.2.1 Centralised and Decentralised Forecasting Processes Run in Parallel and Complement Each Other������������������������������������������  25 3.2.2 SAP’s Transformation Process����������������������������������������������������������  26 3.3 Key Components in the Central Forecast Process����������������������������������������  27 3.3.1 The Satellite Concept������������������������������������������������������������������������  28 3.3.2 Predictive Analytics Models ������������������������������������������������������������  28 3.3.3 Collaboration Model in the Central Forecast������������������������������������  30 3.4 Success Factors and Challenges��������������������������������������������������������������������  31 3.4.1 Process and Organisation������������������������������������������������������������������  31 3.4.2 Operational Work with Predictive Models����������������������������������������  33

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3.5 Areas of Application Today and in the Future����������������������������������������������  34 3.5.1 Central Simulations of Outcome Scenarios��������������������������������������  34 3.5.2 Integration of Predictive Components in Satellites��������������������������  34 3.6 Conclusion����������������������������������������������������������������������������������������������������  35 References��������������������������������������������������������������������������������������������������������������  36 4 From  Success Assurance to Product Development: Data Analysis at Gebrüder Weiss in the Corporate Logistics Department  39 Martin Selb 4.1 Introduction to Controlling at Gebrüder Weiss��������������������������������������������  39 4.2 Logistics Controlling at Corporate Logistics������������������������������������������������  40 4.3 Current Development in Logistics Controlling at Gebrüder Weiss��������������  43 4.3.1 Change in the Framework Conditions����������������������������������������������  43 4.3.2 Change in the Methods Used������������������������������������������������������������  47 4.4 The Path to Product Development����������������������������������������������������������������  54 4.5 Conclusion����������������������������������������������������������������������������������������������������  55 References��������������������������������������������������������������������������������������������������������������  58 5 The  Digital Transformation of Reporting at Swiss Radio and Television (SRF)  59 Kevin Wettstein and Renato Caderas 5.1 Introduction��������������������������������������������������������������������������������������������������  59 5.2 Initial Situation���������������������������������������������������������������������������������������������  60 5.3 Objective ������������������������������������������������������������������������������������������������������  61 5.3.1 Standardisation: Notation, Content, Platform����������������������������������  61 5.3.2 Provision of Information: Self Service ��������������������������������������������  64 5.3.3 Transparency: Need to Know������������������������������������������������������������  64 5.4 Solution Approach for the Introduction of the New Reporting Landscape ���������� 65 5.4.1 Clustering Modules��������������������������������������������������������������������������  65 5.4.2 Reporting Guideline��������������������������������������������������������������������������  66 5.4.3 Definition of Module Contents ��������������������������������������������������������  66 5.4.4 Development of Module Contents����������������������������������������������������  68 5.4.5 Training��������������������������������������������������������������������������������������������  68 5.5 Learnings������������������������������������������������������������������������������������������������������  69 5.5.1 Cultural Change on the Part of Users and Controllers����������������������  69 5.5.2 Enabling��������������������������������������������������������������������������������������������  69 5.5.3 Gamification/Discovering Connections��������������������������������������������  70 5.5.4 Development Use and Influence of Further Developments��������������  70 5.6 Online Reporting: Culture Change ��������������������������������������������������������������  71 References��������������������������������������������������������������������������������������������������������������  73

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6 Benefits  and Stumbling Blocks in the Introduction of a Business Intelligence Solution for SMEs Using the Example of SIGA  75 Nicole Hecht and Peter Scherrer 6.1 Introduction��������������������������������������������������������������������������������������������������  76 6.2 Definition of Controlling and Business Intelligence������������������������������������  77 6.2.1 Controlling����������������������������������������������������������������������������������������  77 6.2.2 Business Intelligence������������������������������������������������������������������������  77 6.3 IT Systems and Data Management of the BI Solution ��������������������������������  78 6.4 Business Intelligence and Analytics in Controlling: Practical Examples ����  79 6.4.1 Standard Report Using the Example of a Sales Report��������������������  80 6.4.2 Drill-Down Report Using the Example of Sales Prices��������������������  81 6.4.3 Alerts Using the Example of a Real-Time Report in Production��������  82 6.4.4 Shopping Basket Analysis����������������������������������������������������������������  83 6.4.5 Forecasting with Prophet������������������������������������������������������������������  84 6.5 Stumbling Blocks and Lessons Learned ������������������������������������������������������  87 References��������������������������������������������������������������������������������������������������������������  92 7 Digitalize  Corporate Management with Business Intelligence  95 Romano Caviezel 7.1 Introduction��������������������������������������������������������������������������������������������������  95 7.1.1 Business Intelligence������������������������������������������������������������������������  96 7.1.2 Business Analytics����������������������������������������������������������������������������  96 7.1.3 From Strategy to Management Cockpit (Descriptive and Diagnostic Analysis) ������������������������������������������������������������������������  97 7.1.4 From Control to Planning ����������������������������������������������������������������  97 7.1.5 Using Business Analytics to Recommend Action����������������������������  97 7.1.6 From Selection to Implementation���������������������������������������������������  97 7.2 Initial Situation���������������������������������������������������������������������������������������������  97 7.3 Objectives of the New BI Solution ��������������������������������������������������������������  98 7.4 Solution: Project Preparation as a Basis for the Successful Implementation of the BI Solution ��������������������������������������������������������������  99 7.4.1 From Big Data to Services����������������������������������������������������������������  99 7.4.2 The Requirements Definition������������������������������������������������������������ 100 7.5 Result: From the Requirements Specification to the BI Solution ���������������� 101 7.5.1 Specifications������������������������������������������������������������������������������������ 101 7.5.2 Why Is the Comment Function so Important?���������������������������������� 102 7.6 Design: There Is No Second Chance for the First Impression���������������������� 103 7.6.1 Design ���������������������������������������������������������������������������������������������� 103 7.7 Success Factors for Successful Implementation ������������������������������������������ 107 7.7.1 Acceptance���������������������������������������������������������������������������������������� 107 7.7.2 Quality/Trust ������������������������������������������������������������������������������������ 108 7.7.3 Care �������������������������������������������������������������������������������������������������� 108

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7.8 Learnings������������������������������������������������������������������������������������������������������ 109 7.8.1 What Have Been the Benefits of Implementing the Business Intelligence Solution? ���������������������������������������������������������������������� 109 7.8.2 Getting Out of the Comfort Zone����������������������������������������������������� 109 7.8.3 Requirements Determine the Technology���������������������������������������� 109 7.9 Conclusion���������������������������������������������������������������������������������������������������� 110 Literature���������������������������������������������������������������������������������������������������������������� 111 8 From  Digital Tools to Digital Methodology 113 Robert Duckstein 8.1 Initial Situation��������������������������������������������������������������������������������������������� 113 8.1.1 Previous Planning ���������������������������������������������������������������������������� 114 8.1.2 Status of Digitisation and Technical Stack �������������������������������������� 114 8.1.3 Definition of BI system�������������������������������������������������������������������� 115 8.2 Design Phase������������������������������������������������������������������������������������������������ 117 8.3 Agile Prototyping for the Development of the Agile Planning Method ������ 118 8.4 Iterative Development Process���������������������������������������������������������������������� 122 8.5 Result Actual Status�������������������������������������������������������������������������������������� 124 8.6 Outlook �������������������������������������������������������������������������������������������������������� 124 8.7 Lessons Learned�������������������������������������������������������������������������������������������� 125 8.8 Conclusion���������������������������������������������������������������������������������������������������� 125 References�������������������������������������������������������������������������������������������������������������� 127 9 Business  Analytics in Marketing Controlling: A Case Study for the Automotive Market 129 Markus Ilg and Alexander Baumeister 9.1 Digitalization in Marketing Controlling ������������������������������������������������������ 129 9.1.1 Analytics: Challenge and Opportunity of Digitalization������������������ 129 9.1.2 Characteristics of Marketing Controlling ���������������������������������������� 130 9.1.3 Controlling Tasks in the Analytics Process Using the Example of CRISP-DM ���������������������������������������������������������������������������������� 131 9.2 Time Series Analysis as an Application Example in Marketing Controlling���������������������������������������������������������������������������������������������������� 133 9.2.1 Characteristics of Time Series Analysis�������������������������������������������� 133 9.2.2 Data Preparation������������������������������������������������������������������������������� 134 9.2.3 Modeling and Evaluation������������������������������������������������������������������ 136 9.2.4 Further Development of the Model: Distinguishing the Type of Drive �������������������������������������������������������������������������������������������� 138 9.3 Competence Requirements in Digital Controlling���������������������������������������� 142 References�������������������������������������������������������������������������������������������������������������� 142

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10 Interactive  Big Data Visualizations: Potential for Management Reporting145 Peter Hofer, Lisa Perkhofer, and Albert Mayr 10.1 Introduction������������������������������������������������������������������������������������������������ 145 10.2 Overview and Use of Big Data Visualizations�������������������������������������������� 147 10.2.1 Types of Visualization: Application and Level of Familiarity ���� 147 10.2.2 Interaction: Taxonomy and Application�������������������������������������� 153 10.3 Design and Usability of Specific Interactive Big Data Visualizations�������� 155 10.3.1 Overview of the Research Methods Used������������������������������������ 156 10.3.2 Multidimensional Visualizations – Multiple Dimensions������������ 159 10.3.3 Multidimensional Visualizations – Multiple Attributes �������������� 162 10.4 Conclusion�������������������������������������������������������������������������������������������������� 168 References�������������������������������������������������������������������������������������������������������������� 169 11 Digital  Transformation in Controlling at the Alpiq Group Ulrich Egle, Anca Frisan, and Markus Steiner

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12 Controller  Profiles in Switzerland: Importance of Digitalization 183 Viviane Trachsel and Christian Bitterli 12.1 Introduction������������������������������������������������������������������������������������������������ 183 12.2 Development of the Controlling Role Models�������������������������������������������� 184 12.2.1 Traditional Controlling Role Models������������������������������������������ 185 12.2.2 Controller as Business Partner ���������������������������������������������������� 186 12.2.3 Digital Controller ������������������������������������������������������������������������ 186 12.3 Analysis of Controller Job Advertisements in Switzerland������������������������ 187 12.3.1 Research Design and Methodological Approach������������������������ 187 12.3.2 Areas of Responsibility Mentioned �������������������������������������������� 188 12.3.3 Required Competences���������������������������������������������������������������� 189 12.4 The Controllers of the Present and Future�������������������������������������������������� 191 References�������������������������������������������������������������������������������������������������������������� 192 13 Standardization  and Automation as the Basis for Digitalization in Controlling at Siemens Building Technologies 195 Ivo Gerig 13.1 Introduction������������������������������������������������������������������������������������������������ 195 13.2 Standardisation and Automation at Management Level ���������������������������� 196 13.2.1 Initial Situation���������������������������������������������������������������������������� 196 13.2.2 Smart Reporting �������������������������������������������������������������������������� 199 13.2.3 Smart Analytics���������������������������������������������������������������������������� 203 13.3 Standardization and Automation in Operational Controlling �������������������� 206 13.3.1 Business Activity (BA) DAsh������������������������������������������������������ 207 13.3.2 Further DAsh Applications���������������������������������������������������������� 210

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13.4 Digitalization Through Predictive Analytics���������������������������������������������� 211 13.4.1 Sales Forecast with Predictive Analytics ������������������������������������ 211 13.4.2 Possibilities and Limits of Predictive Analytics�������������������������� 215 13.5 Learnings���������������������������������������������������������������������������������������������������� 215 13.6 Conclusion�������������������������������������������������������������������������������������������������� 216 14 Digitalization  of the Controlling System in Theory and Practice Using the Example of the ARTS Group 219 Ingo Cassack 14.1 Introduction������������������������������������������������������������������������������������������������ 219 14.2 Digitalization and Controlling Systems������������������������������������������������������ 220 14.3 Digitization of Controlling Subsystems������������������������������������������������������ 221 14.3.1 Digitalization of Controlling Tasks���������������������������������������������� 222 14.3.2 Digitalization of the Controlling Organisation���������������������������� 223 14.3.3 Digitalization of Controlling Instruments������������������������������������ 224 14.4 Case Study: Digitization of the Controlling System at ARTS�������������������� 224 14.4.1 Digitalization of Controlling Tasks���������������������������������������������� 224 14.4.2 Digitalization of the Controlling Organisation���������������������������� 226 14.4.3 Digitalization of Controlling Instruments������������������������������������ 227 14.5 Further Development Trends of Digitalization in Controlling�������������������� 229 14.6 Conclusion�������������������������������������������������������������������������������������������������� 229 References�������������������������������������������������������������������������������������������������������������� 230 15 From  Financial Report to Controlling Cockpit in the Age of Digitalization 233 Paul Sidler and Luca Gerussi 15.1 Introduction������������������������������������������������������������������������������������������������ 234 15.2 Initial Situation������������������������������������������������������������������������������������������� 234 15.2.1 Presentation of the Current Situation by Means of the Maturity Model���������������������������������������������������������������������������� 234 15.2.2 Reporting�������������������������������������������������������������������������������������� 234 15.2.3 Analysis���������������������������������������������������������������������������������������� 235 15.2.4 Planning �������������������������������������������������������������������������������������� 235 15.3 Objective ���������������������������������������������������������������������������������������������������� 236 15.3.1 Target Image�������������������������������������������������������������������������������� 236 15.4 Solution Approach�������������������������������������������������������������������������������������� 236 15.4.1 Prerequisites�������������������������������������������������������������������������������� 236 15.4.2 Reporting�������������������������������������������������������������������������������������� 237 15.4.3 Analysis���������������������������������������������������������������������������������������� 239 15.4.4 Planning �������������������������������������������������������������������������������������� 240 15.5 Learnings���������������������������������������������������������������������������������������������������� 241 15.5.1 Establish Principles at the Outset������������������������������������������������ 241 15.5.2 Putting Yourself in the Role of the Receiver�������������������������������� 241

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15.5.3 Change Takes Time and Resources���������������������������������������������� 242 15.6 Conclusion�������������������������������������������������������������������������������������������������� 242 References�������������������������������������������������������������������������������������������������������������� 244 16 Possibilities  and Limitations of Mobile Applications for Controlling 245 Robin Nunkesser and Jens Thorn 16.1 Introduction������������������������������������������������������������������������������������������������ 245 16.1.1 Current Tasks and Challenges of Controlling������������������������������ 246 16.1.2 Brief History of Mobile Devices�������������������������������������������������� 247 16.1.3 Current Characteristics of Mobile Devices���������������������������������� 247 16.2 Relevant Mobile Devices and their Usage Habits�������������������������������������� 248 16.2.1 Tablets������������������������������������������������������������������������������������������ 248 16.2.2 Smartphones�������������������������������������������������������������������������������� 248 16.2.3 Wearables ������������������������������������������������������������������������������������ 248 16.2.4 Usage Habits�������������������������������������������������������������������������������� 249 16.3 Reporting on Mobile Devices from a Controlling Perspective������������������ 249 16.4 Challenges in Controlling �������������������������������������������������������������������������� 250 16.5 Possibilities of Mobile Devices������������������������������������������������������������������ 251 16.5.1 Development Possibilities������������������������������������������������������������ 252 16.5.2 Security���������������������������������������������������������������������������������������� 254 16.5.3 Information Presentation�������������������������������������������������������������� 257 16.5.4 Mobile Backends and Cloud Computing������������������������������������ 258 16.5.5 Synchronisation and Continuity Between Mobile and Fixed Devices������������������������������������������������������������������������������ 258 16.6 Case Studies������������������������������������������������������������������������������������������������ 259 16.6.1 Use of Microsoft Power BI���������������������������������������������������������� 259 16.6.2 Use of the Microsoft SQL Server BI Platform���������������������������� 260 16.6.3 Using SAP HANA with MicroStrategy �������������������������������������� 260 16.7 Conclusion�������������������������������������������������������������������������������������������������� 261 References�������������������������������������������������������������������������������������������������������������� 262 17 How  Zalando Uses Digital Solutions to Transform Investment Controlling265 Jörg Engelbergs and David Moreira 17.1 Introduction: Zalando and Digitalization���������������������������������������������������� 265 17.2 Initial Situation: Reasons for Introducing Two New Digital Solutions������ 266 17.3 Target Image: Product Vision and Use Cases �������������������������������������������� 268 17.4 Procedure: Project Structure and Implementation�������������������������������������� 269 17.4.1 Investment Boardroom���������������������������������������������������������������� 270 17.4.2 Investment App���������������������������������������������������������������������������� 272 17.5 Lessons Learned: Challenges, Do’s and Don’ts����������������������������������������� 274 17.5.1 Investment Boardroom���������������������������������������������������������������� 274 17.5.2 Investment App���������������������������������������������������������������������������� 274

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17.6 Conclusion: Digitization as an Opportunity for Controlling���������������������� 275 References�������������������������������������������������������������������������������������������������������������� 277 18 Digitalization  of Controlling in Insurance Companies 279 Mirko Kraft and Bianca Drerup 18.1 Basic Understanding of Insurance and Controlling������������������������������������ 280 18.1.1 Basic Understanding of Insurance ���������������������������������������������� 280 18.1.2 Basic Understanding of Controlling�������������������������������������������� 280 18.1.3 Need for a Sector-Specific Concept of Controlling? ������������������ 281 18.2 Application Orientation and Interdisciplinarity in Controlling in Insurance Companies������������������������������������������������������������������������������ 282 18.2.1 Application Orientation �������������������������������������������������������������� 282 18.2.2 Interdisciplinarity������������������������������������������������������������������������ 284 18.3 Selected Applications of Controlling in Insurance Companies and their Digitalization�������������������������������������������������������������������������������������� 285 18.3.1 Contribution Margin Calculations ���������������������������������������������� 285 18.3.2 Internal Models���������������������������������������������������������������������������� 287 18.3.3 Telematics Tariffs������������������������������������������������������������������������ 288 18.4 Effects of Digitalization on the Competencies of Controllers in Insurance Companies���������������������������������������������������������������������������������� 289 18.5 Conclusion and Outlook ���������������������������������������������������������������������������� 291 References�������������������������������������������������������������������������������������������������������������� 292 19 Use  of Smart Technologies in Large Infrastructure and Energy Projects 297 Andreas Langer and Lutz Neugebauer 19.1 Introduction������������������������������������������������������������������������������������������������ 297 19.2 Challenges of Large Infrastructure and Energy Projects���������������������������� 298 19.2.1 Technical and Functional Level �������������������������������������������������� 299 19.2.2 Economic Level �������������������������������������������������������������������������� 299 19.2.3 Political Level������������������������������������������������������������������������������ 300 19.2.4 Project Management Level���������������������������������������������������������� 300 19.2.5 Data Management Level�������������������������������������������������������������� 300 19.3 Data Management and Digital Technologies for Project Controlling�������� 301 19.3.1 Basic Technologies���������������������������������������������������������������������� 302 19.3.2 Obtaining Data ���������������������������������������������������������������������������� 303 19.3.3 Checking the Plausibility of Data and Structuring it ������������������ 305 19.3.4 Analysing and Using Data ���������������������������������������������������������� 305 19.4 Practical Approaches to the Digitalization of Project Controlling�������������� 306 19.4.1 Overview of Controlling Tasks and Digital Tools������������������������ 306 19.4.2 Project Objectives and Feasibility������������������������������������������������ 307 19.4.3 Project Planning �������������������������������������������������������������������������� 310 19.4.4 Risk Controlling�������������������������������������������������������������������������� 311

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19.4.5 Reporting and Analysis���������������������������������������������������������������� 312 19.4.6 Closure and Consolidation���������������������������������������������������������� 314 19.5 Conclusion and Outlook ���������������������������������������������������������������������������� 315 References�������������������������������������������������������������������������������������������������������������� 317 20 Current  Trends and Future Potentials of Digitalization in Procurement Controlling 321 Andreas Jonen 20.1 Objective ���������������������������������������������������������������������������������������������������� 321 20.2 Relevance of Procurement�������������������������������������������������������������������������� 322 20.3 Digital Transformation of Procurement������������������������������������������������������ 323 20.4 Effects of Digitalization on Controlling����������������������������������������������������� 324 20.5 Special Effects of Digitalization on Procurement Controlling ������������������ 325 20.6 Empirical Review: Job Advertisement Analysis ���������������������������������������� 329 20.6.1 Justification of the Method of Investigation�������������������������������� 329 20.6.2 Objectives and Presumptions of Effectiveness���������������������������� 331 20.6.3 Overview of Existing Studies������������������������������������������������������ 331 20.6.4 Procedure ������������������������������������������������������������������������������������ 334 20.6.5 Description of the Sample������������������������������������������������������������ 336 20.6.6 Results of Job Advertisement Analysis���������������������������������������� 337 20.7 Conclusion�������������������������������������������������������������������������������������������������� 339 References�������������������������������������������������������������������������������������������������������������� 340 21 The  Role of the Chief Financial Officer in the Digital Transformation of Business Models 343 Wolfgang Becker, Matthias Nolte, and Felix Schuhknecht 21.1 Introduction������������������������������������������������������������������������������������������������ 343 21.2 Value-Oriented Controlling and the Digital Transformation of Business Models ������������������������������������������������������������������������������������ 345 21.2.1 Value-Added Oriented Controlling: Purpose, Functions, Objects, Tasks and Duty Bearers ������������������������������������������������ 345 21.2.2 The Digital Transformation of the Business Model as an Object Field of Value-Added Oriented Controlling���������������� 346 21.3 Tasks, Duty Bearers and Instruments in the Context of the Digital Transformation of Business Models ���������������������������������������������������������� 347 21.3.1 Initialize �������������������������������������������������������������������������������������� 347 21.3.2 Realize������������������������������������������������������������������������������������������ 349 21.3.3 Evaluate���������������������������������������������������������������������������������������� 351 21.4 The CFO in the Digital Transformation of Business Models �������������������� 353 21.4.1 Scientific Approach���������������������������������������������������������������������� 353 21.4.2 Empirical Validation�������������������������������������������������������������������� 354

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21.5 Target Profile of the CFO���������������������������������������������������������������������������� 356 21.6 Conclusion and Outlook ���������������������������������������������������������������������������� 360 References�������������������������������������������������������������������������������������������������������������� 361 22 Hack  Yourself: A Call for an Artistic Metamorphosis of the Controller in the Digital Transformation 369 Avo Schönbohm and Thea Dymke 22.1 Controlling and Controllers in the Digital Transformation������������������������ 369 22.2 The Controller at the Crossroads���������������������������������������������������������������� 370 22.2.1 The Changing Art of Controlling������������������������������������������������ 370 22.2.2 Modern Art as Agent & Inspirational Surface of Transformation������������������������������������������������������������������������ 372 22.3 L’Invitation au Voyage: Excursions into Artistic Thinking������������������������ 374 22.3.1 Against the Norm������������������������������������������������������������������������ 374 22.3.2 Without Purpose and Open-Endedness���������������������������������������� 376 22.3.3 From Aesthetic Competence to Transfer�������������������������������������� 378 22.4 Drawing Inspiration: Looking Inwards and Outwards ������������������������������ 379 22.4.1 Looking Inwards: Studio Time���������������������������������������������������� 379 22.4.2 The View Outwards: Figure and Ground ������������������������������������ 381 22.4.3 Disrupt Yourself – Creative Destruction�������������������������������������� 381 22.5 Digitalization as a Creative Opportunity for Controllers���������������������������� 384 References�������������������������������������������������������������������������������������������������������������� 384

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Digital Controlling: Basics for the Successful Digital Transformation in Controlling Imke Keimer and Ulrich Egle

Abstract

Disruptive digital technologies and big data are having a significant impact on the corporate world. The platform economy is creating new business areas and ecosystems. Controlling is challenged in two aspects. On the one hand, it must accompany the digital transformation in the company by controlling digitalization. On the other hand, controlling itself must use the potential of digitalization and develop into Digital Controlling.

1.1 Introduction The market environment is characterized by ever-increasing complexity, dynamism and internationality. The rapid development of information technology (IT) is one of the main drivers of these changes. Through the Internet and related technologies, the world is becoming more and more interconnected and distances play less and less of a role (Nixon 2015, p. 2). As a result, entire value chains are being redefined, companies are advancing into new markets, but at the same time the established market is also being worked on and

I. Keimer (*) · U. Egle Rotkreuz, Switzerland e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_1

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threatened by ever new competitors. Due to digitalization and globalization, products and services from all parts of the world are in competition with each other (Kaufmann 2015, p. 2f.). Companies are continuously forced to differentiate themselves from the competition with a convincing value proposition (Keimer et al. 2018, p. 6). The new digital technologies are strongly determining and in some cases radically changing the value chain in companies. Activities and business processes are being standardized and automated. This releases a wide range of resources that can be used elsewhere and create added value for internal and external customers. In addition, there is the potential of value-adding information, which is available due to the exponentially growing amount of data. This information needs to be harnessed. Companies can use digital services to tap new sources of revenue, enhance the customer experience and evolve into a data-driven business model. Controlling is involved in the digitalization offensives in two different ways through the digital transformation. On the one hand, controlling supports and accompanies the entire company in the digital transformation through the controlling of digitalization. It provides controlling systems for measuring the value contribution of digitalization and integrates new performance indicators into the existing key performance indicator systems. The transformation to an ecosystem or the integration of online and offline channels along the customer journey requires modern KPI systems with the interlinking of monetary and non-­ monetary performance indicators. Controlling knowledge must therefore expand to include, for example, the functioning of the platform economy and online key figures. Only in this way can the controller, as a business management consultant, help shape the digital transformation in the company. On the other hand, controlling itself is also affected by the digital transformation. When it comes to digitalization in controlling, controlling itself must face the new challenges, use the opportunities of digitalization and become more efficient through standardization and automation. In controlling, technologies such as integrated ERP systems, cloud applications, big data analytics, and business intelligence (BI) systems can be identified as key technologies of the digital transformation (Bhimani and Willcocks 2014, p. 470; Strauss et al. 2014, p. 1). Through automation, they reduce the susceptibility to errors and increase both the efficiency and effectiveness of the controlling department. Complex data analyses that previously took weeks can nowadays be produced in real time. In addition to speed, the use of Big Data Analytics allows for a multi-layered evaluation of data and the recognition of correlations that would not be obvious without new technologies. Moreover, today’s mobile standards enable high bandwidths for data analysis independent of location and people. By using appropriate technologies, companies can optimize controlling processes and open up new evaluation dimensions. This increases the benefits of controlling and secures its existence in the company. Digitalization in controlling thus has a cumulatively positive effect on the overall success of the company. However, digitalization in controlling is still expandable in a large proportion of companies. In a nationwide survey conducted in 2018, 100 companies (65% of the Swiss companies surveyed) state that they estimate the level of digitalization in controlling to be

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12 companies 43 companies

42 companies

57 companies How high do you estimate the degree of digitalization of your controlling? up to 25%

up to 50%

up to 75%

up to 100%

Fig. 1.1  Subjective degree of digitalization. (Based on Keimer et al. 2018)

below 50% (cf. Fig.  1.1). Only 12 companies (8%) indicate a degree of digitalization above 75%, thus estimating it as high (Keimer et al. 2018). The discussion about digitalization in controlling must not be limited to investments in digital technologies, but must deal in particular with the value-creating use of digital technologies and take a holistic approach. In doing so, the entire range of digitalization must be taken into account, starting with the standardization of data sources and the standardization of processes and extending to questions about the possibilities of real-time analyses or complex algorithms in the context of artificial intelligence. In addition to investments in digital technologies, investments in employee competencies (digital controllers) are also indispensable in order to exploit the possibilities of digitalization in controlling. This article is divided into two sections. First, we briefly discuss the controlling of digitalization (Sect. 1.2) in the company as a whole and then focus on digitalization in controlling (Sect. 1.3). First, the theoretical foundation is described and discussed. In doing so, we will show which requirements must be met for Digital Controlling. Starting with data, technologies, processes and methods and ending with competencies, we provide a description and impetus for the most important domains of digitalization in controlling.

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1.2 Controlling Digitalization In theory and practice, digital transformation is associated with innovative digital business models, digital services and the optimization of value creation through the buzzword Industry 4.0 (Kreutzer et  al. 2018, p.  43; Obermaier 2016, p.  8). By building platform companies and digital ecosystems, established and new companies seek to enhance the customer experience and develop competitive advantages. Through the targeted use of digital technologies, many opportunities arise to overcome digital inertia and further develop business models. Established and new products will be networked to offer customers individualized services throughout the product lifecycle (Soder 2014, p.  15). Digital service business is increasingly becoming a lucrative source of revenue that can go far beyond classic cross-selling measures. In terms of revenue models, companies are therefore increasingly flexible and, for example, offer subscription models for the use of their products and services in many industries (Momsen 2019, p. 9). In doing so, companies are responding to the social trend that owning products has become less important in favour of diversity and flexibility, and are positioning themselves as solution providers (e.g. mobility service providers). Established companies in particular face the challenge of permanently adapting and realigning their business model to dynamic and digital customer needs in order not to lose their digital connection (Kreutzer et al. 2018, p. 12). In addition, the potential of the digital transformation can be further increased through the value-creating use of accessible data and used in a targeted manner for omnichannel concepts, for example. In reality, there is often a lack of coherent business cases to holistically determine the value contribution of digitalization for the company. Lack of transparency, traceability and experience lead to hesitant investments in digital projects (Schönbohm and Egle 2016, p. 4). Even if companies have defined a digital strategy and have a roadmap for implementation, it is often not clear who drives digitalization in the company and how the digital transformation is managed (Kreutzer et  al. 2018, p.  91). Controlling is challenged to realign or rebuild the existing control systems for digital business models in order to steer digital initiatives in line with the corporate strategy (Schönbohm and Egle 2017, p. 233). Key performance indicators (KPIs) must adequately reflect the challenges of the VUCA (Volatility, Uncertainty, Complexity, Ambiguity) environment. Markets are highly volatile, changes are uncertain, complexity increases due to diversity and multiplicity of decision-­making, and the information underlying decisions is often ambiguous. Mapping the customer journey across the entire lifecycle with the interconnection of online and offline channels is technically possible, but the added value must be apparent, transparent and comprehensible to decision makers (Niehaus and Emrich 2016, p. 57). From the abundance of data and metrics, the relevant performance indicators must be determined and integrated into existing control systems. The adaptation of control systems is a continuous process, as previous metrics do not optimally reflect the customer experience, for example, or new digital channels are gaining in importance (Schönbohm and Egle 2017, p. 227f.).

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The Net Promoter Score for measuring customer satisfaction, different characteristics of the conversation rate, the development of sales in the digital channels or the number of subscriptions for digital services are just a few examples of performance indicators in the digital environment. The result is new management tools, such as Objectives and Key Results (OKR), which can be used to successfully manage the dynamics of change. Controlling must accompany the digital optimism in the company as a prudent coach. However, this also requires a cultural change in controlling that is open to change and allows adjustments to the management system (digital mindset).

1.3 Digitalization in Controlling Finance departments are also under great pressure to exploit the opportunities offered by digitalization. In addition to automating their own processes and activities, it is the task of controlling to extract the available information from the data and make it available to the company as a whole in a way that adds value. For many CFOs, the topic of digital finance transformation is logically high on the agenda in order to align the finance department with future tasks and to position themselves accordingly within the company for digital initiatives. Many finance processes are too manual, too expensive and, in particular, too cumbersome and can only support new business requirements such as real-time analyses (e.g. dynamic pricing) to a limited extent. The need for optimization affects all areas and processes in the finance department. The resource-intensive financial processes are often bundled in the controlling department (e.g. operational planning and budgeting) and have so far often been less trimmed for optimization than the processes in financial accounting. One of the reasons for this is the difficulty of standardizing many controlling processes. Kirchberg and Müller (2016), for example, examine the effects of digitalization on controlling processes and find that primarily operational planning and budgeting, project and investment controlling, risk management and business management will only be affected by digitalization to a limited extent (Kirchberg and Müller 2016, p. 91ff.). One reason for this is the different processes that are difficult to summarize in standards. In contrast, processes in financial accounting can be standardized more easily and automated in a second step. A good example of this is digital incoming invoice processing: Invoices are automatically separated from other document types, features such as customer or invoice numbers and the corresponding invoice items are digitally recognized. The required data is automatically extracted and fed into the corresponding downstream systems. The process flow is always the same: document capture, verification with possible release or rejection and the posting. The digital transformation is also becoming increasingly important in controlling and is shaking up existing, and in some cases entrenched, controlling structures and controlling profiles (Schäffer and Brückner 2019, p. 15f.). However, there is no consensus when it comes to how to shape the digital transformation in controlling and at what pace it

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should proceed (Kieninger et al. 2015, p. 11; Schönbohm and Egle 2017, p. 214; Schäffer and Weber 2018, p. 47). It is not enough to improve controlling with partial digital optimizations. The digital transformation must be driven forward in a structured and comprehensive manner (Keimer et al. 2018, p. 1). There must be no patchwork in the controlling or finance department, for example by implementing isolated solutions or the uncoordinated use of Robotic Process Automation (RPA). The digital transformation in controlling must be embedded in the overarching vision of the digital transformation of the finance department and the company.

1.3.1 Digital Controlling The digital transformation presents those responsible for controlling and the controlling departments with a complex, large and also existential task. It is about the elementary question of how the decision-makers can approach this task systematically. In the context of management days, workshops and meetings, the digital possibilities for controlling are debated so that the digital opportunities can be used and at the same time savings potential can be identified. Those responsible are not idle in their implementation and in many companies, extensive and successful digitalization projects are already underway in the finance or controlling departments. Companies are implementing modern ERP systems to provide an up-to-date infrastructure for the finance department. In doing so, they are creating the basis for data evaluation and are also investing in data quality, methods and employee skills. Controlling is increasingly being developed in the direction of Digital Controlling, which automates the transactional and repetitive activities in controlling and at the same time uses the potential of digitalization in a value-creating way. Based on the definition of Digital Controlling, we specify the term below using five domains in the following sections. Digital Controlling refers to digitally positioned controlling that uses the possibilities of digital transformation to generate the greatest possible value proposition for its customers. Digital Controlling is based on the five domains of data, technologies, processes, methods and competencies. Digital Controlling must meet minimum requirements in all domains.

Fundamental to the development of Digital Controlling is a holistic approach that includes all necessary domains and whose digital development has a comparable level of maturity. It is not conducive to the further development of controlling into Digital Controlling if, for example, the technologies for the use of business analytics (BA) are already available in controlling, but the available data is insufficient or the employees do not have the necessary skills for data evaluation. Figure 1.2 shows the domains for the successful transformation to Digital Controlling. The individual domains and their attributes are described below.

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Digital Controlling

Data

Technologies

Processes

Methods

Competencies

Fig. 1.2  Domains of Digital Controlling

1.3.1.1 Digital Controlling: Data In controlling, the data is the basis for the most important task: the evaluation of relevant information to support decision-making by controlling customers. In Digital Controlling, care should be taken to evaluate the potential of tapped, but also as yet untapped, data sources. Technologies, such as the Internet of Things, allow Controlling to access information across the entire value chain. The use of data in Digital Controlling is not limited to structured and internal data. The integration of external and/or unstructured data (e.g. images, voice, customer opinions from social media, product ratings, mobility data or weather data) provides Digital Controlling with in-depth insights. This means that this data can be linked to internal data and integrated into planning and forecasting. This allows the company to react more flexibly to changes in the market, weather, politics or social media and, ideally, to predict them in advance. This makes it possible, for example, to minimize the capital costs of warehousing or to optimize dynamic price management. Furthermore, the domain of data in the context of Digital Controlling includes not only the use of data with the various formats and volumes, but also data management and data governance (Keimer et al. 2018, p. 19ff.). Data management includes the storage of and access to all data processed in the company. Digital Controlling should be given access to all essential data in the company. It is advantageous if access to the data is also simple and the data does not have to be prepared manually. In this way, the data can be used directly for analyses in controlling and, for

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example, flow into real-time analyses. Other important components of data management are data consistency, data stability and data integrity. Data consistency refers to ensuring that a single – and correct – version of the data is always accessed, regardless of the data source (single version of the truth). This can be supported, for example, on a technical level through the use of Data Lakes. Data stability requires that the data is permanently available and that no data loss occurs. Data integrity further ensures the completeness and timeliness as well as the accuracy of the data. Data governance includes all frameworks and guidelines that define the security and handling of data throughout the entire data lifecycle. Data governance guidelines must be comprehensively available in Digital Controlling and regularly updated. In addition, it is helpful to create clear responsibilities and to declare one person responsible for the area of data governance. For Digital Controlling, it is of elementary importance that the data domain is well developed. It forms the basis for the other domains. Data comprises the knowledge and information that is extracted for the company in Digital Controlling. cc

For the domain Data, the following aspects must be fulfilled in Digital Controlling: • Data use: In addition to internal and structured data, external and/or unstructured data is also integrated into the evaluations. • Data Management: Controlling has access to the required and relevant data and the data is of high quality. • Data Governance: A clear and up-to-date policy is in place and responsibilities are defined.

1.3.1.2 Digital Controlling: Technologies Digital Controlling requires the use of technologies and applications as well as a high level of integrity of the systems in order to be able to perform its tasks. The portfolio of relevant digital technologies for controlling is very broad and includes modern hardware and software components. The technologies enable the collection, storage, aggregation and processing of data. They make the existing digital potential of data accessible. Innovative, digital technologies enable in-depth analyses and create the conditions for realizing automation efforts in controlling. Technologies such as cloud technology can make real-time production and customer data accessible. When using and implementing technologies in controlling, not only the short-term perspective should be considered. In order for Digital Controlling to successfully establish itself and fulfill its tasks, the medium and long-term view must also be included. Here, close cooperation with the IT department is beneficial in order to exchange information about technological developments and opportunities and to launch investments in new technologies in a timely manner.

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Even though the design of IT systems is strongly driven by the size, industry and business model of a company, it should be ensured that certain minimum requirements are met for Digital Controlling. For Digital Controlling, a good data basis must be provided via a modern ERP system, supplemented by a business intelligence and/or business analytics solution. Depending on the business area, this technological base should be supplemented by additional technologies and applications for the extended recording, processing, analysis or presentation of operationally relevant information (e.g. self services, cloud computing, dashboards, Internet of Things, etc.). In the domain of technologies, it is not only the use of technologies and applications that plays an important role for Digital Controlling. A decisive success factor is that the systems are fully integrated. Isolated solutions lead to data breaks and interfaces and can result in inaccuracies and considerable additional work during data processing. In the context of Digital Controlling, care must be taken to ensure that new technology is well integrated into the existing system landscape. cc

For the domain Technologies, the following aspects must be fulfilled in Digital Controlling: • Use of technologies and applications: ERP system and BI/BA solution supplemented by other technologies and applications. • Integration: High level of integration of existing systems and applications.

1.3.1.3 Digital Controlling: Processes The essential controlling task is to prepare, provide and explain information as a basis for decision-making. Efficient and optimized controlling processes based on a modern IT infrastructure play an important role here (Keimer et al. 2017, p. 827). The standardization and automation of controlling processes creates freedom for the controller to deal with more in-depth analyses and to focus on the interpretation and communication of the results. Furthermore, the automation of controlling processes and controlling activities can reduce the error rate, increase efficiency and speed. The goal of Digital Controlling should be to digitalize a large part of the controlling processes and to strive for the highest possible degree of automation. As already mentioned, not all controlling processes are equally suitable for automation. In particular, transactional and repetitive processes and activities are considered easy to automate, in contrast to competency-based ones. These include, for example, the compilation and aggregation of data or the creation of standard reports. Digitalization thus predominantly influences the main controlling processes of cost, performance and profit accounting and management reporting (Kirchberg and Müller 2016, p. 91). In addition to the automation of controlling processes, Digital Controlling also strives for high efficiency in the domain of processes. Through innovative technologies, there are

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constantly new and improved tools with which the controlling processes can be handled more quickly and reliably. These tools primarily fulfill process-supporting functions. Robotic Process Automation, Self Services and Workflow Management are currently the most discussed. Complex system adaptations are not always necessary. RPA, for example, is a software robot that imitates a user and mimics user input. This makes it easy to automate processes that are standardized, follow clear rules, and have only digital input and output sources (e.g. SAP, Excel, etc.) (Hermann et al. 2018, p. 29). cc

For the domain Processes, the following aspects must be fulfilled in Digital Controlling: • Degree of automation: A large part of the transactional and repetitive controlling processes and activities should be automated. • Efficiency: Automation of manual activities and process optimization through IT applications.

1.3.1.4 Digital Controlling: Methods Only the existence of the corresponding technologies and data make the use of statistical methods in controlling possible. In general, a distinction is made between four categories of analysis methods in the context of business analytics (Davenport and Harris 2007, p. 8). Starting with the descriptive methods of descriptive analytics (What happened?), through the finding of correlations of diagnostic analytics (Why did something happen?) and the predictions of predictive analytics (What will happen?) to the highest category of prescriptive analytics (How can I influence that something happens?). In Digital Controlling, at least methods of Diagnostic Analytics should be in use and correlations in the data should be analyzed (e.g. using correlations) and thus reporting should go beyond the mere description of actual and plan data. Ideally, these methods are supplemented by further methods from the categories of predictive and prescritive analytics. Many companies focus on the prediction of trends and probabilities as well as the analysis of scenarios. The statistical methods chosen in Digital Controlling differ depending on the company’s situation and the issue at hand. In the context of Digital Controlling, structure-discovering methods can reveal correlations that are not obvious at first glance. The possibilities of big data analytics even partially reverse our previous understanding of statistical methods. The basis of inferential statistics is small samples, which are used to infer the population on the basis of theoretical hypotheses. Sampling is necessary because data has been scarce until now and the technical possibilities for analysis have been limited. This is changing due to the exponential growth of data and the increase in computing power: More data and variables can be included in analyses and, for example, relationships can be found by using correlations – even without first suspecting a causal relationship. Even fuzzy data, i.e. incomplete or even erroneous data, can be integrated into the analyses. Even if analysis programs already

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produce extensive evaluations, these should always be critically scrutinized with methodological understanding. Here, the Digital Controller or an employee with the appropriate methodological and specialist knowledge is needed, on the one hand, to assess the variables taken into account and, on the other hand, to check, evaluate and interpret the results (Keimer et al. 2018, p. 46). cc

For the Methods domain, the following must be met in Digital Controlling: • Methods of Analysis: Use of Diagnostic, Predictive and/or Prescriptive Analytics.

1.3.1.5 Digital Controlling: Competencies In order to successfully manage the digital transformation, it is necessary to adapt the traditional controlling role model. The Digital Controller must accompany both the controlling of digitalization and the digitalization in controlling. Therefore, high demands are placed on him. In addition to the classic competencies of the controller, the Digital Controller also needs technical, statistical and interdisciplinary competencies. Egle and Keimer (2018) define competencies for the Digital Controller in the areas of controlling expertise, business espertise, data science, IT management and performance culture (Egle and Keimer 2018, p. 51). The competencies in these areas are described below. Even though the scope of the controller’s tasks is changing with the digital transformation, solid controlling expertise is still the basis for the professional fulfillment of controlling tasks. In addition to a strong understanding of controlling processes, instruments, and cost and revenue models, the Digital Controller must expand this knowledge to include digital business models and the management of the customer experience across the entire customer journey. He or she must both understand the traditional controlling models and be able to expand them to include the new digital KPIs (e.g. online KPIs) (Egle and Keimer 2018, p. 51). The possibilities of business analytics and the inclusion of external and/or unstructured data also broaden the perspective on business. The focus is not only on internal performance accounting, but also on interrelationships that affect both the company itself as well as the competition, the markets and the customers. In order to be able to correctly classify the results of the analyses, the Digital Controller should have a proven understanding of the market and the industry. The knowledge of digital business models, platforms and digital ecosystems helps the Digital Controller to decisively support the entire company in the digital transformation. In order to accompany digitalization projects in the company, knowledge of agile project and change management is an important success factor (Egle and Keimer 2018, p. 51). In addition, however, the Digital Controller should also cover skills from the areas of data science and IT management. This includes all skills in the area of data retrieval and analysis: from knowledge of IT architecture, data management and scripting and programming languages to statistical skills and the visualization of results (Egle and Keimer 2018, p. 51).

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The changed performance culture should also not be underestimated. The Digital Controller must not retreat into his cubbyhole. He or she must meet management at eye level as well as seek out discussions with the specialist departments and play an active role. Therefore, he should have a high level of communication and teamwork skills. The Digital Controller must show initiative and take a proactive approach to data analysis. In addition, the Digital Controller should have a high level of resilience in order to cope with today’s fast-paced environment and the demands of management and specialist departments, which are not always free of conflict (Egle and Keimer 2018, p. 51). cc

For the domain Competencies, the following aspects must be fulfilled in Digital Controlling: • Controlling Expertise: Controlling processes, controlling instruments, revenue and cost models, online key figures, controlling systems and controlling organization. • Business Expertise: Market and industry understanding, business models, platforms/ecosystems, risk management, project management, change management, and law, ethics and compliance. • Data Science: Business intelligence, business analytics, statistics skills, programming skills, visualization and dashboards. • IT Management: IT architectures, technologies, IT governance, data management, scripting languages, IT security and workflow management. • Performance Culture: Initiative, communication skills, customer orientation, linked thinking, accuracy, ability to work under pressure (resilience) and ability to work in a team.

1.4 Conclusion The influences of the digital transformation are having a strong impact on companies and are forcing them to make massive adjustments in order not to lose their competitiveness. Controlling cannot escape this development either and is increasingly experiencing significant changes. Controlling is faced with the two challenges of both taking on the controlling of digitalization and managing it successfully, as well as driving digitalization forward in controlling and developing controlling into Digital Controlling. Controlling is challenged to establish itself strategically and to perceive the digital transformation as an opportunity. It can position itself as a north star for digitalization in the company by driving its own digitalization forward as Digital Controlling and

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supporting digitalization initiatives in the company as a whole. In order to play an active role in the field of digitalization, it is necessary to build a competent, interdisciplinary and passionate team that has the right competencies and lives digital optimism.

Research Project Digital Transformation in Controlling

This article is part of the Digital Transformation in Controlling (DigiCon) project, which is co-financed by Innosuisse. In addition to the Lucerne University of Applied Sciences and Arts, 9 renowned Swiss companies have collaborated on this project. The project aims to determine the state of digitalization in Swiss controlling functions and to advance the digital transformation in Switzerland. For this purpose, a maturity model measures the digital maturity level of controlling functions within the dimensions of data, technologies, processes, methods and competencies (Keimer et al. 2018). The definition of Digital Controlling is based on these dimensions.

References Bhimani, A., and L. Willcocks. 2014. Digitisation, ‘Big Data’ and the transformation of accounting information. Accounting and Business Research 44 (4): 469–490. Davenport, T.H., and J.G.  Harris. 2007. Competing on analytics  – The new science of winning. Boston: Harvard Business School Press. Egle, U., und I.  Keimer. 2018. Kompetenzprofil “Digitaler Controller”. Controller Magazin 9,10 (5): 49–53. Hermann, K., R.  Stoi, and B.  Wolf. 2018. Robotic process automation im finance & controlling der MANN + HUMMEL Gruppe. Controlling  – Zeitschrift für erfolgsorientierte Unternehmenssteuerung 30 (3): 28–34. Kaufmann, T. 2015. Geschäftsmodelle in Industrie 4.0 und dem Internet der Dinge: Der Weg vom Anspruch in die Wirklichkeit. Wiesbaden: Springer. Keimer, I., M.  Zorn, M.  Gisler, and M.  Fallegger. 2017. Dimensionen der Digitalisierung im Controlling: Grundlagen und Denkanstösse zur Selbstanalyse und Weiterentwicklung. Expert Focus 90 (11): 827–831. Keimer, I., M. Gisler, M. Bundi, U. Egle, M. Zorn, M. Kosbah, and A. Bueel. 2018. Wie digital ist das Schweizer Controlling? Eine schweizweite Analyse auf Basis eines Reifegradmodells. https://blog.hslu.ch/digitalcontrolling/. Accessed on: 15.07.2019. Kieninger, M., W.  Mehanna, and U.  Michel. 2015. Auswirkungen der Digitalisierung auf die Unternehmenssteuerung: Herausforderungen und Best-Practice-Lösungen. In Controlling im digitalen Zeitalter, ed. P. Horváth and U. Michel, 3–13. Stuttgart: Schäffer-Poeschel. Kirchberg, A., and D.  Müller. 2016. Digitalisierung im Controlling. Einflussfaktoren, Standortbestimmung und Konsequenzen für die Controllerarbeit. In Konzerncontrolling 2020, ed. R. Gleich, K. Grönke, M. Kirchmann, and J. Leyk, 79–96. München: Haufe Lexware. Kreutzer, R., T. Neugebauer, and A. Pattloch. 2018. Digital business leadership. Digital transformation, business model innovation, agile organization, change management. Berlin/Heidelberg: Springer.

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Momsen, B. 2019. Controlling wiederkehrender Umsätze. Controlling & Management Review 63 (2): 8–15. Niehaus, A., and K.  Emrich. 2016. Ansätze und Erfolgsfaktoren für die Digitalisierung von Vertriebsstrategien. In Digitalisierung im Vertrieb: Strategien zum Einsatz neuer Technologien in Vertriebsorganisationen, ed. L. Binckebanck and R. Elste, 47–63. Wiesbaden: Springer Gabler. Nixon, R. 2015. Remaining relevant. The future of the accounting profession. Fremantle: Vivid Publishing. Obermaier, R. 2016. Industrie 4.0 als unternehmerische Gestaltungsaufgabe. Betriebswirtschaftliche, technische und rechtliche Herausforderungen. Wiesbaden: Springer Gabler. Schäffer, U., and L. Brückner. 2019. Rollenspezifische Kompetenzprofile für das Controlling der Zukunft. Controlling & Management Review 63 (7): 14–31. Schäffer, U., and J. Weber. 2018. Digitalisierung ante portas. Controlling – Zeitschrift für erfolgsorientierte Unternehmenssteuerung 30 (1): 42–48. Schönbohm, A., and U. Egle. 2016. Der Controller als Navigator durch die digitale Transformation. Controller Magazin 41 (6): 4–8. Schönbohm, A., and U.  Egle. 2017. Controlling der digitalen Transformation. In Digitale Transformation von Geschäftsmodellen: Grundlagen, Instrumente und Best Practices, ed. D.  Schallmo, A.  Rusnjak, J.  Anzengruber, T.  Werani, and M.  Jünger, 213–236. Wiesbaden: Springer Gabler. Soder, J. 2014. Use Case Production. In Industrie 4.0 in Produktion, Automatisierung und Logistik. Anwendung, Technologien, Migration, ed. B. Vogel-Heuser, T. Bauernhansl, and M. ten Hompel, 3–26. Wiesbaden: Springer. Strauss, E., G.  Kristandl, and M.  Quinn. 2014. The effects of cloud technology on management accounting and decision making. Chartered institute of management accountants. https:// www.cimaglobal.com/Documents/Thought_leadership_docs/Management%20and%20financial%20accounting/effects-­of-­cloud-­technology-­on-­management-­accounting.pdf. Accessed on: 07.10.2019. Imke Keimer  is Professor of Mathematics and Business Analytics at the Institute of Financial Services Zug IFZ at the Lucerne School of Business. Imke Keimer researches and teaches in the areas of digitalization in controlling, financial risk management, mathematics and business analytics. She is the head of the MSc International Financial Management programme. Ulrich Egle  is Professor of Digital Performance Management at the Institute of Financial Services Zug IFZ at the Lucerne School of Business. After studying technically oriented business administration at the University of Stuttgart, he completed his doctorate at the Institute of Information Systems at the University of Bern. He supports companies in the digital transformation of business models and on the topic of digital finance transformation.

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Current Trends in Digital Transformation in the Financial Sector Imke Keimer and Markus Zorn

Abstract

Currently, transactional ERP transformations are a high priority in finance departments. However, at the same time, those responsible should push ahead with the implementation of insights methods and business analytics. In an interview with Imke Keimer, Markus Zorn explains why this is so important and which digital trends are currently perceived in the finance sector.

2.1 An Interview by Imke Keimer with Markus Zorn Keimer Mr. Zorn, you are a partner at Deloitte and head of the Finance & Performance team. How far would you say Swiss companies are in the digital transformation of the finance department? Zorn In my estimation, a stage has been reached in Switzerland where many companies are very interested in finding out more and are beginning to experiment. I. Keimer (*) Rotkreuz, Switzerland e-mail: [email protected] M. Zorn Zurich, Switzerland © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_2

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Keimer So in your opinion, digitalization is still in its infancy? Zorn Yes, rather at the beginning. For example, there are only a few pioneers who are really already using the possibilities of predictive analytics and have already implemented solutions. Keimer Why is that? Why is investment not faster and more broadly based? Zorn I wonder about that, too. For example, the investment and the risk are much lower compared to a traditional transactional finance project. If a transactional finance project does not go as expected, then it can be very costly. If something goes wrong when implementing predictive forecasting, then it is easy to go back to the previous processes and create the forecast manually. So nothing happens in the worst case scenario. But compared to the state of 2 years ago, when it was not yet tangible for companies what digitalization exactly means, a lot has already happened. I am convinced that most companies know what the possibilities are. The technologies are ripe. It is just a matter of getting started. Our current online survey on this topic shows that over 90% of companies are interested in this topic. Keimer You just talked about predictive analytics. What about prescriptive analytics? Are these methods already being used in the finance function? Zorn In terms of volume, the focus is currently on predictive analytics. However, it is only a small step from there to the implementation of prescriptive analytics. For example, as soon as I know how to predict my sales, I also know the screws I have to turn to make something happen. For example, prescriptive analytics can be used to model additional restrictions in a value chain. For example, I can determine that a 20% increase in sales is currently not possible at all because production cannot manage this. Nevertheless, predictive analytics is currently more of a topic in the finance functions than prescriptive analytics. Keimer Can you give me an example of the use of predictive analytics in controlling? Zorn Predominantly, it is about forecasting sales. Most of the companies we work with start with historical data series from within the company and then sometimes add external data. However, predictive analytics also makes it possible to predict other P&L items and, based on this, to create what-if and scenario models and thus investigate the question of how the overall result of a company will develop. Keimer As a result, the forecast then delivers a confidence interval. Or how do I have to imagine this exactly? Zorn Exactly. Predictive analytics can be used to model numerous scenarios. If, for example, the company management wants to know how strongly its own profit and loss account will be affected by a negative development of the FX rate, an intervention by the Swiss National Bank, the loss of a main supplier or an economic crisis, corresponding scenarios can be modelled. The influences of these scenarios on the result are finally presented by means of confidence intervals.

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Keimer What developments do you currently see in digitalization in controlling? What will change in the next 5 years? Zorn At the moment, we are noticing two trends in controlling. On the one hand, the question is increasingly being asked whether the classic budget is still relevant and whether it still delivers sufficient added value. Many companies are consciously deciding against drawing up a budget and against the time-­consuming budgeting process. Instead, companies are defining targets. On the other hand, automation in controlling is on the rise, i.e. automation of report generation and commenting and thus the implementation of push reports and self services. By using automatic text generation (Natural Language Generation), it is possible, for example, to turn an Excel spreadsheet into a finished report including text. In doing so, the text is amazingly good and you do not notice that it was written by a machine. These are the two trends that, in addition to the two topics of forecasting and insights, are currently moving controlling. Keimer Which projects currently have the highest priority in the finance departments? Zorn At the moment, transactional ERP transformations are clearly the priority. Especially with SAP S/4HANA, we notice the sword of Damocles of the implementation deadline. Our customers are familiar with ERP system projects. They know that customizations need resources and they are taken seriously and prioritized. It is a different story with the newer topics: many are under the impression that they are nice-to-have and can be taken care of at a later date. However, this is a fallacy. Of course, a transactional ERP system helps to become more efficient in finance and reduce costs in this area. As a KPI, cost reduction is a direct control variable for many companies. Implementing insights methods and predictive analytics, on the other hand, is not directly aimed at reducing costs. Rather, these tools are important for identifying business opportunities and not missing them. Companies that have these tools in place know, for example, that they don’t have to try to promote a particular product next month. It won’t sell because other factors will kick in. Or in 6 months, today’s target customers won’t buy a product because they’ll buy product xyz. If a company fails to recognize these trends, then sales will fail to materialize. Thus, the impact of waiting is much more severe than the cost saved by an ERP transformation. Keimer What should be changed? Zorn I would prioritize differently. I would take a small part of the budget for the transactional ERP system and invest it in a predictive machine. Many companies think, I will complete the transformation of the ERP system first and then look further. But a lot of opportunities are missed during this period! Keimer What data does a predictive analytics or insights tool need? You mentioned internal data earlier, is this sufficient?

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These are very sufficient. I have observed in customer projects that simply including cost and sales data from the ERP system in combination with data from the CRM system, production data, HR data and – if a company is already advanced in digitalization – data from the Internet of Things can generate meaningful insights. Based on this data, good predictions can already be made. For this, I do not necessarily have to include external data such as weather data, geodata or movement data. How do you go about the digitalization of the controlling function? What is usually the first step? Proof of value is the magic word here. You can see that predictive analytics is a new topic for many customers. They first want to experience how it works. We are given a short period of time and a tight budget and have to show what is possible with it. If a mandate comes about, what happens next? Do you first take a look at the current state of your customers? No, that rarely adds value. What is important for us is: What is the vision? Then we can derive the right technologies and tools from it. Then we define the use cases. This can be, for example, sales forecasting, scenario modeling at group level, or a more precise analysis of costs with predictive analytics. The implementation then takes place agilely, step by step, in an iterative process. What about the topic of artificial intelligence? Is artificial intelligence being used in the finance department? We are very much looking at artificial intelligence and are in the process of building new teams. At the moment, we are seeing an increased use of chatbots in the finance functions. This mainly concerns companies that receive mass requests. These can come from both internal and external sources. Can you give me an example of that? A customer or supplier calls the company and has a question about a particular invoice. The chatbot understands the question, finds the invoice, and asks a question back, “Yes, I found the invoice. What is your question?” “The amount is not correct for the third item.” The chatbot is able to conclusively answer the responder’s questions at a very high percentage. We are talking about an order of magnitude greater than 80%. The rest goes on to second level support. Would you already call a chatbot artificial intelligence? Or is the learning aspect still missing? Robots, of course, work based on rules. If something does not work, I have to update the rules. Let us take digital invoicing. This has been working very well for years. An invoice is scanned in, and the machine is programmed accordingly and knows where to find the supplier number, amount, etc. Self Learning kicks

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in insofar as the machine itself recognizes if the supplier changes the form: a new logo, the amount is no longer in the usual place, etc. This can be the first step towards artificial invoicing. This can be considered the first step towards artificial intelligence. What impact do digital developments have on the CFO’s role model? In my opinion, the CFO’s remit is expanding. A CFO has to drive the digital topics in the finance department. It is his task to see the finance area more broadly than just preparing the balance sheet and the P&L as efficiently as possible. Finance needs to evaluate numbers that go beyond traditional financials to be able to generate valuable insights. If the CFO does not move, then marketing starts first with analytics, or IT does. Accordingly, the CFO must actively promote digital topics in the finance function. What exactly does that mean for the controller? What skills would you say are indispensable in the context of digitalization? I would say the ability to model. This includes data science, mathematics, statistics as well as the appropriate tool skills. Here I always find it a bit unfortunate that in the European area, the word controller is still used. This sounds too much like control. As long as this does not change, I would even go so far as to say that the controller sees himself predominantly as a controller. He is still the one who taps his colleagues on the knuckles when costs are not on budget. But that is not the controller of the future. Modeling also always requires a certain amount of creativity. This aspect is completely lost in the word control. Exactly. Creativity is part of the required mindset of a future controller, in addition to technical skills. He does not have to control whether someone does something or not. He has to tell the business what he discovered from the figures. Does this require the controller to understand the market in question? A controller does not have to become a market analyst. But if, as a data scientist, he processes a huge pile of data and identifies a trend, e.g. that a product is collapsing in a market, then the question of why naturally follows. That is exactly what is “new” about Big Data Analytics. Right, right! Until now, it was the case that a hypothesis was made about a suspected relationship and tested using the data. Big Data Analytics is exactly the opposite. I have a huge pot of numbers and I do not know what they tell me at all. But my clever mathematical methods show me the correlations and I then have to interpret them. This means that the previously known methods are used in a completely different way.

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Company Profile Deloitte Switzerland General Guisan Quai 38 8022 Zurich Switzerland Industry: Consulting and auditing Turnover 2018: CHF 475 million Number of employees 2018: approx. 1910 employees Deloitte Schweiz is a leading consulting and auditing firm in Switzerland and offers industry-specific services in the areas of Audit, Risk Advisory, Consulting, Financial Advisory as well as Tax and Legal. With over 1900 employees at its six locations in Basel, Bern, Geneva, Lausanne, Lugano and Zurich (headquarters), Deloitte serves companies and institutions from all sectors of the economy.

Imke Keimer  is Professor of Mathematics and Business Analytics at the Institute of Financial Services Zug IFZ at the Lucerne School of Business. Imke Keimer researches and teaches in the areas of digitalization in controlling, financial risk management, mathematics and business analytics. She is the head of the MSc International Financial Management programme. Markus Zorn  leads the Finance & Performance practice for Deloitte Switzerland. He advises CFOs with the goal of defining the future of the finance organization and advancing digitalization in finance. With more than 20 years of experience in management consulting with a focus on finance topics, he supports CFOs of multinational clients. The goal is to increase efficiency, reduce costs, achieve fast close, improve data and reporting quality, and implement accounting and reporting standards. Markus Zorn’s current focus is on defining the future of the finance function and the role of the CFO of international companies, taking into account digital technologies.

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Further Development of the Financial Forecast in the Context of the Digital Transformation Using the Example of SAP SE Simone Raschig and Mike Schulze

Abstract

The financial forecast process at SAP was traditionally structured along a classic bottom-­up principle. After the inadequacies of this process design in terms of control relevance and simulation options became clear in 2015, the process was fundamentally rethought and finally significantly developed and restructured in 2017. This article describes the transformation process and explains the combined approach used today. This consists of a centrally prepared forecast of the Group’s business development for the calendar year, which is based on standardized and statistical calculation methods, and a financial forecast for the current quarter, which is provided by decentralized units (The authors would like to thank the employees of SAP SE Christian Cramer, Thorsten Rasig, Stephanie Rieder, and Reinhild Rülfing for their helpful suggestions and technical support in preparing this article).

S. Raschig Walldorf, Germany M. Schulze (*) Mainz, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_3

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3.1 Initial Situation The financial forecast process has always been a high priority at SAP. As in other listed companies, management needs an early and reliable insight into the development of the key financial figures relevant to management for the full year. This is to ensure that negative developments are identified at an early stage and that countermeasures can be taken if necessary. Traditionally, SAP’s financial forecast was structured along a classic bottom-up principle. In the first and last month of each quarter1, all of the Group’s business units provided their forecast for the full year along the income statement (P&L), together with an estimate of personnel development. In 2015, the inadequacies of this process design became very clear. While the financial forecast up to the third quarter predicted a rather average achievement of the expectations communicated to the capital market (external guidance) for the year, the actual annual result was at the upper end of the communicated external guidance (see Fig. 3.1). In terms of forecasting accuracy, the forecast process used at that time did not meet expectations. On the process side, potential for improvement was also seen in the area of standardization of calculation methods and thus also in the transparency of results. In the past, uniform calculation standards for sales and costs existed only in isolated cases. As a result, the forecasting process proved to be time-consuming and resource-intensive, as well as difficult to understand.

Range external guidance

FC Mar. 2015

FC Jun. 2015

FC Sept. 2015

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Fig. 3.1  Forecast result 2015 versus actual result

 An exception was made in the month of January; no forecast was prepared here.

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Against the backdrop of these experiences, SAP’s financial forecast process was fundamentally rethought and finally significantly enhanced and reorganized in 2017. To improve the forecasting accuracy of the full-year forecast, a central competence team (CoE Central Forecast) was created at the same time as part of Group Controlling, which deals exclusively with the medium-term financial forecast (focus: full year). The methodological focus in this team is on the consistent application of standardized calculation methods. Statistical methods and predictive analytics concepts play a key role here. The previous forecast methodology showed that the results forecast for the current quarter are very important for the short-term management of the units and are also quite precise. The bottom-up driven forecast approach that had been in place until then was therefore not completely abolished. Instead, the mandatory forecast period was adjusted. Under the new forecast approach, the business units are now required to report a realistic quarterly forecast along the income statement. This is reported to the Management Board together with the full-year projection from the central competence team as the overall Group forecast. In this way, the operational management aspect for the individual business units is retained and is supplemented by an independent, neutral full-year forecast for medium-term corporate management.

3.2 The New Forecast Process 3.2.1 Centralised and Decentralised Forecasting Processes Run in Parallel and Complement Each Other The new forecast approach deliberately avoids reconciling the decentrally prepared quarterly forecast with the Group’s central full-year forecast. Both processes run in parallel, but are separated from each other in terms of content (see Fig. 3.2). Although the decentralized units still have the option of preparing a forecast for the current year and reporting it in the system, the forecast reporting to the Management Board for the full-year view

Decentralised forecast: current quarter forecast Q1

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Fig. 3.2  Centralized and decentralized forecast process

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only shows the results of the central forecast. For its part, the central forecast does not use the quarterly forecast of the decentralized units, but is based on its own calculations. Due to the consistent focus on Group level, there is also no modelling for individual business units from the central competence team. Instead, in addition to a realistic forecast scenario based on a ceteris paribus assumption, the central forecast also shows the range of possible full-year results. Thus, a conservative and an optimistic result is also simulated by adjusting situationally determined parameters. SAP’s experience since the application of the new forecast concept shows that successful corporate management is possible with such a combined concept. The central forecast takes on the role of an early warning system that identifies risks neutrally, without adjustments from the countries or regions, early in the year and can simulate their effects realistically. The decentralized forecast validates these forecasts in the short term, i.e. for the current quarter, and offers starting points for short-term changes in results.

3.2.2 SAP’s Transformation Process The change in the forecast process described above required a number of essential prerequisites in the company in order for it to be implemented successfully. SAP created these in several steps – piece by piece – and expanded them in a targeted manner. The consistent focus on customer requirements is one of SAP’s great strengths. In the early years, customer demand focused on license-based products that the customer purchased and ran on its own system infrastructure – on premises (on-premise product). With the advance of digitalization, demand changed. Today, many customers expect solutions that are quick and easy to install, that they can integrate into their existing infrastructure and that they can use flexibly, without large investments and long implementation cycles. In response to this development, SAP’s product portfolio, which previously focused heavily on on-premise solutions, was expanded to include cloud solutions. These are based on a leasing model in which the customer can use the software for a defined period and consume the associated services. At the end of the contract period, the customer can decide whether to extend the lease period or change its solution portfolio. In order to make this expansion of the product portfolio into the cloud area successful and profitable, fundamental adjustments to SAP’s internal processes were necessary in addition to technical changes. In the financial area, this meant mapping new sales and consumption models, redesigning profitability calculations, and developing forecasts for the future development of the individual product lines. At the same time, newly acquired companies had to be taken into account in the financial processes. Against the backdrop of these diverse changes, it was now necessary to manage complexity in a cost-efficient and holistic manner. Key starting points for counteracting increasing complexity and operating cost-efficiently were seen in the areas of organization, data and employees. All of these areas have been systematically developed and sustainably expanded since the 2000s.

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In the area of organization, all essential administrative functions were centralized as a first step. For example, instead of local purchasing departments, there is now a global purchasing organization that looks after all locations worldwide and thus makes efficiencies in contract negotiations available to several locations. Similarly, the most important transactional processes such as Record-to-Report (R2R) have been transferred to Shared Service Centers (SSC), which are distributed worldwide in such a way that all time zones can be optimally covered. For the local units, this meant on the one hand a more limited range of tasks than before centralization. On the other hand, the local teams were able to concentrate fully on their core sales business. The next step in the finance transformation process was the creation of so-called Centers of Expertise (CoE) as central competence teams. Similar to the Shared Service Centers, these were to work on specific topics from which all locations could benefit by developing standards for core areas of corporate management and thus integrating them into the existing financial infrastructure. One of the first CoEs to be established along these lines was the People & Workforce CoE, which was responsible for standardizing personnel and personnel cost planning. Other CoEs quickly followed, so that today key areas of internal financial management, such as personnel costs, maintenance revenues and cloud revenues, are managed centrally by CoEs and made available for forecast planning worldwide. In the area of data, SAP was able to draw on an ERP system from the very beginning, which over time proved to be a significant advantage for presenting uniform processes and ensuring data quality (single source of truth concept). In order to be able to report more agilely and flexibly on the basis of this data, an upgrade to SAP HANA was started in 2012. With SAP HANA, data is pulled from the transactional system in real time, allowing it to be used quickly and efficiently not only for financial reporting, but also for predictive modeling. Now that the rapid delivery of data in real time is covered by SAP S/4HANA Finance, the internal priority is to provide insights rather than masses of data. User-­ oriented dashboards as well as simple and uniform representations of essential control variables are therefore more important than ever. In the area of employees, internal development programs were carried out to accompany the steps outlined above. The increasing centralization of organizational units led to changes in the task portfolio of individual employees. Transactional, repetitive activities took a back seat; sales support and advisory tasks became more important than in the past. This change was actively supported with the help of appropriate training programs in order to ultimately enable and motivate employees to drive the change themselves and to increasingly focus their tasks in the area of business partnering.

3.3 Key Components in the Central Forecast Process As described before, the central forecast concept aims at increasing the forecast accuracy of the group results and at the same time ensuring transparency of the calculation. Essential components to achieve these goals are so-called satellites and predictive analytics models.

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3.3.1 The Satellite Concept Satellites bring together data at a very granular level from different data sources and combine them in such a way that the internal user of the resulting information can form a comprehensive picture of the current situation. Since satellites each also contain parameters that relate to potentials in the future, satellites are also used to calculate expected values in the future. The transition from the currently available information to future forecasts takes place here on the basis of a uniform, predefined calculation logic that is controlled centrally via the CoEs. An important application example of a satellite is the analysis and forecasting of cloud revenues. Here, information available on an individual contract basis about all current contracts is consolidated and presented in a business warehouse report. To forecast future revenues, information on contract renewals and new contracts is also processed in the satellite. In the last step, these can still be manually adjusted to show known one-time effects, such as a reduction in sales due to the payment behaviour of certain customers or similar. The sales calculated in this way are available to all controllers worldwide for their forecast planning. The integration into the SAP internal forecast planning environment allows a simple and efficient procedure in the planning process. From a controlling perspective, a further advantage is the transparency of the calculation logic used and the certainty that cloud revenues are forecast using the same logic everywhere. In addition to the calculation of cloud revenues, satellite logic is successfully applied in the area of personnel costs and maintenance revenues. Satellite data is used in both the centralized and decentralized forecast.

