Global Manufacturing Management: From Excellent Plants Toward Network Optimization 3030727394, 9783030727390

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Management for Professionals

Thomas Friedli Gisela Lanza Dominik Remling Editors

Global Manufacturing Management From Excellent Plants Toward Network Optimization Foreword by Kasra Ferdows

Management for Professionals

The Springer series Management for Professionals comprises high-level business and management books for executives. The authors are experienced business professionals and renowned professors who combine scientific background, best practice, and entrepreneurial vision to provide powerful insights into how to achieve business excellence.

More information about this series at http://www.springer.com/series/10101

Thomas Friedli • Gisela Lanza • Dominik Remling Editors

Global Manufacturing Management From Excellent Plants Toward Network Optimization

Foreword by Kasra Ferdows

Editors Thomas Friedli Institute of Technology Management University of St.Gallen (ITEM-HSG) St. Gallen, Switzerland

Gisela Lanza wbk Institute of Production Science Karlsruhe Institute of Technology Karlsruhe, Germany

Dominik Remling Institute of Technology Management University of St.Gallen (ITEM-HSG) St. Gallen, Switzerland

ISSN 2192-8096 ISSN 2192-810X (electronic) Management for Professionals ISBN 978-3-030-72739-0 ISBN 978-3-030-72740-6 (eBook) https://doi.org/10.1007/978-3-030-72740-6 # The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Foreword

Perspectives on Global Manufacturing Manufacturing matters. It is a key to prosperity for nations and companies. In 2019, the world generated US$13.8 trillion value added in manufacturing (Worldbank, 2020b), representing 15% of the world’s gross domestic product (GDP). If you add the activities created by manufacturing but not included in these numbers, as explained in Chap. 1 of this book, its effect is much larger: it accounts for almost half of the world’s GDP. Even beyond its economic value, it nourishes research and develops critical skills, and in some cases, it is considered to be important for national security. What happens in manufacturing is of interest to multiple constituencies. Manufacturing is also a hallmark of global cooperation. Nowadays, remarkably, very few products are manufactured in a single country. Parts produced in one country are often sent to another for further work, to a third for assembly, and to other countries for packaging, storage, distribution, and sale, and sometimes for remanufacturing, reuse, or disposal. A cursory examination of aggregate trade statistics shows the extent of this phenomenon: In 2019, world merchandize trade was US$18.89 trillion (the rest, US$6 trillion, was in services) (World Bank, 2020b), and the largest and growing share of goods traded were intermediate goods—parts, components, and semi-finished products—as opposed to primary goods (essentially raw materials) and finished goods (ready to be used by consumers) (United Nations, 2020). This global fragmentation of manufacturing has been happening at an astonishing rate. In the last seven decades, global trade (more than three-fourth of which has been merchandise—i.e., essentially manufactured—trade) has been growing faster than the global GDP, frequently by a wide margin. Only in a few years during this long period, while the world faced extenuating (and exceptional) circumstances, it grew at a slower pace, and even more rarely it actually declined from its level in the previous year. One such year was 2020, which was of course due to the COVID-19 pandemic. But global trade is expected to rebound in 2021 and resume its historical trend.

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The accelerated rate of globalization of manufacturing has naturally created more complexity. For manufacturing companies, it has increased the complexity of two basic decisions: which part of the firm’s value chain should be produced where and how should such a network of factories be managed? For governments, it has made formulating the policies for attracting and retaining manufacturers more demanding. For others, including some of the non-governmental organizations, it has made achieving a variety of objectives—such as persuading the companies to adopt more sustainable practices, protecting labor and human rights in the extended global supply chains, ensuring safety of manufactured products, and leveraging manufacturing to help the poor and reduce poverty—more arduous. The literature has not kept up with this growing complexity because research in global manufacturing has been hampered by high levels of detail complexity (i.e., when a large number of independent variables must be considered), dynamic complexity (i.e., when cause and effect are subtle and where the outcomes of interventions are not well understood) (Senge, 2006), and hysteresis (i.e., when there is a delayed reaction to stimulus). Each of these three obstacles exacerbates the difficulty caused by the other two, making this kind of research more challenging (Ferdows, 2018).

Integrating Strategic and Operational Perspectives It is therefore understandable that much of the literature on global manufacturing is focused on a limited number of issues. These issues range from high-level strategic ones to routine operational ones. A short list of the strategic issues includes design of the global footprint of the firm’s production and supply chain network (including offshoring, reshoring, and sourcing decisions), deployment of new technologies and big data, mitigation of risks in dispersed production, improvement of sustainability of the network, and transfer of production know-how in global networks; some of the routine operational issues include managing allocation of resources and products to plants, inculcation of lean and operational excellence practices in the global network, and tracking of performance of factories in the network. Most books and articles cover issues mostly in one or the other category, seldom both or their interactions. This book is a rare exception. The chapters address a comprehensive list of strategic and operational issues. That is one of the reasons this book stands out. This is perhaps thanks to bringing together scholars in a “business school” and a “technical school.” This kind of collaboration in this field is unique and remarkable. St.Gallen University and Karlsruhe Institute of Technology are influential centers of research in global manufacturing, and merging their strengths and complementary perspectives has allowed the book to address a broad set of issues in unusual depth. Here is an example. Scholars agree that a factory’s design, choice of process technology, work methods, level of digitization, and other structural and infrastructural elements should fit the local conditions. But few delve deep into the specifics. This book does and provides clear illustrations of what may need to be modified. For example, there is a discussion of how some components should be redesigned to

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protect proprietary know-how in risky countries, or how the method for soldering may need to be modified if the requisite skills are difficult to find locally. It is refreshing to see such attention to details and specific examples.

Adding the Policy Perspective Besides discussing strategic and operational issues in managing global manufacturing, it is also useful to consider how constituents other than the manufacturers themselves are viewing the issues in global manufacturing. For example, how do groups such as politicians, industry lobbyists, government officials, and a host of other experts in different institutions, even the media and general public, think about trends in global manufacturing? These groups, collectively, influence taxes, subsidies, tariffs, local laws and regulations, and a host of other important factors which directly affect manufacturers’ decisions of where to locate their factories and how to manage them. They craft the policies that govern global manufacturing. Their views are often shaped by events in the global trade and geopolitics. For example, changes in trade pacts (e.g., the recent Regional Comprehensive Economic Partnership among the ASEAN countries), trade frictions (e.g., USA–China), economic and demographic changes around the globe (e.g., rise of Asia or aging population in the developed world), new global logistics infrastructures (e.g., China’s Belt and Road Initiative), and new international mandates (e.g., limiting carbon footprint, or supply chain transparency, safety and ethical compliance) can evoke strong reactions in many of them, which in turn can affect decisions made by the manufacturing companies. It is therefore useful to view global manufacturing also from their perspectives. However, it is difficult to analyze the interaction of so many perspectives in one book. Several chapters in this book, admirably, address the impact of some of these global trends, but most of the book is rightly focused on the discussion of strategic and operational issues faced by manufacturers. As mentioned, it is a more comprehensive list of issues than one finds in other books. Nevertheless, it is useful to address two over-arching questions that seem to be at the heart of many debates among policy makers about global manufacturing: First, “is manufacturing leaving the rich countries?” Second, “are the global manufacturing footprints in most companies over-extended?”

“Is Manufacturing Leaving the Rich Countries?” This question is a source of never-ending debate among scholars and policy makers; it also influences business managers’ thinking. The conventional wisdom holds that these days most new factories are built in low-cost countries. Consider the discussion in the USA. It is often shaped by sensational headlines in the media, such as “The Death of American Manufacturing” (Morely, 2006), and events in a few industries

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(like apparel and toys). Closure of factories creates real hardship and not surprisingly can shape the public perception. But in general, these views are often based on anecdotal evidence or events in small segments of manufacturing. A closer examination of the macro data suggests a different picture. The assertion for decline of manufacturing in the USA is mostly based on two observed trends: decline of manufacturing jobs and decline of share of value added in manufacturing in the GDP. Indeed, according to the World Bank (2020a), the number of manufacturing jobs in the USA has declined (from 18.1 million in 1998 to 13.5 million in 2018), causing real hardship, and the sector’s share of GDP has fallen (from 16.1% in 1997 to 11.2% in 2017). However, US value added in manufacturing has not declined. It rose from US$1.38 trillion in 1997 to US$2.18 trillion in 2017 (in nominal dollars, and still a larger amount even after adjusting for inflation) (Worldbank, 2020b). The USA has been the world’s largest manufacturer in six of the last seven decades. It lost the number one position to China in 2009 but is still the second largest manufacturer in the world. The USA is even a bigger manufacturer in relative terms. Per capita, the USA produced 2.4 times more value added in manufacturing in 2019 than China (US$6610 in the USA versus US$2780 in China). South Korea, Japan, Germany, and Switzerland, among other rich countries, had even a larger per capita value added in manufacturing (see figure “Per capita manufacturing value added”). 15,310

8,140

8,160

S. Korea

Japan

8,920

6,610

2,780

China

US

Germany

Switzerland

Per capita manufacturing value added (US$, 2019). Data from Worldbank (2020a)

None of these countries are developing or “low-cost” countries. The average person in many advanced countries is producing—and often exporting—more manufactures than the average person in most developing countries (Ferdows, 2019). This is in contrast to the notion that rich countries are not competitive locations for manufacturing.

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In fact, examining the foreign direct investments in manufacturing by multinationals (who supposedly locate their factories in most desirable locations around the world) suggests an opposite picture, at least for the USA and a few other rich countries. According to the United Nations Conference on Trade and Development (UNCTAD) (2020), foreigners have consistently built more factories in the USA than in any other country. For example, in 2019, foreigners invested US$101 billion in the USA to build “greenfield” facilities (meaning new ones, not repurposed structures)—about half were factories, and the rest were power plants, telecom networks, and other infrastructure projects. That was a quarter of the US$402 billion total greenfield investments in the world, significantly more than the greenfield investment in China, which was US$62 billion in 2019. Other top recipients of manufacturing investments that year include several other rich countries: the UK, Germany, the Netherlands, France, Canada, and Spain. Vietnam, Brazil, and India, each with around US$30 billion greenfield investment, were also among the top ten recipients, but the factories in Brazil and India were mostly for serving domestic demand; only the new factories in Vietnam seem to have been mostly for exports, often serving as a second source for multinationals that had depended solely on factories in China for some parts or products (UNCTAD, 2020). The suggestion that the rich countries, with their high-cost environments, have become unattractive for manufacturing is also rebutted by many experts. For example, the ten most attractive countries for manufacturing compiled by the Brookings Institution (West & Lansang, 2018), using 20 indicators (measuring appeal of policies and regulations, tax policy, energy, transportation, health costs, workforce quality, and infrastructure and innovation), were Britain, Switzerland, the USA, Japan, Canada, the Netherlands, South Korea, Germany, Spain, and France (China was ranked at number 13). In short, developed countries, already home to a great deal of manufacturing, remain competitive. Manufacturing is not leaving the rich world.

“Are the Global Manufacturing Footprints Over-Extended?” The disruption caused by the COVID-19 pandemic in 2020, added to the tariff and geopolitical frictions in previous years, has raised the question of whether companies have gone too far in dispersing their production networks. Some experts—in academia, think tanks, and consulting companies—argue that companies should seriously start shrinking their global manufacturing footprints and near-shore or even reshore some of their offshore production. Altering the footprint of a company’s global production network is not new. Companies are always doing that in response to changes in external and internal (i.e., inside their company) conditions; sometimes they do it simply to correct a bad past decision. A new analysis of inventory and transportation costs, impact of potential disruptions, or risks of leakage of intellectual property can trigger, and has triggered, reshoring production. But is there any evidence that the frequency of such decisions has improved dramatically? Have we reached a “tipping point” in reshoring?

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Data on reshoring are patchy. Going beyond the hype in some media—which are frequently based on anecdotes and merely declarations of intentions—there is little evidence of a “tipping point.” Reshoring manufacturing is still only a small fraction of offshoring (i.e., moving or investing in production outside the home base). “There is no evidence of any coronavirus-induced rush by companies to return operations to the United States” (Alden, 2020), or, going even a few years back, as an OECD report (De Backer et al., 2016) puts it, “it is a trickle rather than a flood; [and] reshoring initiatives that are often publicly launched do not always materialize.” A study of a sample of 1500 large German companies found that, between 1995 and 2015, for every company that reshored production, four offshored (Kinkel, 2018). The latest data on foreign direct investment provide additional evidence that companies continue to spread their manufacturing around the globe at historical rates rather than retracting them. Rich countries, while being among the world’s largest manufacturers (see above), are also among the world’s largest investors of manufacturing abroad (See figure “World’s top foreign direct greenfield investors”). 137

75 62 46

S. Ko re a

Ja pa n

Fr an ce

U K

C hi na

G er m an y

U S

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28

27

Sw itz er la nd

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Sp ai n

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World’s top foreign direct greenfield investors (about half of “greenfield FDI” in the USA and many other countries is in building new manufacturing facilities. Rest are telecom, pipelines, and other infrastructural projects) (billion US$, 2019). Data from UNCTAD (2020)

Note that even China, the world’s largest manufacturer (US$3.89 trillion manufacturing value added in 2019) (Worldbank, 2020b) and destination for manufacturing for many companies worldwide, was the third largest investor in manufacturing abroad. It is clear that multinational manufacturers expand their global networks not just to reduce costs but more often to access growing markets and specialized skills. According to a recent report (Kharas, 2017), 88% of the next billion new entrants

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into the middle class will be in Asia, and by 2030, the middle class worldwide is expected to spend US$64 trillion, US$29 trillion more than the 2017 level. Multinational manufacturers cannot ignore these growing markets, and they—especially those that produce consumer products—need factories close to these consumers to be able to respond to their demand for ever-faster deliveries, especially as e-commerce continues to grow astronomically.

Final Words There are no compelling reasons to believe that global manufacturing networks are entering a period of retraction or increasing concentration in the developing countries. Competitive and world-class manufacturing can and will continue to be done in both developed and developing countries, although managing them will become increasingly more complex. That is why this book is timely and we are likely to need more books like it. Washington, DC, USA January 2021

Kasra Ferdows

References Alden, E. (2020). No, the pandemic will not bring jobs back from China. Foreign Policy. Retrieved from https://foreignpolicy.com/2020/05/26/china-jobs-corona virus-pandemic-manufacturing-trump/ De Backer, K., Menon, C., Desnoyers-James, I., & Moussiegt, L. (2016). Reshoring: Myth or reality? (OECD Science, Technology and Industry Policy Papers No. 27; OECD Science, Technology and Industry Policy Papers, Vol. 27). Retrieved from https://doi.org/10.1787/5jm56frbm38s-en Ferdows, K. (2018). Keeping up with growing complexity of managing global operations. International Journal of Operations & Production Management, 38 (2), 390–402. https://doi.org/10.1108/IJOPM-01-2017-0019 Ferdows, K. (2019). Five myths about manufacturing. Washington Post. Kharas, H. (2017). The unprecedented expansion of the global middle class an update, global economy and development at Brookings. Kinkel, S. (2018, January 25). Industry 4.0 application and reshoring of manufacturing – evidence, limitations & policy implications. Presentation at Makers Workshop “Industry 4.0 – Implications for an EU industrial policy,” Brussels. Retrieved from https://www.ceps.eu/wp-content/uploads/2017/11/Pre sentation%20Steffen%20KINKEL_EU%20Industrial%20policy%204.0_ Brussels_25-01-2018.pdf Morely, R. (2006). The death of American manufacturing. The Philadelphia Trumpet. Senge, P. M. (2006). The fifth discipline: The art and practice of the learning organization (Rev. and updated). Doubleday/Currency.

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United Nations. (2020). International Trade Statistics. Retrieved from https:// unstats.un.org/unsd/tradekb/Knowledgebase/50090/Intermediate-Goods-inTrade-Statistics United Nations Conference on Trade and Development (UNCTAD). (2020). World Investment Report. West, D. M., & Lansang, C. (2018). Global Manufacturing Scorecard: How the US compares to 18 other nations. Brookings. Worldbank. (2020a). World Bank national accounts data, and OECD National Accounts data files. Retrieved from https://data.worldbank.org/indicator/NV. IND.MANF.CD Worldbank. (2020b). World Development Indicators, Macrotrends. Retrieved from https://www.macrotrends.net/countries/USA/united-states/manufacturing-output

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dominik Remling and Thomas Friedli

Part I 2

3

5

The Basics of Global Manufacturing Management

The St.Gallen Management Model for International Manufacturing Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dominik Remling and Thomas Friedli

25

Operational Excellence: The St.Gallen Model for Holistic Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marten Ritz and Thomas Friedli

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Part II 4

1

Managing Manufacturing Site & Network Optimization

Managing International Manufacturing Networks in Today’s Business Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jens Kaiser and Dominik Remling

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Unlocking Value with Production Network Optimization: A Strategic Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marian Wenking, Oliver von Dzengelevski, and Torbjørn H. Netland

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Deriving a Network Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Philipp Miedler and Thomas Friedli

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7

Site Selection Processes in Global Production Networks . . . . . . . . . 101 Bastian Verhaelen, Sina Peukert, and Gisela Lanza

8

Design for X – Site-Specific Adaptation of Production Processes and Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Shun Yang, Sina Peukert, and Gisela Lanza

9

Product-Mix Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Felix Klenk, Sina Peukert, and Gisela Lanza

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Contents

10

Order Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Florian Stamer, Sina Peukert, and Gisela Lanza

11

Adding an OPEX Perspective to Network Optimization . . . . . . . . . 155 Mark Grothkopp, Marten Ritz, and Thomas Friedli

12

Process Quality Improvements in Global Production Networks . . . 167 Rainer Silbernagel, Tobias Arndt, Sina Peukert, and Gisela Lanza

13

From Plants to Network: Digitalization as an Enabler for Global Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Christoph Benninghaus

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Enabling Data-Based Applications in Manufacturing . . . . . . . . . . . 189 Paul Buess

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Managing Manufacturing Network Performance . . . . . . . . . . . . . . 203 Dominik Remling

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Operations Research in International Manufacturing Networks . . . 219 Martin Benfer, Sina Peukert, and Gisela Lanza

17

The Role of the Plant Leaders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Michael Wiech

Part III

Practitioner Contributions

18

Strategic Transformation and Operations Management at Bühler AG: A Holistic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Holger Feldhege, Xue-Zhi Liu, and Dominik Remling

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Global Manufacturing at CLAAS: From a Local-for-Local Structure Toward Network Excellence . . . . . . . . . . . . . . . . . . . . . . 269 Hendrik Schellmann and Christian Köbke

20

Applying a Regional Manufacturing Network Analysis for PALFINGER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Martin Friedl

21

Network Optimization in 5-Year Cycles at Lapp Group . . . . . . . . . 301 Georg Stawowy, Boris Katic, and Dominik Remling

22

Holistic Manufacturing Network Management Approach at Jenoptik AG: Light and Production Division . . . . . . . . . . . . . . . 317 Richard Hummel

23

Global Traceability as a Competitive Advantage: The Model-Based Approach of a Tier-1 Automotive Supplier . . . . . . . . . . . . . . . . . . . 335 Fabian Liebetrau

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351

Editors and Contributors

About the Editors Prof. Dr. Thomas Friedli Professor for Production Management, Institute of Technology Management, University of St.Gallen (ITEM-HSG) Thomas Friedli is director of the Institute of Technology Management and a professor for production management at the University of St.Gallen in Switzerland. His main research interests are in the field of managing global production, operational excellence, and industrial services. He is a lecturer in the (E)MBA programs in St.Gallen, Fribourg, and Salzburg. He spent several weeks as an adjunct associate professor at Purdue University in West Lafayette, USA. Prof. Friedli leads a team of 15 researchers who develop new management solutions for manufacturing companies in today’s business landscape. He is also the editor, author, or coauthor of 15 books and various articles. Among his books are Strategic Management of Global Manufacturing Networks (2014), Leading Operational Excellence in the Pharmaceutical Industry (2013), Wettbewerbsfähigkeit der Produktion an Hochlohnstandorten (2012), and Industrie als Dienstleister (1997). Prof. Dr.-Ing. Gisela Lanza Professor for Production System and Quality Management, wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT) Gisela Lanza is member of the management board at the wbk Institute of Production Science of the Karlsruhe Institute of Technology (KIT). She heads the Production Systems division dealing with the topics of global production strategies, production system planning, and quality assurance in research and industrial practice. In 2009, she received the Heinz Maier-Leibnitz Award of the German Research Foundation (DFG) in recognition of her outstanding scientific achievements after the doctorate, and she was awarded in 2016 with the Federal Cross of Merit on Ribbon. She is an active member of the scientific advisory board of the German Academy of Engineering Sciences (acatech) and the national platform Industrie 4.0, as well as of the Steering Committee of the Allianz Industrie 4.0 Baden-Württemberg. The holistic design and evaluation of production systems is a central research issue in numerous research and joint projects. The methodological approach includes the use of quantitative methods to increase efficiency as well as the development and xv

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introduction of innovative technologies into production processes. In addition, a special focus is placed on data-driven planning and control of production networks in order to translate corporate strategy into tactical and operative network design. In order to master the highest process quality, especially in immature production processes, Prof. Lanza is also intensively engaged in the integration of in-line measurement technology in production systems as well as intelligent methods for the analysis of measurement data. The close interaction with numerous companies primarily from automotive, mechanical, and plant engineering secures the practical applicability and the industrial added value of her research work. Dominik Remling Research Associate and Head of Global Production, Institute of Technology Management, University of St.Gallen (ITEM-HSG) Dominik Remling holds a master’s degree in technology management from the University of Stuttgart. Before joining the ITEM-HSG, he gained professional experience in the field of production systems and quality management at Robert Bosch GmbH and Daimler AG. He is part of Prof. Friedli’s Division of Production Management at the University of St.Gallen, where he leads the research group Global Production. He pursues a PhD in management with a focus on performance management in international manufacturing networks.

Contributors Dr.-Ing. Tobias Arndt GAMI – Global Advanced Manufacturing Institute, Suzhou, China Martin Benfer wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany Dr. Christoph Benninghaus Construction Company, Schaan, Liechtenstein Dr. Paul Buess Dürr AG, Bietigheim-Bissingen, Germany Oliver von Dzengelevski Chair of Production and Operations Management (POM), DMTEC, ETH Zurich, Switzerland Dr. Holger Feldhege Bühler AG, Uzwil, Switzerland Martin Friedl Palfinger AG, Bergheim, Austria Prof. Dr. Thomas Friedli Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland Mark Grothkopp Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland Richard Hummel Jenoptik AG, Jena, Germany Jens Kaiser Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland

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Boris Katic U.I. Lapp GmbH, Stuttgart, Germany Felix Klenk wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany Christian Köbke CLAAS KGaA, Harsewinkel, Germany Prof. Dr.-Ing. Gisela Lanza wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany Dr. Fabian Liebetrau Automotive Supplier, Eschen, Liechtenstein Xue-Zhi Liu Bühler AG, Uzwil, Switzerland Philipp Miedler Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland Prof. Dr. Torbjørn H. Netland Chair of Production and Operations Management (POM), DMTEC, ETH Zurich, Switzerland Sina Peukert wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany Dominik Remling Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland Marten Ritz Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland Dr.-Ing. Hendrik Schellmann CLAAS KGaA, Harsewinkel, Germany Rainer Silbernagel wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany Florian Stamer wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany Georg Stawowy Lapp Holding AG, Stuttgart, Germany Bastian Verhaelen wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany Dr. Marian Wenking Mann+Hummel GmbH, Ludwigsburg, Germany Dr. Michael Wiech SGL Carbon GmbH, Wiesbaden, Germany Shun Yang wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

Abbreviations

ABS AHP AI ATS BOM BSC BU CAC CIP CIS DBA DDO DES DfM DfMA DfX FIFO FMEA GDP GPN GPP HU IMN ITEM JIT KIT LM MES MIT NPV OEE OEM OLAP

Agent-based simulation Analytical hierarchy process Artificial intelligence Adherence-to-standard Bill of material Balanced score card Business unit Component assembly center Continuous improvement process Commonwealth of Independent States Data-based application Daily delivered orders Discrete event simulation Design for manufacturing Design for manufacturing and assembly Design for X First-in-first-out Failure mode and effects analysis Gross domestic product Global production network Gross primary productivity Handling unit International manufacturing network Institute of Technology Management Just-in-time Karlsruhe Institute of Technology Lean manufacturing Manufacturing execution system Massachusetts Institute of Technology Net present value Overall equipment effectiveness Original equipment manufacturer Online analytical processing xix

xx

OPEX OR OT PEC PM PSSC ROI RPI S&OP SAC SIOP SKD SM SQCDP TPM TPS TQM VSM wbk XPS

Abbreviations

Operational excellence Operations research Operations technology Project execution center Performance management Project support supply chain Return on investment Risk priority index Sales and operations planning System assembly center Sales inventory and operations planning Semi-knocked-down Smart manufacturing Safety, quality, cost, delivery, and people Total productive maintenance Toyota Production System Total quality management Value stream map Institute of Production Science Company-specific lean production system

List of Figures

Fig. 1.1 Fig. 1.2 Fig. 1.3 Fig. 1.4 Fig. 1.5 Fig. 1.6 Fig. 1.7 Fig. 1.8 Fig. 1.9 Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 2.5 Fig. 2.6 Fig. 2.7 Fig. 2.8 Fig. 2.9 Fig. 2.10 Fig. 2.11 Fig. 2.12 Fig. 3.1 Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 4.4 Fig. 4.5

Motives for the global expansion of companies . . . . . . . . . . . . . . . . . . . . . . Customer requirements of international manufacturing networks [N ¼ 88] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reasons for offshoring of Swiss manufacturing companies . . . . . . . . . Expected capacities of Swiss manufacturing companies . .. . . . . . . . . . . Conflicts of interest in international manufacturing networks [n ¼ 212, N¼88] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The sand cone model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Objectives of Industry 4.0 activities . .. . .. . .. . . .. . .. . .. . .. . . .. . .. . .. . . Impact of Industry 4.0 on international manufacturing networks [N ¼ 85] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Information considered to develop a global production strategy [N ¼ 86] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . St.Gallen Management Model for International Manufacturing Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework for gauging plant subnetworks . . . . . . . . . . . . . . . . . . . . . . . . . . Exemplary Manufacturing Priorities framework . . . . . . . . . . . . . . . . . . . . Exemplary Network Capability framework . . . . . . . . . . . . . . . . . . . . . . . . . Exemplary Site Portfolio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Morphological box to operationalize the Site Portfolio . . . . . . . . . . . . Simplified classification for networks operating many (20+) sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exemplary Centralization and Standardization framework . . .. . .. . . Exemplary Information and Knowledge Exchange framework . . . . Exemplary Resource Sharing framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exemplary Incentive System framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework to prioritize work packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . St.Gallen Operational Excellence Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . Environment of manufacturing networks and its constituents . . . . . Extent to which decisions are affected by risk and dynamic . . . . . . . Exposure to internal and external complexity drivers . . . . . . . . . . . . . . Influence of the COVID-19 pandemic on operations . . . . . . . . . . . . . . . Top five applied approaches for decision support . . . . . . . . . . . . . . . . . .

6 8 9 9 10 11 13 13 17 26 28 30 33 35 38 41 43 44 46 47 49 53 64 65 66 67 69 xxi

xxii

Fig. 4.6 Fig. 4.7 Fig. 4.8

Fig. 4.9 Fig. 4.10 Fig. 4.11 Fig. 4.12 Fig. 4.13 Fig. 5.1 Fig. 6.1 Fig. 6.2 Fig. 6.3 Fig. 6.4 Fig. 7.1 Fig. 7.2 Fig. 7.3 Fig. 7.4 Fig. 7.5 Fig. 7.6 Fig. 7.7 Fig. 7.8 Fig. 7.9 Fig. 8.1 Fig. 8.2 Fig. 8.3 Fig. 8.4 Fig. 8.5 Fig. 8.6 Fig. 8.7 Fig. 8.8 Fig. 8.9 Fig. 8.10 Fig. 8.11 Fig. 9.1 Fig. 9.2 Fig. 9.3 Fig. 10.1 Fig. 10.2 Fig. 10.3 Fig. 10.4 Fig. 11.1

List of Figures

IT tools applied for decision support . . . . . . .. . . . . . .. . . . . .. . . . . . .. . . . . . Assignment of responsibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Centralization of decision-making (the values may not always add up to 100% since we excluded the answer option “Don’t know” and “Not relevant” from this illustration) . . . . . . . . . . . . . . . . . . . Degree of standardization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Top three factors to successfully implement standards . . . . . . . . . . . . . As-is and future level of flexibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Top three challenges in flexible order shifting . . . . . . . . . . . . . . . . . . . . . . Top three approaches to reduce default risk . . . . . . . . . . . . . . . . . . . . . . . . . Finding fit between network capability level and competitive context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The role of production strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Holistic network strategy framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Intended and realized strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Four steps toward an optimized manufacturing network . . . . . . . . . . . Main drivers for relocation abroad and to their origin . . . . . . . . . . . . Site selection process model . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . . Valuation methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Location criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pairwise comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Longlist of potential settlement areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Information table for utility analysis . . . . . . .. . . . . . . .. . . . . . . . .. . . . . . . . Utility analysis . . .. . . . .. . . .. . . .. . . . .. . . .. . . . .. . . .. . . . .. . . .. . . . .. . . .. . . . Best practices of site selection processes . . . . . . . . . . . . . . . . . . . . . . . . . . . Requirements for production processes and product design . . . . . . Optimizing product design by surface treatment . . . . . . . . . . . . . . . . . . Optimizing process design through standard procedures . . . . . . . . . Optimizing product design through modularization . . . . . . . . . . . . . . . Optimizing packaging through standardization . . . . . . . . . . . . . . . . . . . . Translation of standards into clear-cut generalist specifications . . . . Avoid customs through shipping configuration . .. . . . .. . . .. . . . .. . . . Typical development paths for local adjustment . . . . . . . . . . . . . . . . . . Adapt manufacturing engineering by modified product design . . . Simplify application of product label . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simplify production processes through material change . . . . . . . . . . Components of the clustering approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . Components of the optimized production network . . . . . . . . . . . . . . . . Post-optimality approach to integrated product allocation . . . . . . . . Uncertain information in mid-term order planning . . . . . . . . . . . . . . . . Mid-term order planning tasks under uncertain configurations . . . Exemplary variant tree for the A320 family . . . . . . . . . . . . . . . . . . . . . . . Assignment of customer orders and plan orders . . . . . . . . . . . . . . . . . . . Performance-practice framework .. . .. .. . .. .. . .. .. . .. .. . .. .. . .. .. . .. .