3.3.2 Predictive Analytics Models In addition to the satellites described above, predictive analytics models play an important role in SAP’s forecasting process. Predictive analytics is understood as the area of data mining that deals with the prediction of future developments (Larose and Larose 2015, p. 4). Data mining is the application of methods and algorithms from statistics and machine learning to search an available data pool for hidden patterns (Knöll et al. 2006, pp. 60–61). Due to the neutrality of the calculation underlying all statistical methods and the associated objectivity of the results, SAP sees great potential for application in the central forecast. With the changeover to the central forecast concept described above, the Central Forecast competence center, which had been created shortly beforehand, focused on the application potential of predictive analytics in financial forecasting. The goal was to develop predictive models that would allow SAP to forecast revenue and costs from a Group perspective for the time horizon of the current calendar year. When filling this CoE, care was taken to ensure that, in addition to sound controlling know-how, the team also had in-depth mathematical and statistical knowledge. In addition, a mixture of

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experienced colleagues and newcomers to the profession proved helpful in optimally combining sound process knowledge with impartiality and innovative ideas. The four employees of the CoE acquired basic knowledge on the topic of data science and predictive analytics, if they did not already have it due to their training, through internal SAP training courses and by trying out concrete predictive models. To this day, the team is supported by internal data scientists who also contribute further statistical knowledge, which is important and helpful, for example, in the pre-selection of possible predictors for a model. In addition, the employees of the CoE are supported by a competent IT team, which created and continuously develops the infrastructural requirements for the application of predictive analytics solutions in the central forecast process. As far as the IT infrastructure was concerned, it quickly became clear that such a tool-­ based concept would only be successful in the medium term if it could be applied with limited effort. Therefore, from the very beginning, SAP focused on integration and automation of the solutions used in the central forecast. This means that the data used as the basis for the predictive models is pulled directly from the database in real time, and then the calculations are performed in the predictive analytics tool according to the latest data. All (historical) data is drawn in EURO with a normalized currency basis in order not to distort the time series by currency effects. Once the calculation is complete, the EURO-­ based simulations are converted into the foreign currencies relevant to SAP using a specially designed currency conversion application with a realistic currency split2 . The parameters of the currency application, such as the number of reference periods on which the currency split is based, can be changed flexibly by CoE staff, depending on the application scenario3 . The forecast values are then copied directly to the central forecast planning environment. There, the employees of the CoE have the possibility to make further adjustments. Over the course of time, predictive models have been developed for all major P&L cost types, which are used in the central forecast together with other cost and revenue calculations from the satellites. For the CoE staff, the focus today is on continuous improvement of the models with a manageable use of resources. In the early days of the new concept, the focus was on intensive exploration of the possibilities and limits of predictive analytics for forecast modeling as well as on the conception of a collaboration model for the teams involved in the central forecast process.

 SAP currently has operations in more than 78 countries and therefore covers a wide range of currencies in its operating business. Because external guidance is published based on prior-year exchange rates, it is important to anticipate as accurately as possible the currency mix in which revenue and costs will be included in the full-year results. 3  An application scenario could be, for example, a simulation of the influence of certain currencies that have fluctuated strongly in the past periods. In this case, the reference period would be in the near past in order to reflect the most current exchange rates possible in the forecast scenario. 2

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3.3.3 Collaboration Model in the Central Forecast The central forecast approach combines cost and revenue calculations from satellites with predictive analytics models developed in the CoE Central Forecast. The final consolidation of the Group’s P&L forecast for the full year is the responsibility of the CoE Central Forecast. Since the satellites are managed centrally by their own CoEs, this procedure requires close coordination of all CoEs involved in each forecast and also ad hoc, for example in the case of extraordinary simulation requests (see Fig. 3.3). In this process, the CoE Central Forecast can rely on the fact that the turnover and cost data supplied via the satellites from the supplying CoEs have been carefully validated and, if necessary, modified in order to provide as realistic a value as possible for the central forecast. This validation step can be illustrated particularly clearly using the example of personnel costs. The personnel cost projection in the forecast is initially based on the so-called committed personnel, i.e. the existing personnel plus contract offers already sent and confirmed. In order to plan future personnel development, the committed personnel is supplemented by fluctuation and, if necessary, replacement as well as the planned personnel growth. The resulting personnel planning is valuated with country-specific cost information. Personnel costs are calculated on this basis and copied to the forecast planning environment as a default value. When creating the decentralized forecast, all controllers can use a personnel cost proposal from the personnel cost satellite and modify it if special effects are known. For the central forecast, the decentrally supplied personnel development projections and personnel costs are validated in a separate step and adjusted if necessary. This is supported on the process side in that the controllers worldwide are asked in the last month of each quarter to provide a forecast of personnel development for the year as a whole. This forecast for the full year is based on assumptions regarding staff turnover, replacement and actual staff growth. Based on historical patterns, the experts from the People & Workforce CoE are able to determine whether the assumptions provided locally are realistic. If this is

CoE Central Forecast

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Fig. 3.3  Collaboration model central forecast

Forecast input solution Predictive analytics models

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not the case, the experts adjust the personnel development forecasts and have the validated trend monetized accordingly by the personnel cost satellite. This validated personnel cost value then runs into the central forecast together with the appropriate personnel development projection. Predictive analytics models basically assume that certain patterns in historical data will show up again in the future. SAP’s experience shows that these assumptions fit well as long as business models do not fundamentally change or one-time events of a certain magnitude occur. An example of this would be a restructuring program, which is rarely carried out. Such special effects are captured by the central forecast approach through a regular exchange with the relevant central departments as well as with the controlling managers of the business units. The positioning of the CoE Central Forecast as part of Group Controlling has proven to be advantageous at SAP in this sense.

3.4 Success Factors and Challenges As described above, the introduction of a central forecasting process was part of a chain of changes in many areas of the company (Sect. 3.2.2). As in any change process, there were challenges that had to be identified and actively addressed.

3.4.1 Process and Organisation 3.4.1.1 Clear Commitment from Senior Management and Perseverance SAP’s experience shows that such a far-reaching process change can only be successful if senior management is fully behind it. This means not only getting a comprehensive understanding of the new approach, but also clear, continuous communication to the areas affected by the change. In the case of central forecasting, for example, a video message from the CFO was sent to all controllers in the company, which made clear the board’s clear commitment to the new approach. The limitations of the previously used approach were also explained, and the expected improvements and the organizational requirements created for this were described in a manner appropriate to the target group. Another important aspect in this context is a fundamental trust of the management in the teams involved. This trust refers first of all to the innovative strength and competence of the departments involved. In addition, however, trust also manifests itself in patience in the sense of giving the new process time to be tested, shaped, and ultimately continuously improved. SAP’s experience shows that the development of standardized calculation methods and predictive analytics models cannot be achieved in the course of a few weeks. Healthy perseverance, a certain tolerance for mistakes and a focus on continuous change in the company help to develop a concept that is successful in the long term.

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3.4.1.2 Joint Project Between Experts, Citizen Data Scientists and IT The application of predictive analytics methods requires extensive mathematical and statistical knowledge. Although controllers have a basic knowledge of these, their often study-based knowledge is not sufficient to develop predictive models for forecasting sales and costs. As outlined above, SAP met this challenge both through appropriately adapted competence profiles in the staffing of positions and through specific further training measures for the employees of the competence center. In terms of external perception, the colleagues in the team have thus developed over time into Citizen Data Scientists, i.e. experts who, although not necessarily comparable with classic Data Scientists in terms of their training, have developed into competent contacts in the field of Data Science through practical and theoretical further training. The continuous exchange with professional data scientists, who are organizationally assigned to the IT area, proved to be another success factor of the process conversion. This professional pool of experts accompanies predictive analytics projects beyond the financial area and can accordingly contribute experience, methodological knowledge and access to other data sources outside the financial area. This combination of statistical methodological knowledge and the overview of other non-financial predictive projects from the data scientist team, together with the business and process knowledge from the competence team, proved to be an important key to success in the development and introduction of predictive methods for the central forecast. 3.4.1.3 Rethinking Short-Term Management The introduction of the centralized forecast approach with a clear separation between centralized and decentralized forecasts not only meant a change in the focused time horizon and the amount of data provided in the forecast. Rather, the introduction of predictive models in the central forecast also required a change in thinking with regard to the recognition of cause-and-effect relationships. While deviations from the budget or the previous year could usually be assigned and analyzed geographically in the consolidation approach used previously, the use of predictive models for key sales/cost types in the central forecast first provides information about which costs and/or sales lead to deviations. In a second step, it is then possible to analyze in more detail which of the factors included in the respective calculation model (in satellite or predictive models) has changed significantly over time and where, if necessary, one needs to start in order to achieve the annual targets. For short-term, intra-year management, this means that the central forecast objectively reveals significant developments and projects how these trends may affect the year as a whole. By providing detailed forecasts per cost and revenue type, the central forecast also provides starting points for corrective measures. With the help of the decentralized forecast, which focuses only on the current quarter, it is then possible to observe the extent to which certain management-relevant measures materialize.

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3.4.2 Operational Work with Predictive Models 3.4.2.1 Clarity About the Objective of Modelling When it comes to operational modeling and working with concrete data, it became clear that there should be clarity and a common understanding of the goal of the modeling among all participants. In the central forecast, it was already defined at the beginning of the process changeover that the accuracy of the forecast for the Group should be improved for the entire year. This meant that the forecast horizon always extended to the end of the current calendar year. Furthermore, it was defined that cost and revenue forecasts at Group level should be as accurate as possible – in contrast to projections at business unit level or below. Due to the strict separation between decentralized and centralized forecasting as described in Sect. 3.2.1, it was also clearly defined that quarterly forecasting was not a priority in the centralized forecast. What was important was an accurate annual result. The definition of these parameters not only helped SAP to position the new competence center with regard to its value contribution, but also set the course for the selection of the relevant P&L forecast models and for the data selection. 3.4.2.2 Data Quality and Data History Which input factors/data lead to the best predictive accuracy in the predictive models was first tested for each topic area through correlation analyses before being included in the respective revenue/cost model. Then, various input parameters were processed in SAP’s proprietary predictive analytics solution and the results were validated through standardized statistical key performance indicators (KPIs). Outliers were analyzed, input parameters were adjusted again if necessary, and the models were recalculated. An important success factor in the predictive modeling process is data availability. On the one hand, this involves obtaining and processing meaningful and representative data series. On the other hand, a high-quality prediction result requires a sufficiently long data history, which can vary from model to model. Initial tests in the area of travel expense modeling, for example, showed that the data from SAP’s internal reporting system that is included in the model is available at least at the monthly level, and in some cases also at the daily level. However, since daily accuracy is not available for all model parameters, it quickly became clear that monthly-level data would be included in the model. With the resulting 12 data points per year and a maximum time horizon of 1 year to predict, it became apparent that one would need to provide a data history of at least 4 to 5 years in order to calculate valid predictive results (Harrell 2015, pp. 72–73). Special challenges always arise when structural breaks occur in the data history. A classic example in the financial area would be a change in the account structure that results from moving individual posting accounts between account groups and thus cost types. Ideally, such changes are then corrected by repostings in the historical data series. However, since such repostings are based on more or less accurate assumptions, such retroactive

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corrections can have a negative effect in some models. Another possible approach to deal with such structural breaks is to test alternative input parameters or, if no other way is possible, to build up a new history over time.

3.5 Areas of Application Today and in the Future 3.5.1 Central Simulations of Outcome Scenarios As described above, SAP has been using the new forecast concept for more than 2 years. The accuracy of the projection with a fluctuation range of ±1% of the annual actual result to the respective central forecast4 at operating result level speaks for the new concept. In addition to forecasting a realistic expected full-year result, SAP also uses the central forecast to show a possible range of target achievement. As mentioned above, two further scenarios are presented based on the realistic scenario: an optimistic scenario, which assumes a significant overachievement of the revenue target, and a conservative scenario, which simulates an underachievement of the revenue target. The results of the individual scenarios are each measured against the earnings expectations communicated to the capital market and thus additionally show whether and, if so, to what extent there is a need for short-term management. Outside the regular forecasting process, the possibilities of the central forecast with its integrated infrastructure based on predictive analytics are used to simulate certain financial scenarios. A practical use case for this is, for example, the validation of growth assumptions. In the past, such simulations were created quite laboriously outside the planning system; currency developments and the expected currency mix in the actual postings could only be roughly incorporated into such simulations. By linking the central forecast environment to real-time data and integrating the currency application mentioned above, simulations can be created and reported with currency accuracy in the system since the introduction of the central forecast with the corresponding infrastructure. This has advantages in terms of accuracy as well as in the efficiency and speed of creating such a scenario.

3.5.2 Integration of Predictive Components in Satellites While financial data from the satellites is accessible to all controllers, access to predictive model results of the P&L has so far mainly been concentrated on the CoE Central Forecast. This results from the fact that the revenue and cost forecasts were built to predict results as accurately as possible at Group level, as shown above.

 Measured by Mean absolute percentage error MAPE, including weighting according to the number

4

n

of open periods to be predicted MAPE = 1 / n × ∑ ( ISTi − FCi ) / ISTi . i =1

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However, since the results of predictive modeling are very promising, SAP’s next step was to examine where predictive components could be integrated into existing satellites in order to further increase the forecasting quality there. Initial approaches to this already exist in the personnel cost satellite. Here, there is great potential in the integration of a predictive model that allows the fluctuation probability of employees to be forecast with location accuracy. Based on various input factors such as length of service, assignment to specific organizational units and job function, a classification model (decision tree logic) was developed that predicts turnover rates for country clusters resulting from the model. Since fluctuation is a significant factor in the personnel planning of each location and is also frequently underestimated, SAP expects to be able to make personnel planning even more precise by integrating this model.

3.6 Conclusion Digital technologies and innovative process approaches in controlling can make a significant contribution to increasing the efficiency of financial forecasting as well as to significantly improving forecast quality. This is demonstrated by the best-practice approach of SAP SE. Traditional, bottom-up-based forecast approaches of the past proved to be insufficient there in terms of controlling relevance and simulation possibilities. In 2017, SAP’s financial forecast process was therefore radically changed. Instead of a consolidation forecast across all areas, a combined approach is now used. A central competence team prepares the forecast for the Group’s business development for the calendar year using standardized and statistical calculation methods (predictive analytics). In parallel and independently of this, the decentralized units provide a financial forecast for the current quarter, which they require for the operational management of their business. Standardized calculation models play a central role in both components of the financial forecast. With the help of so-called satellites, which combine business data from different sources in real time and apply defined calculation methods to project future values, essential revenue and cost elements of the forecast are calculated uniformly. In the central competence team, which specializes in forecasting the full-year results, predictive analytics methods and tools are also used. These not only make it possible to produce more accurate forecasts of the annual result, but also to simulate ad hoc scenarios for management. By integrating these digital components into the financial planning infrastructure, such simulations can be created directly in the system, based on real-time data, with little time required and with currency accuracy. Such a conversion requires not only process and organizational adjustments. In order to be successful in the long term with a digitization concept, competencies must be built up within controlling that go beyond the traditional understanding of the role of the business unit controller. Knowledge in the field of statistics, mathematical methods and a comprehensive understanding of the available data are crucial here. The controller becomes a so-called Citizen Data Scientist who, together with teams of experts, develops, tests and

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implements predictive models on the system side. In addition to the development of additional competencies in controlling itself, such a project requires a clear and long-term commitment from management to a consistent conversion to digital concepts. Based on the positive experience with predictive analytics methods in controlling to date, SAP is currently working on using predictive models not only in central competence teams. By integrating predictive components into automated tools such as the aforementioned satellites, the benefits of these tools should be available to all controllers in the company.

Company Profile

SAP SE Dietmar-Hopp-Allee 16 69190 Walldorf Germany Industry: Technology Turnover 2018: EUR 25 billion Number of employees 2018: 96,500 FTE SAP SE is the global market leader for enterprise software5 and a leading provider of analytics software for business intelligence. The company offers a broad product portfolio with solutions in the areas of on-premise, hybrid, and cloud. Around the globe, 77 percent of all transactional revenue passes through SAP systems. Globally, more than 437,000 customers in over 180 countries rely on SAP systems to optimize their business management and operational processes. To that end, more than 96,000 employees and more than 18,000 partner companies used SAP in 2018, generating revenue of approximately EUR 25 billion (non-IFRS) (SAP 2019).

References Harrell, F.E. 2015. Regression modeling strategies with applications to linear models, logistic and ordinal regression, and survival analysis. 2nd ed. Cham/Heidelberg/New York/Dordrecht/ London: Springer. Knöll, H.D., C. Schulz-Sacharow, and M. Zimpel. 2006. Unternehmensführung mit SAP BI: Die Grundlagen für eine erfolgreiche Umsetzung von Business Intelligence – Mit Vorgehensmodell und Fallbeispiel. Wiesbaden: Vieweg + Teubner.

 Enterprise software is computer software that is specifically designed to map and automate business processes. 5

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Larose, D.T., and D.C. Larose. 2015. Data mining and predictive analytics. 2nd ed. Hoboken: Wiley. SAP. 2019. SAP Corporate Factsheet. https://www.sap.com/corporate/de/company.html#pdf-­ asset=16b2e4dd-­b67c-­0010-­82c7-­eda71af511fa&page=1. Accessed on: 23.05.2019.

Simone Raschig  is head of the Central Forecast and Subsidiary Controlling Competence Center at SAP SE in Walldorf. After completing her doctorate at the University of Umeå, Sweden, she first did a management trainee program at Robert Bosch GmbH, Stuttgart, before joining SAP in 2007. There she worked in various controlling functions in Germany and abroad. Since 2016, she has been working in Group Controlling with a focus on continuously improving SAP’s financial forecast process using standardized, innovative methods. Mike Schulze  is Professor of Controlling, Accounting and Financial Management at CBS International Business School in Mainz and Senior Research Fellow at the Strascheg Institute for Innovation, Transformation and Entrepreneurship at EBS Universität für Wirtschaft und Recht in Oestrich-Winkel. His current research and consulting focuses include the topics of trends and developments in the CFO area, the digitalization of corporate management, and integrated corporate reporting. Prof. Schulze previously worked as an officer in the German Armed Forces and in the finance department of Ford-Werke GmbH in Cologne.

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From Success Assurance to Product Development: Data Analysis at Gebrüder Weiss in the Corporate Logistics Department Martin Selb

Abstract

Digitalization is changing the role of controlling in Corporate Logistics. There are new customer requirements for information availability. At the same time, the amount and granularity of available data is increasing. An increasingly complex and volatile environment requires flexible and forward-looking planning. In order to ensure adequate decision support and satisfy customer needs, new methods, tools and processes are required in controlling. Machine learning, simulations and network optimizations offer additional possibilities to answer business questions. This requires the use of specific software products. Traditional controlling processes also need to be adapted. In this context, new tasks arise for controlling, which is increasingly involved in digital product development.

4.1 Introduction to Controlling at Gebrüder Weiss Corporate Logistics is one of the business units of Gebrüder Weiss, alongside Land Transport and Air & Sea Freight. It deals with integrative, customer-specific logistics solutions which, in addition to warehouse logistics, also include other services such as transport or customs from the Gebrüder Weiss service portfolio. This ensures that the customer

M. Selb (*) Lauterach, Austria e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_4

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receives high-quality services from a single source, which are tailored to his needs. The tasks of Corporate Logistics are defined as follows: • • • •

Development of the logistics strategy on the basis of the ascertained customer needs. Research and development in the field of logistics, including the analysis of trends. Qualification of employees in the field of logistics. Creating the necessary structures and solutions to make the implementation of the strategy a success.

A separate department, Corporate Controlling, is responsible for group-wide financial, performance and risk controlling at Gebrüder Weiss. It is responsible for the management information system as well as training and qualification in the area of controlling instruments. The latter include cost accounting, forwarding and other reports, which are mapped in SAP-FI/CO, COPA and the Business Warehouse. The financial and personnel data for these are stored in a modern SAP HANA database or an archive database. Other activities include support for the Group’s decentralized controlling network and operational investment controlling. Like the other departments, Corporate Logistics is based on the reports and evaluations of Corporate Controlling. This is where the responsibility for the accuracy of financial and personnel data lies. There are also binding rules for all specialist areas for the mapping of profit, service and cost center structures, as well as other rules relating to cost accounting. The controlling of the business units support the branches with department-specific key figures and information in real time. The focus is on planning, managing and controlling operations in accordance with the goals of the corporate strategy. The requirements of the individual business units can differ greatly. The operation of a multi-customer warehouse, for example, requires a control system similar to that found in production plants. Industrial personnel, machinery (e.g. forklifts) and infrastructure such as shelving are required. The air and ocean freight business, on the other hand, differs in the flow of processes. Tangible assets, such as machinery, are required to a much lesser extent for service delivery. Freight space in aircraft or on ships is purchased from airlines or shipping companies. For this reason, each of the specialist areas has its own software (warehouse management or transport management systems) to control the operational value added.

4.2 Logistics Controlling at Corporate Logistics As already mentioned in the above section, there is a separation of controlling activities at Gebrüder Weiss. Corporate controlling deals with what is summarised in the literature as financial, profit and risk controlling (Lachnit and Müller 2012, p. 3). Firstly, this means the analysis of the financial situation of the company in terms of asset turnover, capital structure and securing liquidity. Secondly, it is about ensuring the operating result through sales and cost planning or also specifications for a meaningful cost and performance accounting

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(Lachnit and Müller 2012, p. 49ff.). The third component is risk controlling, which deals with the identification, assessment and monitoring of risks. These include, for example, changes in crude oil prices, exchange rates or economic fluctuations (Lachnit and Müller 2012, p. 223ff.). As a specialist area, Corporate Logistics, in contrast, deals with activities that are referred to as logistics controlling. The focus is on the optimal control of material and goods movements in the warehouses and beyond the supply chain or value chain. Controlling in Corporate Logistics has three areas of responsibility: • Support of the branch offices by providing specifications or instructions for cost accounting (incl. internal activity allocation) and standardized logistics reports in SAP-BW. • Internal evaluations for the management. • Customer-specific analyses as support for sales or customer implementations. Standard reporting is used for the planning, management and control of operations, based on Group specifications. The key figures include financial, personnel and production figures (e.g. orders processed per day). The aim is to provide the responsible branch, logistics and warehouse managers with an up-to-date and comprehensive picture of business development. The data is updated daily, although some key financial figures (e.g. completed sales) can only be evaluated on a monthly basis. Production data, in turn, is available in aggregated form on an order basis. Standard reporting is presented in SAP-BW. Currently, the outdated SAP Business Explorer is being replaced by the new Business Objects. All reports from SAP BW can be exported to Excel or called up directly in Excel using Analysis for Office. This ensures that users can carry out further evaluations independently. This is particularly important for line managers who want to integrate additional information from other systems (e.g. data from transport reports) into the reports. However, the key figures reported to the management of Gebrüder Weiss are always based on the data from SAP-BW. This ensures the maintenance of a central data truth. Production monitoring in logistics is mapped directly via the warehouse management systems. Gebrüder Weiss has the advantage of having standardised systems worldwide. The previous system will be replaced worldwide by WAMAS (software from SSI Schäfer IT Solutions GmbH in Austria) by 2022. The advantages of this constellation are obvious. A customer can reuse interfaces already implemented in one country to the Gebrüder Weiss warehouse management systems in other countries. For logistics controlling, this leads to a high level of standardisation of data and less effort in maintaining key figures. The monitoring of warehouse production is of great importance. Currently, daily control is primarily ensured by information from the system dialogues and additional reports from the warehouse management systems. Internal activity allocation is also part of logistics controlling. Logistics at Gebrüder Weiss is characterised by its high flexibility. Automated high-bay warehouses or automated small parts warehouses have hardly been able to meet the different customer

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requirements in a cost-efficient manner. This applies to the different types of pallets and boxes as well as storage requirements. However, there are also major differences in the processing of storage and retrieval orders with regard to quality controls and Value Added Services (VAS). The respective processes are defined together with the customers and mapped in the systems. In order to ensure a correct representation of the respective activities in the warehouse, the efforts for the customers or their orders must be recorded. These mappings are very complex depending on the client and the type of service. Using appropriate software, the services are mapped correctly and booked from the corresponding service to profit centers. Thanks to the globally standardized billing processes, a uniform data basis is available in almost real time. This in turn, linked with data from the warehouse management system or SAP-BW, serves as the basis for further evaluations. The traditional controlling techniques used in logistics include the following in the operational warehouse business among other things: • ABC-XYZ analyses on an article basis to optimise storage bin allocation • Warehouse heat maps to identify storage locations that are not efficiently occupied (e.g. in the picking area) • Value stream analyses to determine process optimization potentials • Activity-based costing to determine costs for internal activity allocation • Failure mode and effect analysis to optimize quality The aim of these techniques is to optimize daily warehouse operations. In addition to productivity, this also includes quality. For strategic decisions, such as site openings or expansions, there are further instruments. These include break-even and scenario analyses as well as investment calculations. Another important area of departmental controlling is customer and individual case-­ specific analyses. These include, for example, center-of-gravity calculations to evaluate the possible location of warehouses or logistics hubs. A prerequisite for such analyses is the possibility of locating site data, since software with map programs can only achieve exact results with GPS coordinates. The calculation of truck kilometers for different types of vehicles belongs to this category. In addition, the controlling techniques mentioned in excerpts above are also used for this type of analysis. The controlling function at Corporate Logistics has been developed accordingly. Reporting is largely standardized and based on reliable information. The business warehouse provides a central source of data, which is referred to as the single source of truth. Cost and performance accounting is well implemented thanks to internal activity allocation using proprietary software and the profit service center structure. Anomalies and variances are detected early and corrected by analyzing operating procedures. In the literature, this stage of development is referred to as future and action-oriented, planning and control-­oriented controlling (Lachnit and Müller 2012, p. 49ff.).

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4.3 Current Development in Logistics Controlling at Gebrüder Weiss 4.3.1 Change in the Framework Conditions The increasing digitalization of business processes has a strong impact on freight forwarders. There is an extensive daily exchange of documents or data with customers, shipping companies, freight forwarders and suppliers across the globe. This situation is not new for Gebrüder Weiss, but the requirements for information availability and data security are constantly increasing. The latter point is taken into account through consistent implementation of the General Data Protection Regulation and ISO 27001:2013 certification. The second point is the customer requirements regarding information availability. From the end customer business or the parcel service providers, customers are used to being continuously informed about the status of the shipment. Likewise, documents such as invoices or proofs of delivery are requested via corresponding platforms. Customers also increasingly have larger amounts of data regarding their historical shipments or warehouse movements. In most cases, these are available on a daily basis. In the case of project implementations or changes to the business, service providers are expected to deliver suggestions based on the available data. Another point is the general availability of data in the Group – regardless of the customer. The cost of data storage has fallen in recent years as capacities have increased. This allows for the additional collection of data on the shop floor, but also the use of external sources. Modern warehouse management systems record every scan point in the warehouse and additionally integrate data from other sources (e.g. forklift sensors). An example of the increased external use of data is navigation and other data from modes of transport such as ships or aircraft. Similarly, GPS tracking of trucks is increasing penetration in the market. The points mentioned above, such as customer requirements and data volumes, place new demands on logistics controlling. On the one hand, these present new challenges, but they also offer additional opportunities for proactive management and customer support. Cause-effect relationships are often only inadequately captured by traditional controlling methods. Thus, there are key figure systems, such as balanced scorecards. These take into account not only financial, but also personnel and customer-relevant key figures. The correlations between the different views are often explained in a supposedly logical manner, but are rarely measured using statistical methods. Likewise, cause-effect relationships are usually understood in linear terms, but this is rarely true in practice. Poor understanding of complex systems results in poor decisions. Methods from the field of data analysis, such as statistical analysis and machine learning, make it possible to better interpret relationships. Another point is the focus of analyses. Planning is the starting point of the controlling process. Without the definition of goals and the measures derived from them to achieve them, control or management by means of key figures is not possible (Rüegg-Stürm and

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Sander 2009, p. 26). In the past, it tended to be easier to extrapolate growth rates. In modern times, controlling business processes is increasingly complex. Economic growth in much of the world has slowed, while political conflicts, such as the trade war between the US and China, add to the uncertainty. Budgeting for volumes is becoming more difficult, as is the day-to-day management of logistics volumes. The increasing shortage of truck drivers and warehouse staff in parts of Europe requires planning to be as flexible as possible, which is difficult when environmental conditions are uncertain. Forecasting and machine learning are also ways to improve planning here – whether it’s for strategic or operational business management.

4.3.1.1 New Tools Traditional controlling tools are particularly suitable for cost unit or cost center accounting and for mapping (financial) key figures. For the evaluation of more complex data and forecasts, other tools are absolutely necessary. These include in the area of logistics controlling: • • • •

Self Service Business Intelligence Solutions Statistical software Simulation tools Software for geodata analysis and network optimization

It must be said that the methods and tools mentioned above are not new. Research on machine learning algorithms was already carried out in the 1960s (Buchanan 2006, p. 59). Statistical programs such as R have also been an integral part of university environments for many years. However, larger amounts of data, new customer requirements and more powerful computers are increasingly favouring their use. In the meantime, the knowledge is also being taught by universities – be it in special courses for data analysts or as part of IT or business studies training. In addition, there is an increasing number of continuing education opportunities, including online courses.

4.3.1.2 New Processes A central point, which must be considered, are new processes. In controlling, we usually talk about planning, management and control. More consulting- and data-driven projects require a different approach. First of all, the business issue must be clearly defined. While this is still relatively clear in the case of cost unit or process cost accounting, it becomes more difficult when it comes to the question of how a process can be optimized using machine learning. A second important point that needs to be considered at the beginning of a project is the available data. For example, the monthly sales of the last 3 years are just 36 data points. With this, certain trends and seasonal fluctuations can be derived, but due to the limited amount of data, it is difficult to train algorithms with it. For many business questions, however, the data is not available or not of sufficient quality. Converting systems, for example, can create new data structures. In many cases, the old and new data

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cannot be easily combined. Older warehouse management systems often only know stock transfers in addition to putaways and removals. Newer systems are much more precise and differentiate between various transport and picking movements. Switching to a new system means that new analyses are possible in the future. At the same time, however, it is difficult to reconcile the existing data from the old system with the new system. In some cases, data is not available at all or can only be obtained or collected with disproportionate additional effort. This means that the business question and data availability are highly interdependent. It is often necessary to redefine the business question based on the data basis. Once this step is done, the data must be prepared for modeling and later processing. For this purpose, they should be extracted from the systems and transformed. Quality issues need to be identified and any questions for further processing need to be clarified. This part is very time consuming. Together with the business question and the availability check, data preparation often requires 80% of the total project lead time. Data also plays an important role in classic performance controlling, and there too are always various problems. However, the data volumes are usually smaller and the questions clearly defined. The next step is data modeling. Here, network optimization, simulation or machine learning are used in logistics. The goal here is to first map an existing system as accurately as possible. Then, system parameters can be changed in order to arrive at better decisions. However, in this context it is important to evaluate the models carefully. Seemingly accurate algorithms tend to overfit again and again. Too many variables, some of them irrelevant, are taken into account. This leads to apparent accuracy. If the model is provided with new data that it does not yet know, the results are not very useful. The reverse case of underfitting is less common in logistics practice. If the model does not fit the training data well because important factors are missing, the problem is usually obvious. However, both problems should not be underestimated. Very accurate models often have the disadvantage of not being generalizable. Well-generalizable models, on the other hand, tend to be inaccurate. The right balance must be found here. In practice, it is not possible to find an implementable model in every case. In some cases, the data do not show any patterns or the correlations found cannot be transferred. In others, the added value of the insights gained is too low to pursue the project further. But even if a model is found that adds recognizable value, the question of implementation remains. Even the replication of more complex warehouse processes in corresponding warehouse management systems or controlling reports in SAP-FI/CO is a challenge. The implementation of complex algorithms in purchased software, on the other hand, is often not possible. On the one hand, many software providers do not yet offer suitable interfaces for integrating languages such as R or Python. This means that more complex modeling directly in the application is not possible. On the other hand, there are already providers who offer certain functions (e.g. machine learning) directly in their own software, but the modeling possibilities are limited. In some cases, it is therefore necessary to export the data from the application and perform the necessary modeling in a separate data analysis environment.

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The problem with such solutions is, on the one hand, data consistency. Software providers regularly introduce updates that can also affect the data tables. If controlling is not informed about such changes in good time, this can lead to considerable problems when processing the data in the analysis environment. On the other hand, the costs of such solutions should not be ignored. This concerns not least the additional costs for data storage – even if the productive database of the software manufacturer does not have to be copied completely. On the other hand, the data from the analysis environment must be imported back into the productive systems or shared in some other way. The procedure described above is derived from the Cross Industry Standard for Data Mining (CRISP-DM) process model (see Fig. 4.1). This standard has become widespread in practice, along with a number of very similar models. Currently, there are efforts to extend this standard. As mentioned earlier, there are some technical challenges to consider when extracting data and implementing algorithms. The CRISP-DM process standard lacks important steps – for example, the technical implementation after model evaluation (Huber et al. 2019, p. 403ff.). In practice, it is recommended to adapt the process models mentioned in the literature according to one’s own needs. For controlling, they provide a good basis for better structuring and thus also controlling data-intensive projects. In order to implement process standards such as CRISP-DM, an agile project management method such as Scrum is recommended. This comes from the software development area and is based on regular sprint meetings, which take place approximately every 2 to 4 weeks. In these meetings, the open items from the product backlog are specified (Dåderman and Rosander 2018, p. 16). The advantage over traditional project management with predefined phases is flexibility. Especially in the area of data analysis, goals cannot be defined a priori. Based on insights from the data, the business question may need to be asked anew. Scrum offers a workable framework to deal with these uncertainties.

Business Understanding

Data Understanding

Data Preparation Implementation

Data Modeling Evaluation

Fig. 4.1  CRISP-DM model. (Adapted from Huber et al. (2019, p. 404))

60-80% of the project time

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4.3.1.3 New Methods Other methods that are increasingly finding their way into logistics are network optimizations and simulations. Until now, the use of these tools has not been worthwhile in many cases. First, it was difficult to collect the necessary data and second, software tools were relatively expensive and complex. This situation is beginning to change. In addition, customer requests require more precise planning of logistics networks. Supply chain management is becoming an increasingly important issue for freight forwarders. Smaller companies also do not want to do without the possibilities of lead logistics providers. But they want to do so at a reasonable cost and without the lengthy development of their own software tools – as is often the case with larger customers. The methods will be discussed in more detail in the following.

4.3.2 Change in the Methods Used As already discussed, this also requires additional or different tools and process changes. However, these points are mostly project and company specific. Whether R or Python is used for machine learning depends on numerous factors – not least on the experts available. The methods, on the other hand, are of independent interest to logistics controlling in order to improve decision-making. In a sense, they form the basis for answering specific business questions.

4.3.2.1 Forecasts Forecasts are becoming increasingly important for freight forwarders. In some European countries, a shortage of commercial staff is already becoming apparent. There is a shortage of truck drivers as well as qualified warehouse staff. The relatively good economic situation of the past months and years further exacerbates this problem. A number of measures are needed to get a better grip on these challenges. One of these is better capacity planning. Forecasts are one method of doing this. There are different qualitative and quantitative approaches to this. A statistical method that does not require additional explanatory factors is time series analysis. This is useful, for example, to predict the order development of warehouse logistics for a certain period of time. Especially when there are a number of global warehouse locations with different customers, it is difficult to measure influencing factors. The global economy, country or industry-specific developments and customer projects all have an influence on the order figures for the coming months. To collect all these factors and integrate them into a model means a very large effort with a possibly questionable result. Time series analyses offer the possibility to create forecasts directly from historical sales volumes. The idea behind this is that the data already contains all the information needed to map future developments. Figure 4.2 shows the components of a time series. First, possible seasonal effects are examined. For example, the Christmas season leads to an increase in orders every year,

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Fig. 4.2  Time series decomposition using R: course, seasonality, trend, other

while the holiday season in summer is less labour-intensive. Seasonal effects can occur at different time periods (e.g., quarterly or monthly). However, they always occur at the same times each year. The second component is trends. These patterns are longer-term and show rising or falling trends. For example, after a successful launch, products tend to have an increasing sales trend, which often diminishes over time. Towards the end of the product life cycle, sales figures decline again. Trends do not necessarily have to be linear and can change direction again and again. In addition to these two components of the time series, there are also cyclical patterns. These usually have a duration of at least 2 years, but unlike seasonal patterns, they do not occur regularly. In addition, there is another component of time series which cannot be assigned to the above effects (Hyndman and Athanasopoulos 2018, p. 145ff.). For the prediction of time series, without the direct consideration of influencing factors, there are, for example, methods of exponential smoothing or autoregressive moving average models (ARIMA). These are useful in freight forwarding companies to estimate future order quantities. This applies to both the planning of transport and storage capacities. In order to optimally allocate trucks and personnel, knowledge of the future number of orders is crucial. Figure 4.3 shows the prediction of future orders. It is important to emphasize that the predictions are within a certain range of variation, which is called confidence interval. Hierarchies present a challenge in time series analyses. For example, there is the group level, different regions, countries and locations, which in turn may include external warehouses. The future development of orders or financial results at the upper levels is usually relatively accurate. For example, the development of customer orders at the group level is

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Fig. 4.3  Forecasting with Exponential Smoothing (ETS) using R

well predicted for the next few months. The situation is different for smaller customers who, for example, only have a requirement in a few weeks of the year. Here, volume developments can only be meaningfully estimated in rare cases, as there are too few data points. Regardless of this, the volume forecasts of the customers should correspond in total with those of the respective locations. In practice, however, this is almost never the case. Location forecasts almost always deviate from the individual customer forecasts. The question in this case is which view is better. This is not always easy to answer. One way is to aggregate the customer forecasts. This takes into account the maximum amount of information. As mentioned earlier, individual client forecasts are only useful for larger clients with corresponding regular business. Conversely, it is also difficult to derive individual customer forecasts from location developments. Research is currently intensively engaged in the search for new models that are able to generate hierarchical forecasts (Athanasopoulos et al. 2019, p. 28). Time series analyses are a useful method for logistics controlling to estimate future order or financial developments. They have both a strategic (e.g. for annual forecasts) and a direct operational benefit (e.g. order quantity forecasts for shift planning). Time series analyses have limitations in the case of highly volatile project business or customers with small order quantities. Here the prediction quality is low. For the future, the creation of dynamic models is recommended – especially for operational forecasts. Time series analyses should be supplemented with real-time data in order to achieve better results.

4.3.2.2 Machine Learning Machine learning deals with the creation of (predictive) models from data and is part of the research field of artificial intelligence. There are direct connections to other fields,

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such as applied statistics or pattern recognition. While machine learning also deals with robotics and machine vision, data mining focuses more on the analysis and modeling of data. However, the boundaries between the individual research areas are becoming increasingly blurred (Provost and Fawcett 2013, p. 39ff.). In this context, other important terms need to be clarified. Data Science deals with the principles, processes and techniques to answer questions based on data. The goal is to improve decision making, especially for questions of business relevance. In this context, methods from the field of machine learning play an important role. However, Data Science is not synonymous with the term Big Data. Technologies to process large amounts of data (e.g. Hadoop or MongoDB) are becoming increasingly important. However, data processing is in most cases an upstream process. Only after the appropriate preparation of the data do the algorithms find application. However, the distinction between Data Science and data preparation with Big Data technologies is becoming increasingly blurred. In many cases, models are implemented directly with new data processing technologies (Provost and Fawcett 2013, p. 4ff.). Machine learning or the algorithms from this research field are divided into two basic areas. On the one hand, there is the so-called supervised learning. This involves the prediction of a certain value or class based on dependent variables (see Fig. 4.4). This prediction does not necessarily have to concern the future. For example, algorithms are used to label calculations that have already been issued with a category. While in the English-language

Fig. 4.4  Decision tree using R

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literature time series analysis is usually referred to as forecasting, machine learning is often referred to as predictive. The second area is that of unsupervised learning. This involves the clustering of data sets to gain new insights. An example of this is the clustering of customers based on certain factors (Lantz 2013, p. 19 ff.). In practice, logistics offers many possible applications for machine learning. A typical use case is the prediction of the workload of warehouse locations based on orders. In some cases, the number of orders is already well estimated using time series analyses, but this information is not sufficient for effective resource planning. This requires knowledge of other influencing factors, such as the number of different items or their weight. By means of certain algorithms, such as regressions, the workload per order is estimated. Especially in multi-customer warehouses this information is central for decision support in order to allocate personnel and equipment like forklifts accordingly. Related approaches, such as the prediction of lead times for production orders, are already known from the literature (Ötztürk et al. 2006, p. 683ff.). In the area of supply chain management, the prediction of the estimated time of arrival (ETA) of goods is playing an increasingly important role. On the one hand, this information is increasingly demanded by business customers. Shipment status details are not a new requirement for freight forwarders, but they do not automatically contain predictions regarding the time of arrival. On the other hand, this information is also needed for internal planning and optimization of value chains. A concrete example for such forecasts are the estimated arrival times of container ships. It must be said that today these already provide relatively complete data on ship type, speed and other parameters in a uniform manner via an automatic system (Automatic Identification System – AIS). Using this information and weather data, the time of arrival is estimated by means of various algorithms. The results are significantly better than previous estimates by ship agents (Parolas 2016, p.  72  f.). Similar results have also been obtained in predicting the arrival time of commercial flights. The accuracy of the results concerns the estimates of Eurocontrol (Ayhan et  al. 2018, p. 40 f.).

4.3.2.3 Network Optimization Network optimization (see Fig. 4.5) is already an important component of supply chain management. The topic of location planning is particularly central to this. This is part of operations research (OR). It deals not only with the question of the optimal location of sites, but also their number and role in the network. Location planning thus has a long-­ term strategic impact on the orientation of companies, as it influences investment decisions regarding warehouse and production capacities as well as the flow of material flows. Tactical and operational factors have a major influence in site planning. This concerns the choice of transport type as well as inventory strategies (Melo et al. 2009, p. 401ff.). In practice, simpler models have often dominated location planning to date. The simplest method to find a possible hub, warehouse or production location is a center-of-­gravity calculation. This usually optimizes the location of the site according to airline kilometers and based on shipment weights. Equally common is customer allocation to warehouse

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Fig. 4.5  Network optimization using R

locations or hubs based on capacity and travel time constraints. Together with the classic traveling salesman problem, these methods form a basic building block of optimization possibilities. Although these methods provide initial implications for location planning, the statements are often too imprecise to be used to derive strategic recommendations for action. The number of optimal warehouse locations, for example, is described as the optimum of the intersection of a transport and a storage cost curve. The more warehouses and hubs there are in the vicinity of recipients, the lower the transport costs. In return, however, storage and handling costs usually rise sharply. A central warehouse, in turn, can greatly increase transportation costs and may violate time restrictions. In addition, the question of the appropriate article mix also plays a role. Recipients often have to be supplied with several different products. These can originate from one or more production sites, which can assume a warehouse function. Between factories and recipients there may be several layers of handling points and warehouses. The calculation of the optimal flows of goods with the corresponding transports (e.g. general cargo or direct deliveries) offers corresponding optimization potential. Route optimizations (e.g. due to milk run systems) must not be disregarded. A challenge with many tools available in practice is the temporal consideration of the optimization. Often this is only possible in relation to a point in time. The possible factor combinations can only be simulated in part. In addition, it is usually difficult to reconcile tactical problems, such as route planning, with strategic network design. Regardless of algorithms and tools, however, there is another challenge in practice that should not be underestimated. Companies, whether shippers or freight forwarders, can

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only optimize their own network. However, not everything that is optimal for one player must lead to a reduction in costs in the value chain. For example, a shipper may come to the conclusion, based on network optimization, that a carrier should deliver directly to certain recipients in the future in order to save costs. The service provider, on the other hand, accepts a supposed detour via a transhipment warehouse in order to better utilise his own trucks. Direct transport may reduce handling costs, but it increases the cost of transport. Since the shipper has no insight into the carrier’s network, he cannot calculate an optimum for the entire value chain.

4.3.2.4 Simulation Simulations of (warehouse) logistics processes are not new, but additional data from warehouse management systems offer better application possibilities for process optimization. Until now, one difficulty in mapping real processes has been the information basis. Today, warehouse logistics, even in automated warehouses, still involves certain manual process steps in many cases. In the past, the comprehensive recording of these steps was often dispensed with. One reason for this was the limitations of older warehouse management systems. Value added services, such as the manual repackaging of articles, could not be mapped, or only at great expense. Another point is the large volume of data that accumulates when every process step has to be recorded continuously. For these reasons, the focus in the past was more on billing or customer-relevant information (e.g. number of orders and articles). The limited database and the relatively complex manual processes in warehouses have limited the use of simulations to date. This circumstance is increasingly changing and therefore makes this method interesting for optimizations. In logistics, event-oriented simulations are not exclusively, but particularly, suitable. As the name suggests, a series of events is processed in this method. The input factors, such as the number of orders, are not static. The models allow you to simulate distributions and thus recreate real-world conditions accordingly. The objective of simulations is to reduce waiting time in order to lower costs. Methods such as Just-in-Time or Kanban can optimize processes. However, in order to quantify cost savings, simulations are necessary in many cases (Beaverstock et al. 2017, p. 13ff.). Simulations, as shown in Fig. 4.6, have a decisive advantage over the methods described above, such as time series analysis. They are not necessarily dependent on historical data. It is therefore possible to run through completely new process variants. This in turn makes simulations a basis for time series analyses or machine learning. For example, if warehouse processes are changed due to new customer requirements, previous process data can no longer be used for training algorithms. Here, simulations help to create a new database. Conversely, it is also possible to test the results of machine learning in theory before algorithms are finally implemented. This can be, for example, an improved method of route finding in the warehouse, which is simulated again before release in order to identify possible problems such as blocked aisles.

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Fig. 4.6  Warehouse simulation using FlexSim

However, simulations can also be combined with optimization algorithms (Beaverstock et al. 2017, p. 315ff.). This approach is also of interest for certain use cases in the supply chain. The optimization algorithms already presented are able to allocate hundreds of customers to multiple warehouse locations or hubs based on capacity constraints, travel times, and other factors. However, as described above, a time frame must be defined. This can be an annual, monthly, or peak day view. Simulations, on the other hand, have the advantage of representing the development of systems over time. It is possible to pause a model at any time and look at the situation. Even outside of peak days, it may be that certain customer or order combinations bring a warehouse location to its capacity limits. Conversely, using simulations, it is very time-consuming to look at hundreds of customers and several warehouses or hubs over a longer period of time and to optimize them manually. For such use cases, combinations of simulation and optimization are the best solution.

4.4 The Path to Product Development The methods described above enable logistics controlling to answer new business questions. This changes the role of the controller to that of a data analyst and consultant. Previous activities in the area, such as standardized reports or evaluations for management, remain. However, the increased automation of reporting reduces the time required for this. This frees up resources that can be used for other methods and their value contribution.

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In this context, so-called business intelligence tools should also be mentioned. The term business intelligence itself is defined very generally in the literature. It refers to the collection and, above all, processing of data in order to gain insights (Gurjar and Rathore 2013, p. 82). In practice, this refers to the use of special software, which increasingly also allows the user in the department to analyze larger amounts of data from various systems independently, without the help of IT. In addition, these tools also have the ability to create interactive dashboards and share these views afterwards. This reduces the burden on IT, but also reduces the turnaround time for report creation (Lennerholt et al. 2018, p. 5061). Business intelligence tools play an important role in the interactive presentation of results. The tasks of the controller are thus increasingly moving in the direction of digital product development. For customers, interactive dashboards and network displays or optimizations offer an attractive additional service. In order to offer these, it is first important to understand the customer requirements. Then, the corresponding data sources must be sifted through and data must be prepared. After that, modelling is necessary, which has to be processed to the end. This process includes a variety of topics – such as design issues, IT infrastructure or algorithms. These issues cannot all be solved by the controller or data analyst alone. Intensive cooperation with the sales department, the marketing department, the central controlling department or the IT department is absolutely necessary. Controlling is also more involved in making suggestions for new technologies and their use. For example, more and more manufacturers are including algorithms in their business intelligence tools, which make it possible to ask questions about data in natural language or to obtain analyses of changes from the previous month at the touch of a button. Such possibilities in turn form a basis for the provision of additional services.

4.5 Conclusion Advancing digitalization has greatly changed the role of controlling in the Corporate Logistics department. The tasks of the original logistics controlling, such as cost and activity accounting, have remained. However, these could be more standardized and automated through better tools and methods. Excel or Access have largely become obsolete as data sources. At the same time, larger volumes of data and a more extensive variety of data are available today. The cost of storage space has fallen, while the performance of modern database systems, such as SAP HANA, has risen sharply. This opens up new possibilities for evaluating information, but these in turn require new methods. Network optimizations and simulations have been known in the logistics sector for some time, but have only been used selectively. The effort to extract the necessary data was great and not in all cases was the information even available. Thanks to better warehouse management and transport management systems, this situation has changed. The tools to make the existing data usable are also cheaper and easier to use. Relatively new in logistics at Gebrüder Weiss are time series analyses and also machine learning. Both methods are suitable for recognising patterns in data sets, which in turn improve decision-making. In this way, future orders and their workload are better

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recognised, which improves resource allocation. Optimizing the formation of picking orders is another use case. Here, the proliferation of open source languages, such as R or Python including the corresponding algorithms, plays a crucial role. The knowledge and technologies have become more accessible. All this means that logistics controlling is also increasingly involved in product development. Network optimisations and data analyses are playing an increasingly important role in customer projects at every stage. Competent support in this area by offering specific services improves decision-making for the customer and Gebrüder Weiss. Even away from the project business, key figures, map displays and analyses are of great interest to clients. Clients need clear dashboards and reports to make decisions. Due to the increasingly complex networked world, up-to-date information is more crucial than ever for business success. The stronger interlinking of controlling and data analysis in the Corporate Logistics department has proven to be a success. However, the foundations for this are not exclusively new methods and tools. First, appropriately qualified employees must be found and made familiar with the internal data. Machine learning or network optimizations differ significantly from classic cost unit accounting. Furthermore, it requires a clear make or buy strategy. It is unrealistic that all methods and technologies are mastered in depth by a few people. Close cooperation with innovation management, IT, other departments and external consultants is the key to success. cc

Recommendations for Action   

• Automation/Standardization The existing reporting and other routine activities should be automated and standardized if possible. Outsourcing to a shared service center is also possible. If controlling spends most of its time on activity-based costing or standard reports because they require a lot of manual effort, valuable resources are blocked. It is also important to ensure the quality of the data by means of active data management. • Accepting changed framework conditions Digitalization goes hand in hand with new (customer) needs. At the same time, larger amounts of data and more diverse data are available. New methods, tools and processes are required to make use of these opportunities. This change must be supported by managers. For example, the development of additional personnel capacities or the acquisition of further software may be necessary. Controlling must also be taken into account in the digitization strategy. • Understanding controlling as an active part of digital product development Decision-making using data is becoming increasingly important in companies. Methods such as machine learning offer new possibilities to make predictions. This can generate customer benefits both internally and externally. Controlling must therefore not close its mind to new tasks such as digital product development. Especially in this area, managers are relieved.

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Company Profile

Gebrüder Weiss GmbH Federal road 110 6923 Lauterach Austria Industry: Transport and Logistics Turnover 2021: EUR 2.5 billion Number of employees 2021: 8000 employees With around 8000 employees, 180 company-owned locations and a turnover of 2.5 billion euros in 2021 (preliminary), Gebrüder Weiss ranks among the leading transport and logistics companies in Europe. In addition to its core business of overland transport, air & sea freight and logistics, the company also operates a number of highly specialized industry solutions and subsidiaries under the umbrella of Gebrüder Weiss Holding AG, based in Lauterach (Vorarlberg, Austria). This includes logistics consultancy x|vise, tectraxx (industry specialist for hi-tech businesses), dicall (telephone service, consulting and telemarketing), Railcargo (railway transport) and the Gebrüder Weiss parcel service GWP, a shareholder in the Austrian company DPD. This bundling of services allows us to respond to customer needs quickly and flexibly.

References Athanasopoulos, G., P.  Gamakumara, A.  Panagiotelis, R.J.  Hyndman, and M.  Affan. 2019. Hierarchical forecasting (No. 2/19). Monash University, Department of Econometrics and Business Statistics. Ayhan, S., P. Costas, and H. Samet. 2018, July. Predicting estimated time of arrival for commercial flights. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 33–42. ACM. Beaverstock, M., E. Greenwood, and W. Nordgren. 2017. Applied simulation: Modeling and analysis using FlexSim, Orem, FlexSim Software Products. Buchanan, G.B. 2006. A (very) brief history of artifical intelligence. AI Magazine 26 (4): 53–60. Dåderman, A., and S. Rosander. 2018. Evaluating frameworks for implementing machine learning in signal processing: A comparative study of CRISP-DM, SEMMA and KDD. Gurjar, Y.S., and V.S.  Rathore. 2013. Cloud business intelligence  – Is what business need today. International Journal of Recent Technology and Engineering 1 (6): 81–86. Huber, S., H. Wiemer, D. Schneider, and S. Ihlenfeldt. 2019. DMME: Data mining methodology for engineering applications – A holistic extension to the CRISP-DM model. Procedia CIRP 79: 403–408. Hyndman, R.J., and G. Athanasopoulos. 2018. Forecasting: Principles and practice. OTexts. Lachnit, L., and S. Müller. 2012. Unternehmenscontrolling: Managementunterstützung bei Erfolgs, Finanz-, Risiko- und Erfolgspotentialsteuerung. Wiesbaden: Springler Gabler.

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Lantz, B. 2013. Machine learning with R. Packt Publishing. Lennerholt, C., J. van Laere, and E. Söderström. 2018. Implementation challenges of self service business intelligence: A literature review. In 51st Hawaii International Conference on System Sciences, Hilton Waikoloa Village, Hawaii, USA, January 3–6, 2018 (Volume 51, S. 5055–5063). IEEE Computer Society. Melo, M.T., S. Nickel, and F. Saldanha-Da-Gama. 2009. Facility location and supply chain management – A review. European Journal of Operational Research 196 (2): 401–412. Öztürk, A., S. Kayalıgil, and N.E. Özdemirel. 2006. Manufacturing lead time estimation using data mining. European Journal of Operational Research 173 (2): 683–700. Parolas, I. 2016. ETA prediction for containerships at the Port of Rotterdam using Machine Learning Techniques. Provost, F., and T. Fawcett. 2013. Data science for business: What you need to know about data mining and data-analytic thinking. Sebastopol: O’Reilly Media. Rüegg-Stürm, J., and S. Sander. 2009. Controlling für Manager: Was nicht-Controller wissen müssen. Frankfurt a. M: Campus.

Martin Selb  is responsible for data analytics and controlling in the Corporate Logistics department at Gebrüder Weiss GmbH. His activities include cost accounting, spatial statistics, machine learning, network optimisation and simulations. He also supports the development of digital services and tools.

5

The Digital Transformation of Reporting at Swiss Radio and Television (SRF) The Way from Excel to Online Reporting Kevin Wettstein and Renato Caderas

Abstract

This article highlights the introduction of online reporting for cost managers at Swiss Radio and Television SRF. The focus is on the lessons learned during the implementation of far-reaching digitalization projects. A successful digital transformation in the area of financial control requires intensive cultural work both among those responsible for costs and among the controllers themselves.

5.1 Introduction The media markets have been in a state of upheaval since the 1990s. For more than 20  years, media-specific management literature has been pointing out the effects of digitalization. Digitization, i.e. the transformation of analog carrier media into digital information, is steadily increasing. For over 10 years now, the storage capacity of digital media has exceeded that of analogue media many times over (Schneider 2013, p. 10ff.). Digitization creates the technical basis for media content to be stored, processed and distributed on a uniform basis, regardless of the medium. Production networks are no longer spatially limited, as information can be distributed and processed without loss of quality (Schüler 2015, p.  23ff.). This leads to significantly lower costs for production,

K. Wettstein (*) • R. Caderas Zürich, Switzerland e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_5

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distribution and multiple use. In addition, digital information dissemination eliminates the scarcity of transmission channels. These factors lower the barriers to market entry and thus promote competition within the media industry (Schächter 2003, p. 271ff.). This gives rise to new platforms and usage options (e.g. Netflix), which put pressure on classic broadcasters such as SRG, including with regard to the advertising market. SRF is caught between the traditional distribution channels, which reach the majority of its existing target audience, and the new business of digital information dissemination, which also requires new production processes. This digital transformation ties up funds on the one hand and human resources on the other. Successful implementation with unchanged resources places increased demands on financial management. In addition, the demands of stakeholders regarding cost transparency in the production of content have increased, particularly in the case of public service media companies. In recent years, for example, many German-language media companies have published their broadcasting costs, including Zweites Deutsches Fernsehen (ZDF), Rundfunk Berlin-­ Brandenburg (rbb) and also the Swiss Radio and Television Company (SRG), which is organized under private law. Cost pressure is also increasing for public service media companies. This is reflected in the necessary cost-cutting measures of various media companies. Financial management and control are taking on an increasingly important role. In an industry characterized by change and complexity, such as the media industry, there is a risk of inconsistent action. This results in a need for coordination between individual plans, strategies and initiatives as well as ongoing monitoring of the achievement of objectives. In addition to the need for coordination, there is also a need for information, which is necessary for decision-making and includes aspects of profitability or use of the offer. These two functions of coordination are performed by controlling (Becker and Geisler 2006, p. 904ff.).

5.2 Initial Situation In 2011, the two companies Schweizer Radio DRS (SR DRS) and Schweizer Fernsehen (SF) were merged to form a single company, Schweizer Radio und Fernsehen (SRF). Up to this point, the management tools had developed independently of each other and were geared towards the different needs of radio and television. This created significant differences in these instruments. After the merger, these differences in the instruments led to information asymmetries between the individual members of management, and also posed a challenge in the case of deputies or personnel changes. In addition, SRF’s controlling organization was decentralized until 2016. The business controllers reported directly to the heads of the departments; each controller geared reporting to his or her own needs. Harmonization of the instruments was therefore not a priority. Overarching controlling processes and the interface to the Group Controlling department of the Swiss radio and television company were handled by the central Controlling department.

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SRF currently uses a central ERP system (SAP) with a data warehouse based on it. The handling of authorizations is very restrictive. Before the introduction of online reporting, there was no possibility for cost managers to obtain data directly from the systems. All evaluations were provided by the responsible business controllers. Until the introduction of online reporting, SRF’s reporting landscape comprised three levels, which were only defined in terms of the recipient, but were otherwise not standardized. The individual reports at the department level were mostly based on data from the ERP, which were summarized in Excel. Both in terms of layout and notation, the reports differed greatly between the departments (see Fig. 5.1). The reporting for the attention of the SRF management was created in PowerPoint. The data basis was both the ERP and individual reports from the data warehouse. It was standardized in terms of layout and notation, but not in terms of content and structure (see Fig. 5.2). Group reporting, on the other hand, was fully standardized and based on a collection of detailed reports from the data warehouse. However, the high level of detail mostly resulted in a description of the deviations instead of a derivation of the background (cf. Fig. 5.3). In summary, the SRF reporting landscape showed potential for optimisation in the following points: • Standardization of layouts and notations across departments and levels • Provision of financial management information at the appropriate level • Shifting resources from data preparation to business partnering in controlling

5.3 Objective The reporting sub-project was carried out as part of a larger financial project to improve the financial management and control of the SRG. Its objective was, in particular, to focus more strongly on the needs of middle management with regard to timely management information in the form of self-service. The comprehensibility of financial information was to be increased and consistency maintained or ensured between the financial information of lower management and the Board of Directors. In order to achieve this, the SRG reporting landscape had to be restructured. The new structure was modular, so that the modules could be individually combined into a report to meet different needs (Menninger and Stäheli 2014).

5.3.1 Standardisation: Notation, Content, Platform A uniform visualization concept had to be developed for the presentation of all report content. This concept was to be applied across all management levels and in all SRG

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Fig. 5.1  Individual reporting SRF

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Fig. 5.2  Reporting SRF Executive Board

Fig. 5.3  SRF Group reporting

enterprise units – i.e. also in the other language regions. This was intended to increase comprehension when reading and interpreting the reports as well as the recognition value. In addition, uniform notations should be used in order to minimise any misunderstandings (cf. Fig. 5.4).

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Reporting Notation Application of International Business Communication Standards (IBCS) Notation

Deviations Absolute deviation

Abbreviation

Relative deviation

Abbreviation*

Dark grey

FC-PL

ΔPL

(FC-PL)/PL

ΔPL%

PY

Medium grey/light grey

AC-BU

ΔBU

(AC-BU)/BU

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Forecast

FC

Hatched/half full

FC-BU

ΔBU

(FC-BU)/BU

ΔBU%

Budget

BU

Dark grey bordered

AC-PY

ΔPY

(AC-PY)/PY

ΔPY%

Plan*

PL

Light grey bordered

FC-PY

ΔPY

(FC-PY)/PY

ΔPY%

Credi*

CR

Dark grey bordered

Obligo

OB

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Remaining “Remaining”

RE

Dark grey

Scenario

Abbrevuna

IST “Actual”

AC

Previous Year

Sample

Effect on period target Positive effect (green)

Negative effect (red)

Fig. 5.4  Notation concept reporting

On the technical level, the framework condition was the retention of the central data source of the data warehouse, not least to maintain the principle of a single version of the truth.

5.3.2 Provision of Information: Self Service The SRF Executive Board, the SRG Executive Board and the Board of Directors received quarterly financial management information in the form of a forecast. The new reporting should maintain this cycle. In order to satisfy the need for financial information from middle management, monthly steering information based on actual values should be made available – online in the form of self-service. This should reduce information asymmetries between individual managers and save resources on the controlling side by eliminating the preparation of individualized evaluations.

5.3.3 Transparency: Need to Know With regard to the content of the respective reporting modules, the approach followed was to only present content that can also be influenced at the respective level. The aim was to provide decision-makers with the information they need for their sphere of influence and not to flood them with information. The increase of the financial awareness of the management should be increased specifically through transparency. There should be clarity about financial developments in day-­ to-­day business at all times, so that developments and forecasts can be critically scrutinized by management. To achieve this, all cost managers should be able to view the results of the entire department.

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5.4 Solution Approach for the Introduction of the New Reporting Landscape The introduction of the new reporting landscape at SRG and SRF can be divided into five phases: • • • • •

Phase 1. clustering modules Phase 2: Creation of reporting guidelines Phase 3: Definition of module content Phase 4: Development of module content Phase 5: Training of recipients

5.4.1 Clustering Modules SRG reporting follows the principle of recipient orientation. In order to ensure this principle, the first step was to create a clustering of report recipients and the corresponding report types, whereby the objective and form of dissemination were defined (cf. Fig. 5.5). The online reporting is primarily aimed at middle management. The content is structured according to topic-based modules and is presented in different views through guided navigation. Online reporting follows the pull approach and can be used in self-service.

Fig. 5.5  SRG reporting landscape

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Management reporting builds on online reporting in terms of content and is aimed at senior management. The reporting retains its classic form and is prepared and commented on by Controlling on a quarterly basis. Analysis reporting is available for controlling. Analysis reporting is used by controllers as an instrument for preparing management reporting or for answering ad hoc queries. Although reporting to stakeholders is included in the reporting landscape, it does not undergo any changes for reasons of consistency. The clusters by report recipient and type were supplemented by the dimension of cross-­ cutting areas in order to also cover the information needs of technical experts who do not hold a line management position but a technical management position.

5.4.2 Reporting Guideline The reporting guideline contains fundamental decisions on layout, notation, structure of the reporting landscape and reporting systems. The creation of the reporting guideline was deliberately chosen before the definition of the specific content, so that the subsequent steps could be fully focused on the content and no discussions of principles hindered the development process. The first point of the reporting guideline is the clustering of the modules and the basic decisions on distribution derived from this. The layout and notation is based on the International Business Communication Standard – IBCS. The background to this choice is that this concept is suitable for both dashboards and print reports and focuses the presentation on the essentials, the content. In addition, some corporate units have already applied initial IBCS approaches in the past. The reporting guideline also includes the specifications of the graphical elements in terms of color, arrangement, size, and position, as well as uniform definitions of terms and abbreviations for key figures. The reporting guideline also sets out principles for technical implementation with regard to the system architecture. These principles serve in particular to ensure data integrity and the use of the existing data warehouse. Detailed technical documentation was prepared by IT during the implementation phase.

5.4.3 Definition of Module Contents Online reporting as a middle management tool is an integral part of SRF’s reporting landscape. In order to meet the goal of informing middle management about key financial figures/developments, a link to management reporting must be ensured. This is so that middle management can answer detailed questions that arise from the management reporting of top management. If this is not possible with self-service reporting, the more detailed analysis reporting of controlling provides insights. The outlined reporting landscape is thus

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characterized by a pyramidal structure that allows a drill-down across the different levels and platforms. In order to ensure this drill-down or pyramidal structure, close coordination between the platforms was required as part of the definition of the module content. To achieve this, a combination of top-down and bottom-up approach was followed. The needs of top management were developed in workshops with the CFOs. The focus was on control variables and the dimensions required to interpret these variables. In order to cover the information needs of middle management as comprehensively as possible, an inventory of the existing analysis tools (mostly excel) of the business controllers was made in parallel. These instruments covered the majority of the financial control instruments of middle management. By combining the top-down and bottom-up requirements, the minimum content requirements for online reporting were created. The development of the content in terms of structure, functionality and arrangement was then carried out by a national SRG working group, with the results being periodically mirrored with representatives of the stakeholders (pilot users). The result was a reporting platform that contains five central elements: • • • • •

Costs Services Staff Time credits Projects/Investments

There are different perspectives within the five central elements of online reporting. In the case of costs, for example, these are a classic view by cost element structure or by cost unit. With regard to the self-service functionality, all reports can be drilled down to obtain

Fig. 5.6  Online reporting

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additional information, but not down to the line item level (see Fig. 5.6). Through the combination of elements, perspectives and functions, middle management ultimately receives broadly the same information as previously provided by the Business Controller (Excel lists).

5.4.4 Development of Module Contents One of the central challenges in the project was the new systems for visualization. The IT department of the SRG had no experience with the software used up to this point. The know-how build-up of the new technologies took place synchronously with the development – in cooperation between internal developers and external consultants. This resulted in the problem that the technical feasibility of requirements could in part only be checked during development. To counteract possible delays, an agile approach was chosen for the realization phase. During the three-month implementation phase of the online reporting, project managers and developers held weekly coordination meetings in which the development goals for the coming week were defined and implementation decisions were made for identified technical problems. The overall implementation was divided into sprints according to the individual topics of the online reporting, with individual functions that occurred more than once being handled as separate modules. The results from the sprints and necessary adjustments in the technical implementation were discussed in the national working group and then tested and finally accepted by the respective representatives. In parallel, the module contents were periodically mirrored with the pilot users and their feedback was incorporated into the further development.

5.4.5 Training The introduction of online reporting should be practice-oriented. To ensure this, a train-­ the-­trainer approach was chosen. The project members received technical training from the developers in order to be able to cover as many questions as possible regarding the use of online reporting. The project members then conducted training sessions with the business controllers. In the course of this, concrete questions about business cases and scenarios were dealt with, which the business controllers had brought in as use cases. In the final step, the managers received individual training from their business controllers. The managers’ specific questions were addressed and answered using online reporting. If the respective managers had previously received specific analyses, the training showed how the same information could be obtained using new self-service tools. Special courses for managers and cost managers deal with business management issues and possible applications of online reporting. In these business management courses, concrete examples are used to show how to read online reporting and how to interpret developments in the key figures.

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5.5 Learnings 5.5.1 Cultural Change on the Part of Users and Controllers With the introduction of online reporting, a central cultural change took place in the area of standard reports. Instead of sending out relevant key figures, users must now actively obtain them in self-service. Whereas previously the key figures were prepared by the controller and in some cases already provided with initial findings, users are now expected to make these interpretations themselves. This requires a fundamentally higher interest in financial information. In addition, more pronounced business knowledge is required in order to draw the right conclusions. Users are informed after the month-end closing that up-to-date information is available. However, the cultural change is by no means only evident on the side of the users; this change should not be underestimated among controllers either. Previously, controllers were able to control the flow of information and the focus placed on it through the targeted preparation of data. In self-service, this is no longer the case, and those responsible for costs must assume their financial responsibility to a greater extent. On the controlling side, there is a shift from preparation to interpretation and from information provision to discussion of financial developments. The strengthening of financial competencies and independence in obtaining information also increases the demands on the business partner on the controlling side. This also requires a certain degree of consistency. Evaluations and analyses provided in the past must not continue to be prepared so that a successful transfer to self-service can take place.

5.5.2 Enabling Managers and cost managers have a wide range of tasks, competencies and responsibilities, with financial responsibility representing only a small part of the whole. It is therefore all the more important that the respective managers are empowered to use online reporting. They must be able to obtain the essential information in a short time and interpret it accordingly. The prerequisite for this is that the technical functions are trained and business contexts are shown. Approximately 1 year after the introduction of online reporting, a user survey was conducted to determine the frequency of use and the obstacles to use. The result of the user survey shows a sobering picture. Only 30% of the managers surveyed use online reporting often or at least regularly (at least once per quarter). Half of the users call up online reporting only once to three times a year and around 20% of those surveyed have never used it (cf. Fig. 5.7). One of the main reasons for non-use is a lack of understanding or the fact that online reporting is perceived as too complicated. Within the framework of business management courses, comprehension is particularly promoted when reading online reporting. The interpretation of the results is also trained using practical examples and success factors. Based on the feedback from the user survey, additional coaching sessions are offered to further consolidate the use of the technical functions.