69 70

71 72 72 73 74 75 82 89 92 93 97 102 103 106 108 109 110 110 110 112 119 120 120 120 121 121 122 123 124 126 126 132 134 136 145 146 147 151 157

List of Figures

Fig. 11.2 Fig. 11.3 Fig. 11.4 Fig. 11.5 Fig. 12.1 Fig. 12.2 Fig. 12.3 Fig. 13.1 Fig. 13.2 Fig. 13.3 Fig. 14.1 Fig. 15.1 Fig. 15.2 Fig. 15.3 Fig. 15.4 Fig. 15.5 Fig. 15.6 Fig. 15.7 Fig. 15.8 Fig. 16.1 Fig. 17.1 Fig. 17.2 Fig. 17.3 Fig. 18.1 Fig. 18.2 Fig. 18.3 Fig. 18.4 Fig. 18.5 Fig. 18.6 Fig. 19.1 Fig. 19.2 Fig. 19.3 Fig. 19.4 Fig. 19.5 Fig. 19.6 Fig. 19.7 Fig. 19.8 Fig. 19.9 Fig. 20.1

Linear interpolation to calculate the relative performance score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Input process for regularly updating site performance data . . . . . . . Output result scoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Site performance measurement cockpit across the network . . . . . . Five-step description model for process quality . . . . . . . . . . . . . . . . . . . Evaluation of exemplary site specializations . . . . . . . . . . . . . . . . . . . . . . . Exemplary visualization of Global Quality Value Stream . . . . . . . . Digitalization location factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital technology transfer process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital competence management framework . . . . . . . . . . . . . . . . . . . . . . Share of exploited data of available data . . . . . . . . . . . . . . . . . . . . . . . . . . . Implementation level of performance management systems . . .. . . Development barriers of performance management systems . . . . . Success factors for performance management . . . . . . . . . . . . . . . . . . . . . Site comparison matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Site target profile (automotive supplier example) . . . . . . . . . . . . . . . . . Site target profile (specialty chemical manufacturer example) . . . Multilayer performance management model . . . . . . . . . . . . . . . . . . . . . . . Network dashboard KPI dimension structure . . . . . . . . . . . . . . . . . . . . . . Typical OR problems in manufacturing network management . . . Perspectives on plant contribution to network capabilities (exemplified) . .. . . . . .. . . . . .. . . . .. . . . . .. . . . .. . . . . .. . . . .. . . . . .. . . . .. . . . . . Plant context and implications for plant leaders . . . . . . . . . . . . . . . . . . . Nominated plant leader to assume additional network responsibility .. . .. . .. . .. .. . .. . .. .. . .. . .. .. . .. . .. . .. .. . .. . .. .. . .. . .. . .. . MLS strategy icon .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . . .. . . . .. . . . .. . . . . Core vs. non-core classification process . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bühler Event Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example of daily delivered order monitor . . . . . . . . . . . . . . . . . . . . . . . . . Vertical integration through digital transformation . . . . . . . . . . . . . . . . Standard factory targets . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Development of the BU Grain’s global manufacturing network . .. . . .. . .. . . .. . .. . . .. . . .. . .. . . .. . . .. . .. . . .. . . .. . .. . . .. . .. . . .. . . . Structure of the strategic initiative “manufacturing network” . . . . C3 approach to balance capabilities, capacities, and costs . . . . . . . . Adapted manufacturing priorities (only exemplary values) . . . . . . . Classification of sites by product and process complexity . . . . . . . . Site portfolio showing the development toward the future state . . .. . . . . . .. . . . . .. . . . . . .. . . . . .. . . . . . .. . . . . .. . . . . . .. . . . . .. . . . . .. . . . . . . Site capability assessment (extract) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matrix organization of the service unit manufacturing . . . . . . . . . . . . Quick check approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PALFINGER product overview . .. . .. .. . .. . .. . .. .. . .. . .. .. . .. . .. .. . .

xxiii

160 162 163 164 169 171 173 181 184 186 192 205 205 206 209 210 211 213 214 226 236 240 243 253 256 258 260 261 265 270 272 274 275 276 278 279 280 282 287

xxiv

Fig. 20.2 Fig. 20.3 Fig. 20.4 Fig. 21.1 Fig. 21.2 Fig. 21.3 Fig. 21.4 Fig. 21.5 Fig. 22.1 Fig. 22.2 Fig. 22.3 Fig. 22.4 Fig. 22.5 Fig. 22.6 Fig. 22.7 Fig. 23.1

List of Figures

PALFINGER network configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Revenue and profit contribution of plants in the region . . . . . . . . . . Identified improvement measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Top five strategic initiatives in the past . . . . . . . . . . . . . . . . . . . . . . . . . . . . Product classification matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Make-or-buy decision matrix . . .. . . .. . .. . . .. . . .. . .. . . .. . . .. . . .. . .. . . . The processes of the lead buyer organization . . . . . . . . . . . . . . . . . . . . . . Top five strategic initiatives for the future . . . . . . . . . . . . . . . . . . . . . . . . . Global production network of Jenoptik light and production . . . . Competence and solution for light and production . . . . . . . . . . . . . . . . Historical development of the company . . . . . . . . . . . . . . . . . . . . . . . . . . . . Production locations of metrology in light and production . . . . . . . Production strategy of division light and production . . . . . . . . . . . . . . Site roles in the division light and production . . . . . . . . . . . . . . . . . . . . . Monitoring of production sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model for traceability implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

294 295 297 303 307 309 310 312 319 320 322 324 325 328 332 339

List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 6.1 Table 6.2 Table 7.1 Table 14.1 Table 14.2 Table 14.3 Table 18.1 Table 23.1 Table 23.2 Table 23.3

Manufacturing priorities .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . . Network capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Operationalization of strategic site reasons . . . . . . . . . . . . . . . . . . . . . . . . Examples to direct behavior utilizing incentives . . . . . . . . . . . . . . . . . . Major elements of a production strategy . . .. . . . . . . .. . . . . . . .. . . . . . . . Interaction between priorities and capabilities . . . . . . . . . . . . . . . . . . . . Net present value calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data-based applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . Key challenges of using DBAs in manufacturing . . . . . . . . . . . . . . . Key enablers of using DBAs in manufacturing . . . . . . . . . . . . . . . . . . Parts classification . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . Overview of influencing factors for part traceability . . . . . . . . . . . . Variables for calculating recall potential for given supplier part .. . . . .. . . .. . . . .. . . . .. . . .. . . . .. . . .. . . . .. . . .. . . . .. . . .. . . . .. Variables for calculating potential recall costs . . . . . . . . . . . . . . . . . . .

29 32 38 48 89 95 111 191 193 195 257 346 347 348

xxv

1

Introduction Dominik Remling and Thomas Friedli

This chapter serves as an introduction to the topic of global manufacturing management. First, the development and importance of manufacturing for established and emerging economies is shown on the basis of key statistics. Based on this, the latest trends in global manufacturing management will be highlighted. This includes dealing with global markets, cost pressures, trade-offs, Industry 4.0, and operational excellence and ultimately shifting from a mere manufacturing site focus to a holistic international manufacturing network view. The introduction to the topic of global manufacturing management is followed by a description of the structure and objectives of this book.

1.1

Importance of Manufacturing

To live well, a nation must produce well.—Opening Statement of the MIT Made in America Study, 1989

The primary message of the MIT (Massachusetts Institute of Technology) study is as valid today as it was then (Locke & Wellhausen, 2014). The study was conducted by a team of leading scientists, engineers, and economists—the MIT Commission on Industrial Productivity—to look at the weaknesses of the American industry that were jeopardizing the nation’s standard of living and position in the world. Hence, five national priorities were identified to return it to its former leading status. The priorities addressed for industry, labor, government, and the educational sector included focusing on the fundamentals of manufacturing (technical and organizational excellence), cultivating new economic citizenship in the workforce, D. Remling (*) · T. Friedli Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland e-mail: [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_1

1

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D. Remling and T. Friedli

blending cooperation and individualism, learning to live in the world economy, and providing for the future (Dertouzos, 1989). In contrast to the commonly proposed macroeconomic levers, the reorganization and effective integration of human resources and new technologies were identified as the main drivers for productivity gains (Locke & Wellhausen, 2014). The motivation for proposing these measures back in 1989 resulted from a downturn in US production in terms of productivity and quality. The measures were intended to enable the USA to restore the competitive edge it inherited during the industrialization era. The phase of industrialization started in the mid-eighteenth century in the UK followed by other European countries and the USA as well as later by Japan, the East Asian Tigers, and China (Naudé & Szirmai, 2012). In 1870, the UK accounted for almost a third of global manufacturing output, while the USA represented less than a quarter (Smil, 2013). The tide had turned by 1890, at which point the USA’s share had risen to 36% and the UK’s had dropped to less than 15% (Smil, 2013). From then on, the USA has held the top spot for 120 years (Smil, 2013). The ascent, primarily achieved through advances in productivity, was driven by the use of steam power, the introduction of early production systems with interchangeable, standardized parts as well as new forms of organization, and the establishment of larger manufacturing facilities to replace the manufactories that had dominated until then. Besides, revolutionary inventions during this era by people like Bell, Edison, Westinghouse, and Tesla, to mention only a few, were quickly turned into commercialized products. The first half of the twentieth century, also known as the consolidation phase, was characterized by the electrification of production and the further increase in output toward standardized mass manufacturing. The substitution of steam engines by electric motors in factories made it possible to operate machinery more efficiently and to establish the use of conveyor belts more widely. The improved efficiency of factories also pushed mass production to a new level, which manifested itself particularly in the automotive industry. This trend was further stimulated by a stronger drive toward vertical integration and the introduction of scientific management. The Great Depression could not keep the USA from becoming the world’s leading industrial power. That era in the lead-up to World War II was rather innovative with discoveries like radar and nuclear fission. By 1943, production in the USA had been switched to war material for up to two-thirds of total output (Smil, 2013). The years of war were characterized less by innovation (although innovation remained present during this period) than by the focus on rapid mass production. After the war, the strong involvement of women in the workforce, close collaboration between government, private companies, and trade unions, as well as government-funded research led to further growth. This resulted in increasing prosperity and also boosted imports of manufactured goods. Other important drivers in the development of production were the inclusion of automation, computers, and microchips in production in the 1950s, 1960s, and 1970s (Smil, 2013). The decline of US manufacturing started in the 1970s, initially due to shrinking oil production and increasing reliance on imports. The resulting trade deficit was followed by a devaluation of the US currency. Increasing competition, especially

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Introduction

3

from Japan and Europe in the automotive sector, further exacerbated the situation from the 1980s onward. Advancements in microchips facilitated the rise of personal computers in the 1980s and the Internet in the 1990s. In 2010, only 8.2% of US workers were employed in manufacturing compared to 19% in Germany and 18% in Japan. At that time, the global share of US manufacturing had declined to 20%, equaling China (which had risen from 3% in 1990) (Smil, 2013). Accordingly, industries such as the manufacturing of electronics, furniture, steel, automobiles, and textiles shifted worldwide. Besides the shift of manufacturing between economies, globalization has forced companies to manufacture at several locations around the world, driven by favorable factor costs but also by infrastructure and tax subsidies. Nevertheless, the USA is today an exceptional case for a powerful country that is more dependent on chronic deficit-inducing imports of commodities than any other country (Smil, 2013). The course of events indicates how important manufacturing is for the rise of a nation’s prosperity. At the same time, the example of the USA shows impressively how domination can be lost over time due to certain events and decisions, even when manufacturing was the primary force for becoming a leading economic power. Although this shift is a multifaceted problem, the loss of jobs in manufacturing is seen as the dominant driver (Smil, 2013). For developing countries today, the transition from agriculture to manufacturing still entails increased productivity and the path toward higher living standards (Manyika et al., 2012). Only a small number of countries have managed to achieve high incomes without a solid manufacturing base, if only by accessing natural resources or exploiting other locational advantages (Hallward-Driemeier & Nayyar, 2017). Advocates of purely service-based economies speak of two myths regarding the declining importance of manufacturing (Smil, 2013). The first is that manufacturing is becoming less significant due to the decreasing mass per product and thus lower material input through technical innovation, especially exemplified by the semiconductor industry. This myth can easily be dispelled if one considers the strong increase in these inputs due to population growth and rising per capita incomes. The second myth pertains to the perception that manufacturing in affluent countries is becoming a dispensable activity, as anything could be imported from the profits of services (Smil, 2013). However, this neglects the fact that manufacturing has three main backward-forward connections (Coad & Vezzani, 2017). Productivity Growth Contribution Productivity growth in the industry is significantly higher than in the service sector since industrial production can be rationalized. Services are created through the interplay of people, whereas industrial production is created through the interplay of humans and machines (Bauernhansl et al., 2014). On top of that, every dollar earned from manufacturing leads to US$1.40 of additional economic activity, the highest value relative to other sectors (Smil, 2013). Those effects are reflected in the growth of economies. Kaldor (1966) has based his “growth laws” on

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D. Remling and T. Friedli

empirical evidence, suggesting a positive association between the growth of manufacturing output and average gross domestic product (GDP) growth, manufacturing productivity, and overall productivity of the economy. R&D and Innovation Contribution Manufacturing entails a considerable amount of R&D (research and development) capacities and thus innovation. This further facilitates scale economies, technology diffusion, greater competition, and other spillover effects (Hallward-Driemeier & Nayyar, 2017). Likewise, manufacturing contributes by training and education of employees due to its own demand for well-educated personnel. This also becomes apparent in well-paid employment (Manyika et al., 2012). Trade and Internationalization Contribution A strong manufacturing base is particularly important in a globalized world in which balanced international trade can take place. This is illustrated by the example of the USA with its large trade deficit as a result of its declining manufacturing base compared to export-oriented countries such as Switzerland and Germany. These countries manage to maintain an even trade balance or even achieve significant trade surpluses, which in turn lead to a capital surplus. The following figures clearly reflect the differences in manufacturing shares between the major industrialized countries. The contribution of worldwide manufacturing to the global GDP in 2017 was 15.6%, while countries such as Ireland (33.9%), Korea (29.5%), Germany (22.8%), and Switzerland (18.9%) were well above this average and the former industrialization driver countries the USA (11.6%) and UK (10.1%) were considerably below (OECD, 2020). The major problem, however, lies in the complexity behind these figures, as the definitions and data collections of governments and international organizations only take into account part of the reality (Smil, 2013). Firstly, manufacturing companies employ a significant number of people in manufacturing-related service areas such as management, accounting, R&D, software, and transportation. Many of these functions are outsourced today so that they are not assigned to manufacturing but could not exist without it. The North American Industry Classification System also neglects obvious manufacturing methods such as refining of ores (assigned to mining) or bulk breaking and redistribution in smaller lots (assigned to retail trade) (Smil, 2013). Accordingly, manufacturing reflects far more than is indicated in the existing data records and its benefits cannot be judged merely by, for example, contribution to GDP. According to Smil (2013), statements about the “decrease in importance of manufacturing” and “reduction in value-added is not a cause for concern” and that “the growth of modern economies resides in services and the export of services ensures sufficient income to import all the goods needed” are simply wrong. Instead, manufacturing is far more important to modern economies than its share of the GDP

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Introduction

5

reflects. Manufacturing is deeply rooted in a national economy with connections to a wide variety of spheres affecting various factors that determine the success of the overall political, economic, legal, educational, social, and healthcare systems of a country (Smil, 2013).

1.2

Managing Manufacturing Today

In absolute terms, worldwide manufacturing is expected to reach new heights during the twenty-first century, and great benefits will accrue to those countries that succeed in supplying most of their domestic needs for durables and are also able to tap the immense foreign demand for manufactured goods.—Vaclav Smil, 2013

The importance of manufacturing for both underdeveloped and developed economies is undeniable. Leading scientists are convinced that the current century will also be characterized by production and the manufacturing of goods will be an important pillar for modern economies. We are currently still at the beginning of the twenty-first century and are part of the fourth industrial revolution, which in retrospect will perhaps be seen more as an evolution. The following chapters will first touch on our global value creation structures, the resulting price pressure, and the trade-off between top-quality craftsmanship in high-wage countries and cost pressure caused by new competitors from Asia. Also, we do justice to the overriding role Industry 4.0 and digitalization are currently playing, by repeatedly presenting research results on this topic throughout the book. Furthermore, the topic of Operational Excellence (OPEX) is discussed, which, as is well known, leads to success through continuous execution. This topic is also one of the cornerstones of this book.

1.2.1

Global Markets: Global Value Chains

Globalization had already begun in the late nineteenth century and both international trade and foreign direct investments have ballooned thenceforth (Cheng et al., 2015). Foreign direct investment is the main driver for increasing trade between nations (UNCTAD, 2018). In 2018, the world trade value was 300 times that of 1950 (WTO, 2020). Intra-firm trade as a result of foreign direct investments and multinational operation covers the main share of world trade value (Lian & Ma, 2011). For example, in 1988, 53% of imports into the USA were provided by US foreign subsidiaries and this value has risen to around 80% until 2011 (Lian & Ma, 2011). According to a United Nations estimate, about 80% of global trade is triggered by the international manufacturing networks (IMNs) of transnational companies, whereas one-third of global trade is attributable to trading within firms (UNCTAD, 2018). Hence, manufacturing companies increased their worldwide growth, starting from supplying domestic markets, afterward by supplying global markets through

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Fig. 1.1 Motives for the global expansion of companies. Reprinted from “Global Production Networks: Design and Operation.” by G. Lanza, K. Ferdows, S. Kara, D. Mourtzis, G. Schuh, J. Váncza, L. Wang, and H.P. Wiendahl, 2019, CIRP Annals, 68, 2, p. 823-841

export, and finally by supplying global markets via local manufacturing (Cheng et al., 2015). There are different motives for this global expansion, outlined in Fig. 1.1 by Lanza et al. (2019). The course of further internationalization generally entails four main challenges (Lanza et al., 2019). First, there is increasing uncertainty in terms of varying product requirements and volatile market demands. Second, global expansion results in a higher degree of internal and external complexity. Third, companies must deal, increasingly, with sustainability. Last, there is also the challenge of disruptive innovations that have the potential to erode entire industries. Further, there is a vast number of factors that cannot be influenced by the companies themselves, such as currency differences, trade agreements, new competitors, and new technologies, and thus they are required to constantly adjust IMNs (Ferdows et al., 2016). The US Bureau of Labor Statistics expects a global share of manufacturing output of 17% in 2029 (2020). Looking ahead, growth will be driven primarily by emerging markets. By 2025, global consumption will have doubled compared to 2013, with half of this amount coming from emerging markets (Mancini et al., 2017). A key driver of this growth is the share of the consumer society with disposable incomes greater than ten dollars per day (Mancini et al., 2017). This share amounted to one-third of the world’s population in 2010 and is expected to rise to one half by 2025 (Mancini et al., 2017). For manufacturing companies, the numbers are even more promising. It is expected that by 2025, 65% of all goods will be manufactured

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in emerging countries (Mancini et al., 2017). For manufacturing companies, however, it will not only be important to choose the right location but also to be as efficient as possible in a very diverse environment. Accordingly, a broad set of skills is necessary to be able to meet complexity, productivity, and agility requirements (Mancini et al., 2017).

1.2.2

The Steady Pressure on Costs

Fortunes are not made with inventions, but with improvements.—Henry Ford

Although many skills are necessary to cope with today’s and the future environment, there is a steady pressure on costs regardless of whether the manufacturing strategy itself is mainly cost-driven. We have already highlighted the importance of productivity growth, which contributes significantly to the economy, in an earlier section. In manufacturing companies, the benefit of increasing productivity can be distributed to different stakeholders, such as the customer through lower prices, the workforce through higher wages, and so on. Consequently, there is a direct relationship between productivity and costs. Since productivity is defined as a ratio between output and input, a decrease of inputs for the same output can be achieved through, for example, cost improvement programs. Usually, companies aim to optimize both ends of the formula by increasing the output and decreasing the input at the same time. In the benchmarking survey “Managing Global Production Networks in Today’s Business Environment,”1 the main customer requirements of 88 IMNs were inquired for the fiscal year 2019 (Friedli et al., 2020). The participating companies are mainly headquartered in the German-speaking areas. However, their customer base is truly global. In fact, quality, delivery reliability, and price are the most critical criteria when it comes to competition and differentiation (see Fig. 1.2). This means that although most of the companies studied are generally technology leaders in their specific niche, the price of the product always plays a crucial role. In the annual Swiss Manufacturing Survey, which was conducted between 2017 and 2020 with more than 800 participants in total, in each round it was asked for the main barriers for the successful execution of manufacturing activities in Switzerland. Switzerland is known for its status as a high-wage location, and this is indeed the perception of companies as the primary barrier to local manufacturing. Thus, the

1

The benchmarking survey had been conducted in the period between May 6 and July 30, 2020 and yielded in total a sample of 88 participants. Most commonly, the participants hold positions such as COO, CTO, Head of Manufacturing, Head of Global Operations, etc. The participating companies mainly have their headquarters in German-speaking countries and come from various industries (33% mechanical engineering, 13% electrical engineering, 11% automotive, 10% metal products, etc.). The total number of production sites within the international manufacturing network ranges between less than 5 (24 companies) and more than 50 (seven companies).

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No importance Quality Delivery reliability Price Innovation Service Volume f lexibility Delivery speed Variant f lexibility 2

3 Sucessf ul Practice

4

5

6

Follower

Fig. 1.2 Customer requirements of international manufacturing networks [N ¼ 88]

companies stated labor costs as the prime barrier, which has been unaltered for four years in a row. Consequently, manufacturing companies tend to outsource and offshore capacities to foreign countries with access to best-cost labor. Lowering costs is one of the main reasons for outsourcing and has—according to a study by Edvardson, Oskarsson, and Durst—led to decreases in costs in 48% of the firms involved (Edvardsson et al., 2020). The share of value add in high-wage countries decreased in the 1990s and 2010s, which reflects the offshoring activities of multinational companies (Hallward-Driemeier & Nayyar, 2017). A quick reading of the economic press reveals that many companies have relocated to best-cost countries (Vereecke et al., 2006). However, the reasons for going global are twofold, being attributed to both internal and external pressures described in the eclectic model by Dunning (1980). The reasons for offshoring in Swiss manufacturing companies are shown in Fig. 1.3. It must be also considered what specific processes and products are offshored. While it makes perfect sense to relocate simple manual tasks into low-wage countries, unlimited free trade, strong dependence on imports, and the gradual offshoring of whole industries are threats even to the strongest economies (Smil, 2013). In this respect, the outlook for the Swiss manufacturing industry is positive, as it is expected that despite the current challenges, production volume will remain constant and tend to decline at foreign locations of the Swiss companies, as shown in Fig. 1.4.

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9 Very Unimportant

Very Important

Reduce manuf acturing costs Increase proximity to customer Reduce logistic costs Improve delivery speed Improve quantity f lexibility Ensure availability of qualif ied workers Improve (on-time) delivery reliability Better inf rastructure in target country Better legal & political conditions in target country Improve process quality Improve product quality

1 2017 [N=38]

2 2018 [N=31]

3

4 5 2019 [N=7]

6 7 2020 [N=9]

Fig. 1.3 Reasons for offshoring of Swiss manufacturing companies

Fig. 1.4 Expected capacities of Swiss manufacturing companies

Although there are many cost improvement measures suggested in the literature, we should first gain an understanding of the relationship between costs and the other manufacturing outcomes such as speed and quality and how they influence each

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other. Therefore, in the next section, we will dive deeper into the management of trade-offs and show a different perspective on them utilizing the sand cone model.

1.2.3

How to Deal with Trade-Offs: The Importance of the “Sand Cone Model”

Though we accept that cost improvements remain the ultimate goal of most manufacturers, we see these cost improvements also as an ultimate consequence of resources and management efforts invested in the improvement of quality, dependability, and reaction speed of the company.—Kasra Ferdows and Arnoud De Meyer, 1990, p. 174

A trade-off is a situational decision that involves diminishing or losing one quality, quantity, or property of a set or design in return for gains in other aspects. The triangle of production consisting of cost, quality, and time is widespread. The idea behind the theory is that the improvement of one category is always accompanied by a cut in at least one other category. The most significant conflicting goals in IMNs are shown in Fig. 1.5. Most responses refer to conflicts of interest in terms of costs. The size of the tiles indicates the number of votes in the survey (descending order). What seems trivial at first sight has been questioned by Ferdows and De Meyer with the sand cone model (1990). In principle, trade-offs do not seem to be as trivial as previously assumed. Not only can trade-offs be avoided, but the capabilities that were previously in a trade-off relationship can even complement each other and are

Fig. 1.5 Conflicts of interest in international manufacturing networks [n ¼ 212, N¼88]

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Fig. 1.6 The sand cone model. Adapted from “Lasting Improvements in Manufacturing Performance: In Search of a New Theory.”, K. Ferdows, and A. De Meyer, 1990, Journal of Operations Management, 9, 2, pp. 168–84

therefore cumulative. Furthermore, improving capabilities in a cumulative way is much more lasting than developing them at the expense of the others. Some authors (Crosby, 1983; Deming, 1982; Garvin, 1988) have found, for example, that the introduction of quality improvement measures not only leads to higher quality but also to lower costs (Ferdows & De Meyer, 1990). However, this would not necessarily work the other way around, i.e., that cost-saving programs lead to higher quality. The same appears true for increased dependability and flexibility. Making production processes more stable and reliable enables greater flexibility but not vice versa. The aim of the contribution was not to discard the trade-off theory but to create an understanding of the fact that this theory is not valid under all circumstances and that a more comprehensive theory is needed. Based on a quantitative evaluation of the European Manufacturing Futures Survey, the authors propose the following model (see Fig. 1.6). First, the sand cone analogy has been arbitrarily chosen and represents management effort, which is built upon. The first pile, which is the foundation of all following efforts, is to improve quality performance. After the first results emerge, additional efforts should target the manufacturing dependability. Dependability, in this case, stands for the reliability of delivery through stable processes or availability of goods. As soon as improvements are raised, the speed of response should be addressed, while efforts on the previous categories should be maintained at the same time. After these cumulative capabilities are enhanced, the focus can shift to cost improvement. Following the proposed cumulative steps in this order will lead to more lasting improvements in manufacturing. Furthermore, the efforts in reducing costs after building the underlying foundation won’t lead to a decrease in the other capabilities, which means the trade-off itself is diminished. Retrospectively to the mentioned trade-offs shown in Fig. 1.5, the biggest conflicts companies today still face are between cost and quality, cost and speed,

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as well as cost and flexibility (dependability was not measured). Applying the sand cone model now in IMNs, it could be argued that the main trade-offs can be solved by globally consistent and coordinated improvement programs. Regardless of the strategic site reason or site role, improvement programs should be executed at all sites according to the cumulative sand cone logic. It is crucial to do this consistently globally via standards or central steering. Within this book, a multitude of approaches to support this and to diminish some of the mentioned trade-offs will be shown.