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How often do you use online reporting? Answered: 59

Skipped: 0 Never (0 views p.a.) Rarely (1-3 calls p.a.) Regular (4-8 calls p.a. ) Often ( Passenger cars 24 590 27 575

> Passenger cars 26 737 29 785

> Passenger cars 21 673 21 726

> Passenger cars 18 148 17 610

June

July

August

September > Passenger cars 20 993 19 604

> Passenger cars 20 553 21 089

May

October

November > Passenger cars 20 598 21 973

December

24 769 26 077

31 063 29 562

26 911 26 929

24 729 29 078

26 108 23 672

2010

2011

24 209 28 470 30 590

22 033 25 142 29 462

22 894 24 736 27 729

21 103 22 349 27 464

17 940 20 124 22 643

24 006 25 659 26 280

26 826 31 960 28 862

23 042 25 112 30 324

24 997 26 055 28 496

23 769 28 810 31 382

17 718 18 899 23 286

17 941 19 281 21 437

2009

28 758

26 869

26 631

21 317

22 205

24 487

40 947

30 111

29 373

33 964

24 461

24 922

2012

2014

29 717 32 382

24 944 23 695

26 537 26 401

22 706 21 821

21 080 19 856

26 281 27 610

29 140 28 012

27 468 26 091

28 476 27 905

29 220 26 879

22 713 22 454

21 872 20 977

2013

32 133

26 191

25 447

24 273

23 685

30 175

33 492

27 925

29 339

32 266

23 088

19 129

2015

35 008

26 270

23 354

26 180

21 945

26 189

31 464

26 177

29 029

29 562

23 192

20 961

2016

30 796

26 587

24 614

24 606

23 111

24 798

32 141

28 375

25 995

30 892

23 123

19 994

2017

Fig. 9.2  Registrations of passenger cars (Swiss Federal Statistical Office 2019)

Vehicle group / type The types of vehicle are defined in the Ordinance on Technical Requirements for Road Vehicles (VTS) SR 741.41, Art. 6 to 28.

Comments Each month, the FSO receives an extract of the vehicles newly registered in Switzerland in the current year up to the end of the previous month. The results are always provisional. Subsequent registrations for past months are likely. Every month the data for all previous months are reloaded (overwritten). The municipality of residence is determined by the postcode and postal town of the holder's address.

Meta information: Last changes: new data set 2018 Data collection date: January of the following year. Database status: 03.01.2019 Spatial reference: cantons as at 1.1.1997 Data source: Federal Roads Office (FEDRO) - IVZ vehicles, formerly MOFIS database

Footnotes

20 690 21 695

> Passenger cars 27 792 24 362

April

> Passenger cars 21 131 22 542

21 122 19 089

> Passenger cars 24 348 27 057

March

18 792 21 824

> Passenger cars 16 557 17 590

February

2008

18 313 21 664

2007

> Passenger cars 17 562 18 835

2006

January

2005

27 303

23 986

22 666

20 462

21 680

25 379

31 536

27 942

26 558

28 581

22 536

22 258

2018

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M. Ilg and A. Baumeister Monthly registrations of passenger cars Switzerland, 2010-2018

Registrations per month

40.000

30.000

20.000

10.000

0 01/10

01/11

01/12

01/13

01/14

01/15

01/16

01/17

01/18

01/19

Fig. 9.3  Scatterplot of monthly registrations of passenger cars

of the increase in new registrations in June (Swiss Federal Statistical Office 2013). The outlier is irrelevant for an annual forecast, as it is likely to be a case of early purchases. However, the value may be influenced by seasonal effects.

9.2.3 Modeling and Evaluation Prior to the actual modeling, the time series is presented as a line chart, which makes seasonal effects more visible. In addition, the data is split: the data up to December 2017 is used as training data to calculate the model. The 2018 traffic displacements are used as test data to check the prediction strength of the model. This verification is essential to assess the model quality and allows the detection of overfitted models (Ruppert and Matteson 2015, p. 110; Backhaus et al. 2018, p. 94). The test data are shown dashed in the line plot in Fig. 9.4. Seasonal effects are also apparent. For example, local maxima can be seen in the middle of the year and at the end of the year, while the number of placements is regularly very low in January, for example. The regression model is formulated with a linear trend component and a monthly seasonal component. Here, the months are represented by dummy variables that take the value 1 for data values from the respective month, but are 0 otherwise. January is not explicitly included in the model as a dummy variable because a model with dummy variables for all 12 months would be characterized by perfect linear dependence of the regressors and would not be computable (Backhaus et al. 2018, p. 98). The regression model is

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Monthly registrations of passenger cars Switzerland, 2010-2018

Registrations per month

40.000

30.000

20.000

10.000

0 2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Fig. 9.4  Line plot of monthly registrations from 2010 onwards

yt = β0 + β1t + γ2m2 + ⋯ + γ12m12 + ϵt with β0 as the intercept, β1 the coefficient for the time variable t, the dummy variables m2 to m12 and their coefficients γ2 to γ12. For the implementation in R, months are coded as an ordinal scaled variable; the regression function lm() then automatically generates a regression over time with 11 binary dummy variables to represent the calendar months. Computation of the model results in an acceptable ratio of explained variance to total variance (R2 = 0.7575) and provides a highly significant explanatory contribution (F = 21.73, p-value |t|) (Intercept) 1.852e+04 4.138e+03 4.475 2.41e-05 *** Date 1.606e-01 2.557e-01 0.628 0.5317 Month02 1.576e+03 1.049e+03 1.503 0.1367 Month03 9.291e+03 1.049e+03 8.860 1.25e-13 *** Month04 6.998e+03 1.049e+03 6.672 2.64e-09 *** Month05 6.607e+03 1.049e+03 6.298 1.37e-08 *** Month06 1.091e+04 1.049e+03 10.395 < 2e-16 *** Month07 5.334e+03 1.050e+03 5.082 2.27e-06 *** Month08 7.254e+02 1.050e+03 0.691 0.4916 Month09 2.729e+03 1.050e+03 2.598 0.0111 * Month10 4.566e+03 1.051e+03 4.345 3.92e-05 *** Month11 5.025e+03 1.051e+03 4.779 7.52e-06 *** Monthl2 9.856e+03 1.052e+03 9.369 1.19e-14 *** ––– Signif. Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual Standard error: 2097 on 83 degrees of freedom Multiple R-squared: 0.7585, Adjusted R-squared: F-statistic: 21.73 on 12 and 83 DF, p-values < 2.2e-16

0.7236

Fig. 9.5  Result report in R with computed coefficients for the regression model

The forecast values on the basis of the calculated model are shown by the dashed line in Fig. 9.6. The mean absolute error MAE amounts to 1743 traffic operations, the mean absolute percentage error MAPE was calculated to be 7.2%.

9.2.4 Further Development of the Model: Distinguishing the Type of Drive The model with monthly seasonal components already forecasts the expected sales quite well. In controlling, however, the relevance of the model results must always be questioned. For example, the analysis to date gives no indication of the impact of the increasing importance of electromobility (Electrosuisse 2019; Jannsen et al. 2019). The traffic figures are also available from the Federal Statistical Office, differentiated by fuel type. After a few transformations of the loaded data, R can display a comparison of the fuel types (Fig. 9.7).

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Forecasts for vehicle registration in 2018 (dashed) and actual values Excerpt 2016-2018

Registrations per month

40.000

30.000

20.000

10.000

0 2016

2017

2018

2019

Fig. 9.6  Comparison of actual values with predicted values of the regression model for test data

Registrations per month

Monthly registrations of passenger cars, by fuel type Switzerland. 2010-2018

20.000

Fuel Petrol Petrol-electric Diesel Diesel-electric

10.000

Electric Gas

0 2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Fig. 9.7  Registrations, differentiated by fuel type

The differentiation by fuel type suggests a falling trend for diesel vehicles and an increasing trend for petrol-electric hybrids, but the trajectories for electric and hybrid drives are difficult to interpret as they are small compared to diesel and petrol drives and

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therefore difficult to see in Fig.  9.7. Hence, the use of a logarithmic scale is indicated (Fig. 9.8). Now, an increasing trend in gasoline-electric drives is evident. An initially falling, but in the last six months again increasing trend can be seen for diesel-electric drive. Figure 9.8 suggests that the developments in the number of vehicles placed on the market should be modeled in a differentiated manner according to fuel type. In R, the dataset can be divided into subgroups that contain the traffic volumes for each fuel type. A regression model is then automatically estimated for each subgroup, enabling fuel-specific forecasts. Figure 9.9 shows the actual values of vehicle registrations from 2016 to 2018 and the calculated forecasts for 2018. The analysis of the model ratios shows that the fuel-specific models for diesel-electric drives and gas drives explain less than 23% of the variance (column R2 in Fig. 9.10) – the linear regression model with seasonal component is not suitable here for the time series analysis. There are also serious deviations in the forecast – with the exception of petrol drives (column MAPE in the “Forecast regression” block in Fig. 9.10). For controlling, this means that it can only do justice to its role as a business partner if the calculated models are questioned. Depending on the level of knowledge, controlling will develop improved models itself or commission data analysts to do so. A potential alternative are ARIMA models (autoregressive integrated moving average), which are stochastic time series models. They combine realized historical values and moving averages of past forecast errors of stationary time series. For basics and details on time series and ARIMA models, please refer to the literature (Ruppert and Matteson 2015, p. 307ff.; Schlittgen 2015; Shumway and Stoffer 2017). A challenge in the application of Monthly registrations of passenger cars, by fuel type Switzerland. 2010-2018

Registrations per month

10.000

Fuel

1.000

Petrol Petrol-electric Diesel

100

Diesel-electric Electric Gas

10

1 2010

2011

2012

2013

2014

2015

2016

2017

2018

Fig. 9.8  Registrations, differentiated by fuel type, logarithmized ordinate

2019

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Forecasts for monthly registrations in 2018 (dashed) and actual values Excerpt 2016-2018 Petrol

Petrol-electric

Diesel

Diesel-electric

Electric

Gas

10.000

Registrations per month

1.000 100 10 1

10.000 1.000 100 10 1 2016

2017

2018

2019 2016

2017

2018

2019 2016

2017

2018

2019

Fig. 9.9  Registrations, differentiated by fuel type and comparison with forecast values (dashed) for 2018

Fuel

Petrol Petrol-electric Diesel Diesel-electric Electrical Gas

Regression quality

Forecast (Regression)

R2

p

MAPE

0,79 0,78 0,63

8.00E-23 3.24E-22 2.73E-13

0,0937 0,2068 0,4369

0,11

0,608126

0,77 0,22

RMSE

Forecast (ARIMA) MAPE

RMSE

1.637 297

0,0597 0,1120

1,6749

3.273 58

0,0749 0,8467

1.34E-21

0,2753

124

0,3013

123

0,036826

1,8276

50

1,4631

47

1.137 164 632 70

Fig. 9.10  Selected criteria for assessing regression models and forecast quality of regression and ARIMA models

ARIMA models is the determination of the most appropriate model parameters, e.g., over which past period realized past values or residuals are included in the calculation. In R, algorithms are available that automatically test a number of calibration parameters, e.g. the function auto.arima() from the library forecast. The application of ARIMA models leads to a significant improvement of the forecast quality for all drive types except for the electric drive (column MAPE in the block “Forecast ARIMA” in Fig. 9.10).

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With the analysis shown here, controlling contributes to supporting sales planning. In doing so, it fulfills its service function and provides support in the form of both information and methods. One of the tasks of controlling is to check the suitability of alternative methods for the respective purpose.

9.3 Competence Requirements in Digital Controlling Analyses like the previous one are not yet part of the typical controlling activities in practice. Nevertheless, controlling will not be able to escape digitalization. This gives rise to numerous challenges – ranging from a changed controlling mindset and the business partner approach to mastering the analysis of Big Data (Schäffer and Weber 2016, p. 10). The use of mathematical-statistical methods in controlling currently appears to be rather low (Schillhahn et al. 2019). Success in controlling therefore requires further methodological competencies in statistics and IT, as well as increased personal competencies for business partnering, for example in communication (specific to competencies in business analytics (Seiter 2017, p. 94; Egle and Keimer 2018, p. 51ff.). The controlling profile of the future requires their successful combination with classic controlling competencies.

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The R Foundation. 2019. R: The R project for statistical computing. https://www.r-­project.org/. Zugegriffen: 1. Sept. 2019. Thieme, N. 2018. R generation. Significance 15 (4): 14–19. https://doi.org/10.1111/j.1740-­9713 .2018.01169.x. Timinger, H. 2017. Modernes Projektmanagement: Mit traditionellem, agilem und hybridem Vorgehen zum Erfolg. Weinheim: Wiley-VCH. Troßmann, E. 2018. Controlling als Führungsfunktion: Eine Einführung in die Mechanismen betrieblicher Koordination. 2nd ed. München: Vahlen. Wickham, H., and G. Grolemund. 2017. R for data science. Sebastopol: O’Reilly. Wirth, R., and J. Hipp. 2000. CRISP-DM: Towards a standard process model for data mining. In Proceedings of the fourth international conference on the practical application of knowledge discovery and data mining, 29–39. Zuckarelli, J. 2017. Statistik mit R: Eine praxisorientierte Einführung in R. Heidelberg: O’Reilly. Markus Ilg  born in 1966, has been a university lecturer in controlling at Vorarlberg University of Applied Sciences since September 2009. From 2013 to 2019 he was in charge of the Bachelor’s and Master’s programmes in business administration, and since 2017 he has been Head of the Department of Business and Management at the Vorarlberg University of Applied Sciences. Further professional stations led Markus Ilg, among others, to the Landesbank Baden-Württemberg as well as to the management consultancy zeb/ with a consulting focus on bank controlling. Markus Ilg studied business and economics at the University of Hohenheim from 1992 to 1997, majoring in controlling, industrial management and statistics, and received his doctorate in 2005 with a dissertation on knowledge management with electronic internal knowledge markets. Topics at the interface of controlling and business analytics are the focus of Markus Ilg’s research and consulting activities (www. ilgs.at). Alexander Baumeister  born in 1971, has held the professorship for business administration, in particular controlling, at Saarland University since April 2008. From 2010 to 2014, he was also Vice President for Planning and Strategy there. Between 1990 and 1995, he studied business and economics at the University of Hohenheim, specializing in controlling, accounting and finance as well as foreign trade, and received his doctorate there in 2001 with a thesis on price limit decisions in the case of currency risk. In his post-doctoral thesis, completed in 2007 and awarded the 2008 Südwestmetall Prize and the Werner Kern Prize for Research in Production Economics for 2007 and 2008, he developed a simulation model for lifecycle cost guarantee management in plant engineering. Research stays took him to the Australian National University in Canberra and the University of Southern California in Los Angeles, among others. Baumeister’s research focuses on approaches to project, warranty, and risk controlling, concepts of process and lifecycle accounting, and innovative budgeting methods.

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A Summary of Empirical Studies on the Selection, Use, and Design of Novel Visualization Types Peter Hofer, Lisa Perkhofer, and Albert Mayr

Abstract

This paper highlights the applicability of novel visualization types for the representation of multidimensional data. For this purpose, multidimensional visualizations for several dimensions (Sankey, Sunburst and Treemap visualization) as well as for several attributes (Parallel Coordinates Plot, Heatmap) are presented. For this purpose, on the one hand, more than 100 practitioners from the financial sector were interviewed about challenges in dealing with Big Data, and on the other hand, several online experiments as well as experiments by means of eye tracking technology were conducted. This approach allows to derive and summarize a comprehensive judgment about the usability as well as the advantages and disadvantages of these Big Data visualizations.

10.1 Introduction The rapidly increasing volumes of data in many areas of life, caused by the global spread of the Internet, social media, mobile IT applications and the increasing sensor technology, are a natural side effect of the advancing digitalization. In addition to this high volume, Big Data is also characterized by a large variety of structured and unstructured data (Variety) and an increasing speed in the generation and application of this data (Velocity). Generating relevant management information from this data and making it available to decision-makers for a simple, accurate and fast decision-making process is one of the main

P. Hofer (*) • L. Perkhofer • A. Mayr Steyr, Austria e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_10

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tasks of controlling. Therefore, the concrete analysis and use of big data has become a major challenge for controlling. The business graphics traditionally used in reporting do not always enable optimal cognitive processing of this data and rapid recognition of correlations and trends by users. Due to the digital transformation and Big Data, the following essential and trend-­setting questions arise for controlling in the area of data analysis: • Can Big Data be used efficiently with existing analysis tools? • What new software tools and visualization types are needed to support efficient and effective management decisions? • Which data can be used meaningfully for corporate management? What opportunities and risks does this present for controlling and the company? • How does this change the work of the controller? In order to be able to fulfill his role as a business partner, the controller must face the above challenges and develop solutions for them. To use Big Data in a meaningful way, controllers must know which data and analysis techniques are available and how they can be evaluated and applied. They should also be able to communicate the results from their analysis to management or third parties in a credible and comprehensible manner (Hofer et al. 2018). Visualization is an essential measure for successful communication. Visual stimuli can be processed much faster and, above all, more analytically by the human brain. Thus, along with statistical algorithms for data reduction/summary, they form a central instrument in the control and analysis of Big Data. For the representation of large amounts of data, new types of visualization are continuously being developed in conjunction with suitable interaction techniques, which should help to make the data usable and the insights gained from it comprehensible. In order to increase the quality of decisions and the adequate derivation of measures, the focus of the controller must be directed specifically at the user-friendliness of these visualizations. It is essential that the information overload caused by the large volume of data is reduced by a suitable visualization design and with the help of integrated interaction concepts, thus relieving the decision-maker cognitively. On the one hand, a relief is achieved by transferring the data into a graphic form that corresponds to the task and supports it. On the other hand, the mental load is adapted to the user’s ability by independently controlling the analysis process through interaction and is thus reduced. Especially for large data sets, user-centered interaction concepts enable users to communicate directly with the visualization and the underlying data, thus independently moving from the analysis of the data totality to the analysis of individual sub-areas. This allows users to quickly identify correlations, trends and outliers and thus effectively and efficiently gain new insights, which in many cases would have remained hidden without this extended analysis function.

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Since the data to be visualized often have new or extended characteristics for a fully comprehensive analysis, the visualization types to be used must also be adapted accordingly. The bandwidth of existing visualization types is broad and ranges from the classic business graphics widely used in controlling (e.g. bar charts, column charts, line charts) to geographical (e.g. maps) and multidimensional display types (e.g. heat maps, parallel coordinates). These and the associated possibilities for interaction are presented in Sect. 10.2 as an overview; in addition, their use by Controlling is explained on the basis of an empirical online study. Section 10.3 is then devoted specifically to the novel and interactive visualization possibilities for multidimensional data sets and their optimal design. Current and potential areas of application in controlling, depending on the data selection and the task, are also examined. To ensure that the recommendations made on the novel types of visualization are comprehensible, this chapter is supplemented by the empirical methodology.

10.2 Overview and Use of Big Data Visualizations In this chapter, visualization and interaction types are presented and the current use of interactive Big Data visualizations in Austrian companies is examined more closely before their potential for use in controlling is subsequently analyzed. The following examples are primarily intended to provide an overview of different visualization types – from classic business graphics to geographic visualizations to hierarchical charts and network graphs. In addition, a focus is placed on different interaction types, which form an essential component of new Big Data visualization for the representation of multidimensional or hierarchical data sets.

10.2.1 Types of Visualization: Application and Level of Familiarity Companies use different types of visualizations in controlling, but mostly classic diagram types are used in reporting. In order to find out to what extent interactive visualizations and suitable visual analytics tools for big data are also used, an online survey was conducted in 2016 among the participants of the largest Austrian controlling conference (CIS  – Controlling Insights). 145 participants took part in this survey, 72 of whom had a management function, while the rest classified themselves as employees in controlling. It has been proven that appropriate visualizations in reporting can improve the cognitive load of report recipients and the quality of decision-making (Falschlunger et al. 2016; Perkhofer and Lehner 2019). Since reporting is one of the core tasks in controlling, it was obvious to specifically examine this professional group in more detail in the study. For the study, the respondents were presented with different interactive visualization types and asked how familiar they were with them and which of these types were already used in the companies. The following taxonomy was used in the survey.

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Fig. 10.1  Business charts (column, bar, line and pie charts)

Business Charts This classic form of visualization is very popular in the entrepreneurial landscape and is also widespread. Generally we mean by classic business charts graphs such as bar charts, column charts, line charts or pie charts (cf. Fig. 10.1). This type of visualization has existed since the advent of computer technology in the late 1970s. The results of the survey conducted have once again confirmed the high degree of widespread use. The frequency of use in companies is by far the highest at 94% (Hofer et al. 2018). Business charts are also used in Big Data visualizations, but there mainly in the form of interactive dashboards. However, this results in a central disadvantage: When using conventional business graphics, information must be summarized, i.e. added up. Outliers (individual values or clusters) within the data, which are often regarded as essential sources of information, are smoothed out in the process and are no longer visible at first glance or only with considerable additional effort. This is offset by the advantage that classic business charts have a very high degree of familiarity. Because of this, report recipients have sufficient experience in using these visualization types and can therefore extract information quickly and correctly. In addition, you do not need your own software programs for the standard charts. A reasonably good knowledge of Excel is sufficient to create attractive graphics or to understand them. For the novel types of visualization, you usually need your own software tools to use them. To improve their design, you should also have additional programming skills (e.g. Javascript). Text & Geographic Visualizations Of the novel interactive visualization types, geographic visualizations are used most frequently. 34% of all survey participants declared that they use them in the company. These geographical visualizations include, as shown in Fig. 10.2, above all choropleth or proportional symbol map. Text-based visualizations such as WordCloud are only used in 19% of cases. Here, a text is examined for recurring words and these are displayed in large (high frequency) or small (low frequency) depending on how often they are mentioned. Multidimensional Visualizations – Multiple Attributes Multidimensional visualizations with multiple attributes, such as Parallel Coordinates Plot, Polar Coordinates Plot and Heatmap (cf. Fig. 10.3), are characterized by the fact that

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Fig. 10.2  Text & Geographic Visualizations (WordCloud, Choropleth, Proportional Symbol Map)

large transaction-based databases, which have multiple attributes (e.g. sales, contribution margin) per dimension (e.g. regions), can be displayed very well. They allow detailed information on the respective source data to be identified and, as a result, outliers of the respective attributes can be quickly detected and analyzed. In addition, the results of different key figures can be compared with each other at a glance, whereby patterns and correlations of the measured variables/attributes (e.g. is there a correlation) become visible. Multidimensional visualizations for multiple attributes are still little known in Austrian companies. They are only used by 24% of Austrian companies. This is also their biggest disadvantage. Due to the low level of awareness, many users have little experience in using and interpreting them (Hofer et al. 2018). The possibilities inherent in these visualizations can therefore often not be tapped. Multidimensional Visualizations – Multiple Dimensions (Hierarchies) In contrast to multidimensional visualizations with several attributes, in which several different measured variables/attributes can be displayed at once, this category of visualizations displays several hierarchical levels sorted by dimensions. Thus, a measurand can be summarized or divided in a variety of ways. Typical representatives of these visualizations are sunburst, sankey or treemap diagrams (see Fig. 10.4). It is also true for these visualization types that they are little known or used. Only 18% of the surveyed controllers and managers use this type of visualization in their companies. Network Visualizations Globalization and digitalization are leading to more intensive external and internal networking of companies. In order to grasp these often very complex structures and to be able to analyze the dependencies, network visualizations are an adequate tool. In this way, the interrelationships within these structures and the relationships between them can be presented in a visual form that is easy to understand. Examples of these types of representation are the node-link diagram shown in Fig.  10.5, the chord diagram, and the dependency graph. Network visualizations are used in only 13% of all Austrian companies, showing the lowest degree of diffusion (Hofer et al. 2018).

Fig. 10.3  Multidimensional visualizations – multiple attributes (Parallel Coordinates, Polar Coordinates, Heatmap)

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Fig. 10.4  Multidimensional visualizations – multiple dimensions (Sunburst, Sankey, Treemap)

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Fig. 10.5  Network visualizations (node link diagram, chord diagram, dependency graph)

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10.2.2 Interaction: Taxonomy and Application Many of the visualizations explained in the previous section only become meaningful through the user’s interaction with the data. In analyses, e.g. in reporting, the recipient of the data often starts with a specific analysis task. However, the analysis process does not always end with simply answering the original task, but rather leads to new, often unexpected insights through the work with the data and the visualization, which would usually have remained undiscovered without interaction, but which can have a high relevance (Pike et al. 2009). The general sequence of the interaction process is summarized by Shneiderman (1996, p. 337) in his Visual Information Seeking Mantra as follows: “Overview first, zoom and filter, then details-on-demand”. This can be extended to an iterative process, which consists of the initial generation of an overview-like visualization, subsequent exploration, and then further refinement of the original view (Heer and Shneiderman 2012). However, the order in which the interaction techniques are used is basically individual and user-­ dependent and is determined by the user (Brehmer and Munzner 2013; Dilla et al. 2010; Elmqvist et  al. 2011; Liu et  al. 2017). The interaction process supports the knowledge creation of the decision maker, consequently conditions higher intellectual engagement and ultimately leads to improved decision-making ability (Pike et al. 2009; Shneiderman 1996). Interactions allow the user to filter the displayed data set, highlight or select certain subsections, present the data in a different order, or even change the visualization form itself (Elmqvist et al. 2011; Keim 2001). The most important different interaction techniques are listed below and should give the reader a first overview (Brehmer and Munzner 2013; Yi et al. 2007). Select Select allows data to be highlighted based on specific options presented to the user. The amount of visible data remains the same, but the highlighted data is brought to the fore (e.g. by an increased color intensity). It is often possible to highlight or select several elements at the same time. However, the more selectors are active, the higher the mental load on the user. Examples: Linking&Brushing, dropdown, checkboxes, radio buttons, scrollable lists, sliders, clicking directly on elements in the visualization, etc. Filter Unlike selecting, filtering allows the deselected data areas to be invisible. Filtering therefore actively manipulates or restricts the visual data set. Multiple active filters can be very helpful in identifying details along multiple dimensions or facets, but it is difficult to keep track of them. Examples: Dropdown menu, checkboxes, radio buttons, scrollable lists, sliders, clicking directly on elements in the visualization, etc.

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Navigate Navigate changes the user’s point of view. This can be used, for example, for geographic maps where you can move the analysis area to user-defined locations (directly by mouse click). Examples: Zoom, pan, rotate. Arrange The spatial reorganization of elements of a visualization (data dimensions or attributes) is of great importance for representations where only adjacent data sets can be analyzed (e.g. axis changes within the Parallel Coordinates Plot or the Sankey visualizations). Examples: Rearrangement of axis, rearrangement of rows/columns, rearrangement of spatial layout – should multiple visualizations be involved. Change Changing refers to changing the visual representation itself – that is, choosing a different visualization type. It is often referred to as a high-level interaction. However, tracking and understanding a full display change can be cognitively difficult to process and should be used sparingly. Examples: View reconfiguration (change visualization type, change color schemes). Aggregate Using statistical measures to describe multiple data points with one measured value. This can be very helpful in gaining quick insight. Even though statistical characterization is a very powerful approach, valuable information is often lost. Examples: Mean, median, variance, standard deviation, counts, summations. According to our study of the status quo (Hofer et al. 2018), filtering data is the most frequently used interaction technique in controlling (59% of respondents), while changing the colouring or symbols of data series is the second most important interaction category. The average use of only 1.7 interaction techniques per respondent suggests that interaction has been underused in controlling to date. One possible reason for the sparse use of interaction, but also of novel visualization options for Big Data, is the frequent need to use special visual analytics software. However, the results of our study show that Microsoft Excel remains by far the most preferred analytics tool, followed by analytics software such as Qlik View and Power BI (Hofer et al. 2018) – certainly a major reason why interactions are so sparsely used in reporting. The listed interaction possibilities are bundled into interaction concepts according to the recommendations from the literature and used specifically for each visualization type. Since visualizations and their interaction concepts are also novel in the scientific environment and are constantly being developed further, the next chapter empirically examines the current status for use in the financial sector. The focus is on usability, i.e. the ease of

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use for management decisions, measured by effectiveness, efficiency and satisfaction. The results of our empirical investigations and the method used are presented in Sect. 10.3.

10.3 Design and Usability of Specific Interactive Big Data Visualizations User-centered interactive visualizations enable users of Big Data to interact directly with the visualization in order to easily identify patterns (clusters, correlations, trends), outliers, or even individual values depending on the user. At the same time, an optimal design and interaction concept adapted to the analysis tasks and data sets should reduce the user’s cognitive load and achieve high usability. The examples presented in this chapter focus on new Big Data visualizations for the representation of multidimensional or hierarchical data sets. A multidimensional dataset consists of several information-bearing attributes or facts (e.g. sales, contribution margin) and descriptive dimensions (e.g. dealer, country). In Controlling, evaluating attributes using multiple dimensions is a fundamental part of the reporting logic for describing the performance of an enterprise. In the corporate context, these facts and dimensions are defined, stored in databases, and prepared and made accessible to the user via standard reports or dynamic online queries. However, the aforementioned use of multiple dimensions or facts, as well as the desire for user-specific adjustments depending on the focus of interest, currently pose challenges to the standard software solutions available on the market that are difficult to solve and require new types of user-­ centered visualizations. These JavaScript-based multidimensional visualizations are available in their own web libraries (e.g. D3.js) as standard templates (Bostock et al. 2011). The most commonly used representatives are presented in this chapter, but significantly revised to meet the requirements of user-centricity in design and interaction. Representatives presented for dynamic evaluation of multiple dimensions are the Sankey chart, the Sunburst chart and the Treemap, and for simultaneous evaluation of multiple facts, the Parallel Coordinates chart and the Heatmap. The examples presented contain programmed functionalities that clearly go beyond the freely available standards in order to bring about advances in effectiveness and efficiency. Example

For a better understanding of the prototypes, the same dataset was used for all visualizations. It is a fictitious company that deals with wine. The dataset contains 9961 records, where each record represents the order of a customer and consists of 14 dimensions (e.g. retailer name, order number, grape variety, country of origin, continent of origin) and 12 attributes (e.g. gross and net sales, trade margin, contribution margin and wine quality in points). ◄

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10.3.1 Overview of the Research Methods Used The goal of our research in the area of user-centered, interactive visualization of Big Data is a fundamental improvement of decision support. This is achieved by designing novel visualization and user interface concepts as described above and exploring them with respect to usability and practicality. The empirical examination of the impact of new design recommendations and interaction concepts is an essential part of the research field. The implementation of user studies such as eye-tracking and online surveys enables the quantification of the cognitive effects of innovative control and visualization concepts. Based on these observations and physiological data (eye-tracking), best practice examples and design recommendations are formulated.

10.3.1.1 Online Surveys Design, Data Model and Implementation The studies conducted in the research project were designed as online experiments with LimeSurvey; a separate but identical experiment was created for each visualization type. Each participant evaluated only one type of visualization, but had to rate different statements according to correctness in order to determine the efficiency (the time required was recorded) and effectiveness (there were always clearly correct and incorrect answers) of the visualization. In addition, each participant had to work with different specifications (differences in design – e.g. colour use, orientation – and interaction – e.g. no interaction, limited interaction, multiple interaction) of the same visualization (programming based on d3.js) to assess the impact of the design and interaction. The studies for each visualization type were conducted in 2018 and 2019 at the online vendor Amazon Mechanical Turk, and all participants were required to have at least a bachelor’s degree. In total, 294 test subjects participated in six usability experiments (Sankey, Sunburst, Treemap, Parallel Coordinates, Polar Coordinates, and Heatmap) during this period. In the experiment, the respective visualization type and the interaction options used were presented. A video with the respective possible interaction techniques and a test run for the subjects served as preliminary training. The self-generated data model described above was also used as a basis for comparing the different visualization types. In the experiment, participants were presented with statements for identifying, comparing and summarizing data from the wine trade (cf. Task Classification Model for Big Data Visualizations by Brehmer and Munzner 2013) in random order for evaluation:

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Sample Questions with One Attribute and Multiple Dimensions (Sunburst, Sankey, and Treemap)

1. Identify: a. Wine&Co 9 sells less than 3000 bottles of white wine. b. Most red wine bottles are sourced from France. 2. Compare: a. Wine&Co 11 sells more red wine than Schenki 2. b. Schenki 1 sells less red wine bottles than Schenki Direct white wine bottles. 3. Summarize: a. More than 50% of all red wine bottles are sold by all Wine&Co stores. b.  Overall, more white wine is sourced from North/− and South America than from Europe. ◄

After the experiment was conducted, participants were still asked preferences for the applicable interaction types and their experience with visual analytics or with the explicit visualization types of the study. Usability Measurement In order to assess the quality of a visualization, the usability, i.e. the user-friendliness, must be measured, which according to ISO standard 9241 consists of the following three components: • Effectiveness • Efficiency and • Satisfaction To determine the most important factor of usability for controlling, effectiveness, the number of correctly answered statements is used, while for efficiency the logged time for task execution is measured. Satisfaction is surveyed for each visualization as a whole on a 7-point Likert scale. The surveyed components are used separately for comparisons of the visualizations; for a calculation of the overall usability, a normalized sum (z-scores) is determined from the three components.

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10.3.1.2 Eye Tracking In order for an optimal preparation of information to meet the above central evaluation criteria, physiological measurement methods are required in addition to the survey of users: While satisfaction can be queried as a subjective assessment of the report recipient, the criteria of effectiveness and efficiency (in addition to results from online surveys) can be measured objectively with the help of eye-tracking technology (Eisl et al. 2015). The focus here is not on the total time or the total number of correctly answered questions. The main benefit of the methodology is primarily the investigation of reasons for time delays or the recognition from the gaze path whether statements were only answered correctly by chance. Eye tracking stands for the recording of the gaze paths of the report recipients in order to analyse the visual perception of the users. By measuring the duration of each task and the associated number of saccades (eye movement due to a change of gaze) and fixations (dwell time of the eye on a certain point), important objective findings can thus be obtained when optimizing information processing. The link between eye movement and cognitive processing by humans can thus be made visible and an impact of visual stimuli on downstream decisions can be established (Lund 2016). This requires appropriate eye-­ tracking software and, as hardware, an eye-tracking device. Whether the visualized information is correctly processed and interpreted, i.e. effectively displayed, can be tested in parallel to the eye-tracking with questions asked of the test subjects. Eye tracking has been used since the late 1980s in a variety of applications, including psychology and behavioral theory, website usability studies, and marketing for advertising design and product placement (Lund 2016; Eisl et al. 2012). Figure 10.6 shows an eye-tracking scanpath of a test subject while answering a task, which was performed with a Sankey chart for a multidimensional dataset. The following information can be obtained using this scanpath: The diameter of the blue circles

Fig. 10.6  Eye-tracking gaze path of a subject with a Sankey diagram

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corresponds to the duration of the fixations, and the saccades connecting the fixations are represented by blue lines. The red squares visualize the individual interactions of the subject to select the data streams by mouse click. With the help of this physiological measurement method, the influence of optimal visualization designs and interaction concepts on usability can be empirically demonstrated. The comparison of aggregated gaze trajectories of a visualization concept (cf. Fig. 10.6) with scanpaths of alternative concepts provides significant indications of the suitability or incorrect use of design selection and interaction concept for a specific task. On this basis, and online surveys conducted in advance, it was possible to define optimal design rules for the multidimensional visualization types described below. These are now presented, separated according to their focus on the representation of multiple hierarchies or multiple attributes, for possible use in controlling.

10.3.2 Multidimensional Visualizations – Multiple Dimensions These multidimensional visualization types are characterized by several hierarchical levels arranged according to dimensions, which are integrated into a graphic (Chou et  al. 2016; Losbichler et al. 2016). Thus, a metric that is essential for controlling or management can be summarized or divided in a variety of ways. This class of visualizations is particularly suitable in reporting for comparing different dimensions or summarizing several categories (Brehmer and Munzner 2013). Prominent representatives are the Sankey charts as well as Sunburst and Treemap visualizations presented in the next chapters.

10.3.2.1 Sankey Chart Sankey diagrams are often used to represent material and quantity flows and form an effective alternative to classical diagram types. In economics, this visualization type is often used to describe value chains, and in politics it is applied to visually represent voter flow analyses (Waniczek and Patloch 2018). The Sankey diagram quickly and intuitively demonstrates the proportions of individual data streams in relation to the total quantity. The visualization consists of data streams, also called flows, which connect one node (dimension 1) with another (dimension 2). The width of the stream represents the proportion of the total quantity and also offers the possibility of displaying further subcategories. Prerequisites for the optimal use of a Sankey chart are, on the one hand, the avoidance of negative values in the dataset and, on the other hand, ensuring that the sum of the data streams is always 100%. In the following example, the described data model from the wine trade is represented with a Sankey diagram (cf. Fig. 10.7). The left axis visualizes the highest aggregated level and represents the attribute sales quantity as total quantity. On the other axes, this sales volume is clustered according to the defined dimensions wine trader, grape variety as well as continent and country of origin of the respective wine variety. The connections between the individual clusters illustrate the respective quantity flows and their significance in comparison to the total quantity.

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Fig. 10.7  Example Sankey wine trade with 5 levels

Compared to the original visualization in the d3 library, the visualization design and the interaction concept were extended by means of programming in JavaScript and design recommendations were empirically collected by means of an online survey (Sect. 10.3). The ability to shift dimensions (Arrange interaction technique) in combination with the highlighting of one or more connection streams (Select interaction technique) emerged as interactions essential for usability. In order to focus on the essential hierarchies, the user can add or deselect individual dimensions (Filtering). In addition, the user is guided through the Sankey visualization with the help of an appropriate color concept.

10.3.2.2 Sunburst Chart The Sunburst visualization type enables a further hierarchical perspective on multidimensional data sets and is therefore equally suitable for complex Big Data analyses in Controlling. The diagram represents an extension of the pie charts and ring diagrams so popular in reporting from the area of classic business graphics by introducing further category levels for subdividing the data set in hierarchical dependency. The interrelationships between the individual dimensions or categories can be quickly elicited, analogous to the onion principle, by displaying multiple concentric circles on the outside. The dimension selected as the top hierarchy is always located in the innermost circle, further dimensions follow analogously to the interactive category selection (Rodden 2014). The following Fig. 10.8 visualizes, similar to the Sankey chart, the sales quantities of our wine trade example. The multidimensional hierarchy is now depicted in the sunburst chart as follows: On the first, hierarchically highest level (in the innermost circle), the total sales volume per wine trader is shown. On the next level, this quantity is subdivided into the dimension grape varieties (e.g. red wine, white wine), followed by a further subdivision into the two dimensions continent and country of origin.

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Fig. 10.8  Example Sunburst with user-specific shifting of the layers

For the Sunburst visualization, the following design and interaction concept has been empirically shown to be as user-friendly as possible: • The initial selection or order of the dimensions can be changed by the interaction option Arrange by moving the dimensions. If, for example, a user does not want to primarily analyze the sales volume per wine trader, but instead wants to focus on the respective grape varieties, he can rebuild the visualization by moving the levels (the highest level now represents the grape variety) and thus generate a new hierarchical dependency. • The user is supported by a perception-optimised colour concept, in which colour matching is carried out for each dimensional value, starting from the innermost level. • In addition, the usability of the visualization is improved by the use of a mouse-over interaction (appearance of a tooltip when the data field is activated). • By clicking on a dimension field, the interaction “Zoom” triggers a rotation of the elements and this selected dimension is taken as the new highest level. Hierarchically subordinated dimensions are subsequently arranged in a ring again, higher ranked categories are hidden.

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10.3.2.3 Treemap Another method for the hierarchical visualization of information  – i.e. a possibility to analyze an attribute in detail while simultaneously observing several dimensions – is the treemap. This visualizes a hierarchy on a rectangular area (Johnson and Shneiderman 1991) and uses 100% of the available area. Each selected dimension is assigned to a rectangular area, with all hierarchically subordinate subdimensions visually forming the sum of the respective higher dimension. These sub-dimensions can either be visible on the first level or made visible by interactive selection via mouse click. The value of a selected dimension element (paragraph of wine trader A) is represented by the size of the respective rectangle. In addition, colors are often used to distinguish different structures on the same hierarchical level. This allows a larger number of hierarchical levels to be represented and facilitates the comparison of proportions (Severino 2015; Wang et al. 2015). Thus, when displaying the hierarchy levels, it is possible not only to display the hierarchically highest dimension in the visualization, but also to expand the hierarchy by up to two additional sub-dimensions. In this case, in each rectangle of the first dimension, the respective sub-dimensions are displayed nested as further rectangles (Severino 2015). Since, according to previous studies, too many added sub-dimensions reduce the perceptual ability of the decision maker, the total number of visualized hierarchies should be harmonized with the individual cognitive receptivity of the user. The treemap visualization type offers a great deal of design freedom with many possibilities, especially due to the individual arrangement of the elements. By far the most frequently used types are the treemap visualizations with square shapes (the squarified layout) or those with elongated rectangles (the slice & dice layout) (Bruls et al. 2000). Based on the empirical studies conducted, a clear square layout, uniform labeling, and labeling according to the numerical values stored in the task proved to be advantageous for the treemap visualization in particular (see Fig. 10.9). In terms of interaction techniques, the Treemap visualization type requires the same functionalities as the Sunburst visualization type. Thus, the interaction concept presented for the Sunburst (arrange, zoom and mouseover) also proves to be optimal for the Treemap with regard to usability and user-­ centeredness and is therefore not mentioned in detail again. In summary, the treemap visualization is characterized by its clear design and supports a comprehensible and clear presentation of hierarchical structures.

10.3.3 Multidimensional Visualizations – Multiple Attributes Multidimensional visualizations such as the Parallel Coordinates Plot or the Heatmap serve as representatives for the dynamic evaluation of multiple attributes. They thus form solution approaches for the representation of large, transaction-based databases, which visualize several attributes or facts for a selected dimension and thus enable the recognition of detailed information about the respective original data. This enables management to identify individual values in a user-friendly way on the one hand, but also to make

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Fig. 10.9  Treemap with squarified shapes and two hierarchies

comparisons between several key figures that are essential for corporate management (Brehmer and Munzner 2013). The opportunity for controlling to access the original transaction data of the ERP system again by means of in-memory databases from the CPM tool is thus also expanded by visualization options for this data stock.

10.3.3.1 Parallel Coordinates Plot Parallel Coordinates Plots project multidimensional data structures onto a two-­dimensional display surface (Inselberg 1985). Normally, multidimensional data sets cannot be captured due to the perceptual limitation of humans to three dimensions. In contrast to the classical coordinate system (XY diagram or scatter plot), however, the axes representing the attributes (e.g. turnover, sales, contribution margin, …) of an object are arranged in parallel in the Parallel Coordinates Plot and run at the same distance from each other. The selected attribute values are then connected by lines/polygons. Two parallel coordinates can be compared with a classic scatterplot. Only the horizontal x-axis is changed in its position and arranged vertically next to the y-axis. The data points, which are created by the x and y coordinates, are not displayed as points, but are visualized according to their height by a connecting line between the two axes. A Parallel Coordinates Plot, in contrast to the Scatterplot, allows several attributes to be displayed at the same time and thus allows the simultaneous analysis of outliers or correlations of multiple attributes. The results of different key figures can be compared with each other at a glance. However, most important is the recognition of patterns and correlations between the individual measured variables, which can only be carried out without restrictions in the case of adjacent axes.

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The Parallel Coordinates Plot in Fig. 10.10 shows essential key figures of orders of different wine traders as parallel arranged attribute values (e.g. sales, turnover, contribution margin, wine quality), which are displayed with differentiated scaling per axis. Due to the mapping of all original transactions and their resulting key figures (sales, turnover, contribution margin, …, per order), Parallel Coordinates Plots appear complex and confusing at first glance. Especially for inexperienced users, the recognition of correlations between the different attributes is difficult at first, since there are only direct connections between the selected axis to the two neighboring axes (Lehmann et al. 2010). The special added value for controlling is provided by this type of visualization only through the additional use of interaction techniques. The original arrangement of the attribute axes and their subsequent modification using the Arrange interaction are essential in order to be able to recognize correlations between axes that were not originally directly connected. Using the interaction Select, any intervals on the axes can be fixed and thus individual data records can be highlighted. In this way, trends of individual KPIs or correlations between individual key figures for selected data areas can already be identified visually, without statistical evaluation. Consistent color concepts also result in added value for this type of visualization in terms of efficiency, effectiveness, and satisfaction. The usability of the Parallel Coordinates Plot of our sample in Fig. 10.11 is increased by means of perception-optimized color differentiation or gradation per retailer. Thus, it can be quickly seen that, for example, the retailers Meinl, Schenki 1, and Schenki 2 achieve the highest trading margins. After highlighting (selecting) these three retailers and rearranging (arranging) the axes, it also becomes apparent that these retailers have no delivery costs and as a result a high percentage contribution margin can be achieved.

10.3.3.2 Heatmap Analogous to the Parallel Coordinates Plot, the heatmap is also a method that can represent multidimensional data sets using a color-coded system, so that the properties of key figures/attributes of complex data are easier for the user to understand. In the heatmap, a color concept is used in particular to efficiently compare data values that would be much more difficult to understand if presented numerically in a spreadsheet. In the case of color codes or color scales, dark and very light colors indicate extremities (Perrot et al. 2017). Visually, the heatmap visualization form resembles a tabular format, which consists of colored cells (depending on attribute expression) based on a row and column structure. When displaying values, a distinction is made between categorical data, which are based on a defined color code (e.g. low, medium, high), and numerical data, which require the use of a color scale due to their measurability. The color scale represents smooth color gradients from minimum to maximum, whereby both single-color (monochrome) and multicolor color gradations (e.g., two-color with the display High in red and Low in blue, or two-color with the display for Good in green and Bad in red) can be used. A legend for understanding the color logic should be depicted in the visualization for both variants (Severino 2015). The use of colors should be chosen optimally according to the task to be

Fig. 10.10  Parallel coordinates plot of several key figures from the wine trade (total data set)

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Fig. 10.11  Parallel coordinates plot of several key figures from the wine trade (coloured highlighting of selected traders)

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Fig. 10.13  Heatmap with Two-Color Design and Row and Column Highlighting

accomplished (Silva et  al. 2011). However, visualizations that are too colorful quickly overwhelm the user’s ability to perceive. An essential difference between the heatmap and the parallel coordinates plot is its aggregated representation of the data behind it (e.g. representation of sums, mean values, maxima, minima, …). Therefore, in a heatmap, depending on the purpose of the data analysis, different aggregations per attribute can always be displayed (Severino 2015). By means of an option list or drop-down field, this selection can also be designed interactively for the flexible display of several criteria. For the heatmap visualization, a color concept with business logic, i.e. red for negative and green for positive values, has proven to be optimal as a result of our study based on the wine trade example (cf. Fig. 10.12), whereby the color intensity is to be selected depending on the strength of the attribute expression. Additionally, an improvement in usability is achieved if, as shown in Fig. 10.13, instead of highlighting the selected cell alone (quality rating of trader Spar Wines), the complete row (quality rating) and column (trader Spar Wines) are highlighted. Also, the dynamic changeability of the arrangement of rows and columns (interaction arrangement) and the

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display of attribute values using mouse-over interaction (Brehmer and Munzner 2013; Heer and Shneiderman 2012) proposed in the literature contributes to an increase in efficiency, effectiveness and user satisfaction. Key features of the heatmap visualization are the reduction of the data set to a selected attribute metric (e.g., mean, sum, maximum, or minimum of sales), which provides the opportunity for efficient and effective work with the data.

10.4 Conclusion Controlling is responsible for the efficient and effective supply of information to decision-­ makers. The digital transformation will bring new challenges for the controlling function in the future, especially in the area of reporting, and will also change the role of controlling. Many controlling processes can be automated, freeing up resources for other important functions such as data analysis and management support (controlling as a business partner). At the same time, this requires considerable adaptation processes at the technological, organizational, process-related – but also human level (Egle and Keimer 2018). Due to the growing amount of data, management is increasingly confronted with the phenomenon of information overload, i.e. the amount of data to be processed leads to an impairment of the filtering of relevant information and thus to excessive demands. It is also becoming increasingly difficult for management to assess the relevance of the data presented. In the age of Big Data, information must therefore be presented in the most effective and efficient way possible, so that the most correct and quickest decisions can be made. In addition, suitable interaction concepts should enable a user-centered process of data exploration. Novel interactive Big Data visualization types promise exactly this high level of user-­ friendliness, whereby an empirical verification of this promise is necessary with regard to usage and visualization and interaction design. This is precisely where the research of the University of Applied Sciences Upper Austria, Faculty of Business and Management in cooperation with the Faculty of Informatics, Communication and Media comes in, offering not only best practice examples in terms of design and interaction, but also functional prototypes of the innovative visualization possibilities. An online survey conducted at the congress “Controlling Insights Steyr” has shown that the possibilities of Big Data visualizations that exist in science are still little tapped in practice. Classic business graphics continue to be the most common and are used almost comprehensively, but interactive Big Data visualizations are used significantly less. In the future, the controller in his function as a business partner must achieve a greater familiarity of the user with these interactive visualizations and thus implicitly also create an increase in usability. This is the only way to increase the use of these visualization types. By applying empirically verified design recommendations in the visualizations used, precisely this increase in usability will be achieved, which will ultimately also increase the degree of use compared to the current status quo.

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The primary task of our research field is therefore the empirical investigation of perception-­optimal designs and interaction concepts. The findings to date confirm the need to focus on user-centered visualizations due to the increase in data volume and complexity. Up to now, the information variety of reporting could be made manageable by suitable classical visualizations, but for the volume of Big Data, today’s standard visualizations of controlling are no longer sufficient for many tasks. With the addition of interactive Big Data charts to the visualization spectrum, the user’s mental resources can be used for the actual task and the cognitive processing of the larger data volumes. One-size-fits-all solutions – analogous to the recommendations for classic business charts – do not exist for these new types of Big Data visualizations either. The selection of the visualization type depends on the type of data (representation of multiple attributes or multiple dimensions); in addition, different task types require different optimal visualization designs and interaction concepts. So far, controllers have only been able to draw on overarching design recommendations in this context, such as Shneiderman’s Visual Information Seeking Mantra and a set of interaction techniques independent of visualization. Empirical design recommendations for the usability of specific visualization types and their perception-optimized design were missing in reporting. In general, the standard visualizations available online in the D3.js library show significant potential for improvement for use in controlling. More complex tasks for decision-­makers, such as comparing individual dimension characteristics or capturing interrelationships across the entirety of objects, require an expansion to include user-­ centric interaction techniques and improved design. A consistent color concept and an interaction concept adapted to the visualization type, which includes basic techniques such as moving the axes (arrange) and filtering or highlighting information, are crucial for high usability. Among all investigated visualization types, the Treemap, the Heatmap and the Parallel Coordinates Plot achieved the best performance in our online usability survey. Based on the status quo of Big Data visualizations, the current paper features a set of specific design implications for multidimensional visualization types. These are intended to ensure a high level of user familiarity with the visualizations, which ultimately results in an increased level of usage so that the full potential of these multidimensional Big Data visualizations can unfold.

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Peter Hofer  is Professor of Controlling and Business Intelligence at the Upper Austrian University of Applied Sciences, Faculty of Management. His teaching focuses on planning and budgeting, management reporting and ERP systems. His research focuses on information design, especially in the field of Big Data visualizations. Lisa Perkhofer,  is Assistant Professor of Digital Accounting at the Upper Austrian University of Applied Sciences, Faculty of Management. Her research mainly concentrates on information design and digital accounting. Albert Mayr  is Professor of Controlling at the Upper Austrian University of Applied Sciences, Faculty of Management. His research areas are in controlling and cost management. He is Regional Director Austria of the International Controller Association and a management trainer.

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An Interview by Ulrich Egle with Anca Frisan and Markus Steiner Ulrich Egle, Anca Frisan, and Markus Steiner

Abstract

Ulrich Egle’s interview with Anca Frisan and Markus Steiner provides a basic assessment of the status and impact of digitalization in controlling at Swiss companies. In addition, they report on the digital transformation in the finance department and in controlling at the energy company Alpiq. The focus is on reporting, robotic process automation and the personnel and organizational challenges of digitalization in controlling.

The Digitalization of Swiss Controlling: From the ICV’s Point of View Egle Mr Steiner, before we come to Alpiq, you are also the Swiss delegate of the International Association of Controllers (ICV). What is your impression of the digital transformation in controlling at Swiss companies? Steiner The digitalization of controlling is high on the agenda of Swiss controllers! A mega-trend that, after a pilot phase of early adopters mainly in large companies, has now reached controlling departments of all sizes and industries. Switzerland is no exception, and we may even be leading the way in the digital transformation of the finance function in our key industries. The Swiss labor market is

U. Egle (*) Rotkreuz, Switzerland e-mail: [email protected] A. Frisan • M. Steiner Olten, Switzerland © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_11

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considered one of the most competitive in the world. Cost pressures at the Swiss production site, as well as innovation budgets from headquarters and leadership positions in global organizations, are ideal conditions for Swiss controllers to learn and implement the new digital opportunities. As more and more mediumsized and smaller companies are now asking themselves what they can implement within the scope of their possibilities in the area of digitalization, the discussion of digital controlling is gaining more depth, practical reality and pragmatism. For us at the ICV, it is very exciting to observe which paths are now being taken in practice, because theoretical treatises with visionary concepts abound. How is the controller affected by digitalization? In our view, the controller, as a business partner of management, is doubly affected by digitalization: The modern controller should understand the implications of the digital transformation on the value chain and the business model of his company and contribute his methodological expertise to the development and implementation of digital business models. Which digitalization projects in the financial sector are Swiss companies currently carrying out/will be carrying out soon? As part of the Controller Conference Switzerland 2019, which we at the ICV organize annually together with the Institute of Financial Services Zug IFZ from the Lucerne University of Applied Sciences and Arts, we conducted a live survey. The majority of conference attendees responded to conduct projects in the areas of Big Data/Business Intelligence (36%) as well as Predictive Analytics (19%). Surprisingly, only a few of the attendees deal with the implementation of Robotics/RPA (7%), while the mentions of Machine Learning (13%) and Artificial Intelligence (11%) were quite high. In a broad Swiss sample, however, I believe the proportion of automation projects would be higher. What are the goals of the conference topics with your digitalization projects in the financial sector? Interestingly, the participants indicated that the primary goal of these projects is not currently cost cutting and increasing the efficiency of the controlling department (15%), but rather to develop methodological competence in order to better support management as a business partner (49%). In this case, the business case of the project may even be negative in the medium term. At least a zero-sum game is the goal of 36% of the respondents. Will the companies achieve these goals? Most respondents recognize that the implementation costs exceed the planned costs in many cases and thus, if efficiency gains remain the same, even more projects will fall into the category method development, it may cost something (actual: 73% instead of plan: 49%). Accordingly, fewer projects will cover their costs (actual: 20% instead of plan: 36%) or provide real cost savings (cost cutting, actual: 9% instead of plan: 15%).

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You then explored these survey results in greater depth at one of the topic tables in the Digital Controlling Excellence World Café at the conference. What came out of this? Currently, most participants are implementing business intelligence (BI) and predictive analytics projects in controlling and production areas. Machine learning and AI initiatives, on the other hand, are more likely to be pursued in production and at the customer interface (CRM), and RPA in accounting and controlling. The fact that digitalization projects in controlling do not currently have a primary cost-saving goal is due to the fact that the development of digital methodological competence in controlling has become an almost mandatory consequence in competition  – both within and outside the company. Quality enhancement, improved business partnering, empowerment of internal customers with more controlling self-services, the use of new analysis options and, last but not least, efficiency levers for the controlling function itself are cited as goals. Only a few participants manage to deliver digitalization projects on time and in full, while also staying within budget. Success factors are realistic planning with regard to scope, time and effort, staged implementation with skilful prioritization and support through effective change management – these were also mentioned remarkably often. To prevent project costs from getting out of hand, respondents today like to choose agile implementation methods (e.g. Scrum), at least for subprojects, tend to define smaller project assignments, pay attention to a clear requirements specification and the greatest possible standardization.

The Energy Company Alpiq in the Digital Transformation: Focus on Reporting Egle Ms Frisan, where does Alpiq stand in terms of the digital transformation in the Finance and Controlling departments? Frisan We have been seeing a fundamental transformation in the energy industry for several years. Digitalization is a strong driver of this. For us in Controlling, this means supporting our business in this development on the one hand and becoming more efficient, faster and more digital ourselves on the other. In recent years, we have made significant leaps in the finance department in this regard. As a first concrete example, I would like to mention the consolidation, planning and reporting system we implemented in 2017, the OneMIS. The new tools have enabled us to achieve a significant increase in the availability of key figures relevant to management in the area of the balance sheet and cash flow statement. We are able to determine balance sheet and cash flow ratios down to the profit center level and thus meet our management’s focus on the important drivers. We have also significantly enhanced our controlling tools for analysis and reporting, which allow us to perform much more flexible evaluations, drill-downs and also presentations in the reporting dashboards. We have also implemented the first RPA cases in the finance area. We see a lot of potential in this area to make our work even more efficient.

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Egle What requirements must modern reporting meet? Frisan Our experience has shown that the core requirements for internal reporting have not changed: Data must be presented reliably, transparently and comprehensibly in order to ensure the basis for efficient management. What has changed, however, is the fact that this data and the underlying analyses must be available at much shorter intervals  – in our case, for example, monthly cash flow figures down to profit center level. This would not have been possible for a company with our structure until a few years ago. The way this information is available has also changed dramatically: Whereas we used to send out Excel files and Power Point slides, now decision-makers have access to up-to-date data anytime, anywhere, for example in our online dashboards. Probably the most important change concerns the focus of the analyses – away from monthly P&L reporting towards balance sheet and cash flow analysis. In addition, our new Quality of Earnings reporting highlights the portion of business unit earnings that provide relatively repeatable long-term returns, normalized against prior year or budget – rather than variance analysis as a collection of effects that focus management’s attention on the more or less successful detailed aggregation of business cases. Egle Does self-reporting allow more time for the controller as a business partner and what new activities does it create for the controller? Frisan Thanks to the new tools at our disposal, we can now spend more time on complex financial analyses and decision support for the business. However, this also means that the profile of the controller is changing. In addition to technical expertise, IT affinity is also required in order to not only be able to use the increasingly complex tools, but also to exploit and further develop their potential. Egle What changes do you expect in reporting in the next 5 years? Frisan The direction continues to be set by digitalization. Increasingly complex business models and ever larger data volumes require new controlling processes and tools such as RPA. We also expect a significant increase in flexibility requirements not only in the area of actual reporting but also in planning, whether shortor long-term, bottom-up or driver-based. This means that these processes must also become even faster and more efficient. Digital Technology Robotic Process Automation Egle Mr Steiner, what is the significance of automation in controlling at Alpiq? Steiner Automation is one of our focus topics in order to increase the scope and quality of our controlling output, improve speed and exploit the efficiency potential of the new tools. We are initially aiming for automation within our controlling systems (SAP ERP, BW and Analysis for Office, BPC). We can now use Excel, Analysis for Office and BW to merge, analyze, plan and present operational data, e.g. from energy trading, with our financial data very efficiently. To do this, we first had to lay the foundations. After consolidating the ERP systems, we put a lot of time and effort into our aforementioned central OneMIS (BW, BPC,

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Analysis for Office and Design Studio): an integrated system that provides and returns transaction data from the ERP for consolidation, reporting and planning as consistently as possible without aggregation losses. We have harmonized transaction and master data, standardized reporting and planning processes and already implemented numerous automation functions for reporting and planning: Think, for example, of currency conversion functions, scenario and simulation techniques, prepopulation of budget and plan data, automatic calculation of balance sheet and cash flow items, comprehensive checks and workflows. We work on complete data sets for P&L, balance sheet and cash flow calculation. In particular, the introduction of time-dependent hierarchies for our management structure as well as for the chart of accounts is very advantageous for us, since we can carry out light restatements after reorganizations with relatively little effort, in order to establish comparability of actual values to previous year values, budgets and long-term plans throughout. All of this can already be described as automation, for which we no longer need an RPA. Egle What added value does Robotic Process Automation deliver for Controlling at Alpiq? Steiner Despite our integrated OneSAP system with OneMIS, we have a need for further automation, especially across tools and processes. In addition to the automation goals mentioned earlier, we also want RPA to provide job enrichment for our controllers so that repetitive, tedious tasks can be replaced, such as regularly calling up validation reports, copy and paste activities, or working at off-peak times in the morning or evening. And of course, we wanted to learn about and try out this new technology, not just for ourselves – but also to be able to show other departments from the business, as well as other cross-functional areas such as HR or IT, what the possibilities are. Since we have already built up sound system knowledge on the controlling side with our power users with the OneMIS implementation, it was the logical next step to now give these digitally affine power users the opportunity with RPA to remedy missing automation options, inadequacies in terms of functions and usability of the tools and interface problems. Controllers are now able to independently set up analyses, reconciliations and checks across multiple systems, e.g. from the detailed data of the trading systems to the reporting of energy trading to the financial data in ERP and finally BW. We make more checks than before and find errors faster. Egle What did the introduction of RPA look like at Alpiq? Steiner More than a year ago, we conducted a pilot with UiPath in Controlling for use cases from Controlling, Accounting and IT.  Our colleagues from the support functions for energy trading also started a pilot for their use cases at about the same time – so we initially gathered experience independently of each other. Since both were convinced of the potential of RPA for Alpiq in general and UiPath as software, we teamed up in a joint implementation project and established an overarching business case. We initially opted for a decentralised

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structure with RPA developers and decision-makers in the respective departments, with important decisions being taken in central committees. RPA governance, organization, processes and IT infrastructure were jointly defined. Design rules for development, mutual quality assurance, building shared modules and logics for early detection of bot run time errors (e.g. due to changes in underlying systems) were also addressed centrally. We started lean and are now growing to meet demand. After initial external support, including in the development of the bots, we are now focusing more on internal development and maintenance of the bots. To keep the customization of the bots as efficient as possible, we follow our design principle of not coding as much parameterizability of the bots in the RPA software as possible, but making it easily accessible to the user. The evaluation logic of the reports, for example, is completely adjustable in the report itself. Egle Did you encounter any resistance during the RPA implementation? Steiner There was no resistance, but there were critical questions. However, curiosity quickly prevailed. We talked a lot about what the goal of automation with robots can be in the administrative areas. The media’s unfortunately somewhat threatening portrayal of the upheaval of the job market through robotics and above all artificial intelligence naturally also led to reservations and concerns among our employees. We have discussed this openly. I think we all realized pretty quickly that with RPA, we have a new tool in our toolbox that enables evolutionary improvement steps in many facets for every employee. In our opinion, RPA is part of the contemporary equipment of a finance department. We will therefore also see more RPA functionality directly in ERP, office, production and CRM systems in the coming years. We had some lively discussions on the topic of standardization before robotization. In principle, a process should always be standardized before it is robotized. However, this has to be decided on a case-­ by-­case basis, as standardization can take a disproportionately long time or cost a lot. Regardless, it should always be checked whether a use case requested for robotization will still be necessary in the medium term. Finally, we have agreed in our governance to clearly coordinate with all parties involved which use cases are to be robotized. On a case-by-case basis, we decide whether RPA or optimization within a system is the more cost-effective and faster alternative. This also prevents RPA efforts from becoming obsolete due to IT or process changes. System and process changes are of course unavoidable in the medium term, which is why we have consciously decided to also develop and use interim solutions with RPA until better options exist. Egle What use cases in finance have you robotized to date? Steiner In Controlling, we have automated the cost and variance analysis. We are now planning to expand the use case to include push information to the line managers when limits are exceeded and in the event of anomalies. In the closing

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process, numerous work reports are automatically updated and made available to the controllers. We have had very good experience with the robotization of validation reports for actual and plan data: By means of numerous check reports, data reconciliations are carried out every morning to ensure consistency of content across various systems. When the controllers come into the office in the morning, corrections have already been made and they can start immediately. This is particularly valuable in time-critical process phases. In Accounting, the daily posting of transaction data (e.g. debtors, accruals, etc.), especially from interfaces that could not yet be fully automated, is mapped with bots. A bot provides numerous validation reports to our consolidation colleagues through a time-intensive night run. Treasury uses RPA to reconcile bank data – a sticking point for our RPA experts, as various two-factor authentication methods had to be used in web-based applications. Finally, the Master Data team uses robotics to automatically create and maintain master data. RPA is used here to supplement missing or too expensive automation options between systems. Further use cases are also planned in HR, Compliance and IT. Egle How can possible applications of RPA in controlling be identified and when does the business case for RPA pay off? Steiner We have defined a framework to identify potential use cases for automation with RPA in a structured way. In addition, we naturally ask our colleagues for suggestions and ideas. The number of transaction steps in a use case is not decisive, because a productive bot can always be expanded later with process steps at the beginning or end and thus workflows can be connected over time via RPA. The use case candidates are then evaluated based on a catalog of criteria. Among other things, transaction volumes, the degree of standardization or the suitability for standardization, further qualitative benefits such as the increase in quality and speed, the reduction of operational risk, etc. are considered. The effort of the original use case as well as the estimated effort after automation is measured in hours and evaluated at personnel costs. In addition, the effort required for implementation and maintenance must be determined. The business case also includes the RPA licenses as well as infrastructure costs, e.g. IT services such as dedicated servers and integration costs. External and internal development costs, training costs and other costs are also cost components. We assess the RPA business case as positive if the payback can be achieved within a useful timeframe. The prioritization of the implementation of the use cases is then done according to the expected efficiency gain, the procedural and technical complexity and taking into account the feasibility, availability of developers and current stakeholder demands. The expected lifetime of the bot is also included and thus the stability of systems and processes is considered. Egle Ms Frisan, how is Alpiq’s Controlling facing up to the personnel and organisational challenges of digitalization?

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Our controllers continue to need the proven basic competencies such as technical expertise in finance and the ability to present information in a management-­ oriented manner. In future, controllers will increasingly need to have more IT know-how, or at least an affinity for it. At Alpiq, we have trained power users for the various systems in the Controlling division, as mentioned earlier. The power users have a finance background, but they were given the opportunity as well as the time to gather the necessary expertise in relation to the IT systems. While at the beginning they were mainly troubleshooters and first level support for their team colleagues, they now independently take on further development tasks on the specialist side and regularly give internal training courses so that their expertise can be passed on to the controllers.

Company Profile

Alpiq Ltd. Bahnhofquai 12 4601 Olten Switzerland Industry: Energy Turnover 2018: CHF 5.2 billion Number of employees 2018: 1290 FTE Alpiq is a leading Swiss electricity producer and energy services provider active throughout Europe in the fields of energy production and marketing, energy optimisation and electromobility. Alpiq has been producing climate-friendly and sustainable electricity from CO2 -free Swiss hydropower for more than a hundred years. The power station portfolio also includes interests in two Swiss nuclear power stations as well as flexible thermal power stations, wind farms and photovoltaic plants in Europe. As an international energy trader, Alpiq is active in all major European markets and boasts unique expertise in flexibility marketing and cross-border trading. Thanks to digital tools, electricity production, consumption and the flow of energy between producers, prosumers and consumers are optimised and the electricity grids are stabilised.

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Ulrich Egle  is Professor of Digital Performance Management at the Institute of Financial Services Zug IFZ at the Lucerne School of Business. After studying technically oriented business administration at the University of Stuttgart, he completed his doctorate at the Institute of Information Systems at the University of Bern. He supports companies in the digital transformation of business models and on the topic of digital finance transformation. Anca Frisan  heads the Group Controlling and Reporting team at Alpiq. She studied Business Administration at Eberhard Karls University in Tübingen, Germany, and also at Babes Bolyai University in Cluj-Napoca, Romania. After several years of experience as a controller, she worked as team leader Regulatory Controlling at an E.ON subsidiary in Romania. Markus Steiner  is Head of Business Reporting & Controlling at Alpiq in Olten and is Regional Delegate Switzerland for the International Association of Controllers. He studied business informatics at the University of Regensburg with studies abroad in Växjö (Sweden) and Oxford (England). After several years as a Management Consultant for Financial Management at Detecon in Zurich, he worked as Team Leader Planning Optimisation & Modelling in Group Controlling at SBB in Bern.

Controller Profiles in Switzerland: Importance of Digitalization

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Job Advertisement Analysis in Switzerland Viviane Trachsel and Christian Bitterli

Abstract

Digitalization is changing the role of the controller to that of the digital controller. An analysis of 270 job advertisements in Switzerland in 2019 shows that the controllers sought are primarily concerned with reporting, analyses, projects and planning/budgeting. Accordingly, industry-specific knowledge dominates the required professional competences and Excel, ERP and other MS Office skills the IT-specific competences. A degree in business studies, often with a focus on accounting, controlling and finance, is a prerequisite for most positions. The digital controller is therefore (still) rather a peripheral phenomenon on the Swiss job market. As soon as companies are further along in the digitalization of controlling processes, however, this is likely to change. Companies are therefore well advised to review the composition of the controlling team with regard to digital skills and to initiate any necessary measures.

12.1 Introduction A controlling role model represents an aggregated summary of the tasks, functions and required skills of a controller (Rehring et al. 2011, p. 14). There is currently a discussion about how the role of the controller is changing under the influence of digitalization. In a first part, this paper addresses the development of controlling role models. These role C. Bitterli V. Trachsel (*) Rotkreuz, Switzerland e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_12

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models are subsequently characterized and the competencies derived that the controller must have in order to assume the corresponding role. Afterwards, it is shown which controller profile is currently sought by companies in Switzerland, and a conclusion is drawn. The article is based on a comprehensive literature analysis of scientific and practice-­ oriented publications on the changing role model of the controller as well as on an analysis of job advertisements in Switzerland.

12.2 Development of the Controlling Role Models Seefried (2017, p. 60) has illustrated the development of the role model of the controller from the traditional role models to the business partner. The traditional role models include the controllers who, as technical experts, primarily take care of the annual financial statements, cost accounting and costing, as well as the role models of the reporter and the navigator described in Sect. 12.2.1. Today, this diagram must be adapted and supplemented to reflect the new role model of the digital controller. Figure 12.1 shows in the middle the role models of the controller and on the right the associated, aggregated key tasks. The left part of the figure shows the development of the role models. The left column shows a situation that has long existed in this or similar way in many controlling departments, and in some cases still today. In future-oriented controlling, the preparation of figures, reporting and planning require significantly less time because the processes are largely automated with the use of new technologies. This leaves more time for supporting and advising management, for in-depth analyses with the help of business analytics, and for further development and successful use of digitalization. In addition to the new role models of the business partner and the digital controller, the traditional role models of the technical expert, the reporter and the navigator are still represented in controlling departments.

Relevance

Knowledge of the current controlling role models and the competencies required for them are relevant for • The controllers who want to succeed in the changing environment, • The CFO, who leads and develops a controlling team, • The human resources managers responsible for recruitment and personnel development.

12  Controller Profiles in Switzerland: Importance of Digitalization Development over time Business Partner Navigator

Reporter

Technical expert

183

Task contents

Digital Controller Business Partner

• Digitalization of controlling processes • Business Analytics • Drivers of automation and digital business models • Architect of the control system • (Proactive) idea provider and driver for the management • Ensuring fit between organization and management control

Navigator

• (Reactive) optimization and decision support • Strategy implementation

Reporter

• Planning • Reporting and commentary

Technical expert

• Cost accounting / Calculation • Consolidation / Annual financial statements

Fig. 12.1  Developmental stages of the understanding and tasks of controlling. (Adapted from Seefried 2017, p. 60)

12.2.1 Traditional Controlling Role Models The role model of the technical expert is based on tasks such as consolidation, preparation of the annual financial statements, cost accounting and calculation. Due to the strong number orientation and a certain obsession with detail, the role image is often also referred to as bean counter or number cruncher (Rehring et al. 2011, p. 14). The controlling role model of the information provider or reporter focuses on reporting and planning. It includes the preparation, commentary and thus interpretation of the figures by the controller. With the development of information technologies (IT), in particular the development of enterprise resource planning systems (ERP), the traditional tasks of the technical expert, but also reporting, require less time and strategy finding and implementation as well as (reactive) decision support gain in importance. Controllers who support the manager in these tasks correspond to the role model of the navigator (Seefried et al. 2015, p. 558 f.). Controllers who represent a traditional understanding of the role must have good technical skills in accounting and controlling. They must be able to retrieve the required data from ERP systems and prepare it using spreadsheet programs (especially Excel). In terms of personal competencies, reliability is emphasized (Seefried 2017, p. 58).

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12.2.2 Controller as Business Partner Driven by a dynamic market environment, further developments in IT, increased requirements on the part of management and a changed self-conception of controllers, the role model continues to develop into a business partner (Nobach and Immel 2017, p. 79). This role model has been intensively discussed for more than a decade (see, for example, Nobach and Immel 2017, p. 78ff.; Möller et al. 2017, p. 64ff.; Goretzki and Weber 2010, p. 163ff.). The controller is a reliable partner of the management. He is the designer of the control system and proactively supports management by contributing ideas. In the role of business partner, the controller ensures the fit between organization and control (Seefried 2017, p. 59). Thus, the role of the business partner corresponds to a more active role than the traditional role models. In terms of the required competencies, particular emphasis is placed on understanding the business model and thus comprehension skills as well as leadership and communication (Möller et al. 2017, p. 67). Management acceptance is crucial to make business partnering work. In order to support decisions and be involved in the design of business models, business partners must not only have analytical skills, but also a certain degree of intuition and the ability to think their way into the market, customers and competitors (Goretzki and Weber 2012, p. 23).

12.2.3 Digital Controller The digitalization of the finance department leads to new challenges and tasks for controlling. Controllers must have skills in dealing with Big Data, an understanding of digital business models, and competencies in change management (Schäffer and Brückner 2019, p. 15). The new role model has not yet been fully sharpened. The literature is guided by specific technologies or data analyses that shape the role model and from which the necessary competencies are inferred (cf. Ploss 2016, p. 60ff. on cloud solutions; Seiter et al. 2015, p. 472 f. on Industry 4.0; Grönke and Heimel 2015, p. 242 on Big Data). Egle and Keimer (2018) paint a more comprehensive picture of the digital controller. They describe the digital controller on the basis of five competence areas. The competence areas are shown in Fig. 12.2. Specialist knowledge and business skills are still key. This basic knowledge is supplemented with an understanding of cost and revenue models of digital business models and adequate key figures. Competencies in the areas of risk management, agile project management, change management and compliance will become increasingly important (Egle and Keimer 2018, p. 51). The dimension Data Science comprises the competencies that are necessary to successfully use business analytics; first and foremost, this is in-depth knowledge of statistics. The

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Fig. 12.2  Dimensions of digital competencies. (Egle and Keimer 2018, p. 51) Performance Culture

ITManagement

Specialist Knowledge

Digital Controller Business

Data Science

controller must know which analyses he can use for which questions, what the possibilities and limits of an analysis are and how the results can be visualized. Depending on the depth of the analysis, standard programs may not be sufficient. Programming skills for advanced methods are then required (Egle and Keimer 2018, p. 52). Based on an analysis of job advertisements, Freistühler et al. (2019, p. 66) found that, in practice, Controlling increasingly works with a Data Scientist for such in-depth analyses. One of the tasks of the digital controller is to design efficient controlling processes and to drive automation forward. This is done, for example, with the use of software robots (Robotic Process Automation, RPA). In addition, controllers must recognize the developments of new technologies and assess them in terms of their value contribution. In order to perform these tasks, knowledge of IT management is required. This includes existing and new technologies, data management, workflow management and IT security (Egle and Keimer 2018, p. 52 f.). With regard to social and self-competence (performance culture), personal initiative, communication skills, customer orientation, networked thinking, accuracy, ability to work under pressure as well as teamwork skills are mentioned (Egle and Keimer 2018, p. 53).

12.3 Analysis of Controller Job Advertisements in Switzerland 12.3.1 Research Design and Methodological Approach In order to gain an overview of the current controller profile in Switzerland, an empirical analysis of job advertisements was conducted (Barmettler 2019). Between February 1, 2019 and March 22, 2019, a total of 270 controller job advertisements were collected and

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analyzed across Switzerland. The sample of advertisements was based on an automatic search for the term controller on the most important online job platforms in Switzerland: Jobs.ch, JobScout24.ch, Monster.ch and Linkedin.com. All job advertisements were recorded in a Microsoft Excel file. The evaluation categories were defined on the basis of theory, used in a pretest and adjusted again. A systematic approach to data collection ensured that each job advertisement was only included once. The results are presented in the following chapters.

12.3.2 Areas of Responsibility Mentioned The analysis of the job advertisements shows that the controller’s area of responsibility in Switzerland is broad. In almost all job advertisements, reporting is part of the controller’s area of responsibility. In the 270 advertisements examined, this task was mentioned 236 times, which corresponds to a rate of a good 87%. In-depth/ad hoc analyses are mentioned second most often (in a good 60% of the advertisements) as a (further) task of the controller. Project management/project controlling is only slightly behind. This also includes working on projects. Planning/budgeting is listed as a task in significantly more than half of the job advertisements examined (57% of the advertisements). Other tasks are only mentioned in approximately one in three or even one in four job advertisements. Figure 12.3 shows the ten most frequently mentioned controlling tasks in Switzerland. The evaluation of the scope of tasks shows that reporting continues to be the central task of the controller in Switzerland. In addition, control and planning tasks continue to be important for today’s controller.

Reporting

87.4%

In depth / Ad-hoc analyses

61.1%

Project management / Project controlling

59.3%

Planning / Budgeting

57.0%

Contact person for internal

35.9%

Forecast

31.1%

Optimization of controlling systems

31.1%

Cost accounting

27.4%

Collaboration in monthly, quarterly and annual financial statements

27.0%

Process management 0%

26.3% 20%

40%

Fig. 12.3  The most important controlling tasks in Switzerland

60%

80%

100%

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12.3.3 Required Competences In order for controllers to be able to perform their tasks well, they must have the appropriate competencies. In this study, a distinction is made between specialist knowledge, IT-specific and personal competencies. Figure 12.4 shows the specialist knowledge listed in the job advertisements. In the 270 job advertisements examined, industry-specific knowledge, such as knowledge of the real estate, asset management, telecommunications or media industries, was mentioned 62 times. This corresponds to a rate of 23%. Other skills that controllers need for their job are accounting skills according to IFRS/US GAAP/Swiss GAAP FER and general accounting skills (approx. 10% each). Project management skills (approx. 9%) round off the range of professional skills. Statistics and business analytics skills – both important competencies for the digital controller – are not explicitly mentioned in the advertisements. It is also interesting to note that more than 5% of the job advertisements do not list any specialist knowledge at all. IT-specific skills are requested significantly more often than the specialist knowledge presented above. This is illustrated in Fig. 12.5, where knowledge of how to use the MS Office program Excel comes first and is listed in approx. 56% of all advertisements. This is almost 2.5 times more often than the most frequently mentioned specialist knowledge (industry-specific knowledge). Excel skills are followed by ERP skills (approx. 46%) and general Microsoft Office skills (approx. 39%). In fourth place come general IT skills (approx. 18%), which controllers should have. Database and BI skills are listed in 10 and just over 7% of the advertisements respectively. Knowledge of programming is mentioned in only three advertisements. Of these, knowledge of Visual Basic for Applications (VBA) is required twice and knowledge of a programming language once. Programming skills are subsumed under Excel skills (VBA) and IT skills (programming language). A further analysis shows that in almost two thirds (approx. 63%) of the job advertisements, controllers are only required to have Microsoft Office application skills. In only 20

Industry-specific knowledge

23.0%

Accounting knowledge IFRS / US GAAP / Swiss GAAP FER

10.4%

Accounting skills

9.6%

Project management skills

9.3%

Accounting knowledge Swiss Code of Obligations Cost accounting skills Process management skills Other 0%

2.6% 1.9% 1.1% 3.7% 10%

20%

30%

40%

50%

60%

Fig. 12.4  Specialist knowledge required in Swiss job advertisements

70%

80%

90%

100%

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56.3%

ERP system knowledge

45.9%

General MS-Office skills

38.9%

IT knowledge

17.8%

Database knowledge

10.0%

BI knowledge

7.4%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

70%

80%

90%

100%

Fig. 12.5  Required IT-specific skills in Swiss job advertisements Business studies

44.3%

Studies with focus on accounting, controlling and finance

38.5%

Technical studies

1.1%

Other studies

1.1%

No studies required 0%

14.9% 10%

20%

30%

40%

50%

60%

Fig. 12.6  Required studies in Swiss job advertisements

job advertisements (approx. 7%) are more extensive competencies required. In most cases, this involves the implementation or further development of business intelligence or MIS solutions. In summary, it can be said that the majority of the IT-specific skills required are still very modest. In addition, 37 job advertisements (approx. 14%) do not provide any information at all about IT requirements. Specialist knowledge and IT-specific competences are often closely related to the education or training completed. Figure 12.6 provides an overview of this. In approx. 85% of the job advertisements with details of the educational background, a university degree is required. This is either generally required as a degree in business studies (approx. 44%) or specifically as a degree with a focus on accounting, controlling and finance (approx. 39%). In a very small number of cases, a technical or other degree is required (approx. 1% in each case). Overall, there is no difference between university degrees and degrees from universities of applied sciences in the job advertisements analysed. Only about 15% of the advertisements analysed do not explicitly require a degree. In addition to expertise and educational background, personal skills are also very important for a controller. This is shown in this study by the fact that the available job advertisements place significantly more demands on personal competencies than on specialist knowledge. The ten most important personal skills for a controller in Switzerland are shown in Fig. 12.7. The most frequently required personal competence of a controller is analytical thinking. This is required in every second job advertisement. In second place comes social

12  Controller Profiles in Switzerland: Importance of Digitalization Analytical thinking

50.0%

Social skills / teamwork skills

43.0%

Communication skills

40.7%

Personal initiative / Readiness for action

34.1%

Indipendent working

30.4%

Accuracy / Reliability

28.1%

Efficiency / Goal orientation

21.9%

Structured working / Adherence to deadlines

20.0%

Flexibility

19.3%

Solution-oriented behaviour 0%

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16.7% 10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Fig. 12.7  Expected personal competencies in Swiss job advertisements

competence/teamwork skills (43%), followed by communication skills (approx. 41%), as well as personal initiative/readiness for action with approx. 34% and independent working with approx. 30%. Overall, it can be stated that, in addition to the expected analytical thinking skills, many competencies relating to personal responsibility are required in the job advertisements.

12.4 The Controllers of the Present and Future So what skills do the controllers of the present and future need to have? If one compares the role models described in the literature with the analysis of job advertisements, one notices a discrepancy: Digital competencies are demanded by a broad authorship, but Swiss practice is not (yet) looking for them! This supposed contradiction can be explained. Most companies are not yet so far along in the process of digitalization that they could do without a large number of technical experts and reporters. This is evidenced by the study by Keimer et al. (2018, p. 15ff.), which determined a level of digitalization in the midfield for most Swiss controlling departments. The quality of the available data is often a problem here. An examination of the development of the role profile gives the impression that the demands on the controller have risen steadily and have been greatly expanded once again, especially with digitalization. Does the controller now have to be an accounting, consulting and IT specialist in one person? Yes and no! Yes, because the requirements have increased and the controller must be familiar – to varying degrees – with digital technologies and data analysis options. He is also expected to have communication skills. No, because these requirements are not necessarily placed on each individual controller, but on the controlling function as a whole. In many companies, a team holds the controlling function. So the requirements mentioned must be met across the entire team. A good

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controlling team today is made up of technical experts, reporters, navigators, business partners, and digital controllers, where one person can perform multiple roles, but does not necessarily have to. An SME cannot afford a large controlling team. This raises the question of what expectations management has of controlling. These must be in line with the team size and the competencies of the controlling staff. If the expectation is that controlling will drive digitalization forward, this can probably not come from a single controller. A small team is also hardly capable of digitizing the controlling processes. The use of standardized software with appropriate consulting or even outsourcing can be the next step here. Controlling teams should be deliberately put together on the basis of a clearly defined competence portfolio. This is tailored to the specific situation of the company. The situation is characterized on the one hand by management’s expectations of controlling and on the other hand by the maturity of digitalization in controlling. Particular attention is paid to new hires. Here, central questions arise: Which roles are already covered in the team? Which controller optimally complements the role portfolio in the team? Which tasks should the controlling team handle and to what depth? In addition to new hires, the development of competencies in an existing team is also crucial. The demand for digital controllers will increase and can hardly be met without corresponding development measures in the existing teams. Employees who show interest in new technologies should therefore be actively promoted. If the competence profile of the controlling team is based on the situation of the company, it also means that it has to keep up with the development of the company. As already mentioned, the current analysis of job advertisements shows that companies today still need many technical experts and reporters. However, in the future, there will be much less need for controllers whose main expertise is limited to preparing figures in Excel. As soon as companies have standardized their ERP systems and brought them up to date, they will need many more digital controllers who know how to use business analytics. The need for digital controllers is undisputed, but how their role will develop and sharpen will become clear in the coming years.

References Barmettler, P. 2019. Controllerprofile in der Schweiz. Luzern: [Unveröff. Bachelorarbeit], Hochschule Luzern – Wirtschaft. Egle, U., and I.  Keimer. 2018. Kompetenzprofil „Digitaler Controller“. Controller Magazin 43 (9/10): 49–53. Freistühler, S., J.A. Kempkes, F. Suprano, and A. Wömpener. 2019. Controller und Data Scientist in der Unternehmenspraxis – Eine empirische Analyse der Aufgabenprofile im digitalen Zeitalter. Controlling 31 (3): 63–69. Gönke, K., and J.  Heimel. 2015. Big Data im CFO-Bereich  – Kompetenzanforderungen an den Controller. Controlling 27 (4/5): 242–248.

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Goretzki, L., and J. Weber. 2010. Der Wandel der Controller – Eine rollentheoretische Betrachtung am Beispiel der Hansgrohe AB. Zeitschrift für Controlling & Management 54 (3): 163–169. ———. 2012. Die Zukunft des Business Partners – Ergebnisse einer empirischen Studie zur Zukunft des Controllings. Zeitschrift für Controlling & Management 56 (1): 22–28. Keimer, I., M. Gisler, M. Bundi, U. Egle, M. Zorn, M. Kosbah, and A. Bueel. 2018. Wie digital ist das Schweizer Controlling? Eine schweizweite Analyse auf Basis eines Reifegradmodells. https://blog.hslu.ch/digitalcontrolling/. Accessed on: 15.07.2019. Möller, K., J.  Seefried, and F.  Wirnsperger. 2017. Wie Controller zu Business-Partnern werden. Controlling & Management Review 29 (2): 64–67. Nobach, K., and Ch. Immel. 2017. Vom Controller zum Business-Partner bei Bosch. Controlling & Management Review 29 (3): 78–85. Ploss, R. 2016. Der digitale Controller. Controlling & Management Review 60 (2): 60–65. Rehring, J., L. Voußem, and J. Weber. 2011. Die Rolle(n) der Controller: Eine Einordnung durch den WHU-Controllerindex. Controller Magazin 36 (5): 14–19. Schäffer, U., and L. Brückner. 2019. Rollenspezifische Kompetenzprofile für das Controlling der Zukunft. Controlling & Management Review 31 (7): 14–30. Seefried, J. 2017. Kompetenzsteuerung im Controlling – Ein Vorgehensmodell auf Basis des AHP zur Entwicklung der Finance Business Partner Funktion. Disseratation, Epubli GmbH, Berlin. Seefried, J., F. Wirnsperger, J. Schulte, and K. Möller. 2015. Business Partnering durch individuelles Kompetenzmanagement – Ausgestaltung der Rolle des Performance Managements am Beispiel von Hilti. Controlling 27 (10): 558–564. Seiter, M., G. Sejdic, and M. Rusch. 2015. Welchen Einfluss hat Industrie 4.0 auf die Controlling-­ Prozesse? Controlling 27 (8/9): 466–477.

Viviane Trachsel  has been a professor at the Institute of Financial Services Zug IFZ at the Lucerne School of Business since 2001. Previously, she worked in Group Controlling for the Migros Group. At the University of Applied Sciences and Arts she is responsible for the specialisation in Controlling and Accounting of the Bachelor’s programme in Business Administration. She teaches and conducts research with a focus on controlling. Christian Bitterli  has been a lecturer, project and study leader at the Institute of Financial Services Zug IFZ at the Lucerne School of Business since 2009. Before joining the Lucerne University of Applied Sciences and Arts, he worked in the fiduciary and auditing department of the SME BalmerEtienne AG, after which he took on various functions as controller and finance manager at Shell Switzerland AG and Shell Brands International AG.  His main areas of expertise are financial accounting, in particular consolidated financial statements and reporting.

Standardization and Automation as the Basis for Digitalization in Controlling at Siemens Building Technologies

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Ivo Gerig

Abstract

For more than ten years, Siemens has had a uniform data platform that brings together details from all transaction systems. Work on standardization and data quality is ongoing. Using practical examples from Siemens Building Services, the author describes the development of various applications and their use in controlling and management self-­ service. Beyond the pure efficiency gain through standardization and automation, real added value for business management is created through the use of predictive analytics. This creates new opportunities for the timely identification of risks and opportunities. The use of innovative tools provides controlling with new analysis and visualization options. In addition to a high level of business understanding, this requires the controller to have in-depth skills in reading and interpreting data as well as in information technology.

13.1 Introduction Digitization has already been one of the most important challenges in controlling for several years and will remain the big topic in the future. But what does that mean exactly? How do I deal with it as a controller and how is my job changing? Using practical examples, the author would like to show how the tools of controlling have changed in recent

I. Gerig (*) Schongau, Switzerland e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_13

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years at the former Siemens Building Technologies (BT) and what the next development steps could be. In the 2018 financial year, BT generated global revenue of around EUR 6.6 billion in more than 40 countries, employed around 29,000 people and was organised into five operating business units (BUs). BT’s decentralised value creation in the solutions and services business, the control of 19 research and development sites and nine production cities around the globe, are a particular challenge for controlling. The paper first looks at the standardisation and automation of financial reporting and describes how BT’s activities in this environment lead to efficiency gains in controlling on the one hand and provide easy and reliable access to financial information on the other. This is followed by a discussion of the use of enhanced customer and product data to support operations. The focus here is on the timely and reliable preparation of management information as a basis for decision-making. While the first part of the article mainly focuses on standardization and automation aspects, the second part describes the step towards true digitalization in controlling. Specifically, a predictive analytics example for the creation of a sales forecast is presented. Finally, the author summarizes his learnings and gives an outlook on possible further digitization activities.

13.2 Standardisation and Automation at Management Level The representation of the decentralised value chains and the provision of decision-relevant key figures at the various management levels with responsibility for results place high demands on BT controlling and the associated tool landscape. Ongoing changes, which are part of the group’s daily routine, represent a further challenge. Innovation in the range of products and services, changing markets and mergers and acquisitions activities require constant adjustments to the organisational structures and the responsibilities derived from them. Irrespective of the organizational set-up, management must be able to rely on the figures provided by the controlling department at all times in the course of dialogues and decision-making. In the following, the data basis and the system landscape in BT controlling are first discussed before the standardisation and automation of management reporting is described. Subsequently, the focus is expanded from pure reporting to the analysis of key financial figures.

13.2.1 Initial Situation Two of the main challenges in controlling at management level are management reporting and the analysis of key financial figures. The data basis for both at BT is the central corporate consolidation system in the Siemens Group called ESPRIT, and in the past also a division-specific data warehouse based on ESPRIT.

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These two technical building blocks serve or served BT Headquarters Controlling as the basis for communication with the countries on the one hand and for the preparation and analysis of data on the other. To better understand BT’s journey towards digitisation, it is important to first understand the basis of number crunching.

13.2.1.1 ESPRIT Database ESPRIT is an application that was introduced centrally in 2000 and is technically based on SAP Business Warehouse (SAP BW). It compiles the financial data of the fully consolidated companies on a monthly basis. The various hierarchies of the Siemens matrix are mapped – on the one hand the vertical structure according to regions, zones and countries and on the other hand the horizontal structure according to divisions and business units (so-called Siemens depth structure). In addition to the pure financial consolidation for external reporting, ESPRIT has an area that contains the reporting for the standardized operational financial reporting (business unit reporting) and thus represents an essential basis for controlling activities. The key performance indicators (KPIs) required for the management and control of business activities are divided into five categories (see Fig. 13.1). The individual areas of activity are defined as follows: Sales Activity The companies’ sales actitvity reports include new orders, backlog, gross margin in backlog, sales revenue and sales margin. The required reporting level is the BU, business segment, or subsegment. Profit and Loss Account The operating result is calculated from the sales revenue using the income statement. The income statement is prepared using the cost of sales method. Costs are classified according

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to the functions in which they are incurred. The income statement at Siemens shows profits or losses at the BU level (not at the segment or subsegment level). Statement of Net Assets Net working capital (NWC) is reported internally from the statement of financial position. It is determined by deducting current liabilities from current assets at BU level. It can thus serve as a measure of the liquidity of an individual BU. Net capital employed (NCE) is also reported. NCE is arithmetically based on the balance sheet and represents the capital base of a BU. Thus, this ratio serves to assess the capital intensity of an individual BU. Cash Flow Statement The cash flow statement of the BU is only prepared up to the free cash flow. This corresponds to the sum of cash flows from operating activities and investing activities. Employees Each reporting unit also reports the headcount on a monthly basis. Each employee is counted as one employee – regardless of contractual working hours. Employees are also broken down by functional area. They are assigned to the functions of production, sales and marketing, research and development, and general administration on the basis of cost centers.

13.2.1.2 BT Business Warehouse With the introduction of the ESPRIT system, the desire arose in various Siemens units to be able to make extended use of both the centrally generated consolidation values and the associated logics and master data for their own specific reporting requirements. To avoid burdening the ESPRIT system with such use cases, central Siemens IT developed an SAP BW template that was provided to all divisions as a basis for building their own Business Warehouse (BW) systems. These BW systems formed the basis for covering the specific reporting requirements of the divisions (e.g., for consolidation according to local laws, a more detailed form of reporting, or individual depth breakdowns). At the Building Technologies Division, this BW template was in use on a dedicated system with approximately 220 users worldwide until 2016. The advantages and disadvantages of this BW solution are listed below: Advantages • High flexibility to implement division-specific needs • Low coordination and agreement effort with other units of the Group • Common division data basis • High acceptance due to integration with Microsoft Excel

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Disadvantages • High IT effort due to separate infrastructure • High adjustment effort in case of changes • Data access only for trained employees • No possibility to store comments The increasing demands on controlling in terms of quality, costs and time require continuous process and tool optimizations. Although the BW system made it possible to meet many requirements, it also created a lot of time-consuming reports with overlapping content. Due to the high technical complexity, the management did not have its own system access and was dependent on obtaining all information via controlling.

13.2.2 Smart Reporting The introduction of the Smart Reporting application represents the first major standardization and automation step in management reporting. Due to the very decentralized global organization of Building Technologies, communication between the countries and central management at division and business unit level is an important prerequisite for supporting successful business development. This includes a regular exchange of information on current key financial figures in performance review meetings. In the past, the preparation of these dates required a great deal of manual effort because data from the consolidated financial statements had to be retrieved, prepared for controlling purposes and converted into a management-friendly format. Together with another Siemens division and a pilot country, BT Performance Controlling decided in spring 2011 to develop a joint IT application that can be used across divisions and countries.

13.2.2.1 Challenge In the past, employees in BT’s central controlling department and in the countries were able to access a common database via Excel and create corresponding reports and analyses. The division also provided the countries with templates to structure the preparation and execution of performance review meetings with management in a uniform manner. However, a standardization of communication was only partially achieved, as the high flexibility in the preparation in Excel led to individual presentations and it even happened regularly that centrally reported data, especially forecast values, were overwritten at short notice by changes that had become known in the meantime. This often led to major discussions and irritations in the interpretation of the financial figures in the respective management meetings.

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Therefore, a standard template with a comment option for the key KPIs of the division and the BUs per country was introduced via Excel/Word and stored in a document management system with access rights. While this approach supported the systematic exchange of information, it was very cumbersome to use and access to the reports was still limited. In particular, many countries had to submit additional comments in different formats for their country management, which led to duplication of work and the associated frustration. The first step in process and tool optimisation at BT was therefore to make the standardised figures from group accounting available as efficiently as possible and in a user-­ friendly form, without negatively affecting the high quality of the data.

13.2.2.2 Structure of Smart Reporting The objective was to develop an easy-to-use and intuitive management frontend based on the ESPRIT data that allows timely and reliable access to financial information. In addition, a flexible commenting of selective KPIs across all levels should be made possible. A name was also quickly found – Smart Reporting was to be the name of the management-­ suitable application. With a small, interdisciplinary project team, the requirements for the tables and graphics were defined in close cooperation with the central ESPRIT IT team. This allowed for an interactive and quick implementation, as the IT team had all the knowledge about the sometimes highly complex KPI calculations and organizational structures as well as the corresponding technical know-how. In addition, there was agreement from the outset that the definition of the content of the new application should be based exclusively on corporate standards. It was particularly important to establish a common language with regard to the time axis (month/quarter/year) and the data categories (actual values/forecast/budget) and their respective comparisons. 13.2.2.3 Requirements for Smart Reporting Together with the other Smart Reporting project participants, BT Controlling defined the following requirements for the application: • Simple and intuitive graphical user interface – only functionalities that can be logically applied on the corresponding level may be displayed. Otherwise, the corresponding navigation option is hidden (e.g. local currency only on the corresponding uniform currency level – a country bundle without currency union can by definition only be displayed in group currency). • Regular, timely transfer of ESPRIT data already during the closing process to support the closing process. • Fast response times, i.e. a few seconds after calling up a report, the end user must have the requested display on the screen. • Simple and flexible commenting option for tables and graphics – the name of the author with the date and time of saving must be visible.

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• Geographical and organizational authorization concept to support the matrix organization. • Views in local and consolidation currency. • Ability to generate and export reports to PDF and Excel. • Reduction of complexity through limited, targeted selection options. It was clear to everyone involved that this list of requirements was no easy task for the technical implementation. Nevertheless, everyone was willing to turn the ambitious goals into reality.

13.2.2.4 Implementation by the IT Team A proof of concept, which envisaged developing Smart Reporting on the basis of a solution that was highly traded on the market for business intelligence solutions at the time, did not deliver the hoped-for results. Thus, the Oracle APEX platform, which was already widely used in the Siemens Group, was chosen as the technical basis for Smart Reporting. From the beginning, there were two strands of requirements. A common base of standard tables and graphics on the one hand and individual, separately financed presentations on the other. A community was formed to agree the common requirements, and decisions on each were confirmed by a high-level Steering Board. The IT team initially worked according to the waterfall model, i.e. with a milestone logic and the classic separation of business and IT responsibilities, and set updates live on a quarterly basis. All members were highly specialized, i.e. each could only operate one technology or one component of Smart Reporting. Since the end of 2011, the Blue Sheet in the Financial Hub China had already been a parallel activity to Smart Reporting in the Siemens Group, which had a great deal of overlap in terms of content and technology. The biggest difference was that various Oracle standards were used in the Blue Sheet instead of components implemented in-house. This also included a framework for the description of reports and charts. At the same time, the number of users and thus the need for more reports/timelines/charts in Smart Reporting had grown strongly, while the performance problems became more obvious. A comparison of the two tools made it clear that the sustainability of uniform reporting throughout the company could only be achieved in the medium term by merging the applications. Due to the higher degree of technical standardization, the Blue Sheet was declared the new basis for the still young Smart Reporting Tool. From the user’s point of view, this technical change of direction was not noticeable at the time of the changeover. However, even at this early stage, it reduced the complexity of the application and opened up new possibilities for future development. 13.2.2.5 Rollout and Acceptance The rollout of Smart Reporting into the organization began in November/December 2011 after a development period of about eight months. Thanks to the simple tool access and intuitive handling, the new reporting application very quickly found a high level of

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acceptance in the BT Division as well as in other Siemens Groups. Also important in the first phase was the support of management, which very quickly accepted the standard and used the various reports to prepare for and conduct meetings. The quality and efficiency of the cooperation between countries and headquarters was significantly improved. Above all, individual communication channels were curbed by the comments stored centrally in the tool. Country queries via e-mail regarding special influences were reduced to a minimum and multiple queries were prevented. Building Technologies recorded approx. 250–300 active users worldwide shortly after the introduction of Smart Reporting. In turn, the number of users in SAP BW was reduced to around 70. Smart Reporting also caught on relatively quickly in the other Siemens divisions. It was primarily the countries that strongly supported the Common Language that increased the pressure on the divisional headquarters to adopt Smart Reporting as a standard. What also contributed to the rapid adoption was the fact that Siemens had reorganized as of October 1, 2011 (fiscal year 2012). Unlike many other IT processes, the direct link to ESPRIT in Smart Reporting made it possible to display key financial figures according to the new organizational structure at the push of a button from day one based on the official restatement of the consolidated financial statements. This functionality has proven itself many times over in recent years in an increasingly dynamic corporate environment. In the meantime, Smart Reporting has become the Siemens standard tool. Nevertheless, it continued to be a great challenge to do justice to the many new regions and divisions that successively came into play. Many freely funded individual requirements combined with the quarterly release logic made it increasingly difficult to maintain an overall product without being exposed to constant conflicts of interest. So in 2013, the IT team decided to switch to agile software development. For a transitional period, a proxy product owner was nominated in IT before the then still largely unknown procedure was introduced step by step to the business departments and fully implemented. The release cycles were linked to the monthly closing. The IT team members began to spread themselves more widely by rotating tasks. Change requests were consistently prioritized according to their added value, which led to a new push and caused the user numbers to grow to over 3000. The tool, which was developed through the initiative of individual divisions and countries and is now recognized as a de facto standard, was transferred to the responsibility of Corporate Finance in 2016. The number of users has since risen to 4600. Thanks to the acceptance of the standard, the number of freely financed special topics decreased from 2016 onwards to such an extent that it was finally possible to dismantle all special areas of the application.

13.2.2.6 Further Development of Smart Reporting During 2018, a project for the technical redesign of Smart Reporting based on the open source JavaScript software library REACT was launched under the name NextGen. This new Smart Reporting application was rolled out in the 2019 financial year. This enables a broader range of functions and, in particular, use on mobile devices with touchscreens.

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Furthermore, new ideas are continuously being developed as to how individual solutions can be derived from a standard as required without having to set up the business logic anew in each case.

13.2.2.7 Success Factors There are a number of reasons why the Smart Reporting application is now the de facto standard for management reporting in the Siemens Group. The most important ones are listed below: • Management acceptance: Smart Reporting was very quickly accepted as the standard in the central units and especially in the countries. In particular, the simple and intuitive handling was highly appreciated by the users. • Commenting functionality: The possibility to comment on special influences of the current reporting period or forecast promoted that Smart Reporting is systematically used for the exchange of information. The quality of commenting is strongly dependent on the feedback culture. Constructive criticism with suggestions for improvement is usually well received. However, it is equally important to give positive feedback – motivation is an important key to success. • Esprit as the basis for the management ratios: Initially, ESPRIT had a majority focus on ensuring external reporting. This was mainly evident in the case of reorganizations, which could usually only be calculated comparably on a global level. With the introduction of Smart Reporting, the pressure on the finance department increased to perform comparability calculations, i.e. the conversion of past business periods into a new organizational structure, also at the lowest reporting level and thus to ensure a common language at all reporting levels.

13.2.3 Smart Analytics Despite the great success of Smart Reporting, only parts of the controlling requirements were implemented with this application. In particular, the flexible analysis of data was still solved very individually, which regularly led to coordination efforts and a lack of comparability of results. With the introduction of the Smart Analytics application, the standardization and automation efforts were extended from management reporting to the analysis of key financial figures.

13.2.3.1 Challenge and Structure of Smart Analytics Although the use of divisional BWs has decreased significantly due to the introduction of Smart Reporting, they continued to be used due to specific requirements that still existed. In particular, the necessary flexibility for the central controllers to perform analyses and create detailed reports could not be covered by Smart Reporting due to its universal standardization. A further expansion of the tool to meet these requirements would have massively increased the complexity of Smart Reporting and thus endangered its acceptance.

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For this reason, Siemens launched the Smart Analytics project in January 2015 with the goal of discontinuing the financial reporting and controlling activities on the divisional BWs and migrating them to a central data platform with ESPRIT as the data basis. The greatest challenge was to find the right balance between the highest possible evaluation flexibility for the individual controller and a group-wide standardization for smart analytics. Through close cooperation and consultation with all stakeholders, we succeeded in defining a common denominator and building the platform accordingly.

13.2.3.2 Objective of Smart Analytics In contrast to Smart Reporting, where standard views and deliberate limitation of flexibility are part of the concept, Smart Analytics with standard functionalities should allow analyses that are as flexible as possible in order to serve individual controlling requirements. The following points were defined as objectives of Smart Analytics: • Smart Analytics will become the standard tool for financial management reporting and ad-hoc analysis of ESPRIT figures in the central finance organization and the divisions. • Report templates are maintained centrally to the extent that value is added to the divisions; other reporting services are provided as needed. • ESPRIT is the primary data source for Smart Analytics. • A Smart Analytics Demand Board will be implemented to aggregate and prioritize the requirements and mandate the appropriate implementation. • The central finance organization is the governance owner that, for example, orchestrates the demand board and sets the central budget for smart analytics. • IT is responsible for technical service management. Consequently, this project did not focus exclusively on BT’s needs either. Rather, the aim was to create a Siemens-wide solution that could leverage synergies, particularly in terms of development and operating costs.

13.2.3.3 Implementation by the IT Team The project organization for the development of Smart Analytics was set up with great care. Representatives from all divisions as well as from the central finance and IT departments were involved in the project. Although the large number of project participants caused an above-average effort for communication and coordination, it ensured that all stakeholders were able to cooperate from the beginning and represented the emerging solution as theirs. As with the introduction of Smart Reporting, a lot of energy had to be spent on evaluating an adequate user interface at the beginning of the Smart Analytics project. The project was helped by the fact that the central Siemens finance department had shortly before introduced a financial statement-related controlling application with a different focus but similar objectives to Smart Analytics. Based on this experience, the decision was made in favor of EPM (Enterprise Performance Management), an add-in for Excel from SAP. The

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EPM frontend was to be based on a central SAP BW backend. Although the data source ESPRIT is technically already a SAP BW, it was necessary to transform the ESPRIT data model for Smart Analytics so that the end user can move around EPM as intuitively as possible. For example, certain detailed aspects of the ESPRIT data model needed for legal closing were interpreted for Smart Analytics; complex key figures were precalculated and persisted to achieve better performance. Also, items and metrics were given speaking titles instead of technical labels so that text search could be used in EPM. Thanks to the content-­ related and technical proximity to ESPRIT, the Smart Analytics project team was able to benefit from the existing knowledge, especially on the IT side. In addition, the high level of commitment of the technical project participants in the test phase and in the rollout preparation made it possible that only a little more than seven months passed from the kick-off at the end of April 2015 to the go-live at the beginning of December.

13.2.3.4 Rollout and Acceptance The introduction of a standard instead of an individual solution requires a certain willingness to compromise on all sides. Accordingly, the introduction of Smart Analytics was not an easy transition for all divisions. On the one hand, the controllers had to be trained to access data via EPM. On the other hand, it was initially necessary to transfer all individual reports and tables for internal reporting to the new tool. Letting go is always difficult! The Building Technology division joined forces to shut down its decentralised BW. As of the end of 2018, approximately 40 BT users were active on Smart Analytics. 13.2.3.5 Additional Visualization and Analysis Options With Smart Analytics, a successful, group-wide standardized data model geared towards financial analysis was created. However, the queries via EPM were only limited in terms of end-user friendliness. In recent years, great progress has been made in the field of data analysis and visualization software. There is now a wide range of not only low-cost but also end-user centric applications that can be operated without in-depth IT expertise. With this impetus, a project was launched at BT in the 2018 financial year to make smart analytics data even easier to analyse using the front-end tool Tableau. On the one hand, a selection of dashboards/reports, some of which are complex, was developed independently by Division Controlling – without the involvement of external help – and made available in the organization. On the other hand, additional controllers with a certain affinity for data in the organization are continuously being enabled to independently create content with Tableau and to automate their individual, previously Excel-­ based evaluations and reports and thus to use the corresponding capacities more profitably. It is obvious that such a front-end tool offers enormous potential in terms of productivity increases in the controlling organization. Furthermore, with the reduction of manual activities for data collection and preparation, resources can be increasingly used for the actual data analysis and interpretation. However, it is also clear that such a change cannot happen overnight and requires not only a change in skills, but also in particular in the mindset of each individual controller.

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Fig. 13.2  Schematic representation of the Smart Family at Siemens

13.2.3.6 Success Factors The following success factors can be highlighted for the cross-divisional Smart Analytics project: • Expertise in the project team: Subject matter experts from all divisions as well as from the central finance and IT departments worked together as a team from the very beginning, making Smart Analytics a common tool. • Acceptance in controlling: By involving the divisions at an early stage, it was possible to cover the essential needs of all parties involved. However, standardization also means compromises in flexibility – this remains a challenge even in regular operation. • Best Practice Sharing: The introduction of the Demand Board and the associated regular exchange of information between controlling staff also created a platform for best practice sharing. The schematic representation of the Smart Family at Siemens (see Fig. 13.2) shows the positioning of the tools on the different levels (Management, Analysis, Reporting & Auditing). The differentiation of these levels is an important prerequisite for the design of the front ends in terms of functionality and usability.

13.3 Standardization and Automation in Operational Controlling Thanks to the now highly automated reporting of internal and external key financial figures based on ESPRIT, Siemens has a reliable basis of figures for the global financial development of the various business units. However, for an in-depth analysis of a business development of products and customers, the number basis is below the ESPRIT reporting depth in the various local transaction, i.e. Enterprise Resource Planning (ERP) systems, for which there has been no central consolidation for controlling purposes to date. In the

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past, management inquiries in this regard inevitably led to Excel-based country queries, each with a high level of manual effort. A particular challenge was the adequate consideration of the factors time and quality. Different definitions and interpretations led to a high coordination effort. For business support, the individual countries built their own, often Excel/VBA-based reporting tools. This made it difficult to manage the business units across countries. The task of central controlling consisted mainly of collecting and compiling data – there was usually not enough time for analysis.

13.3.1 Business Activity (BA) DAsh With the introduction of the Business Activity (BA) DAsh application, financial data below the group reporting level therefore became the focus of standardization and automation in controlling for the first time. Specifically, a solution was created that enabled uniform, daily access to the key figures of sales, incoming orders and sales margin worldwide and thus a standardized form of operating volume reporting.

13.3.1.1 Objective The introduction of BA DAsh was intended to massively reduce the manual effort required to create and reconcile global volume figures through standardization and automation. In addition, information on products and product groups, customers and vertical markets should be made available to management in a timely manner to support business management. 13.3.1.2 Requirements for BA DAsh Even more than with the ESPRIT-based smart solutions, BA DAsh had to perform the balancing act between standardization with regard to technical and content aspects and the necessary individuality with regard to use. In summary, the following requirements had to be met: • Automated provision of cross-system standardized data below the central reporting level (subsegment) for products, product groups and customers. • Daily reporting for worldwide volume development in the product business with comprehensive analysis options. • Standard reports for management in graphical and tabular form. • Flexible evaluation options for incoming orders, sales and sales margin in all dimensions such as product, region, customer and depth structure.

13.3.1.3 Implementation by the IT Team In the wake of the Siemens compliance affair, there was a need to centrally monitor certain decentralized processes in the early 2000s. For example, central monitoring of the socalled segregation of duties specifications was set up to ensure that the same person who

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had generated an order could not also release it and receive the associated goods. For this purpose, certain local ERP tables were extracted to a central Oracle database, where they were normalized and evaluated. Since the ERP extractors already provided a standardized way of making decentralized ERP tables available centrally, from a technical point of view it was no longer a big step for Siemens to also derive tables from other ERP areas for central evaluations. Many SAP ERP systems, especially in the Siemens product business, use the COPA tables (SAP ERP tables for profit and loss and market segment accounting) to map volume data, which was accordingly included in the extractor logic as the basis for central volume reporting in BA DAsh. What sounds relatively simple from a purely technical point of view led to many coordination loops in the first months and years in order to ultimately be able to centrally store a correct interpretation of all tables from all systems. Efforts had been made for many years to standardize the Siemens ERP world. However, the operational use of tables and the detailed implementation of value flows are influenced in many places by local peculiarities that have to be interpreted for a central evaluation. These reconciliation activities of the volume data as well as the development of the QlikView-based application BA DAsh and the associated rollout were already started in 2010. However, several years passed before the Siemens divisions were able to integrate BA DAsh and the associated controlling options into their processes across the board. Only with the broader use of BA DAsh did the data quality gradually improve. The fact that the central database, on which all ERP tables are aggregated and standardized, was migrated from Oracle to SAP HANA after a few years had just as little impact on content as the conversion from classic extractors to an SAP SLT process. This merely took into account the constantly growing volume of data as well as the demand for better performance for the user. Today, the resulting Siemens HANA Data Lake forms the cross-­ domain database for many reporting and analytics use cases at Siemens.

13.3.1.4 Result With the rollout of BA DAsh in 2010, it was possible for the first time to create transparency with regard to data quality in the individual ERPs of the various countries. Initially, there was often feedback that BA DAsh was showing incorrect values, but on closer inspection, in most cases the data maintenance in the ERP systems was not up to date. Often, when changes were made, the local systems were not adjusted to the latest depth breakdown specifications and reported via reconciliation tables in ESPRIT. Thanks to BA DAsh, these inconsistencies were uncovered and the adjustment process was initiated with the country managers. The larger units were cleaned up within a year. The entire process took about two years and is still a particular challenge in reorganizations today. Acceptance of BA DAsh was achieved very quickly, as the benefits were equally felt by local and central units. At the end of 2018, approximately 2600 employees across Siemens were working with BA DAsh. Below are some functionalities of BA DAsh:

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Management Overview The management overview in BA DAsh mainly contains graphical representations. They represent monthly, quarterly and annual values. Of particular interest is the view with the daily volume development, which can be tracked within a month. The management view is easy and intuitive to use and therefore has a high acceptance. However, navigating for deeper analysis requires in-depth knowledge of data structures and content. Filter Criteria for Analyses Standardized filter and selection criteria are displayed on the user interface to support analyses. Here are some examples: • Time query: Selection option between fiscal years, quarters and individual months. • Regional hierarchies: representation of regions, countries or individual legal entities. • Depth Breakdown: Provides information on divisions, business unit, business segment or business sub-segment. • Product hierarchy: The Group Classification Key (GCK) and the Product Classification Key (PCK) are the common keys in the ERP systems. The user can use these dimensions to analyze products and sales. • Volume Dimensions: The main functionality is to distinguish between third party business and Siemens internal business. Siemens internal revenues are presented using a special logic (ESPRIT like) and map the relationships with other legal entities or other business units within the same legal entity but different depth breakdown elements. • Currency: The values can also be displayed in national currency within a currency union. • Customer: Possibility to filter by sold-to party, payee or payer (invoice recipient).

Bookmark BA DAsh allows the user to save a filter selection to allow faster access to user customized reports. This is done with a simple mouse click. This functionality is especially important for users who regularly need the same reports or want to provide them to other users.

13.3.1.5 Success Factors The more directly an application relies on an organization’s business processes, the more critical are issues such as data quality and operational collaboration. For BA DAsh, the following factors can therefore be named for success: • Use of BA DAsh at all management levels: Data quality can only be achieved if reports for all business reviews are used 1:1 from the system. This increases the pressure on the units to get their data in order.

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• Limitation of Excel download options: The majority of users should be able to cover their information needs via the standard templates. Therefore, the Excel download function is only available to selective users who are also appropriately trained. • Creation of user communities: The involvement of key users in the exchange of experience and the implementation process of changes and new ideas promotes the acceptance and quality of the tool. However, great care must be taken to ensure that not all change requests create real added value and often massively increase complexity. Therefore, IT-technical and content-related support is very important.

13.3.1.6 BA DAsh Outlook Since the introduction of the QlikView application BA DAsh in 2010, the technical possibilities have continued to develop. Due to the increasing demands of users for additional functionalities and data, performance was severely impaired and maintenance became more and more costly. Therefore, at the beginning of 2019, after a pilot phase, Siemens decided to introduce SAP Analytics Cloud (SAC) as an alternative front end. The objective was to return the logics previously anchored in the QlikView frontend to the backend, thereby increasing performance and reducing maintenance effort. At the same time, this should create the basis for more flexibility in data access and thus also for future requirements in the analytics environment.

13.3.2 Further DAsh Applications Based on the BA DAsh application, further specialized applications were developed. For example, the Wall-to-Wall (W2W) application for displaying the effectively achieved margin of a product from the country perspective. Here, the transfer price between headquarters and country is replaced by the effective costs and thus allows a through profit and loss calculation. The W2W DAsh is an important basis for controlling the product business. The Vertical Market DAsh was also developed based on the BA DAsh. It supports the transparency of strategic initiatives for the further development of the vertical markets. (e.g. universities, hotels, data centers, etc.). Thanks to this application, the corresponding progress is measured in the head office as well as in the decentralised units and can be easily compared with the respective objectives. In addition to volume reporting, the DAsh idea has also been implemented in other areas. For example, today there is an Asset Management DAsh (AM DAsh) based on the receivables sub-ledgers of the ERP systems and a Customer Relationship Management DAsh application (CRM DAsh) based on the various CRM tools used locally. The schematic representation of the DAsh Family (cf. Fig. 13.3) represents only a selection of DAsh applications for reasons of simplification. As with the representation of the Smart Family, the distinction between the levels (Management and Analysis) is an important prerequisite for the design of the frontends in terms of functionality and user-friendliness.

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Management

Analysis

Asset Management (AM DAsh)

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AM DAsh

BA DAsh

Access to open invoices and stock levels  Risk management support

CRM DAsh

Daily ERP ERP

ERP

Customer Relationship Management (CRM DAsh)

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Incoming orders. Sales and distribution margin for products and solutions  Sales and sales support

Data lake

Transaction Tools

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ERP

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Customer data with sales funnel and business potentials (opportunities)  Support customer service

Fig. 13.3  Schematic diagram of the DAsh Family at Siemens

13.4 Digitalization Through Predictive Analytics In the author’s view, the introduction of software in a previously manually operated process can help to increase efficiency, but this measure cannot yet automatically be described as a genuine digitisation solution. Accordingly, the previous chapters do not deal with digitisation, but with standardisation and automation aspects of reporting and business analysis, which has already enabled BT to achieve significant productivity improvements. However, the pressure to perform more tasks with fewer employees and to increase flexibility at the same time does not stop at controlling. The next steps are clearly moving towards the expanded use of existing data through the application of predictive analytics and thus towards true digitalization.

13.4.1 Sales Forecast with Predictive Analytics With the introduction of predictive analytics logics in the Sales Forecast, for the first time a central BT controlling solution goes beyond standardisation and automation and supports the digitalization of controlling.

13.4.1.1 Initial Situation The Siemens units provide a monthly forecast (FC) in ESPRIT. The input from the various countries forms an important basis for the global top-down estimates of the divisions and BUs. The quality of a forecast is one of the most important prerequisites for the external guidance of the Group as well as for the management of the business units. The forecasting process in the organisation ties up a lot of capacity, and the quality to date has largely depended on the assessment of the employees involved. Particularly due

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to the decentralised value creation of BT’s solutions and services business, reliable short-, medium- and long-term revenue planning for the timely build-up and reduction of capacities in the individual countries is a particular challenge. The idea of better supporting the forecasting process with software and minimizing the manual effort and the risk of work errors with the help of algorithms has existed for a very long time. However, reliable technical solutions are only slowly gaining acceptance. With the support of a Siemens internal audit team from Corporate Finance Audit (CFA) and Zone South (Switzerland and Italy), BT launched a pilot project in 2018 to create an analytics-based forecast (FC) process.

13.4.1.2 Requirements The following requirements should be covered by the novel predictive analytics solution: • Creation of an analytics-based FC template to automatically generate a sales forecast for a short-term plan (3 months) and a medium-term plan (18 months). • The short-term model should take into account order intake and order backlog. • The medium-term model is intended to represent seasonality in the quarterly view. • Consideration of the different types of business in the solutions and services business. • Only a few manual interventions to take account of major projects and other significant effects (e.g. IFRS adjustments/structural changes) in the basis of figures.

13.4.1.3 Objectives The introduction of the Sales FC application was intended to massively reduce the manual effort required to create and reconcile sales planning with the various branches and central functions of a country/zone organization. By setting up on a standardised basis of figures, a higher focus should be placed on operational control. 13.4.1.4 Pilot Project Procedure In the first step, the local team conducted a workshop under the leadership of CFA. This deepened the understanding of the business with the specifics of the individual business types and documented the forecast process with its strengths and weaknesses. At a second workshop, the prepared data from the Siemens HANA Data Lake was validated with the stakeholders. The data provision was based on close collaboration between CFA, Siemens IT and SAP representatives. Any data inconsistencies were discussed and solutions sought. In order to include only the statistically relevant data in the forecast simulation, special influences had to be identified and excluded. These are, for example, recently added portfolio elements or projects with very large volumes. Data from the last seven years was prepared and made available to the experts.

13  Standardization and Automation as the Basis for Digitalization in Controlling… MODEL SET Data to train the models, optimize the parameters and select the best model

TEST SET For the evaluation of the predicted performance

MODEL History 5-7 years

TEST last 12 months

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FORECAST DATA To create forecasts for the next 18 months

FORECAST Preview 18 months

Fig. 13.4  Project phases for predictive sales forecast

Based on the first two steps, experts prepared initial data models according to the different business types. Naturally, the different portfolio elements also have different characteristics, e.g. seasonal fluctuations. Therefore, no single model can be the best for predicting all portfolio elements, but each of these elements has its own best model. To find this, two types of models were investigated: ARIMA and Prophet. ARIMA is a classical statistical approach which decides, based on various metrics, how many of the past data points are included in the prediction and with what importance. Prophet was developed by Facebook’s research department and pays special attention to the seasonality of historical data as well as individual outliers and data points after which a sudden turnaround occurs and incorporates this information into the forecast. By combining various parameters (e.g. seasonal fluctuations), in some cases several hundred potential models were reviewed for each portfolio element and the best one selected. The result was then discussed with the business representatives in a third workshop for further optimization. On the occasion of the fourth workshop, the developed data models and simulations could be discussed and agreed with the business representatives on the basis of actual data. During operation, the application is supplied with current data on a monthly basis. Based on this, the models are also checked on a monthly basis and adjusted if necessary. Splitting the data according to the given scheme produces a reliable estimate of the forecast accuracy (cf. Fig. 13.4).

13.4.1.5 Result CFA has come a long way in developing sustainable sales forecasting accuracy across BT’s project, service and product businesses. The data-driven approach provides forecasts with similar accuracy to the current manual process, but with significantly less effort, at any desired time, and with high reliability. Figure 13.5 shows a sample graphical visualization of the sales forecast of a business unit. The focus is on a graphical representation of the sales development with the corresponding growth forecasts as key figures.

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Forecast drill-down by location, business type and technology

Historical, current and future sales including 18-month forecast ose

Fig. 13.5  Application example predictive sales forecast

Of course, the data can also be transferred to an Excel file at the click of a mouse and processed further if required.

13.4.1.6 Success Factors When using modern technologies, for which experience and historical knowledge can only be accessed to a limited extent, somewhat different success factors apply than with established processes and platforms. For the predictive analytics solution at BT, the following points were essential for a successful start: • Close cooperation and regular exchange between SAP key users and BT business experts as well as CF A Data Analytics. • Standardized processes in the SAP system with only minor manual intervention at the end of the fiscal year. • Time for experimentation. The project plan took into account sufficient time for experimenting with different algorithms as well as for regular exchanges with the business representatives to clarify any questions that arose.

13.4.1.7 Next Steps After a project time of eight months, the data models for programming the application were handed over to BT IT. The new application for the South Zone will be implemented on the BT Data Analytics platform by the end of 2019. This will lay the groundwork for the extended rollout in other countries. It is planned to start with a 2–3 day workshop in the respective country. This serves to understand the data structure, SAP as a basis for go/no-go decisions and to estimate the effort required for data preparation. On average, an implementation time of approx. 10 weeks per country is assumed.

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13.4.2 Possibilities and Limits of Predictive Analytics In addition to the classic topics such as budgeting and forecasting, the author sees many other opportunities for the use of predictive analytics in controlling, especially in the early detection of opportunities and risks. For the BT business, the avoidance of negative margin deviations in the solutions business, the planning of own products in the solutions and services business for timely capacity planning in the factories as well as the early detection of customer and supplier risks can be mentioned as examples. Here too, however, it must be emphasized that standardization and automation are important prerequisites for true digitization, i.e. homework still needs to be done in many areas, especially in master data management and master data quality. Due to the regular reorganizations in large companies, the comparability of historical data as well as the know-how and the handling of special influences are special challenges. The topic of predictive analytics is currently being worked on and further developed by various parties. New algorithms are constantly being developed. However, the corresponding results must be tested and implemented in close cooperation with the business partners. This takes time and ties up capacities that are often not available. Both the initial and the regular effort involved in the use of algorithms are often still underestimated.

13.5 Learnings In the following, the author describes his learnings in connection with digitalization in the controlling of building technologies: • The prerequisite for digitization is the standardization of data and processes. The biggest challenge here is data quality. Instead of cleaning up the quality at source, attempts are often made to cure symptoms on the surface by intervening in the front-end logic. Although this brings short-term success for the user in many applications, it leads to high maintenance costs for the applications and makes the next step of automation more difficult because the problem has not been solved at the root. When trying to automate or digitize, you are quickly caught up with reality, meaning data inconsistencies and inadequacies in the process. • There is no such thing as one fits all when developing applications. The target group with the corresponding end users must be clearly defined from the beginning and remain in focus throughout the entire development process. For example, management tools must be easy to use and intuitive. This factor is often underestimated because the front ends are designed by IT and controlling specialists who often underestimate the complexity of the menu navigation. It is important that despite the target group specific design of applications, the same database is always accessed, whether it is a management tool or an operative controlling tool. If possible, the logics should be stored in the backend of the systems in a way that can be calculated from bottom to top and from top to bottom. This procedure ensures the consistency of the information across the various applications and creates the absolutely crucial trust in the tools.

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• The development of an application requires extensive expertise and a high level of resource commitment in the project team. It is important that the controlling experts work closely with the IT partners from the very beginning. Mutual knowledge exchange is the key to success. It is often attempted to transfer an Excel template 1:1 into a new state of the art controlling application. However, this approach prevents the exploitation of innovative analysis and reporting possibilities. Anchoring this basic attitude in the project team at the time of commissioning is an essential element of the cultural change that is required to make digitization in controlling sustainably successful. • A clear and easily communicated IT strategy for data analytics (business intelligence) is essential. For some applications, BT started small and created isolated solutions. This still leads to multiple data stores with high operating costs and limited consistency between the different applications. That’s why the focus continues to be on standardising and merging the various platforms and technologies. This step is not only important for reducing cyber security risks and operating costs, but is also a prerequisite for predictive analytics solutions, which must systematically access different data categories and histories in order to create added value.

13.6 Conclusion In recent years, Siemens has been able to lay an important foundation for digitalization in controlling by focusing heavily on the standardization and automation of reporting and the provision of management information. The use of innovative tools offers new analysis options and supports visualization and thus a quick, timely identification of strengths and weaknesses of a business unit. From a controlling perspective, however, there is still great potential in standardization and automation, and especially in true digitalization in the sense of predictive and prescriptive analytics, machine learning, and digital support services, to name just a few. Therefore, competencies related to handling data and value flows, combined with informatics expertise in controlling, will become increasingly important. This change will present some employees with a major personal challenge, despite training and development programs. There will no longer be a sharp separation between the specialist department and IT in controlling. The author has personally had very good experiences in working closely with IT. The joint use of IT and controlling competencies in the design and continuous development of data analytics platforms and end-user tools is the key to success. Controlling will become increasingly dependent on its technical infrastructure. System failures or faulty data can lead to massive delays or even misinterpretations. Therefore, corresponding security concepts and quality controls are indispensable, which require extensive professional and technical knowledge. Whether we like digitalization or not – we have no choice but to take this path in order to be able to make qualified decisions in the company in a timely manner in an increasingly fast-paced competitive environment.

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Company Profile

Siemens Switzerland AG Smart Infrastructure, Global Headquarters Theilerstrasse 1a 6300 Zug Switzerland Industry: Technology Revenue 2019: EUR 15.2 billion Number of employees 2019: 72.000 FTE Siemens is a global leader focused on electrification, automation and digitalization. In fiscal 2019, the Group generated sales of 87 billion and employed 385,000 people worldwide. The Building Technologies Division (BT), with global headquarters in Zug (CH), was founded on October 1, 1998 through the acquisition of the industrial activities of Elektrowatt AG (Zurich), thus combining several decades of experience in the field of comfort in buildings as well as the protection and safety of people, processes and infrastructures. Optimal building performance at low operating costs enables customers to optimize the energy costs and reliability of their buildings. There is a particular focus on industry-specific solutions for data centres, hospitals, life science companies, airports, hotels and utilities, as well as commercial buildings for international companies and cities and their infrastructures. BT generated global revenues of around EUR 6.6 billion in the 2018 financial year in more than 40 countries, employed 29,000 people. In spring 2019, BT was merged with large parts of the Energy Management Division and a small part of Siemens’ former Digital Factory business to form the new operating company Smart Infrastructure.

Ivo Gerig  was born in eastern Switzerland in 1959 and lives with his family in Schongau (Canton Lucerne). After several years of fiduciary and auditing practice in Western Switzerland, he returned to German-speaking Switzerland in 1985 and completed his training as a certified public accountant in 1990. Already during his auditing activities, his heart beat particularly for international group accounting. When he moved to industry, he gained extensive practical experience in international accounting, consolidation and controlling. Since 1994 Ivo Gerig has been working for Siemens in the Building Services Division, which emerged from the former Landis & Gyr. Various M&A activities, reorganizations as well as changes in the corporate accounting system have shaped his day-to-­day controlling activities over the past 25 years. As Head of Performance Controlling, Ivo Gerig attaches great importance to standardization, automation and digitalization in controlling. Since spring 2019, he has been working full-time at Smart Infrastructure on the development of the Digital Office with a focus on the optimization of internal processes and data analytics.

Digitalization of the Controlling System in Theory and Practice Using the Example of the ARTS Group

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Ingo Cassack

Abstract

The evolution of digital technologies has a major impact on the traditional environment of finance and controlling. Starting from a general classification of the terms digitalization and controlling systems, a consolidation and critical analysis of the various subsystems is carried out. The theoretical presentation considers the controlling tasks (including operational and strategic), the controlling organization (structural and process organization) and the relevant controlling instruments under the influence of digitization. The subsystems presented are then illustrated by relevant practical examples from the ARTS Group.

14.1 Introduction “Digitalization is changing the rules of the game” (Schäffer and Weber 2016, p. 16). This statement by Schäffer and Weber in 2016 represents a fact from a practical perspective today. New business models and modifications in elements of business models are significantly changing companies. For example, platform concepts such as Amazon, Google and Uber are revolutionizing entire industries. But even in industries that are not currently being changed holistically by a platform economy, an evolution of individual elements is taking place as a result of digitalization. These elements can, for example, consist of a digitalization of the finance or controlling area. The following article will explain these

I. Cassack (*) Dresden, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_14

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changes for controlling in more detail with a view from practice. First, general changes for the entire controlling system will be described. In particular, studies and empirical work will also be included. In the third chapter, the tasks, organization and instruments of the digitalization of controlling are described. Finally, these are presented in concrete terms for the service company ARTS in an implementation-oriented manner. The article concludes with theses on the further development trends for digitalization in controlling as well as a short conclusion.

14.2 Digitalization and Controlling Systems The innovations of digitalization and the implications for controlling have now been the subject of numerous studies, some of which are essential from the author’s point of view and will be examined in more detail below. With a relatively large sample of 454 participants from the WHU Controller Panel, Schäffer and Weber provide a very interesting empirical insight. Key results are shown in Table 14.1, so the results of this study will be discussed in more detail below. The answers of the participants of the controller panel can be given on a scale from 1.0 (very low) to 7.0 (very high). A change is also evident as a result of the multi-year survey. The participants surveyed see a particularly strong increase for future topics in controlling in digital skills (plus 1.5) and digital business models (plus 1.4). In the case of digital competence, the assessment on a scale of 1.0 (very low) to 7.0 (very high) changed from 4.2 to 5.7. In the case of digital business models, the approval rating rose from 3.7 to 5.1. Table 14.1  Results WHU Controller Panel Future topics 1 Information systems 2 Data management 3 Efficiency & controlling 4 Digital competences 5 Business partner 6 Participation in strategic planning 7 Self-service reporting 8 Agile corporate management 9 Volatility 10 Internal communication 11 Digital business models 12 Young controllers 13 Business analytics Schäffer and Weber (2018, p. 43)

Meaning 2011 5.6 – 5.4 – 4.4 4.7 – – 4.5 – – 4.0 –

Meaning 2014 5.7 – 5.1 – 4.7 4.4 – – 4.3 4.5 – 4.2 –

Meaning 2017 5.3 5.2 4.9 4.2 4.6 4.4 3.7 3.9 4.4 4.3 3.7 4.1 3.2

Meaning 2022 (E) 6.2 6.1 5.8 5.7 5.6 5.4 5.3 5.2 5,1 5.1 5.1 5.0 5.0

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It is also interesting to note that, according to the study participants, the importance of information systems declined from 2014 to 2017 (minus 0.4 from 5.7 to 5.3), although an increase in weighting is expected in the future. Thus, the design of information systems remains a core task for controlling. In the study by Schäffer and Weber, it has the highest value of all the subject areas with 6.2. It should be noted with regard to the informative value of the study that the WHU Controller Panel is a self-assessment or self-perception of the controlling or finance sector. Regarding the selection of the sample or the data of the study participants (Schäffer and Weber 2018, p. 43). In this context, only three percent (CEOs) are not directly assigned to the finance area. The other 97% of the study participants are controllers, heads of controlling and other financial management functions as well as CFOs. The task of providing information in particular could certainly be assessed diversely by employees from other corporate divisions. A similar trend of strengthening and changing the IT requirement profile of controllers is also noted by Drerup et al. (2018) in their 2018 study (n = 231), which the authors vividly compare with older studies. Here, it seems particularly interesting that older studies already postulated the importance of unspecified IT skills. From the perspective of the results of Drerup et al. (2018), a strengthening and concretization of these requirements can be seen in the context of the digitalization of controlling. This can be seen in detailed expectations of concrete software solutions, but also in a problem-solving competence of the controllers. In addition to empirical studies from the traditional academic environment, there are numerous other practice-led studies that reveal similar tendencies. However, the empirical basis and the scientific, adequate procedure are not always comprehensible to the external reader. The McKinsey study, for example, illustrates that repetitive activities in finance and controlling will be automated in the future. This concerns repetitive activities in suband general ledger accounting as well as in cash accounting. According to this study by Plaschke, Seth, Whiteman, analytical activities (e.g. in the area of business development) are less affected by the changes of digitalization (Plaschke et al. 2019).

14.3 Digitization of Controlling Subsystems Subsystems are understood to be parts of a basic overall system. This is used didactically and also in communication to make a large concept easier to understand. Various differentiations can be used as subsystems for the controlling system. In the following, a distinction is made between controlling tasks, the controlling organization and the controlling instruments. This is also done by some other authors for the area of controlling and this also seems adequate for digitalization (Horváth and Michel 2015). The explanation of the effects of digitization on controlling follows this structure.

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14.3.1 Digitalization of Controlling Tasks There are numerous concepts in theory for understanding controlling according to tasks or the general function. Baum et al. (2014) provide an illustrative overview of the various controlling approaches (cf. Fig. 14.1). These can be vividly structured into narrower and broader definitions. The narrowest definitions, are the information-providing approach (1) and the regulation-oriented approach (2). Beyond these, the limited leadership shaping coordination approach (3), integrates the outcome goal oriented coordination and adaptation of planning and control and information supply system. The comprehensive coordination approach extends to other subsystems of the enterprise (4). Finally, the rationality-­assuring approach also includes the management of management in controlling (5). 5) Rationality safeguarding approach 4) Comprehensive approach to coordination

Human

2) Regulatory approach

Organisation

ControllingSystem

3) Limited Leadership Shaping Coordination 2) Regulatory approach 1) Informational approach

Planning system

Information system

Coordination Control system

Behavioural aspects of controlling Goods & Services

Execution system

Fig. 14.1  Comparison of controlling systems. (Baum et al. 2014, p. 4)

Money

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This preliminary understanding is necessary in order to identify the subsystems of controlling that will be analyzed in the following with regard to digitalization. The focus is on the digitalization of the planning and control system as well as the information supply system. When looking at empirical studies, it becomes apparent that even in the context of digitalization, the focus continues to be on the classic tasks. For example, recent studies also show that reporting, planning, budgeting and variance analyses are in focus (Losbichler and Ablinger 2018, p. 52). The automation and digitalization of these tasks are considered by numerous authors (Plaschke et al. 2019, p. 3). The focus is often still on operational activities, such as accounts receivable, accounts payable or cash accounting. In general, however, digitization also appears to generate significant added value for strategic tasks. Some authors also see the function of data steward in controlling (Marmonti 2019, p.  64ff.). In order to generate automation, digitalization and changes to the controlling organization often take place, which will be discussed in more detail below. In some cases, publications on digitalization also call for new controlling concepts (Kenfenheuer 2019, p. 32). From the author’s point of view, however, these are often not completely new controlling concepts for digitalization, but rather these following the listed presentation. Therefore, this article will draw on the established approaches and explain the innovations in the context of digitization.

14.3.2 Digitalization of the Controlling Organisation When digitizing the controlling organization, it makes sense to differentiate between the process organization and the organizational structure. In publications on digitalization in controlling in general and digitalization of the controlling organization in particular, the focus is often on the process organization. A business process redesign or a customer- and process-oriented redesign takes place. Digitization makes it easier to drive forward the process orientation of finance and controlling processes, such as the order-to-cash process. This order-to-cash example then also reveals the challenge that a change in the process also affects other subsystems of the company. In the case of order-to-cash, this can affect the interface to sales (e.g. new contracts) or between accounting and controlling (invoicing and analysis). For a detailed description of an order-to-cash process before and after optimization, see Momsen (2019, p. 11ff.) as an example. In addition to reorganizing controlling activities, it also makes sense to change the structure and organizational structure. For example, linking a new, digital controlling process with a clear person in charge can drive the digitalization of the organizational structure and process organization. For processes (process organization) and structure (organizational structure), see Pampel (2018, p. 22) for an example, who further extends this to include a governance area. The change in the controlling organization can refer to evolutionary and radical innovations from digitalization. Evolutionary, controlling hubs could emerge that, for example, perform data analysis or operational controlling. In addition, revolutionary, major effects of digitalization can result from innovations in business

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models. These then also lead to organizational restructuring in terms of processes and structure. A new digital business model leads to a new, digitally adapted performance management (Engelhardt et al. 2019, p. 20 f.). In terms of organization, the impact of digital platforms on the organization of controlling in particular must be integrated (Sambol 2019, p. 40). Concrete impact is, for example, the necessary measurement and control of the marketplace. This also implies new instruments of controlling from digitalization, which will be discussed in more detail below.

14.3.3 Digitalization of Controlling Instruments The aforementioned shifts in the focus of controlling from digitalization for tasks and organization also require innovations by innovating controlling instruments. A broader information base from additional data sources (Big Data) leads to new instruments and possibilities in controlling. Thus, prescriptive analyses, simulations, driver-based planning and statistical modeling can also be applied more strongly in controlling through digitalization. On the new possibilities offered by analytics as a controlling instrument, see also Schäffer and Weber (2016, p. 14f.). Scientific analyses continue to show additional potentials of Big Data. In addition to these innovations that were previously assigned to the ideal instruments or methods, there are also further developments in software solutions and mobile applications that significantly change the instruments of the controller. Real-­ time data enable faster reactions and put controllers in a position to directly support managers in their decision-making. In this way, the new possibilities of digitalization turn the pilot for goal achievement or navigator into a real advisor (or business partner). Horváth goes so far as to state, “It is fair to say that only digitalization turns the controller into a real business partnetr” (Horváth 2018, p. 12). The innovations of digitalization for tasks, organization and instruments of controlling will be explained in detail below for the medium-sized ARTS Group.