1.2.4

The Impact of the Industry 4.0 Discussion

In Sect. 1.2.2, the outlook for the Swiss manufacturing sector in terms of manufacturing capacities was already highlighted. One of the main drivers for keeping manufacturing at the high-wage location is Industry 4.0, as we at the Institute of Technology Management (ITEM) have already proclaimed in numerous publications since 2016. Our first benchmarking survey on the topic of Industry 4.0 was conducted in 2015, incorporating a management perspective unlike most studies with a more technical focus (Friedli et al., 2016). Back then, it was already clearly visible that relocating production to a high-wage location was not the intention of Industry 4.0 but would at best only lead to keeping production. It also became clear that several companies were already working on individual solutions and successful practices could implement a greater range of technologies. Our second benchmarking survey on the topic of digital technologies revealed further insights regarding the evolution of production at high-wage locations (Friedli et al., 2018a). In two combined studies, we investigated how Industry 4.0 affected the highwage location (Friedli et al., 2018b). The sample examined is made up of 273 companies, originating from the German-speaking area, but in many cases maintaining manufacturing facilities worldwide. Almost 75% of the companies surveyed support the statement that, thanks to Industry 4.0 activities, they can maintain production in high-wage locations. In summary, the interviewed companies can hold their production activities at high-wage locations through Industry 4.0. But at the same time, Industry 4.0 does not lead to new production facilities in high-wage countries. This means that once a production facility is offshored, it will not return thanks to Industry 4.0 and digitalization. In another study (Remling et al., 2020), we researched the degree of implementation of Industry 4.0 in small- and medium-sized as well as large companies. A total of 56% of companies have fully implemented at least one digitalization technology; at 27%, remote maintenance accounts for the largest share of this. Regarding the reasons, there are significant differences: Small- and medium-sized companies primarily aim to increase manufacturing flexibility. Large companies, on the other hand, focus on increasing manufacturing efficiency. The reasons for implementing Industry 4.0 in Swiss manufacturing companies over time are depicted in Fig. 1.7.

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Increase manufacturing flexibility

Strongly agree

Strongly disagree

Increase manufacturing efficiency Integrate customers/suppliers in business processes Increase performance of existing business models Offer new, digital services Keep manufacturing in high-wage countries Creation of a new business model Improve coordination between sites Offer new, digital products Establish new manufacturing sites in high-wage countries 2018 [N=158]

2019 [N=201]

2020 [N=183]

Fig. 1.7 Objectives of Industry 4.0 activities

Fig. 1.8 Impact of Industry 4.0 on international manufacturing networks [N ¼ 85]

Going beyond the study of high-wage locations by looking at entire manufacturing networks, Fig. 1.8 shows the impact of Industry 4.0. It becomes clear that, for successful practices, the biggest influence will be on the definition of site roles. In terms of network management, both groups of the sample expect a comparatively high influence. In this regard, for example, new tools can support decision-making or specific digital technologies can support the coordination of the network. The top five technologies to coordinate manufacturing networks are machine-to-machine communication, cloud computing, identification or communication technologies, mobile devices in production, and big data analytics (Remling & Friedli, 2019).

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In line with our results from practice, the IMN research community believes that Industry 4.0 and new technologies will change manufacturing in general and thus also influence the decision of where manufacturing will take place in the future (Cheng et al., 2019; Demeter, 2017; Ferdows, 2018). However, research currently provides little more than naming the under-researched topics. Ferdows (2018) openly addresses this research gap and suggests learning from companies that already use a high degree of data in production, such as Siemens, GE, RollsRoyce, and Honeywell, just as lean management was taught by Toyota. That explains perfectly why we adhere to our benchmarking approach and consequently integrate the technological development here.

1.2.5

The Necessity of Systematic Management of Operational Excellence

Lean management in the Western world emerged from the publication The Machine That Changed the World by Womack et al. (1991). The starting point at that time was the superiority of the Japanese auto industry over the American one. Today the term is taken further as OPEX, includes a holistic perspective, and stands for continuous improvement efforts within an organization (Friedli et al., 2012). OPEX can be explained with the St.Gallen OPEX model. It is based on the wellknown principles of the Toyota Production System (TPS), Total Quality Management (TQM), and Just-in-Time (JIT), combined with a technical subsystem and social subsystem, where the latter is enhanced through an Effective Management System (Friedli, 2010). OPEX, carried out through programs, consists of “structures, methodologies, tools and activities [. . .]” (Friedli, 2010, p. 202). Hereby, the success of such a program highly depends on the leadership and soft skills of the participants involved rather than on technical skills. Friedli (2010, p. 24) defines OPEX as follows: “[. . .] [it] constitutes the continuous pursuit of improvement of a production plant in all dimensions. Improvement is measured by balanced performance metrics comprising efficiency and effectiveness, thus providing a mutual basis for an improvement evaluation.” Due to the relevance of the topic in manufacturing networks, Chap. 3 will further elaborate on the theoretical background of OPEX. Further, Chap. 11 will show a practical example of OPEX in an IMN.

1.2.6

Adding the Global Dimension: From Site to Network Optimization

Production management is generally strongly concentrated on the site level, where it deals intensively and primarily with the production organization at a single site. The

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research stream of international manufacturing developed from this classic site perspective and stems originally from the production/operations management (P/OM) and manufacturing engineering disciplines (Cheng et al., 2015). The primary interest was the question of optimal localization of manufacturing sites in a manufacturing network. Supplemented by further site-specific questions—e.g., according to the strategic added value in the network—a research stream was established, which allows the design of sites in the context of the company’s manufacturing network. The change from traditional site-specific manufacturing systems has moved toward a total consideration of worldwide dispersed sites. Those are considered as IMNs and “are generally defined as a coordinated aggregation (network) of intra-firm plants/factories located in different places” (Cheng et al., 2019, p. 91). In this regard, factories are the nodes of a network, and managing them in isolation is not effective (Shi & Gregory, 1998). Over time, the research in international manufacturing developed following practice from international sales to international manufacturing (Cheng et al., 2015). The development of growing companies is similar in the way that they expand their offering abroad by means of export via foreign distributors and sales subsidiaries, up to local manufacturing close to the customer globally. The IMN field has steadily gained in importance as a result of the internationalization of manufacturing companies (Cheng et al., 2015). However, this global perspective within the operations management research field is addressed by a comparatively low number of researchers because of the high degree of complexity and the unsuitability of analytical models (Ferdows, 2018).

1.2.7

Summary

We described how manufacturing has developed over the three major revolutions, the contribution of manufacturing to the success of an economy, and how its importance will manifest itself in the future. The provision of employment opportunities, the contribution to R&D, and the reliance of many other jobs in other sectors are the main reasons why production is still indispensable in developed economies. However, internationalization harbors several internal complexity drivers as well as external variables that can often not be influenced. Furthermore, there is high pressure on costs, which prompts companies to relocate more and more production to best-cost countries, which in turn can endanger a healthy manufacturing base at the high-wage location. In Switzerland, for example, many companies have recognized the advantages of the location and are consciously maintaining manufacturing capacities. Accordingly, in a global network, it must always be precisely evaluated what the plants at certain locations can contribute to the network and what effects relocation decisions will have. Purely cost-based decisions neglect important factors and drive the trade-off discussions. However, not all performance dimensions are in a trade-off relationship but can also enrich one another, as illustrated in the sand cone model. Further, the topic of Industry 4.0 and digitalization has a decisive influence on IMNs because it helps to justify

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manufacturing at strategically important locations. Also, this development provides technologies to better manage globally distributed networks in particular. Accordingly, every company needs to select the appropriate technologies and apply those internally in a meaningful way. In addition to the drive of digitalization, lean management continues to play a decisive role and thus OPEX must not be neglected in the network perspective. Accordingly, the importance of viewing site-level-driven issues from a global perspective becomes clear. Orchestrating them optimally in the manufacturing network will lead to a global optimal target state.

1.3

Aim and Structure of the Book

After the success of the first book in the area of IMNs by the ITEM (Friedli et al., 2013) and almost a decade of further research including the multiple application, adaptation, and extension of the underlying frameworks in the course of several research and industry projects, it is time for a successor. This book will not replace the previous book but is rather intended as a kind of enrichment and temporal update. It will show in particular how the “St.Gallen Management Model for Global Manufacturing Networks” (Friedli et al., 2014) was applied in a practical business context, how the frameworks can be adapted to the specific individual cases, and how possible gaps in the model can be closed in the context of further discussions, such as digitalization. A new aspect is the cooperation between the ITEM and another leading institute in the field of IMNs, the Institute of Production Science (wbk) of the Karlsruhe Institute of Technology (KIT). Forces were deliberately joined for this book to bring together the expertise of both institutes. One of the reasons for this is the necessity of combining strategic and data-based approaches. Today, more and more decisions are based on data. In particular, “successful practice” companies take into account a higher bandwidth of information when developing global production strategies (see Fig. 1.9). This also had a great influence on the daily work at the ITEM in the past couple of years. The management frameworks in the previous book were over time increasingly enriched by data and the ITEM started to encourage their customers to include different functions and management levels into the projects. Consequently, the information content increased significantly and brought valuable issues to light. Data-based conclusions complemented former merely qualitative discussions with operations executives. When making decisions, the gut feeling alone is no longer sufficient. Instead, the frameworks were further operationalized and supplemented with data. One example of this is the operationalized site portfolio, which will be described in Chap. 2. Further, new opportunities from increased data availability and homogenized IT systems in combination with new technologies such as cloud computing and data analytics are arising. Accordingly, the book is structured as follows. It is divided into three parts. Part I explains the basics of global manufacturing management and OPEX. Chapters 2 and 3 lay the foundation for optimization at the site and network level.

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Financial site data

Severe benefit

Not considered

Operational site data Market / customer data Sales & operations planning data Political inf luences Technological trends Supplier data Competitor data 2 3 Sucessf ul Practice

4

5

6 Follower

7

Fig. 1.9 Information considered to develop a global production strategy [N ¼ 86]

Part II builds on that, providing numerous quantitative and qualitative models and approaches derived from practice. Chapter 4 outlines survey results on today’s contextual factors of IMNs and identifies success factors to consider in an uncertain and volatile environment. Based on this, Chap. 5 quantitatively ascertains the importance of aligning contextual factors with a network strategy. To develop a network strategy, Chap. 6 provides a conceptual approach. Coming from the strategy, the configuration context in Chap. 7 uses the example of an automotive manufacturer to illustrate the process of site selection. Chapter 8 outlines how production processes and products can be adapted to the specifics of sites using a sensor manufacturer as an example. In line with the St.Gallen approach, Chap. 9 then explains qualitative and quantitative methods for product allocation and presents a practical application at an aviation manufacturer. Chapter 10 covers a typical coordination aspect by presenting a method for order planning in IMNs. This approach will be explained based on two practical examples at an automotive and an aviation company. OPEX and quality have not yet been explicitly mentioned in the St.Gallen approach but are essential for the operation in IMNs. The two topics often discussed at site level are considered in the IMN context in the following two chapters. Chapter 11 presents an approach to measure the maturity of OPEX in one of the largest, globally operating pharmaceutical companies. Chapter 12 provides a description model to systematically analyze process quality across sites and identify improvement measures using a value stream-based approach.

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Another emerging topic that has so far been neglected in the IMN context is Industry 4.0 and digitalization. For this purpose, Chap. 13 provides a framework that helps to manage technologies on a global level, to make the right location decisions, and shows a structured technology transfer process. Further, Chap. 14 highlights data-based applications in manufacturing as well as their key challenges and key enablers to apply them successfully. The increasing availability of data and new tools arising from digitalization are in turn leading to new opportunities in performance management. Chapter 15 provides therefore an overview of how performance management can effectively be implemented not only at the site level but also in the network as a whole. The approaches described in this chapter are based on practical applications at four globally operating companies. In addition, Chap. 16 presents quantitative methods from the field of operations research to support network-relevant decisions. Finally, Chap. 17 adds a leadership perspective by explaining how site managers can be integrated into strategic and coordinative management aspects by operations executives. These insights also emerge from numerous cooperations with manufacturing companies. Part III includes chapters originating directly from companies presenting their approaches from a practitioner’s point of view. Chapter 18 describes the holistic approach of managing the production network at Bühler. Starting with the strategy, through the management of complexity, to the avoidance of risks, pragmatic concepts are listed here. The topic of digitalization and performance management is also a core topic within the chapter. Chapter 19 explains the adaptation of the St.Gallen approach for the CLAAS production network based on strategy, configuration, and coordination. Chapter 20 describes in particular the procedure of the St.Gallen approach at Palfinger and the subsequent development of the network in the following years. In Chap. 21, Lapp makes use of a framework from the St.Gallen approach to present the core topics of network optimization over a 10-year period. Chapter 22 shows the strongly quantitatively supported approach to manage the network at Jenoptik. Chapter 23 finishes with the digitalization topic of traceability within a manufacturing network. Although the chapters are aligned and most widely structured along the St.Gallen Management Model for IMNs, they don’t build on each other. This means that readers can choose the topics they are interested in and read them in random order. However, if the reader is not familiar with the St.Gallen Management Model for IMNs and new to global manufacturing management in general, we highly encourage to read this chapter and Chap. 2 first. Further we suggest to use the index function of this book to jump into the relevant sections of specific topics of interest. To avoid any possible confusion, some frequently used terms and synonyms are explained below: • Production and manufacturing throughout this book mean the creation of physical goods, whereby the creation of services can occur as a co-product, but not exclusively. Not meant, for example, is the creation of electricity, although approaches from this book can also be adapted to those industries.

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• Manufacturing sites and plants are the physical locations where manufacturing takes place. These may include several manufacturing units at one location. R&D may also be located at sites, but not exclusively. • IMN and Global Production Network (GPN) refer to a “coordinated aggregation (network) of intra-firm plants/factories located in different places” (Cheng et al., 2019, p. 91). • Operations executives or network management refers to management positions accountable for several manufacturing sites within a firm (typical position titles in practice: COO, CTO, Head of Manufacturing, Head of Global Operations, etc.).

References Bauernhansl, T., Ten Hompel, M., & Vogel-Heuser, B. (Eds.). (2014). Industrie 4.0 in Produktion, Automatisierung und Logistik: Anwendung, Technologien, Migration. Wiesbaden: Springer Vieweg. Bureau of Labor Statistics. (2020). Output by major industry sector. Employment Projections. Cheng, Y., Farooq, S., & Johansen, J. (2015). International manufacturing network: Past, present, and future. International Journal of Operations & Production Management, 35(3), 392–429. https://doi.org/10.1108/IJOPM-03-2013-0146. Cheng, Y., Farooq, S., Johansen, J., & O’Brien, C. (2019). The management of international manufacturing networks: A missing link towards total management of global networks. Production Planning & Control, 30(2–3), 91–95. https://doi.org/10.1080/09537287.2018.1534273. Coad, A., & Vezzani, A. (2017). Manufacturing the future: Is the manufacturing sector a driver of R&D, exports and productivity growth? JRC Working Papers on Corporate R&D and Innovation, 06/2017. Crosby, P. B. (1983). Quality is free: The art of making quality certain. New American Library. Demeter, K. (2017). Research in global operations management: Some highlights and potential future trends. Journal of Manufacturing Technology Management, 28(3), 324–333. https://doi. org/10.1108/JMTM-02-2017-0030. Deming, W. E. (1982). Quality, productivity, and competitive position. Cambridge, MA: Massachusetts Institute of Technology. Dertouzos, M. L. (Ed.). (1989). Made in America: Regaining the productive edge. Cambridge, MA: MIT Press. Dunning, J. H. (1980). Toward an eclectic theory of international production: Some empirical tests. Journal of International Business Studies, 11(1), 9–31. https://doi.org/10.1057/palgrave.jibs. 8490593. Edvardsson, I. R., Óskarsson, G. K., & Durst, S. (2020). The outsourcing practice among small knowledge-intensive service firms. VINE Journal of Information and Knowledge Management Systems. https://doi.org/10.1108/VJIKMS-06-2019-0083. Ferdows, K. (2018). Keeping up with growing complexity of managing global operations. International Journal of Operations & Production Management, 38(2), 390–402. https://doi.org/10. 1108/IJOPM-01-2017-0019. Ferdows, K., & De Meyer, A. (1990). Lasting improvements in manufacturing performance: In search of a new theory. Journal of Operations Management, 9(2), 168–184. https://doi.org/10. 1016/0272-6963(90)90094-T. Ferdows, K., Vereecke, A., & De Meyer, A. (2016). Delayering the global production network into congruent subnetworks. Journal of Operations Management, 41(1), 63–74. https://doi.org/10. 1016/j.jom.2015.11.006. Friedli, T. (Ed.). (2010). The pathway to operational excellence in the pharmaceutical industry: Overcoming the internal inertia. ECV.

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Friedli, T., Benninghaus, C., & Lützner, R. (2016). Industrie 4.0 from a management perspective [Benchmarking report]. Institute of Technology Management. Friedli, T., Budde, L., Benninghaus, C., Elbe, C., & Pejic, T. (2018a). Digital Technologies: Evolution of production in high-wage countries [Benchmarking report]. Institute of Technology Management. Friedli, T., Elbe, C., & Remling, D. (2018b). Hochlohnstandorte brauchen Industrie 4.0. Produktion - Technik Und Wirtschaft Für Die Deutsche Industrie, 31. Friedli, T., Lanza, G., Remling, D., Kaiser, J., Verhaelen, B., & Benfer, M. (2020). Managing global production networks in today’s business environment [Benchmarking report]. Institute of Technology Management. Friedli, T., Mundt, A., & Thomas, S. (2014). Strategic management of global manufacturing networks. Berlin: Springer. https://doi.org/10.1007/978-3-642-34185-4. Friedli, T., Schuh, G., & Mundt, A. (2012). Wettbewerbsfähigkeit der Produktion an Hochlohnstandorten (2. Aufl). Wiesbaden: Springer Vieweg. Friedli, T., Thomas, S., Mundt, A., & Lützner, R. (2013). Management globaler Produktionsnetzwerke: Strategie, Konfiguration, Koordination. Munich: Hanser. Garvin, D. A. (1988). Managing quality: The strategic and competitive edge. London: Free Press. Hallward-Driemeier, M., & Nayyar, G. (2017). Trouble in the making?: The future of manufacturing-led development. World Bank. Kaldor, N. (1966). Marginal productivity and the macro-economic theories of distribution: Comment on Samuelson and Modigliani. The Review of Economic Studies, 33(4), 309. https://doi. org/10.2307/2974428. Lanza, G., Ferdows, K., Kara, S., Mourtzis, D., Schuh, G., Váncza, J., Wang, L., & Wiendahl, H.-P. (2019). Global production networks: Design and operation. CIRP Annals, 68(2), 823–841. https://doi.org/10.1016/j.cirp.2019.05.008. Lian, L., & Ma, H. (2011). The magnification effects of intra-firm trade of multinational corporations. International Journal of Business Administration, 2(3), 94. https://doi.org/10. 5430/ijba.v2n3p94. Locke, R. M., & Wellhausen, R. L. (Eds.). (2014). Production in the innovation economy. Cambridge, MA: MIT Press. Mancini, M., Namysl, W., Pardo, R., & Ramaswamy, S. (2017). The great remake: Manufacturing for modern times. McKinsey & Company. Manyika, J., Sinclair, J., Dobbs, R., Strube, G., Rassey, L., Mischke, J., Remes, J., Roxburgh, C., George, K., O’Halloran, D., & Ramaswamy, S. (2012). Manufacturing the future: The next era of global growth and innovation. McKinsey & Company. Naudé, W., & Szirmai, A. (2012). The importance of manufacturing in economic development: Past, present and future perspectives.. UNU-MERIT. OECD. (2020). Value added by activity (indicator). https://doi.org/10.1787/a8b2bd2b-en. Remling, D., Elbe, C., & Friedli, T. (2020). Werkplatz Schweiz und Digitaliserung: Von der Einsicht zur Auswirkung. Technische Rundschau Das Schweizer Industriemagazin, 112, 6–9. Remling, D., & Friedli, T. (2019). Digital technologies to coordinate manufacturing networks: A Swiss pioneering perspective. EurOMA Conference Operations Adding Value to Society, 26, 1781–1790. Shi, Y., & Gregory, M. (1998). International manufacturing networks-to develop global competitive capabilities. Journal of Operations Management, 16(2–3), 195–214. https://doi.org/10. 1016/S0272-6963(97)00038-7. Smil, V. (2013). Made in the USA: The rise and retreat of American manufacturing. Cambridge, MA: The MIT Press.

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Introduction

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UNCTAD. (2018). Global value chain and development: Investment and value added trade in the global economy. United Nations Publications. Vereecke, A., Van Dierdonck, R., & De Meyer, A. (2006). A typology of plants in global manufacturing networks. Management Science, 52(11), 1737–1750. https://doi.org/10.1287/ mnsc.1060.0582. Womack, J. P., Jones, D. T., & Roos, D. (1991). The machine that changed the world: How Japan’s secret weapon in the global auto wars will revolutionize western industry. Harper Perennial. WTO. (2020, September 15). Evolution of trade under the WTO: Handy statistics. World Trade Organization.

Part I The Basics of Global Manufacturing Management

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The St.Gallen Management Model for International Manufacturing Networks Dominik Remling and Thomas Friedli

In this chapter, the St.Gallen Management Model for International Manufacturing Networks will be described along the three layers of strategy, configuration, and coordination as well as the necessary strategic fit between them. The idea of the framework is to provide operations executives with a discussion framework that enables them to address the key aspects of managing global manufacturing networks and derive conclusions. The numerous underlying concepts provide a pragmatic way to elaborate company-specific solutions based on a transparent overview about the as-is situation of the network as well as the requirements for future competitiveness.

2.1

Overview

The approach to analyze and optimize International Manufacturing Networks (IMNs) originated out of several research and industry projects conducted by the Institute of Technology Management (ITEM), University of St.Gallen, and was first published in German in 2013 (Friedli et al., 2013). While scientifically derived, the framework is intended to be applied and used by practitioners. The English version followed in 2014 in order to promote the concept internationally (Friedli et al., 2014). Over the following years, the concept became more and more widespread in the manufacturing industry. Companies have applied the model or parts of it internally, either inspired by the publication or in direct collaboration with the research team at the ITEM. The triggers for the use of this approach can be manifold. The motives range from refining corporate strategies, restructuring efforts, need to increase specific D. Remling (*) · T. Friedli Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland e-mail: [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_2

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Manufacturing Priorities

Network Capabilities

Strategy

Strategic Gaps Competence Allocation

Plant Allocation

Configuration Product Allocation

Process Allocation

Incentive System

Degree of Centralization & Standardization

Coordination Resource Sharing

Information & Knowledge exchange

Fig. 2.1 St.Gallen Management Model for International Manufacturing Networks. Adapted from Strategic Management of Global Manufacturing Networks, by T. Friedli et al., 2014, Berlin Heidelberg: Springer

performance attributes, and post-merger integration to the reconciliation of already existing approaches to ensure the correct path is followed. Further, for globally producing companies seeking to reap the benefits of a harmonized production network, the St.Gallen approach is also a valid option for regularly revisiting the taken decisions. This is probably the biggest lesson learned from the large number of projects undertaken. Consistent application over several years helps companies not only to adapt systematically to changing environmental conditions (e.g., to a pandemic) but also to ensure that the framework itself is constantly challenged and modified if needed. The long-term proactive perspective given by the consideration of a sound global manufacturing strategy can even prevent reactive and costly short-term restructurings at one point or another. Beyond that, various insights emerged during the application by the ITEM regarding the three superior layers of the framework. The framework presented in the following section is accordingly the updated version, enriched by the empirical work of practical application in previous years (see Fig. 2.1). The framework presented in Fig. 2.1 is a transition from a structural to a procedural framework. The three levels of strategy, configuration, and coordination are still important, but they are regarded as iterative steps. Within each of the three levels, consecutive steps are required. This logic corresponds to the application of the framework in practice. The consecutive application will be described in the following: • Strategy: The process starts with the clarification of the strategic baseline and evaluation of the Manufacturing Priorities. Thereafter, the Network Capabilities are elaborated. By comparing Network Capabilities and Manufacturing

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Priorities, strategic gaps of the network are identified, which need to be closed by the levers “configuration” and “coordination.” • Configuration: After the strategic starting position has been clarified, the optimal number and localization of the sites is determined first (see Chap. 7 for more detailed information on choosing optimal site locations). This is followed by the allocation of the most appropriate processes, products, and necessary competencies (see Chap. 9 for a quantitative product allocation approach). In practice (not coming from a greenfield approach), the configuration lever should be run through several times to ensure an optimal fit between allocated processes, products, and competencies. Once the structure is finally set, the respective site roles are determined. • Coordination: After the configurative setup has been defined, the coordination level can be elaborated accordingly. In this stage, the degree of Centralization and Standardization is determined, before further structures for the exchange of information and knowledge, the sharing of resources, and the setting of incentives are specified. Finally, the initial strategy discussion is resumed in order to establish a strategic fit and to check whether the measures taken within the levers configuration and coordination are in harmony with each other and can fill the derived strategic gaps.

2.2

Manufacturing and Network Strategy

2.2.1

Unit of Analysis

As the complexity of IMNs has increased continuously over the years, it is essential to find a sound unit of analysis first. This is usually the start of network analysis as part of network optimization. Depending on the specific company, selecting the entire company for analysis might be too complex. Consequently, a project scope that is suitable must be defined. It is conceivable to execute the narrowed network analysis then as a pilot project referring to one defined unit of analysis. This helps to gain experience with the methodology and to subsequently transfer the approach to other subnetworks. In many cases, the unit of analysis is derived from the already existing structures of the company. The unit of analysis can be, for example, all sites of a company, a division, a business unit, or other individual areas of responsibility. However, the demarcation may not be clear if, for example, different business units operate at the same sites (see Lützner, 2017, for more background information on shared factories). Furthermore, many companies sell both high-end and commodity products in order to generate economies of scope. This also leads to a high degree of complexity involving conflicting goals within the unit of analysis. Chapter 1 already highlighted the exposure of IMNs to several conflicting goals. The most common conflicting goals occur between cost, quality, time, and flexibility. Consequently, the magic

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D. Remling and T. Friedli Complex / Proprietary Production Processes

Process Innovation Subnetworks Lead with process technology

Rooted Subnetworks Integrate new product and process technologies

Simple / Standardized Products

Complex / Proprietary Products

Footloose Subnetworks Coordinate production in the network

Low Investment Subnetworks Protect proprietary product knowledge

Simple / Standardized Production Processes

Fig. 2.2 Framework for gauging plant subnetworks. Reprinted from “Delayering the global production network into congruent subnetworks.” By K. Ferdows, A. Vereecke, A. de Meyer, 2016, Journal of Operations Management, 41, 1, pp. 63–74

triangle of production also plays a role in IMNs. To resolve these trade-offs, Skinner’s (1974) seminal work proposed the focused factory. He observed that a factory performs better when it focuses on specific manufacturing outputs. Ferdows et al. (2016, p. 64) advanced Skinner’s approach to manufacturing networks: “The notion of focus, with a few modifications, can be applied also to a group of factories that work together to accomplish a manufacturing mission.” That means manufacturing networks with a focus on specific manufacturing outputs perform better than those trying to achieve multiple conflicting goals. Accordingly, it makes sense to divide networks by comparable manufacturing outputs. Further, Ferdows et al. propose a framework (see Fig. 2.2) to divide manufacturing networks and refer to so-called congruent subnetworks, implying a coherent production strategy and the corresponding capabilities to achieve it within the network (2016). Ideally, congruent subnetworks are located on a diagonal line in the framework, starting from low product and process complexity and progressing to high product and process complexity. By dividing networks into subnetworks, the complexity and thus the effort for management and coordination can be reduced. The authors of the framework believe that more such frameworks are needed to reduce complexity instead of developing more profound optimization models (Ferdows et al., 2016). Those are useful for operational/tactical tasks rather than for strategic tasks. In line with Ferdows et al. (2016), we recommend breaking up the given structures and organization charts within companies and to create meaningful

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Table 2.1 Manufacturing priorities Meeting or beating customers’ expected price levels. Being price competitive.

Price Quality Specification Conformance Delivery Speed Reliability Flexibility Design (product range) Volume (order size) Innovation (product/ process) Service

Providing products whose features fully meet or exceed customers’ requirements. Providing products to their defined specifications reliably and consistently. Providing good, consistent, and stable product quality. Meeting and even exceeding the expected delivery speed. Being fast. Keeping delivery promises on time and in full. Being reliable. Providing a wide product range or developing new designs quickly to meet every customer’s specific wish. Meeting customers’ individual product expectations. Changing order sizes or delivery times quickly if required by the customer. Being fast and flexible to customer’s delivery expectations. Providing innovative and novel products (by such processes) or products that enable the customer to be innovative. Being innovative or enabling innovation. Enhancing products with additional service offers or providing outstanding customer service. Being service-oriented.

Note: Adapted from Slack and Lewis (2002), Miltenburg (2005)

complexity-reducing units of analysis. Certainly, it is possible that the prevailing structures also allow a reasonable division. However, this is not our experience, since other criteria than manufacturing outputs also play a role in the definition of, for example, business units. One reasonable approach to define subnetworks is to follow the Manufacturing Priorities concept (see Friedli et al., 2014). Manufacturing Priorities are directly derived from the respective markets and customers and define where a company must excel to sell its products (see Table 2.1). We assume that similar Manufacturing Priorities that are required by customers are a good basis to determine the partition of a network. Thus, subnetworks can have a specific focus on cost, quality, reliability, flexibility, etc. In this case, a best cost-oriented subnetwork, for example, can focus on the make-to-stock production. This means that the network is separated according to its production principles. However, various other classifications are also possible for the partition based on the Manufacturing Priorities.

2.2.2

Manufacturing Priorities

Regardless whether the unit of analysis has already been defined, the first step in the practical application of network analysis is to assess the Manufacturing Priorities (see Table 2.1) based on, e.g., specific product clusters, specific customer segments, or specific markets. Product clusters may consist of the company’s product groups

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D. Remling and T. Friedli Lighthouse products

Important for not losing orders

Main products Commodity products

t

an

ific ign Ins

r de Or

qu

r fie ali

Important for winning orders

y y rit ity rit r al rio er ior er rio er itic ifie tp pr n h p winn Cr qual es winn w win g h i o g L er r H er Hi rder d d de o or or or

Manufacturing Priorities

Price Specification Quality Conformance Speed Delivery Reliability Design flexibility (product range) Flexibility Volume flexibility (order size) Innovation

Product/process

Service

Fig. 2.3 Exemplary Manufacturing Priorities framework. Adapted from Strategic Management of Global Manufacturing Networks, by T. Friedli et al., 2014, Berlin Heidelberg: Springer

but also clusters formed using the Pareto principle can be used, for example, by focusing on the top five products contributing more than 80% of turnover. It is recommended to include market and product experts with good knowledge about the market environment, current trends, and customer requirements when assessing the Manufacturing Priorities. A common method to assess the Manufacturing Priorities is to conduct interviews or group discussions. Here it is especially valuable to include several departments or functions, such as product managers, key account managers, research and development, and sales and marketing together with the operations executives. It adds additional value to bring different functions together since such exchange can be rather rare in corporate environments and enables, on the one hand, the operations department to better understand the ultimate customer requirements and, on the other hand, the market experts to understand the issues of operations. In Fig. 2.3 you can see a typical example of three assessed product groups. In this example, three product groups were identified: lighthouse products, main products, and commodity products. To get a common understanding of the Manufacturing Priorities, they are classified along with an order qualifier/order winner logic. Order qualifier means that a criterion must be met to a certain degree but performance above that degree is not further appreciated by the customer. In contrast, an increase in performance of an order winner criterion has a positive impact on the business and enables a company to win orders against its competitors. Furthermore, we included a nuance in the framework that distinguishes between order qualifiers, critical order qualifiers, and a three-point scale for order winners. To force a clear prioritization, we set the rule that for each product category, there can be only one highest priority winner and a maximum of two high-priority order winners. Coming back to our example, we can see that for commodity products, price and speed are most important to win orders, whereas most other criteria are order qualifiers. This applies in the real world, for example, for the manufacturing of standard components, such as screws. It is crucial to adhere to common norms and

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quality standards to be able to supply the market. If those criteria are not met, the customer won’t buy those products. Being in the market, a competitive advantage can be achieved by being cheaper than the competitors offering similar products. For main products, it is necessary to offer certain design flexibility for the customers, and for lighthouse products, innovation is most important. However, both product categories need to adhere to the expected quality standards; otherwise, the customer will also not buy the product. To our experience, quality conformance is mostly an order qualifier or critical order qualifier, except the competition is not able to achieve consistent quality. The same applies to delivery reliability. In the automotive industry, for instance, speed is not as important as in other industries since delivery quantities and dates are often transparent and set months in advance. However, it is crucial to be able to deliver just in time or even just in sequence, which typically makes delivery reliability a critical order qualifier. This means that a company should not only aim for the high-priority winners, but that it is also necessary to maintain the order qualifiers at the same time. The example shows three different product categories, which form the basis to divide the network accordingly. While assessing the Manufacturing Priorities, it is crucial to conduct thorough documentation of the discussion and to write down the reasons for each dot’s position. This is important to make decisions transparent and to justify the subsequent steps of the network analysis.

2.2.3

Network Capabilities

After deriving meaningful subnetworks and assessing the Manufacturing Priorities for each of them, the next step is to assess the prevailing Network Capabilities for each subnetwork (see Friedli et al., 2014, for more background information on Network Capabilities). The Network Capabilities describe the strategic network orientation to be achieved along the dimensions of access to markets, access to resources, efficiency, learning, and mobility (see Table 2.2). Please refer to Chap. 5 if you are interested in how Network Capabilities relate to manufacturers’ value generation. Part of the network analysis is the assessment of the Network Capabilities. The assessment is based on the Network Capability framework (see Fig. 2.4), which includes a five-point Likert scale to define the current and intended contribution of each capability. The best way to assess the network capabilities is to conduct a workshop together with the responsible executives of the network or subnetwork. We sometimes also include the site managers within the discussion, but this depends on the leadership style of each company (participative vs. authoritative). Furthermore, it needs to be considered that an effective discussion is not guaranteed if too many participants are included. Thus, the participants for this task must be chosen wisely in every individual case.

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Table 2.2 Network capabilities Assure access to strategic market and competitive factors, like . . .

Markets/ customers Competitors

Sociopolitical factors

Image Assure access to resources of strategic importance, like . . .

Supplier/raw material

Best cost labor Skilled labor External knowhow

Increase efficiency by . . .

Economies of scale Economies of scope Reduction of duplication

Provide mobility of . . .

Processes, products, and personnel Production volume and orders

Explore and exploit know-how and innovation about . . .

External factors

Internal factors

The network provides access/proximity to markets and customers The network provides access/proximity to competitors to fight them in their markets The network enables to benefit from sociopolitical factors such as to overcome trade barriers, to hedge exchange rate fluctuations, to exploit financial subsidies, etc. The network enables to benefit from image factors such as “made in ...” The network provides access to suppliers, for example, to assure local low-cost supply, rapid delivery, highquality raw material, etc. The network provides access to a cheap workforce The network provides access to a highly qualified workforce The network provides access to external know-how such as universities, competence clusters, engineering services, etc. The network provides cost benefits by concentrating on identical products The network provides cost benefits by concentrating products with similar manufacturing processes The network provides cost benefits by concentrating support functions, administrative functions, etc. The network enables a flexible and fast transfer of products, production processes, machines, and personnel between the sites The network provides a flexible and fast exchange of production volume between the sites or flexible order allocation The network provides the possibility to unlock and share knowledge about external factors such as local market needs and customer expectations, buying behavior, cultural aspects, etc. The network provides the possibility to unlock and share knowledge about internal factors such as local improvements, best practices, technology improvements, etc.

Note: Adapted from Shi and Gregory (1998), Miltenburg (2005)

The St.Gallen Management Model for International Manufacturing Networks

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Very high

Current contribution of the network (As-Is)

Very low

2

Importance for being competitive (To-Be) 









Markets/customers

Assure access to strategic Competitors market and competitive factors, Socio-political factors like … Image Supplier/raw material Assure access to resources of strategic importance, like …

Best cost labor Skilled labor External know-how Economies of scale

Increase efficiency by … Provide mobility of … Explore and exploit know-how and innovation about …

Economies of scope Reduction of duplication Processes, products & personnel Production volume & orders External factors Internal factors

Fig. 2.4 Exemplary Network Capability framework. Adapted from Strategic Management of Global Manufacturing Networks, by T. Friedli et al., 2014, Berlin Heidelberg: Springer

2.2.4

Strategic Gaps

After assessing the current situation (as-is state) of the network, the strategic gaps as improvement potentials should be elaborated. To do this, the subnetwork is to be compared with the respective Manufacturing Priorities required by the customers of the network. We chose to depict the Network Capabilities of the commodity product network according to the Manufacturing Priorities of the commodity products shown in Fig. 2.3 as an example in Fig. 2.4. As you can see in Fig. 2.4, the arrows display the improvement potentials. The first arrow targets to the left, which means that there is an over-fulfillment of this specific capability. In this case, we could argue that being close to the customer is not necessary to fulfill short delivery times, since the product can be transferred via airfreight. Being close to the customer would mean the company would need to have many sites around the world, which would eventually impede its capability to achieve high economies of scale. Another factor that should be increased is access to sociopolitical factors. In doing this, the company can achieve lower costs by avoiding trade barriers and high taxes through an accordingly aligned network footprint. Access to best cost labor is already on a high level but still could be improved since labor costs have increased in countries that were targeted as best cost countries previously. Further factors to achieve low prices are focusing on economies of scale and exploiting internal knowhow through knowledge exchange. The example shows how to apply the framework. We refrain from giving further examples for the product group’s main products and lighthouse products. Bringing the two frameworks to assess the Manufacturing Priorities and Network Capabilities together is a crucial final step to align the necessary capabilities with the customer requirements. If a customer, for example, expects low price products, the implication should be to have access to and to exploit best cost labor in the network. For each

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individual customer requirement, whether order qualifier or order winner, the implication should be specified for the network. Further, the Network Capability framework is also well suited to assess the contribution of each site to the network. This is an effective task to increase the awareness and understanding of the benefits of a manufacturing network for site managers. We did this, for example, in global site manager meetings and printed out a framework for each plant manager to indicate what their contribution to the network was. This often resulted in very interesting insights in the form of widely different perceptions between site managers and operations executives. One operations executive stated: “If our low-cost sites start to employ engineers because they see themselves as competence leaders, they miss their task to have simple work steps carried out by cheap labor.” As a consequence, the intended role of a site is sometimes not clear to the site managers, and the proposed task using our framework leads to creating this awareness in the organization. If you are further interested in engaging site managers for the manufacturing network, please refer to Chap. 17.

2.3

The Configuration Lever

The configurative lever describes the structure of an IMN, including the specific locations and allocation of resources (Meijboom & Vos, 1997). We speak of a configuration “lever” since this is one of the two design levers to address the strategic gaps out of the Network Capability discussion. It is important to note that our configuration approach is not a greenfield planning of the network footprint, as it is often applied for layout planning of new sites. Rather, we first analyze the current state of the network configuration to derive feasible solutions that address the strategic gaps based on the initial situation. In this context, we utilize the Site Portfolio approach, which we have used as a decision support tool in numerous projects. It is always impressive to see how much added value the visualization of data in the form of the Site Portfolio offers for decision-making in this context. The approach will be described in the following section.

2.3.1

Site Portfolio Framework

A visualization of the Site Portfolio framework is shown in Fig. 2.5. The Site Portfolio is a hexagonal playing field on which the sites can be arranged like tokens. For this purpose, the strategic site reasons are listed on the outside, which determine the corner or edge of the playing field in which a site is located. Also, the level of competence determines whether a location is positioned further inside or further outside on the playing field. The processes carried out and their utilization of products and product performance are visible on each token in the form of a colored wedge. In addition, a small star on each wedge indicates whether a site has a superior rank in the network about a process. Further, certain areas can be marked on the playing field, which is useful for assigning site roles.

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Proximity to market Competence Sites

s

te

ad

Site 4

Si

Le

Site 3

Emerging Sites Em Site 6 Site 7

IV. Focused Follower

to ss t ce s Ac w-co lo

Site 1

III. AllroundFollower

sk

II. Focused Performer

A ills cces & k s to no w-h ow

Site 5

Site 2

I. AllroundPerformer

Processes performed by the sites Site token diameter Process n

Process 1

Capacity Process …

Process 2

Process/activity performed for the network

Utilization Process 4

Process 3

Fig. 2.5 Exemplary Site Portfolio. Adapted from Strategic Management of Global Manufacturing Networks, by T. Friedli et al., 2014, Berlin Heidelberg: Springer

Once the current state of a network’s configuration is depicted, the basis for modifications is provided. The Site Portfolio can be applied in the context of the four decision-making layers according to the St.Gallen Management Model for IMNs introduced at the beginning of this chapter. The first layer concerns the allocation of sites on the world map. At this point, typical decision situations cover the consideration of whether existing sites should be discontinued and which ones are particularly suitable to enhance, for example, the Network Capability “economies of scale” or whether new sites are needed to ensure “access to market.” The second layer concerns the allocation of products to the sites. In this regard too, the Network Capability to be influenced plays a key role, for example, if redundancies are deliberately created to increase the resilience of the network. Next, processes are allocated to sites so that they can manufacture the respective products. Competence levels are assigned to the sites accordingly. The combination of product, process, and competence level should be coherent according to a Focused Factory (Skinner, 1974). In practice, this process is highly iterative, running through each level several times. However, it is not necessarily required to start with the product allocation. In some cases, the visualization of the Site Portfolio already indicates which site has a

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high level of competence, automatically assigning in particular complex products. Nevertheless, it must be taken into account that the Site Portfolio is dynamic over time and that not only the products change but also processes, technologies, and ultimately the capabilities of the sites. Accordingly, the definition of the target state of a network configuration should not end with the assignment of the currently manufactured product portfolio but should comprehensively cover the view into the future.

2.3.2

Operationalizing the Site Portfolio

The Site Portfolio is built up in three steps and can be used qualitatively as well as quantitatively through operationalizations. However, we recommend the latter variant to ensure a data-based decision basis. First, one or several perspectives for the overall Site Portfolio depiction has to be chosen. This determines the displayed sites of the network on the Site Portfolio. Afterward, the dimensions of the playing field must be defined, which determines the position of the sites. In the third step, the site tokens are configured. These steps are described in detail in the following. We will also provide the operationalizations we usually applied within our projects. Step 1 Select Network Depiction Perspectives Before the Site Portfolio is set up, it must be decided which network or subnetwork should be mapped in the first step. This is a similar consideration to the network delayering mentioned in Sect. 2.2. Here, however, the decision situation rather than complexity reduction should be in the foreground. From our experience, it makes sense to display an overall network and to display the subnetworks formed in the strategy discussion separately. However, other representations are also possible, such as regional Site Portfolios or production principle (make-tostock vs. engineer-to-order) subnetworks. It is important to mention that this step makes it possible to question the existing organizational structures by deliberately mapping cross-structural Site Portfolios. For example, in product-oriented organization structures, it can be meaningful to create Site Portfolios for different process steps within the value chain to reveal hidden synergies. Step 2 Playing Field Structuring The design of the Site Portfolio requires the definition of the playing field structure, where the sites will be allocated. The operationalization of the strategic site reasons and the classification level will determine the position of the sites within the framework. Both concepts are described in the following section. (a) Edges (Strategic Site Reasons) The second step initially involves the concept of strategic site reasons. The strategic site reasons indicate the mission of a plant given its location and thus its exploited access factors. Within the Site Portfolio, we focus on the three most

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important site reasons—access to markets, access to know-how, and access to best cost—which we are already familiar with in the context of the Network Capabilities (Miltenburg, 2005; Shi & Gregory, 1998). If other access factors, such as access to suppliers, are important for a network, they can be exchanged, provided a reasonable depiction is granted. A site can fulfill several access factors, but in the sense of a focused factory, we reduce the exploitable access factors to a maximum of two per site. For example, a manufacturing site in China can have all three strategic site reasons—“access to market,” “access to best costs,” as well as “access to skills and knowledge”—at the same time. However, what the site should concentrate on should be clearly defined. The following three examples cover three different possible orientations. First, if the site’s mission is to serve the local market as cost-effectively as possible, the access factors “access to best cost” and “access to market” should be focused on by employing low-skilled employees to manufacture products for the local market. Second, however, if the site is intended to manufacture sophisticated products for the local market, the access factors “access to market” and “access to skills and knowledge” should be exploited by hiring a higher number of engineers. Third, if the site’s mission is to contribute to the overall network by conducting process development tasks for the entire network, the site should exploit “access to best cost” and “access to skills and knowledge.” In this case, the site would contribute globally comparatively cost-effective but at the same time highly skilled work to the network. The clarification of the strategic site reasons will accordingly influence, among other things, personnel decisions and the allocation of investments at the site. Further, it should be noted that the strategic site reason can also change with the network perspective. For example, Eastern European sites can provide “access to best cost” from a regional perspective, whereas from a global perspective, there are comparatively more cost-effective locations, and therefore, Eastern European sites can then be classified as having “access to (the European) market.” To assess which strategic site reasons exist in a site, we suggest the operationalizations shown in Table 2.3. Based on the existing site reasons, it can then be decided which should be exploited as a priority. However, other strategic site reasons can also be considered, as suggested in Fig. 2.6. (b) Inner Circles (Classification Level) After the strategic site reasons have been defined and operationalized, the site classification level must be defined, too. The classification level differentiates the sites according to their competencies using four levels and can include a wide variety of dimensions. Initially, a decision can be taken as to whether one four-point scale or two combined two-point scales are preferred. For example, according to the former variant, a four-level competency level can be defined to classify the sites (low/medium/high/very high competency). Alternatively, for the latter case, the competence level can also be defined in two levels (low/high competence) and combined with another dimension like product diversity (low/high number of manufactured products). In this case, the sites would be classified according to the

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Table 2.3 Operationalization of strategic site reasons Strategic site reason Access to market Access to best cost

Access to skills and knowledge

Step 1

2.1

Network Delayering

Playing Field Structuring

Dimension Perspective Edges (3 Strategic Site Reasons)

2.2

Inner Circles (Classification Level)

3.1

Wedges

3.2

Inner Diameter

3.3 3.4

Site Structuring

Operationalization
Local distribution share
Local sales share
Average blue-collar costs
Average white-collar costs
Energy costs (electricity, gas, etc.)
Raw material/supply costs
Availability of low-/high-qualified workforce
Competitive situation regarding other companies also looking for employees
Average onboarding time for new employees
Share of education level within site
Innovativeness of local environment
Relevance of locally available knowledge
Exploitation of locally available knowledge

Outer Diameter Star Label

Site Portfolio Characteristics Regional subnetwork

Global network

Divisional subnetwork

Production principle subnetwork

…

Access to… markets/ customers

competitors

Financial Performance Products

sociopolitical factors

Strategic Importance Processes

Operational performance Turnover Activity performed for the network

image

Bandwidth of products

suppliers / raw material

best cost labor

Bandwidth of processes

Technologies

Product capability Assets

Utilization (OEE) Value added

Standards defined for the network

skilled labor

external knowhow

…

Process capability

…

Resources

…

Productivity Maximum capacity

Center of competence

… … …

Fig. 2.6 Morphological box to operationalize the Site Portfolio

two scales in a two-cross-two matrix. The former variant is often easier to understand, but the choice depends strongly on the company context and should reflect the actual picture as realistically as possible instead of simplifying it. Figure 2.6 shows exemplary dimensions for the site classification level (see row 2.2). Step 3 Site Structuring The last step of building the Site Portfolio is to design the site tokens, which can depict specific site-related measures to support the configurative decisions. In total, four dimensions are usually incorporated, described as follows.

2

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(a) Wedges The first dimension includes the “pieces of cake,” as we affectionately call them, which can represent what kind of product, process, or asset—to name a few examples—is physically present in the site. The wedges are usually marked in color or with a texture and represent a presence or absence of the specific dimension. This helps to identify duplicates in the network and to facilitate decisions regarding Product Allocation or Resource Sharing. (b) Inner Diameter The inner diameter can refer to a single wedge or the whole circle. The first option can refer to the individual dimension chosen for the wedge. For example, in the case of processes, a process utilization can be listed for each individual process. This example is shown in the exemplary Site Portfolio in Fig. 2.5. However, since the utilization is difficult to measure for indirect processes, a mean utilization for the direct processes averaged over the respective site can also be calculated and represent, in this case, the whole inner diameter. The values of the inner diameter are usually given as a ratio and therefore represent a percentage of the outer diameter. For example, the wedge would reach the outer diameter at 100% utilization (see Process 1 at Site 1 in Fig. 2.5). Figure 2.6 shows additional examples beyond the utilization for defining the inner diameter. The measures greatly depend on the chosen representation of the wedges. (c) Outer Diameter The outer diameter refers to the whole outer circle of the site token and relates to the diameters of the other site tokens. This helps to ensure a relation between the sites. A useful reference value is the turnover of the products manufactured at the site or the value-added generated in each case. The maximum available capacity of the site is also reasonable as a size indicator (see Fig. 2.6 for further examples). (d) Star Label The star label indicates a certain superiority in comparison to other sites. This can be, for example, a process that is also performed for other sites, the definition of standards that become valid for other sites, or a center of competence that supports other sites in a certain way (see Fig. 2.6). The possibilities are also infinite and depend strongly on the dimension chosen for the wedges. Operationalization can be carried out via corresponding quantitative dimensions. However, measurability becomes a challenge at one point or another, especially when it comes to making indicators comparable across sites. A pragmatic but less precise approach is to use qualitative data for operationalization, for example, based on Likert scales. An indicator for a well-chosen operationalization is the full exploitation of the scales. It makes little sense, for example, to use the utilization

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as an axis from 0% to 100%, if all sites vary between 85% and 95%. In this case, the scale should be restricted or normalized. For Likert scales, sites should aspire to full exploitation. Once operationalization is accomplished and the sites are visible in the Site Portfolio, specific areas on the playing field can be defined that indicate a higher role of the sites according to the site role concept (see also Ferdows, 1997). The exemplary Site Portfolio in Fig. 2.4 shows three different target areas. The emerging sites feature a lower performance than other sites within the network. The competence sites exhibit higher performance, whereas those sites achieving a high degree of performance and access to skills and know-how are considered as lead sites. Depending on the site role, different responsibilities and resources can be allocated to the specific site. Further, the site role concept features a dynamic perspective, meaning that sites can achieve higher roles over time. The Site Portfolio approach combined with the site role concept can transparently show a pathway toward higher roles within the network. Thus, it is possible to create certain incentives to develop in a certain direction as a site.

2.3.3

Application of the Site Portfolio

In practice, the Site Portfolio approach is used to show the current state of the network on the one hand and to define the development direction for the network on the other hand. Therefore, the configuration layer of the St.Gallen Management Model for IMNs can be applied as a process utilizing the Site Portfolio as a decision basis (see Fig. 2.1). In the first step, the sites are allocated. Therefore, the Site Portfolio shows if there are certain gaps and if additional sites are needed or if sites are superfluous. In the next step, processes are allocated to the sites, which depends among other things on the existing competence level. Depending on the assigned processes the products can then be allocated. Based on the defined process and product portfolio, each site should receive the necessary resources to develop the required competence level. This approach manifests itself as a closed iterative process using the current state of the manufacturing network to define the future state of it. In Chap. 19, we explain how the Site Portfolio approach is implemented and utilized in a practical example. However, one of the limitations of the Site Portfolio is that a suitable visualization is restricted to a certain number of sites. If more than 20 sites are depicted, the Site Portfolio often becomes too complex, so the network should either be divided into subnetworks or can be limited to a simplified two-dimensional matrix. To focus on the classification level can be one solution for the simplification for the latter case (see Fig. 2.7). This allows viewing of more than 20 sites at a glance. As already shown, in each of these two dimensions, several variables can be included. A common example in practice is the consideration of sites based on their financial performance, such as gross margin, combined with their strategic importance. The latter can, for example, be composed of the dimensions of knowledge

2

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Fig. 2.7 Simplified classification for networks operating many (20+) sites

outflow, competence level, innovativeness, and/or market growth rate. We have successfully applied this approach in a project for an automotive supplier with more than 50 sites (see Fig. 2.7). The combination of the two dimensions provides the network manager with a direct indicator of which sites contribute negatively to the result. Further, it shows which sites have high strategic importance and thus justify the negative result (Question marks), which sites are important and perform well (Prime Sites), and which sites are not strategically important but contribute positively to the result (Cash Cows). For sites that do not fulfill either of these dimensions to a sufficient extent (Basic Sites), development solutions must be found. Those can aim at either increasing the strategic relevance or improving the operating result. Figure 2.6 shows an example of a simplified classification for large manufacturing networks operating many sites. Our vision is to implement the Site Portfolio according to the Online Analytical Processing (OLAP) logic. OLAP retrieves data directly from the operational databases or a central data warehouse. The aim of OLAP is to obtain an analysis result that supports decision-making through a multidimensional view of the data. The implementation follows a multidimensional, data point-oriented logic (Gluchowski & Chamoni, 2006). This means that decision-makers have the possibility of combining the dimensions shown in the morphological box (see Fig. 2.6) as required depending on the context of the decision. In addition, the Site Portfolio can be updated in real time, show the status at different points in time, or even show the development over time. The OLAP-based Site Portfolio is being implemented as part of a research project co-financed by Innosuisse. Please refer to Chap. 15 for further reading.

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The Coordination Lever

Our concept of coordination manifests itself in the definition of structures for guiding and influencing the behavior of the actors within an IMN. The coordination can also be described as the governance of the network to regulate hierarchy relationships, promote cooperation and competition, and set incentives to achieve goals. Consequently, the coordination lever is divided into four main concepts.

2.4.1

Centralization and Standardization

The first concept comprises the degree of Centralization and Standardization. It is strongly connected with the organizational structure and the allocation of responsibilities in the form of competencies. Before organizational structures are defined, it makes sense to apply the Centralization and Standardization framework. The framework consists of one axis for the degree of centralization and another axis for the degree of standardization. Within the framework, systems, decisions, and processes are allocated. Centralization is related to the assignment of responsibility which may be held by a central unit (e.g., headquarters) or delegated to a region, selected sites, or each site individually in the network. Responsibility can be held for either definition of systems, taking decisions, or the definition of processes. The standardization axis differs for systems and decisions/processes. Standardization of systems ranges from using individual tools at each site to standard tools with homogenous implementation in the network. Standardization of decisions and processes ranges from only local standards to controlled routines and to common (IT) tools or methods. Figure 2.8 shows an exemplary Centralization and Standardization framework. In practical use, the axes must first be adapted to the individual company context. For example, the regional level is not organizationally available in all companies, but others are, which the framework doesn’t reveal in the template shown here. The category of several sites can be further detailed by listing certain competence centers or a category for lead sites if the approach is applied. The areas of responsibility can be adjusted and expanded freely. Based on this, the classification of the areas of responsibility is determined according to the current situation. Also, the optimal target state should be determined. If a dot is allocated in the framework to another position for the target state, there is optimization potential, which should be tagged with an arrow starting from the current state. Each arrow in the framework can be viewed as an independent work package. Usually, it makes sense to do this exercise above the site management level. You can find a practical application of the Centralization and Standardization framework in Chap. 21 in a practical context. The Centralization and Standardization discussion is a sensible basis for the development of organizational structures. In addition to the common manufacturing network context, we used the framework, for instance, to establish a COO organization at an aircraft structural component manufacturer. Based on the allocation of

The St.Gallen Management Model for International Manufacturing Networks Group level

2

2.2

1.4

2.6

2.1 2.5

3.1 3.2

Region Several sites

Degree of centralization

2.9

2.7 2.8

2.4

2.3

Each site individually

1.7 3.7

standardized 1.6

3.5 3.6

2.10

P No/local standardization Individual tools/ heterogeneous implementation level at each site

Responsibility areas

3.3 3.4

1.2 1.5

autonomous

1.1 1.2 1.3 1.4 1.5 1.6 1.7 … 1.n

1.1 1.3

centralized & standardized

centralized

S

43

Documented rules, guidelines & processes

Audited/controlled processes & routines

Standardized (IT-) tools or methods

Individual tools/ homogeneous implementation level at each site

Standardized tools/ heterogeneous implementation level at each site

Standardized tools/ homogeneous implementation level at each site

Systems Production system Product data management system Quality management system Management system Improvement programs HR System Know-how-exchange system …

D

Degree of standardization Decisions

2.1 Site strategy & roles 2.2 Organizational structure 2.3 Manufacturing IT decisions 2.4 Make-or-Buy Decisions 2.5 Product allocation decisions 2.6 Transfer pricing 2.7 Production process decisions 2.8 Manuf. technology decisions. 2.9 Long-term capacity development 2.10 Short-term capacity development … 2.n …

P

3.1 3.2 3.3 3.4 3.5 3.6 3.7 … 3.n

D S

Processes Strategic sourcing Strategic logistics Production cost calculation Long-term S&OP Internal SC-planning/ order allocation Short-term manufacturing pl. Production/Operations …

Fig. 2.8 Exemplary Centralization and Standardization framework. Adapted from Strategic Management of Global Manufacturing Networks, by T. Friedli et al., 2014, Berlin Heidelberg: Springer

systems, decisions, and processes, the responsibilities at a central level had already been clarified. Since the COO was not able to do all the assigned tasks on one’s own, appropriate staff units have been set up. Also, a blueprint organization was developed for the sites. This helps to identify the appropriate contact persons and counterparts in the sites and consequently leads to improved collaboration between sites. The organizational structure can be detailed using the RASCI logic,1 whereby the type of responsibility is assigned to the corresponding functions.