14.4 Case Study: Digitization of the Controlling System at ARTS 14.4.1 Digitalization of Controlling Tasks As a service company, the controlling tasks at ARTS have been more focused on intangible aspects, the process-related creation and the integration of the external factor ever since the company was founded in 2000. The general definition of services will not be discussed further in this article. As an example, reference is made to Meffert et al. (2018, p. 12ff.) and the explanations of the term and systematisation given there, as well as the literature references there. This also applies to services that are provided very closely or in production as contracts for work. The main driver of digitalization in controlling is the introduction of a group-wide enterprise resource planning system (ERP system). In addition, the

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motivation is to achieve greater efficiency and transparency by driving forward digitalization in Controlling. The ERP system has been introduced in its entirety since 2015 and strengthened at the beginning of 2017. As a medium-sized company, ARTS has chosen the ERP system Odoo. A screenshot for the navigation is shown in Fig. 14.2. Both the CEO and CFO of the group are driving the rapid introduction of all modules as completely as possible. For the finance and controlling area, digitalization leads to a change in tasks for all employees. In order to be able to use the workflow and the strengths of the ERP system in Controlling, preparatory work is necessary in other modules. In the purchasing module, for example, purchase orders must be created so that scanned invoices can be automatically assigned and posting proposals can be created. At the same time, tasks from the project module can ensure tracking. The following figure shows the different modules that are used for controlling tasks. The supply of information at ARTS in particular has become much easier thanks to the ERP system. In addition to more and better data, the speed of information use in particular has also increased. The ERP system Odoo used by ARTS enables easy access via mobile phones or other mobile devices. This has simplified planning and control in particular. The

Fig. 14.2  Screenshot ERP-Odoo at ARTS

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simplification of access in practice means that controlling tasks can also be carried out by managers on the move. Specifically, this digitalization leads to an acceleration of forecasting and rapid variance analysis. This leads to and supports the goal of more controlling with fewer controllers.

14.4.2 Digitalization of the Controlling Organisation At ARTS, digitization is changing both the process and the organizational structure. As described above, the introduction of a comprehensive ERP system leads to significant adaptations of processes and responsibilities. The conversion of previously partially manual work steps into automated, digitized standard processes initially required a clear description of the workflows. From ARTS’ point of view, it was important to adapt essential processes to the possible standards of the ERP system (and not vice versa). Reflection and redefinition of internal processes were important in order to be able to use all functionalities. The increased process orientation also resulted in adapted workflows. Processes such as Hire to Retire, Record to Report, Purchase to Pay and Order to Cash were defined more precisely. This results in clear process responsibility and measurability, which required reorganization. This reorganization of the processes in turn requires good project management, which is also controlled via the ERP system Odoo. In addition to the process orientation, the fast execution of projects is also necessary. The use of a project dashboard should lead to an increase in speed. Such a dashboard is shown in Fig.  14.3. This overview leads to transparency and acceleration. Not only is speed of execution beneficial, but also consistent, timely decision-making. This is also postulated by numerous other practitioners, for example by Deutsche Lufthansa AG (Langer and Schäffer 2019, p. 26ff.) who demonstrate this under the title “Digitalization needs speed in decision-making”).

Fig. 14.3  Project management with Kanban status in the ERP system

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Finally, the change in processes also entails changes in the organizational structure of controlling. For example, clear process owners must be defined for the newly defined processes mentioned above (purchase to pay, record to report, etc.). For the core controlling process Record to Report, this also provided ARTS with the opportunity to improve the efficiency of its creation. From the author’s point of view, controlling with clear key figures (adherence to delivery dates, lead time, costs) was also advantageous.

14.4.3 Digitalization of Controlling Instruments At ARTS, the holistic introduction of an ERP system led to the use of new digital tools as well as new and expanded data sources in the sense of Big Data. As part of the digitalization strategy, prescriptive analyses, simulations, driver-based planning and modeling are now increasingly being used. Simulations and driver-based planning currently supplement the analyses from traditional budgeting. Through the use of e.g. Google Analytics as well as mobile real-time reports, innovations in controlling instruments are also being driven forward at ARTS. However, these very positive additions and opportunities of digitalization must also be contrasted with the risks. New analysis options, new reports and key figures (click rates, process key figures and qualitative key figures) must not lead to the neglect of classic profitability key figures. At ARTS, as a medium-sized group, the question of prioritization is thus always at the forefront. The customer of the report must be involved in the further development of new process-oriented controlling instruments. At ARTS, this is done by involving the division managers. In this way, a company cockpit was created, supplemented by key figures for sales or human resources. The resulting process and the process key figures are regularly communicated as an HR process with the key figures. Figure 14.4 shows the visual representation of the application process and the key process indicators. In the current reporting, these KPIs also serve to make transparency measurable for successful and unsuccessful measures. In combination with quick feedback and self-controlling, this should increase the speed of implementation of effective actions. The ARTS-ERP system focuses on the mobile and up-to-date presentation of controlling options. Condensation and focusing (e.g. in line or bar charts) for evaluation is quick and easy. Figure 14.5 shows one of many evaluation options for project staff, which highlight critical values and necessary measures in real time. Furthermore, an essential tool is the business management of digital projects. Driver-based planning and activity-based costing replace traditional overhead calculations. In addition to a cost analysis, pricing is also essential. Digitized services offer the possibility of decoupling costs and price. However, the controlling instruments must also provide important information about value generation for the customer. (On product costing and pricing, see also Spitzenpfeil and Adelt (2015, p. 22f.)). In ARTS, target pricing is created via benchmarking. Big Data and business analytics certainly offer optimization potential here for the future. The simplicity of the ERP system also supports gamified learning. This means that the controlling instruments can also be used via a mobile application.

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Contact points

24.000 + 10% to 2018

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General Offers

per

„“

Customer interview 1440 Interview rate 5% „

Job Offer HIRE

720 Positive quota 50% 540 Bounce rate < 25%

750 chances Fig. 14.4  Key figures of the HR process at ARTS

4.3.2020

Statistics attendance - Odoo Grouped

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Stacked

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January 2020 July 2019

May 2018 August 2019

September 2018 September 2019

October 2018 October 2019

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11.000.00 10.000.00 9.000.00 8.000.00 7.000.00 6.000.00 5.000.00 4.000.00 3.000.00 2.000.00 1.000.00 0.00

Fig. 14.5  Graphical evaluation option for project members

Nov ember 2018 November 2019 July 2017

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14.5 Further Development Trends of Digitalization in Controlling Digitalization has arrived in controlling. Numerous quantitative studies as well as qualitative analyses confirm this trend. From the author’s point of view, the further development will affect tasks, organization as well as instruments. In the tasks of controlling, an answer to new, innovative business models must be found in particular. Companies whose business model is based on a digital platform (e.g. Amazon, Google, Uber) focus more on growth than on profitability. Controlling that only focuses on the profit target/profitability does not go far enough here. Corporate value-­ oriented controlling must become more of a focus here. At the same time, controlling also has the task of keeping the other stakeholders in focus. In addition to the development of controlling tasks from the new business models, it is also necessary to adjust the classic tasks to the digital challenges. Accordingly, the organization of controlling is also subject to further development. The increase in importance of process orientation, for example through the automation of end-­ to-­end processes (e.g. record to report, hire to retire, purchase to pay, order to cash), is still not reflected in the solutions for the controlling organization, either in theory or in practice (KPMG 2019). The focus on a process organization of controlling will thus certainly continue to advance in the coming years. At the same time, this process orientation also affects the instruments of controlling. This is also not a new development trend. Since the introduction of activity-based costing, many authors have postulated a trend away from classical costing towards activity-based costing. This trend is reinforced by the development of the corporate structure towards larger shares of service companies. These companies often provide their services as intangible goods in processes. It is important to emphasize that the forecast of the future development of digitalization in controlling remains a snapshot. It is important to reflect on this again and again. For example, the ten theses listed by Kieninger et  al. (2015) serve as a basis. The general analysis and general conclusions are still valid today in the view of the author.

14.6 Conclusion In practice, digitalization offers enormous opportunities for increasing effectiveness and efficiency. For the ARTS Group, only some of these potentials have been realized. At the same time, there is also a shift in entrepreneurial focus. Digital tools and digitalization in the organization leads to faster decisions and higher realization pressure of tasks at ARTS. For the controlling tasks, there is a tendency away from data preparation activities towards increased analysis. With regard to the controlling organisation, a strong shift from an organisational structure to a process organisation is evident at ARTS. This is mainly due to the (production-related) services that ARTS provides. Finally, this entrepreneurial focus is also reflected in the instruments used.

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Overall, the role of the controller is also changing. He has long since ceased to be just a number cruncher, and the role as a guide to achieving goals only partially describes the job. New opportunities arising from digitalization are also changing the role of the controller. Or in the words of Horváth: “It is fair to say that it is digitalization that makes the controller a true ‘business partner’” (Horváth 2018, p. 12).

Company Profile

ARTS Airport Center Dresden Hermann-Reichelt-Str. 3 01109 Dresden Germany Industry: Services Turnover 2018: >EUR 30 million Number of employees 2018: 500 FTE ARTS is an expert in engineering and manufacturing services, technology consulting and HR services. At five branch offices and over 25 project locations, more than 500 technical and commercial employees work daily to realize the visions of the customers. In the process, it will become clear which practical design potentials exist in the use of digitalization in the controlling system. The article concludes with an outlook on some development trends and a brief conclusion.

References Baum, H.-G., A.G. Coenenberg, and T. Günther. 2014. Strategisches controlling. 5th ed. Stuttgart: Schäffer-Poeschel. Drerup, B., F. Suprano, and A. Wömpener. 2018. Controller 4.0 – Anforderungsprofil des Controllers im digitalen Zeitalter. Zeitschrift Controlling 30 (Spezialausgabe): 13–19. Engelhardt, P., O. Gassmann, and K. Möller. 2019. Innovative Geschäftsmodelle steuern und skalieren. Zeitschrift für Controlling und Management Review 31 (2): 16–25. Horváth, P. 2018. Vorwort. Zeitschrift für Controlling und Management Review 30: 12. Horváth, P., and U. Michel. 2015. Vorwort. In Controlling im digitalen Zeitalter, Herausforderungen und Best-Practice-Lösungen (V), ed. P. Horváth and U. Michel. Stuttgart: Schäffer-Pöschel. Kenfenheuer, K. 2019. Neue Controlling-Konzepte gefragt. Controlling & Management Review 63 (2): 32–37. Kieninger, M., W.  Mehanna, and U.  Michel. 2015. Auswirkungen der Digitalisierung auf die Unternehmenssteuerung. In Controlling im digitalen Zeitalter, Herausforderungen und Best-­ Practice-­Lösungen, ed. P. Horváth and U. Michel, 3–13. Stuttgart: Schäffer-Pöschel.

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KPMG. 2019. https://hub.kpmg.de/digitalisierung-­im-­rechnungswesen-­2019. Accessed on: 23.08.2019. Langer, C., and U. Schäffer. 2019. Digitalisierung braucht Geschwindigkeit in der Entscheidung. Zeitschrift für Controlling und Management Review 31 (2): 26–31. Losbichler, H., and K. Ablinger. 2018. Digitalisierung und die zukünftigen Aufgaben des Controllers. In Digitalisierung und Controlling: Technologien, Instrumente, Praxisbeispiele, ed. R.  Gleich and M. Tschandl, 49–71. München: Haufe. Marmonti, S. 2019. Der controller als data steward. Zeitschrift für Controlling und Management Review 31 (2): 64–67. Meffert, H., M. Bruhn, and K. Hadwich. 2018. Dienstleistungsmarketing: Grundlagen – Konzepte – Methoden. 9th ed. Wiesbaden: Springer Gabler. Momsen, B. 2019. Controlling wiederkehrender Umsätze. Zeitschrift für Controlling und Management Review 31 (2): 8–15. Pampel, J.R. 2018. Digitale Horizonterweiterung – Begleitung der Innovation von Geschäftsmodellen durch das Controlling. Zeitschrift für Controlling und Management Review 30: 8–17. Plaschke, F., I.  Seth, and R.  Whiteman. 2019. https://www.mckinsey.com/business-­functions/ strategy-­and-­corporate-­finance/our-­insights/bots-­algorithms-­and-­the-­future-­of-­the-­finance-­ function. Accessed on: 10.05.2019. Sambol, S. 2019. Digitale Geschäftsmodelle prüfen und bewerten. Zeitschrift für Controlling und Management Review 31 (2): 38–44. Schäffer, U., and J.  Weber. 2016. Die Digitalisierung wird das Controlling radikal verändern. Zeitschrift für Controlling & Management Review. 28 (6): 8–17. ———. 2018. Digitalisierung ante portas – Die Veränderung des Controllings im Spiegel der dritten WHU Zukunftsstudie. Zeitschrift für Controlling und Management Review 30 (1): 42–48. Spitzenpfeil, T., and I. Adelt. 2015. Winning in the digital world: Controlling und Digitalisierung. In Controlling im digitalen Zeitalter, Herausforderungen und Best-Practice-Lösungen, ed. P. Horváth and U. Michel, 15–26. Stuttgart: Schäffer-Pöschel.

Ingo Cassack  studied and received his doctorate under Prof. Dr. Dr. h. c. mult. Péter Horváth. He then worked for a German group for ten years in responsible finance and controlling management positions in Germany, France, Brazil and Poland. After working as CFO for a large German automotive supplier (3700 employees, EUR 700 million in sales), he has been CFO and Commercial Director at ARTS Group since July 2016.

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Practical Example EKZ Eltop AG Paul Sidler and Luca Gerussi

Abstract

In the past, there was a lack of up-to-date management information, planning was very time-consuming and not targeted enough, and reporting was not appropriate for the various levels. A few years ago, a transformation process was initiated. The following article describes the most important changes. For example, with the introduction of Advanced Budgeting, the entire planning process was significantly simplified, accelerated and made more effective. Using a driver-based approach, reporting was changed so that the previous key figures and management information were redefined and made available to management via self-service. Business processes were also simplified and lead times shortened. These and other measures have made a gratifying contribution to the significant increase in EBIT – while at the same time ensuring a high level of service quality.

P. Sidler (*) Zürich, Switzerland e-mail: [email protected] L. Gerussi (*) Baar, Switzerland e-mail: [email protected]

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_15

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15.1 Introduction Until six years ago, Eltop was divided into two areas, the electrical installations and the specialist electrical shops. For strategic and economic reasons, it was decided to discontinue the specialist shops division. This decision was a step towards the initialization of the improvement of the financial management and the increase of the transparency of Eltop. In the past, there was a lack of up-to-date management information and planning was very time-consuming and not targeted enough. In addition, reporting was not appropriate for each level and the data relevant for decision-making could not be made available in a standardized way. Profitability targets were significantly increased and Eltop was converted into a public company in 2018. A transformation process was initiated in order to be able to achieve the targets. The strengthening of financial management and various digitalization measures in the areas of planning, reporting and analysis play an important role here.ger

15.2 Initial Situation 15.2.1 Presentation of the Current Situation by Means of the Maturity Model As a starting point for financial transformation, the most important financial characteristics were recorded and rated on a scale of 1–10 (see Fig. 15.1). This included, for example, the availability of control information, standardization or automation. The same characteristics were also used to define the target picture per characteristic, so that the most important thrusts of control are clearly visible on the maturity model.

15.2.2 Reporting The reports were extensive, historically grown and contained a lot of information. Important key management figures were only available with a time delay. For example, Feature

Scale 1

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Availability Data Sources Meaningfulness Automation Standardisation Target group Indicators / KPIs Scale 1-10 1 low expression, 10 very high expression

Fig. 15.1  Maturity model

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income statements were only available at quarterly level. Readability was limited, among other things, by the lack of visualizations and the lack of focus on top key figures. As a result, it was difficult to derive insights. The reports also did not contain any recommendations for action. The entire order processing, from opening a customer order to invoicing, took place in a conversion system. The order-related data was then periodically transferred via an interface to the ERP system, where, among other things, results analysis was performed. Monitoring the interface was resource-intensive and error-prone. The degree of standardization and automation was limited. Pull reporting did not take place; instead, the reports were prepared individually and delivered personally to the recipients. Individual needs were taken into account to a large extent. This was very user-­ friendly for the individual recipient, but required a lot of resources. The different needs for level-specific control were not defined. Also, the terminology was not handled in a uniform way; this is particularly important as a common language is crucial to avoid misunderstandings and to be able to work efficiently and effectively.

15.2.3 Analysis The control parameters were mainly financial key figures, i.e. there was no balanced presentation with other levels such as market, customers, processes or employees as is known from the balanced scorecard, for example. In addition, values related to the past dominated. There were few early indicators and future-oriented key figures (e.g. incoming orders, rolling projections, concrete measures in case of deviation from targets). The analysis took place in the Um system because the relevant data was not available in the ERP. Since the value drivers had not been recorded, there was no common understanding of the main control variables and levers. This led to a lot of detailed analyses being prepared manually.

15.2.4 Planning In the past, management had a very high demand on the accuracy and level of detail of the budget. The preparation was extremely detailed and tied up a great many personnel resources across all hierarchy levels over a long period. In addition, the budget values of the various cost types have been distributed to the more than 30 branches. It was a mixture of a bottom-up approach, top-down adjustments, various rounds of distribution of revenues and costs to the branches, so that the targets could finally be met. Organizational changes, new developments and expectations had to be mapped in a laborious and time-consuming manner. It was also very difficult to map scenarios. It was a time-­ intensive iterative procedure that had grown historically and whose benefits were not in proportion to the effort involved.

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15.3 Objective 15.3.1 Target Image In a workshop, the target picture of corporate management was discussed and defined with the relevant stakeholders. Table 15.1 shows the most important key figures divided into income statement, balance sheet, cash flow and key figures. Since not every control variable is important for the stakeholders business, branch and project management, the relevance was marked with an x (Peter and Pfaff 2008). In addition to the discussion regarding the relevant control variables, it was stated in the target picture that timely and level-appropriate control should be made possible. As a result, managers and project leaders must be further enabled to derive findings and possible corrective measures from the key figures and to make them available in a timely manner (Barkalov 2015). The target picture was defined using a maturity model and compared with the current situation. It became apparent that the greatest changes should be aimed for in the areas of availability, standardisation, informative value and indicators (cf. Fig. 15.2).

15.4 Solution Approach 15.4.1 Prerequisites 15.4.1.1 Value Driver Tree One of the most important prerequisites for successful management is an understanding of the value-driving variables of the unit. In order to further strengthen this understanding, a value driver tree was sketched in a first step (see Fig. 15.3). To define the value driver tree, a Table 15.1  Measurement and control variables Income statement Proceeds Production costs Gross profit Balance Work in progress Cash flow Forecast Key figures Productivity Contribution margin per employee Utilization preview

Management

Branch management

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x x x

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Actual Target Scale 1-10 1 low expression. 10 very high expression

Fig. 15.2  Target image of the maturity model

multi-step approach is appropriate. In a first step, the top financial ratios are determined (e.g. gross profit, EBIT). These are then subdivided into earnings figures such as sales and variable costs. The final step is to determine the value drivers (e.g. volume sold, times price, per region). This is done with the aim of distinguishing the relevant and value-­driving parameters from the irrelevant ones, which forms the basis for setting up the controlling cockpit.

15.4.1.2 Control of the Organisation In order to clarify the question of how Eltop is managed, a proposal was drawn up by Controlling and submitted to the stakeholders. The discussion focused on the key figures with which Eltop can be managed and the frequency with which the information must be made available. 15.4.1.3 Standardisation Versus Individual Needs It is important to note that the highest possible degree of standardisation should be achieved. This creates a uniform understanding, which serves as an important basis for future discussions and decisions. In addition, maintenance costs can be reduced to a minimum. This requires persuasion on the part of Controlling, but this will pay off later.

15.4.2 Reporting 15.4.2.1 Level- and Addressee-Oriented Real-time queries are indispensable for operational employees such as order, project and branch managers for a smooth and profitable processing of customer orders. These are displayed in the order cockpit (see Table 15.2).

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+Revenue Turnover per hour x number of hours Profit surcharge per hour

+Gross profit

-Own work Charge rate per hour x number of hours

-Material and external services

-Structural costs

=Result

Fig. 15.3  Section of the value driver tree Table 15.2 Reporting Real time Order cockpit for managing orders/customer projects

Weekly Order report – overview of current sales orders Invoice journal Cash flow forecast

Monthly Key figure cockpit per organizational unit Detailed reports for analysis purposes Performance reviews and measures

A weekly overview of the current orders provides information on the respective capacity utilisation, the financing of the orders and the settlement volume (invoice journal). The addressee group is intended for the management and the branch managers. The key figure cockpit is to be made available to the business and branch management on a monthly basis. Reports are made to the Board of Directors on a quarterly basis.

15.4.2.2 Real-Time Key Figures The changeover from quarterly to monthly financial statements forms the basis for a monthly key figure cockpit. The focus is placed on a constant monthly performance. This facilitates the monitoring of the entire branch network and the early initiation of any corrective measures.

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15.4.2.3 Self Service Portal To ensure that the reports are available at any time and from any location, a self-service portal was set up. This was done with the aim of providing stakeholders with management-­ relevant content quickly, but also cost-effectively. As a result of the chosen pull principle, great importance was attached to empowering stakeholders to the extent that they are able to generate and interpret reports themselves. As further measures, the branch manager was further strengthened in his function as local entrepreneur and a performance-related bonus-malus wage system was introduced. The interest in financial key figures was strengthened in the process. 15.4.2.4 Visualizations When designing the reports, great importance is attached to the visualization. Figure 15.4 shows the actual order intake (AE), which is compared with the planned revenue on a rolling basis over 12 months. The planned revenue is 60 MCHF in the current fiscal year 2019. Of this, 25% will be generated with service orders. For these small orders, no measurement of order intake is performed. The remaining 75% will be generated with large orders in the contract business. Here, it is essential to measure incoming orders as an early indicator of future capacity utilization. The view can be selected in the Self Service portal as a whole or per organizational unit.

15.4.3 Analysis 15.4.3.1 Structure and Integration of Order Cockpit in ERP For the successful completion of customer projects, it must be possible to view the order-­ related data, such as orders for external services and confirmations of hours worked by the assembly personnel, at any time. This forms the basis for project control. For this purpose, a separate order cockpit was developed and integrated into the existing ERP.  The user interface, also called GUI, uses standard transactions of the ERP system in the background. Customizing is therefore only necessary for the display in the GUI.  The rest remains in the standard, which brings decisive advantages for updates or upgrades. The 50’000 40’000

25% 75%

Object Service

OI Planned OI Actual

30’000 20’000 10’000 0

Jul

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Fig. 15.4  Order intake actual versus planned

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order cockpit thus becomes the indispensable user interface of every project or branch manager for processing and controlling his customer orders.

15.4.3.2 Structure of Data Sources, Data Cube, Business Warehouse Since the order cockpit is primarily limited to the processing of an individual project, it does not provide a complete overview per organizational unit. To answer higher-level questions, it must be possible to aggregate the data of the individual orders per organizational unit. For this purpose, a data cube was created that contains all order-related information and serves as a source for reporting. Answers to the following questions can be derived from this: • • • •

How high is the worklist of an organizational unit or the entire company? How is the work started financed by partial payments? How much was billed to customers per organizational unit? What are the top customers per organizational unit?

15.4.4 Planning 15.4.4.1 Consistent Key Figs A decisive point in the implementation was that the control-relevant key figures are reflected in all processes and instruments, i.e. from reporting and planning to the individual objectives of the employees. This helps to establish a common language, to focus on the essential aspects and thus to ensure a concrete and consistent implementation. It is also important that all financial figures are recorded and made available in only one place (single source of truth). During planning, for example, the control-relevant key figure contribution margin per FTE was defined and thus the target results per store were defined. This is a key figure on which the store manager has a direct influence. The variable wage component of the store manager is also linked to this key figure. 15.4.4.2 Significant Reduction of Complexity In the traditional budgeting method used to date, a separate branch income statement and cost center plan were prepared for each of the 40 organizational units. The twelve cost centers were also planned individually. The planning of the newly applied advanced budgeting method is carried out by Controlling in a short top-down approach. After defining the most important financial targets for Eltop, such as EBIT, net working capital, etc., these are broken down into individual sub-areas (Rieg 2015). Budgeting is now done on the basis of fewer key figures. In addition, only very few people are involved in the planning. Thus, the number of people involved could be reduced from almost 50 to six. The most important positions are recorded by Controlling on the basis of empirical values and assumptions. Furthermore, only very few budget values are stored in the ERP, since the management of the units and the stores is carried out via the most important target figures (e.g. contribution margin per FTE, contribution margin per store, productivity). The total budgeting time could also be significantly reduced (see Fig. 15.5).

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Involved persons

Cost center

Income statement

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New Advanced Budgeting

Number: 40 (40 branches) Qty: 1

Number: 52 (40 branches, 12 cost centers)

Planning ERP

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Fig. 15.5  Simplification of budgeting

15.5 Learnings 15.5.1 Establish Principles at the Outset At the beginning of the project, principles should be defined which are relevant for the control. This helps all those involved to orient themselves to these standards again and again. Thus, in the reports or planning, it was asked again and again whether information was relevant to control and was actually needed by the management for control. If the information was not relevant to management, it was not included because otherwise there was a risk of getting lost in the details. In the principles, it was also determined which are the important key figures that should be evaluated consistently across all processes.

15.5.2 Putting Yourself in the Role of the Receiver Visualization plays a not insignificant role in reports. The reports should be prepared with graphics and tables in such a way that they attract the attention of the recipients and the necessary actions can be clearly derived. Managers only deal with something that is

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relevant to them and also visually appealing. In addition, the core of the message must be clear and logically comprehensible. The topic of visualizations and user-friendliness will certainly gain in importance in the future.

15.5.3 Change Takes Time and Resources The introduction of new processes and tools takes time and must be supported and exemplified by top management. Therefore, sufficient time must be allowed for pilots, necessary adjustments, training, communication, introduction, etc. For example, a self-service portal means extra work from the recipient’s point of view. After all, not everyone will be immediately enthusiastic about the changes. Many recipients feel that controlling delegates work to them that the finance department used to do. In this case, the controller must take on a different role than that of the number cruncher; he must get the recipient on board, show him the benefits and accompany him during the changeover phase. For this, the controller needs the appropriate time, patience and communication skills.

15.6 Conclusion In the past, there was a lack of up-to-date management information and planning was very time-consuming and not targeted enough. The reporting was also not level-appropriate and the data relevant for decision-making could not be made available in a standardized way. A few years ago, among other things, the profitability targets were significantly increased, Eltop was converted into a public limited company in 2018 and a transformation process was initiated. The measures in the areas of control, planning, reporting and analysis play a central role here. Important control information must be available in an up-to-date and targeted manner in order to be actively used as a competitive advantage. On the occasion of the introduction of advanced budgeting, the value-driving variables and costs that can be influenced were analyzed so that the entire planning process could be significantly simplified, accelerated and made more effective. This driver-based approach also has an impact on reporting. The previous key figures and reporting were redefined and made available in a cockpit for each organizational unit (push and pull). The interfaces to the ERP environment systems were eliminated and replaced by an order cockpit integrated in the ERP. This significantly improved the processing of customer orders. Business processes were simplified and throughput times shortened. It was also important that management information be made available to management via self-­ service. In addition, the variable salary components were consistently aligned with the target figures that could be influenced. All these measures have made a gratifying contribution to the marked increase in EBIT – while at the same time maintaining a high level of service quality – at Eltop.

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Recommendations for Action 

• Additional competencies for controllers Controllers need a much higher IT affinity than before, i.e. the competence profile of the controller expands and becomes much more extensive and demanding than before. In addition to all the competencies that have been necessary up to now, the controller must acquire the corresponding knowledge and experience in the areas of IT management, data procurement and evaluation, value flows, analytics, business intelligence, etc. This means that the controller’s competency profile is expanding and becoming much more extensive and demanding than before. If the controller or the controlling department only has a limited amount of such competencies, measures must be taken to close the delta as quickly as possible. Even with small financial means, a lot can be achieved here. In the future, the above-mentioned competencies will become even more important for the controller and this cannot be delegated to external specialists, but the competencies must be built up internally (Egle and Keimer 2018). • The downside of digitization In connection with digitization, the one-time and recurring operating costs (e.g. for IT systems, licenses) as well as the associated human resources must not be disregarded. In particular, the maintenance of ERP systems and Um systems can cause recurring costs that should not be underestimated. For this reason, a classic project cost/benefit analysis must be carried out for all new solution approaches and the recurring maintenance expenses must be examined particularly critically. For this reason, an attempt was always made to remain within the ERP standard for the new solutions. Because the automations, no matter how attractive they may appear, have their corresponding price. • Streamlining and focusing of reporting In most companies there are far too many reports, which have often grown historically. Since the reports are nowadays either automatically generated or prepared with very little personnel effort, this leads to the fact that the raison d’être of reports is not critically questioned. In fact, it would be necessary to delete all reports from time to time and use a green field approach to define which reports are actually needed by management and are relevant for controlling. The classic W-questions can already achieve a lot here: What happens with this report? Who really needs it? What decisions and concrete measures are derived from it? • Measure orientation in reporting Management must actually want and support the transformation of the financial instruments (Tone at the Top). Analyses and reports must be integrated into the management rhythm and controlling must be part of this management body. The best reports do not bring any added value if they are not used to take measures to improve the operational units.

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Company Profile

EKZ Eltop AG Querstrasse 17 8951 Fahrweid Switzerland Branch: Electrical installation Turnover 2018: 60–70 mCHF Number of employees 2018: approx. 450 employees EKZ Eltop AG is a 100% subsidiary of the EKZ Group. The EKZ Group is one of the leading energy suppliers in Switzerland with over 1400 employees. EKZ Eltop AG plans and implements customised solutions in the fields of electrical installation, telecommunications, IT and building automation for private and business customers. A key success factor of EKZ Eltop AG is its proximity to customers. This is ensured by a unique decentralized structure with over 30 electrical installation branches. The company currently employs around 450 people, including 120 apprentices, and generates sales of 60–70 mCHF.

References Barkalov, I. 2015. Effiziente Unternehmensplanung  – Weniger Aufwand, mehr Flexibilität, mehr Geschäftserfolg. Wiesbaden: Springer Gabler. Egle, U., and I.  Keimer. 2018. Kompetenzprofil „Digitaler Controller“. Controller Magazin 43 (5): 49–53. Peters, G., and D. Pfaff. 2008. Controlling: wichtigste Methoden und Techniken. Zürich: Versus. Rieg, R. 2015. Planung und Budgetierung: Was wirklich funktioniert. Heidelberg: Springer Gabler.

Paul Sidler  is a member of the Board of Directors of EKZ Eltop AG and Head of Controlling for the EKZ Group. He holds an MBA from the University of St. Gallen and a degree in business administration from the University of Applied Sciences. He was previously an Executive Director at Ernst & Young in the Risk & Financial Advisory division.  Luca Gerussi  is a certified expert in accounting and controlling and a business economist FH. He has worked for a number of renowned companies as a divisional controller. Today he is responsible for finance and a member of the divisional management of EKZ Eltop AG.

Possibilities and Limitations of Mobile Applications for Controlling

16

Robin Nunkesser and Jens Thorn

Abstract

In an almost unprecedented success story, mobile devices and their software (apps) have become ubiquitous utensils for end users. In controlling, there is – despite potential benefits and clear interest  – still no widespread use of apps. In this chapter, the possibilities of mobile devices and their usefulness for the tasks of controlling – especially under the aspect of digitalization – are highlighted. At the same time, challenges in the use of apps and reasons for the hesitant use of apps to date are mentioned and analyzed. The main reasons are security concerns, limited space for information presentation and necessary IT integration. Suitable recommendations for action are intended to contribute to increased usability in the future.

16.1 Introduction Mobile devices and their software (apps) have enjoyed an almost unprecedented success story since the release of the iPhone in 2007 at the latest. After the opening of the App Store in 2008, it took less than a year for one billion apps to be downloaded. In many areas, the success with end users has also led to the consumerization of corporate IT, i.e. the increasing use of mobile devices for tasks that were traditionally performed with stationary computers. This naturally leads to points of contact with controlling, which is greatly influenced by digitalization.

R. Nunkesser (*) • J. Thorn Hamm, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_16

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16.1.1 Current Tasks and Challenges of Controlling The essential tasks of controlling, understood as ensuring the rationality of corporate management, are (Weber and Schäffer 2016, p. 47): • Planning • Control • Information supply Planning considers the mental, systematic anticipation of future actions across all areas of the company and can be divided into operational and strategic planning with regard to the planning horizon. In control, the planned values are compared with the actual values. Appropriate countermeasures are initiated in the event of possible deviations. In addition, information is supplied to the decision-makers in the company through reporting. The objective of reporting is to create transparency about the company and its environment. Reporting in particular plays a central role as an information supply system for controlling and decision-makers in a company. Reporting should inform decision-makers quickly and reliably about current developments in the company and its environment. Thus, reporting can also be described as the core product of controlling (Weber and Schäffer 2016, p. 237). Controlling has to cope with the challenge of increasing complexity, uncertainty and dynamics of changes in the business environment. Reporting mainly uses costs and revenues as control variables; however, the importance of quantity data for reporting, such as quality or time values, is increasing (Weber and Schäffer 2016, p. 83). At the same time, a large amount of data is increasingly being generated, for example in sales, production or finance, primarily as a result of the digitalization of processes in companies.

Required Features of the Reporting System

In summary, we can state that in order to fulfill the information function for decision makers in the company, the reporting system must have the following characteristics (Schön 2018, p. 20): • • • • •

Quick Reliable Flexible Relevant Comprehensible

In the best case, mobile devices can provide support here. But what exactly constitutes mobile devices from today’s perspective? Let’s first take a look back.

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16.1.2 Brief History of Mobile Devices The first distinguishing feature of mobile devices compared to stationary devices is their location independence. Many milestones of the early development phases were reached at the Xerox Palo Alto Research Center (Xerox PARC). Since some of the most important features of today’s mobile devices can be traced back to the developments there, we will focus on them first. In 1976, prototypes of the Xerox Notetaker, a precursor of today’s notebook, were built there. In 1991 Marc Weiser describes research results on two new mobile devices: tabs and pads (Weiser 1991). These were already networked, locatable and equipped with sensors and thus able to act context-sensitively. Of course, much more happened between 1991 and 2007 (introduction of the iPhone), but little that extended the core properties of location independence, networking and context sensitivity. The introduction of the iPhone in 2007 added – besides many technical developments – simplicity and more direct interaction.

16.1.3 Current Characteristics of Mobile Devices The aforementioned properties of location independence, networking, context sensitivity, simplicity and direct interaction are now inseparably linked with the mobile end devices smartphone and tablet. With the exception of networking, the other properties also include unique selling points that are only possible in this quality with mobile end devices. Even if stationary end devices are equipped with touchscreens, for example, a good part of the simplicity and directness is lost due to the design. In addition, there are topics such as augmented reality, which are also only possible with mobile devices, but also topics such as artificial intelligence, which are now of enormous importance for both mobile and stationary devices.

Core Features of Mobile Devices

The most important features of tablets, smartphones and wearables are • • • • •

Location independence Networking Context Sensitivity Simplicity Direct interaction

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16.2 Relevant Mobile Devices and their Usage Habits Since Microsoft released Windows 10, several mobile devices such as laptops, tablets and their hybrid variants can be used with the same operating system. For controlling purposes, there are few differences between laptops and stationary devices. However, the usability outside the workplace increases the probability of loss or theft. In addition, a smaller display area is available compared to stationary devices. The differences in tablets and hybrid devices are significantly greater, for example, due to touch operation. In the case of pure tablets and smartphones, however, there is a duopoly in the operating system area consisting of devices with the iOS operating system from Apple and the Android operating system from the Open Handset Alliance (with Google as the leading member). Among tablets and smartphones, they currently (August 2019) reach a share of over 99% of data traffic in Germany, according to Statcounter (2019). Therefore, notebooks as end devices and Microsoft Windows as operating system are only marginally considered in the following. The other relevant mobile device classes are considered below in ascending order according to the degree of difference to notebooks.

16.2.1 Tablets Tablets have been established as a new end device class by Apple’s iPad in 2010. Compared to notebooks, they are characterized primarily by touch operation, more built-in sensor technology, even better mobility in terms of weight and battery life, and other operating systems (iOS and Android).

16.2.2 Smartphones Smartphones are – apart from feature phones, which are not relevant in the context considered here – not the only type of mobile phone, but the one that has become dominant. Compared to tablets, they are characterized by telephony, even more built-in sensor technology, smaller screen sizes and different usage habits. Smartphones are typically always at hand these days.

16.2.3 Wearables The term wearable is currently mainly associated with smartwatches, but actually refers to all end devices that are worn directly on the body. We limit ourselves here to the consideration of smartwatches with the Android and iOS operating systems, which have been available since 2014.

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16.2.4 Usage Habits The usage habits of mobile devices sometimes differ significantly from the usage of stationary devices. In 2007, Leland Rechis (User Experience Designer at Google; cited by Wellmann 2007) presented three groups of smartphone users: Repetitive now is someone who searches for the same information over and over again. This information can be, for example, the current weather, emails, social media updates or stock prices. So it’s all about repetitive tasks. Bored now is someone who has some time on their hands and wants to take advantage of it, such as waiting at the bus stop or in a café. Urgent now as a behavior means that it is about finding something quickly. This is often related to where one is at the moment. If someone is sitting on the tram, he wants to quickly find out at which stop he has to get off in order to get to a certain location. So this is about tasks that have a certain urgency. At the latest with the appearance of the iPad, usage habits were added that also take up significantly longer periods of time and cover more and more professional activities. Certain professional activities can be completed entirely with mobile devices. However, many IT-intensive tasks – including tasks in controlling – still cannot be performed exclusively with wearables, smartphones or tablets. These devices are more likely to complement them in areas where advantages such as location independence, context sensitivity, simplicity, or direct interaction contribute to more efficient task completion.

16.3 Reporting on Mobile Devices from a Controlling Perspective Based on the information provided by reporting, decisions are made in various departments such as production, sales or human resources of a company or by top management. The quality of the decisions depends on the quality, speed and flexibility of the information provided by the reporting system. Moreover, the comprehensibility of the reports used by the decision makers plays an important role. However, decision makers more often face an information dilemma (Schön 2018, p. 20). On the one hand, extensive, historical and unstructured information is available  – also caused by the digitalization of many processes. On the other hand, decision-makers lack fast, relevant and, above all, future-related information for decision-making. The question is to what extent mobile devices can help decision makers to ultimately make better decisions. Mobile devices are constantly available to decision-makers due to their transportability. Thus, information on mobile devices can be used by decision makers in a timely manner, in relation to the addressee and, above all, independent of location (in a survey by Legenhausen et al. 2018, location independence and speed are considered the key requirements for reporting on mobile devices). This means that decision-makers can, in principle, react quickly and efficiently to changes in the company or the corporate environment and make decisions on the basis of the available information. The rapid

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availability of information can also in turn trigger timely reactions, for example with the help of comments by a decision-maker. In addition, reports must be comprehensible and, above all, self-explanatory for decision-makers. Self-explanatory means that the relevant data in the required level of detail is flexibly made available to decision-makers by the reporting system on mobile devices (Rohe and Hoffjan 2018, p. 18). High flexibility of a reporting system also includes that the relevant data quickly achieves the attention of a decision maker. On the other hand, this means that decision makers no longer necessarily have to rely on controllers to analyze reports (Weber et al. 2012, p. 108). Another advantage of reporting on mobile devices is the context sensitivity already outlined (Wehrum and Heinrich 2013, p. 321). In the context of reporting, this means that reports can be tailored to the preferences and needs of the decision maker in a way that is appropriate for the addressee. For example, applications that automatically display the relevant information when decision-makers visit a plant are conceivable. The advantages described and the success in the consumer sector make mobile devices highly attractive. Surveys (e.g. cited in Scheffner and Pham Duc 2012) showed a high level of interest in mobile reporting solutions, at least for top management, early on after the establishment of tablets as an important end device class. However, implementation is proving to be slow.

16.4 Challenges in Controlling In 2015, the Institute for Business Instruments and Technologies (IBIT) at Bremerhaven University of Applied Sciences conducted a survey on reporting on mobile devices for SMEs (Legenhausen et al. 2018). The result showed that less than a quarter of all respondents use reporting on mobile devices. Hopes were expressed for simplified access to information and a good overview, for example. However, the majority of respondents (88%) did not see mobile reporting as a replacement for traditional reporting, but rather as a supplement to it. However, there was a lack of confidence in the solutions on the market, especially with regard to security. Monthly reports or even other reports usually contain sensitive data of a company that must not fall into the hands of third parties. It is necessary to prevent third parties from accessing the data from outside as well as to develop measures in case a mobile device is lost. In addition, the authorization systems available in a company for company data on mobile devices must also be taken into account. Wegener and Faupel (2018) present a study according to which classic report presentations continue to dominate. Tables are still the most popular form of presentation (used by 85.7% of the companies surveyed). On the other hand, tables are also among the least usable forms of presentation for small screen sizes. The screen size of mobile devices generally makes reports more difficult to read, especially when they contain extensive graphics or tables (Noä 2017). At the same time, a complex data structure with different

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layers limits an implementation of a reporting system on mobile devices. For example, it may be relevant for a decision maker that the revenue figures from the sale of products can be classified by customer, country, sales region or product type. For a multi-level classification, on the one hand, the data must be available to the decision maker in a timely manner and in a high quality. On the other hand, the reasons for the deviations must also be recognizable for the decision maker from a report. Therefore, the question of suitable visualization forms for mobile devices arises on several levels. The constant availability of reports on mobile devices leads to another challenge for decision makers (Schön 2018, p. 466.). In contrast to traditional reporting with a printout and dispatch of reports, with mobile devices the information is made available to decision-­ makers immediately, possibly unfiltered. However, this can also result in a quantitative burden on decision-makers with this flood of information, so that decision-makers have to filter out the decision-relevant information that requires an immediate response from this flood of information. Finally, solutions for new classes of end devices often only work with adaptations to the existing IT. Depending on the type of solutions required, this can also be a major challenge.

Challenges in Controlling with Mobile Devices

Important challenges in controlling with mobile devices are • • • •

Security Presentation of information in a limited space Higher information frequency Integration of the company IT

16.5 Possibilities of Mobile Devices There are unique selling points that make the increased use of mobile devices in controlling attractive and actually even indispensable in the future. On the other hand, there are justified and partly unjustified reservations regarding some aspects of mobile devices. Dedicated apps are necessary for meaningful use in controlling. First of all, it is important to distinguish between standard products and individually developed software. Can the intended purpose be achieved with a standard product, or is custom development necessary? If necessary, a standard product must also be extended by individually developed components. However, not all apps are the same. There are different development possibilities with partly enormous effects on e.g. application possibilities, performance, user friendliness and security. For an individual development it is therefore important to know the different development possibilities.

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16.5.1 Development Possibilities In 2007 and 2008, the iPhone and the App Store gave rise to three development options for mobile applications (apps) whose terminology is currently very widely used: Web apps refers to applications built with web technology that run in the browser and can access a limited set of device-specific features. Native apps refer to applications created with software development kits (SDKs) provided by Apple and Google that have nearly the same capabilities as apps preinstalled on devices. Hybrid apps combine web technology and the possibilities of SDKs in order to enable fast and possibly cross-platform developments with web technologies on the one hand and to provide access to more device-specific functions than pure web apps on the other hand. Effects of the Implementation Technology

When deciding on an individually developed app for controlling, the technology used for development is important. Depending on the technology chosen, there are differences, for example, in terms of: • Development effort and thus costs • Possible scope of functions • Availability

As Nunkesser (2018) describes, the division into web apps, native apps, and hybrid apps is outdated for both Android and many cross-platform approaches. More important than presenting an alternative division here, for practical reasons, is to present popular concrete development options with some advantages and disadvantages. SAP, for example, offers a whole range of different development options for individual apps with its Cloud Platform Mobile Services (see Fig. 16.1). Fig. 16.1 Development options for SAP Cloud Platform Mobile Services

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In addition, SAP also purchased Roambi in 2016, which continues to be marketed as a separate product under SAP Roambi.

16.5.1.1 Development with Cross-Platform Language Many important approaches to using a cross-platform language for apps leverage web technologies. Progressive Web Apps (PWAs) go beyond the capabilities of pure web apps by, for example, providing a similar installation to apps distributed through app stores and offering a higher set of device-specific features. Popular tools for developing PWAs include React and Ionic. SAP Cloud Platform Mobile Services also provides the ability to develop PWAs. Classic hybrid apps rely almost exclusively on Apache Cordova. This enables distribution via app stores and theoretically unlimited access to device-specific functions. This technology is used by Ionic and SAP Cloud Platform Mobile Services, for example. Finally, React Native goes a bit beyond these approaches by allowing to use native interfaces in the apps with web technologies, i.e. to implement the display in the apps not via HTML/CSS, but via the display elements provided by Apple and Google. Criticism of these approaches are possible limitations in functionality, performance and user-friendliness. Positive aspects are the potentially lower development effort and the flexibility to address more platforms. 16.5.1.2 Development with Platform-Specific Language Full flexibility is achieved by using the official SDKs from Apple and Google. However, when developing for both platforms, it is virtually impossible to share code and resources, which means a high development effort. If the individual development is to implement an app for the SAP Cloud Platform Mobile Services, the libraries provided there can be used. 16.5.1.3 Development with Non-platform Language In the best case, the advantages of the other approaches can be combined when using a common language that is actually foreign to the platform, especially with regard to a high range of functions, good usability and low development effort. Therefore, there are many manufacturers who offer frameworks for this purpose. Two of the most successful and relevant come from Microsoft (.NET MAUI) and Google (Flutter). SAP also offers its own solutions in the Mobile Development Kit in the Cloud Platform Mobile Services. 16.5.1.4 Specific Solutions To complete the example of the SAP Mobile Platform, the SAP Fiori Client and SAP Content to Go should also be mentioned. When using SAP Fiori, tasks can also be completed on the SAP Fiori Client App. SAP Content to Go, on the other hand, allows content from SAP S/4HANA to be brought directly into the corresponding Content to Go app. Similar concepts are also offered by other product manufacturers. For example, Microsoft Power BI (Power BI Mobile) and Tableau (Tableau Mobile) also offer apps that provide access to dashboards and reports. SAS Visual Analytics also offers companion apps that provide all data visualizations for mobile devices as well.

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16.5.2 Security One of the most important obstacles to the widespread use of mobile applications in controlling are security concerns. These concerns are fundamentally understandable, as iOS and Android initially came onto the market as operating systems for consumer end devices. However, Apple started to focus on business customers and applications with iOS quite early on. Google has also placed a lot of emphasis on security since Android 5. Nowadays, iOS and Android typically meet the security requirements placed on an operating system when updated regularly. Since the systems are still relatively new and have been able to learn from the mistakes of previous decades of operating systems for stationary end devices, they are even superior to other operating systems in many areas. The security of iOS, especially since hardware and software come from a single source, can be considered higher. However, if you compare iOS with special Android variants, such as the variants of current BlackBerry smartphones, a similar amount of security features becomes apparent. The following overview lists security features that are implemented for both iOS and BlackBerry smartphones: Software/Apps • App Sandbox: Specially protected/isolated execution environment for apps • App Signing: Mandatory signature for apps that uniquely identifies the source of the app. • Centralized App Distribution: Distribution of the app via controlled, central stores Software/OS • • • •

Integrated VPN: Integrated possibility to establish VPN connections Disk Encryption: Encryption of the file system Integrity Protection: Continuous checking for integrity violations Hardened Kernel: Adapted operating system kernel for higher security

Firmware/Hardware • • • •

Secure Boot: Verification of signature and integrity of the system at system startup Crypto Engine: Own implementation of cryptographic algorithms Root of trust: Hardware integration of cryptographic material Trusted Manufacturing: Verification of the production process

However, this does not mean that Android smartphones from manufacturers other than BlackBerry are unsuitable or not secure enough. However, there is a clear problem with Android in the speed of the provision of security-relevant updates. Manufacturers that change as little as possible to the Android provided by Google do better there than

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manufacturers that change a lot. This is because the integration of security patches is more difficult with highly customized systems. In a case study by SecX13 (2018), the update speeds of various smartphone manufacturers were compared and evaluated. Figure 16.2 shows a converted achieved percentage of the possible maximum rating. Android One is also worth mentioning here. Participants of this program, such as Nokia, Motorola and BQ, use an Android that has been changed as little as possible and thus achieve a fast availability of security updates. cc

Mobile operating systems and their infrastructure offer more security features than usual operating systems for stationary end devices. However, the operating system should be kept as up-to-date as possible.

If operating systems are currently considered secure, what are the main security issues? Typically, two main problems are considered: loss/theft of the end device and malware (this can be expanded to include problems that are less mobile-specific and are not the focus here: Social Engineering, poorly secured WLAN, weak passwords, etc.). Both problems are exacerbated as soon as sensitive data is permanently stored on the device. While encrypted data is typically at risk primarily in the event of password loss, less secure data such as photos or contact data can also be misused by malware. So let’s first take a look at the problem of malware. The Nokia Threat Intelligence Report annually examines data networks and their traffic that are powered by Nokia solutions. In the 2019 report (Nokia 2019), malware also shows progress in relation to the security efforts of Apple and Google. For example, since 2016, the percentage of infected devices detected has decreased significantly to an average of 0.31% in 2018, which is the lowest since measurements began in 2013 (though some of the decrease is also due to side

83 %

Apple

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Google

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FairPhone

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BlackBerry

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25 %

Sony Huawei

22 %

LG

22 % 22 %

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Fig. 16.2  Evaluation of the update speed of different smartphone manufacturers

100 %

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iOS

0,85 % 47,15 %

Android

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IoT Devices 0%

25 %

50 %

75 %

100 %

Fig. 16.3  Malware found by operating system. (Nokia 2019)

effects such as reclassification and better network protection). It is interesting to note the distribution of malware by operating system (see Fig. 16.3). It should be noted that only data traffic in mobile networks was examined. A clear picture emerges in the area of malware: Android is initially much more susceptible to malware. This is due to installation options of apps outside the central store, rooted Android devices and a lack of manual controls in the Android publishing process. While this is a distinct disadvantage compared to iOS, stationary operating systems offer the same opportunities for malware from the outset. This is also reflected in the high percentage of affected Windows systems on mobile networks. Still, this is a security vulnerability that should be addressed on Android. While this problem is significantly less with iOS, the problem of lost/stolen devices remains with iOS as well. One solution to both problems is typically the use of solutions marketed under names such as Mobile Device Management (MDM), Enterprise Mobility Management (EMM), or Unified Endpoint Management (UEM). The basis of these technologies is that iOS allows remote device management via the management framework and Android via the management APIs. Well-known solutions on the market include BlackBerry UEM, IBM MaaS360, Microsoft Enterprise Mobility + Security, MobileIron UEM, and VMware Workspace ONE. Typical functions are: • • • • • • • • •

Separation of private and business data Localization and locking of missing devices Possibility of remote data deletion of business data in case of device loss Monitoring of operating system and app versions in use Whitelists or blacklists for allowed/prohibited apps App store for businesses Password policy enforcement Simplified onboarding Enhanced security for business email

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Summary Security

Mobile devices without stored sensitive data are well protected by current operating systems, with iOS offering higher overall security than Android. For sensitive encrypted data, the risk of device loss and the use of outdated operating systems should be addressed above all. At least for sensitive unencrypted data, device management features should be implemented.

16.5.3 Information Presentation Many reports are still based on tables, which are only suitable for mobile devices to a limited extent. However, this should often not be a problem for two reasons. On the one hand, mobile devices are able to present more compact representations and, if further figures are required, to enable a smooth transition to software on stationary systems. Secondly, more modern representations of information than tables are often useful and can replace them completely. Decker (2016) provides a good overview of visualization on mobile devices. According to Lund (2016), 57% of the managers surveyed in a study complain about irrelevant reports. In an interview with Tödtmann (2016), Lund sees reasons in financial technical number graveyards, among other things. Even the classic graphical data representations such as dot, line, pie, donut, column and bar charts can help here and are better suited for mobile devices than tables. Not least in the course of Big Data problems, however, alternative forms of representation are also used. For example, boxplots, network diagrams, correlation matrices, sunburst diagrams, bubble maps, heat maps or tree maps can also be interesting. Fussan (2018) provides an example of more modern presentations with the use of tree maps at the Berliner Sparkasse. Compared to the presentation with classic tables, a faster information uptake by the report readers and a reduced error rate were achieved there. Mobile devices also offer the possibility of making the presentation of information more interactive. The possibility of interaction with gestures offers a great advantage. Interactive diagrams also make it possible to deepen data representations as needed. Appealing demos for JavaScript-based displays can be found, for example, at Observable (2019). So-called dashboards offer a way to combine important information displays to get a quick overall view that can be deepened as needed. Popular products such as Tableau and Microsoft Power BI offer configurable dashboards with ready-to-use mobile applications. The displays in the mobile applications are optimized for mobile devices in terms of display and interaction, so they also support touch gestures, for example. The composition of the dashboard can be configured externally and the mobile applications do not require any further customization. The dashboards can be designed in such a way that they adapt to the size of the mobile device, e.g. offer an optimized display for tablets and smartphones. • Decker (2016) provides the following recommendations for reports on mobile devices: • Simple • High quality

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• Consistent • Interactive

16.5.4 Mobile Backends and Cloud Computing Serious applications for mobile devices can only be realized with access to remote resources via networks. Naturally, the advantages can only be used properly if the access can happen location-independently. Both Android and iOS support VPN connections that can securely enable this access. However, some added values such as push messages require dedicated backend systems. Especially in connection with cloud computing, there are still many reservations, which is why it is worth taking a closer look at cloud computing. First of all, it must be stated that cloud computing is merely a model for simple, ubiquitous access to remote resources on demand. It is mainly about more efficient provisioning. Mell and Grance (2011) list the following core characteristics of cloud computing: • On-demand operation of, for example, computing and storage capacities according to the self-service principle • Accessibility via a wide range of end devices (including mobile devices) • Pooling of required resources with dynamic allocation • Resource elasticity in the sense of rapid scaling/expansion of the resources provided • Measurable services These properties are achieved via technologies that will not be discussed in detail here. However, it is important to note that these technologies can be used regardless of whether the resources are operated in-house or in a private or public data center. Cloud computing in itself therefore harbors only a few inherent security risks; these mostly only arise through external public operation, for which there are also alternatives. cc

Cloud computing in itself offers interesting technologies that can also upgrade existing infrastructures. These can be used in own, private and public data centers. For the most part, security risks are not inherent, but are primarily related to the chosen deployment method.

16.5.5 Synchronisation and Continuity Between Mobile and Fixed Devices Often, mobile devices are to be used as a supplement in order to access information more quickly and easily. Mature technologies exist, for example, to start or prepare processes with hardly noticeable media disruption on a mobile device and continue them on a

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stationary device. Often a shared backend is sufficient. However, there are also technologies, such as Apple’s Handoff, which comfortably solve the seamless continuation of work with a separate product.

16.6 Case Studies The implementation of reporting on mobile devices faces the aforementioned challenges of security concerns, limited space for information display and necessary IT integration, for which there are various solutions. At the core are often existing systems, for example from SAP or Microsoft. These are then extended either with the solutions offered by the manufacturers or with individual solutions. In the following, various companies and their solutions are briefly presented. All of the companies described were already using products from SAP or Microsoft before the introduction of mobile controlling solutions. However, different strategies were then pursued in each case for the introduction of mobile solutions.

16.6.1 Use of Microsoft Power BI The company agileBI uses Microsoft Power BI as a modern solution for Business Intelligence at various customers. Implementation examples show quite well the way from a rather less integrated solution with limited suitability for mobile devices to higher integration and efficient use of mobile devices. As Wegener and Faupel (2018) show, work is still largely carried out with tables in Microsoft Excel, for example (if necessary, in combination with Power Pivot for the purpose of self-service BI). Since 2013, Microsoft has offered Power BI as a sister system with a different focus. Among other things, apps for mobile devices (also iOS and Android) are available, for which own optimized dashboards and reports can be created. A major advantage is a comparatively smooth transition, as Power BI uses Excel and Power Pivot as data sources and can also be based on existing Office365 infrastructure, for example. In this way, security-relevant issues of authentication and role management can often be implemented with existing solutions. The integration can also be quite flexible and iterative. In the concrete implementation examples, it has been shown that this works very well with an agile approach. For example, an Excel-­ based workflow can be retained temporarily and functionality can be flexibly added or migrated until, in the best case, self-service BI is enabled on mobile devices. Figure 16.4 shows such a process that is not very IT-intensive, in which existing workflows and tools are used and only a few additional steps are added. Starting from this process, higher integration of the systems can gradually eliminate intermediate manual steps.

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Data transmission

Monthly

obtain data extract

Integration in Excel template

Validity check

Integration in PowerBI

Data transmission Distribution

Data transmission

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Data Analyst

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Fig. 16.4  Excel-based process for creating mobile-enabled dashboards

16.6.2 Use of the Microsoft SQL Server BI Platform Petras (2016) describes the implementation of mobile reporting at the E-Plus Group based on the Microsoft SQL Server BI platform. Here, too, the implementation succeeded through agile approaches. The problem of presenting information on mobile devices was addressed in a structured manner by creating a reporting guideline. The apps for iOS and Android were implemented as web applications, primarily for cost reasons. The security aspects and access management are regulated in the SQL server, the web apps implement a pure presentation.

16.6.3 Using SAP HANA with MicroStrategy Willert (2016) describes a project to provide sales figures and store data on the smartphone at adidas. The existing systems here were SAP HANA and MicroStrategy. As one of the first steps, a reporting guideline was also created and adapted for mobile devices. Willert (2016) names the main challenges as: • How can the space on a smartphone be used optimally? • How much information is necessary for the user group? • How do I use the new navigation options efficiently? Since the existing MicroStrategy solution is mobile-capable in principle, prototype implementations could be implemented quite quickly. Therefore, this approach can also be described as agile. Due to the implementation of mobile variants on an existing standard solution, the security risks were also rather low here.

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16.7 Conclusion Security concerns as one of the main barriers to the widespread use of mobile solutions in controlling are typically manageable when using appropriate solutions. Challenges of information presentation due to limited display space can be well addressed by innovative visualizations, multi-level displays and use of continuity to stationary solutions. Unique selling points such as context sensitivity and simplicity offer distinct advantages. Nevertheless, it is worth taking a closer look at when the use is really worthwhile in terms of noticeable added value. Otherwise, there is a risk of a lack of acceptance of such applications. The possible applications are to be considered under the following aspects: • User groups • Report content • Report form In terms of user groups, reporting on mobile devices appears to make sense if there tends to be a large number of decision-makers who need to be informed promptly with a standardized report (Mladenova et al. 2011, p. 5). Furthermore, it should be decision makers who spend a lot of time away from their office in order to take advantage of location-­ independent reporting. Furthermore, the user group should be characterized by the fact that they need up-to-date data quickly due to the presented complexity and dynamics of the business environment. This enables decision-makers to make timely and, above all, location-independent decisions outside their own offices on the basis of the reports made available. This requires a corporate culture in which decisions are not only made within meetings, possibly with several decision-makers. However, studies show that currently rather few decisions are made outside the company using a mobile device. On the other hand, it should also be avoided that short reporting intervals lead to actionism or hasty decisions by decision-makers (Strauß et al. 2015, pp. 316–317). With regard to the report content, a decision must be made as to whether a report for a decision-maker covers the entire company or only relates to a specialist area such as purchasing, personnel, production or sales. The data structures that bundle data across the entire company are usually significantly more complex than reports for individual departments. The authorization structures for company-wide reports are also more diverse and hierarchical. Furthermore, due to the simpler data structures, reports from the user departments may be more self-explanatory than, for example, the company-wide monthly report. Particularly in the case of company-wide reports, it can be difficult for a decision maker to find the relevant information independently in a short time without the support and commentary of Controlling. Overall, simple, departmental reports are currently more suitable for implementation on mobile devices than company-wide, hierarchically structured reports with a large number of different data structures.

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The report format is about the use of graphics, tables and comments in a report (Weber and Schäffer 2016, p. 242). Here, user-friendliness plays a central role due to the small display formats on mobile devices. Decision-makers expect the ease, clarity and simplicity of mobile applications that they are accustomed to from other applications – even in the private environment (Noä 2017, p. 75). cc

Recommendations for Action   

• Weigh the advantages of mobile devices such as location independence, simplicity, context sensitivity and directness against your controlling needs. Mobile devices offer many advantages, but an added value in the use must be recognizable. • Approach the key challenges of security, information presentation and IT integration in a non-judgmental and structured way. A lightweight and complementary use of mobile devices should almost always be possible. • Take an agile or iterative approach when implementing reporting on mobile devices. Often, existing solutions already contain mobile-capable components that can be used to quickly achieve prototypical results.

References Decker, J. 2016. Visualisierung von interaktiven Managementberichten auf mobilen Geräten. In Managementberichte gekonnt visualisieren, ed. C.  Schneider, K.-U.  Stahl, and A.  Wiener, 263–280. Freiburg u.a.: Haufe. Fussan, A. 2018. Verdauliche Controlling-Berichte anstatt Buchstabensuppe und Zahlensalat – Ein Praxisbeispiel. Controller Magazin 4: 49–56. Legenhausen, C., C.M. Hoff, and J. Backhaus. 2018. Mobile Systeme im controlling und reporting. Controller Magazin 3: 41–42. Lund, T. 2016. Unternehmenssteuerung 2016. Berg Lund & Company Studie. http://berg-­lund.de/ publikationen/unternehmenssteuerung-­2016. Accessed on: 04.06.2019. Mell, P., and T. Grance. 2011. The NIST definition of cloud computing. Special publication 800–145. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-­145. Mladenova, V., M. Homann, H. Kienegger, H. Wittges, and H. Krcmar. 2011. Towards an approach to identify and assess the mobile eligibility of business processes. AMCIS 2011 proceedings. https://aisel.aisnet.org/amcis2011_submissions/264. Accessed on: 30.08.2019. Noä, S. 2017. Reporting für die mobile Nutzung: Usability und user experience auf hohem Niveau erzielen. In Reporting und Business Intelligence, ed. A.  Klein and J.  Gräf, 3rd ed., 61–83. Freiburg u.a.: Haufe. Nokia. 2019. Nokia threat intelligence report – 2019. White paper. https://pages.nokia.com/T003B6-­ Threat-­Intelligence-­Report-­2019.html. Accessed on: 30.08.2019. Nunkesser, R. 2018. Beyond web/native/hybrid: A new taxonomy for mobile app development. In MOBILESoft’18: 5th IEEE/ACM international conference on mobile software engineering and systems, May 27–28, 2018, Gothenburg, Sweden, 214–218. New York: ACM.

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Observable. 2019. Notebooks. https://observablehq.com/@d3. Accessed on: 09.10.2019. Petras, A. 2016. Mobiles Top-Management Reporting zur Vertriebssteuerung eines Mobilfunkunternehmens. In Managementberichte gekonnt visualisieren, ed. C.  Schneider, K.-U. Stahl, and A. Wiener, 219–240. Freiburg u.a.: Haufe. Rohe, M., and A.  Hoffjan. 2018. Konzeptionelle Analyse von Self-Service Business Intelligence und deren Gestaltungsmöglichkeiten. Controlling  – Zeitschrift für erfolgsorientierte Unternehmenssteuerung 30 (4): 17–23. Scheffner, J., and K.-M.  Pham Duc. 2012. Umfassende Beratung: Neue Herausforderungen für das Controlling. In Controlling als Inhouse-consulting, ed. U. Krings, 135–150. Wiesbaden: Springer Gabler. Schön, D. 2018. Planung und Reporting im BI-gestützten Controlling  – Grundlagen, Business Intelligence, Mobile BI und Big-Data-Analytics. 3rd ed. Wiesbaden: Springer Gabler. SecX13. 2018. Smartphone security update availability report. https://twitter.com/secx13/status/968225118517452800. Accessed on: 09.10.2019. Statcounter. 2019. Mobile & tablet operating system market share Germany. https://gs.statcounter.com/ os-­market-­share/mobile-­tablet/germany/#monthly-­201907-­201907-­bar. Accessed on: 09.10.2019. Strauß, E., M.  Quinn, and G.  Kristandl. 2015. Möglichkeiten und Grenzen eines IT-gestützten Controlling- und Reportingsystems für mittelständische Unternehmen mit mobilen Endgeräten. Controlling – Zeitschrift für erfolgsorientierte Unternehmenssteuerung 27 (6): 313–317. Tödtmann, C. 2016. Vielen Managern fehlen Informationen für wichtige Entscheidungen. WirtschaftsWoche Online. https://www.wiwo.de/erfolg/management/unternehmensberater-­torge-­ doser-­vielen-­managern-­fehlen-­informationen-­fuer-­wichtige-­entscheidungen/14870454.html. Accessed on: 04.06.2019. Weber, J., and U. Schäffer. 2016. Einführung in das Controlling. 15th ed. Stuttgart: Schäffer-Poeschel. Weber, J., E. Strauß, and S. Spittler. 2012. Controlling & IT: Wie Trends und Herausforderungen der IT die Controllingfunktion verändern. Zeitschrift für Controlling und Management 56 (2): 105–109. Wegener, B., and C. Faupel. 2018. Reporting design. Controller Magazin 3: 4–8. Wehrum, K., and T. Heinrich. 2013. Mehrwerte und Erfolgsdeterminanten mobiler BI-Lösungen für die Unternehmenssteuerung. Controlling – Zeitschrift für erfolgsorientierte Unternehmenssteuerung 25 (6): 320–325. Weiser, M. 1991. The computer for the 21st century. Scientific American 265: 94–104. Wellmann, S. 2007. Google lays out its mobile user experience strategy. The InformationWeek Blog. https://web.archive.org/web/20070823202202/, http://www.informationweek.com:80/blog/ main/archives/2007/04/google_lays_out.html. Accessed on: 04.06.2019. Willert, T. 2016. Reporting auf dem Smartphone bei adidas. In Managementberichte gekonnt visualisieren, ed. C. Schneider, K.-U. Stahl, and A. Wiener, 241–262. Freiburg u.a.: Haufe.

Robin Nunkesser  has been Professor of Mobile Computing at Hamm-Lippstadt University of Applied Sciences since 2014. Previously, he worked first in Zurich as a software engineer and consultant at ELCA Informatik AG, then at the IT service provider adesso mobile solutions GmbH in Dortmund. Here he gained extensive experience first as a project manager, later as head of application development and finally as head of IT. He has worked on numerous large mobile application projects and feels at home on both iOS and Android.  Jens Thorn  has been Professor of Accounting and Controlling at Hamm-Lippstadt University of Applied Sciences since 2013. Before joining Hamm-Lippstadt University of Applied Sciences, Jens Thorn held various positions at Elster Group SE. As Head of Group Controlling at Elster Group SE, he was responsible, among other things, for the establishment and further development of a risk management system, the harmonization and acceleration of planning and reporting processes, and the conception of a capital market-oriented reporting system.

How Zalando Uses Digital Solutions to Transform Investment Controlling

17

Jörg Engelbergs and David Moreira

Abstract

The digital transformation has had a substantial impact on financial processes in recent years through agile working methods and the implementation of innovative efficiency standards. This article provides insight into two innovative projects in finance controlling at Zalando and highlights them in the context of digital transformation. The specific projects are (I) a digital platform and process in an investment app developed in an agile approach and (II) a new, automated investment boardroom used to monitor Zalando’s fixed assets across all business units and central functions.

17.1 Introduction: Zalando and Digitalization Zalando’s business model with both customers and partners is largely based on digitalization and the innovative use of information technology. It is therefore hardly surprising that these two factors have an equally significant impact on the company’s internal processes. From company-wide to smaller projects in individual areas, improvements in performance are regularly addressed in many places with the help of technology and digital transformation. Looking specifically at cases where financial processes are being evolved, these efforts regularly require collaboration and coordination across numerous teams in diverse areas such as accounting, treasury, finance controlling, procurement, technology, operations, or corporate governance. In this article, we will present two projects where the Finance Controlling team has taken the initiative to drive the digitization of finance processes. J. Engelbergs (*) • D. Moreira Berlin, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_17

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The finance function at Zalando has two key mandates. First, it oversees investment initiatives across the company and intervenes to optimize value creation. In doing so, it benefits from the effectiveness and efficiency of a central function. Second, with flat hierarchies and ways of working, the finance function is focused on cultivating continuous improvement, innovation and change within the company. Since Zalando – especially considering its size – is still a relatively young company, you will not find a fully established picture of internal processes. Rather, these are still being created or developed on a regular basis. For example, a team for investment controlling has been established in Finance Controlling in recent years. Due to the continuous increase in capital expenditures, the team has become increasingly important, as has the number of employees involved in investment projects. In view of these conditions, the need arose for a solution to effectively and efficiently control, evaluate and monitor investments throughout the company. A more detailed look at the initial situation with the necessary improvements and how these led to the pursuit of two digital solution approaches is provided in Sect. 17.2. Especially for the large number of companies that need to take action when introducing advanced digital solutions (Seufert et  al. 2019), these two examples hopefully provide food for thought and contribute to gaining experience.

17.2 Initial Situation: Reasons for Introducing Two New Digital Solutions As part of the mission to improve transparency, systems, and governance and to establish financial aspects in the decision-making process, the recently established Investment Controlling team has taken an initial inventory to identify key stakeholders in the investment process, highlight the status quo of systems and processes in managing investment decisions, and systematically identify key challenges faced by stakeholders. In this initial effort, the team focused on the stakeholders responsible for large investments of at least five million euros per year. Through a series of interviews, the team was able to learn about the existing processes, systems and people who participate throughout the investment cycle. As the volume of investments has recently increased significantly, it was not surprising that impetus for improvement was uncovered in various aspects: • Planning showed only a low degree of accuracy. Since in only a few areas are investments regularly made that are comparable in form, timing and volume, planners must make numerous assumptions subjectively with only limited ability to analyze past data. • As an e-commerce company, the income statement and working capital play an essential role in Zalando’s day-to-day business. The financial impact of investments is hardly known in the wider organization and receives comparatively little attention.