1 Management framework to allocate responsibilities according to the following categories: responsible, accountable, support, consult, inform.

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2.4.2

Information and Knowledge Exchange

The Information and Knowledge Exchange framework defines the structure of how information and knowledge are shared between manufacturing sites. It is a guideline and should ensure the most efficient exchange possible. Information can be formalized, whereas knowledge is difficult to formalize and therefore requires different exchange mechanisms. As shown in Fig. 2.9, there are various categories for information, which can be further divided into internal and external information as well as knowledge. The application in the practical context works similarly to the

3.1

Exchange structure

Centrally provided

3.5

1.2

transparency

limitation

1.1

Centrally coordinated

1.3

Centralized & decentralized

3.4

network

isolation

Decentralized No exchange

No access

Access to limited data/information

Access to most data/information

Access to all data/information.

No access

Limited access for selected sites

Access for all requiring sites

Access for all sites

I K

Degree of transparency Information & knowledge categories: External information 1 .1 Markets/customers 1.2 Competition 1.3 Suppliers … 1.n … Internal information 2.1 Site strategy/roles 2.2 Financial site performance 2.3 Market-& sales performance 2.4 Operative Site performance 2.5 Sales & operations planning 2.6 Administrative production data … 2.n …

Knowledge 3.1 Product innovation 3.2 Product changes/improvements 3.3 Technology/process innovations 3.4 Best practices production 3.5 Management know-how & practices 3.6 Business & supporting process improvements … 3.n …

Exchange mechanisms information Informal channels such as:  Ad-hoc calls, meetings & e-mails  Social activities

I

Formal channels such as:  Databases, sharepoints& intranet  Regular, formal meetings Exchange mechanisms knowledge Moving people/ Job rotation Competence groups

Customized projects/ project support

K

Qualification & training

Manuals, systems, databases I: Availability of information K: Intensity of exchange

High

Average

Low

No use of mechanism

Fig. 2.9 Exemplary Information and Knowledge Exchange framework. Adapted from Strategic Management of Global Manufacturing Networks, by T. Friedli et al., 2014, Berlin Heidelberg: Springer

2

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approach of the Centralization and Standardization framework. First, the axes need to be aligned with the individual company requirements. However, we seldom changed the axes within projects for this framework. Information and knowledge categories can be adjusted and enriched freely. The exchange mechanisms were also seldom adapted in practice, which manifests their applicability. A workshop on the definition of Information and Knowledge Exchange structures can be carried out well with the involvement of the site managers since the inclusion of this group of people increases the buy-in for the implementation and thus the success of the exchange structure. Here, too, in the best-case scenario, an initial state and a target state are defined, whereby each change from current to target state can be defined as a dedicated work package. In our experience, an indicator of a good information exchange structure is an exchange of information that is as formal as possible. In the exchange of knowledge, the success of the mechanism depends heavily on the knowledge category. Further, the success of the structure strongly depends on the underlying IT infrastructure and tools to support exchange. In this area, notable innovations have been generated in the past few years, which are particularly useful for multi-plant networks. One example of this is smart glasses that are used for crossplant maintenance instructions (Remling & Friedli, 2019).

2.4.3

Resource Sharing Framework

Resource Sharing within a manufacturing network is focused on the evaluation of resource categories according to their degree of scarcity in the network and the allocation to requiring and possessing sites. Sharing can occur either as a physical exchange, for example, by transferring machines and tools, or by moving workers, or non-physically by granting access to permanently installed resources. Moreover, the kind of chargeback for Resource Sharing is evaluated. Figure 2.10 shows an exemplary filled Resource Sharing framework. The first category comprises development capacity, which is organized as a resource pool since sufficient quantity is available and exchange takes place very frequently. Since the development department in this example is centralized at the lead site within the manufacturing network, there are more requiring sites than possessing sites, which reflects the diameter of the circle. There is no chargeback for the occupation of this resource in place since sufficient quantity is ensured. Figure 2.10 shows several more self-explanatory examples.

2.4.4

Incentive System Framework

Another important part of manufacturing network coordination is to define an Incentive System. Incentive Systems provide mechanisms to motivate an intended behavior by facilitating desirable or restricting unwanted actions. In a network, incentives are crucial for coordinating the behavior of site management. Many companies understand incentives merely as setting financial rewards that are

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Extent of resource availability

Sufficient quantity

1

dedication Rather sufficient quantity

pooling

5

4

Balanced quantity

Rather limited quantity

6

2

3

competition

cooperation

Limited quantity

No sharing

Very seldom

Seldom

Occasionally

Frequently

Very frequently

Frequency of sharing Resource categories 1 2 3 4 5 6 … n

Development capacity Engineering capacity Production engineering capacity Production capacity Special production capacity Support f unctions …

Allocation of resources More resource possessing sites than resource requiring sites Resource possessing sites equals resource requiring sites Less resource possessing sites than resource requiring sites

Type of chargeback Per cause

No chargeback 1-n

Apportionment (royalties)

Fig. 2.10 Exemplary Resource Sharing framework. Adapted from Strategic Management of Global Manufacturing Networks, by T. Friedli et al., 2014, Berlin Heidelberg: Springer

reflected in the remuneration of certain executives. However, our framework extends well beyond this and includes—in addition to the financial aspect, which is not negligible but difficult to change—compensation in the form of reputation and awards as well as autonomy and responsibility. Designing an Incentive System for a manufacturing network includes defining the right level on which targets are agreed on. At the highest level, this can be a target identically for all sites reflecting the performance of the entire network, a division, or even the entire company such as the company turnover. One level below, targets are agreed on individually for sites or groups of sites at (sub-)network level, such as the return on investments placed within the network. The next level specifies identical targets for all sites at the site level, for example, a globally uniform quality level in the form of cost of poor quality (COPQ) targets that shouldn’t be exceeded by any site. At the lowest level, individual targets are set for the respective site, such as the successful local launch of defined products. The second dimension of the framework defines how the reward is granted. This can either be based on the individual contribution of the plant or in the form of equal shares between plants. The task of the network manager is to distribute the incentives in such a way that the desired behavior is achieved.

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…identically at or above network level

1

collaboration

Targets are agreed …

coopetition …individually at network level

2

…identically at site level

3

individualism …individually at site level

collectivism

5

4

Remunerations are given out…

No targets agreed upon

…on the basis of the individual success / contribution of the sites

Performance categories 1 Financial overall performance 2 3 4 5 … n

Market- & sales performance Operational performance Contribution for learning/qualification Accordance with strategic goals …

…in equal shares among the sites

Ways of rewarding: Autonomy & responsibility

Financial remuneration 1-n

No use of mechanism

Reputation & awards

Fig. 2.11 Exemplary Incentive System framework. Adapted from Strategic Management of Global Manufacturing Networks, by T. Friedli et al., 2014, Berlin Heidelberg: Springer

In the exemplary framework in Fig. 2.11, we can see the first performance category “financial overall performance” as a classification in the target field collaboration. The financial result of the company could serve as a target that applies equally to all locations. If the target is achieved, a financial reward is given to all sites, regardless of whether the site has contributed directly to the financial result or not. For example, sites that have poor financial performance are incentivized to surrender processes to sites with a better financial performance to improve costs and thus the overall financial result. Table 2.4 shows some further examples for each performance category from Fig. 2.11. The design of an Incentive System should start with a consideration of the expected behavior of the sites in the network. After the Incentive System has been defined, it must be ensured that conflicting targets are avoided to ensure acceptance in the organization. Furthermore, the incentives and target achievement should be transparent to achieve the desired behavior.

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Table 2.4 Examples to direct behavior utilizing incentives Performance category 1. Financial overall performance

Possible indicator Company gross margin

2. Market and sales performance

Turnover growth rate

3. Operational performance

Ratio of cost of poor quality and conversion costs

4. Contribution to learning/ qualification

Number of conducted on-site trainings for staff from other sites

5. Accordance with strategic goals

Fulfillment of local strategic initiatives

2.5

Target description Every site receives the same partial financial bonus when reaching the corporate financial target

Site receives a bonus and an award based on generated additional turnover Sites with the lowest costs of poor quality are announced in the intranet and receive an internal quality award Sites receive more autonomy from the central level (e.g., decisionmaking competence for process technologies) for conducting trainings Sites achieving their individual strategic initiatives receive an individually defined financial bonus and more responsibilities

Intended behavior Sites collaborate in reducing costs (definition of cost structures, alignment of cost calculations, driving cross-site cost-cutting programs) to improve the overall financial target Sites compete in increasing their local turnover Every site tries to improve the internal quality level even beyond the predefined target Sites engage in cross-plant exchange and information sharing on both sides (giving and receiving entities) Every site fosters their individual strategic contributions according to their specific role in the network

Toward Harmonization

Now that dealing with the complexity of network management by breaking it up into subnetworks and various frameworks with different thematic focuses has been discussed, the implications must first be consolidated and brought into alignment. We have realized this in our projects by creating a strategy roadmap containing detailed work packages. The work packages verify if the coordinative measures are in line with the intended target configuration and support it optimally. The work packages are described in tables containing the following columns: Number, Topic, Action, Structural Improvement, Next Steps, Responsible, and Date. They include the configurative and coordinative implications that came out of the discussion of the initial and target state of the Site Portfolio and coordination frameworks. Each arrow within the framework can comprise a single work package. Usually, many work

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Qualify

Temporize

Avoid

Medium low

Medium high

Execute

Low

Strategic Importance / Potential Benefit

High

2

Low

Medium low

Medium high

High

Effort and complexity

Fig. 2.12 Framework to prioritize work packages

packages arise after such an exercise and must therefore be prioritized. Figure 2.12 shows a possible prioritization scheme. Further, the roadmap should be critically reviewed concerning the required Network Capabilities (see Table 2.2) to best support them. The Network Capabilities, however, should be harmonized with the Manufacturing Priorities so that the customer requirements are best met with the intended network structure. On top of that, the PARTS2 approach described by Friedli et al. (2014) can be applied to harmonize the implications with the overall network environment. Finally, one must return to the initial selection of the unit of analysis. If several units of analysis have been analyzed, they must be harmonized as well.

2.6

Summary

The optimization of IMNs based on our systemic approach is carried out within the three introduced levers, which can also be viewed as subsequent projects within a superior framework (see Chap. 19). The unit of analysis must be defined in advance

2 Strategy framework considering Players, Added Value, Rules, Tactics, Scope (see Friedli et al., 2014).

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and, if necessary, subdivided to reduce the complexity of the object under consideration. The first block comprises the definition of strategic positioning by ascertaining the customer requirements in the form of Manufacturing Priorities and the necessary Network Capabilities. The next two main blocks comprise the strategic levers to achieve the defined network strategy through configuration and coordination. In the configuration, the locations of the plants are specified including the required processes, products, and capabilities. The coordination is about defining the interaction structures in the network, meaning the cross-plant behavior, competition, and cooperation. This is defined by stipulating the degree of Centralization and Standardization, the Information and Knowledge Exchange structures, the Resource Sharing structure, and the incentive structure. Ultimately, all three blocks should be aligned to optimally adapt the network to the external requirements. Furthermore, a clear definition of work packages with responsibilities is essential for the implementation of the measures.

References Ferdows, K. (1997). Making the most of foreign factories. Harvard Business Review, 75(2), 73. Ferdows, K., Vereecke, A., & De Meyer, A. (2016). Delayering the global production network into congruent subnetworks. Journal of Operations Management, 41(1), 63–74. https://doi.org/10. 1016/j.jom.2015.11.006. Friedli, T., Mundt, A., & Thomas, S. (2014). Strategic management of global manufacturing networks. Cham: Springer. https://doi.org/10.1007/978-3-642-34185-4. Friedli, T., Thomas, S., Mundt, A., & Lützner, R. (2013). Management globaler Produktionsnetzwerke: Strategie, Konfiguration, Koordination. Munich: Hanser. Gluchowski, P., & Chamoni, P. (2006). Entwicklungslinien und Architekturkonzepte des On-Line Analytical Processing. In P. Chamoni & P. Gluchowski (Eds.), Analytische Informationssysteme (pp. 143–176). Cham: Springer. https://doi.org/10.1007/3-540-33752-0_8. Lützner, R. (2017). Shared factories—A focused factory perspective on the management of co-located manufacturing units in international manufacturing networks (Dissertation, University of St.Gallen). Meijboom, B., & Vos, B. (1997). International manufacturing and location decisions: Balancing configuration and co-ordination aspects. International Journal of Operations & Production Management, 17(8), 790–805. https://doi.org/10.1108/01443579710175565. Miltenburg, J. (2005). Manufacturing strategy: How to formulate and implement a winning plan (2nd ed.). Portland, OR: Productivity Press. Remling, D., & Friedli, T. (2019). Digital technologies to coordinate manufacturing networks: A Swiss pioneering perspective. EurOMA Conference Operations Adding Value to Society, 26, 1781–1790. Shi, Y., & Gregory, M. (1998). International manufacturing networks-to develop global competitive capabilities. Journal of Operations Management, 16(2–3), 195–214. https://doi.org/10. 1016/S0272-6963(97)00038-7. Skinner, W. (1974). The focused factory. Harvard Business Review, 50(3), 113–145. Slack, N., & Lewis, M. (2002). Operations strategy. Financial Times Prentice Hall: Pearson education.

3

Operational Excellence: The St.Gallen Model for Holistic Optimization Marten Ritz and Thomas Friedli

Literature about International Manufacturing Networks usually neglects Operational Excellence or Lean Production Systems. Coming from a long history, both are often solely approached and seen as stand-alone topics. However, the enterprise-wide introduction of a Lean Production System and the launch of a comprehensive Operational Excellence programme directly impact the coordination of global production networks. Above-site organizations introducing and sustaining Operational Excellence are a crucial asset to optimize global production. Additionally, Operational Excellence supports the knowledge exchange throughout the network and is closely linked to the network capability “learning”. Therefore, this chapter introduces, explains, and highlights the St.Gallen understanding of Operational Excellence.

3.1

A Refined Definition of Operational Excellence

Operational Excellence (OPEX) unifies the balanced management of cost, quality, and time. OPEX always focuses on the individual need of internal and external customers. Thus, it is the philosophy used to systematically enable organizations to continuously improve themselves. Improvements should be measured and evaluated by a set of well-defined metrics covering both effectiveness and efficiency. In order to achieve such a status within an organization, pushing Operational Excellence comprises structural and behavioural changes. It is neither a pure top-down nor a bottom-up approach but equally requires top management commitment as well as involvement of every single employee. M. Ritz (*) · T. Friedli Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland e-mail: [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_3

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Obviously, Operational Excellence is not only about showing superior and balanced performance in operations. True excellence also comprises the way this level of performance is achieved, building capabilities to sustain the current achievements, implementing practices to improve even further in the future, and embedding a culture of never being satisfied with the current status across the entire organization.

3.2

An Operationalization of Operational Excellence

The St.Gallen Operational Excellence Model acts as a reference to break down the above-explained understanding in actionable subject fields. Furthermore, it serves as framework to measure the current status of Operational Excellence within an organization and therefore provides a full operationalization. Following the idea of operationalizing and measuring excellence in order to define improvement priorities and actions subsequently, a continuous benchmarking study of pharmaceutical production structured along the St.Gallen Operational Excellence Model has been launched. This benchmarking programme is still active. On that basis the St.Gallen research team compiled the largest academic database of operational performance metrics from around 400 pharmaceutical production sites worldwide. The model itself was initially designed back in 2004 and has been continuously advanced since then. Structure, content, and detailed explanations have been published in four comprehensive St.Gallen books on Operational Excellence (see Friedli et al., 2006, 2010, 2013, 2018). It is built on a profound theoretical foundation and likewise enables practical applications incorporating learnings from groundbreaking operations research across industries. Understanding OPEX as a holistic and integrated system is always the highest priority and the central guideline for all implications drawn based on the model and the corresponding benchmarking programme. The current model is exhibited in Fig. 3.1 (Friedli et al., 2006, 2010, 2013, 2018). Elements of both a social and a technical sub-system form the excellence model. The technical sub-system comprises well-known practices in the three main technical categories: Total Productive Maintenance (TPM), Total Quality Management (TQM), and Just-in-Time (JIT). Within all three categories, concrete measures to quantify the current status and enforce further optimization are defined and allow a comprehensive evaluation. In addition to that, the social sub-system, which focuses on supporting and encouraging humans to strive for continuous improvement, addresses especially the question of how to enable Operational Excellence through managerial actions and steers an organization accordingly. There are four main social categories—Direction Setting, Management Commitment and Company Culture, Employee Involvement and Continuous Improvement, and Functional Integration and Qualification—that structure appropriate practices.

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Fig. 3.1 St.Gallen Operational Excellence Model. Reprinted from Friedli et al. (2013)

It is crucial to emphasize that neither the high-level sub-systems nor the underlying measures and practices should be understood as isolated items or managed individually. Even more important than the impact of each single lever is the way different elements reinforce each other. Hence, the model is designed as a holistic system in which single elements or interventions have a direct and indirect impact on both other components and the entire system. Truly understanding the overall performance of an organization and defining the right actions for improvement is much more complex than technically summing up results from an individual evaluation of all different components.

3.3

The Technical Components

The technical sub-system of the OPEX model structures, formalizes, and summarizes a set of technical practices defining an excellent production environment. The associated benchmarking allows researchers and practitioners to assess the implementation level of these practices. Selection and categorization of practices was done based on first observations indicating a lack of lean tool terminologies (e.g. Poka-Yoke, Andon, etc.) back in 2004. That is why general principles of Operations Management are stated rather than specific lean techniques and tools. In accordance with the very basics of Operations Management (see e.g. Hayes & Pisano, 1994), TPM, TQM, and JIT are incorporated as the main technical guiding

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principles of the OPEX reference model to address the major challenges of production. Based on the first round of benchmarking companies along the OPEX model, it became obvious that all three categories are heavily interconnected and that there seems to be a logical sequence in their implementation, namely: 1. Total Productive Maintenance (TPM) 2. Total Quality Management (TQM) 3. Just-in-Time (JIT) The concept of TPM, explaining how to clean, inspect, and maintain equipment, is the crucial foundation of optimized production systems. TQM cannot be introduced and meaningfully sustained without TPM: Stable processes require stable and fully reliable equipment first. Subsequently, mastering of both TPM and TQM is a prerequisite to sustainably eliminate waste and finally increase efficiency through JIT without facing the risk of a crash of the underlying system. Over the years, the St.Gallen OPEX team experienced a number of examples in various industries that underpinned the importance of adhering to the sequence listed above. Lowering inventories to directly force improvements in financial indicators without first doing the homework on stabilizing processes and equipment, often leads to quality and delivery issues due to the lack of ability to use buffers for reacting to anomalies in production. Usually, these types of events are the starting point to question and retract OPEX and Continuous Improvement programmes. Consequently, the maturity of the entire production system slides back, requiring the revision of the entire approach to optimization, creating a nightmare from both an effectiveness and efficiency perspective. Total Productive Maintenance (TPM) The category TPM comprises three major practices: preventive maintenance, housekeeping, and effective technology usage. Furthermore, autonomous maintenance and cross-functional training act as key enablers to stabilize equipment. A combination of all practices contributes to maximize equipment effectiveness along the lifecycle while establishing the most efficient way of conducting maintenance at the same time. TPM details both short- and long-term elements: Actionable maintenance strategies enabling the structured prevention of breakdowns through appropriate skill and capability development lead to short-term improvement. Screening, assessing, and using new technologies jointly form the long-term strategy. Housekeeping tasks are performed daily by machine operators themselves and include, among others, cleaning, inspecting, lubricating, and precision checking tasks. Housekeeping does not solely focus on equipment but also on the handling of all tools utilized to do so. State-of-the-art housekeeping comes along with broad application of the 5S1 principles. In this role, this is one of the very basics when it

5S is a workplace organization method that uses a list of five words: “sort”, “set in order”, “shine”, “standardize”, and “sustain”.

1

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comes to striving for excellence in light of a comprehensive production system. Systematically cleaned-up working environments as an established standard at all times forms one of the early indicators of a site’s ability to deploy and sustain any types of more ambitious optimization practices. Total Quality Management (TQM) Four elements are defined to be the major principles of the TQM sub-system: process management, customer integration, supplier quality management, and crossfunctional product development. In a nutshell, TQM is a rigorous fact-based approach to problem-solving. Even if the concept of Six Sigma has become much more popular nowadays, TQM as the underlying basis is still relevant. The difference between both concepts lies in the even stronger orientation of Sig Sigma on statistical analyses and the quantified measure of sigma/standard deviation in processes. Finally, an interplay of both concepts allows companies to monitor all parameters steadily by isolating variables that cause deviation, master them, and— by doing so—be able to continuously improve the underlying processes. TQM itself goes far beyond statistics by definition. It includes customer focus and continuous process improvement, as well as people and supplier development. It can be summarized as management’s total commitment to quality including appropriate prioritization and resource allocation striving to achieve the highest quality at the source of production. Process management as the core of TQM comprises documenting, measuring, analysing, and improving processes comprehensively. Thus, it aims to reduce process variances to a minimum level and includes all common tools of quality management aiming to find and control root causes of deviation (Cause and Effect Diagrams, Pareto Analysis, Design of Experiments, Statistical Process Control, etc.). In order to avoid unintended human and organizational dysfunction (e.g. unmotivated workforce, high absenteeism) caused by a higher level of documentation and standardization, successful process management is achieved by peers working in cross-functional teams. Following the overarching idea of ensuring quality at the source, supplier quality management aims to integrate suppliers into the existing internal quality system to ensure high-quality levels already for all incoming goods and materials. It is equally important to understand customer’s expectations to achieve excellent quality along the entire value chain. For this purpose, cross-functional product development completes the set of TQM practices considered in the St.Gallen Operational Excellence Model. It has proven its worth as a powerful instrument to better translate customer requirements into high-quality products. Just-in-Time (JIT) Pull production, setup time reduction, layout optimization, and planning adherence jointly build the JIT category of the St.Gallen Operational Excellence Model. JIT manufacturing has become a crucial element to increase flexibility without building up huge inventories in most industries. In this way both (1) increasingly heterogeneous customer requirements and (2) unpredictably varying demand can be

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addressed. Furthermore, reducing inventories and working capital are two levers to increase overall efficiency of operations. Especially pull production contributes to decreased overproduction and inventories: Instead of pushing a completed lot onto the next step of a production sequence, it is up to subsequent steps to define the pace of the overall flow. Tools such as Kanban allow to establish pull principles for all workflows and can even be deployed to indirect and supporting tasks close to the production line. Systematic reduction of setup times supports working efficiently. It enables producers to handle smaller lot sizes and a smooth material flow along the manufacturing processes. Planning adherence must be a given to level out the production schedule in both volume and demand variety the best way possible. It is a crucial enabler to keep the JIT system stable and allows for minimum inventory. Layout optimization based on optimizing the interplay of operator and equipment in a processing sequence, is an additional principle of JIT implementation. Intelligent production layouts support reducing all unnecessary movements of raw materials, intermediates, and finished goods. Basic Elements Besides the above-described elements assigned to the three categories of TPM, TQM, and JIT, some common practices are shared by all three and are not unique to one of the categories. However, these are considered and underpinned as crucial components of excellence production: Standardization and Visual Management are both prerequisites for successfully implementing almost all TPM, TQM, and JIT principles. Therefore, these form the model’s category basic elements alongside social components such as employee empowerment and cross-functional training. Both practitioners and researchers (e.g. Imai, 1986) are in agreement that it is nearly impossible to improve any process before it has been standardized and thus stabilized. Standardization not only refers to processes in the understanding of how a task is executed or the sequence different steps are performed. It also includes the standardization of production technologies and equipment. Clearly, standardization is a common supportive element for TPM, TQM, and JIT. Visual Management provides the workforce with updated information on process and performance data as well as the targets and objectives in place. Hence, it acts as an interface between performance review and shop-floor management. Used in the right way, visual boards are not only descriptive sources of information but also vehicles for allowing supervisors to actively steer daily operations by conducting structured huddle meetings. Visual Management assists the deployment of all TPM, TQM, and JIT principles applied in this way. It can equally provide timely information regarding JIT (such as the actual takt time) as well as variability in process parameters (TQM) or unavailability of specific equipment due to preventive control (TPM).

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The Managerial and Social Components

The social sub-system of the St.Gallen OPEX model aims at “motivating and aligning people to work for a common goal”. It was developed based on different sources. In particular, Demeester et al.’s model (Demeester et al., 2004) inspired the St.Gallen team back in 2004, in addition to some of the classic motivation theories such as Maslow’s Need Hierarchy, Herzberg’s Enrichment Theory, and Taylor’s Scientific Management. Targets must be understandable and consistent, as well as challenging and supported by senior management to provide clear guidance to the workforce. Experiences in science and practice show that employees need to feel that they have control over their job (what requires a certain degree of autonomy) and belong to a team at the same time. Operators should be supported by frequent and timely feedback on their progress. Multiple skills should be developed according to the individual potential and needs. Thus, the components of the managerial and social sub-system of the Operational Excellence Model are summarized as Direction Setting, Management Commitment, Employee Involvement, and Continuous Improvement, as well as Functional Integration and Qualification. Direction Setting A strategy that helps to set clear and consistent objectives formulated by the management is a needed prerequisite to implement any improvement practices in a structured way (Demeester et al., 2004; Hayes et al., 2005; Skinner, 1974). Finally, both the definition of a strategy—summarized as setting a direction—and acting on the strategy through deploying concrete tailored improvements, summarized as following a direction, are puzzle pieces of excellent production. Management Commitment Management commitment is one of the key success factors to achieve and sustain improvements according to prominent TQM studies and academic literature (Crosby, 1979; Ghobadian & Speller, 1994; Juan, 1993). Only management’s own commitment to quality provides confidence of the priority assigned. However, the importance of management commitment is not solely vital for TQM; it is equally important for all elements of JIT and TPM. Management always needs to promote a culture of supporting and enabling people in doing their work. That includes to provide all resources needed and regularly spend time on the floor, e.g. by attending structured Gemba Walks. Employee Involvement and Continuous Improvement Getting all employees involved in continuously thinking about how to improve the current situation is one of the major managerial challenges. Thus, it must be clear that process improvement is a common task for everybody and not just for a few smart industrial engineers. Such an understanding comes along with establishing a

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speak-up culture as well as implementing systematic ways to enable and to force operators to bring in improvement ideas, e.g. suggestion and recognition systems. Functional Integration and Qualification Even if the right culture is in place and the workforce is eager to contribute to the objectives set by the management, proper know-how is needed to do so. The concept of autonomous problem-solving contains of delegating complex decisions. Such empowerment can only succeed if employees are continuously supported in acquiring new knowledge and building capabilities. Furthermore, the flexibility of the technical system introduced above requires a multi-skilled workforce able to fulfil different roles at and close to the production line. Consequently, functional integration and employee development is a basic pillar to achieve the target of excellent production.

3.5

Application of the Model: Understanding Real Excellence in a Balanced Way

As outlined above, an established benchmarking program along the Operational Excellence Model allows researchers and practitioners to assess the current status of organizations. Different to other benchmarking approaches, the present understanding of an excellent manufacturing site is influenced by the traditional St.Gallen school of systems theory and cybernetics. The complexity of situations and problems has not only to be recognized but considered, rather than comparing isolated, single aspects and inexpedient problem definitions (Ulrich & Krieg, 1974). Holistic consideration comprises the connectivity of problems and interrelations within the social system besides the technical interplay of different components (Bleicher, 1995). Following the paradigms above, the St.Gallen benchmarking relies on the model’s entire technical sub-system in order to distinguish excellent plants from low-performing ones. Quantified transparency about manufacturing operations of an organization is achieved by assigning a distinctive set of KPIs to TPM, TQM, and JIT. Consequently, excellent manufacturing sites show higher performance on a holistic level, instead of outstanding performance in single KPIs. This approach helps to address the usual problems in industrial excellence programmes, namely: • Taking into consideration trade-offs between cost, quality, and time • Identifying the improvement areas with the highest potential for all individual plants The integrated assessment of outcome performance measures in combination with technical and social enablers not only highlights the as-is situation. Additionally, it indicates which improvement levers might be the right ones to pull.