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• Due to restrictions in the flow of information and an ever-evolving system landscape, the initial postings regularly require reworking. As a result, rework is regularly required and final values are only transparent comparatively late. • Structures for controlling have only been systematically created for a few investments. For example, investments are only incompletely assigned to cost centers and a project system, such as work breakdown structures, is only used sporadically. • Analyses and reports were largely created manually based on data extracts and with the help of Microsoft Excel. This approach is typically error-prone and requires data to be repeatedly reprocessed to arrive at relevant insights. To address these key challenges, the team proposed a roadmap that had at its core the launch of two digital products. Both products are now live and have very Zalando-specific names, so for the purposes of this article we will use Investment Boardroom and Investment App as simpler terms. The Investment Boardroom is an enhanced business intelligence system. The main area of application covered is periodic reports and evaluations on the development of fixed assets (primarily operating and office equipment and internally generated software) over time and in plan comparison. The core users of this solution are employees in the central finance area (especially in Asset Accounting and Finance Controlling) as well as the decentralized units of Controlling in the business units (especially Fashion Store and Offprice) and the functional areas (for example in Logistics). The Investment Controlling team was entrusted with the task of coordinating and monitoring the development of the system. This application aims to make analysis and reporting more seamless and automated. Possibilities to view the data from different angles and in detail create starting points to improve the accuracy of the entries and completeness of the recording. Finally, the supportive use of the system substantially reduces manual work in the closing process and significantly accelerates the record-to-report process. The Investment App is a workflow solution that accompanies the approval process for investments across departments for Zalando in a digitally supported format. Accordingly, the target group is broadly distributed across all organizational units involved in the decision-­making process and the execution of investments. Originally, we assumed that this project would exclusively involve the approval process in fixed assets. However, as we discussed the design of the app with stakeholders, we found that the clear separation of Capital Expenditures (Capex) and Operating Expenditures (Opex) was not easy to maintain outside of the finance areas where this distinction is relevant. Therefore, we have opened the scope of the project to all forms of investments. Nevertheless, we have left the focus on non-recurring expenses. The Investment App achieves several improvements. It fundamentally promotes awareness and attention for the financial relevance of investments. Greater accuracy in planning can also be achieved, as more stakeholders are actively involved in the process and transparency increases across the board. The project also strengthened the use of project

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systems, as investments are already more thoroughly examined and structured in the approval process. The following section provides further insight into the product vision of the Investment Boardroom and the Investment App. The key use cases of the two products are also considered.

17.3 Target Image: Product Vision and Use Cases Building on the fundamental idea of driving the transformation of investment controlling through two digital products, we began to formulate a product vision as a target image. Similar to the approach Zalando uses to drive the optimization of general business activities, we chose a customer-focused approach and designed the vision specifically for the product’s core customers. For example, for capital expenditures, the key customers within Zalando are in the areas of logistics, corporate real estate and technology. From interviews and surveys, we were able to compile the relevant requirements that should deliver value. At the same time, we were able to identify a central theme that ran across the feedback we collected. For the Investment Boardroom, it became apparent that an integrated solution would be most appealing to key users. The broader vision statement includes other elements such as: • Have a comprehensive library of automatically populated dashboards that provide an overview of actual spend versus plan over the period. • The solution should be so easy to maintain that there is no need to involve specialists from the technology teams for the usual application. • With intuitive operation not only to show the trends, but also to enable dynamic setting of parameters as well as drill-downs to individual postings at cost center level. We aligned this product vision with some use cases that had emerged in the course of analyzing the baseline situation and determining the target picture. One example is the activities of financial controllers during the month-end closing process to locate and analyze specific planned investments. Another use case is the ability to easily evaluate time series data in preparation for an upcoming planning cycle. The product should also cover analytical use cases, such as looking at the investment portfolio by different asset types or by age of the investments. To communicate the investment app specifically, we used the simple slogan „It’s an app“. This allows users to immediately associate the product with the user-friendly, simple app interaction familiar from smartphone use. The vision for this product also includes: • Approvals and comments can be submitted in a fast and seamless approval process (see Fig. 17.1 for an overview of the four-step workflow) in a responsive UX/UI interface and are immediately available to additional users in the organization.

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4.* Executive Board Approval 3. Manager Approval 2. Review

1. Request

Project manager submits request for investment for review and approval

Leaders and experts review and comment on the request

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Fig. 17.1  Four-stage workflow of the Investment App

• The tracking of investments should follow a simple classification logic so that an easily accessible overview (e.g. for internal audit purposes) can be ensured. • The workflow should enable the simple integration of already existing documents so that the effort for the users is minimal and the various existing formats can still be used depending on the specifics of the investment. Typical use cases addressed by this product vision are the coordination and information flow between involved departments, e.g. to include the review of investments by the tax or legal department. A use case arises for decision makers who want to have quick access to the system even when traveling or in meetings. Finally, internal audit is also interested in better audit chains and access to structured documentation. Basically, in this phase we aimed to have an open discussion about the different types of usage with internal customers and transfer insights from the customer experience and the design principles Zalando uses for technology in its own apps.

17.4 Procedure: Project Structure and Implementation Once we had secured approval and support from senior finance executives, and the vision for the two projects had been agreed, we began to implement the transformation projects. This is described in more detail in the following two sections (for further insights into

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product management see, for example, Banfield et al. 2018). In order to have a systematic framework both for our general Ways of Working and for building technology products in Finance Controlling, we used the 4D product management approach. An overview of the four phases of the 4D model is shown in Fig.  17.2. The 4Ds stand for the Discovery, Definition, Design, and Delivery phases and are closely related to Lean and Agile methodologies. Based on these phases, we performed the rollout and change management of the two projects company-wide.

17.4.1 Investment Boardroom 17.4.1.1 Discovery In this initial phase, teams from SAP Analytics, Tech Product, and Finance Controlling worked together to drive the discussion. The teams were able to orient themselves to the specific roles in the project: • The role of the SAP Analytics team to transparently bring in what options are available internally. • The Tech Product Team was tasked with exploring other (external) solutions and bringing the teams’ discussions together. • The requirements and specifications for the project were provided by Finance Controlling. During this phase, we established jour fixe meetings to gather input from the different areas, identify the best approach to move the project forward, and share experiences to best manage the upcoming next steps. We have summarized the results from these discussions in a preliminary Project Charter. This gives a first overview of the problem, the goal and the process to get there, together with the internal customers and parties involved in the project. The project charter also clearly formulates the goal of digital transformation to create business intelligence dashboards for automated reports and analysis as well as to avoid manual work that is error-prone and time-consuming.

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Fig. 17.2  4D product management approach

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17.4.1.2 Definition In this phase, we began to precisely define the customer needs (for more on development with customer orientation, see e.g. Cagan 2018). To do this, we first further specified and agreed on the problem and, based on this, prioritized the most important aspects in a backlog. In addition to the teams that were already involved in the discovery phase, those responsible for the operational areas were also represented in this phase. 17.4.1.3 Design In order to solve the previously defined problem, the selection of the technical solution was made in the design phase. This task was highly relevant due to the large number of internal and external systems. For example, the solutions used in the financial area for specific use cases include: SAP, Jedox, MicroStrategy, Tableau, as well as several other apps created in-house by the internal development teams. For the selection decision, we used transparent criteria from both a functional and technical perspective. The main aspects that were decisive for the selection of the best-fitting solution are: ) Existing systems are to be preferred (due to better know-how, interfaces, efficiency). A B) The database for fixed assets (in SAP BW) should not have to be changed and should be used more intensively (minimization of effort, consistency). C) As far as possible, internal resources should be used for development (knowledge transfer, depth of detail). D) As a result, scalable, flexible and easy-to-use business intelligence dashboards should be available in an automated way (product vision). Considering all these conditions, we finally chose SAP Analytics Cloud as the technical solution, as it best met the specific requirements.

17.4.1.4 Delivery For this phase, we used a schedule with a concrete end-to-end division of work. This work plan divides the tasks of the development teams into sprints, testing, and enhancements. For the development sprints, we started building models (mockups) for the future dashboards to visualize the data. Weekly meetings as a touchpoint with the technology teams were useful to track software development, resolve bottlenecks, and align upcoming activities. Finally, with the creation of a Minimum Viable Product (MVP) already in test runs of the functionalities, debugging and prioritization of improvements could be started. 17.4.1.5 Rollout As soon as the MVP included the first automated dashboards, we made sure that these were immediately shared with the first test users, whom we had identified as early adopters and multipliers for communication. In the sprints that immediately followed, we were already able to improve the product based on the feedback we received. This approach

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allowed us to get the word out about the product very early in the process, promote cross-­ team involvement and communication, and thus address the necessary changes in the transformation.

17.4.2 Investment App 17.4.2.1 Discovery As previously explained, one of the team’s main responsibilities in investment controlling is to improve the approval or decision-making process for investments. For this assignment, we first needed to better understand the needs of internal customers in detail. Therefore, we began this phase with a series of one-on-one interviews with executives responsible for large fixed asset investments in technology, logistics infrastructure, and corporate real estate. These interviews gave us the opportunity to gather all relevant information from the user’s perspective about the existing process, the teams involved, and the systems used. The key project participants in this phase were Finance Controlling, experienced managers and project managers from the investment projects. 17.4.2.2 Definition After compiling the specific customer needs, we went into prioritization with the finance executives. This was a helpful step to clearly define for which of the mentioned needs the investment app should bring solutions. The final problem definition was created in co-­ creation between Finance Controlling and the Finance Product Owner in the technology area. The key elements of the problem are lack of clarity in the process (traceability of who, what, where and why), a missing platform (need to further digitalize the activities in controlling) and lack of governance principles (approval thresholds and roles not comprehensively defined). 17.4.2.3 Design In the design phase, we aimed to find the optimal system solution for the digital platform. For this purpose, we explored both internal and external solutions. The systems we looked at included SAP PPM (project management), Jira with a Capital Expenditure Management add-in, Google Forms with custom plugins, Asana, Kissflow, Oracle Capital Asset Planning, Sharepoint and SAP Fiori with specific additions. The process of selecting the best solution took about three months, with most of the time spent on testing the options listed – a tedious and lengthy job at times. However, the testing activities allowed us to address the relevant questions in a very targeted way: A) Is one of the systems we already use capable of serving as a workflow solution for the approval process? B) What are the implementation and maintenance costs and how long is the expected time span until the solution is fully implemented?

17  How Zalando Uses Digital Solutions to Transform Investment Controlling

CONSUMERS

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Fig. 17.3  System architecture of the Investment App

C) Does the system provide a simple UX/UI with an approval process that allows a RACI (Responsible, Accountable, Consulted and Informed) matrix to be implemented? D) What achieves the better overall fit? An external ready-made solution or internally developed software? After extensive testing, we summarized all of the above options in a decision tree, including the respective cost drivers and a buy/not-buy or make recommendation for each solution. Considering the specific requirements and the best congruence with the project goal, we chose SAP Fiori App as the technical platform. An overview of the system architecture of the Investment App is shown in Fig. 17.3.

17.4.2.4 Delivery Similar to the Investment Boardroom, the work plan and schedule were broken down into several work packages, sprints. The distribution of roles and responsibilities was directly oriented to the specific technology area. The key people assigned to the project were one expert each in full-stack, backend, and coding workflows. After we scheduled and timed the work in sprints, we stored them in a project management system as a work plan. This allowed us to improve collaboration between the parties involved with simple means. We took a radical approach to agile development of the app. This means that we spent very little time in working out the design and models. Instead, we focused on creating a rough overview of the flow for the user and a few sample pages, and based on that, we quickly moved into the development of the software. Part of this agile approach was to create a Minimum Viable Product (MVP) very quickly. This allowed us to ensure that users could get a feel for the solution early on. In addition, we were able to immediately go into iterations for improvement based on the feedback we received. The development of the MVP took about six months. During this period, we used weekly meetings to (I) track the completion of tasks from the previous week, (II) resolve bottlenecks, and (III) coordinate upcoming tasks. With the creation of the MVP, the testing or alignment with the expectations of the use cases (user stories) also began. The purpose of the user stories was to give the test users a

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clear framework and to collect all bugs and improvement suggestions in specific histories in order to arrive at a fully functional MVP. The user group in this phase consisted mainly of stakeholders in the various controlling teams in the organization who would be working with the app in the future. At the end of the testing, and after all relevant technical flaws were fixed, we invited a broad number of employees as part of the piloting. The piloting lasted 60 days and upon completion, the official communication for the rollout and go-live of the app took place.

17.4.2.5 Rollout For the rollout, we prepared a series of preliminary steps to promote change management and acceptance in the organization. First, we invited the large number of test users from the pilot and registered them as initial users of the final version. In the second step, we used the good feedback from the test users to address more teams. We leveraged this further in the third step by addressing awareness and engagement with the product through a roadshow across all relevant organizational units. Fourth, we ensured that the benefits and content of the app were included in all relevant internal company training and communication channels (such as the intranet). Finally, we used regular management events within and outside of Finance to further consolidate knowledge.

17.5 Lessons Learned: Challenges, Do’s and Don’ts In this section, we summarize the lessons learned and the do’s and don’ts from the post-­ mortem analysis of the two projects. The goal is to share what went well and what went poorly during product development so that best practices can be developed and risks avoided in future projects. We present the different aspects in tabular form, distinguishing between the platform (i.e. the technology used), the product (elements concerning the self-driven developments), the process (issues concerning project timeline and roadmap), and people for all observations concerning the teams. The summary for the Investment Boardroom can be found in Table 17.1, and that for the Investment App in the following Table 17.2.

17.5.1 Investment Boardroom See Table 17.1.

17.5.2 Investment App See Table 17.2.

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Table 17.1  Lessons learned from the Investment Boardroom project Platform Challenge The basic infrastructure we used (SAP Analytics Cloud) is under continuous development, so updates and new functionalities had to be taken into account Do’s Development of an MVP and subsequent improvements based on user feedback

Don’ts

Extensive use of dashboards, as they require intensive maintenance work

Product As we did not have any comparable products so far, we were not able to draw on any empirical values, e.g. for estimating the effort required

Process The teams involved (including Accounting and IT) had different peak workloads, making it difficult to agree a common timetable Broad and open Planning of approach of sprints, work potential systems packages and for full business weekly check-ins intelligence to the reporting development process Product owner with Assumption that hands-off mentality existing systems or laissez faire will work management. Close smoothly. Allow involvement is enough time to recommended deal with deficiencies in databases or systems

People People involved had no to little experience in translating functional requirements into executable technical measures

Involvement of all relevant stakeholders right from the start of the project

Waiting for stakeholders to seek coordination on their own. Better to set up daily stand-up meetings or short online check-ins as effective communication channels in advance

17.6 Conclusion: Digitization as an Opportunity for Controlling In order to meet their own goals and the needs of their internal customers, controllers today not only have to develop their own competencies, but also drive initiatives for the further development of technical tools. Picking up on the effects of digitalization, controllers – illustrated here by two examples from Zalando – are visibly tapping into new ways of working and expertise in technologies in order to be able to adequately meet the already present challenges of the profession. The new ways of thinking are gradually becoming part of their DNA. Fewer and fewer projects in finance are being driven forward without the involvement of technology teams. To build a finance function that actively embraces technical innovation to increase efficiency and effectiveness, controllers should be at the forefront, driving appropriate skills and competencies to support change. Controlling must evolve and move, for example, from simply annotated reports explaining variances against posted values to data-driven

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Table 17.2  Lessons learned from the Investment App project Platform Challenge We had no standard technical solution as a benchmark. The selection of the solution for the digital platform was correspondingly difficult Do’s Definition of roles and responsibilities in a collaborative project management system

Product As this product is rather unusual, the coordination of the customer problems and requests to be solved was challenging

Process In addition to the introduction of the technology, existing processes and behaviors in change management also had to be solved

People Strong advocates for this far-­ reaching product had to be found in all business units and integrated into the project

Agile approach to software development, including approach based on MVP

Initial interviews with the decision makers for the major investments were essential to gain understanding of needs and support for the project

Don’ts

Selection of the technical solution too strongly influenced by functional representatives. This should be done primarily by experts in technologies

Invest disproportionate time and effort in the fundamental directional decisions at the outset. E.g. through a joint memo from executives and experts on governance Ambitious schedule requirements when there are no comparable products or previous projects at the same time

Introduce bulky or unclear functionalities before the MVP goes live, as this will make the platform too costly to use

Strong dependence on the know-how of specific individuals. Rotation in technology teams due to parallel projects or also fundamental departures is to be expected

control centers in digital form. The role of technology in automating recurring tasks, optimizing processes, and even gaining insights is growing steadily. Greater use of technical resources in finance teams can free them up to focus more on partnering with business units and helping them implement strategic plans (on the role of financial controllers, see for example Stanton and Sandwell 2008). In this article, we have described the Investment Boardroom and the Investment App, two projects in investment controlling that enable the organization at Zalando to focus more on transforming processes and monitoring mechanisms for the effectiveness of financial investments in order to fundamentally improve performance. By integrating these digital solutions, investment decisions can be made in a more targeted, faster and (hopefully) even more value-enhancing way. All in all, these two projects have been instrumental in improving management opportunities at Zalando through digital

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transformation. They provide a significantly higher level of transparency for the company, enabling it to identify and counteract risks, uncover developments in a timely manner and adjust the strategy if necessary, and set up a resource-saving process through digital workflow support.

Company Profile

Zalando Zalando SE Valeska Gert Street 5 10243 Berlin Industry: Internet trade: Mail order fashion Turnover 2018: EUR 5.4 billion Number of employees in 2018: 15,619 FTEs Founded in Berlin in 2008, Zalando is Europe’s leading online platform for fashion, connecting customers, brands and partners. In the 2018 financial year, revenues grew by 20% to around EUR 5.4 billion, with adjusted EBIT of EUR 173.4 million and 15,619 employees as of the balance sheet date. The main drivers of this growth were an increase in active customers to 26.4 million (2017: 23.1 million), who placed 116.2 million orders during the year (2017: 90.5 million), while the number of site visits increased to 3.1 billion (2017: 2.6 billion). For 2019, Zalando is also targeting growth in gross merchandise volume (GMV) of 20–25% with revenue growth at the lower end of this range. Zalando expects to continue to grow profitably while making investments, primarily in logistics and technology, of around EUR 300 million.

References Banfield, R., M. Eriksson, and N. Walkingshaw. 2018. Product Leadership: How top product managers launch awesome products and build successful teams. Sebastopol: O’Reilly. Cagan, M. 2018. Inspired: How to create tech products customers love. Hoboken: Wiley. Seufert, A., J.  Engelbergs, M. von Daacke, and R.  Treitz. 2019. Digitale Transformation und Controlling. Erkenntnisse aus der empirischen Forschung des ICV. Controller Magazin Januar/ Februar: 4–12. Stanton, J. and R.  Sandwell. 2008. The changing role of the financial controller. Ernst & Young Research Report.

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Jörg Engelbergs  has been Vice President of Controlling at Zalando SE since 2013 and, among other things, was responsible for the company’s IPO in 2014. Prior to that, he spent eight years at The Boston Consulting Group as a consultant and project manager in various industries. He completed his doctorate at RWTH Aachen University on issues of decision research. He is a member of the board of trustees of the International Controller Association and head of the start-up controlling expert group. David Moreira  has been Senior Finance Controller for Investment Controlling at Zalando SE since 2017 and is responsible for the digital transformation projects in this area. Previously, he was active in the digital environment in retail companies, most recently at Zoot a.s.. He completed his doctorate at the University of Economics in Prague in the field of cross-border M&A in banking.

Digitalization of Controlling in Insurance Companies

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Along the Limits of Insurability in Theory and Practice Mirko Kraft and Bianca Drerup

Abstract

This article deals with the digitalization of  management accounting / controlling in insurance companies, which goes hand in hand with the digital transformation of the insurance industry by Big Data, Artificial Intelligence (AI) and Blockchain. The insurance business requires an industry-specific design of the controlling instruments, but not of the controlling concept itself. Managing insurance as a service in a value- and risk-oriented way requires cost transparency, e.g. through contribution margin calculations. Risks, on the other hand, can only be understood from a balance sheet perspective, e.g. through internal models. These interdisciplinary fields of application of controlling are undergoing digitalization. In addition, there are new market developments such as telematics tariffs, in which the digitalization of controlling is essential in order to address the limits of insurability. The fields of application result in new competence profiles in distinction to actuaries and data scientists.

This article is based in part on Kraft and Tillmann (2017). Furthermore, considerations on insurability from a research project with Prof. Dr. Martin Eling, University of St. Gallen, have been incorporated into. Thanks are also due to the student assistants at Coburg University, who provided support in the preparation of the article. M. Kraft Coburg, Germany B. Drerup (*) Duisburg, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_18

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18.1 Basic Understanding of Insurance and Controlling 18.1.1 Basic Understanding of Insurance Insurance is a real phenomenon. It is constituted by policyholders as demanders of insurance cover who make use of this service of an insurance undertaking as provider (cf. Fig. 18.1). Legally, the insurance product is defined by an insurance contract between the policyholder and the insurance company, which includes as its economic core the payment of a premium as the price for the insurance cover and the (partial) payment of claims by the insurance company (Kraft 2014, p. 276 f.). The insurance industry is one of the most important economic sectors in Germany: more than half a million people work there, Germans have concluded around 438 million insurance contracts, more than EUR 160 billion in benefits are paid out each year and the approximately 1400 German insurance companies hold more than EUR 1.7 trillion in investments (GDV 2019). The function of insurance is particularly important because it creates more security for society and the economy in an increasingly complex and uncertain world. By analysing and assuming risks from people (e.g. from motor vehicle accidents or illnesses) and from companies, private insurance companies enable social, economic and technical innovations and contribute to sustainable growth and prosperity. The use of key technologies such as Big Data Analytics (BDA) and Artificial Intelligence (AI) and the digitalization of their business models will be a key factor for the future of the insurance industry.

18.1.2 Basic Understanding of Controlling Controlling from a functional point of view is the “procurement, processing and analysis of data for the preparation of target-oriented decisions” (Berens and Bertelsmann 2002, p. 282). This basic understanding can be attributed to the information goal or information supply-oriented approaches of (german-language) controlling concepts. For an overview of (german-language) controlling approaches, see e.g. Coenenberg et al. (2016, p. 39ff.); on the importance of information supply for the management of insurance companies, Kirchner (1986).

Fig. 18.1  Basic understanding of insurance – business management view. (Kraft and Tillmann 2017, p. 419)

pays a premium

Insurer

Insured pays damages

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This basic understanding of controlling can be extended and contains, at least implicitly, aspects of planning and control as well as coordination. The rationality-assuring function is also inherent (goal-oriented). Instead of emphasizing the differences between the various controlling concepts, significant commonalities can be identified (Berens et  al. 2013, p. 224 f.; Coenenberg et al. 2016, p. 43ff.). For example, despite the differing and changing views, it can be regarded as indisputable that managerial accounting, and in particular cost and performance accounting, is an essential pillar of the information supply system and thus of controlling (Coenenberg et al. 2016, p. 43). There is a hig level of agreement – at least in the literature on controlling (Küpper et al. 2013) – on methods and instruments of controlling (Coenenberg et al. 2016, p. 43). The data foundation of decisions and therefore also of controlling is becoming even more important due to the volumes of data and the possibilities of evaluating them in real time (Big Data Analytics, BDA). For more advanced controlling approaches, the independence of controlling as a business sub-discipline can be assumed (Coenenberg et  al. 2016, p.  43). Going beyond accounting-based decision support for management, coordination and rationality assurance functions of controlling are theoretically sound and suitable for independent problem solving (Coenenberg et al. 2016, p. 43). In addition, controlling is proving its worth in practice, especially in insurance companies that are caught between market-induced transformation pressures and, in some cases, traditional group structures. On the one hand, insurance management is a sub-science of insurance science as a collective science with the phenomenon of insurance as the object of knowledge (Schmidt 1988, p. 1244). On the other hand, it is a branch of business administration. Controlling is  – as just shown  – a function-oriented business administration subfield and thus not industry-specific. Digitalization is also a cross-industry development. Controlling in insurance companies is therefore an interesting area of intersection that will be examined in more detail in this paper. First, however, the question of the necessity of an industry-­ specific or, more specifically, an insurance-specific concept of controlling in the sense of insurance controlling will be discussed, also with regard to the special features of the digitalization of controlling (in insurance companies).

18.1.3 Need for a Sector-Specific Concept of Controlling? There are approaches to a sector-specific formulation of the concept of controlling with regard to insurance business theory (Happel 1999, p. 15ff.).1 However, there was and is no need for a modification of a general understanding of controlling with regard to insurance companies (Happel 1999, p. 18; Kraft 2008, p. 28). In principle, the tasks of controlling in insurance companies do not differ from the tasks of controlling in non-insurance  For an introduction to controlling in insurance companies, see e.g. Kirchner and Wiegard (2014) and Junglas and Wiegard (2014a). For this section, cf. below Kraft (2008, p. 28). 1

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companies. Admittedly, it must be conceded that the insurance business and the insurance industry contain special features, such as capital intensity, which other industries do not have or do not have to the same extent. But the differences between other industries are not small either. As a result, neither an original insurance controlling (or also insurance industry controlling) nor other industry-specific controlling terms can be conceptually justified. However, controlling in insurance companies must be designed to suit the purpose and should therefore take account of both company-specific and sector-specific circumstances – such as the random (aleatory) nature of the insurance business (Schöffski 1996, p. V). This also applies, for example, to controller competence profiles (Sect. 18.4). The specifics of the insurance product naturally have effects on controlling which must be taken into account (Busse 1980, p. 159), without, however, being able to justify a controlling of a completely different nature in insurance companies. However, differences to controlling in industrial companies (or other material service companies) should not be reduced to the (banal) statement that services and material goods differ from each other (Kislat 2005, p. 297 f.). The regulatory framework conditions of the insurance industry, which was and is a highly regulated industry (including through the German Insurance Supervision Act, VAG 2016), cannot substantiate controlling of its own kind either (Kislat 2005, p. 297). Rather, the legal framework conditions form industry-specific contextual factors that must be included as ancillary conditions in decision preparation and decision-making. Insofar as controlling processes are prescribed by supervisory law, they only require a (sector-­ specific) minimum level – their company-specific expansion and supplementation is not blocked by this, but is even required from a management point of view. The deregulation of the insurance industry in Germany in 1994 certainly increased the importance of controlling in the practical management of insurance companies. However, the regulation that has intensified since 2008 with the financial crisis is not associated with a practical loss of importance for controlling; rather, the requirements for risk management and thus for risk controlling have been significantly increased. Further regulatory requirements can also be expected as a result of digitalization. For example, the German Federal Financial Supervisory Authority (BaFin), as the German insurance supervisory authority, published a circular on the insurance supervisory requirements for IT (VAIT) in 2018 (BaFin 2018).

18.2 Application Orientation and Interdisciplinarity in Controlling in Insurance Companies 18.2.1 Application Orientation It is inherent in the real phenomenon of insurance that a scientific examination of insurance and especially of controlling in insurance companies in research and teaching should have a considerable degree of application orientation. Questions in insurance science are inherently linked to (insurance) practice.

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The question of insurability is taken as an example of an applied problem in insurance science: Which risks are (still) insurable? The question of insurability cannot be answered in general terms (Berliner 1982, p. 1). In addition to the subjectivity to which risk decisions are fundamentally subject, the answer is dynamic and can only be answered practically (by example). In order to objectify the insurability of risks, criteria of insurability are used. One catalogue of such criteria that has been frequently used in the literature is that of Berliner (Fig. 18.2). Risks reflect technical and social developments, so that only climate change, fraud risks, terrorism and cyber risks should be mentioned here as examples of the limits of insurability. The catalogues of criteria for assessing insurability remain correct, but must be applied anew in each case (for a fundamental discussion of insurability criteria, see Berliner (1982, p. 29ff.). By linking different data sources, including unstructured data, Big Data and AI in particular are suitable for providing new answers to (old) questions about insurability limits (on digitalization and insurability, see Kraft 2018, p. 89ff.). The effects of digitalization on the value chain in insurance companies (Eling and Lehmann 2018) result in corresponding needs for adaptation in controlling in insurance companies, which has these limits of insurability in mind. This is illustrated below using crop insurance as an example. Example: Crop Insurance

In February 2016, the Indian government introduced a crop insurance programme for the first time, which has been further developed several times since then (on this and the following Rai 2019, p. 2ff.). This business poses new challenges for Indian property insurance companies, especially in the event of a claim. Failure to use key technologies results in disproportionately high loss ratios due to insurance fraud, which is, however, difficult to prove and very costly in relation to the insurance premium (violation of insurability criteria). Ground recordings from drones and automated image analysis can remedy this from a technological point of view. ◄

actuarial Loss events

independent

Maximum damage

controllable

Average damage

moderately

Loss frequency

high

Information-asymmetry moral hazard and adverse selection not excessive

market Insurance premium

reasonable & affordable

Cover limits

acceptable

Industry capacity

enough

societal Public policy

consistent with societal values

Legal system

allows the coverage

Fig. 18.2  Criteria of insurability according to Berliner (1982) and Eling and Lehmann (2018, p. 362)

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Numerous sectors are affected by weather-related business risks that cannot yet be adequately quantified and thus insured. Examples include clothing retail, gastronomy and tourism. To make such business risks insurable, weather data can be documented using blockchain technology and evaluated by algorithms such as artificial neural networks (ANN). This combination helps to better quantify weather-related business risks on the one hand and thus prevent insurance fraud on the other. However, general business cost management approaches cannot be easily transferred to the insurance industry: short-term production and program planning procedures, inventory management and optimal order quantity models are far from being directly adopted. However, the theoretical concepts can also be applied to controlling in insurance companies. If, for example, the claims settlement process in motor insurance is to be mapped using activity-based costing, knowledge of the individual process steps and their costs is necessary (Schäffer and Weber 2016, p. 159). This results in interfaces to process management, but also to customer care. However, if the process is observed including current technological and market developments, further interfaces and effects on activity-based costing and thus on controlling itself arise. If claims settlement is to be designed in line with the needs of a hybrid customer, additional contact options need to be developed and designed. A web-based claims notification can include the option of uploading photos of the damage directly (if necessary, even directly via smartphone app). If a corresponding image recognition system is stored in the background, which uses Big Data Analytics (BDA) to determine the damage within seconds and can quantify it within a certain tolerance interval, the customer can be made an offer to pay out the damage directly. Such a design of the claims settlement process has an enormous impact on process throughput times and costs, which have always been relevant objects of consideration for controlling in insurance companies.

18.2.2 Interdisciplinarity Since insurance is a synthesis of diverse elements (Sect. 18.1.1), its scientific study is characterised by interdisciplinary approaches (Farny 2011, p. 16).2 As a collective science with the phenomenon of insurance as its object of knowledge (Schmidt 1988, p. 1244), insurance science is therefore interdisciplinary in nature: the sub-sciences of insurance management, actuarial science and insurance law are applied together in insurance practice. In addition, there may be insurance medicine or social insurance theory as well as insurance economics with economic approaches (Schulenburg and Lohse 2014, p. 2 f.). As an interplay of insurance law, actuarial science and insurance economics, insurance science is thus fundamentally to be regarded as interdisciplinary in itself (internal-­ interdisciplinary) and thus to be characterised as an interdiscipline. A complete decomposition of the real phenomenon of insurance in application-oriented contexts 2

 The following section is based on Kraft (2014, p. 276ff.).

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cannot be meaningful. An integrating view is needed, especially in controlling. Digitalization means that there is an increased need for competences that are described by the buzzword Data Science (Sect. 18.4). The criteria for assessing insurability can also be differentiated, for example, into actuarial, actuarial-economic and (insurance) legal criteria, although these are not independent of each other (Sect. 18.2.1). A second example is the new EU insurance supervision system Solvency II (Solvency II Directive 2009). Although this is essentially insurance (supervisory) law, the underlying concepts are partly of an (insurance) mathematical nature (e.g. value-at-risk) or also of a business management nature (e.g. in the case of the market value balance sheet).3 They are combined, for example, in internal models (Sect. 18.3.2). However, controlling also interfaces with other sub-disciplines within insurance business theory: The company-specific implementation of concepts from process management and marketing (Hirzel et al. 2013, p. V), especially sales, such as the hybrid customer, customer journey design or the cross-functional design of end-to-end processes, has a profound impact on the business model and thus also on the current business situation and the data basis of controlling. In the following, selected (interdisciplinary) controlling applications in insurance companies and the influence of digitalization on them are discussed.

18.3 Selected Applications of Controlling in Insurance Companies and their Digitalization 18.3.1 Contribution Margin Calculations Contribution margin accounting architectures in insurance companies are the conceptual extension of the (combined) ratio as the quotient of claims + operating costs and premiums. As a controlling instrument, they provide detailed, better information than cost and activity accounting with its sub-areas of cost element, cost centre and cost unit accounting.4 They effectively create cost transparency (Kraft 2008, p. 62ff.) in composite insurance companies, especially as an informational basis for product, customer, sales and process controlling. In addition, contribution margin accounting concepts are at least the preliminary stage to business intelligence (BI) applications, whereby the term business intelligence (BI) can be understood to mean procedures and processes for the systematic procurement, preparation and analysis as well as presentation of data in electronic form (DAV 2019, p. 28). The

 For an introduction to the new insurance supervision Solvency II, see e.g. Gründl and Kraft (2019).  For a general discussion, see Flacke et al. 2018. On the insurance-specific design of cost accounting, see Brenner and Zeyher (2014). 3 4

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characteristic of data analysis based on contribution margin accounting is that it is not yet Big Data Analytics (BDA) with AI methods (machine learning) in true sense. Proposals for (multi-level) contribution margin accounting in insurance companies are not new (e.g. Busse 1980; overview in Kraft 2008, p. 198ff.). They can be represented by means of a reference scheme (cf. Fig. 18.3), which is based on the degree of allocability of the reference cost categories (Kraft 2008, p. 104ff.) to reference object hierarchies (for insurance-specific reference object hierarchies, see Kraft 2008, p. 211ff.). It is apparent that the design of contribution margin accounting concepts has to be insurance-specific, as the classical multi-level contribution margin accounting concepts are not directly transferable: This applies to both multi-level fixed cost recovery accounting and relative direct cost and contribution margin accounting (according to Riebel). In order to support decisions in insurance companies that are more in line with objectives, a combination of these partial costing methods with full costing methods (such as process costing) is required. Although it limits the theoretical informative value of such contribution margins, it increases the informative value in an architecture of a contribution margin concept without ending up with the flatness of overhead calculation. One way to achieve this is the attributability according to process dependency (activity quantity induced vs. activity quantity neutral) in addition to the differentiations according to attributability (direct costs vs. overhead costs) and according to the variation of a cost influencing factor (variable vs. fixed costs) (Kraft 2008, p. 295ff.). BI applications continue the idea of multidimensional controlling in insurance companies, which can be described as a cost cube. Multidimensional evaluation options provide indications for recognizing limits of insurability (e.g. combined ratio >100%).5 Cost management and process management linked with procurement management, outsourcing and reorganisation are possible with contribution margin accounting concepts and other key

Fig. 18.3  Reference scheme for contribution margin calculations in insurance companies. (Kraft 2008, p. 160)

Premiums +/-

Investment result

-/+

Claims costs Reinsurance result

=

Contribution marting I

-

Commissions

=

Contribution margin II Operating costs

-

Contribution margin III (risk) capital costs

=

Contribution margin IV

=

 For multidimensional controlling and cost management in insurance companies, see Pelizäus (2018).

5

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performance indicators (KPls). The goal is operational efficiency, which is the basis for generating value contributions (Wilson 2015, p. 241).

18.3.2 Internal Models In the following, internal models are understood to be stochastic models that not only calculate individual risks (e.g. investment risks or underwriting risks) or are used to value underwriting provisions, but also enable a holistic, risk-based management of the insurance company or group (Tillmann 2005). Since the start of Solvency II, internal models approved by the supervisory authority can be used to determine regulatory capital requirements. The prerequisite for this is the use of the internal model for decision support (so-called use test). The internal advantages of improved risk management and controlling through company/group-specific internal models, as opposed to a blanket standard formula, are thus now also recognised in supervision. Insurance undertakings can adapt the standard approach through undertaking-­ specific parameters (USP), whereby these parameters only affect parts of the Solvency Capital Requirement (SCR) calculations. If the individual risk profile is not reflected in other parts, an internal partial model can be applied for. Alternatively, an insurance undertaking/group may decide to use a full internal model. Stochastic simulation models are usually used. Implementation here is also possible with Big Data Analytics (BDA), for example by the use of artificial neural networks (ANN). According to BaFin data, of 342 individual insurance companies with a reporting obligation in the German market at the start of Solvency II, 15 insurance companies used a full internal model and 17 a partial internal model; seven used USPs (BaFin 2016). Although the spread of approved internal models remains limited and is often associated with capital market-oriented, international insurance groups, models for corporate management are increasingly being used in medium-sized insurance companies as well. These come close to internal models or partial models, but without (initially) seeking supervisory certification. The distribution of tasks between controlling and risk management departments as well as actuarial departments can vary. This potentially results inshifts in competencies for (risk) controllers. Political risks such as climate (policy) risks, default risks on government bonds or greater creditor participation must also be integrated into risk models. Scenario analyses as well as stress tests help to better understand the impact of such risks, even if they cannot be managed in a classical sense. Internal models are ultimately indispensable with regard to risk-return considerations ((risk) capital allocation for value and risk-oriented management) and for risk and limit controlling (Wilson 2015, p. 589ff.) in an insurance company/ group for the integration of operational and strategic corporate management (Tillmann 2005, p. 316).

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18.3.3 Telematics Tariffs We will now look at another field of application for controlling in insurance companies that is closely linked to the keywords Big Data and digitalization: so-called telematics tariffs. In these tariffs, data on the insured risks is collected by sensors and used for ongoing pricing. The policyholder can thus also be given direct feedback on his or her driving style, thus expanding existing bonus-malus systems (no-claims bonus). While claims controlling also traditionally looked at historical claims data, in particular claims frequencies and claims amounts, completely different data (types) are now taken into account in addition and virtually in real time. Telematics tariffs in motor insurance were only recently introduced on a broad basis in Germany (Hering and Kraft 2015). The tariffs were initially restricted to young people, but are now open to all age groups. Providers of telematics tariffs include Allianz (BonusDrive), HUK-COBURG (Telematik Plus) and Generali (Mobility). The amount of the premium, which is controlled by a discount, takes actual driving behaviour (pay-how-you-drive, PHYD) into account and expands mileage-based pricing (pay-as-you-drive, PAYD). The basis for this is that driving data is recorded by sensors in the car and forwarded via mobile communications. Among other things, geodata (GPS location) is also recorded. In some cars, these recording systems are already installed by the manufacturers. In particular, this is the case with the automatic emergency call system eCall, which must be installed by the manufacturer in new vehicles in the EU from 2018. If this is not the case, a telematics box can be retrofitted or a so-called OBD dongle, a chip (on the window pane) or the driver’s smartphone can be used. The data, e.g. on speed, acceleration and braking behaviour, are aggregated into a score (with values usually between 0 and 100), which is intended to reflect the driving style. If the score is above certain thresholds (e.g. 90), discounts on the car insurance premium are granted (sometimes up to 30–40%). The amount of data generated from telemetry data collection is large and requires processing using Big Data methods. Only AI methods can be used to correlate driving patterns with claims data. Traditional statistical (regression) models can thus possibly be replaced. The same applies to health insurance tariffs using health data collected by health apps, for example (such as Generali’s Vitality programme in Germany), and the use of smart home components in household or homeowners insurance. The aim in each case is, among other things, even more efficient management of risk balancing in the collective on the basis of data on the insured risks. Data analysis and the digitalization of business models significantly expand controlling in insurance companies, because this is where interdisciplinary aspects come together. The legal perspective on data protection, for example, is also of fundamental importance. Assessing the opportunities and risks of these new business models is a classic controlling task. Finally, the effects of digitalization on the future competencies of controllers in insurance companies will be discussed.

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18.4 Effects of Digitalization on the Competencies of Controllers in Insurance Companies In the following, the effects of digitalization in controlling in insurance companies on the competence profiles of employees in insurance companies directly affected by this are presented. Then, new competence profiles will first be presented and then compared with that of the “digital” controller in insurance companies. Competencies are understood to mean the ability to “solve problems in practice in a self-organised manner” (IGC 2015, p. 22 f.). Digitisation has generally led to the development of “data science”, which is expressed in new occupational fields of data processing (cf. Table  18.1). There is also a need for competencies among employees in insurance companies. As a result, these competency profiles are emerging alongside the classic image of a controller in insurance companies, but also alongside that of a (pure) actuary. In recent years, some insurance companies have also already hired a so-called Chief Digital Officer (CDO) to support the company regarding digitalization. The Ergo Group, for example, gives this position and the associated functions particular relevance by mapping it as a third division, Ergo Digital Ventures, at the same level as the two divisions for insurance business (Munich Re 2019, p. 27). However, this position is likely to disappear in the next few years or be integrated into the operational business units as soon as digitalization has been sufficiently implemented in a company. The rapid progress of digitalization means that insurers cannot invest in all technologies themselves, which is why they are increasingly entering into partnerships. For example, the carpooling provider BlaBlaCar entered into a cooperation with the insurer AXA (BlaBlaCar 2019). The development of new technologies is steadily increasing the need for data experts, but also in the companies themselves. In France, it is estimated that 2000–3000 data experts are needed per year in all sectors. Despite the development of training courses for the profession of data scientist, this demand cannot yet be adequately met in Germany either (despite increased student numbers, e.g. also in computer science courses). As a result, data experts are in high demand on the market. Stakeholders who do not align compensation with market standards have difficulty recruiting. In addition to business knowledge, which is usually learned directly in the companies, data experts also need knowledge in the areas of computer science and insurance. Therefore, they should initially work with actuaries in order to benefit from their (actuarial) knowledge of insurance, e.g. on product specifics such as guarantees. Table  18.2 shows the competencies expected from actuaries and data scientists in comparison to the controller competencies. The levels indicated in Table 18.2 are indicative and depend on company-specific circumstances, e.g. the size and risk profile (depending on the lines of business) as well as the degree of digitalization of the insurance company. The additional need for competencies among controllers in insurance companies on the way to becoming digital controllers (see Egle and Keimer 2018) will also depend on the respective specific competency profile of the controllers’ role. However, the roles that are generally distinguished (head of

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Table 18.1  Main occupations in data processing Functions Chief Data Officer

Competencies Coordination of data collection (data identification of relevant data for the company, funding of action plans, purchase of software packages or data, etc.) Organization of the data exchange with the different departments Preparation of business-oriented recommendations Ensuring compliance with data protection requirements (e.g. compliance with the EU-GDPR) Consideration of digital ethics in relation to data use Data Scientist Contribution to the assessment of business needs Identification, cleansing and preparation of internal and external, structured or unstructured data (e.g. from scanned documents such as claims reports using the software program R) Implementation of algorithms or assemblies of machine learning algorithms (“Machine Learning”) Formulation of hypotheses to be tested via the developed algorithms Big Data Architect Building distributed computing architectures (possibly in a cloud), integrating data, facilitating data availability, and optimizing performance Adaptation of the IT architecture to the processing needs (e.g. real-time) Data Visualization Expert Use data visualization tools to implement renditions (dashboards, (Expert for data mappings, synthetic indicators, etc.) that enable understanding of visualization) information Data Analyst Organization, summary and translation of extensive information Master Data Manager Collection and optimisation of the information available in the (person responsible for company in order to improve its optimal use (data on products, master data) guarantees and services offered, customer and contractual data, regulations, etc.) Ensuring that data are used lawfully and thus properly integrated into information systems Data Protection Officer Ensuring the protection of personal data and assisting data controllers (transversal function requiring computer, legal and communication skills) Berthelé (2018, p. 152)

controlling, strategic controller, sales controller, personnel controller, investment controller, plant controller according to IGC 2015, p. 116ff.) would first have to be reformulated for specific industries (Sect. 18.1.3). In summary, the competence profiles of controllers, actuaries and data scientists can be clearly distinguished, but there are also overlaps. For BI applications as discussed in Sect. 18.3.1, controllers will certainly depend on preparations and support by data scientists. Conversely, the data scientist who evaluates telematics data (Sect. 18.3.3) will depend on actuaries (regarding product design) and controllers (cost structures) for context classification. Actuaries will play a leading role in risk modelling (e.g. for internal models, Sect.

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18.3.2), with the models then being scrutinised by risk managers or risk controllers. Different competence profiles will therefore continue to be justified in the controlling of insurance companies even after digitalization.

18.5 Conclusion and Outlook Insurance as a special service business requires the insurance-specific design of controlling, whereby the aspects of application orientation and interdisciplinarity must be adequately taken into account. The developments in controlling in insurance companies (Utecht 2009, p. 93) have hardly changed since the financial crisis of 2008. Even at the turn of the millennium, a stronger value and risk orientation were already apparent as an empirical trend, even though complementary to other discussions, including the balanced scorecard concept and process orientation (Wallasch et al. 2000; Bogendörfer and Estorff 2014; Junglas and Wiegard 2014b). However, new aspects are now being added that extend or even exceed classic business models of insurance companies, usually using Big Data Analytics (BDA) and Artificial Intelligence (AI). Ecosystems and a platform economy as well as start-ups in the insurance sector, so-called InsurTechs, could also disruptively change the insurance business and thus also controlling and controllers in insurance companies. Controlling in insurance companies remains an exciting field both in practice and in research and teaching, also in view of increasing digitalization. From cost accounting, control and management (Sect. 18.3.1) to risk modelling (Sect. 18.3.2), there are theoretical and practical fields of application and challenges. Application-oriented instruments must be designed and further developed accordingly. Value and risk-oriented controlling Table 18.2  Competencies expected of actuaries and data scientists as well as controllers Knowledge of internal data Sensitivity to the use of external data Product knowledge Customer knowledge Accounting skills Technical and financial skills Business Intelligence (BI) skills Predictive algorithms Prospective modeling and special tools Legal restrictions Strategic management Compliance with decision-making/governance processes Berthelé (2018, p. 160)

Controller +++ ++ + + +++ ++ ++ − ++ ++ +++ +++

Actuaries +++ + +++ ++ + +++ + + +++ + ++ ++

Data scientists +++ +++ ++ +++ − + +++ +++ + + − ++

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as the supreme discipline is a daily necessity in insurance companies, with interesting questions that cannot yet be considered solved, either practically or theoretically. The EU insurance supervisory regime Solvency II, which was launched on 1 January 2016, has given a boost to many issues relating to the management of insurance companies. For example, the importance of accounting has increased and, for insurance companies that prepare their accounts in accordance with IFRS, it will increase considerably with the introduction of the new IFRS standard for insurance contracts (IFRS 17). But even for (pure) HGB users, the new detailed and extensive reporting requirements to supervisors and the public (Solvency II Pillar 3) have become an additional accounting task. This external communication has to be consistent with other disclosures (HGB individual or consolidated financial statements), so that it is not primarily departments of internal accounting (controlling/corporate planning department) that are in demand here, but those of classic external accounting (accounting/balance sheet department). The strongly expanded areas in risk management (usually the central risk management departments) play a supplying and technically coordinating role (on the tasks of controlling in risk management Kraft 2013). All of these developments should also be reflected in the future competence profile of a digital controller (Sect. 18.4). In addition to strategic decisions and risk management, pricing and sales decisions should in future be considered more closely from an actuarial and value-oriented controlling perspective (e.g. in the case of new life insurance products and further telematics tariffs). Risk assessment is mandatory in the new product process and should, however, also lead to appropriate consequences so as not to “test” limits of insurability. The increased tasks require more automation (e.g. through robot-controlled process automation, Robotic Process Automation, RPA), so that the digitalization of controlling has also found its way into insurance companies and will continue to do so. In view of the persistently challenging economic  environment and new competitors from the InsurTechs field, as well as politically induced economic uncertainties (such as trade wars and Brexit), combined with strong regulatory tendencies, the German insurance industry is facing considerable challenges and the associated pressure to transform. Controlling cannot eliminate these challenges, but with the help of big data and AI methods, it can contribute to the appropriate management of insurance companies so that they can continue to fulfil their social task of assuming risks from people and companies with competitive cost structures and integrated service offerings.

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Mirko Kraft  Professor of Insurance Management at Coburg University of Applied Sciences and Arts since 2012, teaches and conducts research on controlling and risk management in insurance companies. Prof. Kraft studied mathematics at Heinrich Heine University in Düsseldorf and completed his doctorate at University of Münster in the School of Business and Economics (Chair of Controlling, Prof. Dr. Wolfgang Berens). His dissertation dealt with cost transparency in insurance companies. Recently, Prof. Kraft investigated limits of insurability through telematics and is involved in other research projects with digitalization and Artificial Intelligence (AI) aspects. From 2019 to 2021, he was a member of the consultative expert group on digital ethics of the EU insurance supevisory authority EIOPA. Bianca Drerup  teaches at KBM Duisburg in the areas of business informatics, project management and accounting/controlling. Her dissertation on “Controlling Transformation” examines 4 transformation projects in controlling holistically, taking into account the associated digitalization projects and the impact on the role of the controlling function. She previously worked as Group Controller at the Haniel Group and Finance Expert at Deloitte Consulting and most recently worked in Controlling International as well as in the Digital Transformation Office of the Ergo Group. Dr. Drerup conducts research in the area of end-to-end controlling processes, particularly with regard to their digitalization, organization and impact on the role of the controlling function.

Use of Smart Technologies in Large Infrastructure and Energy Projects

19

Andreas Langer and Lutz Neugebauer

Abstract

In the past, major projects in the infrastructure and energy sector have repeatedly revealed enormous potential for improvement in terms of planning, control and stakeholder management. The challenge of such projects lies primarily in the complexity and volume of the data to be processed and the specific requirements for controlling that arise from this. This makes the support potential of digital technologies and artificial intelligence greater. The following article examines which specific tools from the field of digitalization are particularly suitable for the usage in the controlling of major projects, the extent to which such tools are already being used in practice today, and what future developments might look like. The focus is on the planning process, risk management, data evaluation, progress measurement, and reporting in general.

19.1 Introduction “In exactly six months, the new Willy Brandt International Airport will open – after more than twenty years of planning,” announced the Tagesspiegel on 02.12.2011 (2011). Just 27 days before the planned opening in June of the following year, the airport company Berlin Brandenburg cancelled the ceremonial inauguration and promised instead: “BER A. Langer (*) Stuttgart, Germany e-mail: [email protected] L. Neugebauer Düsseldorf, Germany © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_19

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Airport will be launched after the summer holidays”. Some time has already passed since then. Alongside the Elbe Philharmonic Hall in Hamburg, which has now been completed, the construction of the capital city’s airport is an example of many large-scale projects and illustrates their problems: The framework and constraints of large infrastructure projects are becoming increasingly complex and challenging for their management. Outsiders often cannot understand how budget deviations of well over one hundred percent can occur. In a study (2015a), Kostka and Anzinger determined an average cost increase of 73% by the time the project was completed, based on 170 large-scale public infrastructure projects in Germany, where 119 were completed. The BER (so far 365% estimate as of May 2019, (Tagesschau 2019) and the Elbe Philharmonic Hall (with 1125%, Frankfurter Allgemeine Zeitung 2016) are significantly higher with their values, but are not isolated cases. In particular, large public IT infrastructure projects often run the risk of exceeding the planned costs very significantly. The introduction of the truck toll system by Toll Collect and the FISCUS tax system, for example, both have cost overruns of over 1000% (Spiegel online 2015). All these examples show the high demands on project management and especially project controlling. In order to strengthen project execution in accordance with the plan, project controlling must therefore also be given greater consideration in the data management for project planning and control. Due to the increased complexity, new digital technologies must be used more intensively as tools. On the one hand, in order to be able to recognize and explain complex relationships in large projects more quickly, and on the other hand, in order to enable the focus of one’s own work from strongly operational activities to a more strategic orientation as a business partner in the project. In this context, the following chapters will therefore first systematize the central challenges of large-scale projects (Sect. 19.2) and examine the technologies that are fundamentally available for project controlling that is more strongly supported by digitalization (Sect. 19.3). Section 19.4 brings together the individual phases of project controlling with the available technological approaches. The article concludes with Sect. 19.5 and an outlook on future developments.

19.2 Challenges of Large Infrastructure and Energy Projects The question of the causes of the cost overruns in major projects outlined above is not new and has also been the subject of academic research for decades. As early as the 1960s, the German development economist Albert O. Hirschman developed the theory of the Hiding Hand (Handelsblatt 2019a). This theory states that people fundamentally underestimate the risks of large, innovative projects. This phenomenon can be traced back to a healthy optimism and thirst for action. In this context, for example, the statement by the CEO of Deutsche Bahn, Richard Lutz, who said in 2018 with regard to the major Stuttgart 21 project that the project would not have been started, if there had been an accurate assessment of the project’s progress at the beginning (Handelsblatt 2019b).

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In addition to the purely psychological aspect, the reasons for budget deviations can also be found in particular at the technical-functional, economic and political, data technology and project-specific levels (Kostka and Anzinger 2015a). The individual levels are outlined below as possible causes for budget deviations:

19.2.1 Technical and Functional Level Especially in projects with a pioneering technical character, there are often no references that have already proven the fundamental feasibility of the intended functions. If new or little-tested technologies are used for the first time beyond laboratory conditions in new operating environments or combined with each other for the first time, technical problems often arise that can have a massive influence on the course of the project and the corresponding costs. As an example, reference is made to the North Sea East project of offshore wind energy production. In their case study, Kostka and Anzinger refer to severe time delays in grid connection due to difficulties in the construction of the converter platform and power transmission (Kostka and Anzinger 2015b). Furthermore, there was a subsequent need for fundamental clarification between transmission system operator Tennet and plant operator Innogy regarding the distribution of liability in the event of a possible failure of the grid connection (Manager Magazin 2012). Overall, the project was delayed by 18 months due to these issues.

19.2.2 Economic Level Deviations from the plan due to economic decisions are not infrequently linked to the public procurement practice of large infrastructure projects. This frequently gives rise to the phenomenon that bidding companies initially keep the expected costs artificially low during the tendering phase, in order to win contracts. Only later, in the implementation phase, it becomes apparent that completion can only be secured by means of further supplements. This is a phenomenon often summarized as survival of the unfittest (Flyvbjerg 2009). This contributes to the increased likelihood of misplanning and subsequent significant cost increases. Another major issue is due to the need to comply with regulatory requirements. It is not unlikely, particularly in the case of long-running large-scale projects, that requirements imposed by the authorities on the technical design of buildings and facilities may become more stringent, leading to potentially cost-intensive adjustments in project planning and implementation. More common, however, are cases in which changes in the project’s plans cannot easily be brought into line with regulatory requirements. For example, the decision to add an additional mezzanine floor at BER Airport could not be immediately implemented within the existing planning for the fire protection system, taking into account the fire protection specifications, and led to massive delays (Die Welt 2016).

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19.2.3 Political Level With regard to political factors, for example, it must be taken into account that the basic chances of support for a public project decrease the higher the forecast costs and the expected risks are. This relationship can lead to both project costs tending to be calculated too low from the outset and project risks being communicated much more optimistically (Pfeiffer 2017; Grewe 2014). This mechanism is reinforced when politically responsible parties have to prove their ability to act through large, public projects. In addition, in large public projects, such as BER, the supervisory project committees are often also staffed with political representatives, who complicate project planning with their wishes for project implementation and with resulting changes to plans, and who may contribute to deviations in costs and schedules.

19.2.4 Project Management Level The challenges outlined above are also exacerbated by a large number of factors internal to the project (cited below from Fiedler and Schuster 2015 using the example of BER), although only a few will be mentioned here as examples: • A lack of planning depth at the beginning of a project as well as simultaneous planning and construction can lead to significant and thus cost-increasing time delays due to subsequent plan changes. • The division of responsibility among too many project participants, for example 50 different partial lots at the BER (Der Tagesspiegel 2014), without establishing an adequate project governance structure, is a further risk factor and makes subsequent changes to the plan even more difficult. • Organizational structures on both the client and contractor side are often not sufficiently documented and coordinated with each other, which leads to ambiguities regarding responsibilities, decision-making paths and general processes within the organizations. • The sometimes very late identification of budget variances is often due to insufficiently independent project controlling, inadequate quality controls and risk management that needs improvement. • Lack of communication and coordination between project participants prevents an early response to developing risks and problems.

19.2.5 Data Management Level Today’s major projects involve very large volumes of data. In the case of BER, it was assumed in 2014 that there were around 800 architectural plans with 8000 implementation

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plans and 50,000 work and assembly plans, i.e. around two million individual documents (Der Tagesspiegel 2014). In addition to this highly technical information, there is also commercial data (e.g. from procurement processes, invoices, ongoing cost calculations) and project management data (e.g. schedules with dependencies and resource planning). The regularly collected and evaluated information on external constraints (e.g. requirements and guidelines, environmental conditions) must also be added. Not only are considerable amounts of data to be collected, structured, analyzed and further processed – in addition, the sometimes complex interfaces and dependencies in the information and data management used must also be taken into account (Wagenitz et al. 2013, p. 493), referred to Grewe (2012). At BER, for example, up to 60 engineering offices were employed in parallel (Der Tagesspiegel 2014), which had to exchange data with each other. A lack of comprehensive and adequate data management is often the cause of a lack of transparency regarding costs, deadlines and services in the project and therefore project managers cannot react early enough and accurately to deviations. A variety of causes can lead to deviations from the plan in projects – with the effects on costs and deadlines outlined above. Not all of the levels and causes addressed can be influenced by optimized controlling. For this reason, the following section will focus in particular on the challenges of procuring and processing the necessary project data and on how controlling can be supported digitally in this regard.

19.3 Data Management and Digital Technologies for Project Controlling In the previous section, it becomes clear that the management of project-related data is a critical success factor, also driven by increasing requirements for technical design and more complex regulation (Auth et al. 2018). Nevertheless, even today at the start of large infrastructure projects, the required information is often only available in limited quantity and quality – which is the only way to explain many of the project deviations outlined above. This is just as true for the assessment and decision phase (Singh 2015) as to whether a project should be implemented as it is for the implementation phase, in which, as practice shows, the timeliness of project information relevant to control is often a weak point. Project controlling faces two main challenges here: • In order to enable a higher reliability and quality of the planning and control information, the data basis for project-internal and -external facts (Sect. 19.2) must be expanded, its structure must be defined in an even more granular way and the corresponding data must be collected regularly. However, this may involve considerably higher volumes of data that first have to be collected and processed. • On the other hand, project controlling must ensure that information on costs, deadlines and services is available in the shortest possible time or, if possible, in real time, in order to enable targeted control interventions by project management.

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Data management can therefore be understood as one of the central tasks of project management or project controlling in today’s large-scale projects. In the following, the central database is therefore seen as a data hub, i.e. as a core function to which all essential data flows from internal and external data sources lead and from which all digital tools on the other side are supplied with data. For effective data management within the framework of the data hub, the following questions must be answered: • Which project areas and objectives are to be covered by data management and which data (e.g. costs, deadlines, services, risks, purchase controlling, personnel controlling, contract management and claim management; communication with authorities, etc.) is required for this? • Which requirements arise with regard to the data structures of the data hub (which data in which form and in which correlation to other data must be available), so that all digital tools (e.g. for plan-target-actual comparisons, forecast and scenario formation, risk management, document management, action tracking, etc.) of project management and controlling can access it? • Which functions for data identification and procurement are to be defined, i.e. which data are collected with which tools from which sources at which point in time or subsequently checked for plausibility before they enter the data hub (this is particularly important if unstructured data are collected by automated processes)? • Which digital tools are based on the data hub and what information contribution should they make to project management? On the basis of the questions raised here, which cover all steps from data procurement, structuring, plausibility checking and analysis to data use, digital tools are outlined below which support project management and controlling in its challenges.

19.3.1 Basic Technologies Proven basic technologies are already available today for processing large amounts of data. These include big data solutions, cloud computing and in-memory computing, which can also be specially adapted to the requirements of large infrastructure projects. Big Data solutions comprise the infrastructure for collecting, providing and analyzing very large amounts of data, with high necessary processing speed as well as a high bandwidth of data types or data sources. The requirements to process large amounts of data exists in almost all large-scale infrastructure projects and often cannot be implemented with conventional database solutions. Distributed data storage and analysis programs that use a large number of processors and servers in parallel are used (Döbel et al. 2018). Since technical and functional requirements can change during the course of large-­ scale projects, flexible and scalable systems that do not depend on a fixed IT environment

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but are provided as a service in the cloud, for example, are often the appropriate solution. This applies, on the one hand, to the increasing storage requirements with a constantly growing volume of data, and, on the other hand, to the only temporarily very high demand for computing power and special applications, if, for example, a large number of different, complex project execution scenarios are to be modeled via simulation processes in order to optimize time and costs. In-memory computing is a technical solution that relocates all data processing procedures as well as the functions for calling up and storing data to the fast main memory of a computer. This avoids the time-consuming intermediate storage in external storage media. This approach is essential for the functioning of Big Data solutions and is usually part of a cloud infrastructure. In addition to the processing of large amounts of data, the individual preparation and visualization of data for a large number of different target groups can also be considered a basic technology today. By using already simple Business Intelligence (BI) tools, such as MS Power BI, which are especially geared towards the presentation of already evaluated and structured data, dashboards for project management or management, for example, can be created without much effort. For this purpose, the tools are connected to the corresponding data sources (e.g. relational databases) via simple selection functions, the basic structure of the contents is read into the tool and linked to suitable, already prefabricated, graphical representations (diagrams, graphs, tables). Today, so-called collaboration tools are also to be seen as basic tools. These usually combine web-based communication tools such as chat, email, telephony and conference functions as well as scheduling, coordination and task management with the possibility of working jointly and, if necessary, in parallel on plans, software, documents and the advantages of comprehensive document management. For projects with large, heterogeneous teams, a uniform user environment is essential, as well as the ability to automatically generate standardized project documents and send them in a user-specific and time-­ controlled manner (T3N Magazine 2018).

19.3.2 Obtaining Data For the identification and procurement of project-relevant data, project controlling has a variety of different classical and IT-supported approaches at its disposal, whereby the focus here will be on four innovative digital tools: • Robot-assisted process automation (Czarnecki 2019) supports the automated execution of previously manually performed routine tasks. In small programs, so-called software bots, recurring tasks are programmed and automatically processed based on rules and context. In the context of data retrieval, for example, information from a licensing authority that is regularly provided in a certain format as a text document or on its

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website can be automatically searched for defined keywords and the associated, project-­ relevant data can be provided in a structured form in a project database for archiving and further tracking (e.g. requirements for the technical design, conditions imposed by the authority, other ancillary provisions or costs of the procedure). Software bots are always useful when recurring or similar data retrieval tasks are required for a variety of data in a project. • Cognitive agents (Computerwoche 2018; Gaton 2017) are able to simulate human communication in interviews and obtain required information through questioning techniques geared towards human interlocutors. In doing so, they also use natural language processing in combination with underlying artificial intelligence. A possible use case is the procurement of information on a project in which qualitative values play a role in addition to purely quantitative values (motivation of the personnel deployed, maintenance status of the equipment, personal assessment of different risks, etc.). The use of cognitive agents is particularly useful when data about a large number of people must be collected at regular intervals and personal contact would be too costly in terms of space and time. • Systems for automated information extraction enable the provision of relevant information from a large number of unstructured digital data available in various formats. For the transfer of data into a database, the so-called ETL (Extract-Transfer-Load) function is of crucial importance. ETL enables data to be extracted from different data sources (Extract), normalized to the same format (Transfer) and then loaded into the data structure of the database for further use (Load) (Hummeltenberg 2012). Further intelligent methods are used to identify relevant information even in unstructured data sources (e.g. text documents in natural language). This approach is helpful, for example, when many different companies or consortia work together in projects in different trades and administrative activities, whose IT systems and project documentation are only compatible to a limited extent in terms of data technology. For example, if all the information on resource and material planning for several project participants for a particular construction phase is to be extracted from a large number of documents and data sources, this procedure can provide support. Although this innovative approach has been discussed for a long time, there are still no standard products for project management or controlling. • Natural language processing technology can also be usefully employed to extract information from any text or even audio recordings and make it available for further analysis (Computerwoche 2018). An application example for this could be the logging and analysis of long telephone conferences with a large number of participants, whereby the information is logged quickly and comprehensively, thus supporting project documentation (open points, risk factors, etc.). In conjunction with procedures for automated information extraction, data can also be systematized and stored in a structured manner.

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19.3.3 Checking the Plausibility of Data and Structuring it One of the critical success factors of comprehensive data management is the plausibility check and structuring of the collected data. An essential task here is the intelligent comparison of the data against already existing information and sets of rules. In the case of individual data (e.g. the forecast resource consumption and costs of an external company until completion or information on the project progress of a work package manager), this can be done by means of relatively simple, IT-supported plausibility checks. For more complex structures (e.g., an extensive, nested project schedule using diverse hypotheses on relevant boundary conditions, or extensive cost planning of a complex trade provided by a supplier), expert systems, or artificial intelligence (Russell and Norvig 2010; Kirste and Schürholz 2019), in particular, can be used. In such systems, in addition to expert knowledge for the recognition and structuring of data, e.g. for technical-functional or commercial contexts, stored empirical values and structures from comparable projects are also used in particular. This makes it possible to identify supposedly conspicuous or inconsistent data. In the case of projects spanning many years or with corresponding complexity, digital solutions are certainly conceivable here that independently develop the algorithms used through machine learning during the course of the project (Döbel et al. 2018).

19.3.4 Analysing and Using Data For the analysis and use of data from large infrastructure projects, innovative digital solutions should be mentioned that can be assigned to the area of predictive analytics (Fauser et al. 2015) – in particular predictive planning and forecasting tools (CXP Group 2018). In this process, a preparation of the existing data takes place first, in which the information relevant for the forecasts is extracted. In particular, the data stock is classified according to defined categories. With regard to project controlling, for example, the execution time for various work steps could be used here. Subsequently, the data is analyzed exploratively. This involves association, correlation, and regression analyses, i.e. statistical procedures to identify corresponding patterns in the dataset (for example, which factors influence the execution time of a work step and to what extent). Subsequently, models and algorithms can be formed on this basis, which in conjunction with assumptions (if necessary also on the basis of time series analyses) and recognizable boundary conditions of the future can finally be used to forecast future values (execution time of the work step under future boundary conditions). Due to the high computing power of the systems available today, a large number of possible scenarios and interrelationships can be run through and evaluated for large, complex projects. Here, large-scale projects generally make use of scenario technology, which is offered as system by various manufacturers. The literature (Gausemeier and Plass 2014, p. 44ff.) often distinguishes between different phases in the use of scenario technology.

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These include narrowing down the scope of the investigation, describing the influencing factors and the causal relationships, projecting trends and determining various scenarios (usually these are complex mathematical models of the influencing factors and their dependencies), and evaluating and comparing scenarios in terms of their probability of occurrence. Many providers of IT-based solutions initially offer a basic structure to ensure a targeted approach to model building and evaluation. However, influencing factors and their reliable correlations for model building, as well as corresponding quantitative forecast values, must be developed individually for corresponding large-scale projects, unless reference models can be used. The outline of innovative digital tools made here is discussed in the following along the project management phases for the tasks of controlling.

19.4 Practical Approaches to the Digitalization of Project Controlling 19.4.1 Overview of Controlling Tasks and Digital Tools Project controlling is an elementary component of every project management (Gessler 2016) and supports the project over the entire course of the project – from the definition of goals and strategy formulation, through project planning (formal and material goal planning) and, of course, subsequently during project implementation with project-­ accompanying reporting. The controlling tasks (Horváth et al. 2015) during the individual project phases can in turn be differentiated into: obtaining, plausibilising, structuring, analysing and using data. The following matrix provides an overview of the tasks and activities of project controlling and already makes an initial assignment of possible digital tools outlined in the previous section (cf. Fig. 19.1). This overview shows the fundamental potential for the use of automated processes and modern analysis and forecasting tools and will be discussed in more detail in the following sections for the individual project phases. In order to illustrate the use of digital tools in controlling, the following short project example will be referenced again and again: Example

Due to the worldwide increase in passenger numbers, an existing airport must create additional capacity in terms of passengers to be processed or aircraft movements to be carried out. The planning of the terminal buildings and the logistics systems used, such as baggage conveyor belts or check-in counters, must reflect the expected passenger numbers over the next few years or decades and provide sufficient and appropriately sized parking positions for aircraft. Developments in aircraft technology and dimensions must also be taken into account in the planning process. Capacity reserves and expansion options for even greater increases in passenger numbers than assumed must be taken into account in the structural engineering designs. ◄

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Tasks of Project Controlling

Project Goals

Project Planning

Acquire Data

Check the Plausibility of Data

Structure Data

Analyze Data

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Use Data

Review Goals and Strategy

Support Planning Carry out Budgeting Make Forecasts

Risk Management

Manage Risks Monitor Compliance

Reporting & Analysis

Management Reporting Carry out Target/ Actual Comparison Perform Performance Analyses

Closing & Consolidation

Support Project Completion

Robot-supported process automation

Predictive Planning & Forecasting

Scenario analyses

Cognitive Agent

Natural Language Generation and Processing

Bl Data Visualization Machine

Expert systems / Kl

Automated Information Extraction

Learning

Fig. 19.1  Possible areas of application for digital tools in project controlling

Even this brief description makes it clear that a large amount of information is required for project planning and implementation and that, in addition, a large number of assumptions have to be made.