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References Bleicher, K. (1995). Das Konzept integriertes Management. Herausgeber: Campus Verlag. Crosby, P. B. (1979). Quality is free – The art of making quality certain. New York: McGraw-Hill. Demeester, L., Eichler, K., & Loch, C. H. (2004). Organic production systems – What the biological cell can teach us about manufacturing. Manufacturing & Service Operations Management, 6(2), 115–132. Friedli, T., Basu, P., Belm, D., & Werani, J. (2013). Leading pharmaceutical operational excellence – Outstanding practices and cases. New York: Springer. Friedli, T., Basu, P., Mänder, C., & Calnan, N. (2018). 21c quality Management in the Pharmaceutical Industry – The journey from compliance to excellence. ECV. Friedli, T., Gronauer, T., Werani, J., Basu, P., Werani, J. (2010). The pathway to operational excellence in the pharmaceutical industry. ECV. Friedli, T., Kickuth, M., Stieneker, F., Thaler, P., & Werani, J. (2006). Operational excellence in the pharmaceutical industry. ECV. Ghobadian, A., & Speller, S. (1994). Gurus of quality – A framework for comparison. Total Quality Management, 5(3), 53–70. Hayes, R. H., & Pisano, G. P. (1994). Beyond world-class: The new manufacturing strategy. Harvard Business Review, 72(1), 77–86. Hayes, R. H., Pisano, G. P., Upton, D. M., & Wheelwright, S. C. (2005). Operations, strategy, and technology: Pursuing the competitive edge. Indianapolis, IN: Wiley. Imai, M. (1986). Kaizen – The key to Japanese competitive success. New York: Random House. Juan, J. M. (1993). Why quality initiatives fail. Journal of Business Strategy, 14(4), 35–38. Skinner, W. (1974). The focused factory. Harvard Business Review, 52(5), 113–121. Ulrich, H., & Krieg, W. (1974). St. Galler Management-Modell. Bern: Paul Haupt.

Part II Managing Manufacturing Site & Network Optimization

4

Managing International Manufacturing Networks in Today’s Business Environment Jens Kaiser and Dominik Remling

To profit from cost differences, growth opportunities in emerging markets, lower transaction costs, and fewer trade barriers, manufacturing companies have strongly driven their internationalization efforts over recent decades (Friedli et al. 2014). Today, these companies produce at globally distributed manufacturing sites and the individual steps of the value chain take place at different locations in the network. However, uncertainties caused by shifting economic forces, conservative winds, and protectionism as well as events such as the COVID-19 pandemic reveal the strong vulnerability of International Manufacturing Networks. Consequently, companies are reevaluating their optimization efforts of often opposing target dimensions such as efficiency, flexibility, time, and quality. In this chapter, we examine today’s business environment of International Manufacturing Networks and its implications on the management of those networks by presenting relevant success factors.

4.1

Exposure to Environment

To better understand the environment, its constituents, and its impact on International Manufacturing Networks (IMNs), we follow the common categorization approach of research (Osborn & Hunt, 1974; Wenking, 2020) and distinguish with increasing granularity between the macro environment and the task environment (see Fig. 4.1). Constituents of the macro environment describe the broad economic biosphere of manufacturing networks (e.g., legal, environmental, political, governmental, and environmental) (Lanza et al., 2019), whereas constituents of the task environment J. Kaiser (*) · D. Remling Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland e-mail: [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_4

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J. Kaiser and D. Remling Macro environment of international manufacturing networks (chapter 4.1.1) Task environment of international manufacturing networks (chapter 4.1.2) Success factors to cope with today’s environment (chapter 4.2)

Markets & economic environment

…

Suppliers

…

…

Organization (International manufacturing network)

Legal Shareholders environment … …

…

Customers

Technology complexity

People & cultural environment

…

Political & governmental environment

…

Fig. 4.1 Environment of manufacturing networks and its constituents

(suppliers, customers, etc.) directly affect the decision-making of operations executives. As shown in Fig. 4.1, we will firstly analyze the exposure of IMNs to their environment in Sect. 4.1 (macro environment, task environment). In Sect. 4.2, we will examine how organizations can use their IMN to successfully cope with the environmental factors placed upon it. To depict the real-world situation of IMNs, we mainly base this chapter’s findings on data from a benchmarking study conducted by the Institute of Technology Management, University of St.Gallen, in cooperation with the Institute of Production Science, Karlsruhe Institute of Technology, in 2020.1

4.1.1

Macro Environment

The macro environment or general environment includes different influences such as economic, technological, social, cultural, political, and legal influences (Lawrence & Lorsch, 1967; Shrivastava, 1994; Terreberry, 1968). Since those influences cannot directly be controlled by the organization, it is important to be aware of the degree to which the IMN is exposed to them and what the implications of this are. Once the 1

The benchmarking survey had been conducted in the period between May 6 and July 30, 2020, and yielded in total a sample of 88 participants. Most commonly, the participants hold positions such as COO, CTO, Head of Manufacturing, Head of Global Operations, etc. The participating companies mainly have their headquarters in German-speaking countries and come from various industries (33% mechanical engineering, 13% electrical engineering, 11% automotive, 10% metal products, etc.). The total number of production sites within the international manufacturing network ranges between less than 5 (24 companies) and more than 50 (seven companies).

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Managing International Manufacturing Networks in Today’s Business. . . Not at all 1

N = 88

Markets & market development

2

3

4.30

4.50

4.08

Cost factors

3.93

3.25

Political & governmental factors

3.15

People and culture Legal factors

Successful Practice

2

3.64 3.50

3.07

1

4.43

3.34

Highest spread

Logistics

Follower

To a very great extent 5

4

Lowest spread

65

3.29 3

4

5

Question: Please evaluate to what extent your decisions regarding the management of the global production network are affected by risk and dynamic of the following external factors.

Fig. 4.2 Extent to which decisions are affected by risk and dynamic

different influences are transparent, manufacturing executives shall establish sufficient forecasting processes and invest in risk mitigation strategies for the case of unforeseeable events. For IMNs, Lanza et al. (2019) propose six different influencing factors: markets and market development, cost factors (e.g., labor cost, capital cost, material cost, communication cost), logistics (e.g., lead time restrictions), political and governmental factors (e.g., taxes, subsidies), people and culture (e.g., different languages, mentalities), and legal factors (e.g., protection of intellectual property, legal system) (Abele et al., 2008; Lanza et al., 2019). As shown in Fig. 4.2, we distinguish in our benchmarking study between successful practice companies and followers. The six influencing factors are sorted by the extent to which the manufacturing networks of the successful practice companies are exposed to risk and dynamics of the factors. In the benchmarking study, the successful practice companies are the 15% most advanced companies with regard to their IMN practices and performance. They had been chosen based on a multicriteria approach that considers the company’s performance (temporal development of indicators such as EBIT, manufacturing costs, delivery reliability, etc.) as well as their maturity (e.g., formalization of network strategy, usage of defined approaches, usage of digital decision support tools). We will use this differentiation throughout the whole chapter. When considering Fig. 4.2, it is noticeable that for both successful practice companies as well as followers, the order of the asked factors is the same. Risks and dynamics concerning markets and market development, and cost factors influence management decisions of IMNs to the greatest extent. Hence, markets and market development, which describe the demand for products and related services, seem to be the decisive driver when managing manufacturing networks. As one of the most important reasons to move operations abroad, cost factors and their risk and dynamics still play a major role. The highest spread between successful practice

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companies and followers can be observed for the factor logistics, which includes lead time restrictions and demand for a high ability to deliver (see, e.g., Lanza et al., 2019, p. 828).

4.1.2

Task Environment

As described in the preceding section, the macro environment only exerts an indirect influence onto the organization. Furthermore, manufacturing executives have only limited control over these influences. In contrast, the task environment exerts direct influence on a firm’s decision-making. The task environment constantly changes and is hence the primary source of information for decision-makers (Wenking, 2020, p. 36). In our benchmarking study, we specifically addressed the topic of complexity of manufacturing networks and how they are exposed to it. As shown in Fig. 4.3 we inquired from the study participants selected complexity drivers proposed by Budde (2016). Besides the external complexity drivers coming from customers, suppliers, N = 88

Not at all 1

2

3

4 4.29

Cost pressure Quality pressure Supplier reliability

3.93 Highest spread

4.00 3.93

3.60

Time to market pressure

3.45

3.93

Demand variability

3.78

Product complexity

3.79

Technology complexity

3.39

Network complexity

3.17

Size of supplier base

2.95

Size of customer base

Bold internal complexity driver Follower Successful Practice

Highest 2

2.93

3.36

3.11 3

Spread: higher exposition for follower Spread: higher exposition for successful practices

Fig. 4.3 Exposure to internal and external complexity drivers

3.73

3.29

3.00 1 spread

4.08

3.36

3.23

Globalization of customer base

3.93

3.71 3.64

Process complexity

4.36 4.07

3.30

Number of products per site

Actions of competitors

To a very great extent 5

3.38

4

5

Question: To what extentis your entire global production network exposed to the following internal and external complexity drivers?

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Managing International Manufacturing Networks in Today’s Business. . . Not at all

N = 87

1

To a very great extent 2

3

4

4.07 4.00

3.16

Disruption in the external supply chain Highest spread

Transport difficulties

3.86

2.93 3.50

2.76

Absent personnel

3.36

3.18

Delay of strategic projects

3.29

2.56

Disruption in the internal supply chain

Successful Practice

4.21

3.44

Losses / cancellations of orders

Change in the order mix

5

3.76

Decrease of internal production utilization

Follower

67

1

Lowest spread

2

2.79

3

2.86

4

5

Question: Please evaluate to what extent the current corona pandemic influences the operation of your global production network.

Fig. 4.4 Influence of the COVID-19 pandemic on operations

and competitors, we also asked for aspects of internal complexity such as product, process, and network complexity (bold marked in Fig. 4.3). The complexity drivers are sorted by importance for the successful practice companies. The successful practice companies differ significantly from the followers in two areas. On the one hand, they are less influenced by the actions of competitors, and on the other, they differ regarding the reliability of suppliers. The high exposition of successful practice companies to supplier reliability aligns with a comparably large size of the supplier base and the previously observed high exposition to dynamics and risk of logistics such as lead time restrictions and demand for a high ability to deliver (see Fig. 4.2). Besides cost pressure being the most important complexity driver for both groups, followers are highly affected by quality pressure as well as rather technical complexity drivers such as product complexity and process complexity. Aside from the impact of growing complexity, we also asked the study participants how the operation of their IMNs had been affected by the global COVID-19 pandemic (see Fig. 4.4). As depicted in Fig. 4.4, the successful practice companies were influenced by the COVID-19 pandemic to a greater extent. Due to a potential higher degree of internationalization of the successful practice companies, they were more strongly affected by measures such as the border closures. Interrupted external supply chains led to disruptions in the internal supply chains and thus to numerous product failures. Followers with a more local supplier base were less affected. The high transport difficulties in comparison to the followers again indicate a high exposure of successful practice companies to risks and dynamics in terms of logistics (see Fig. 4.2).

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Success Factors

To successfully cope with the described environmental factors, manufacturing executives have myriad different tools on hand. In the following section, we will cover some of these “success factors” we have identified from our benchmarking study. Without any claim to completeness, we will focus on measures coming from improved network decision-making, the definition of an optimal degree of centralization and standardization, increased network flexibility, and risk mitigation strategies.

4.2.1

Improve Network Decision-Making

To learn more about the decision-making procedures of manufacturing executives, we asked which qualitative (e.g., workshops, lessons learned) and quantitative approaches (e.g., business cases, internal IT system) are used to make or rather support decisions. The three layers of the St.Gallen Management Model for Global Manufacturing Networks “strategy,” “configuration,” and “coordination” proposed by Friedli et al. (2013) serve as an orientation. Figure 4.5 shows the average ratings of the approaches. The study participants were allowed to choose their five mostoften used approaches. The calculation of business cases is also used by the followers, but data from internal IT systems are used more frequently. This approach is also used for decisions in the area of coordination of follower companies. However, the majority of follower companies make decisions in the area of coordination based on personal experience. Successful practice companies consider the results of internal workshops as well as documented lessons learned as a decision support method. Regarding the topic of digitalization, there already exist several IT tools to support decisions in the manufacturing network. In our benchmarking study, we distinguished between five different types of tools. By using visual elements such as charts and graphs, decision-makers are supported by visualization tools and dashboards to detect trends, outliers, etc. of data of their manufacturing networks. Whereas simulation tools are used to generate specific scenarios and examine a manufacturing network’s behavior under certain operating conditions, mathematical optimization tools consist of a model with an objective function and several constraints (Lanza et al., 2019). Process mining tools aim at turning event data, e.g., production-related data from ERP systems, into actions and insights (van der Aalst, 2016, p. 2). Finally, artificial intelligence applications can be characterized by their ability to learn and may hence be able to act and make decisions that are usually done by human minds (Ertel, 2017, p. 2). Figure 4.6 shows which tools are used to support network decision-making and how great the added value of such tools has been. Successful practice companies report in principle across all tools a higher added value in the application than the followers. The use of visualization, simulation, and

Managing International Manufacturing Networks in Today’s Business. . .

4

Successful Practice

N = 87 Conduct internal workshops

Strategy

Apply scienfic models, methods, frameworks

Configuration

Retrieve data from internal ITsystems

93% 93%

Make use of personal experience / gut decisions

Make use of personal experience / gut decisions

71%

Calculate business cases

71%

Calculate business cases

86%

Calculate business cases

71%

Analyze standardized performance management/ dashboards

64%

Retrieve data from internal ITsystems

57%

Conduct internal site benchmarkings

64% 58% 55% 45%

Apply scienfic models, methods, frameworks

86%

Consider documented lessons learned

45%

Conduct internal site benchmarkings

57%

Conduct internal workshops

45%

Conduct internal workshops

71%

Make use of personal experience / gut decisions

51%

Consider industrial magazines or research literature

Retrieve data from internal ITsystems

Retrieve data from internal ITsystems

59%

Calculate business cases

86%

79%

64%

Retrieve data from internal IT-systems

Conduct internal workshops

Consider documented lessons learned

Coordination

Follower Conduct internal workshops

79%

69

41%

Make use of personal experience / gut decisions

55%

Retrieve data from internal ITsystems

51%

Conduct internal workshops

50%

Analyze standardized performance management/ dashboards

57%

39%

Consider documented lessons learned

38%

Question: What kind of approaches do you use to support decisions in the following areas (Strategy, configuration, etc.)?

Fig. 4.5 Top five applied approaches for decision support Not in use 1

N = 85

Insignificant benefit 2

3

4

Lowest spread

Visualization tools

5.15

4.12

Optimization tools

5.13

4.46

Process mining tools Artifical intelligence applications

Follower

5

4.62

Simulation tools

3.80 Highest spread

Successful Practice

Fig. 4.6 IT tools applied for decision support

3.55

Severe benefit 6

5.11 4.64 4.55

Question: Have you been able to use the following tools to support decisions regarding your global production network to date and how significant is the added value?

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optimization tools seems to be particularly useful, whereas process mining and artificial intelligence provide for fewer companies added value in decision support.

4.2.2

Define Degree of Centralization and Standardization

As already introduced in Chap. 2, the degree of centralization refers to the concentration of responsibility held by a central unit (e.g., headquarters) in comparison to the regional units or the sites. The term “standardization” refers to the degree of existing standards of systems, decisions, processes, etc. (local vs. global standards). Figure 4.7 shows the opinions on the statement that there is a clear assignment of responsibilities between the sites and the corporate headquarters. Successful practice companies agree with this statement to a large extent, while only 61% of the followers either agree or strongly agree. In the next step, we asked the study participants at which level specific decisions2 in the field of IMNs are taken. The results are depicted in Fig. 4.8. Decisions regarding transfer pricing, supplier selection, long-term planning, and long-term control principles, and make-or-buy decisions are made centrally by half of the successful practice companies. Product allocation decisions are also made centrally by 43% and by 50% individually per business unit. Distribution decisions are made individually by each business unit in 43% of the successful practice companies. Decisions regarding production processes are made individually by selected sites related to site competence by 43% of the successful practice companies

Strongly disagree

N = 87

Sucessful Practice

Strongly agree

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Follower 4%4% 7%

71%

23%

Strongly disagree Undecided Strongly agree

42%

Disagree More or less agree

19%

More or less disagree Agree

Question: "There is a clear assignment of responsibility between sites and the corporate headquarter or central departments."

Fig. 4.7 Assignment of responsibility

2 See Mundt (2012, p. 73) for a detailed description of decisions in the field of international manufacturing networks.

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N = 86 Distribution decisions

0% 11%

14% 7%

7% 7%

43% 24%

36% 39%

Short-term planning and control principles Long-term planning and control principles Improvement programs Manufacturing technology decisions Production process decisions

21% 51%

21% 17%

7% 6%

21% 13%

29% 14%

Transfer pricing Supplier selection

14% 24%

7% 26%

7% 1%

21% 17%

50% 31%

Product allocation decisions Make-or-buydecisions Manufacturing IT decisions Establishment of additional capacities

0% 6%

7% 14%

0% 7%

50% 25%

43% 45%

0% 19%

0% 13%

7% 1%

43% 28%

50% 36%

0% 11%

21% 13%

7% 3%

14% 4%

57% 67%

7% 24%

7% 8%

7% 10%

43% 19%

36% 38%

Capacity utilization

14% 33%

14% 17%

7% 8%

36% 21%

29% 19%

Organizational structure of sites Production site strategy & roles

21% 37%

14% 17%

0% 7%

36% 20%

29% 20%

0% 6%

14% 10%

7% 8%

36% 21%

43% 56%

Each site individually

Selected sites related to site competence

Each region individually

Each business unit individually

7% 22%

14% 7%

0% 7%

29% 26%

50% 36%

14% 28%

21% 24%

7% 7%

29% 18%

21% 24%

7% 14%

36% 21%

0% 3%

21% 26%

36% 36%

7% 26%

43% 25%

0% 4%

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14% 23%

0% 7%

7% 5%

7% 3%

29% 15%

50% 68%

Centrally

Degree of centralization of the network Successful Practice

Follower

Question: Please indicate at which level the following decisions are taken.

Fig. 4.8 Centralization of decision-making (the values may not always add up to 100% since we excluded the answer option “Don’t know” and “Not relevant” from this illustration)

since production processes often depend on the conditions of the locations. Shortterm decisions regarding production planning are made individually at each site by about half of the follower companies. In addition to the decision-making levels, we also asked the study participants about the degree of standardization. Following the proposition of Friedli et al. (2013, p. 121), we asked the study participants for the degree of standardization of systems, decisions, and processes and added the categories resource/infrastructure and employee qualification (see Fig. 4.9). Successful practice companies use standardized tools and methods, especially for systems and processes. For the categories “resources/infrastructure” and “decisions,” a 57% majority of the successful practice companies use individual tools that are, however, homogenously implemented at each site. Regarding the qualification of employees, no uniform pattern can be recognized. This is carried out both in a standardized way and with the help of individual tools. In connection with the degree of standardization, in the next step, we consider the three most important factors for the successful implementation of standards in IMNs (Fig. 4.10).

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Processes Decisions

Resources/ Employee l Systems Infraqualistructure fication

N = 87

53% 15%

7%

19%

21%

20%

15% 51%

39%

4%

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0% 22%

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Individual tools / heterogeneous implementation level at each site

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24%

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27%

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5%

14%

48%

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Individual tools / homogeneous implementation level at each site

Standardized tools / heterogeneous implementation level at each site

26%

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Standardized tools / homogeneous implementation level at each site

Degree of standardization of the network Follower

Successful Practice

Question: Please indicate at which level the following decisions are taken.

Fig. 4.9 Degree of standardization

N = 87

21% 21%

Top-management involvement

19%

Feasible implementation on a global scale

6% 17%

Employee involvement

15% 12% 11% 10%

Employee qualification Justification by business case

5% 10%

Clear definition of responsibilities

18% 7%

Monitoring, auditing Incentives for employees and management Structured documentation Follower

Successful Practice

14% 2% 2% 2% 9%

Question: What are the three most important of the subsequently listed factors to successfully implement standards inthe global productionnetwork?

Fig. 4.10 Top three factors to successfully implement standards

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For the successful practice companies, the participation of the top management is the most important factor with 21%. With 19%, a feasible implementation on a global scale, as well as employee involvement with 17%, belongs to the most important success factors. The involvement of top management is also one of the most important factors for follower companies. In addition, the clear definitions of responsibilities, employee involvement, monitoring, and auditing are among the most important factors for the successful implementation of standards.

4.2.3

Increase Network Flexibility

Key to coping with external disruptions such as protectionism or a pandemic is to increase flexibility within the IMN. In manufacturing, there are various dimensions of flexibility. In our study, we asked the participants about seven commonly used flexibility dimension ranging from production process flexibility (i.e., transferability of production processes between sites) to new product flexibility (i.e., flexibility regarding the introduction of new products in the manufacturing network) (Koste et al., 2004). Figure 4.11 shows the as-is and planned degree of fulfillment of the different flexibility dimensions. In particular, the degree of fulfillment of process flexibility, volume flexibility, product mix flexibility, as well as the flexibility for new products will be developed further by successful practice companies. Follower companies instead strive for the as-is degree in fulfillment of the dimensions of successful practice companies as a planned goal. One top approach to achieve volume flexibility within the IMN is to flexibly shift manufacturing orders between plants. Figure 4.12 lists the three main challenges associated with the flexible movement of production orders within IMNs. N = 80 / 88* *80 for current level and 88 for planned level

Fully applies / Should fully apply

Does not apply / Should not applyy 1

3

Production mix flexibility

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Production process flexibility

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Material-handling flexibility

New product flexibility

2.94 2.91

1

/ /

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Successful Practice (current / planned level) Follower (current / planned level)

Fig. 4.11 As-is and future level of flexibility

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Question: To what extent does the global production network already fulfill the following flexibility dimensions and which dimensions do you plan to expand further due to the current situation?

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Transfer costs

32% 36% 38%

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42%

Regulatory restrictions

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36% 36%

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Insufficient inventory space

57%

0% 4% 0%

4%

Question: What are the three most important challenges in the context of the flexible shift of production order within your global production network?

Fig. 4.12 Top three challenges in flexible order shifting

Transfer costs are the biggest challenge by successful practice companies. Further challenges are an unsuited production infrastructure, prohibition by customers, and the heterogeneity of production sites. Follower companies, on the other hand, see the heterogeneity of production sites as the most important challenge. Other important challenges are an unsuited production infrastructure and transfer costs.

4.2.4

Mitigate Risks

In times of protectionism and pandemics, individual production sites are exposed to an increased default risk. In Fig. 4.13, the three most important methods for the reduction of such default risks within the manufacturing network were queried. Successful practice companies see contingency plans as the most important tool to combat such failures, followed by flexibly shifting of manufacturing orders between sites. Followers see the greatest potential in the latter point.

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N = 88 71%

Contingency plans

47%

Flexible shifting of manufacturing orders between sites Collaborative sharing of information/ best practices

57% 59% 43% 32% 36% 31%

Redundancies

29% 31%

Lean principles Modularity of process and product designs Predictive maintenance

21% 30% 14% 11%

Higher levels of inventory

Follower

Successful Practice

14% 20%

Question: During times of disruptions such as protectionism or pandemics, singlesites can face a default risk. What are the three most important approaches / methods to reduce default risks in the global production network?

Fig. 4.13 Top three approaches to reduce default risk

4.3

Summary

In this chapter, we systematically examined today’s business environment of IMNs and proposed specific success factors to deal with the increasing uncertainties such as shifting economic forces, protectionism, and greater competition. We mainly analyzed and discussed the results of a benchmarking study conducted in 2020. The environment of IMNs can be divided into the macro environment, which describes their broad economic biosphere, as well as the task environment, which exerts a direct influence on decision-making. Markets and their development pose the highest risk and dynamics on the management of IMNs. Besides a high internal complexity such as a high number of products, high product, and process complexity, the participants of the study are particularly challenged by high cost and quality pressure. The main influence of the global crisis on the operation of IMNs lies in a vast loss of orders, which consequently leads to a great decrease in internal production utilization. To successfully cope with the environmental factors placed upon manufacturing firms, we identified specific success factors in the field of improved network decision-making, definition of the degree of centralization and standardization, increased network flexibility, and the consideration of risk mitigation strategies. We observed that successful practice companies use various structured methods such as workshops, lessons learned, and internal benchmarking to advance their strategic decision-making. Additionally, they consequently back their decision-making with IT decision support tools with visualization, simulation, and optimization tools being

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the ones used with the highest benefit. Successful practice companies clearly define responsibilities between central functions such as headquarters and individual sites. Once such a clear definition of responsibilities has been established, top management commitment, as well as employee involvement, accounts for the most important levers to establish global standards. Key solutions to mitigate default risks within an IMN are the development of contingency plans as well as the opportunity to flexibly shift manufacturing orders.

References Abele, E., Meyer, T., Naeher, U., Strube, G., & Sykes, R. (Eds.). (2008). Global production: A handbook for strategy and implementation. New York: Springer. Budde, L. (2016). Integriertes Komplexitätsmanagement in produzierenden Unternehmen. Ein Modell zur Bewertung von Komplexität. Dissertation. University of St.Gallen. Ertel, W. (2017). Introduction to artificial intelligence. New York: Springer. Friedli, T., Mundt, A., & Thomas, S. (2014). Strategic management of global manufacturing networks. Berlin: Springer. https://doi.org/10.1007/978-3-642-34185-4 Friedli, T., Thomas, S., Mundt, A., & Lützner, R. (2013). Management globaler Produktionsnetzwerke: Strategie, Konfiguration, Koordination. Munich: Hanser. Koste, L. L., Malhotra, M. K., & Sharma, S. (2004). Measuring dimensions of manufacturing flexibility. Journal of Operations Management, 22(2), 171–196. Lanza, G., Ferdows, K., Kara, S., Mourtzis, D., Schuh, G., Váncza, J., Wang, L., & Wiendahl, H.-P. (2019). Global production networks: Design and operation. CIRP Annals, 68(2), 823–841. https://doi.org/10.1016/j.cirp.2019.05.008 Lawrence, P. R., & Lorsch, J. W. (1967). Differentiation and integration in complex organizations. Administrative Science Quarterly, 12(1), 1. Mundt, A. (2012). The architecture of manufacturing networks—Integrating the coordination perspective (Dissertation, University of St.Gallen). Osborn, R. N., & Hunt, J. G. (1974). Environment and organizational effectiveness. Administrative Science Quarterly, 19(2), 231. Shrivastava, P. (1994). Castrated environment: Greening organizational studies. Organization Studies, 15(5), 705–726. Terreberry, S. (1968). The evolution of organizational environments. Administrative Science Quarterly, 12(4), 590. van der Aalst, W. M. P. (2016). Process mining (2nd ed.). New York: Springer. Wenking, M. (2020). International manufacturing networks: Interrelations between network capability, environment and performance (Dissertation, University of St.Gallen).

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Unlocking Value with Production Network Optimization: A Strategic Perspective Marian Wenking, Oliver von Dzengelevski, and Torbjørn H. Netland

5.1

Introduction

Companies’ global operations strategy plays a pivotal part in their competitive success. According to one of the COOs we interviewed as part of our research, one-third of the total value generation [. . .] is contributed by the network [. . .] but [. . .] the network [can also] destroy the other two-thirds of value generation, [turning good financial results] into minus figures in no time.

In this chapter, we would like to engage with this notion, exploring under which conditions network optimization adds to companies’ value and under which conditions companies should set other competitive priorities. Put differently, we will focus on two questions. Firstly, what can a network do for a company? And secondly, what should a company do for its network? To answer the first question, we revisit the idea of network capabilities and explore how they relate to manufacturers’ value generation. To answer the second question, we focus on the strategic context in which companies compete and provide a strategic perspective of how companies can achieve fit between their network and their competitive challenges.