19.4.2 Project Objectives and Feasibility The more concretely the objectives are defined with regard to the project results to be achieved, costs and deadlines for a project, the better the existing technical, political, regulatory and economic framework conditions can be delimited. The more precisely the framework conditions have been analyzed, the more concretely they can be taken into account in the formal and objective planning and later in the implementation. In this first phase of project planning or decision-making, it is therefore crucial for success whether the assumptions made and the subsequent formal and objective planning

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based on them are realistic or feasible – i.e. do not contain distortions due to factors internal and external to the project (e.g. political interests or highly simplified technical assumptions). Due to the complexity of large infrastructure projects, such questions are usually not easily answered. Three possible approaches will be outlined to help clarify the feasibility:

19.4.2.1 Project Databases and Benchmarking In addition to governmental organisations that collect data on major projects, such as the Major Projects Authority in the UK (Gov.uk 2019), and provide reference structures and data for successful project procedures, there are a number of commercial approaches that support the structuring, risk identification and quality assurance of major projects. For example, so-called predictive project analytics (PPA) databases have been built in the past, often analyzing several thousand projects of different sizes and providing benchmarking data with regard to diverse project focal points (Fauser et al. 2015, p. 66ff.). Examples of focal points are: • Project organisation and project processes (target setting, planning, implementation, completion, etc.), • Data and methods on human and technical resources, • Control and involvement of participating organisations and partners in the project, • Contract management with suppliers and service providers, • Approaches and methods for planning objectives, in particular for scheduling and structuring activities (e.g. work breakdown structure), • Approaches and methods for formal target planning, in particular for the transfer of material target planning into cost planning, • Structure of risk management. In this way, technical and commercial parameters and general conditions of a planned project can be imported into the system in order to find comparable projects and contexts that show realistic estimates or deviations from best practice cases. Lessons learned from past large-scale projects can also be communicated, although it must be emphasized at this point that due to the singularity of each large-scale project, such databases can only provide reference points for project management and controlling, which nevertheless contribute to the review or optimization of the structures, calculations and assumptions recorded in the project planning.

19.4.2.2 Cognitive Agents in the Collection of Information PPA databases outlined above may reflect experience from individual large-scale projects. However, in an initial phase of project planning and for validation of the planning, it is quite common to consult or involve a large number of experts and experienced individuals. For example, there is a wealth of experience in the market regarding new airport construction or expansion, and the challenge is to collect and evaluate this information in a structured manner.

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Cognitive agents that capture human speech in spoken or written form (for example, in a chat) or respond specifically to answers from the human counterpart (for example, by asking in-depth questions or requesting concretization) could provide support here (Leviathan and Matias 2018). The collected data is converted into a normalized form for storage in databases and can be analyzed and interpreted according to topic area, user groups, etc. A key advantage of cognitive agents is the capture of additional soft factors that accompany the data being collected. For example, similar to a human interviewer, it is also possible to record how the respondent answers. For example, does he answer quickly and with high confidence or rather hesitantly-slowly with only approximate information. If one now evaluates the response behavior of different interviewees in comparison, it may be possible to classify statistically significantly different answers as more likely and others as less likely. In the airport environment, for example, it would be conceivable to interview a large number of airline managers responsible for business development and passenger and ground processes about the future functional design of the airport. Furthermore, a survey of passengers or residents of the airport would be conceivable, for example in order to quickly and comprehensively carry out a survey of future flight behavior or acceptance with regard to noise and environmental aspects. The responses collected can be incorporated into the technical and commercial planning and contribute to evaluating the project from other perspectives. However, it must be stressed at this point that the effort involved in setting up and adapting appropriate systems is only worthwhile if this technology is also used during the implementation phase of the project, for example to survey a defined group of people on risks on a recurring basis.

19.4.2.3 Scenario Technique for Validation of Project Assumptions The use of scenario techniques in major projects makes it possible to obtain a transparent picture of various possibilities, risks and chances of success of different aspects of a project projected into the future. A large number of parameters and variables must be defined for a range of conceivable future developments. In large projects with considerable investment volumes and sequential, irreversible technical work sequences, the use of modern scenario technology is becoming increasingly common and can help to reduce the probability of economic and technical incorrect forecasts and subsequent sunk costs. Taking the example of an airport project, scenario analyses are helpful if the basic structure and function of the buildings to be constructed have been described (design planning/architect’s draft) and a capacity and cost estimate based on this is available, e.g. from the gross floor area determined and the functional areas of the terminal buildings. By varying different values for the forecast passenger development, the break-even point for the total investment can be presented, but also sensible expansion strategies (successive expansion of check-in capacity, security controls and boarding infrastructure) can be determined. In addition, conclusions can be drawn about scheduling and critical paths for the overall project, but also about appropriate cash outflow planning for the course of the project.

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If the example of the airport project is considered, the German Aerospace Center (DLR 2019) has a large number of studies and forecasts from the aviation industry. This provides essential data for the design and dimensioning of airport infrastructures, which can also be used specifically in modelling different scenarios or in simulations. Project controlling should already be involved in this phase of goal definition and feasibility assessment and can provide support in data procurement and analysis. This has the advantage that assumptions, risks and success factors are known from the beginning and can be taken into account in the further project phases by controlling in project planning and control processes.

19.4.3 Project Planning The goal of the planning phase is to develop all planning documents in varying degrees of detail, including the work breakdown structure with the work packages, the schedule, the resource and purchasing planning, the cost planning, and the funds requirements planning. Depending on the size and complexity of the project, the individual documents are subdivided even further, e.g. into general, rough and detailed schedules. The individual planning contents are interlinked or build on each other, creating a complex information network. Large volumes of data are already generated during project planning, for which standard ERP systems are generally used even in large projects. This statement is particularly true for effort and cost planning. For the scheduling and performance planning (personnel hours, use of external companies, use of materials, etc.) of individual trades, the common ERP systems often have their own project modules, but in practice – and especially for large projects – these do not have the necessary flexibility and user-friendliness. These are sometimes the reasons why independent project planning applications and digital tools are used in the practice of many large projects, whose functionality also includes the automated creation of proposals for scheduling and resource planning for trades and the optimization of their critical paths. This also includes a variant simulation of different project sequences and their influence on the cost development or the outflow of funds. Modern integrated project management systems go even further and link functional-­ technical descriptions of the project, for example, with the commercial and scheduling view (bba 2018). Thus, so-called 5D Building Information Management (BIM) systems are increasingly used in the construction industry. The technical-functional view – of what is to be implemented – is reflected in a three-dimensional representation (3D) of the construction project as a digital model. In this model, it is possible to quickly switch between an overview of the entire project and the detailed views of subsections or individual trades, and the additional consideration of the dimensions of cost and time can also make the direct effects of different variants on the schedule and cost development visible. Ideally, these integrated project management systems also include functions for managing the

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planning documents (in the sense of document management) and support the structuring of the project according to different process methods. In recent years, agile approaches and methods in projects have increased, also due to a changing understanding of management and organization (VDI Wissensforum 2019), which combine innovative approaches with flat hierarchies and classic project management methods (such as PMI or Prince2). Therefore, adapted possibilities for coordination and information exchange between the teams and the project management become all the more important in the context of planning. The use of collaboration tools is helpful here. These support the collaboration of project participants through a structured and targeted exchange of information via various channels and enable joint work within the IT platform or on online documents, for example, by retrieving planning data from project participants via workflows and structured templates and making it available to the defined group for discussion. After a status meeting, for example, the meeting results, decisions, open items and defined measures are stored in the appropriate classification and can be tracked. The main task of project controlling in this phase is to support planning and to check the plausibility and consolidation of planning data and planning documents, to take this structure into account in a data hub and to select the necessary tools for data analysis. In terms of content, it must already be determined in the planning phase which information will later be required for controlling in the implementation phase in order to enable corresponding plan, target, actual comparisons and forecasts. Controlling must therefore also ensure that when ERP systems and separate project planning systems are used, as well as the use of other digital tools, the schedule, performance and cost data are consistent and available in the same granularity. Practice shows that this is not always the case.

19.4.4 Risk Controlling In the context of project management and controlling, risks are primarily factors that can usually have a negative impact on the timing and economic progress of the project. In order to be able to identify and evaluate project risks at an early stage, a project-­ accompanying risk management process must be implemented as part of project management (Gleißner 2014), which starts before the project begins. During the planning phase, the strategic risks – which may already have been identified during the definition of the project objectives (such as political influence or a negative reaction from the general public) – must therefore be supplemented by further operational risks (Gleißner 2017). Risk controlling takes on a coordinating and conceptualizing role here and develops the necessary project instruments for identifying, analyzing, assessing, and monitoring project risks. In principle, economic, technical, personnel, contractual, socio-cultural and political risks can occur in major projects, e.g. in the construction of a new airport, as well as deadline risks, supplier risks, environmental risks and planning risks.

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Which risks are relevant for the respective project, individual project phases and, if applicable, work packages and how high the risk (probability of occurrence and extent of damage) is to be assessed here must first be determined with a large number of technical experts. Here, project risk controlling can, among other things, make use of evaluated workshop formats in which the project participants and, if necessary, external experts anonymously evaluate identified risks via live voting systems and submit their personal assessment of them. Such formats are also particularly suitable when it comes to evaluating and documenting risks of project changes in the ongoing implementation with regard to costs, extension of duration and project result quality. The data collected can then be statistically evaluated in a variety of ways. Cognitive agents can also be used in the context of project execution when there is a large number of project participants to be questioned and when project durations are very long. They reduce the risk recording effort by questioning in-house employees, service providers, participating external companies, etc. about the project risks on a recurring basis and on the basis of defined criteria, and by providing the information in a structured manner, irrespective of time and location. Up to now, risks have all too often been considered separately from other project information such as costs, deadlines and services  – although this information is subject to considerable interactions. This is one of the reasons why data on risks should be recorded in the project data hub and thus be accessible to project controlling and the use of business intelligence tools.

19.4.5 Reporting and Analysis Current and consistent data play a central role in project management. In order to counter deviations from the plan effectively and without delays, the project managers must be informed promptly about them with regard to costs, deadlines and services. Figure 19.2 provides an exemplary overview of the data types and data sources in an integrated data hub as the basis for project reporting. Up-to-date project data is the dream of every project manager. New web-based technologies and web-based databases can help to make this a reality and to record and consolidate the data of a large number of project employees, service providers, work groups, personnel planners, material purchasers, etc. on a daily basis. Via mobile applications, operational employees, e.g. on construction sites, can also enter their hours worked into the system themselves, so that these are immediately available on the same day. In this context, not only the resource consumption plays a role, but also, in particular, which project progress is associated with it. In practice, it can be seen that the evaluation of the project progress or individual work packages often depends on the respective assessment of the project or work package managers and does not always objectively reflect the project status. Closely defined milestones in the respective work packages and the calculation of the overall project progress, e.g. using the earned value method, can help here

Cost view

Fig. 19.2  Data types and sources of a data hub

Deadline view

Risk Controlling

Data Analysis and Reporting

Performance view

Risk view

Data hub for project data

Completion & Acceptance

Progress Resources Costs Liquidity Risks Overall reporting

Cost and performance analyses

Risk analyses

Project reports and ad hoc information are adapted to the needs of the recipients.

Other defined views

Forecast data

Actual data

Plan data

Data structure

Project Management Tools

Technical systems

Implementation

Commercial systems

Project controlling

External systems

Planning

Project phases

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(Gille and Kocsis 2014). Since a large number of work packages are processed in parallel in large-scale projects, software and database solutions should be used that, on the one hand, automatically calculate the project progress and, on the other hand, have the flexibility that, for example, a work package manager can use a workflow to change, justify, and comment on the progress calculated by the system. Controlling accesses this data hub for analysis and reporting and is now faced with the challenge of providing the respective decision-makers  – project managers, sub-project managers, work package managers, financiers/investors, etc. – with the information relevant to their decision-making area and visualizing it as accurately as possible. As in other industrial sectors, Business Intelligence (BI) solutions are therefore increasingly used in the project controlling of large projects. Depending on the project complexity, these range from simple MS Power BI solutions to more complex OLAP tools with which multidimensional data can be analyzed – in real time or simultaneously with other analysis methods. For use in a large-scale infrastructure project, such as an airport, a large number of employees with a wide variety of training backgrounds, who are to be orchestrated over a long period of time in a wide variety of trades, can thus be provided in a simple manner with the information relevant to them in the form of presentation suitable for them. In addition to the traditional plan-target-actual comparison of costs and services, the scope of analysis of controlling can thus be considerably expanded and made possible: • Comparison or alignment of different project results via multivariate tests, • Predicting project outcomes through predictive modeling and presenting cause-and-­ effect scenarios for identified project risks, • Insights into patterns or relationships of project progress and work package progress, as appropriate, including monitoring the results of service providers and their service delivery or billing. Whereas conventional reporting approaches aim to define report content and management cockpit KPIs in advance based on the respective decision maker and to report the information on a recurring basis, BI solutions also enable a flexible selection of information and visualization that the respective decision maker can flexibly compile and adapt himself. In large projects, this functionality is advantageous, especially when the focus of control and information requirements change depending on the project phases.

19.4.6 Closure and Consolidation Classically, the final phase of a project involves the acceptance of the services performed as well as a consolidation and safeguarding of the project experiences (Burghardt 2016, p. 749).

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An essential task of project controlling in this context should be the preparation and evaluation of the “project balance sheet”. In particular, lessons learned play a role here, in which the reasons for achieving or not achieving project goals as well as the occurrence of special challenges and how they were dealt with are documented. To process this now very comprehensive data stock, the digital tools of the previous phases can be used again and significantly reduce the effort of the structured project completion evaluation and data archiving. In particular, automated procedures such as computer-­aided process automation or evaluation via natural language processing make it possible to efficiently search through the large number of documents for relevant data (this certainly includes detailed information) and to store this data in a structured manner for further analysis. The tools analyze electronic data and documents according to defined keywords and bring together the related information in a structured way based on rules. With special data mining tools, it may also be possible to subsequently determine correlations and patterns from the detailed data that were previously not immediately apparent. For example, certain construction site tools from one manufacturer fail more frequently at outdoor temperatures above 25 °C and in dusty environments than the tools from another supplier. The possible resulting project delays and cost increases can thus be avoided in subsequent activities. The values determined in this way as extracted empirical knowledge can be used in the context of machine learning as a basis for the further development of expert systems, which will in turn provide support in upcoming large-scale projects of a similar nature, e.g. for the evaluation of planning and forecast data.

19.5 Conclusion and Outlook The possibilities of increasing digitalization of controlling functions, especially in project management, were outlined in the previous section. Fast and automated collection and processing of data as well as intelligent visualization and preparation are necessary, but not yet sufficient prerequisites for the successful use of corresponding solution approaches in a large-scale project. Experience shows that an essential prerequisite for using the advantages of modern information technologies for effective and efficient project management or controlling is the merging and linking of different databases, applications and digital tools – if necessary of all functions and companies involved in the project – and thus the integration and provision of a variety of different data with high data volume. In other words, it should be ensured that all project participants access a uniform, central database. Ideally, this data structure is mapped in IT terms in the form of a project data hub. Project controlling can access this data structure via prefabricated tools, e.g. from the area of business intelligence (BI) or analytics. The resulting use of modern, digital technologies offers enormous opportunities for project controlling. Intelligent, automated and partly web-based processes promise a

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significantly faster collection and processing – up to almost real-time speed – of significantly more data than could be achieved by previous controlling processes. At the same time, the use of expert systems and artificial intelligence can also positively influence data quality. Consequently, this means for project controlling that it can provide more detailed and, above all, more reliable forecasts on the future course of the project already starting with the definition of the project objective. The challenges outlined in Sect. 19.2 can certainly not be solved in their entirety, but the increased transparency creates the possibility of being able to intervene more quickly and in a more targeted manner in the event of project deviations. Investors and financiers can also better assess whether the economic viability of their project plans is guaranteed before and during project implementation. With the increased use of digital processes, the role of project controlling will fundamentally change in the future. With the elimination of time-consuming, manual collection and consolidation of data from various sources and costly procedures for data analysis and preparation, project controlling will be much more involved with conceptual tasks, determination of required data structures (with regard to the source, level of granularity, etc.) and the recognition of correlations or forecasts. The project controlling function would thus be significantly upgraded by taking strategic tasks into account and become a real project business partner.

Company Profile

Deloitte GmbH Auditing Company Rosenheimer Square 4 81669 Munich Germany Industry: Consulting Turnover 2018: EUR 1465 million Number of employees 2018: 8452 FTE Deloitte is one of the “Big Four” auditing firms and, in addition to auditing, provides services in the areas of risk advisory, tax advisory, financial advisory and consulting for companies and institutions from all sectors of the economy. Legal advice is provided by Deloitte Legal. With a global network of member firms in more than 150 countries and more than 312,000 employees, Deloitte combines outstanding expertise with world-class services to help clients solve their complex business challenges. In 2018, Deloitte generated revenues of EUR 1465 million in Germany with 8452 employees.

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Gausemeier, J., and C.  Plass. 2014. Zukunftsorientierte Unternehmensgestaltung. 2nd ed. München: Hanser. Gessler, M., and GPM Deutsche Gesellschaft für Projektmanagement. 2016. Kompetenzbasiertes Projektmanagement. 8th ed. Nürnberg: GPM. Gille, M., and F.  Kocsis. 2014. Earned Value Management sorgt für Transparenz und hält Großprojekt auf Kurs. https://www.projektmagazin.de/artikel/earned-­value-­management-­sorgt-­ fuer-­transparenz-­und-­haelt-­grossprojekt-­auf-­kurs_1092189. Accessed on: 19.09.2019. Gleißner, W. 2014. Quantifizierung komplexer Risiken – Fallbeispiel Projektrisiken. Risiko Manager 22 (1): 7–10. ———. 2017. Grundlagen des Risikomanagements. 3rd ed. München: Vahlen. Gov.uk. 2019. Major projects authority. https://www.gov.uk/government/groups/major-­projects-­ authority. Accessed on: 19.09.2019. Grewe, K. 2012. Das Projektmanagement der Olympischen Spiele 2012 in London. https://www.projektmagazin.de/artikel/das-­projektmanagement-­der-­olympischen-­spiele-­2012-­london_1061676. Accessed on: 19.09.2019. ———. 2014. Herausforderung Großbaustelle – Ein Lösungsvorschlag auf Basis der HOAI. https:// www.projektmagazin.de/artikel/herausforderung-­grossbauprojekte-­ein-­loesungsvorschlag-­auf-­ basis-­der-­hoai_1086732. Accessed on: 19.09.2019. Handelsblatt. 2019a. Stuttgart 21, Flughafen BER: Warum Großprojekte häufig zu finanziellen Desastern werden. https://www.handelsblatt.com/politik/deutschland/stuttgart-­21-­flughafen-­ ber-­warum-­grossprojekte-­haeufig-­zu-­finanziellen-­desastern-­werden/23960406.html. Accessed on: 19.09.2019. ———. 2019b. Deutsche Bahn hätte Stuttgart 21 „mit heutigem Wissen nicht gebaut“. https://www. handelsblatt.com/politik/deutschland/bahnhofsneubau-­deutsche-­bahn-­haette-­stuttgart-­21-­mit-­ heutigem-­wissen-­nicht-­gebaut/21196626.html. Accessed on: 19.09.2019. Horváth, P., R. Gleich, and M. Seiter. 2015. Controlling. 13th ed. München: Vahlen. Hummeltenberg, W. 2012. ETL. http://www.enzyklopaedie-­der-­wirtschaftsinformatik.de/lexikon/ daten-­wissen/Business-­Intelligence/ETL. Accessed on: 19.09.2019. Kirste, M., and M. Schürholz. 2019. Einleitung: Entwicklungswege zur KI. In Künstliche IntelligenzTechnologien, Anwendung, Gesellschaft, ed. V. Witenpahl, 21–35. Wiesbaden: Springer Vieweg. Kostka, G., and N.  Anzinger. 2015a. Large infrastructure projects in Germany: A cross-sectoral analysis. https://www.hertie-­school.org/fileadmin/2_Research/2_Research_directory/Research_ projects/Large_infrastructure_projects_in_Germany_Between_ambition_and_realities/1_WP_ Cross-­SectoralAnalysis.pdf. Accessed on: 19.09.2019. ———. 2015b. Ausbau der Offshore-Windenergiegewinnung in Deutschland Umfang, Muster und Ursachen von Zeitverzögerungen und Kostensteigerungen. https://hertieschool-­f4e6.kxcdn. com/fileadmin/2_Research/2_Research_directory/Research_projects/Large_infrastructure_projects_in_Germany_Between_ambition_and_realities/4_Offshore-­Windenergiegewinnung_in_ Deutschland.pdf. Accessed on: 19.09.2019. Leviathan, Y., and Y. Matias. 2018. Google duplex: An AI system for accomplishing real-world tasks over the phone. https://ai.googleblog.com/2018/05/duplex-­ai-­system-­fornatural-­conversation. html. Accessed on: 19.09.2019. Manager Magazin. 2012. Offshore-Anschluss verzögert sich länger. https://www.manager-­magazin. de/unternehmen/energie/a-­840632.html. Accessed on: 19.09.2019. Pfeiffer, M. 2017. Biases bei betriebswirtschaftlichen Entscheidungen in Großprojekten und Lösungsansätze: Aktueller Stand der Theorie und Empirie. Junior Management Science 2 (3): 48–72. Russell, S.J., and P.  Norvig. 2010. Artificial intelligence  – A modern approach. 3rd ed. London: Pearson.

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Andreas Langer  is a partner at Deloitte GmbH, where he heads the Energy, Resources & Industrials and Management, Technology & Regulation divisions. As a management consultant, he is particularly involved in large-scale projects in the areas of reorganization of organizational structures and processes, personnel concepts, controlling, digitalization, IT implementation concepts, and the introduction of internal control and risk management systems in the energy industry. He has managed and implemented numerous projects for public companies and energy suppliers. Lutz Neugebauer  is a Senior Manager in the Energy, Resources & Industrials division at Deloitte. As a Director at TÜV Austria, Head of Security at the BITKOM Group and Business Information Manager and Deputy Head of Infrastructure Development at Frankfurt Airport for many years, Lutz Neugebauer brings more than 20  years of professional experience to his auditing and consulting work. His consulting focus includes the areas of digitalization of commercial, human resources and technical processes for public sector companies and critical infrastructures.

Current Trends and Future Potentials of Digitalization in Procurement Controlling

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Andreas Jonen

Abstract

The summarized consideration of the digitalization effects on procurement and controlling and their consequences for procurement controlling have highlighted the fields of forecasting, reporting, supplier evaluation, risks, the make or buy decision and strategic planning as areas with particular potential for change. For the evaluation of the factually derived conclusions with the help of job advertisements in the area of the procurement controlling four developments were identified as relevant. A tendency for the intensified relevance of IT knowledge could be identified, as well as for the concentration on strategic tasks. With regard to the increase in analytical activities and stronger cooperation with the IT area, follow-up investigations are necessary in order to be able to make a statement.

20.1 Objective The consequences of digitalization for the procurement area and procurement controlling have been given little focus to date, particularly in corporate practice. In contrast, areas such as production, sales and finance have been included much more intensively in digitization initiatives (Schentler and Schlünsen 2016, p. 85). In this context, some argue that the importance of controlling support in procurement, as well as logistics and administration, will decrease as a result of cyber-physical systems (Lingnau and Brenning 2018, p. 158).

A. Jonen (*) Mannheim, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_20

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Empiric

Impact hypotheses

Causes

320

Digitization

Controlling

Procurement

Procurement controlling Job ad analysis

Fig. 20.1  Derivation of the effects of digitalization on procurement controlling

In the present work the effects of the digitization on the procurement controlling are to be derived starting from the specific effects for the procurement and the controlling separately. Both fields will have thereby implications for the organization of the procurement controlling in the surrounding field of the digitization, as Fig. 20.1 shows. Finally, the formed impact assumptions are analyzed empirically. For this purpose, the method of job advertisement analysis is used.

20.2 Relevance of Procurement For at least two decades it has been possible to state that procurement is becoming increasingly important due to a lower own production and an associated higher proportion of material costs (Kaluza 2010, p.  140). With regard to the share of manufacturing costs, there are estimates of an average of 50–80% (Grochla 1983, p. 20; Kaluza 2010, p. 69). Further evidence is the increasing R&D share of suppliers (in some cases over 50%). Procurement is thus the driving force behind research and development activities (Jonen 2019, p. 921). The relevance grown due to the increase of the procurement intensity finds its expression in the enormous lever effect on the target sizes of the enterprise (Grochla 1983, p. 19). The intensive leverage that can be achieved through material cost reductions is illustrated by the following example: A company with a material cost share of 50% has the same profit effect at a constant return on sales of 5% if it can realize a 2% material cost reduction or a 20% increase in sales (Wildemann 2015, p. 2). Procurement is characterized by internationalization and the shortage of raw materials. Internationalization leads to a significantly expanded spectrum of potential suppliers and the shortage of raw materials to corresponding positions of power with a few suppliers.

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20.3 Digital Transformation of Procurement Following the summary term for the possibilities of digitalization in the production sector based on intelligent, self-controlling objects through the use of cyber-physical systems (Industry 4.0), the changes already implemented and expected in procurement are described with the term Purchasing 4.0 (Feldmann and Henke 2016; Kleemann and Glas 2017). In this context, purchasing is attributed a significant co-responsibility for the implementation of Industry 4.0 (Feldmann and Henke 2016, p. 25). In addition to changes in purchasing objects and processes, intensive savings are also expected. In a survey of purchasing managers, an average value of 21% savings from the effects of digitalization was determined (Kleemann and Glas 2017, p. 32). The key influencing factors in the context of digitalization are intelligent systems, real-time communication and digital networking between the company and the supplier as well as the value creation stages. Developments in information and communication technologies are leading to the emergence of so-called value networks (Nobach 2019, p.  252  f.; Gleich et  al. 2016, p.  26), which no longer involve a one-dimensional arrangement of members in a supply chain, but a multi-dimensionally usable supply network (Gleich et al. 2016, p. 32). This promotes the flexibility of the individual company and the intensity of possible cooperation (Lingnau and Brenning 2018, p. 142; Schlüchtermann and Siebert 2015, p. 461). The establishment and use of these networks is seen as a key success factor in competition (Mödritscher and Wall 2019, p. 392). This can be observed in particular in digitization projects, which are increasingly being handled in corporate collaborations, as individual companies are often unable to provide the necessary technologies on their own (Pampel 2018, p. 24). Digitalization will also change the nature of products (Feldmann and Henke 2016, p. 9). The changes in relation to digital procurement objects are threefold (Kleemann and Glas 2017, p. 9): • The smart products that will need to be procured in the future, for example to ensure a self-controlling workpiece (Seiter et al. 2015, p. 467), will include three main components (International Controller Association 2015, p. 14): –– Physical components (sensors, actuators, control technology), –– Intelligent components (processors, software, data storage), –– Networking components (wireless, wired). • 3D printing will make it possible to produce small batches efficiently, so this will have to be included in make or buy decisions. • Suppliers themselves offer new digital solutions that need to be evaluated and integrated where appropriate. The consequence of these changes in procurement objects could be an (even) stronger involvement of purchasing in product development (Feldmann and Henke 2016, p. 8).

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E-procurement, i.e. electronic tenders or web-based negotiation tools, will become more widespread in the selection of suppliers and the conduct of negotiations. E-procurement offers support potentials of organizational procurement activities. For example, electronic purchasing auctions offer competitive ways of negotiation techniques and the use of electronic procurement portals or virtual marketplaces enables a reduction in transaction costs (Wirtz and Kleineicken 2005, p. 339 f.). When purchasing these products, new categories must be included in the evaluation of suppliers, such as digitalization capability (Kleemann and Glas 2017, pp.  9, 24; Jonen 2023), data security (Kleemann and Glas 2017, p. 10), compliance and adherence to regulations (Muhic and Johansson 2014, p. 553 ff.). Supplier evaluation will increasingly be based on a greater variety of sources and implemented with the help of analytical methods (Kleemann and Glas 2017, p. 10). For the area of warehousing and specifically for safety stocks, it is assumed that a significant reduction in inventories and thus the associated costs can be achieved here. Forecasts assume potential savings of 30–40% based on the exploitation of real-time information (Feldmann and Henke 2016, p. 9; Kleemann and Glas 2017, p. 9) as well as methods such as predictive planning and the associated mitigation of the bullwhip or burbidge effect (whip effect, in which fluctuations in demand lead to build-up deviations in the supply chain) (Kersten et al. 2014, p. 111). With regard to order processing, it can be assumed that this will become much more automated and that demand and reordering will coincide in time (Kleemann and Glas 2017, p. 9 f.). To implement this automation, Robotic Process Automation (RPA) continuously analyzes processes (Czarnecki and Auth 2018, p. 113) and subsequently takes them over by software robots (Mödritscher and Wall 2019, p. 396). The extensive autonomization of operational purchasing (Kleemann and Glas 2017, pp. 9, 18) will lead to a massive shrinking of this area (Feldmann and Henke 2016, pp. 8, 21). Figure 20.2 shows a summary of the effects in the procurement area.

20.4 Effects of Digitalization on Controlling With regard to the changes in controlling due to the influences of digitalization, there are a number of publications that discuss the various starting points of change. These will be summarized below, structured according to the techniques as well as the functions, institutionalization and instruments. The changes represented in Table  20.1 are the starting point for the deduction of the transformation needs in the procurement controlling.

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Effects

Digitisation of procurement

Driver

Autonomization of processes (warehouse, order processing)

Intelligent systems

Intelligent supplier evaluation

Supply Chain Network

Digitization of procurement markets

Horizontal integration

Real-time communication

intensive influence

323

Digital networking

weakened influence

Fig. 20.2  Changes in the context of Purchasing 4.0. (Adapted from Kleemann and Glas 2017, p. 14)

20.5 Special Effects of Digitalization on Procurement Controlling Building on the changes for the procurement area and general controlling, the adjustments for procurement controlling due to digitalization are presented below. In the operational area, demand forecasting and the purchase requisitions based on it represent an essential area of application for predictive analytics. With the help of this technology, forecasts of the required procurement objects can be created on the basis of past data, current inventory and order data, and the inclusion of external data. In this way, orders can be triggered automatically (Schentler and Schlünsen 2016, p. 86; Kleemann and Glas 2017, p. 18). Furthermore, prescriptive methods can be used to calculate commodity price forecasts. Here, it is first of all necessary to build a value driver model in the form of hypotheses. This can then be tested and validated accordingly. A promising area of application could be the forecasts for the material costs of different products, as Fig. 20.3 shows. Ceiling prices, optimal order quantities, the ideal purchase time or procurement strategies such as natural hedging can be derived (Mödritscher and Wall 2019, pp. 400–402; Schentler and Schlünsen 2016, p.  93). This information in turn influences the financial planning, so that the use of flexible budgets1 makes sense and can be expected to significantly improve the quality of forecasts (Seiter et al. 2015, p. 469).  Nobach (2019) argues that rigid annual planning will become less important in perspective (Nobach 2019, p. 257). 1

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Table 20.1  Summary of effects of digitalization on controlling Category Techniques

Change object Raw and master data Methods of analysis

Functions

Fields of action Operational decision fields Strategic level Objects Risk Controlling

Strategic planning

Changes Responsibility for freedom from errors and decomposition to the smallest level Real-time evaluations Amplifier use of methods for the foresight of future developments and evaluation of interdependencies of effects Significantly more prognostic Increasingly automated Gains in importance Growing importance of service controlling New risks (digitization risks) to be identified and assessed Support risk identification and assessment with new forecasting techniques Reduced use of BSC Use of instruments with analytical approaches to uncover cause-effect relationships Increasing relevance reduction of manual tasks

Green Controlling Institutionalization Operational controlling Competence profile Role of Data Scientist gets important, either to take over tasks or to be able to fill interfaces Instruments Reports Self-service reporting Avoiding information overload Stronger forecast orientation Key figures Development of key figures to determine the degree of digitisation and cost/benefit aspects of digitisation initiatives Flexibility-oriented key figures Cost accounting Linearity less relevant for cost relationships Better allocation basis for production overheads Investment Linear interpolation calculation method Use of scenario-based simulation models More intensive use of real options Project controlling Identification of digitization projects Quantifying the benefits of digitisation Maturity assessment Becker et al. (2016), Cole (2017), Egle and Keimer (2018), Gleich et al. (2016), Jonen and Harbrücker (2019), Lingnau and Brenning (2018), Mödritscher and Wall (2019), Nobach (2019) and Pampel (2018)

In the context of digital reporting, it is expected for the procurement area as well as for the other functional areas that ad hoc analyses can be realized more quickly and more easily with regard to requirements due to the real-time data. Furthermore, deviations from

20  Current Trends and Future Potentials of Digitalization in Procurement Controlling

Generated pieces Product Learning curve effect Product X

Material input Z for product X Material costs for product X

Data

Actual price material Z Price function

Forecast price for material Z

Fluctuation range Material Z Generated pieces Co-product Y Learning curve effect Product Y

325

Material costs for product Y

Material input Z for product Y

Fig. 20.3  Example value driver model for material costs. (Adapted from Mödritscher and Wall 2019, p. 401)

the plan should be detected much earlier and communicated automatically as part of continuous reporting. The key figures will have to adapt to the automation of certain processes in procurement, especially through RPA. At this point, metrics will be needed to indicate the extent to which expanding the use of RPA can increase the efficiency of processes and how many processes in procurement are digitized. In addition, key figures on the degree of digitization of purchased products will be necessary. The possibilities of processing data from different – also external – data sources (e.g. data from supplier self-disclosures and from social networks) can be helpful in supplier evaluation and identification of potential new suppliers (Schlüchtermann and Siebert 2015, p. 462). For example, the evaluation of suppliers can be largely automated and – with regard to interdependencies  – carried out with the help of artificial intelligence (Schentler and Schlünsen 2016, p. 90). One assessment criterion should also be the supplier’s digitization capability in order to determine the extent to which it can be integrated into the supply chain (SC) digitization strategy (Kleemann and Glas 2017, p. 24). This digitalization capability in conjunction with the digital innovation significance, i.e. the extent to which digitalization plays a role in the respective objects, can be represented in a supplier portfolio for Purchasing 4.0, as shown in Fig. 20.4. This differentiates between digital latecomers that have a low level of digitization that is at least currently sufficient, digital traditionalists that should have a higher level of digitization capability for their products than they currently have, digital overperformers that have a higher level of digitization capability than is necessary in their product area, and digital champions that meet the high digital innovation importance in their area.

high

A. Jonen

Digital overperformers

Digital champions

Digital latecomers

Digital traditionalists

low

high

low

Digital innovation capability

326

Digital innovation significance

Fig. 20.4  Supplier portfolio for purchasing 4.0. (Adapted from Kleemann and Glas 2017, p. 25)

In the area of supplier evaluation, a constantly updated potential supplier default risk can be calculated by combining supplier data, early warning indicators and country ratings (Schentler and Schlünsen 2016, p. 91). This can either be taken into account when selecting the supplier or as an early warning indicator to initiate appropriate measures. Analyses of supply security can be carried out with the help of agent-based models that simulate the decisions and actions of suppliers and thus provide corresponding estimates (Mödritscher and Wall 2019, p. 397). Digitalization also entails changes in the risk profile for the procurement sector (Jonen 2008, p. 5 ff.). In particular, the risk of dependence on technology providers should be analyzed intensively in this context (Kersten et  al. 2014, p.  112). Other procurement-­ relevant risks that are growing in importance are different security standards along the supply chain and IT interface problems in the process (Seiter et al. 2015, p. 472). Table 20.2 summarizes the specific risks that can arise along the supply chain as a result of digitalization or whose extent of damage or probability of occurrence increases. In general, the risks must be compiled on a company-specific basis in order to be able to take into account the specifics of individual industries/material groups or other situational aspects (Kersten et al. 2014, p. 115). In summary, it can be assumed for the risk controlling of the digitalized supply chain/network that it will become more transparent and flexible and that a number of new types of risks will become relevant (Kersten et al. 2014, p. 122). Digitization requires corresponding IT resources within the company itself (e.g., processing capacities, data storage) as well as the expansion of existing objects with digital components (e.g., sensors, transmitter-receiver systems). At this point, the make-or-buy

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Table 20.2  Specific digitization risks in the procurement area Cause Requirements of electronic portals/high technical requirements for training Different IT security standards along the SC

Intensive cooperation in developments Supply chain network

Digital procurement projects with high uncertainties due to the degree of novelty

Effect Loss of suppliers (technology barrier) Qualification risks among employees Data security threatened/industrial espionage Manipulation of production (sabotage) Dependence on technology providers IT interface problems along the SC (deficient data) Sabotage of the SC from the outside Incorrect benefit assessment for high-­ investment digitisation projects

Based on Kersten et al. (2014, p. 114) and International Controller Association (2015, p. 18 f.)

analysis must determine the extent to which these aspects are provided by the company itself or by a service provider. IT security must be included as a particular criterion in this analysis (Seiter et al. 2015, p. 471). Due to the automation of operational activities made possible by digitalization (Kersten et al. 2014, p. 111), such as variance analyses, strategic activities will also become more relevant in procurement controlling. It can also be assumed that it will be easier to link purchasing data to data from other areas and that strategic decisions can therefore be better prepared by procurement controlling (Schentler and Schlünsen 2016, p. 86). In summary, Table 20.3 shows the individual areas of change, the triggering factors and the consequences, i.e. the adjustments caused by digitalization.

20.6 Empirical Review: Job Advertisement Analysis In order to be able to evaluate the extent to which the logically derived consequences of digitalization are already being implemented in corporate practice, this chapter aims to obtain corresponding indications on the basis of job advertisements. To this end, it will first be shown to what extent this method is suitable for the purpose of the study. Furthermore, the relevant impact assumptions are presented in order to subsequently analyze them with the help of job advertisement analyses that have already been carried out and that are specifically geared towards digitalization in procurement controlling.

20.6.1 Justification of the Method of Investigation The present study is on a purely descriptive level. The lack of application of inferential statistics can be attributed to the fact that this work represents the first analysis for the specific functional area of procurement controlling on the basis of job advertisements.

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Table 20.3  Effects of digitalization on procurement controlling Area Demand forecasts Commodity price forecasts

Factors Big data, advanced analytics

Reporting

Real-time data, advanced analytics, process automation

Supplier evaluation

General

Big data, advanced analytics

Default risk Risk profile

Supply chain network/ Cooperations IT interfaces

Make or buy

Intensive procurement of IT objects or digitised procurement objects Automation of operational activities Analytical methods

Strategic planning

Consequences More accurate forecasts of needs More accurate forecasts of purchase price and price hedging needs Flexible budgets Acceleration of ad-hoc reporting, communication of deviations from plan Adjustment of key figures (degree of digitization of processes, procurement objects) Continuous reporting More qualified assessment of suppliers (e.g. default risk) New evaluation categories, such as the supplier’s digitization capability Simulation of supplier behavior Early development of risk management measures possible New risks Change in probability of occurrence and extent of damage for existing risks Re-evaluation of make or buy decisions taking into account the new digitized components Increased relevance of strategic activities Qualitatively better decision support

Thus, a temporal course of developments with regard to tasks or knowledge can only be carried out in a makeshift manner via job advertisement analyses created in the past for the entire controlling area. For the empirical analysis of the factually derived consequences of digitalization, it is necessary to derive an adequate methodological approach (Bott 2007, p. 109). In principle, qualitative methods can be used, which involve an open and understanding-oriented approach, and quantitative methods, which can statistically verify and validate (Wildgrube 2018, p. 65). With regard to data collection, the question arises whether a primary survey is necessary or secondary data is sufficient. Since it can be assumed that job advertisements have a fundamental informative value regarding the current skill requirements on the labour market, they should be a suitable source of data (Wildgrube 2018, pp. 66, 84; Gege 1981, p. 1294; Bott 2001, p. 85). In addition to information on the current situation, job advertisements can also be assigned a prognostic value (Mehra and Diez 2017, p. 2).

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Compared to a direct survey, the job advertisement offers the advantage of non-­ reactivity, i.e. it provides a time-independent, researcher-independent and repeatable analysis option (Mehra and Diez 2017, p. 2). Further advantages lie in the area of research economy, i.e. that the information can be obtained comparatively easily and at low cost (Bott 2007, p. 110; Price 2012, p. 44). Compared to other text types, such as newspaper articles or narratives, job advertisements are a relatively well-structured text type (Werner and Vester 2017, p. 57) – they usually consist of several sections that can be assigned to specific content classes. For example, the requirements for the applicant are found in the section in which the job advertiser formulates its corresponding expectations. However, there are a number of critics of the job advertisement analysis (e.g. Fröhlich-­ Glantschnig 2005, p. 38), who – partly differentiated according to the question – classify it as not useful. Table 20.4 shows a structured overview of the criticism and, in some cases, possible solutions. As a conclusion of the different advantages and disadvantages of the job advertisement analysis, it can be stated that this instrument is a suitable method for the identification of the required qualification, competences and tasks by companies in the context of digitalization (Bott 2007, p. 112; Wildgrube 2018, p. 85). In this context, the various limitations in the interpretation must be taken into account (Preis 2012, p. 44), as well as the possibilities for reducing the disadvantages in the design of the approach.

20.6.2 Objectives and Presumptions of Effectiveness The impact assumptions are presented in Table 20.5. The derived consequences of digitalization for procurement controlling were included and those cause factors and effects were selected that appeared suitable for the analysis instrument of job advertisements.

20.6.3 Overview of Existing Studies In the past, analyses of job advertisements have been carried out at regular intervals for controllers in order to find out what the current status and possible developments of institutionalized controlling are. Table 20.6 provides an overview of the studies already conducted and included in this analysis. The previous studies include at most only marginally (0.7% (Gege 1981, p. 1294) – 2.0% (Eschenbach and Junker 1978, p. 4) of the job advertisements analysed) job advertisements specifically within the area of procurement controlling, since all of them included the entire controlling area and only showed in isolated cases how high the proportion of specific procurement or purchasing controlling positions was. Thus, only the results for all controlling areas can be used as comparative values.

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Table 20.4  Criticism of job advertisement analyses Area Limited representation

Starting point Bias due to non-inclusion of internal job advertisements (Bott 2007, p. 110; Weber and Schäffer 1998, p. 228; Fenzlein 2009, p. 13). It can be assumed that the probability of placement in the newspaper or a portal is higher in the case of a particularly relevant position (e.g. on the basis of organisational suspension) (Weber and Schäffer 1998, p. 227).a This does not ensure that the job advertisements considered reflect a clear cross-section of all currently available jobs (Fenzlein 2009, p. 12) Unrealistic ideal Job advertisement is often maximum possible ideal image image, which is thus unrealistic (Wildgrube 2018, p. 135; Berens et al. 2013, p. 223). It is not possible to read from the job advertisement whether this desire for an adequate position has also been implemented in reality (Gege 1981, p. 1294; Bott 2007, p. 112), i.e. whether there is a mismatch between the desire for a qualification and the existence of this qualification (Bott 2007, p. 118) Population not Drawn samples do not refer to a known population as in known a representative company survey. This limits the informative value of the data (Bott 2007, p. 116). For example, the sample will include in particular companies that are currently growing and therefore need new employees Limited capacity Due to the limited tender text, only an excerpt of the by description requirements is given. Special features will be overrepresented (Weber and Schäffer 1998, p. 227 f.; Price 2012, p. 45; Fenzlein 2009, p. 13) Dependence on The willingness to place job advertisements is influenced the economic by the economic situation (Fenzlein 2009, p. 13; Weber situation and Schäffer 1998, p. 228) Repeated posting It is difficult to determine whether job offers are repeated of job or only included in the survey set with minimal advertisements adjustments (Berstorff 1990, p. 10) Fictitious job It cannot be ruled out that fictitious job offers are offers included which are merely used to build up a head-hunter index (Berstorff 1990, p. 10) Difficulty in Certain tasks, such as group accounting, imply certain separating tasks knowledge and are therefore often not mentioned under and requirements requirements. This gives the recording an interpretative element (Borchers and Trebes 1999, p. 24)

Reduction

If determination of a future objective and not the status quo is the goal (Berens et al. 2013, p. 226),b criticism is not relevant

Applies to internet job offers to a significantly reduced extent

Key date analysis avoids duplicates

This is contradicted by Borchers and Trebes (1999), who assume that “employees at lower and middle hierarchical levels” are more likely to be sought through job advertisements (Borchers and Trebes 1999, p. 24) b This is the case, for example, in Grob and Lange (1995), who examine the development of demand for business information specialists a

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Table 20.5  Summary of presumed effects # 1

Causal factor Intelligent systems

2

Increase in data volume (sources, depth, quality)

3 4

Real-time data

Presumptions of effect The focus of tasks is shifting from operational to strategic tasks, since a number of tasks in the operational area can be taken over by IT systems IT skills are sought much more frequently in order to be able to analyse the available data Tasks with IT relevance will be represented significantly more often Increased use of forecasting procurement controlling instruments for decision recommendations

Table 20.6  Overview of German-language job advertisement analyses carried out Author Eschenbach and Junker (1978)

Year 1976–June 1977

Bramsemann (1980)

August 116 1974–March 1976 1976–1979 300

Gege (1981)

Number 386

Pfohl and Zettelmeyer (1986)

1986

Berstorff (1990)

October 304 1987–March 1988 1st half-year 397 1996

Kalwait and Maginot (1998)

86

Preißner (1998)

1998

600

Weber and Schäffer (1998) Borchers and Trebes (1999)

1990–1994

9798

1996–1998

103

Sources Focus Controller tasks Five daily newspapers: Frankfurter Allgemeine Zeitung, Die Welt, Süddeutsche Zeitung, Kurier, Neue Züricher Zeitung, Schweizerisches Handelsblatt. National daily newspapers Tasks, comparison with American companies German national daily Controlling tasks newspapers and requirements for employees Unspecified Comparison of job descriptions with target profile derived from Controller literature Frankfurter Allgemeine Zeitung Change of controller tasks Frankfurter Allgemeine Zeitung, Areas of Süddeutsche Zeitung responsibility, requirements, assignment level, age Frankfurter Allgemeine Zeitung Fields of activity, knowledge, functional areas Frankfurter Allgemeine Zeitung Development of (first weekend edition February, control positions May, August and November) over time Frankfurter Allgemeine Zeitung, Personal and Süddeutsche Zeitung and Welt professional am Sonntag requirements for group controllers (continued)

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Table 20.6  (continued) Author Littkemann et al. (2007).

Year 2004, 2006

Number 244

Fenzlein (2009)

January 2003– December 2007 2012

1337

Berens et al. (2013)

Traxler and Greiling (2014) Werner and Vester (2017)

2012

Wild Pit (2018)

2012

189 (DAX-30 and MDAX) 1050

March–May 200 2016

427

Sources Newspapers and online job ads (FAZ, jobpilot, jobstairs, jobscout24 and monster) Frankfurter Allgemeine Zeitung/ Süddeutsche Zeitung

Focus Different types of investment controllers Job description (tasks and requirements)

Corporate career sites

Tasks Requirements

Online job portals in Germany, Austria and Switzerland

Change of job profiles

Online job advertisements (company website, online job exchanges, job exchange of the Federal Employment Agency) Online (employment agency and Monster)

Crystallizing the characteristics of the ideal controller Competencies for employees in procurement

When comparing the current study with work carried out in the past, it must be taken into account that the typification of tasks and requirements was not always congruent (Fenzlein 2009, p. 9).

20.6.4 Procedure To carry out the job advertisement analysis, various sources are available, as shown by the studies already carried out in the past. Job portals, business networks (e.g. Xing), company-­ specific career sites and print media (e.g. FAZ) appear to be relevant. The disadvantage of print media is that longer texts are expensive, which does not apply to Internet offers. Thus, on the Internet, the characteristics and tasks are likely to be described in more detail. In addition, it can be assumed that only particularly important jobs are selectively advertised in print media (Weber and Schäffer 1998, p. 227), this restriction will not be relevant for online job advertisements, as these are significantly cheaper (Berens et al. 2013, p. 226). Another argument in favour of online media is that they have an important role in recruitment for several years (Weitzel et al. 2012, p. 20; Bott 2001, p. 88), which has also been demonstrated for SMEs (Berthel and Becker 2017, pp.  338–343). Moreover, data from online advertisements are easier to process due to the digitization that has already

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Table 20.7  Evaluation of the different sources Representativeness Options/ Criteria Company website Newspaper

Company size (small/ Topicality large representation) High (3) Controllable (3)

Digitalization maturity (all maturity levels represented) Effort Low (1) High (1)

Total 8

Low (1)

Low (1)

High (3)

7

Medium (2)

Medium (2)

Medium (2) Low (3)

Medium (2)

Medium (2)

Medium (2)

8

Job platform Medium (2) Business Medium network (2)

9

taken place and the uniformity of the structure (Mehra and Diez 2017, p. 3). With regard to job portals, there is some criticism that distortions can occur because some companies may only publish via their own company websites due to personnel policy principles (Weber and Kosmider 1991, p. 18) (Wildgrube 2018, p. 135). The various types of sources were evaluated according to the criteria of timeliness, representativeness (all size classes and all levels of digitisation maturity represented) and the effort required to collect the data (Table  20.7). The criteria were assessed with the classes high (3 points), medium (2 points) and low (1 point). In an equal weighting of the four selection criteria, the job platforms have the highest rating. For this category, there are a number of offers, such as Monster, Stepstone or Jobstairs. Since the risk of duplicates2 would have increased significantly if several job portals had been included in parallel, one of the portals had to be selected. Relevant characteristics for the selection are the reach (Crosswater Job Guide 2018), the awareness among users (Statista 2017) as well as the relevance for jobs in procurement controlling (spec. quantity), measured by the number of hits for the keyword procurement controlling. It does not make sense for the number of hits to be the sole criterion, as it can often be observed that completely unsuitable job advertisements are offered in the hit list, particularly in the lower positions (Mehra and Diez 2017, p. 3). The weighted result of the investigation shows the portal Stepstone as the most relevant for the intended purpose of the investigation (Table 20.8).3 With all types of sources, there is a risk of duplicates, in that job advertisements may be slightly adjusted over time and then included in the portal or published in the newspaper. This can be avoided by conducting a key date analysis (Berens et al. 2013, p. 226).

 Definition: records that describe the same real-world object (Draisbach 2012, p. 5).  Mehra and Diez’s (2017) research also identified Stepstone as the best alternative (Mehra and Diez 2017, p. 4). Crosswater Job Guide’s (2018) ranking has Stepstone in first place in 2016–2018. 2 3

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Table 20.8  Selection of job portal Range 33.3% Portal Stepstone Indeed Employment Agency Monster Jobstairs Jobs Jobware

Rank 1 2 3

Scale (1–10) 10 8.6 7.1

Notoriety 33.3% Scale Percent (1–10) 48 4.8 2 0,2 48 4.8

5 5 6 7

4.3 5.7 2.9 1.4

63 2 33 19

6.3 0.2 3.3 1.9

Spec. quantity 33.3% number of Scale hits (1–10) 24 4.4 17 3.1 18 3.3

SUM

46 55 1 0

6.3 5.3 2.1 1,1

8.4 10.0 0,2 0

100% 6.4 4.0 5.1

20.6.5 Description of the Sample 20.6.5.1 Framework Data The cut-off date for the survey of the job advertisements was 22-05-2019. To select the advertisements, all Controlling advertisements were first searched and these were filtered with the criterion Purchasing, Materials Management and Logistics in the Occupational field.4 In this way, 228 job advertisements were determined. These were then manually checked in order to deselect, for example, job advertisements in which control is to be exercised over something or which contained internship offers or consultants (Littkemann et al. 2007, p. 138). After completion of this step, 55 job advertisements formed the analysis set. In this sample, 47% of the advertisements have the title Controlling or Controller, 38% of the offers have the title Purchaser or Purchasing, 13% have a management reference (Manager/Leader) in the job title. In 18 advertisements, a classification with regard to junior (61%) and senior position (39%) is also given. 20.6.5.2 Representativeness: Sector and Size Distribution In order to check the homogeneity of the sample, it is analysed how the industry and size distribution of the sample is structured. The sector distribution shows a large concentration in the industrial sector (57%). The second largest sector is trade (20%). Fenzlein (2009) had shown for his survey that the industry distribution detected there is largely consistent with the comparative surveys (Fenzlein 2009, p. 71; Kalwait and Maginot 1998, p. 60). The present survey shows a high correlation with the distribution in Fenzlein (2009).

 See Weber and Schäffer (1998, p. 228) on the procedure for selecting controller positions and positions similar to controlling. 4

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Table 20.9  Distribution of strategic and operational activities Pfohl and Zettelmeyer Activity (1986) Strategic 26% Operational 5%

Weber and Schäffer (1998) 4% –

Preißner (1998) 9% –

Borchers and Trebes (1999)a 52% 24%

Berens et al. (2013) 23.3% 34.4%

Werner and Vester (2017) 15% –

Jonen (2019) 53% 35%

The figures relate to investment controllers, for whom the strategic component should be more pronounced from within the function a

Based on the information in the job advertisement, 2/3 of the companies could be identified as large companies. A further 22% are medium-sized companies. Small companies are not included in the sample. In the case of 6 job advertisements (11%) placed by recruitment agencies, no size information could be traced.5 The preponderance of larger companies can be explained by the specialisation of the positions sought, which smaller companies do not usually fill with a dedicated position.

20.6.6 Results of Job Advertisement Analysis There are two ways to evaluate the job advertisements. One is the historical comparison and the other is the comparison between different groups (e.g. small versus large companies). Since the four presumptions of effect are aimed at an increase in certain knowledge or tasks, the historical comparison will be used in the following, with the restriction that the past studies used have always referred to general controlling and not to the procurement area.

20.6.6.1 Impact Assumption 1: Increase in Strategic Tasks In order to check the increase or dominance of individual fields of activity, the named areas of responsibility were classified as strategic or operational. It was determined that 53% of the job advertisements describe strategic activities and 35% operational. As Table 20.9 shows, this is the highest value that could be determined for strategic tasks with reference to the surveys conducted so far for the controlling area. Thus, there are indicators that this assumption of impact does not have to be rejected. It should be noted that the ratio of mentions of strategic and operational activities is very inconsistent over the years (0.7–5.2) and no increase in the relevance of strategic to operational activities can be observed for this indicator.

 The SME definition of the European Commission was used for classification: up to 49 employees up to ten million turnover small enterprise up to 249 employees, up to 50 million turnover as medium enterprise and all above as large enterprise. 5

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100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

1976

1977

1979

1986

1988

1994

1996

1998

1998

2006

2007

2012

2012

2018

2019

Fig. 20.5  Development of the relevance of IT skills

20.6.6.2 Impact Assumption 2: Increase in the Need for IT Skills 87% of the job advertisements for procurement controllers in the analysis carried out contain the requirement that IT skills are present. This is the third highest value6 demonstrated in a job advertisement survey for controller positions in the past, as Fig. 20.5 shows.7 For the analysis carried out, IT knowledge can be broken down into experience in the use of MS Office (58%), or specifically Excel (45%), SAP (49%) and general IT or database knowledge (33%). A tendency of the increase of the necessity of IT knowledge for the controlling and/or specifically for the procurement controlling can be pointed out with this analysis. 20.6.6.3 Impact Assumption 3: Increase in Analytical Activities Analytical activities can be found in 82% of the job advertisements. This is significantly higher than the value determined by Berens et al. (2013) in 2013. At that time, it was 57%. In the current survey, planning was only listed as a task in 22% of the job advertisements.  Fenzlein (2009) had a value of 92.8% and Drerup et al. (2018) of 90.5%.  The basis of this statement is the basic assumption that there is a parallelism in job advertisements in general to controlling and specifically to procurement controlling. 6 7

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Due to the small number of studies that have examined the proportion of analytical activities, no trend statement can be made in this area. With regard to digitalization, direct references to this keyword were currently identified in 24% of the job advertisements.

20.6.6.4 Impact Assumption 4: Intensified Cooperation with IT Department Cooperation with other departments was identified in 78% of the job advertisements. The most common type of cooperation was with unspecified or a large number of departments (58%). In isolated cases, logistics (two mentions) or production (one mention) were specified. The IT area was explicitly mentioned as an interface area in 5 of the 55 job advertisements (9%). The extent to which this area was also included in the interdisciplinary mentions cannot be determined. In order to verify the correlation of effects, a follow-up investigation, possibly in the form of a direct survey, will be necessary, which would have to show an increase in interface activities with IT in order to confirm this.

20.7 Conclusion The factual derivation of the effects of digitisation on the field of procurement controlling, which has so far been illuminated to a limited extent, via the analysis of the effects of digitisation in controlling and procurement has been able to identify a number of focal areas and is able to make forecasts for resulting changes. The subsequent review of selected developments with the help of job advertisements has revealed clear trends for the increase in the relevance of IT skills and the processing of strategic tasks. With regard to the research method, the fact that there are no comparative studies specifically for procurement controlling and that no statement can be made about the digitisation status of the companies surveyed on the basis of the job advertisements must be seen as a major disadvantage. In this respect, the representativeness could not be examined. Starting points for further research to close the knowledge gaps are: • Carrying out a higher number survey of job advertisements for procurement controllers with the possibility of carrying out corresponding hypothesis tests. • The evaluation of the job advertisements was partly based on the hypothesis of a parallelism of the job profiles for general controlling and procurement controlling. Here it would be interesting to investigate whether the requirements and tasks regarding the specific functional area actually show no differences to the results for general controlling job advertisements. • Inclusion of foreign companies in order to identify any differences. • Supplementing job ad analysis with surveys or expert interviews in areas where job ads could provide little indication (Bott 2007; Jonen 2019). Im Literaturverzeichnis ist Bott 2007 und Jonen (2019) noch nicht korrekt angegeben. Name wird nicht angezeigt, Formatierung bei Jonen entspricht nicht der einer Zeitschrift.

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Gleich, R., C. Munck, and M. Schulze. 2016. Industrie 4.0: Revolution oder Evolution? Grundlagen und Auswirkungen auf das Controlling. In Unternehmenssteuerung im Zeitalter von Industrie 4.0, ed. R. Gleich, H. Losbichler, and R. Zierhofer, 21–41. Freiburg: Haufe Gruppe. Grob, H.L., and W. Lange. 1995. Zum Wandel des Berufsbildes bei Wirtschaftsinformatikern. Eine empirische Analyse auf der Basis von Stellenanzeigen. Münster. Grochla, E. 1983. Erfolgsorientierte Materialwirtschaft durch Kennzahlen. Leitfaden zur Steuerung und Analyse der Materialwirtschaft. Betriebswirtschaftliche Beiträge zur Organisation und Automation. Vol. 24. Baden-Baden: FBO. Internationaler Controller Verein. 2015. Industrie 4.0. Controlling im Zeitalter der intelligenten Vernetzung. https://www.icv-­controlling.com/fileadmin/Assets/Content/AK/Ideenwerkstatt/ Files/Dream_Car_Industrie4.0_DE.pdf. Accessed on: 31.08.2019. Jonen, A. Beschaffungsportfolios Überblick – Bewertung – Referenzmodell Springer Fachmedien Wiesbaden Wiesbaden. Jonen, A. 2008. Kognitionsorientiertes Risikocontrolling. Lohmar: Josef Eul. ———. 2019. Beschaffungsstrategien – WISU – Das Wirtschaftsstudium. 48: 921–927, 977. Mannheim: Duale Hochschule Baden-Württemberg. Jonen, A., and U. Harbrücker. 2019. Investitionsrechenverfahren in der Praxis: Aktueller Stand und historische Entwicklung. In Mannheimer Beiträge zur Betriebswirtschaftslehre. Mannheim. Kaluza, C. 2010. Konzeption eines erfolgsorientierten Beschaffungscontrolling. Theoretische Betrachtungen und empirische Untersuchungen. In TCW Wissenschaft und Praxis, vol. 44, 2nd ed. München: TCW-Transfer-Centrum. Kalwait, R., and S. Maginot. 1998. Wenn Controller wechseln wollen: Controller’s Anforderungsprofil. Controller Magazin 23 (1): 57–60. Kersten, W., M. Schröder, and M. Indorf. 2014. Industrie 4.0: Auswirkung auf das Supply Chain Risikomanagement. In Industrie 4.0. Wie intelligente Vernetzung und kognitive Systeme unsere Arbeit verändern, ed. W. Kersten, H. Koller, and H. Lödding, 101–126. Berlin: Gito mbH. Kleemann, F.C., and A.  Glas. 2017. Einkauf 4.0. Digitale Transformation der Beschaffung. Essentials. Wiesbaden: Springer Gabler. Lingnau, V., and M.  Brenning. 2018. „Big Data  – Bad Decisions?“  – Implikationen der digitalen Transformation für das Controlling. In Management der digitalen Transformation. Interdisziplinäre theoretische Perspektiven und praktische Ansätze, ed. V. Lingnau, G. Müller-­ Seitz, and S. Roth, 137–167. München: Vahlen. Littkemann, J., D.  Eisenberg, and S.  Lerchl. 2007. Der Beteiligungscontroller in der Praxis. Ergebnisse einer Längsschnittsuntersuchung zum Aufgabengebiet und Anforderungsprofil des Beteiligungscontrollers. Controlling – Zeitschrift für erfolgsorientierte Unternehmenssteuerung 19 (3): 137–144. Mehra, S.-R., and K.  Diez. 2017. Stellenanzeigenanalyse zur Ermittlung von zu vermittelnden Kompetenzen im Rahmen des neuen berufsbegleitenden Studiengang „Master Online Akustik“. https://de.mintonline.de/fyls/482/download_file. Accessed on: 31.08.2019. Mödritscher, G., and F.  Wall. 2019. Controlling von Beschaffungsprozessen  – Wirtschaftliche Nachhaltigkeit, Chancen und ausgewählte Potenziale durch Digitalisierung. In Nachhaltiges Beschaffungsmanagement. Strategien – Praxisbeispiele – Digitalisierung, ed. W. Wellbrock and D. Ludin, 389–406. Wiesbaden: Springer Gabler. Muhic, M., and B.  Johansson. 2014. Cloud sourcing  – Next generation outsourcing? Procedia Technology 16: 553–561. Nobach, K. 2019. Bedeutung der Digitalisierung für das Controlling und den Controller. In Wertschöpfung in der Betriebswirtschaftslehre. Festschrift für Prof. Dr. habil. Wolfgang Becker zum 65. Geburtstag, ed. P. Ulrich and B. Baltzer, 247–269. Wiesbaden: Springer Fachmedien.

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Pampel, J. 2018. Digitale Horizonterweiterung. Begleitung der Innovation von Geschäftsmodellen durch das Controlling. Controlling – Zeitschrift für erfolgsorientierte Unternehmenssteuerung 29 (2): 21–29. Pfohl, H.-C., and B. Zettelmeyer. 1986. Anforderungen an den Controller in der Literatur und in Stellenanzeigen. krp – kostenrechnungspraxis 40 (4): 125–132. Preis, A. 2012. Controller-Anforderungsprofile. Eine empirische Untersuchung. In Schriften des Center for Controlling & Management, vol. 46. Wiesbaden: Gabler. Preißner, A. 1998. Was machen Controller? – Eine Analyse von 600 überregionalen Stellenangeboten. Controller Magazin 23 (3): 217–221. Schentler, P., and H. Schlünsen. 2016. Digitalisierung im Einkauf: Chancen, Anwendungsbeispiele und Erfahrungen bei der Umsetzung. In Einkaufscontrolling. Instrumente und Kennzahlen für einen höheren Wertbeitrag des Einkaufs, ed. A.  Klein and P.  Schentler, 83–99. München: Haufe-Lexware. Schlüchtermann, J., and J. Siebert. 2015. Industrie 4.0 und Controlling: Erste Konturen zeichnen sich ab. Controlling – Zeitschrift für erfolgsorientierte Unternehmenssteuerung 27 (8/9): 461–465. Seiter, M., G. Sejdić, and M. Rusch. 2015. Welchen Einfluss hat Indsutrie 4.0 auf die Controlling-­ Prozesse? Controlling  – Zeitschrift für erfolgsorientierte Unternehmenssteuerung 27 (8/9): 466–474. Statista. 2017. Welche der folgenden allgemeinen Job-Portale bzw. Stellenmärkte im Internet kennen Sie, wenn auch nur dem Namen nach? https://de.statista.com/prognosen/980794/umfrage-­ in-­deutschland-­zur-­bekanntheit-­von-­jobportalen. Accessed on: 12.08.2019. Traxler, A., and D.  Greiling. 2014. Wie sich Stellenprofile von Controllern gewandelt haben. Controlling & Management Review 58 (3): 57–63. Verlinkung zu Jonen, A. 2023. muss noch eingefügt werden. Weber, J., and A. Kosmider. 1991. Controlling-Entwicklung in der Bundesrepublik Deutschland im Spiegel von Stellenanzeigen. Zeitschrift für Betriebswirtschaft 3: 17–35. Weber, J., and U.  Schäffer. 1998. Controlling-Entwicklung im Spiegel von Stellenanzeigen 1990–1994. krp – kostenrechnungspraxis 42 (4): 227–234. Weitzel, T., A.  Eckardt, C.  Maier, S.  Laumer, and A. von Stetten. 2012. Recruiting Trends im Mittelstand 2012. Eine empirische Untersuchung mit 1000 Unternehmen aus dem deutschen Mittelstand. Frankfurt a. M. Werner, P., and A. Vester. 2017. Was macht den „idealen“ Controller aus? Eine Analyse von Online-­ Stellenanzeigen. Controller Magazin 42 (2): 57–61. Wildemann, H. 2015. Einkaufspotenzialanalyse. Leitfaden zur Kostensenkung und Gestaltung der Abnehmer-Lieferanten-Beziehung. 22nd ed. München: TCW. Wildgrube, M. 2018. Kompetenzen in der Beschaffung. Kompetenzmanagement für den Beschaffungsbereich eines Automobilunternehmens. Wiesbaden: Springer Fachmedien. Wirtz, B.W., and A. Kleineicken. 2005. Electronic Procurement. Eine Analyse zum Erfolgsbeitrag der internetbasierten Beschaffung. zfo – Zeitschrift Führung und Organisation 74 (6): 339–347.

Andreas Jonen  is a professor at the Baden-Wuerttemberg Cooperative State University in Mannheim and a consultant for topics in the areas of risk management, project management and internal auditing. He worked for many years in various industrial companies in the area of auditing, including as Head of Auditing. Subsequently, he was Vice President Strategic Projects and Risk Management at an international engineering company and professor at the University of Applied Sciences in Stuttgart.

The Role of the Chief Financial Officer in the Digital Transformation of Business Models

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Wolfgang Becker, Matthias Nolte, and Felix Schuhknecht

Abstract

They were once referred to as the grey mice of corporate management – the chief financial officers who were primarily involved in operations. However, the Chief Financial Officer of the modern era is involved not only in classic forecasts and profitability considerations, but also in risk management and strategic decisions, for example regarding supply chains, pricing and production (Heinen and Noeth, Controlling Manag Rev 2(Sonderheft):6–9, 2010, p. 8 f.). The CFO should also play a central role in the digital transformation of business models, which is one of the most defining megatrends of the twenty-first century. This is because the CFO is responsible for the value creation of the company and, with his or her individual background, is in a position to actively shape organizational structures and processes, and thus the digital business model transformation.

21.1 Introduction The digital transformation is making its way into all areas of daily life and is advancing at a great pace (King 2014, p. 20). Companies cannot ignore this megatrend and must therefore drive their own digital transformation of the business model ever faster and in a more targeted manner in order to remain sustainably competitive (Botzkowski 2017, p. 27). In W. Becker (*) • F. Schuhknecht Bamberg, Germany e-mail: [email protected] M. Nolte Warburg, Germany © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_21

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this context, buzzwords such as Industry 4.0, artificial intelligence, Big Data, cloud services or smart devices are mentioned in the scientific discussion. So, in the recent past, achieving, securing and increasing value creation has changed drastically due to the emergence of new information and communication technologies. Value creation, which can be divided into the (partial) purposes of meeting needs, securing remuneration (including the generation of profit) and satisfying the needs of all stakeholders involved in the company, represents the fundamental purpose of a company and forms the determinant prerequisite for securing its long-term existence and thus fulfilling the going concern principle of any company (Becker et al. 2014, p. 53). The way in which value is created, but also secured and increased, will be increasingly influenced by technological and digital developments in the coming years (Becker et al. 2016, p. 97). However, the digital transformation of business models is not to be understood as a one-off act, but represents a complex innovation process that should ultimately lead to the aforementioned value creation. The transformation of business models as a structural innovation then represents a profound organizational change. Such innovation processes are triggered by various endogenous and/or exogenous impulses and are based on strategically oriented, comprehensively founded decisions. Strategic decisions, which relate to securing the company’s existence, its ability to act financially, and the resources to be deployed, must always be made by top management (Carpenter et al. 2004; Collins and Clark 2003; Becker and Pflaum 2019, p. 38). Due to this, business model transformation should be interpreted as a top management task (Bloching et  al. 2015; Buxmann and Zillmann 2016; Hess et  al. 2016; Jahn and Pfeiffer 2014; Kane et al. 2015). According to (Finkelstein et  al. 2009, p.  10), top management is a relatively small group of people consisting of the Chief Executive Officer and all managers who report directly to the CEO. The question of which persons besides the CEO belong to the top management team who, ceteris paribus, are also allowed to make decisions in the context of the transformation of business models has been discussed extensively in business administration for several years and, if necessary, cannot be answered conclusively (Carpenter et al. 2004, p. 754ff.). However, the Chief Financial Officer can undoubtedly be seen as a member of the top management team (Becker and Pflaum 2019, p. 105). In summary, it can be stated that the transformation of business models that has become necessary requires (pro-)active control by top management in order not to be exposed to digital Darwinism. This article then deals with the scientific question of the role of the Chief Financial Officer in the context of the digital transformation of business models. To this end, the fundamental relationship between value-added-oriented controlling and the digital transformation of business models will first be discussed. Based on these considerations, the Chief Financial Officer will be characterized as the central role bearer in the digital transformation, and generalized target profiles of the CFO in the phases of the digital transformation will also be offered. Since business administration, as an applied real science, aims

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to define, describe and explain its object fields and to derive recommendations for action for business practice from them, this paper will also derive concrete recommendations for action.