Marian Wenking, Oliver von Dzengelevski and Torbjørn H. Netland contributed equally to this chapter. M. Wenking (*) MANN+HUMMEL GmbH, Ludwigsburg, Germany O. von Dzengelevski · T. H. Netland Chair of Production and Operations Management (POM), DMTEC, ETH Zurich, Switzerland # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_5

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Network Capabilities

Output-oriented managers should be interested in the capabilities of their networks. The term network capabilities describes the abilities of a production network to achieve specific objectives—which might often be of high strategic value. The pioneers of this concept, Shi and Gregory (1998) at the Institute for Manufacturing (IfM) in Cambridge, suggested the existence of four distinct network capabilities, which we will describe below and add additional practical examples from our case research with leading companies: 1. Strategic Targets Accessibility Strategic targets accessibility describes the ability of a network to access ‘strategic targets’. Certainly, what target is deemed strategic depends heavily on a company’s judgement. While cheap energy is certainly a strategic target for aluminium producers to power their furnaces, this might not be true for textile manufacturers, more reliant on manual labour. However, despite such differences, some consent in the literature exists as to the general categories of strategic targets networks could access (e.g. Dunning, 1998; Ferdows, 1989), which coincides with what we hear from practitioners. Market access and access to production factors, such as labour or materials, are key. These can be guaranteed by plants located in close proximity to them. Access to production factors is especially important if they are immobile and cost differences prevail between locations. For instance, since about the 1990s, companies have moved parts of their production to East Asia, aiming to save labour costs. Other access motivations might also play a role, or constrain companies’ location choice, such as access to skilled managers, engineers or suitable suppliers (cf. Shi & Gregory, 1998). Practical example: One of the COOs of the companies we conducted our research with emphasized that so-called ‘best-cost countries’ are what his company seeks. They are closer to markets than typical ‘low-cost countries’ but tend to have somewhat higher labour rates. The manager said that considering the level of automation in the company ‘the difference between $10 and $15 per hour is not so decisive, but the difference between $10 and $30 still is’. This way, the company accesses relatively inexpensive labour force and strategic markets at the same time. 2. Thriftiness Ability A networks’ thriftiness describes its ability to use the resources at the disposal of the network with greater efficiency (Colotla et al., 2003), particularly by means of economies of scale and economies of scope. Economies of scale describe efficiency gains by means of bundling production activities at a particular location, rather than dispersing them. This way, duplication of activities (Miltenburg, 2009) can be avoided, machine utilization might be improved and purchase benefits from bulk can be realized. Economies of scope in a network describe efficiencies by

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broadening the scope of activities at a given location, possibly achieving a range of benefits, including greater efficiency of asset usage. Practical example: Emphasizing the importance of overhead costs, another senior manufacturing manager we interviewed narrated how the company builds what he called ‘zebra factories’, i.e. factories within which different production lines are placed next to one another to decrease per product overhead costs, in case no production technology-related economies of scope do not exist elsewhere in the network. 3. Manufacturing Mobility Manufacturing mobility refers to a company’s ability to transfer activities between facilities (Colotla et al., 2003; Shi & Gregory, 1998). This mobility can exist with respect to different aspects of companies’ production system. Of particularly high practical importance is the mobility of whole product lines or individual processes between facilities. Reasons for such shifts might for instance be the introduction of tariffs that penalize imports from specific countries, the closure of a location or other strategic reshufflings. A second to be considered dimension is the mobility of volumes between locations, for instance, due to shifts of market demand or to seek a more efficient distribution of capacity. Of course the concept of manufacturing mobility in a network could also be extended to other spheres, such as the mobility of managers or even facilities themselves (Miltenburg, 2009). Ferdows, Vereecke and De Meyer (2016) even identify a special type of subnetwork they call ‘footloose’ in which products could be transferred with relative ease, seeking minimal production costs. However, this would only be possible if standard processes are used and products are commodity-type. Practical example: The COO of one of our case companies recalled how their company was affected by an anti-dumping case a large country with a key market for the company had filed against producers in a particular location—where also the company in question produced. Some of the competitors could leverage their manufacturing mobility and shift their production to an alternative location. However, the company could not achieve this, making losses for several years, after which it made sure it is able to ‘to shift production volumes relatively quickly between regions’, avoiding such situations in the future. 4. Learning Ability The last network capability is learning, which should be understood in a twofold way. Firstly, there is an internal component to network’s learning ability, corresponding to ‘knowledge-sharing within the network’ (Thomas et al., 2015, p. 1711). For instance, plants might share best practices for productivity improvement among one another (cf. Netland & Aspelund, 2014). The greater the ease with which such exchange between plants in the networks takes place, the more advanced the network’s learning ability. Moreover, Shi and Gregory (1998) suggest that there is an external component to network’s learning, determined by their ability to

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perform successful ‘external comparison, exchange and benchmarking’ (p. 209). Subject to external comparisons could be competitors in key markets or industry leaders. Exchanges with customers or suppliers might engender opportunities for product innovation or allow to better understand the market situation. Practical example: Another COO summarized the importance of their company’s network’s learning ability, saying that it saves the company from reinventing the wheel. Illustrating the efficiency gains from the mobile expert teams in his network, he stated ‘There is an issue in South America? No problem, we send two people’, adding that without a high learning ability in the network ‘lessons learned will always remain theory’.

5.3

Configuration and Coordination

Companies can achieve these network capabilities by means of configuration and coordination—two concepts initially suggested by Porter (1986). Network configuration roughly translates to ‘network dispersion’, i.e. the way how operations are distributed in a network. In turn, network coordination refers to network-internal management. Along these lines, Colotla et al. (2003) make a resource-based argument (cf. Barney et al., 2001), suggesting that network configuration is about picking the right resources (e.g. access to strategic targets), whereas resource coordination is about higher efficiency of already existing resources in the network (Makadok, 2001). Following this line of thinking, thriftiness and learning in a network are more coordination-based capabilities. In other words, they ensure the internal efficiency of the network. In contrast, strategic target accessibility ensures networks’ external efficiency (external as the resources accessed are external to the network). For instance, networks high in market access allow manufacturers to save on shipping costs, extra inventory bound up in deliveries, possible tariffs and time. To some extent, manufacturing mobility appears to combine elements of both. On the one hand, it allows for a more efficient use of internal resources within the network (e.g. facilities, machines, etc.). However also the configurative component matters, to the extent that network mobility allows to take greater advantages of external resources, for instance, by shifting additional manufacturing volumes of a product into a market with growing demand. Of course, the configuration and coordination of networks are interrelated. For instance, it could be easier for a close-knit, low-dispersed network to develop strong learning mechanisms, which would mean that network configuration also plays a role for networks’ learning ability. Likewise, networks’ thriftiness, though mainly a coordination-based capability, is affected by networks’ configuration. Economies of scale—a sub-dimension of the thriftiness capability—correspond partly to the degree a network is not dispersed, but focussed at a particular location.

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Network Capabilities as Value Drivers

In our research, we have investigated under which circumstances a higher capability can serve as a value driver for manufacturers (Flaeschner et al., 2020). We considered that manufacturers compete in different ‘environments’, which can be characterized by three main features (Castrogiovanni, 2002; Child, 1972; Dess & Beard, 1984): Dynamism—What is the pace of the industry the manufacturer is competing in (cf. Fine, 1999)? For instance, how fast do products and services become outdated? How fast do customer preferences change? What’s the rate of innovation? Hostility—How ‘hostile’ is the competition in local and foreign markets? How large are the pie competitors fight over and how tough is the competition over it? Can manufacturers rely on vendors and suppliers, or are they competing ‘alone’? Complexity—How complex is the industry manufacturers are competing in? Are there many customers and suppliers? Are sales ‘all over the globe’ or concentrated in a particular place? Is sourcing predominantly global or everything comes from one spot? We rolled out a survey, covering 105 multinational manufacturers in charge of more than 2178 manufacturing plants in their networks to study the value contribution of production networks and understand whether different competitive circumstances demand greater reliance on network-focussed strategies. Our data and analysis shows that manufacturers in hostile competitive environments—i.e. companies in shrinking markets, with falling margins, who cannot rely on outside support—would enjoy the greatest benefits from network optimization. Manufacturers in more munificent markets have less to gain and are even at risk of destroying value. How dynamically the industry develops or how complex it is appears to play a secondary role in this regard. What really matters for the value manufacturers can gain from upgrading their network capability level is how hostile the competition is manufacturers face (Flaeschner et al., 2020). Figure 5.1 describes the relationship we find. It shows financial performance of manufacturers (green/light grey areas signifying good performance, red/black/dark grey areas indicating the opposite) in relation to networks’ capability level and the degree of competitive hostility they face (all variables are standardized, so that the zero shows the average value and the steps correspond to standard deviations). Let us walk through the main take-aways from this graph and discuss why it is that we observe what we observe. For improved orientation, we added letters in the different regions of the graph and will discuss them clockwise. Companies in region ‘A’ in the lower left of the graph operate in munificent environments, where profits margins are relatively safe, competition is limited and manufacturers can rely on vendor quality. Networks in this region have belowaverage capability level, from which follows (as elaborated on above) a relatively low resource efficiency. However, companies in this region still do well—why? Because their munificent environment is permitting of this, allowing them to build up slack resources (Lin et al., 2016), innovate and this way stay in their market niche, well outside the fairway of potential rivals.

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Fig. 5.1 Finding fit between network capability level and competitive context. Adapted from Flaeschner et al. (2020), with added in-figure annotations

If companies in region ‘A’ were to focus on optimizing their networks to gain greater network efficiency, they might land in region ‘B’, still enjoying the benefits of a relatively munificent environment. However, the majority of companies in region ‘B’ seems to perform relatively poorer than their peers in region ‘A’. We suggest this is because they use their available resources in a way that conflicts with their strategic position. Given their relatively low competitive pressure, companies in this region are situated in relatively favourable niche-type markets in which other competitors did not yet manage to enter, perhaps, because they could not match the quality of these companies’ offers or because they do not have the technical ability to enter into competition with them. One of the COOs we interviewed whose company operates in a similar environment manufacturing premium products noted that their competitors ‘do not attack [the company] on eye level’, giving it a relative independence in product pricing as long as ‘the price distance [to the non-premium competitor] is [not] too large’. Under these circumstances, companies should focus on their unique edge that allows them to hold this market and serve their customers, rather than attempting to increase their network efficiency to prepare undercutting competitors that are not encroaching them sufficiently to present a strategic threat to them. For instance, the above-quoted company reinvests one quarter of its profits into R&D to keep its market position. If companies in this region over-focus on

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network efficiency, they risk to be ‘stuck in the middle’ (Porter, 1980) and waste resources by making costly investments into additional efficiency measures that might be of only secondary importance to the competitive success of the company. Next, let us look at region ‘C’. Region ‘C’ is populated by companies with fit between the demands posed by their competitive context and their global operations strategy. They operate in above-average hostile markets, where profit margins are falling, competition is keen and their products are under threat of commoditization, disallowing them to differentiate themselves on the basis of quality and innovation. Under these conditions, competition starts to be increasingly price-based, challenging manufacturers’ ability to undercut their competitors. In order to hold step with their competitors, manufacturers in this region have to be most concerned with efficiency improvement, so that despite falling margins they can still earn profits. In this situation, improving the internal and external efficiency of their networks by improving their network capabilities is a winning step in manufacturers’ global operations strategy. The COO of the company we studied in this type of setting emphasized how their customers can choose from an increasing number of low-cost suppliers from, e.g. Turkey or Mexico, and are not held back by the lower durability of their products. Considering this commoditization in the market in which quality and technology play an only subordinate role, the COO judged that the already slim profit margins could only be held by meticulous network optimization, starting from the footprint strategy ending with the network’s learning channels. Manufacturers in region ‘C’ are rewarded with good profitability compared to their peers, being able to defend their share of the pie. Region ‘D’ shows networks that compete under about the same conditions as companies in region ‘C’ but are not fully committed to their network capabilities. Overall, their level of network capabilities is noticeably higher than average, but not as excellent as that of the companies in region ‘C’. The effects are evident: they do not enjoy the same outstanding financial performance as their more network-savvy rivals. Even though their efficiency is sufficient to mostly hold step with their competitors in terms of price competition, it is not enough to really thrive in this hostile environment, as companies in region ‘C’ do. If companies in this region would focus their efforts and hone their network capabilities even further, they could transition into region ‘C’ where their network excellency would be rewarded with even greater profitability. Region ‘E’ shows what awaits competitors under hostile competitive conditions who underinvest in their production networks, leaving them with capabilities that are not up for the task. Manufacturers in this region are under high price pressure, but cannot withstand it, which is clearly expressed in their financial performance. Companies in this region should urgently invest in their network capabilities to suit their global operations strategy to the demands of their competitive context.

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Summary

In this chapter, we have treated the concept of network capabilities, as proposed by Shi and Gregory (1998), elaborating on the notion of what the network can do for a company, notably improving its overall efficiency, both with regard to networkinternal and external resources (Colotla et al., 2003). Next, we answered the question what a company should do for its network which decisively depends on the competitive circumstances it is facing. We have shown how companies’ profitability in hostile competitive environments is highly interlinked with their overall network capability level. Under these circumstances, manufacturers do well in emphasizing their network capabilities and placing a strategic focus on network efficiency in their global operations strategy. Aligning their global operations strategy with the demands of their competitive environment allows them to exceed their peers in financial performance (Flaeschner et al., 2020). Companies which only pay secondary focus on their network capability level are penalized with lower performance. Due to the disregard for their networks, some companies in hostile industries lack decisively behind in terms of their profitability. However, under more munificent circumstances the picture reverses. If companies operate in a munificent niche, they should focus on maintaining it, rather than on network efficiency investments, which might direct significant amounts of resources at objectives that are of lesser importance for the competitive success of the company.

References Barney, J., Wright, M., & Ketchen, D. J., Jr. (2001). The resource-based view of the firm: Ten years after 1991. Journal of Management, 27(6), 625–641. Castrogiovanni, G. J. (2002). Organization task environments: Have they changed fundamentally over time? Journal of Management, 28(2), 129–150. Child, J. (1972). Organizational structure, environment and performance: The role of strategic choice. Sociology, 6(1), 1–22. Colotla, I., Shi, Y., & Gregory, M. J. (2003). Operation and performance of international manufacturing networks. International Journal of Operations & Production Management, 23(10), 1184–1206. Dess, G. G., & Beard, D. W. (1984). Dimensions of organizational task environments. Administrative Science Quarterly, 29(1), 52–73. Dunning, J. H. (1998). Location and the multinational enterprise: A neglected factor? Journal of International Business Studies, 29(1), 45–66. Ferdows, K. (1989). Mapping international factory networks. In K. Ferdows (Ed.), Managing international manufacturing. Amsterdam: North-Holland. Ferdows, K., Vereecke, A., & De Meyer, A. (2016). Delayering the global production network into congruent subnetworks. Journal of Operations Management, 41(1), 63–74. Fine, C. H. (1999). Industry clockspeed and competency chain design: An introductory essay. In Automation in automotive industries. New York: Springer. Flaeschner, O., Wenking, M., Netland, T. H., & Friedli, T. (2020). When should global manufacturers invest in production network upgrades? An empirical investigation. International Journal of Operations & Production Management, 41(1), 21–53. Lin, H., Zeng, S., Liu, H., & Li, C. (2016). How do intermediaries drive corporate innovation? A moderated mediating examination. Journal of Business Research, 69(11), 4831–4836.

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Makadok, R. (2001). Toward a synthesis of the resource-based and dynamic-capability views of rent creation. Strategic Management Journal, 22(5), 387–401. Miltenburg, J. (2009). Setting manufacturing strategy for a company’s international manufacturing network. International Journal of Production Research, 47(22), 6179–6203. Netland, T., & Aspelund, A. (2014). Multi-plant improvement programmes: A literature review and research agenda. International Journal of Operations & Production Management, 34(3), 390–418. Porter, M. E. (1980). Competitive strategy. New York: The Free Press. Porter, M. E. (1986). Competition in global industries. Boston, MA: Harvard Business Press. Shi, Y., & Gregory, M. J. (1998). International manufacturing networks-to develop global competitive capabilities. Journal of Operations Management, 16(2–3), 195–214. Thomas, S., Scherrer-Rathje, M., Fischl, M., & Friedli, T. (2015). Linking network targets and site capabilities: A conceptual framework to determine site contributions to strategic manufacturing network targets. International Journal of Operations & Production Management, 35(12), 1710–1734.

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Deriving a Network Strategy Philipp Miedler and Thomas Friedli

Strategy without process is little more than a wish list. —Robert Filek

6.1

Introduction

Strategic decisions like off- and re-shoring, outsourcing, product allocation, investments in technology, merger and acquisitions, and many others, often have immediate, conclusive implications, which can massively reduce structural and infrastructural options within a global production network for a long time. Consequently, it is crucial that every decision is made in consideration of existing capabilities and overall business objectives. With the business and corporate strategy aligned, functional strategies and sub-strategies ensure that each employee acts in the best interest of the company. This chapter aims to illustrate how a comprehensive network strategy can be derived from the production strategy. For this purpose, an existing, sound production strategy is a prerequisite. Therefore, initially, a deeper understanding of the content and development of a production strategy is built up. The production network strategy must support this production strategy by taking into account existing plant and network capabilities. The aim is to create a comprehensive foundation for the subsequent analysis and, if needed, adaption of network configuration and coordination. P. Miedler (*) · T. Friedli Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland e-mail: [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_6

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Requirement: A Sound Production Strategy

In the past, it used to be sufficient to set priorities once and implement them with the necessary capabilities to distinguish oneself from the mass of competitors. The competition was often not able to close the gap at the necessary speed. Nowadays, we live in an extremely fast-changing society. Today’s business environment is especially challenging for globally operating companies. Not only do increasing customer requirements, volatile demand, and product complexity put pressure on production in general and the global production networks, but also unpredictable risks such as trade wars, pandemics, and environmental concerns increasingly threaten global value chains and reveal their limitations. A sustainable competitive advantage can be achieved by those companies that are able to holistically reflect these multidimensional influences in their global production strategy. Therefore, it is necessary: • To have a sound strategy • To be able to adapt it if the environment is changing • To know the levers regarding production network configuration and coordination to accommodate new requirements quickly In most cases, the reason for mismanagement of the strategy might not be the wrong contents or goals, but rather the wrong context, which is not suitable for the intended strategy (Müller-Stewens & Lechner, 1999). The integration of the production strategy into the appropriate context is so difficult because silo thinking dominates at various levels in a company, whether it is on location, network, or functional level. The important factor for a successful production strategy is the harmonization of silo-dominated strategies between different functions and a vertical cascading of goal setting from top floor to shop floor. Figure 6.1 visualizes the role and the interaction of the production strategy with other strategies within a company. The continuous line illustrates the vertical progression, starting with a business and corporate strategy, followed by production, network, and location strategy. The orange connections illustrate the need for coordination with horizontally equally positioned sub-strategies. So, what does a successful production strategy look like? There are many different definitions of production strategy, both in literature and in practice. One reason for this is that the context factors of production strategies can vary significantly due to the different types of industries and competitive environments (Kulkarni & Verma, 2016; Platts et al., 1998; Ward & Duray, 2000). In general, a production strategy defines the exploration and exploitation of production capabilities by guiding structural and infrastructural decisions in order to achieve a unique strategic position in the market, which is consistent with the overall business objectives (Brumme et al., 2015; Hayes & Gary, 1994; Hayes et al., 2005; Miltenburg, 2008; Platts et al., 1998; Skinner, 1996).

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Fig. 6.1 The role of production strategy

Table 6.1 Major elements of a production strategy

Content

Process

Context


Competitive priorities
Distinctive competence
Linkage with business strategy
Structure and infrastructure decisions
Strategy formulation process
Decision patterns and resource deployment
Operational plan and improvement programs
Firm-specific emerging notions
Market and competitors

Note: Adapted from Kulkarni et al. (2019)

The definition has a wide scope. As a result, there are great differences in the included content and processes when formulating production strategy in practice. This chapter is not intended to describe how to elaborate a successful production strategy. Rather, it is about gaining an understanding of the elements that a successful production strategy should address. This is important as these elements define the starting point for the network strategy discussion. Kulkarni et al. (2019) assessed the primary elements that constitute a production strategy. They analyzed 936 articles using a seven-step text-mining process. What they found were 34 academic and eight practitioner’s definitions of Production Strategy. Table 6.1 presents the nine major elements within three dimensions that constitute a production strategy definition according to Kulkarni et al. (2019). The first dimension describes content factors, namely: • Competitive priorities—The factor “competitive priorities” specifies the set of manufacturing objectives derived from the market. Although there are differences in terminology, there is a consensus in the literature on the main dimensions,

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which are defined as the following production priorities: Cost, quality, delivery, flexibility, innovation, and service (Miltenburg, 2005; Slack & Lewis, 2002; Thomas, 2013). While these six have long been acknowledged, recently the subject of the environment is beginning to gain importance, as a production priority (Sansone et al., 2017). Industry experts also mention safety, sustainability of operations, user experience, agility, and co-creation as important competitive priorities. • Distinctive competence—Successful manufacturing companies are not only those who respond to the demands of the market, but also those who are able to achieve distinctive competencies. These competencies may be specific operation or network capabilities. A company could also be outstanding because of its particularly strong innovative power, global responsiveness, ability to change quickly, strong resistance, or unique reputation. Competencies that enable a distinctive characterization as well as meeting the demands of the market should be identified and strengthened. • Linkage with business strategy—It is crucial that the production strategy is consistent with the corporate and business strategy, and the objectives designed in this context. This includes, for example, vision, mission, organizational growth, and goals. Scholars have noted that the link between manufacturing strategy and business strategy is more pronounced in high-performing businesses (Anderson et al., 1989; Fine & Hax, 1985; Swink & Way, 1995). • Structure and infrastructure decisions—Structural decision categories represent decisions regarding the physical attributes, which require substantial capital investments and are difficult to alter or reverse, while infrastructural decision categories describe the systems, policies, and practices that determine how the structural aspects should be managed. The structural decision categories include process, capacity, facilities, and vertical integration, while the infrastructural includes quality systems, planning and control system, organization, and workforce (Hayes & Wheelwright, 1984). The choices made for each of these different types of decisions have varying effects on a company’s operating costs, quality, dependability, flexibility, speed/responsiveness, and new product capabilities (Hayes et al., 2005). The three factors that belong to the second dimension “process” are: • Strategy formulation process—Elements such as strategic focus decisions, overall strategic direction, forward strategic thinking, and reformulation of plans and actions are part of the strategy formulation process. This process justifies all structural and infrastructural decisions. In practice, planning is just a part of the continuous stream of events that determine the strategy (de Wit & Meyer, 1998). • Decision patterns and resource deployment—An operations strategy is determined by the pattern of the decisions made. It includes key terms such as resources, deployment, coordinated decisions, and people, exhibiting the details of resource allocation decisions. This element highlights the internal decisions to

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be taken to realize production strategy goals. It signifies the linkage with a resource-based view of strategy (Gagnon, 1999; Paiva et al., 2008; Schroeder et al., 2002) • Operational plan and improvement programs—Encompass key terms like action plan, operational plan, action programs, and coordinated decisions and highlights the alignment of operational plans, action and improvement programs coherent with manufacturing strategy (Barad & Gien, 2001; Clark, 2009; Netland, 2013; Porter, 1996). In addition to the two well-established streams of content and process, Kulkarni et al. (2019) have also identified context as a third dimension, which covers the macro as well as the micro level: • Firm-specific emerging notions—Comprises all internal characteristics of a company and is referred to as micro-context. It provides the critical link between the role of manufacturing strategy and all operational functions and emerging paradigms to enable them to be proactive in developing future markets (de Wit & Meyer, 1998). • Market and competitors—Ability of the firm to integrate, build, and reconfigure manufacturing tasks and resources, aligning with changing competitive structure, industrial competition, and global customers’ expectations. Future alignment, market needs, complex business environment requirement, competitors, severe economic changes, worldwide basis, and network reformation are the terms that attribute to global and industrial context and is called the macro context (Kulkarni & Verma, 2016; Platts et al., 1998; Ward & Duray, 2000). Competitive priorities as well as structural and infrastructural decisions are identified as the significant factors for a production strategy. Certainly, there may be overlaps between these elements, and some elements may be more important than others for certain industries and sectors. Nevertheless, the elements mentioned above are important to ensure that the production strategy covers all aspects and is fully sustainable for ongoing discussions. The methodology proposed by Kulkarni et al. (2019) can be adopted by firms to audit their manufacturing strategy statement and its alignment with the competitive priorities and the decisions taken.

6.3

Network Strategy

Today’s fast-changing, fiercely contested, and increasingly global competitive environment makes it very difficult for companies to differentiate themselves sufficiently through accurate production priorities and capabilities. Firms need a wide range of operation capabilities to compete successfully in the face of an accelerated pace of technology, innovation, globalization of markets, and ever-increasing customer expectations. Defining the best production strategy under these current conditions is a tremendous challenge and is becoming even more complex due to the

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Fig. 6.2 Holistic network strategy framework

pre-dominant globally dispersed production, where manufacturing takes place at various locations. Taking into account the increasing variety of products, it is also evident that each product family might have unique requirements and therefore, the complexity of the production strategy is increasing, meaning that competitive priorities are defined at a product or product family level, rather than at a factory level. Globally distributed manufacturing companies tend to consist of individual sites that differ in terms of their capabilities and characteristics depending on factors such as their geographical location, historical development, and strategic accesses. These companies can benefit from a wide range of distinctive site capabilities, and additionally generate network capabilities through proper network management. By dividing the sites into specific (sub-)networks, companies with products or product families that compete on different competitive priorities can benefit from the various emerging capabilities. Consequently, products or product families can be grouped according to their respective priorities and can thereafter be assigned to the most appropriate network design. It is remarkable that the focus of most scholars in the field of production networks tends to turn toward the network or factory level, instead of the product level. This means that they adopt an inside-out perspective (network capabilities) rather than an outside-in perspective (competitive priorities) (Rudberg, 2004). The configuration and coordination of a production network has a significant impact on the cost structure, delivery times, responsiveness, flexibility, quality, customer service, and innovation of the company. Hence, it is important that the management of production networks is based on a holistic approach that includes both external and internal factors and is supported by an overall network strategy.

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Fig. 6.3 Intended and realized strategy. Reprinted from “Strategic planning for human resource management in construction”, W.F. Maloney, 1997, Journal of Management in Engineering, Vol. 13 No. 3, pp. 49–56

Figure 6.2 illustrates such a holistic approach, which supports the clarification of the network strategy. This framework represents a continuous network strategy process and is based on the three core areas, “Context”, “Content”, and “Process”, described in more detail in the following sections. These core areas include the elements from the overall production strategy described above. Depending on the requirements, the elements of the network strategy can be derived from the production strategy. If adjustments or extensions are necessary, it is crucial to ensure that these changes are in accordance with the overall business objectives. As shown in Fig. 6.3, the realized strategy usually deviates strongly from the intended strategy due to changing context factors or not implemented strategic measures. In some cases, some companies do not even have a specified intended strategy and thus the realized strategy is the product of many different, individually made decisions and thereby unintentional or emergent. Deviations between the intended and realized strategy are also often found in companies that sequentially formulate and implement the strategy: First, the strategy is planned, then the concept of implementation is approached. If the deviations are too significant, companies try to integrate strategy formulation and implementation sequentially to enable a continuous process. The disadvantage of the two methods is that a lot of time passes between formulation and the finished implementation and therefore changes in the dynamic environment often cannot be addressed. In this case, an almost simulated strategy formulation and implementation is recommended. However, this requires that the strategy is not only developed in the circle of the

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closer management team, but also follows a bottom-up approach to enable fast implementation (Müller-Stewens & Lechner, 1999).

6.4

Strategy Context

As described in the previous section, context factors include both external factors (economic, social, political, competitors, market, and trends) and internal factors (e.g. organizational structure, culture, site capabilities, subnetworks, innovations, and improvements). These factors within which a strategy is developed ensure an outside-in as well as an inside-out perspective and also indicate the demand for sub-networks. The influence of contextual factors becomes clear in the example of Roos and Kennedy (2014), comparing high-cost with low-cost environments: In a low-cost environment, the focus is very often on efficiency. This results in higher productivity and the elimination of activities that do not contribute to value creation. The operating system is reduced to the absolute minimum. On the other hand, the objective in a high-cost environment is often effectivity. Focusing on innovation and R&D, the aim is to improve products and processes to compete on superior value. It is important to note that wage levels, but also other external and internal factors, are constantly changing. The competition is highly dynamic, and the success of a company depends on its ability to anticipate and adapt to these changes.

6.5

Strategy Content

The network strategy content is the link between a network’s external environment and its internal target setting. The internal and external context factors describe the business environment of market and competition. The competitive priorities describe the exact position the network aims for in this environment. Thus, it is important to set competitive priorities that are consistent with and supportive of the business strategy. These priorities can be achieved by exploiting and extending certain core production capabilities. In order to generate a sustainable successful competitive advantage, a company must ensure that it additionally identifies and develops distinctive, hard-to-imitate capabilities within its production network, which consistently provide superior value to the customer. It is worth mentioning that production capabilities and their dimensions keep changing over time. Hence, it is important to both modernize and identify new operation capabilities that serve the current competitive environment more efficiently. The interaction between priorities and capabilities was analyzed by Sansone et al. (2017). Table 6.2 shows an overview of the critical capabilities identified. The capabilities of a production network are not only based on the capabilities of the individual sites, moreover, active network management (through configuration and coordination) can provide additional, so-called “network capabilities”

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Table 6.2 Interaction between priorities and capabilities Priorities Cost Quality Delivery Flexibility Service Innovation Environment

Capabilities Total cost efficiency, productivity, resource efficiency, process efficiency Performance, conformance, durability, supplier quality Dependability, delivery time, time to market Volume flexibility, production mix flexibility, customization flexibility, broad product line, labor flexibility, supplier flexibility Customer service, after-sale service, advertising, broad distribution Product innovation, process innovation, technology innovation, service innovation, market innovation, supply chain innovation Environmental-friendly products, environmental-friendly processes, environmental-friendly supplier

Note. Adapted from Sansone et al. (2017)

(Miltenburg, 2009; Shi & Gregory, 1998). These are accessibility, thriftiness, mobility, and learning, and are described in Chap. 2. Accessibility, thriftiness, and mobility are determined by or derived from the global footprint and the configuration of the network. They might be in a certain trade-off relationship. Attempting to position many sites that produce the same products as close as possible to the customer worldwide means that economies of scale or a reduction of duplicates will hardly be achieved. On the other hand, networks with high economies of scale and hardly any duplicates are likely to have trouble providing the mobility of products, processes, or production volumes. The aim of the network strategy formulation is to determine the demanded priorities and to develop the necessary capabilities that are provided by the network. The necessary management activities to identify and develop the required capabilities are explained in the next section. Skinner (1996) argues that there is incompatibility between individual strategic objectives and that a trade-off must be accepted between them, in which a company achieves high levels of performance in one manufacturing capability at the cost of lower levels of performance in one or more other capabilities. Ferdows and De Meyer (1990) with their Sand Cone Model, which is described in Chap. 1, claim that strategic goals are interrelated and build on each other. The degree of interrelation between the strategic goals depends to a large extent on the circumstances and contextual factors. The studies mentioned above tend to focus only on production and thus on their respective processes, resources, and capabilities. In the past, economic, cultural, and regulatory contexts have been neglected and therefore should be considered when choosing strategic goals (Gold et al., 2017).