21.2 Value-Oriented Controlling and the Digital Transformation of Business Models 21.2.1 Value-Added Oriented Controlling: Purpose, Functions, Objects, Tasks and Duty Bearers Controlling is an integrated task of corporate management. It serves the optimization of effectiveness and efficiency by ensuring the initialization and alignment of the actions of companies to the purpose of value creation. Consequently, the existence of companies should be sustainably secured (Becker et  al. 2014, p.  53ff.). Figure  21.1 illustrates the conceptual framework of value creation-oriented controlling. Based on the overarching objective described above, value creation-oriented controlling is assigned three basic functions (Becker et al. 2014, p. 50), which are exercised at different objects. The original locomotion function of controlling comprises the initializing and thus partly shaping initiation, securing as well as the continuous (further) development of the operational purpose of value creation, which is executed through conscious shaping and steering tasks. In order to fulfil it, the functions derived from it, the information and coordination function, must be ensured (Becker and Brandt 2014, p.  61). The information function serves to create information congruence within leadership and

Controlling (Structural Components) Targets

Functions

Objects

Tasks

Support

Participate

Taskmaker

Leadership

Share responsibility

Methods, Instruments and Tools

Value creation – Value preservation – Value enhancement Situational Context

Fig. 21.1  Conceptual framework of controlling

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execution. This is done by coordinating information needs, supply and demand. The coordination function, on the other hand, subsumes all tasks of the management cycle on the basis of integrated planning and control of the management and execution of entrepreneurial action. In order to perform these basic functions on specific objects, different controlling tasks must be derived and performed by the corresponding duty bearers. Managers primarily assume the locomotion function and controllers the information and coordination function (Becker et al. 2014, p. 61). In this context, the CFO is considered the top controller in the company (Preis 2012, p. 120). In order for the emerging tasks of controlling to be fulfilled at all, appropriate methods, instruments and tools are used (Becker et al. 2014, p. 100). In order to achieve a corresponding value creation, controllers have to use such tools with which information about the creation and renewal of success potentials, success and liquidity can be provided (Baltzer 2013, p. 79). In the future, it can be assumed that the cooperation of controllers and managers in both system issues and management decisions will grow closer and closer. (Losbichler and Ablinger 2018, p. 67).

21.2.2 The Digital Transformation of the Business Model as an Object Field of Value-Added Oriented Controlling In the course of ongoing digitalization, controlling is not only responsible for evaluating necessary investments, but also for developing digitalized business models (Drerup et al. 2018, p. 13). The digital transformation of business models is an object field on which the functions of controlling described above act. The process of digital transformation can be divided into the phases of initializing, realizing and evaluating, as deliberately shown roughly in Fig. 21.2, and applied situationally for the respective company. With the help of the value, market, resource and process perspectives, the strategic orientation of the digital transformation can be considered holistically in each phase (Becker and Kunz 2009, p. 225). The entire process can be controlled from both a strategic and an operational perspective and must be aligned in such a way that the digital business model makes a greater contribution to the value of the company than the analog business model that existed before digitization. The corresponding change must therefore be viewed from a strategic perspective, with controlling primarily performing design tasks in this context, but also value-oriented steering tasks. The design tasks primarily include clarifying which information and communication technologies will be used in the long term and establishing and harmonizing the process landscapes required for digitization. The latter also refers above all to the indirect processes that must be integrated into the digitization efforts, as a lack of inclusion can jeopardize the effectiveness of the entire digitization project. In addition, the streamlined orientation of all processes with the corresponding product-market references takes on a special significance, since the customer-benefit-generating demand-covering function of a business model is in the foreground. This perspective has a special

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Locomotionfunction

Controlling

Reconiliationfunction Informationfunction

Situational Context

Digital Transformation

Initial bisiness Model t0

Initialize

Realize

Evaluate

Ressourceperspective Processperspective Marketperspective Valueperspective

Partially/totally Digitized bussiness model

Fig. 21.2  Controlling digitalization

significance because the increase in value that is ultimately to be aimed for from the value perspective through digitization is also inevitably jeopardized if the customer benefit is jeopardized. In addition to the strategic perspective, the digital transformation process, consisting of the steps initialize, realize, and evaluate, must also be supported by controlling at the operational level (Becker et al. 2016, p. 114). In the operational perspective, the controlling tasks are also derived from the perspectives of value, market, resources, and processes. Business management tools can be used to support specific tasks when controlling functions are applied to the digital transformation process. Task areas that arise as a result of the condensation of tasks are those of design, target formation and planning, control and regulation (steering), management accounting, information by means of special reports and ongoing advice for the management pursuing digitization (Becker et al. 2014, p. 86 ff.). In the following, we will describe which controlling tasks arise as soon as controlling actively accompanies the digital transformation of business models.

21.3 Tasks, Duty Bearers and Instruments in the Context of the Digital Transformation of Business Models 21.3.1 Initialize 21.3.1.1 Locomotion Function Management should set corresponding goals for the digitization process, which can be of a technical, temporal or economic nature (Braun and Siegel 2001, p. 276; Vahs and Brem 2015, p. 366). The increase in value creation is located at the center of the target formation.

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This is achieved through corresponding innovation measures, which are understood here as digitization measures (Becker and Nolte 2019, p. 80). The impetus for initiating the digital transformation usually comes from management, which acts as a power promoter (Witte 1973, p. 17). The reason for this is that several competing innovation initiatives exist in the company at the same time, and resources must be made available for their realization (Beckurts 1983, p. 31; Gebert 2002, p. 192). At the same time, care must be taken to ensure that it is possible within the organization to communicate good ideas to upper management levels, following the bottom-up procedure. Especially in areas driven by technology push, ideas often develop in lower hierarchical levels. In order to use this potential, a functioning bottom-up communication is of high importance (Beckurts 1983, p. 31). For the successful use of the controlling of the digital transformation, acceptance within the company is essential in this regard. This requires clear integration into the company organization, precisely defined tasks, and sufficient staffing (Munck et al. 2015, p. 77 f.). It is the responsibility of management to discuss customer analyses in cooperation with employees in order to identify precise customer needs (Van de Ven 1986, p. 596) as well as to carry out a realistic assessment of the current situation (Gebert 2002, p. 172).

21.3.1.2 Information Function The assessment of the economic benefit of the respective digital transformation is to be fulfilled by the information function. This information is to be communicated to the management. Based on the information communicated, management can make the decision to implement (Hoogen and Lingnau 2009, p. 105; Munck et al. 2011, p. 54). This includes, among other things, the examination of time and budget targets as well as information on the respective status of cost reduction potentials, throughput reductions and quality improvements (Munck et al. 2011, p. 54). In addition, a systematic procurement, evaluation and preparation of relevant information is necessary. In order to evaluate arising uncertainties, especially finance-related information has to be prepared for the management. Questions on which information must be provided relate to the market to be served, the technologies involved, necessary resources and the organizational handling of the digitization measure (Tkotz et al. 2015, p. 36 f.). This information can be made available through instruments and tools from accounting (Gemünden and Littkemann 2007, p. 9). When using efficiency and effectiveness indicators, the problem at the beginning of a digitization process is that the input is not matched by a direct output (Langmann 2011, p. 74). One solution for improving innovation management in this case is the use of qualitative as well as quantitative measurement instruments (Janssen et al. 2011, p. 122 f.). A basic stock of information should be available so that innovations can emerge (Small et al. 2011, p. 128). For this reason, an information and communication structure that covers all phases of the digital transformation process is of central importance. This is also a fundamental success factor for increasing and sustainably securing digitalization capability. At the beginning of the process, it can be assumed that the degree of freedom of the

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respective information is relatively high, which results in a reduction of the time required for decisions. In addition, an increase in the problem-solving perceptiveness of the participants is achieved through an informal design of the structures. This, in turn, leads to an improvement in the overall digitization process. Meanwhile, the exchange of information is continuously standardized, which leads to a more efficient exchange of information (Schön 2001, p. 215). Controllers are given the task of enhancing the information level of employees. For this purpose, controllers have a large amount of precise data at their disposal, which makes this task easier for them. However, this task should not be the focus at the beginning of the digitization process, especially since the results are still very vague at an early stage. In doing so, promising research approaches could be dead-calculated, as these early results do not stand up to time and cost evaluation (de Pay 1994, p. 80). In this context, both strategic and operational controlling is applied, which acts along the entire value chain. The respective targets for controlling the digital transformation, which are derived from the market, focus on costs, time and quality (Horváth 1999, p. 66). To provide a multifunctional business consulting service, controllers can work together with experts from other areas in multifunctional performance management teams (Daum 2006, p. 436).

21.3.1.3 Reconciliation Function At the beginning of a digitization process, it can be assumed that the division of labor is low, resulting in optimal use of the innovative employee potential within the process. The division of labor and the associated coordination effort increase in later phases (Schön 2001, p. 213).

21.3.2 Realize 21.3.2.1 Locomotion Function The willingness of employees to innovate should be encouraged by management within the realisation phase. Possibilities for this are a targeted supply of information, participation in decision-making processes, training efforts, special incentives, etc. (Thom 1980, p. 61). However, intrinsic motivation of employees is indispensable. In addition, management should work to ensure that the transformational situation is accepted by employees (Gebert 2002, p. 195). Furthermore, the management has the task to promote the existing creativity of the employees in the company through appropriate measures (Thom 1980, p. 62). This can be achieved, for example, through agile leadership (Kenfenheuer 2019). Corresponding cornerstones for this are the already described value creation orientation as a maxim for action, self-direction of teams, changed roles of managers, design of structures and processes, leading with metrics, and the willingness to change (Weinreich 2016, p. 150 f.). There should also be some implementation support from management. Possible forms can be discussing problems with the employees and finding possible solutions, offering

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needs-oriented assistance so that the employees can work effectively, or clear communication of what is expected of the employees and appropriate praise for good work results. If such support takes place, there is a significantly higher implementation rate of new ideas (Axtell et al. 2000, p. 272 ff.). Accordingly, participative leadership behavior on the part of management is of great importance for the successful implementation of the digitization project (Marr 1993, p. 1810). Within the entire process, it is essential that management motivates employees to be open to digitization measures (Röhrl 2017, p. 41). This is made possible by appropriate motivational work. In addition, new approaches to digital transformation must be actively implemented by management, whereby management assumes a pioneering role in the implementation of the digital transformation process (Szczepańska-Woszczyna 2015, p. 400).

21.3.2.2 Information Function The controller forwards budget and planning data to management. Furthermore, trends should be recognizable from the data provided. Furthermore, information from different areas should be bundled and analyzed. Based on a sound foundation, management can then determine the further direction of the company (Tkotz et al. 2015, p. 40). Due to the information function, a corresponding data flow between the employees, departments, etc. involved in the respective object should be ensured. This is made possible by an appropriate supply in the right form, quantity and time. It should be noted that the recipients of the respective business information usually come from non-specialist areas. In addition, a purpose-oriented preparation of the information must be considered (Vahs and Brem 2015, p. 365). In this context, care must be taken to ensure that information is supplied consistently, efficiently and effectively throughout the entire innovation process (Hoogen and Lingnau 2009, p. 112; Maier et al. 2015, p. 1165), resulting in a corresponding reduction in innovation time (de Pay 1994, p. 76). 21.3.2.3 Reconciliation Function Within the digitization process, the coordination function aims to optimally coordinate the interfaces and interdependencies between the individual participants (Pistoni et al. 2018). This avoids duplication of work, delays and bottlenecks of a temporal, financial or personnel nature (Vahs and Brem 2015, p.  365). Furthermore, in addition to the stakeholders within the company, there should be coordination between the interests of external stakeholders. Within the digitization process, the controller assumes an interface function between the company and stakeholders. They perform coordination, consolidation and moderation tasks (Vinkemeier and von Franz 2007, p. 42 f.; Andersen 2016, p. 59). Within the stakeholders, customers receive special attention, as the existence of the company is secured by meeting their needs. Due to this, the implementation of digital transformation should always be done in coordination with customer needs. The controller is responsible for

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promoting a balance of interests between the individuals. This is necessary because the interaction of the individuals creates friction points, which must be reduced (Andersen 2016, p. 59; Gadatsch et al. 2017, p. 75). By working together in interdisciplinary teams, mutual understanding is promoted and problems can be solved consistently (Daum 2006, p. 432; Gemünden and Littkemann 2007, p. 15). In order to reduce the number of interfaces between the individual departments and promote cross-departmental cooperation, management measures would have to be taken to reduce communication and coordination problems between the individual departments (de Pay 1994, p. 59). Employees are to be coordinated according to their digitization skills in order to reduce deviations. In order for innovative ideas to emerge, it is necessary to bring together distributed knowledge stocks of different actors (Barthel 2001, p. 53). Here, it is the controller’s responsibility to create a resource allocation within the transformation process that guarantees an optimal level of efficiency and effectiveness in relation to the project implementation (Tkotz et al. 2015, p. 40). This aspect is particularly important in the area of IT, where the aim is always to improve the coordination function between the systems involved. On the part of controlling, care must be taken not to restrict the degrees of freedom in project implementation too much. If there is too much restriction at this point, projects with a high degree of innovation are often not feasible (Munck et  al. 2011, p.  55  f.; Andersen 2016, p. 59). The respective business case is to be reviewed by the controller both at the beginning and regularly during the implementation. Appropriate measures should be derived from the respective results. Routine tasks should be automated or outsourced as far as possible (Daum 2006, p. 430 f.). For the operational implementation of the respective digitization measures, it is of particular importance that a workplace improvement takes place that is coordinated with the digital challenges. In addition, the workplaces must be designed in such a way that cross-departmental collaboration is made possible.

21.3.3 Evaluate 21.3.3.1 Locomotion Function Within the evaluation phase, it is the responsibility of the management to carry out an accompanying control of the defined digitization goals. The corresponding controls are directed at all previously defined target dimensions (Vahs and Brem 2015, p. 366). 21.3.3.2 Information Function In principle, controlling measures are particularly important within a change project, as they enable deviations from the planned concept to be identified and assistance to be provided in the event of problems and difficulties. Open communication with those involved is of great importance here (Plog 2011, p. 176).

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The success of the respective digitization measures should be measured on the basis of their respective characteristics. Accordingly, incentive structures should be created that include both qualitative and quantitative aspects (Andersen 2016, p. 61).

21.3.3.3 Reconciliation Function If a formalized digital innovation process exists within an organization, it has a positive influence on innovation performance (Labitzke et al. 2014, p. 245). This innovation process should be mapped by controlling. In addition, connecting points for innovation-­ oriented behavior should be created (Eickhoff 2003, p. 101). Formalized processes within transformation processes make it possible to identify deviations in digitization activities that have already been carried out and to control them by aligning the deviations (Bürgel and Zeller 1997, p. 277). Controls carried out at the end of the process lead to proactivity on the part of employees, which in turn has a positive influence on innovation activities (Labitzke et al. 2014, p. 245). In addition, controlling should ensure constant monitoring and flexible coordination of the financial, human and material resources used (Vahs and Brem 2015, p. 364). The tasks within the digital transformation can be summarized as shown in (Fig. 21.3).

Initial business model t 0

Controlling

• •

Information function • Assessment of the economic benefit of the digitisation project • Creation of a central information base • Securing a cross-departmental flow of information • Purpose-oriented information processing for external areas

Value creation – Value protection – Increase in value

Initialize Resource perspective

Realize

•

Process perspective Market perspective Value perspective

Evaluate

•

Locomotion function

Digitale Transformation

•

Reconciliation function Mapping of the digitisation process and creation of starting points for innovative behaviour Coordination of interfaces/interdependencies between stakeholders within the digitisation process Promotion of cross-sectoral Cooperation Coordination of employees on the basis of digitalization skills Identification of deviations within the digitisation activities

Partially/totally digitized business model t1

Fig. 21.3  Tasks within the digital transformation of business models

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21.4 The CFO in the Digital Transformation of Business Models The fourth section of this paper will characterize the role of the Chief Financial Officer in the digital transformation of business models. To this end, the scientific concept of the Chief Financial Officer will first be presented. Based on this, selected empirical findings on the Chief Financial Officer in the context of industrial digitalization will be offered.

21.4.1 Scientific Approach Although the existence of a Chief Financial Officer is not required by law, the management of a corporation in Germany is subject to paragraph 76 of the German Stock Corporation Act. Paragraph 2 stipulates that “the management board […] [may] consist of one or more persons”. However, a significant increase in the importance of this function cannot be denied in academic literature (Brandt 2016, p. 59). Despite the increasing specialization within the functions of controlling, finance and accounting, an integration of the functions is increasingly manifesting itself at the institutional level in the form of the role of the chief financial officer (cf. Fig. 21.4). The significance of the position of Chief Financial Officer should by no means be underestimated in the digital world. In the pre-digital world, for example, the CFO’s function tended to be inward-looking and characterised by administrative activities (Daum 2008, p.  390). However, this range of activities is more similar to the role of a senior accountant (Becker et al. 2011, p. 21). Thus, in English-speaking countries, the CFO is also referred to as a “watch-dog”, who is exclusively responsible for the quality of

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financial reports (Feng et al. 2011, p. 22). However, this extraordinarily narrow interpretation of the CFO’s job no longer seems appropriate in today’s practice (Angel and Rampersad 2005, p. 34). Accordingly, the CFO’s range of tasks has undergone a significant upgrading and simultaneous expansion in the present (Fabozzi et al. 2008; Uhde et al. 2017). In addition to the traditional areas of controlling, finance and accounting (cf. Fig. 21.4), further tasks within the scope of increased compliance requirements, strategic planning and communication tasks with other stakeholders are coming to the fore (Becker et al. 2011). Due to these essential areas of responsibility, the CFO is considered the second man in the company’s management level behind the Chief Executive Officer, whom he advises and supports and thus acts as a business partner (Schäffer et al. 2008, p. 375) and opportunity agent (Becker et al. 2011, p. 21). Goodman (2010, p. 1) postulates in an interview with a board member of an American company that “[…] there are pressures on the CEO to do acquisitions, for example. You need to have someone who is the CEO’s intellectual equal to balance this […]. In our company, it’s part of the CFOs job description and he has personal incentives to stand up to the CEO […]”. It seems little wonder, then, that some CFOs rise to the position of CEO during the course of their careers. Prominent examples are Joe Kaeser of Siemens AG (Trojanovski 2013), Indra Nooyi (PepsiCo), James Ziemer (Harley Davidson) or Lynn Good (Duke Energy) (Uhde et al. 2017, p. 117). The CFO can then be interpreted as the institutionalization of a modern understanding of controlling, which can be subsumed under the umbrella term of value-based management (Böhl 2006; Becker et al. 2016). On the one hand, his area of responsibility is determined by the organization; on the other hand, it is possible for the CFO with his individual distinctive background to actively shape organizational structures and processes (Becker et al. 2011, p. 109). At the performance level, the primary goal is to secure and enhance performance (Becker and Ulrich 2012, p. 74). Thus, a not insignificant number of – mainly US – companies postulate that the Chief Financial Officer is centrally responsible for the value creation of the company (Zorn 2004; Higgins and Gulati 2006). This can certainly be interpreted as an indication of alignment with the value creation-oriented controlling concept (Sect. 21.2) and as essential for the successful digital transformation of business models. The CFO can then be characterized as the central role bearer in the context of this digital transformation of business models.

21.4.2 Empirical Validation The findings to be discussed in the following are based on an explorative, quantitative survey conducted in September and October 2018 by the European Research Field for Applied SME Research (EFAM). The data collection was based on Homburg and Giering (1996) and Schnell et al. (2005). In total, 117 subjects participated in this study. The generated data base of the completely filled out questionnaires was then recorded and analyzed with the statistical program IBM SPSS Statistics. Various univariate and bivariate evaluation procedures were used to evaluate the closed and semi-closed questions. While

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univariate evaluation procedures only consider one variable or its expression, for which they usually show one-dimensional frequency distributions, position and scatter parameters, bivariate evaluation procedures, on the other hand, attempt to uncover causalities between two or more variables (Becker et al. 2016, p. 45). However, it should be noted that respondents may differ greatly in their decision-making behaviour as well as in their expertise on the topic. Due to this, there is an urgent risk of a possible response bias, which is why a (systematic) error in this regard cannot be completely ruled out in the context of such a study. Although the study aims to examine industrial digitalization in the German-­ speaking world, these findings can also be applied to the digital transformation of business models (Sect. 21.2.2). In order to examine these possible effects on the basis of mean or median values, but also in a frequency-distributed manner, it is necessary to group the respective subjects of the sample. However, it should be noted that the number of objects, in this case the subjects, should not vary too much in the respective groups (Altobelli 2011, p. 466). In the specific case, 33 subjects attest a significant influence to the Chief Financial Officer, whereas 42 respondents do not or do not have a Chief Financial Officer (this was elicited through a simple yes/no question). A fixed guideline for the minimum size of the groups is not found in the scientific literature, as small sample sizes can also be sufficient, provided that the grouping of the respondents is comprehensible and methodologically correct (Bacher et al. 2010, p. 465; Laufs et al. 2016). First of all, the degree of preparation of the respondent groups for industrial digitalization should be considered (cf. Fig. 21.5). These were queried using a 5 rating scale (where one is equivalent to very poor and very good was operationalised with a five). Comparing the mean values of the two groups, it becomes apparent that companies in which the Chief Financial Officer has an influence in the context of industrial digitalization are better prepared for it. In addition, this group of respondents rates their level of preparation higher than that of their direct competitors. Furthermore, it can be stated that companies in which the CFO has an influence have a higher average degree of digitization (also based on the mean value of a 5 rating scale, where one is equivalent to very weak and very strong was operationalized with a five) in the individual functional areas (see Fig. 21.6). This is particularly evident in the functional areas of purchasing, marketing and sales, but also in the functional area of IT, which is highly relevant in the context of digitalization. But also in the area of top management and in the area of controlling, companies in which the CFO has an influence are more digitized on average. Only in the functional area of customer service, which in addition to general service also includes any aftersales activities, does this group show a lower degree of digitalization. Interesting findings can also be generated with regard to the assessment of the economic situation of the two groups (cf. Fig. 21.7). The companies in which the CFO has an influence are generally more liquid. This statement can also be formulated with regard to the growth of the test persons. Marginal differences arise in the context of the cost of capital and the debt of the test persons. However, these are always observations of the mean value of a 5 rating scale (1 = very weak; 5 = very strong), not statistical correlations.

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21.5 Target Profile of the CFO In German business administration, the term role is usually used in the sense of functions and tasks performed by a defined task holder (Becker et al. 2011). The central functions and tasks of the CFO in the context of the digital transformation of business models have already been highlighted in this paper. If one limits the view of the CFO’s role to the pure fulfilment of tasks, one would disregard the personal dimension of the CFO (Becker et al. 2011, p. 83). Further aspects of the CFO as a person should therefore also be examined in order to arrive at a comprehensive perspective (Goretzki and Weber 2010, p.  163ff.). Accordingly, possible competencies of the CFO will be considered in the course of this paper. The basis for the examination of target profiles is then the competency-based management approach, which pursues the fundamental goal of using competencies as a unifying construct to link both the management of resources and thus the creation of success potentials, as well as strategic implications of the realization of success potentials in the markets (Schiller 2000, p. 75). The competence-based management approach, which is referred to as the competence-based view, is a conceptual development of the resource-­ based view (Barney 2001). In Western society, the concept of competence has become widely established in the private and professional spheres (Kaufhold 2006, p. 21). The concept of competence also attracts attention in the scientific context, but it must be noted that it varies greatly depending on the disciplines under consideration (e.g. sociology, psychology, business

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administration). A comprehensive literature review is offered by Becker and Pflaum 2019. In this article, we follow Schiller’s (2000, p. 105) view, which defines competencies as a combination of the resource characteristics knowledge, ability, willingness and ability to do, and defines them as follows: Competencies are understood as consciously [sic!] achievable bundles of properties of resources, which are suitable for the creation of competitive advantages and are therefore to be used specifically in the course of value creation. Resource characteristics can be interpreted as knowledge, ability, willingness and permission that are relevant to competitive strategy.

The right to do so in the context of the digital transformation of business models is determined by the hierarchical position of the decision-maker in the company and by the corporate constitution/form. The permission of a decision-maker, using the example of a corporation, results from the foreign ownership right through special legitimation. Decision makers must not only possess the skills and abilities to make a decision, but also have the actual intrinsic motivation to act (Probst 1993, p. 180). In order for the digital transformation of business models to actually be carried out, the will of the decision maker must be present. With regard to ability, a clear delimitation of the terms ability and knowledge must first be carried out. Kleinhans (1989, p. 9) understands skill as a knowledge of how to do something, whereas knowing is a knowledge that something is the case or what is the case. Thus, proficiency is the skills and abilities of an individual or a group of individuals. Knowledge is originally something stored in the memory of an individual or group of individuals. Knowledge therefore does not exist in the strict sense, but is always the intellectual disposition to solve problems and tasks (Rothe and Hinnerichs 2005, p. 674). Value Creator The increased dynamism and differentiation means that the traditional view of the CFO as a bean counter no longer meets today’s requirements (Hiebl 2013). Empirical studies show that internal and external complexity are the biggest factors influencing the role of the CFO (Becker and Brandt 2014, p. 118). Thus, Sharma and Jones (2010, p. 1) state that “the traditional image of a CFO is one of glorified bean counter, whose primary responsibility is to prepare the books and report back to higher level management on the overall financial risk and performance of the enterprise”. In addition to processing and integrating large amounts of data and compliance, involvement in strategic decision-making is playing an increasingly important role (Ruthner and Feichter 2013). Becker et al. (2011, p. 89) also attest to the increasing importance of strategic aspects for the CFO. Although there is no uniform understanding of the term strategy in the existing management literature (Becker and Pflaum 2019, p.  39), a strategy can be understood as a long-term pattern of action affecting the entire company, which describes how a company uses its strengths to counter changes in the opportunity-risk constellations in the situational environmental conditions. The purpose of the digital transformation of business models is always to create value (for the concept of value creation, see Sect. 21.2)

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(Becker and Pflaum 2019). Accordingly, a CFO, which is particularly significant for the success or failure of a company (Gison-Höfling 2008, p. 8), has to interact as a value agent. Thus, Malmi et al. (2001, p. 497) state long before digitization that “there has been a transformation of CFOs from bean counters to business partners during recent years”. In order to be able to fulfil this role in a goal- and purpose-oriented manner, the CFO must have certain bundles of competences. With regard to the bundle of competences, a distinction must be made between decision-making authority in the internal relationship and in the external relationship (Becker and Pflaum 2019, p. 111). While the decision-­ making authority in the internal relationship includes the right of management, performance as well as instruction authority (Bühner 2004, p. 64), the decision-making authority in the external relationship focuses on the right of representation authority in order to be able to conclude binding legal transactions with third parties (Becker and Pflaum 2019, p. 111). In order to ensure that all activities (Sect. 21.2) are geared towards value creation, the value agent should be endowed with a high degree of authority to issue directives, thus “prescribe[ing] to other subordinate bodies which actions are necessary or should be refrained from” (Bergmann and Garrecht 2016, p. 63). In terms of will, openness to change is a key prerequisite for the CFO as a value agent to be able to (pro)actively address new opportunities for action in the context of the digital transformation of business models. This openness ultimately helps to find creative solutions and thus to actually shape the digital transformation of business models in a way that creates value in the long term (International Group of Controlling 2015, p. 201). The CFO should also have a high level of decision-making ability. This is the personal ability to self-determine and actively perceive the alternatives for action that present themselves in order to perform a task in a goal- and purpose-oriented manner (Heyse et al. 2010, p. 148). The ability to make decisions is strongly determined by the leadership style. Here, a distinction can be made between an authoritarian and a cooperative leadership style (Tannenbaum and Schmidt 1958, p. 97). In the context of the digital transformation of business models, it is advisable to resort to a cooperative leadership style in order to prevent silo thinking (Trachsel and Fallegger 2017, p. 43) at an early stage. In addition, the CFO should apply a systematic, methodical approach, which is particularly necessary in situations where the focus is on the design of existing work processes (Heyse and Erpenbeck 2009, p. 521). Change Agent Against the backdrop of the digital transformation and the accompanying change within the company, the role of the change agent is becoming increasingly important (Laval 2015, p. 59; Plag 2016, p. 61 f.; Tschandl and Kogleck 2018, p. 55). Within volatile and complex environments, the change agent should initiate and shape change processes within the company (Gleich and Lauber 2013, p. 513). As can be seen from the above, the CFO has a significant role within the digital transformation. Accordingly, the CFO will have to take on tasks that can be attributed to the role of change agent. At the same time, it is evident

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that a successful digital transformation can only succeed if the employees within the CFO organization support and contribute to the digital transformation (Kirchberg and Müller 2017, p. 88). If the decision-making competencies listed above are considered, it becomes apparent that the CFO must actively initiate the digital transformation in his role as a change agent under the aspect of will (Langmann 2019, p. 45). In addition, he should actively implement digitization measures and take on a digital pioneering role in order to motivate employees accordingly (Szczepańska-Woszczyna 2015, p. 400). Due to his or her position in the company, the CFO acts as a power promoter during the digital transformation, which gives him or her the authority to issue technical and disciplinary directives (Becker and Pflaum 2019, p. 111). This aspect describes the competence of being allowed. This enables him to implement the appropriate digitalization measures in the company (Gemünden 2003, p. 123) and to protect against barriers that arise (Witte 1973, p. 15). At the heart of the competence of capability, the CFO in the role of change agent has to fulfil various aspects. Basically, a well-founded digitization decision can only be made if precise knowledge of the respective business model is available (Becker and Pflaum 2019, p. 111). Based on this, holistic process-related thinking by the CFO must take place within the planning and implementation. For the successful implementation of the respective changes, they have to be coordinated and moderated accordingly (Mayer and Wiesehahn 2018, p. 32). This gives rise to the need for the CFO to train his social and communication skills accordingly (Langmann 2019, p. 45; Tschandl and Kogleck 2018, p. 67). A high level of conflict skills, sensitivity, and empathy are necessary to successfully overcome the resistance that arises. Furthermore, the competencies of teamwork and willingness to cooperate are of increased importance, as close cooperation with the operational management is necessary to implement the corresponding digitalization measures (Gleich and Lauber 2013).

21.6 Conclusion and Outlook The preceding explanations make it clear that the CFO plays an important role in the digital transformation of companies. The influence of the CFO has a positive effect on the success of digitization measures. At the same time, it can be seen that the higher the CFO’s influence, the higher the degree of digitization in the respective company. The CFO must initiate and implement digitisation measures that create, secure and increase value. Corresponding targets are to be set for this purpose. After the digital transformation has been initiated by the management, it is the CFO’s responsibility to ensure successful implementation. Once the corresponding digitalization measures have been completed, the CFO must monitor the targets that have been set. The role models of the CFO cited in this article, in the form of the value agent, aim to ensure that digitization measures are not pursued as an end in themselves, but are always

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aligned with the purpose of creating value. By exercising the role of change agent, the smoothest possible course of the digital transformation is to be ensured. This article makes clear that the role of controlling within the digital transformation of business models is gaining in importance. This is accompanied by an increasing need for research that examines the role of controlling within the respective phases of the digital transformation. cc

Recommendations for the CFO 

• Finally, recommendations for action are to be derived in the form of (bundles of) measures, but it must be borne in mind that these are always based on evaluations, which often have a normative character (Kornmeier 2007, p. 26).  • The CFO should be involved in the process of digital transformation of the business model in an anticipatory manner. • The CFO also urgently needs design and decision-making powers in order to be able to shape, steer and manage the digital transformation of the business model in a target- and purpose-oriented manner. • The CFO does not necessarily have to be a digital native, but should have a high digital affinity in order to be able to evaluate alternatives in the context of digitalization. In addition, he or she should be fundamentally open to change, i.e. actively exemplify a culture of change. • In order to prevent silo thinking among employees at an early stage, a cooperative management style is recommended. • In addition, it is the CFO’s responsibility to proactively influence the value creation awareness of the other decision-makers. Specific value creation knowledge is indispensable for this.

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Wolfgang Becker  is a full professor of business administration and holds the chair of Corporate Management & Controlling and is a member of the board of directors of the Competence Center for Business Models in the Digital World at Otto Friedrich University Bamberg. In addition, as Scientific Director, he heads the European Research Field for Applied SME Research (EFAM), the Research Field for Value Based Management and the Research Field for Digital Business Models. These topics are also his main areas of research. Professor Becker also represents the subject area of Corporate Management & Controlling in the MBA program in Business Management at the University of Erlangen-Nuremberg and in the Executive MBA program at the Johannes Gutenberg University in Mainz. Finally, he is founder, shareholder and chairman of the advisory board of Scio GmbH Professor Dr. W. Becker in Erlangen, which offers consulting services to business practice in the field of integrated strategy and organization design. Matthias Nolte  is Head of Project Controlling at Meinolf Gockel GmbH & Co KG in Warburg. He is also a doctoral candidate at the Chair of Business Administration, in particular Corporate Management and Controlling at Otto Friedrich University in Bamberg. His main research interests are: Controlling in the context of digital transformation and innovation controlling. Felix Schuhknecht  is a research assistant at the Chair of Corporate Management & Controlling and a doctoral candidate in the European Research Area for Applied SME Research (EFAM) and the Competence Center for Business Models in the Digital World at the Otto Friedrich University of Bamberg. His main research interests are: Value Based Management, Business Models in the Digital World, Cost Management and KPI Management.

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Hack Yourself: A Call for an Artistic Metamorphosis of the Controller in the Digital Transformation Avo Schönbohm and Thea Dymke

Abstract

Digitalization has taken hold of controlling and made it more powerful than ever through BI systems and data analytics. However, controllers are in danger of becoming superfluous in this new controlling world. They have to reinvent themselves. In doing so, an excursion into artistic thinking and working can be a source of inspiration: This article offers creative development perspectives along analogies to modernist art (Impressionism, Dadaism, Surrealism and Cubism) and asks the controller questions about the courageous development of his or her own professional identity. The aim of the paper is to take controllers out of the deceptive comfort zone of an indispensable employee in order to encourage and inspire them to apply the process of creative destruction to their own professional lives.

22.1 Controlling and Controllers in the Digital Transformation Let us engage with the idea that controlling, like other professional fields (Keese 2018), is undergoing a radical process of change: Automation and digitalization have led to powerful BI systems, sophisticated business analytics, and offerings of self-controlling Rationalism and experience are only instruments of control. The irrational faculties alone unlock for us the gates of the universe. Art is a school of deep knowledge and initiation. (Salvador Dalí) A. Schönbohm (*) • T. Dymke Berlin, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 I. Keimer, U. Egle (eds.), The Digitalization of Management Accounting, https://doi.org/10.1007/978-3-658-41524-2_22

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(Appelbaum et al. 2017), which largely make the controller obsolete as a collector, translator, and interpreter of key performance indicators and as a ruler of large Excel models (Schäffer and Weber 2015). At present, many controllers seem to be busy learning BI systems  – and thus prospectively rationalizing themselves away (Schäffer and Weber 2018a). More ominously, the methodological requirements for programming IT architectures and Big Data evaluations are beyond the insight horizon of current controllers and mainstream controller education (Schäffer and Weber 2018b). Contemporary interactive performance tools such as Kanban, Scrum or Objectives and Key Results (OKR) (Doerr 2018) are still uncharted territory for many controllers. Due to their limited cultural connectivity to digital business models, as well as their not (or only slightly) developed digital mindset, controllers are deliberately kept out of the digital ventures of corporate incubators (Schönbohm and Egle 2017, p. 224). Afflicted with the image of the spoilsport, they thus miss the opportunity to help shape a digital future and secure their own place in it (Schönbohm and Egle 2016, p. 7). Since BI system architect and data scientist are not mandatory development perspectives for controllers, in this article we outline a creative development process of one’s own professional identity (Heinzelmann 2018). We are inspired by modern art: using catchy analogies, we trace approaches tried and tested by artists whose work is always characterised by innovation, disruption and reinvention. Thus, after an excursion into artistic thinking, we offer selected strategies for reflection, renewal of perspective, and techniques for creative destruction and deconstruction. Here, however, it is not a matter of stopping, but of finding a new creative power by crossing the boundaries of one’s own role and task, which could manifest itself, for example, as a designer of digital performance cultures. The aim of this article is to get controllers out of the deceptive comfort zone of an indispensable employee in order to encourage and inspire them to transfer the process of creative destruction to their own professional life  – and to proactively develop a new value-creating role for themselves step by step and fill it with life.

22.2 The Controller at the Crossroads 22.2.1 The Changing Art of Controlling “The Art of Controlling” was the title of the 1997 anthology for Péter Horváth on his 60th birthday (Gleich and Seidenschwarz 1997). What was meant was, in differentiation from (natural) science, a pragmatic and also primarily instrumental approach to the prominent concept of practice and research. The rationality assurance of corporate management was also brought into play as a definition of controlling during this period (Weber 1996, p. 8). In this “definition cloud”, however, it was already laid out that controlling and the controllers were precisely not seen as creative, but rather as the incarnation of rationality-guided corporate cybernetics. Since then, digitalization has progressed at an accelerating pace.

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Controlling is at the heart of the digital transformation of companies: Where this process is already advanced, the future of digital controlling can already be seen: Powerful business intelligence systems, data analytics and self-controlling approaches with automated visualizations allow virtually all authorized persons access to all data in real time. Predictive analytics simplify forecasting, planning, and attention management for business stakeholders. At the same time, new agile thinking in the absence of long-term planning certainty has made performance management a socially charged experience with Scrum and OKR.  The next logical step would be data-driven individualized micro-­ controlling of workers through algorithms that determine a psychogram and performance profile of employees from real data, thus individually balancing and optimizing the work experience. All in all, controlling as the game engine of the corporate game (Schönbohm 2018) has made a dramatic leap forward and the triumph of controlling (Schönbohm 2005, p. 8 ff.) seems to be completed in the industrialization of controlling (Schäffer and Weber 2015, p. 188). While controllers are in the midst of digitalization and recognize information systems, data management, efficiency & controlling, and digital skills (Schäffer and Weber 2018b. p. 43) as the four most important topics of the future, it is becoming clearer that many old-­ school controllers will hardly meet the emerging requirements of the new controlling. Advanced statistical knowledge, programming skills and game design knowledge are neither found in their training nor in further education courses that are promising for the target group. The trend here is rather for physicists, mathematicians, psychologists and computer scientists to run the data centers in companies. The hypothesis that these were subordinate suppliers for the controllers may have quickly turned into its opposite. The point on efficiency and controlling illustrates the dilemma of the controller in the brave new world of controlling: The business cases for BI systems and business analytics only pay off through priced-in savings in human resources in the functional area of controlling. In other words: In the last phase of real-existing controlling, controllers rationalize themselves away. In any case, the automation-induced elimination of many job profiles in controlling on the one hand and the existence of shared service centers and centers of expertise on the other will lead to new career paths for controllers and possibly also to a new self-image of the profession. (Schäffer and Weber 2016, p. 13)

The vague hope that many controllers may draw from the idea of business partnering presupposes, on the one hand, that the managers provided with all the information nevertheless do not understand the business fundamentals. On the other hand, it relies on the fact that controllers can accompany these same managers (comparatively efficiently and effectively) as business educators and internal consultants in both old and new business models (Schäffer and Weber 2015). However, this only increases the demands on the competence profile of controllers (Schäffer and Weber 2016): Should they fail to meet these demands, they will in all likelihood become “superfluous” (ibid.).

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In the long run, the number of controllers will decrease drastically, and controlling will become even more of a management philosophy. (Schäffer and Weber 2016, p. 8)

In summary, it can be said that controllers see themselves thrown into an existential and professional identity challenge (Heinzelmann 2018) with an innovation imperative as a result of digitalization. Consequently, radical changes are on the horizon in order to weigh the opportunities and risks of this transformation individually and to draw the right conclusions for their own career development.

22.2.2 Modern Art as Agent & Inspirational Surface of Transformation In times of disruptive change, it is worthwhile for controllers to let their gaze wander and look at other areas of society that have driven and reflected cultural change: At the latest with the advent of modernism in the twentieth century, the visual arts made it their business to break with the traditional, to question (visual) habits and to establish new perspectives. In this respect, modernist art was an agent of cultural change. The French-American painter and object artist Marcel Duchamp (1887–1968), as a protagonist and philosopher of change, said “I consider taste – bad or good – the greatest enemy of art” (Kuh 1962, p.  92). For artists who submit to the prevailing taste will never produce anything truly innovative: The Impressionists, with their new mode of representation focusing on light reflections and mood, were just as little respected by their contemporaries as the now world-famous Spanish painter Pablo Picasso (1881–1973) when he dismantled familiar points of view in his works in order to construct new perspectives – thus founding Cubism. They were all initially ridiculed or even insulted. If we apply Duchamp’s words to the advancing realignment of controlling, we might say, “The controller’s greatest enemy is rationality.” On the one hand, because rational processes can be automated and, on the other, because controlling must see itself as a shaper and actor within the radically changing social plastic (not the cybernetic machine!) of the enterprise in order to remain relevant. In a synopsis, Table 22.1 shows how controlling and thrusts of modern art could be creatively linked. Four currents of modern art – Impressionism, Dadaism, Surrealism and Cubism – are taken out of the art historical context. Impressionism abandons the pictorial aesthetics of its predecessors and replaces them with a style of painting that takes the respective mood of a scene as its starting point, with its play of color and light and the sensory impressions that accompany it. In a controlling context, this could be reflected in new metrics or even new Big Data visualization options such as Sunburst, Sankey, or Treemap (Perkhofer et al. 2019), which change the perception of corporate reality and, consequently, the options for action. Dadaism, for its part, likes to wear the jester’s cap and, through nonsense and sometimes biting humour, leads to a turning away from prevailing patterns of interpretation. A

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Table 22.1  Controlling and modern art Art movement Core idea Impressionism New representation of reality by relying on one’s own perception Dadaism

Surrealism

Cubism

Protagonists Paul Cézanne (1839–1906), Claude Monet (1840–1926) Satirical rejection of Hugo Ball context and common (1886–1927), patterns of Hans Arp interpretation (1886–1966) Dare to take on new Salvador Dalí (psychologically (1904–1989), inspired) perspectives Max Ernst (1891–1976) Breaking with Pablo Picasso practiced ways of (1881–1973), seeing, analytical play Marcel with different Duchamps perspectives and (1887–1968) forms

Controlling context New key figures, new visualizations Radical questioning of the old business models Gamification, behaviour-­ oriented controlling Innovation in analysis through Big Data and Business Analytics

Controller rolls Introduction of new reports with new key figures and visualizations Internal consultant and change agent

Designer of performative work experiences Introduction of new analysis tools such as Qlik Sense, Tableau, Power BI, Signavio or Celonis

Dadaist inspirational foil would rather lead to the questioning of the ancestral (analogue) business models (and the controlling that accompanies them). Here (if necessary, in the tradition of the court jester) an internal consultant perspective is perceived, which accelerates the digital transformation. Some quotes by Schäffer and Weber (2016) may be interpreted in this direction. It is also through such radical statements that new models of thinking are made possible. Surrealism explores the unconscious and irrational in people, while drawing inspiration from psychology and creating new motifs to create something more real than reality itself (Gombrich 2006, p.  457). In the controlling guild, this could be the first indication of gamification and behaviour-oriented controlling. In this variant, the controller becomes the designer of performative work experiences, also through tools such as Kanban, Scrum and OKR (Schönbohm 2018, p. 65). Cubism breaks with the representation of (apparent) reality and creates a space for new orders of thought in its dialectical play of proportions, perspectives and objects. This radical reconstruction could also be seen as an approach for a proactive realignment of the controller, his roles and tasks. Big Data and digital analysis methods thus provide new perspectives and insights that a human would hardly come up with. In this respect, the cubist task for controllers can be to actively participate in the installation of these very BI and analysis tools such as Qlik Sense, Tableau, Power BI, Signavio or Celonis.

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22.3 L’Invitation au Voyage: Excursions into Artistic Thinking Loosely based on the French writer Charles Baudelaire (1821–1867), we would like to invite readers, controllers or otherwise interested parties to embark on a transformative journey. This article, in its limitations, is intended as an initiation into an artistic way of thinking as well as a first chance for the transfer of the same to controlling and controllers. The challenge is above all to connect two worlds that seem difficult to reconcile at first glance.

22.3.1 Against the Norm Evading prevailing preferences and valid norms or actively positioning oneself against them has defined a basic concern in the visual arts at least since modernism. This applies all the more to the works of Impressionism, which are now part of the established art canon and seem so harmless from today’s perspective. The new ways of seeing and representing by Paul Cézanne (1839–1906) and Claude Monet (1840–1926), as well as their contemporaries, were received with indignation and vehemently rejected by the public and critics of their time. Breaking with the (supposedly) naturalistic, sharply contoured painting style of their predecessors, the Impressionists (impressionio from the Latin corresponds to the German word Eindruck) sought techniques for the atmospheric, light-based depiction of a subjective perceptual situation. However, when their new style of painting in the twentieth century captured reflections of light in predominantly soft colors, stipples, and gradients, art critics and the public scorned them as lunatics, incompetents, and blowhards. It was not until much later that they gained recognition and the commercial success that came with it that the critics were proved wrong (Gombrich 2006, p.  393  ff.). The episode of the Impressionists is exemplary for the struggle of modern art for renewal, differentiation and recognition, as E.H. Gombrich also makes clear in his fundamental work The Story of Art: The struggle of the Impressionists became the treasured legend of all innovators in art, who could always point to this conspicuous failure of the public to recognize novel methods. In a sense this notorious failure is as important in the history of art as was the ultimate victory of the Impressionist program. (Gombrich 2006, p. 402)

Creative Questions for Controllers

• Aren’t controllers, like all other traditional professions, under natural pressure to change in order to maintain their market value? • What can controllers do that artificial intelligence (AI) and algorithms can’t? • What can controllers do differently and in a new way in the existing environment? • What possibilities exist in controlling to reflect the entrepreneurial reality? • Are these new visualization methods of old numbers or new key figures?

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• What innovation potentials do behaviour-oriented approaches to planning and performance measurement hold for this? • Do entrenched thinking on the part of the controllers themselves and the outdated role models of their stakeholders prevent them from breaking new ground? No less radical was the position of an art movement founded in Zurich in 1916 called Dadaism a few decades later. Disillusioned and deeply shaken by the senselessness of the First World War, the Dadaists directed their rejection against art itself and undermined every form of customary sense-making systems and contexts. At the first public Dada evening on 14 July 1916 in Zurich, the Dada artist Hugo Ball (1886–1927) read from his manifesto: I read verses that intend nothing less than: to renounce language. Dada Johann Fuchsgang Goethe. Dada Stendhal. Dada Buddha, Dalai Lama. Dada m’ dada, Dada m’ dada, Dada mhm’ dada. … I don’t want words invented by others. All words invented by others. I want my own mischief, and vowels and consonants to match it. (Pörtner 1960)

Shortly after, Ball asked into the room: Why can’t the tree be called Pluplusch, and Pluplubasch when it has rained? And why does it have to be called anything at all? (Pörtner 1960)

As absurd as these lines may sound, they impressively illustrate the strong driving force in the creative process of artists that lies in constantly setting themselves apart from standardized guidelines and dominant thought patterns. In their reflections on the potential of artistic thinking for managerial action (Sandberg and Frick-Islitzer 2018), they formulate this approach in their systematics as follows: “Artists make the organized breaking of rules into a maxim for action” (Sandberg 2017). Being inspired by this calculated rule-breaking inherent in artistic creation can also encourage controllers to radically question and – possibly – break with prevailing norms and systems. On the other hand, this possibility is also echoed in the reception, i.e. the perception of and engagement with art. For even Dadaist works of art, with their obvious penchant for nonsense, seem to appeal to our analytical abilities. We want to wrest an understanding from them, grasp their patterns and embed them in our existing understanding. The controller, too, wants to fit the new systems as seamlessly as possible into his old world of work and thought. Dadaistically, he could say loosely after Ball: “I implement controlling systems that intend nothing less than: to do without controllers.” Creative Questions for Controllers

• Are controllers drivers of the digital transformation or brakemen and worriers? • Are controllers allowed to ask critical questions about old and new business models and are they answered seriously? • Is the role of business punk in digital transformation possible for the controller?

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• Why shouldn’t controllers, through their numbers-driven nature, also act as advisors and facilitators of digital transformation? • Doesn’t digitalization actually increase the power of controlling? • Who is supposed to learn BI and business analytics systems if not the controllers? • Isn’t the definition of leadership and management also being radically questioned by the digital transformation? Does this present an opportunity for controllers? Surrealism is another artistic movement that does not accept the rational as an explanation of our everyday world, but turns to the forces and phantasms that are hidden in the human psyche. Artists such as René Magritte (1898–1967), Max Ernst (1891–1976), or Salvador Dalí (1904–1989) sought to create a reality that was far more real in their eyes than the existing one – based on the insights and theories of psychoanalysis: Many of the Surrealists were greatly impressed by the writings of Sigmund Freud, who had shown that when our wakening thoughts are numbed the child and the savage in us takes over. It was this idea which made the Surrealists proclaim that art can never be produced by wide-­ awake reason. (…) The Surrealists, too, hankered after mental states in which what is deeply known in our minds may come to the surface. (Gombrich 2006, p. 457)

For the Surrealists’ practice, this meant experimenting with hypnosis and dream diaries, putting themselves in a state of intoxication and fever, experimenting with automatic writing machines and moments of chance. Viewers of their works may to this day be torn between “reasonable” explanations and the pure imagination of their artistic creators. However, since works of art, in the field of tension between production and reception, resist complete rational exploration, they constantly challenge our thinking anew. Creative Questions for Controllers

• • • • • • •

What do controllers know about behavioral psychology and its limitations? Can the controller find himself not only in homo oeconomicus but also in homo ludens? Can’t social interaction controls like Kanban and Scrum also produce performance? Can effective work be fun? Do we need controllers for that? Can the controller help shape and enable performative work experiences? What if controllers saw themselves as game masters and UX designers? Aren’t (artistic) game mechanics, game aesthetics and game dynamics also learnable for controllers and usable as part of their toolbox?

22.3.2 Without Purpose and Open-Endedness So what can be learned from art and artists? All art is quite useless, wrote Oscar Wilde in his 1891 novel The Picture of Dorian Gray, a story about a portrait that ages in place of the sitter himself, thereby helping him to eternal youth. Wilde’s words are in no way an

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expression of annoyance at the futility of art, but rather a reference to one of its fundamental traits: purposelessness. For at the latest with the Enlightenment, art began to struggle for autonomy, in orderto allow itself to be taken over neither by universally valid standards of taste nor by fixed functions. Following this discourse, art always belongs to the society that produces it, but at the same time it is a system to describe and reflect it. In a sense, it is simultaneously part and non-part of society, committed only to its own existence. The French theory of art in the nineteenth century refers to the claim of purpose-free art with its formula L’art pour l’art, as does the founder of the Frankfurt School, the sociologist Theodor W. Adorno in his Aesthetic Theory 1970: “Insofar as a social function can be specified for works of art, it is their functionlessness” (Adorno 1970, p. 337). Incidentally, this criterion often serves to distinguish art from design, since the latter is always assigned a practical use, no matter how artistically a design object has been created. This very freedom of purpose in the artistic creative process also opens up new possibilities for other industries. For if it is only possible to free oneself from the dictates of predictable utility in the course of individual thought and work processes. Thus, undreamt-of scope opens up for the development of innovative ideas. In her book Art Thinking, published in 2016, Amy Whitaker characterizes an artistic way of thinking as setting out from a starting point A and striving for a yet unknown state B – or rather, “inventing” this B in the first place. You are inventing Point B. You are creating something new – an object, a company, an idea, your life – that must make space for itself. In the act of creating that space, it changes the world, in however big or small a way. By this definition, art is less an object and more a process of exploration. (Whitaker 2016, p. 8)

In this context, point A could be the current professional controller identity in the midst of overarching digitalization, while point B represents the new meaningful professional identity of controllers, but one that has yet to be defined in a creative act in the current context. In this thinking, point B is characterized by radical (anxiety-inducing) uncertainty (cf. Fig. 22.1). In contrast, the currently very widespread Design Thinking (Kumar 2013) with a prejudiced process and inherent outcome is more comparable to paint-by-numbers, where the flirtation with the comfort zone never quite breaks off. The artistic exploration of overarching themes and issues is always one that is open to process and outcome, following playful principles of trial and error, adaptation and change, repetition, failure and starting again. Or, as the Irish writer of modernist drama Samuel Beckett (1906–1989) almost fatalistically put it, “Always tried. Always failed. Monotonous. Try again. Fail again. Fail better.” (Beckett 1989, p. 7). It is this sometimes exhausting, but essentially playful and experimental way of working that allows artists to (re)act creatively, to process the unexpected, and finally to produce something new based on it.

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Self Controlling

Agile control

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Fig. 22.1  Art thinking for controllers

22.3.3 From Aesthetic Competence to Transfer In classical artistic training, the training of the gaze is of particular importance. Observing one’s own surroundings attentively and with curiosity, analysing their constellations and mechanisms and reflecting on them – along with one’s own position within them – is an essential part of the artistic creative process. Thus a work of art always refers to the artistic attitude of its creator. According to the art historian and image scientist Wolfgang Ullrich, developing this attitude requires comprehensive training that makes a different, complex way of seeing and understanding possible in the first place. It needs categories according to which what is seen is differentiated, background knowledge so that what is otherwise invisible first becomes apparent, a broad education in order to develop a coherent view from individual independent perceptions. (Ullrich 2005, p. 333)

By means of a trained eye and the necessary background knowledge, gaining a literal overview in order to bring individual, fragmentary impressions and insights into corresponding contexts of meaning is a particularly valuable empowerment in times of high complexity if one wants to reflect on and actively shape change. In this holistic view, Berit Sandberg recognizes the possibility of producing one’s own interpretations of reality and ultimately accelerating and improving decision-making processes (2017). Amy Whitaker also speaks of a wide-angle perspective, while she moves the perception of creative people close to a perspective influenced by the Far East, in which not only the foreground is focused on, but also the environment is included. In place of an object-based approach, a more complex picture is being created, which favors the description and understanding of contextual relationships: In an object-based world, if you were shown a picture of the ocean below the surface, you might name a fish or an anemone, or a shark. In an environment-based world, you might describe the ocean. (Whitaker 2016, p. 35)

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Creativity requires courage, is a much-used quotation from the painter Henri Matisse, and it is precisely this courage that is demanded of controllers with regard to their future self-­ image. Berit Sandberg (2017) crystallizes creativity as the starting point of all artistic action and the omnipresent requirement to face, indeed to exist in, an uncertain future “makes creative courage in a business context a desirable virtue in dealing with open-­ ended, unstructured situations where conventional methods and approaches fail.” (Sandberg 2017, p.  68) Accordingly, what is called for is no longer the pursuit of predefined, meticulously planned solution procedures – for here we bring ourselves all too close to processes that have long been automated. In the digital age, the challenge lies rather in developing, testing and optimizing new, innovative approaches. Putting oneself in the shoes of artistic thought and work processes, analysing and working on one’s own problem and question with foreign means, promises irritation at first and then the chance to reflect on and consciously (re)shape entrenched roles and patterns of action. Because: In artistic action, unlike in management, uncertainty is not minimized with rational-logical thinking, but it is managed through situational action and the use of subjectivity. (Sandberg 2017, p. 3)

General Education for Controllers

• When was the last time I, as a controller, went to an exhibition, read a novel, attended a concert? • When was the last time I was at a trade fair relevant not only to my industry, my function? • How intense is my involvement with topics like AI, programming languages, BI, analytics, Big Data, etc.? • When was the last time (or ever) I did any free creative writing, painting, or music? • Which activities and topics arouse my interest and my play instinct beyond the usual everyday routines? And why? • Do I lack interest, aptitude, or courage to engage in any of the above? • Are formats like Working Out Loud feasible for my professional development?

22.4 Drawing Inspiration: Looking Inwards and Outwards 22.4.1 Looking Inwards: Studio Time But where do artists find their inspiration? How do they generate new ideas and what helps them to cast them into form? Probably the most widespread collective image of the prototypical artist shows him – possibly standing or sitting in front of the canvas – in his studio. It is within these four walls that the nucleus of artistic genius is thought to be found, where ideas are born and masterpieces are created. From the nineteenth century onwards, the

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studio itself increasingly became a subject and was captured by artists in a variety of ways. In this context, the studio can combine numerous functions and facets. In Studio and Gallery. Studio and Cube, Brian O’Doherty outlines some of the manifestations of this myth-enshrouded place, for instance as a social centre, an incubator of new ideas, a revolutionary cell, a church of a new religion, a businessman’s workshop, a conventional housing of new ideas, a place of worship, a production factory […], a clinically clean kitchen, a chaotic garret, a place of experimentation, and a lair of the lone hero. (O’Doherty 2012, p. 20)

But while a classical studio seems to be reserved for classical artists, the concept of a physical or mental retreat for free, playful research and experimentation can be applied to other areas of life and work. Amy Whitaker uses the term studio time to describe a zone in our lives that is reserved for purposeless being and thinking – and can be as multifaceted as an artist’s studio. Studio time is a patch within the composition of your life that is protected ground. […] The hallmark of studio space is that – physically and temporally and economically and mentally – it is open. (Whitaker 2016, p. 44 f.)

If controllers succeed in temporarily escaping the constant routines of daily business and reserving studio time for themselves, an important prerequisite has already been created. This creates space for observing and questioning themselves, pursuing ideas that may initially seem outlandish, and brooding over new ways of thinking, working, or doing business. However, turning away from the noise of everyday life is essential for this, as it is often the seemingly self-evident routines that nip any creative spark in the bud. For while the obedient employee always wants to live up to the expectations of his environment, the artistically empowered idea developer seeks to minimize this pressure by escaping it – at least temporarily. Here, artists, scientists, inventors and entrepreneurs are closer to each other than initially assumed, as Peter Himmelman also points out in his article The Idea-Revealers: Where Business and Art Intersect: As idea-revealers winnow their ideas, they also pare down what is extraneous in their own lives, the noise in their lives – a noise, which, so often derives from people’s expectations. (Himmelman 2017)

Self-Observation Questions for Studio Time

• • • • •

What are my own strengths and weaknesses? What can I do as a controller that a BI system cannot? What risks and opportunities am I exposed to through digitalization? What is my current and future value contribution to the company? What is my role in the company’s digital issues and startups?

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Do people approach me, seek my advice and am I regularly involved? Do I still have the authority to interpret the numbers? If not me, then who? How can I use new techniques and technologies to increase my relevance? What makes the new employees seek advice from me?

22.4.2 The View Outwards: Figure and Ground The training of the gaze has already been discussed above: The perspective described by Amy Whitaker as a wide angle allows one to extend one’s attention from the foreground to the surroundings, and thus to take a look at the context, its constellations and interdependencies. A central principle of artistic pictorial composition also consists in the deliberate arrangement of figure and ground: artistic motifs are not created merely by placing different forms next to and on top of each other. Equally important is the background on which they are positioned. It is this negative space that creates the scene in its entirety, forms contrasts or gradations, and gives the events in the foreground their meaning. Observing one’s own environment and locating oneself in it, for example analyzing the market and competitive situation, classifying relevant social developments, examining related – or completely foreign – industries, increases the chances of gaining at least an approximate impression of the entire ocean instead of the fish. These and similar questions could serve as a starting point, for example: What stories does advertising use today to convince people of the indispensability of their products? How do restaurateurs deal with digitalization? What drives top athletes to peak performance? Inspiration lurks everywhere, but it requires a holistic worldview to discover and harness it. The combination of self-observation and observation of others – the critical analysis of one’s own work in relation to the individual and overarching context  – initially promises freedom for the development of new ideas, but in the second step also a meaningful prioritization of both levels. Together, these tools of composition – prioritization and blank space – give you access to more elegant and imaginative forms of 80-20 thinking: You are aware of the power of efficiency but not limited to it. (Whitaker 2016, p. 40)

22.4.3 Disrupt Yourself – Creative Destruction This process of creative destruction is the essential fact of capitalism. (Schumpeter 1993, p. 138)

What options are left for the controller? We see roughly three options: Ignore, Assimilate, and Creative Destruction. Figure 22.2 illustrates the relationship between the respective strategy, the willingness to change and the potential value creation.

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Potential value creation

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Creative Destruction

Assimilate

Ignore

Readiness for change

Fig. 22.2  Controller strategies in dealing with digitalization

Ignore: The controller can, of course, ignore the topic of digital transformation for the time being and hope that digitalization will have less dramatic effects than described above. Likewise, the next recession could again curb the current hype surrounding digitalization. This option preference may correlate all the more with an approaching retirement age and may well be rational. Adherents of this option will, in case of doubt, be busy for a few more years incorporating their process knowledge into the new IT infrastructure. Assimilate: Adapting to the new challenges of digitalization goes hand in hand with changing the industry-specific language, the necessary further training of existing skills and updating current activities. This will be a sustainable opportunity for a limited number of controllers who are already adaptable to IT, Big Data and digital culture (Schönbohm 2019). Creative destruction: The departure from the norm described at the beginning, which can be understood as the driving force of visual artists at the latest since modernism, is not infrequently accompanied by a destructive moment: the Dada artist Hugo Ball renounces structures of language and grammar in order to destroy contexts of meaning. With his cubist painting style, Pablo Picasso breaks down visual axes into their individual pieces in order to establish a new style of art. A recent example of artistic destruction is provided by the British street artist Banksy, whose works are now being auctioned off at record prices on the art market: Just as an art collector had won his painting Girl with Balloon for almost EUR 1.2 million at an auction held by London auction house Sotheby’s in October 2018, the painting began to self-destruct. It was shredded in half (anonymously at the push of a button with the help of a cutting machine built into the frame). Banksy himself commented on the action the following day on the social media platform Instagram, with a quote attributed to Pablo Picasso: “The urge to destroy is also a creative act,” thus charging the

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cut-up work with meaning once again. Today, the work has doubled its original market value and can be seen in its half-destroyed form in changing museum exhibitions. Destruction as a creative act and creative activity can thus pay off handsomely. In the following box we provide a list of selected techniques borrowed from artistic practice.

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Artistic Creative Techniques   

• Collect and (re)arrange: Start a collection. Collect for a motif, a theme, a colour, a format or simply according to your interests. Rearrange your collection again and again according to different criteria – e.g. according to formal criteria such as colour, shape, size or material properties – and discover what you can learn from the respective arrangements. • Reduce: Remove individual or nearly all elements of a subject, object, or activity to bring out both its essence and its incidental aspects. • Exaggerate: Extend a chosen theme, characteristic, or activity to the extreme by multiplying, enlarging, diminishing, or multiplying existing components to challenge taken-for-granted standards and evoke new insights. • Decontextualize: Detach an object (or activity), from its original functional context and transfer it to a new, preferably foreign context. What reassessment does the out-of-place object experience? • Copy: Study existing solutions, ways of working or business models and copy them – either down to the smallest detail or only to a certain degree. Experiment with how you can integrate the copied elements into your working environment.

In our thought experiment, controllers can thus choose between these three options to shape their own professional future. Their fate is shared by many other professions, which digitalization is also forcing to transform, to take on an almost artistic identity and to reinvent themselves, with no clear anchor points in sight. We do not believe that we can provide the right answers for everyone, but limit ourselves to presenting questions and creative techniques. By appropriating new cultural elements on the one hand and crossing the boundaries of your own role and task on the other, a genuine transformation is possible: Identify the limitations within which you currently work and function and then try to consciously overcome these boundaries: Mix fun and work, design and bureaucracy, swap positions and hierarchies in your company. Grow beyond your defined role and provoke surprising reactions in your environment. Performance Manager, BI Specialist, Scrum Master, Change Agent, Agent Provocateur, Consultant, Business Partner, Data Scientist, CFO, Entrepreneur or Early Retiree. Where do you dare to go?

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22.5 Digitalization as a Creative Opportunity for Controllers Digitization has reached controlling, and for many controllers this means that they will have to reorient themselves professionally and personally in the medium term. Controllers also have personal strengths as well as knowledge and method profiles that put them in a good starting position for reinvention. SAP and Excel are already part of the digital reality of controlling. Qlik Sense and similar applications usher in the next stage of digitalization. This seems to the authors to be reason for creative tension, but not for despondency. The processes of digitization sometimes take longer than expected, yet strike with surprising speed. In this sense, the Corona-induced recession can also be a digitalization accelerator in controlling. Who would have thought that rationality could become the enemy of the controller? Could this situation even lead to a happy professional new beginning? Modernist art is an example of radical transformation in perspective, form and representation of reality, of provocation and innovation: we hope that in times of digitalization it can also be an inspiration and guide to creative destruction for the controller. Learning from artists also means engaging with irrational sources of subjectivity and broadening one’s own view. We generally consider this to be an opportunity. Artistic creative techniques can contribute to the emergence of something truly new. Taking the path alone, in self-moderated groups or in guided workshops are, in our view, viable and promising options. To speak with Joseph Beuys: Every human being is an artist. In this respect, there is also creative power in controllers. We see the limitations of this contribution in its pre-conceptual nature, creatively collating without proving, raising questions without providing answers, rubbing salt in the wound without promising a cure. In the field of tension between rationality, controlling and artistry, there still seems to be plenty of room for experimentation, research and publications. In this sense, this contribution is also a call for joint artistic and scientific work in controlling and management. For following in the footsteps of Salvador Dali, quoted at the beginning, we too see modern art as a (sometimes uncomfortable) school of initiation and knowledge.

References Adorno, T.W. 1970. Ästhetische Theorie. In Gesammelte Schriften, ed. G. Adorno, R. Tiedemann, and Theodor W. Adorno, vol. 7. Frankfurt a. M.: Suhrkamp. Appelbaum, D., A.  Kogan, M.  Vasarhelyi, and Z.  Yan. 2017. Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems 25: 29–44. Beckett, S. 1989. Worstward Ho. Aufs Schlimmste zu. Frankfurt a. M.: Suhrkamp (Aus dem Englischen von Tophoven-Schdningh, E.). Doerr, J. 2018. Measure what matters: OKRs: The simple idea that drives 10× growth. London: Penguin.

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Gleich, R., and W. Seidenschwarz. 1997. Die Kunst des Controlling-Festschrift zum 60. Geburtstag von Prof. Dr. Péter Horváth. München: Vahlen. Gombrich, E.H. 2006. The story of art. 7th ed. London: Phaidon. Heinzelmann, R. 2018. Occupational identities of management accountants: The role of the IT system. Journal of Applied Accounting Research 19 (4): 465–482. Himmelman, P. 2017. The idea-revealers: Where business and art intersect. Forbes Online Magazin. https://www.forbes.com/sites/peterhimmelman/2017/12/27/the-­idea-­revealers-­where-­business-­ and-­art-­intersect/#33692ba291bb. Accessed on: 07.12.2019. Keese, C. 2018. Disrupt Yourself: Vom Abenteuer, sich in der digitalen Welt neu erfinden zu müssen. München: Penguin. Kuh, K. 1962. The artist’s voice: Talks with seventeen modern artists. New York: Harper & Row. Kumar, V. 2013. 101 design methods – A structured approach for driving innovation in your organization. Hoboken: Wiley. O’Doherty, B. 2012. Atelier und Galerie. Studio and Cube. Berlin: Merve (Übersetzung aus dem Englischen von Setton, D.). Perkhofer, L., P.  Hofer, and C.  Walchshofer. 2019. BIG Data Visualisierungen 2.0  – Optimale Gestaltung und Einsatz neuartiger Visualisierungsmöglichkeiten. In Konferenzband der CARF Luzern 2019 Controlling. Accounting. Risiko. Finanzen, ed. L. Nadig, 76–104. Zug: IFZ. Pörtner, P. 1960. Literatur Revolution 1910–1925, Dokumente, Manifeste. Programme. Bd. I “Zur Aesthetik und Poetik”. Neuwied: Luchterhand. Sandberg, B. 2017. Mut in den Berufskulturen von Managern und Künstlern. Interculture Journal: Online-Zeitschrift für interkulturelle Studien 17 (27/28): 67–86. Sandberg, B., and D.  Frick-Islitzer. 2018. Die Künstlerbrille  – Was und wie Führungskräfte von Künstlern lernen können. Wiesbaden: Springer Gabler. Schäffer, U., and J.  Weber. 2015. Controlling im Wandel-Die Veränderung eines Berufsbilds im Spiegel der zweiten WHU-Zukunftsstudie. Controlling 27 (3): 185–191. ———. 2016. Die Digitalisierung wird das Controlling radikal verändern. Controlling & Management Review 60 (6): 6–17. ———. 2018a. Digitalisierung ante portas. Controlling 30 (S): 4–11. ———. 2018b. Die Controlling Community muss sich öffnen! Controlling & Management Review 62 (6): 8–11. Schönbohm, A. 2005. Reflexives Controlling  – Revolution und Rationalität unternehmerischer Wirklichkeit in der Postmoderne, Dissertation. Lohmar: Eul. ———. 2018. Ludic Control-Entwurf eines hedonischen Controllingsystems. In Konferenzband der CARF Luzern 2018 Controlling. Accounting. Risiko. Finanzen, ed. L. Nadig and U. Egle, 64–74. Zug: IFZ. ———. 2019. Ludic Leadership – spielerische Antworten auf die kulturellen Herausforderungen der Digitalisierung. In Digitalkultur  – Facetten digitaler Transformation, ed. A.  Schönbohm, 1–19. Ludeo: Stahnsdorf. Schönbohm, A., and U. Egle. 2016. Der Controller als Navigator durch die digitale Transformation. Controller Magazin 41 (6): 4–8. ———. 2017. Controlling der digitalen Transformation. In Digitale Transformation von Geschäftsmodellen, ed. Schallmo et al., 213–236. Wiesbaden: Springer Gabler. Schumpeter, J.A. 1993. Kapitalismus, Sozialismus und Demokratie. 7th ed. Tübingen: UTB. (Übersetzt aus dem Englischen von Preiswerk, S.). Ullrich, W. 2005. Ohne Hände und auf kurzem Weg: Wie aus Künstlern “Cultural Hacker” werden. In Cultural Hacking. Kunst des Strategischen Handelns, ed. Düllo et al. Wien: Springer.

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Dr. Avo Schönbohm  After several positions in industry, Dr. Avo Schönbohm has been Professor of General Business Administration, in particular Controlling, at the Berlin School of Economics and Law since 2010. Digital transformation, enterprise gamification and performance management are his main research interests, which he fills in an interdisciplinary way. With LUDEO, he has been playfully advising renowned companies on the path of digital cultural transformation since 2016. Thea Dymke  works at the intersection of business and culture: on the one hand, she provides strategic advice to players in the cultural sector, and on the other, she teaches creative methods in companies on the way to a new work culture. As a coach, speaker or lecturer, she pursues her mission to dissolve the supposed gap between artistic freedom and entrepreneurship.