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Strategic Process

Once the company has defined certain production and network capabilities in order to achieve a unique strategic position in the market, which is consistent with the overall business objectives, it is necessary to determine which actions are required to realize these capabilities. The exploitation and development of the desired capabilities involves two steps: Step 1 The first step deals with the design of the structure and infrastructure of the production. Decision choices including patterns of decisions at structural and infrastructural levels in the context of production network management are divided into the design layers “network configuration” and “network coordination”. The individual design layers and their respective relevant decision dimensions and decisions variables are well described by Friedli et al. (2014). Configuration and coordination determine, for example, where activities are geographically located and how these activities are integrated, they explore the degree of plant focus and trade-offs, and helps to align chosen competitive priorities and network capabilities, and translate strategy into action. Step 2 The second step in the development of the desired capabilities includes improvement initiatives. On the one hand, it is important to maintain a competitive edge by continuously improving and optimizing existing capabilities. On the other hand, it is possible to gain new capabilities based on existing structure and infrastructure by establishing an improvement culture. Competitive advantages provided by superior internal capabilities are much more sustainable than one achieved through purchases (Hayes et al., 2005). Since the goal of design decisions is always an improvement of the network, the implementation or realization of the decision can also be regarded as an initial improvement activity. The above-mentioned steps design and improvement and their respective layers configuration and coordination can be derived into a consistent approach, which is visualized in Fig. 6.4. The initial phase is the configuration of the network, the physical design of the individual sites, and the network as a whole. Based on this, the organization and management of the activities distributed in the network is defined, i.e., the coordination. The implementation of coordination measures and a continuous optimization is now attempted through improvement initiatives. An optimal design of the coordination mechanisms offers the necessary prerequisites for the most effective and efficient implementation and further improvements at the configuration level for the individual sites and the network. Every improvement and optimization can set an impulse for a necessary change process. The new or improved capabilities developed through optimization require a rethinking of the structural and infrastructural decisions made in order to continue to achieve the strategic goals/contents efficiently. A significant improvement of the

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Fig. 6.4 Four steps toward an optimized manufacturing network

capabilities can even open strategic opportunities that may even lead an organization to adjust its competitive strategy. These considerations lead to the conclusion that strategy development for production networks can never be a static event, but rather must always be re-evaluated based on the many dynamic variables.

6.7

The Alignment of Different (Sub-)Networks at One Site

As mentioned at the beginning, products or product families can be grouped according to their respective priorities and then assigned to the (sub-)network with the most suitable design. In practice, it is not unusual for two or more different product families with different priorities to be located at one site. Consequently, two or more (sub-) networks intersect at the same site. The production activities are usually independent from each other, which is due to the different characteristics of the product families and businesses of the (sub-)networks. Nevertheless, the networks produce under the same roof, and share certain resources, assets, and services, although they are hierarchically located in different networks, and may even have different organizational structures. The more the (sub-)networks attempt to create net firm benefits by using synergies to save costs and improve the exchange of know-how, the more they are interwoven. The disadvantage of strongly integrated co-located units is the resulting increase in complexity, possible conflicts regarding resources, and more difficult decision making. As a matter of fact, the (sub-)networks have different goals in terms of priorities and necessary capabilities, which are derived from the different network strategies.

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In his thesis, Lützner (2017) focused on the question of how to systematically support the strategic production management of shared factories (sites accessed by several networks). During his research, he developed a qualitative approach to derive a suitable organizational design for these sites. The slightly adapted approach consists of the following seven steps: 1. Measure the degree of compatibility between the manufacturing units of the (sub-)networks. 2. Verify the consistency of the respective network strategies and with the overall business strategy. Adapt network strategies if necessary. 3. Formulate the manufacturing focus by allocating resources and responsibilities to the respective units of each (sub-)network. 4. Identify potential strategic and financial net firm benefits. 5. Define the degree of integration. Strategic and financial goals and the respective manufacturing focus of each (sub-)network must be considered. 6. Identify necessary organizational changes and implement them. 7. Formulate rules and guidelines that regulate the relationship between the co-located (sub-)networks. It is important to define responsibilities and establish guidelines to ensure the continuity of organizational structure. Different degrees of integration and thus different organizational structures are possible if two or more production units of different (sub)networks are placed in one site. Like the production strategy, the defined structure should be continuously reviewed. A change of the context factors and thus a possible change of one of the existing network strategies may also require a change of the organizational structure.

6.8

Conclusion

The success of formulating and implementing the perfect network strategy depends on several variables and there are many of them in a global production network. In addition to variables related to products, processes, markets, stakeholders, competitors, supply chains, technological innovation, and environmental concerns, there are also a number of variables related to the location of each site, such as the cost of production factors, culture, taxes, laws, currency fluctuations, trade pacts, tariffs, and political risks (Ferdows, 2018). These circumstances make it impossible to define a strategy that fits all companies perfectly. The framework, which is presented in this chapter, assists in the formulation and design of the perfect network strategy by considering as many of the above-mentioned variables as possible. It is anticipated that using the holistic approach will help managers to: 1. Understand the context of their production network by looking at external and internal factors

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2. Identify essential production priorities and distinctive capabilities that underlie the content of the strategy 3. Evaluate potential decisions, to exploit and develop the desired capabilities The monitoring of structural and infrastructural decisions as well as external and internal contextual factors always has to ensure consistency. Nevertheless, the most important thing is to communicate the network strategy clearly to everyone involved.

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Lützner, R. (2017). Shared Factories—A focused factory perspective on the management of co-located manufacturing units in international manufacturing networks (Dissertation, University of St.Gallen). Maloney, W. F. (1997). Strategic planning for human resource management in construction. Journal of Management in Engineering, 13(3), 49–56. https://doi.org/10.1061/(ASCE)0742597X(1997)13:3(49). Miltenburg, J. (2005). Manufacturing strategy: How to formulate and implement a winning plan (2nd ed.). Portland, OR: Productivity Press. Miltenburg, J. (2008). Setting manufacturing strategy for a factory-within-a-factory. International Journal of Production Economics, 113(1), 307–323. https://doi.org/10.1016/j.ijpe.2007.09.001. Miltenburg, J. (2009). Setting manufacturing strategy for a company’s international manufacturing network. International Journal of Production Research, 47(22), 6179–6203. https://doi.org/10. 1080/00207540802126629. Müller-Stewens, G., & Lechner, C. (1999). Die Gestaltung unternehmerischer Einheiten: Der General Management Navigator als ein Konzept zur integrierten Strategie- und Wandelarbeit. Organisationsentwicklung, 18(2), 24–43. Netland, T. (2013). Exploring the phenomenon of company-specific production systems: One-bestway or own-best-way? International Journal of Production Research, 51(4), 1084–1097. https://doi.org/10.1080/00207543.2012.676686. Paiva, E. L., Roth, A. V., & Fensterseifer, J. E. (2008). Organizational knowledge and the manufacturing strategy process: A resource-based view analysis. Journal of Operations Management, 26(1), 115–132. https://doi.org/10.1016/j.jom.2007.05.003. Platts, K. W., Mills, J. F., Bourne, M. C., Neely, A. D., Richards, A. H., & Gregory, M. J. (1998). Testing manufacturing strategy formulation processes. International Journal of Production Economics, 56–57, 517–523. https://doi.org/10.1016/S0925-5273(97)00134-5. Porter, M. E. (1996). CLASSIC-What is strategy HBR.pdf. Harvard Business Review, 74(6), 61–78. Roos, G., & Kennedy, N. (Eds.). (2014). Global perspectives on achieving success in high and low cost operating environments. Hershey, PA: Business Science Reference. Rudberg, M. (2004). Linking competitive priorities and manufacturing networks: A manufacturing strategy perspective. International Journal of Manufacturing Technology and Management, 6 (1/2), 55. https://doi.org/10.1504/IJMTM.2004.004506. Sansone, C., Hilletofth, P., & Eriksson, D. (2017). Critical operations capabilities for competitive manufacturing: A systematic review. Industrial Management & Data Systems, 117(5), 801–837. https://doi.org/10.1108/IMDS-02-2016-0066. Schroeder, R. G., Bates, K. A., & Junttila, M. A. (2002). A resource-based view of manufacturing strategy and the relationship to manufacturing performance. Strategic Management Journal, 23 (2), 105–117. https://doi.org/10.1002/smj.213. Shi, Y., & Gregory, M. (1998). International manufacturing networks-to develop global competitive capabilities. Journal of Operations Management, 16(2–3), 195–214. https://doi.org/10. 1016/S0272-6963(97)00038-7. Skinner, W. (1996). Manufacturing strategy on the “S” curve. Production and Operations Management, 5(1), 3–14. https://doi.org/10.1111/j.1937-5956.1996.tb00381.x. Slack, N., & Lewis, M. (2002). Operations strategy. Harlow, UK: Financial Times Prentice Hall. https://books.google.ch/books?id¼e1upWYAGbM0C Swink, M., & Way, M. H. (1995). Manufacturing strategy: Propositions, current research, renewed directions. International Journal of Operations & Production Management, 15(7), 4–26. https://doi.org/10.1108/01443579510090381. Thomas, S. (2013). Produktionsnetzwerksysteme—Ein Weg zu effizienten Produktionsnetzwerken (Dissertation). University of St.Gallen. Ward, P. T., & Duray, R. (2000). Manufacturing strategy in context: Environment, competitive strategy and manufacturing strategy. Journal of Operations Management, 18(2), 123–138. https://doi.org/10.1016/S0272-6963(99)00021-2.

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Site Selection Processes in Global Production Networks Bastian Verhaelen, Sina Peukert, and Gisela Lanza

7.1

Introduction

Production-related location choices are long-term, complex, difficult to reverse, and based on uncertain information. They constitute a high risk for a company as they are crucial for its long-term financial strength and performance. International Manufacturing Networks (IMNs) have become very complex as most companies have production sites all over the world. The increasing interconnectedness between these sites leads to even more factors that need to be taken into account when deciding on where to locate another production site (Mühlenbruch et al., 2006). According to Kinkel (2009), only one in five companies succeeds in this process. This chapter is intended to give an overview of this pivotal topic for companies. In the first part, a short introduction to the importance of a meaningful site selection process is given. Afterward, an analysis of important selection criteria, motives, and factors influencing the choice of location will be shown. To illustrate what an optimal process should look like, the second part presents a dedicated process model for location planning. Further, different valuation methods are elaborated. As a wrap-up, an example of a representative choice process, examples of best/bad practices as well as a prospect on the future are given. Considering the three layers of the St.Gallen Management Model for Global Manufacturing Networks, the site selection process can be classified as an element of the configuration layer (footprint) as it affects the number of sites in the production network (see Chap. 2). The increasing number of influencing factors and the progressive fragmentation of the supply chain, however, make this planning process more and more complex. Incomplete planning and wrong decisions (e.g., not considering all knock-out criteria such as qualification of employees or preservation of flexibility) might B. Verhaelen (*) · S. Peukert · G. Lanza wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany e-mail: [email protected]; [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_7

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Fig. 7.1 Main drivers for relocation abroad and to their origin

even force companies to relocate their production back to their origin. Figure 7.1 depicts the main drivers for back shoring to the origin on the right-hand side and criteria which originally prompted the company to relocate abroad on the left-hand side (VDI, 2009). It needs a structured planning process for a site selection that integrates all relevant factors and criteria. At the start of this process, the motives for the establishment of a new site are of importance. Here, various motives influence a company’s decision to relocate its production into foreign countries. The main drivers are, e.g., the “cost pressure motive”, which involves lower wages, as well as the material and energy costs in the target country. This is particularly important to companies with a high share of manual work in production, e.g., in the assembly of wire harnesses. The “market motive” indicates that a company can open up to new markets, improve its image by producing on-site, and settle at a closer distance to the customers. This motive is important, e.g., in the automotive industry in China, where a company needs a local partner and needs to fulfill local content criteria to serve the

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market. Further motives are the “risk motive” (avoidance of economic and political risks, risk diversification, etc.) and the “resource motive” (additional know-how, production close to essential raw materials, and use of qualified local employees). Access to resources is important to companies with a need for innovation, thus, a site near Silicon Valley is a good choice to stay in contact with state-of-the-art tech and IT companies.

7.2

Site Selection Process Model

The site selection process model allows treatment at three progressive planning levels and therefore enables an integrated evaluation. A special workflow is prescribed at each level. After the description of each level, a brief examination of why consideration of the corresponding level is relevant to real planning processes is given. Figure 7.2 presents an overview of the entire process model.

7.2.1

Company Level

At the company level, the corporate objectives are examined, which include, e.g., the strengths and values of a company or the principles of the markets in which the company operates. A detailed approach to analyzing a manufacturing network’s environment is explained in Chap. 5. In the next step, these corporate objectives are aligned to the corporate strategy to find out what is relevant to the future success of the company. In particular, the strategy concerning the business areas (i.e., price leadership, high delivery capability, quality leadership, technology leadership, etc.) must be clear as it is of great importance to the further planning process. A comparison of the status quo and the desired state of the company determines the 1) Checking the input variables Company Level

1.1 Check company objectives

1.2 Alignment with the strategy

1.3 Determine need for action

2) Development of the site strategy Production Network Level

2.1 Analysis of the portfolio

2.2 Develop network alternatives

2.3 Decision on the site strategy

3) Systematic site selection Production Site Level

3.1 Create requirement profiles

Fig. 7.2 Site selection process model

3.2 Search for potential sites

3.3 Evaluation & Decision

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need for action. Please refer to Chap. 6 for our proposal of a specific strategy process related to IMNs.

7.2.2

Manufacturing Network Level

At the manufacturing network level, an analysis of products, processes, and network structures is carried out to get an overview of the current production network. Please refer to Chap. 2 for our proposal for a profound configuration assessment using the Site Portfolio approach. To complete the network structure analysis, the supplier network is also analyzed. After this analysis has been carried out, several network alternatives can be developed in the next step. At a network level, it can be differentiated between different structures like the “world factory”, “web”, “chain”, “local-for-local”, or “hub-and-spoke” (Lanza et al., 2019). Different network structures fit different manufacturing characteristics. For instance, products with a high manual assembly effort should be manufactured in low-wage countries, while components with a high technological content should be produced in countries where the risk of product piracy and know-how transfer is lower (Lanza et al., 2019). An example of a wellperforming “hub-and-spoke” production network comes from a North American automotive company. Components with a high-value density are produced in two central “hubs” in Mexico (a low-wage country) to benefit from high economies of scale, whereas components with a low-value density and a high assembly effort are manufactured in “spokes” in the US close to customers. This enables the company to pare down logistics and customs costs. Since the spokes are very versatile, the automotive company can easily adapt to future changes (Mahle, 2019). A similar development can be observed in the example of a German automation manufacturer: What began as a “world factory”, quickly developed into a “hub-and-spoke” structure. With the increasing importance of the size and value of the American and Asian markets, the company is striving for a higher regionalization of its supply chain in the form of a “local-for-local” manufacturing network to be able to react more quickly to changes in local demands and requirements (Festo AG & Co. KG, 2019). However, there are also examples where other network structures are highly efficient. The Airbus aviation group follows a “web” structure with four big assembly sites around the globe, which enables it to produce its aircraft according to specific customer requirements with a high degree of flexibility and at the same time proximity to the customer (Hochdörffer et al., 2018). Taking into account the motives of global production, the strategy coming from the company level, and the current and aspired network structure, one can conclude that different reasons lead to the necessity of building up a new site to cope with global competition.

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Manufacturing Site Level

If a company’s locations strategy indicates the need for a new manufacturing site, the third phase of production planning begins. The first step is to create a requirements profile for a new site. When determining this profile, a precise distinction must be made between location factors and process factors. While location factors serve to describe and compare the characteristics of sites and thus to evaluate their attractiveness to production activities of a company in general, process factors describe the manufacturing process of a defined product and therefore weigh up the location factors concerning their relevance to a company. Both location and process factors are divided into quantitative and qualitative factors. The specific requirements that a company places on a new production site are the so-called “location criteria” which are derived from the location and process factors and can be distinguished into knock-out criteria, minimum criteria, and wish criteria. The location criteria in their entirety establish the requirements profile (Abele, 2006). The second step is the search for potential sites. This process works like a funnel. In the quest for potential sites, searches are carried out at the global, regional, and local levels, which are explained in the following three paragraphs. The first stage of the funnel process is a global pre-selection of locations. In this initial stage, subjective assessments are included in the narrowing down of the search, in addition to the requirements profile that has been worked out. Personal contacts, recommendations, and existing relationships often lead to an early limitation to a few options. Therefore, the inclusion of the objective requirements profile is important to fully grasp and exhaust the number of possible options. The search at the regional level aims to limit the search to a few regions or countries. However, no concrete site is sought as yet. Here, it is meaningful to correctly record the actual situation using company-relevant location criteria for local requirements. The local search now attempts to determine the optimum from a few options. To achieve good comparability of the individual options, the search should be conducted in independent regions. The direct comparison of different options in a competitive situation should finally balance all negotiated factors (rents, leases, subsidies) so that a decision can be made (VDI, 2009). In the process of site evaluation and selection, a rough selection is first made at the global pre-selection level. At this point, knock-out criteria and minimum criteria are mainly used. At the level of regional and local search, a detailed evaluation of the individual options or regions is aimed at. Using static and dynamic methods of investment appraisal, the regions are evaluated and, if necessary, compared with existing sites. By this comparison, possible weaknesses of a potential site can be determined, which can lead to its exclusion. For a company with high street transport due to bulky products, it is important to be less than five kilometers away from a highway crossing to be reached in a good manner, which is an important criterion for the company. The result of this level is a narrow selection of regions, cities, or, under certain circumstances, even individual plots of land. On average, about four to six alternatives are considered.

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At the local level, the remaining sites are compared using profitability analyses when focusing on cost aspects. For this purpose, all relevant information is gathered and a site inspection is carried out to obtain a comprehensive picture, on the basis of which a decision can then be made (VDI, 2009). Especially in today’s uncertain times, it is important to think through a comprehensive site selection. Flexibility and adaptability are key success factors for a sustainable global footprint (Lanza et al., 2019). Various valuation methods can be used for site selection, which is explained in the following section.

7.3

Valuation Methods

To reach a well-founded location decision, an evaluation of different criteria to achieve the most fitting site needs to be done. The valuation methods used for this purpose can be distinguished into qualitative methods, which take into account non-quantifiable factors like political stability, and quantitative methods which are often referred to as investment calculation methods. Quantitative methods compose the expected costs to the expected revenues. They can be divided into static methods and dynamic methods. Figure 7.3 shows the most common valuation methods of each category (Abele, 2006). For qualitative methods, utility analysis can be considered a useful addition to the exact arithmetical methods by the recognition of aspects that are not quantifiable in monetary terms. Before a company can carry out a utility analysis, a complete list of criteria (knock-out, minimum, and wish criteria) is needed. Besides the completeness of this list, no criteria on it must be mutually exclusive, overlapping, or conflicting. Conflicting criteria can be terminated by setting priorities. Now, all criteria can be weighted based on their importance. To weigh them, the method of the “comparison of pairs” is suitable. Hereby, every criterion is compared to all other criteria on a binary basis and the more important (or equally important) one is determined. Afterward, the degree of fulfillment for the individual criteria can be defined for every site alternative. The ranking of the alternatives for sites can now be done by multiplying the degree of fulfillment with the weight factor of every individual Qualitative Methods

Quantitative Methods Static

Dynamic

 Comparison of Pairs

 Cost comparison Calculation

 Net Present Value

 Utility Analysis

 Profitability Calculation

 Internal Rate of Return

 Return on Investment (ROI)

 Annuity Method

 Amortization Calculation (static)

 Amortization Calculation (dynamic)

Fig. 7.3 Valuation methods

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criterion and then by summing these results up over every criterion. Finally, the ranking can be interpreted, and the most fitting site can be chosen. For quantitative methods, one differentiates between static and dynamic valuation methods. Static methods assume a continuous course of business and are therefore severely simplified methods since interest effects are not considered. Nevertheless, their reason for existence lies in their simplicity. Static methods can be used to get a fast and rough overview of the different site alternatives or to justify the gut feeling. Nevertheless, they are never accurate. Therefore, one should consider using a more sophisticated dynamic valuation method which lays the basis for the financial department in every company. Dynamic valuation methods take into account interest rates and time effects when calculating the benefits of different site alternatives. Therefore, they represent the real financial situation in a much more accurate way. One of the most frequently used dynamic methods is the calculation of the net present value (NPV). The net present value C is the value of all revenue streams which are related to an investment project discounted to the present by the interest rate. Consequentially, the NPV is the sum of cash flows which is the difference between receipts and expenditures in every period discounted at the financial assets of the first period. If the NPV is positive, an investment is profitable, if it is negative, the investment is gainless (Abele, 2006). In addition to the methods presented, companies often perform a sensitivity analysis in which the estimated input/factor values can vary within a defined range. This enables a company to depict instabilities in the planning process by a systematic variation of these values within the predefined range. The result of the analysis shows which factors have a major influence on the efficiency of an investment and additionally, the risks of an investment can be assessed by taking uncertainties into account in the analysis. A sensitivity analysis can be combined with the application of the presented methods, or can be integrated into computerbased decision support systems, which are also a helpful tool for companies to master the complexity of production site decisions (Moser et al., 2016; Wöhe & Döring, 2010).

7.4

Example of a Representative Site Selection Process

To show how a systematic approach to strategic site selection can look like, a concrete example is presented in the following. The site selection process model with a special focus on the production site level serves as a basis for this. The example is based on a globally operating German automotive group that is looking for a new location for the production of electric engines. For this purpose, a qualitative pre-selection of possible sites has been carried out in a first step, to subsequently subject the results to quantitative analysis.

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Location Criteria

Global Level

Regional Level

Local Level

Quantitative: Raw materials and supplies Distribution cost Tax- and subsidy laws Property cost Construction Cost Realisation time Wage Level Qualitative:

x x x

x x x

x

x

x

x

Market potential Population structure Infrastructure Availability of employees Availability of suppliers Political stability Protection against product piracy

x x x x x x x

x x x x x x

x x x

x x x x

Fig. 7.4 Location criteria

7.4.1

Location Criteria

First, company-specific location criteria have been selected based on the collection of locations. The level of relevance of the criteria is drawn from an intensive analysis of the three levels “global”, “regional”, and “local”. At each level, different criteria are relevant to the site selection. For the German automotive group, this selection looks as follows (see Fig. 7.4). The crosses in Fig. 7.4 symbolize which criteria are relevant to the respective level.

7.4.2

Weighing of the Criteria

The criteria must now be prioritized into knock-out, minimum, and desired criteria. For the knock-out criteria, the company uses the method of pairwise comparison with the six criteria political stability, availability of workers, availability of suppliers, favorable wage levels, infrastructure, and protection against product piracy (see Fig. 7.5). These knock-out criteria are chosen due to the production profile of an electric engine. In this case, a stable environment as well as a good availability of workers is important to ensure an efficient production of new electric engines.

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Position

Percentage

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Political stability

Availability of suppliers

Protection against product piracy

Favourable wage level

Availability of suppliers

Comparison of Pairs 2 = is more important 1 = is equally important 0 = is less important

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27% 100%

Fig. 7.5 Pairwise comparison

In the column “Position”, one can find the ranking of the criteria by importance. Points 4.1 and 4.2 result in a weighted requirements profile for the production of the electric engine based on the knock-out criteria.

7.4.3

Location Longlist

Now that all internal requirements for the company are clear, the external environment must be examined more closely. It is essential to gather important information about potential settlement areas. The following map can be seen as a longlist of potential settlement areas, which contains the most important information about them (see Fig. 7.6). The usage of colored bullet points supports the quick identification of fulfillment (green) / non-fulfillment (red) of important knock-out criteria. In this case, the selection is limited to Germany and China (see Fig. 7.6).

7.4.4

Qualitative Analysis

Following the preselection of the two remaining alternatives, a qualitative comparison between these can be carried out. For this purpose, a utility analysis is made, which is an analysis to guide decisions based on the utility of different options. Necessary information for the utility analysis can be found in Fig. 7.7. The information in the table, e.g., transport connections or political stability, needs to be transformed into a utility scale. For example, the favorable wage level can be scaled from low wage to high wage. Different scales need to be taken into account, whereas one has to consider the different requirements of a company on the specific criteria. The resulting utility analysis can be found in Fig. 7.8.

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• Close to customer • Suitable premises • Good infrastructure

• Low wage level • Close to resources • Good infrastructure

• High wage level • High transportation cost

• High wage level • Lack of suitable premises

• Low wage level • Unstable political situation

• Low wage level • Close to resources • Bad infrastructure

Fig. 7.6 Longlist of potential settlement areas

Alternatives

Infrastructure

Labour market

General information

Location Factors

Germany

China

Transport connections

motorway, rail, airport

road, waterway

(Legal) Competition Acts

well-established

emerging

Political stability

very stable

stable

⌀ Hourly-Rate for a qualified employee per hour

€ 50

€ 15

⌀ Hourly-Rate for unskilled employees per hour

€ 15

€3

Availability of qualified employees

good

bad

Supplier Network

well-established

Availability of resources and suppliers

weak

moderate

very good

Fig. 7.7 Information table for utility analysis

WeightFactor

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Infrastructure

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Protection against product piracy

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Fig. 7.8 Utility analysis

Germany

China

Value

DoF

Value 18

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16 130

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Table 7.1 Net present value calculation Period 0 1 2 3 Net present value C

China
21,500,000€ 4,476,000€ 8,201,328€ 15,630,965€ 1,090,823€

Germany
12,750,000€ 1,534,560€ 4,478,397€ 10,424,699€ 178,440€

The weight factor is derived from the comparison of pairs (1–10), DoF—the degree of fulfillment (1–5)—is based on the information table as stated above. The result of this qualitative analysis indicates that Germany would be a better site alternative with a utility of 164 compared to China’s 130.

7.4.5

Quantitative Analysis

However, for a well-founded decision, an additional quantitative analysis is needed. We use the method of the net present value for this evaluation (see Table 7.1). The values in the table stand for the cash flow after taxes (Et
At) in the respective period. Periods 0–3 can be considered as the corresponding years where year 0 corresponds to the year of investment and year 3 to the third year after the investment. The results row depicts the net present value of the two alternatives. For calculation of the NPV, we set an interest rate of z ¼ 10% and use the following formula: C¼

T ðt¼0Þ

ðE t
At Þ ¼ ð1 þ zÞt

T t¼1

ðEt
At Þ
I 0: ð1 þ zÞt

ð1Þ

The net present value of China is thus calculated by C China ¼


21, 500, 000 € 4, 476, 000 € 8, 201, 328 € þ þ ð1 þ 0, 1Þ0 ð1 þ 0, 1Þ1 ð1 þ 0, 1Þ2 15, 630, 965 € þ ¼ 1, 090, 823 €: ð1 þ 0, 1Þ3

It is evident that China, which previously scored worse in the utility analysis than Germany, is now, according to the net present value calculation, the significantly better alternative.

7.4.6

Evaluation of the Results

For the final selection of a site, the conflicting results of the two evaluation methods must be analyzed and weighed up. On the one hand, there is the possible location of

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Germany with its qualitative advantages, e.g., a better availability of suppliers or employees and the protection of know-how. On the other hand, China has the quantitative advantage of an expected higher profit. A possible approach to decision-making could be to find ways that enable to compensate for the qualitative disadvantages of China, i.e., through supplier development, training of employees, and protective measures. Thus, supplier development and the training of employees will cost a non-neglectable amount of money. On the contrary, long-distance supply chains to China should be considered, which are a thread, as can be seen in the COVID-19 pandemic. Further, transportation would be more expensive, and the delivery time from China to other markets has to be taken into account. Current trends indicate that there is a movement from Asia to countries that are close to or even in Europe but still offer low wages. Possible locations are Turkey or Tunisia which need to be considered for back-shoring activities. Furthermore, the rising possibilities of digitization enable high-wage countries to produce much more cheaply and to relocate production capacities (Lanza et al., 2019). Thus, the company needs to follow this site selection process and consider more criteria and the current trends of digitization and nearby supply chains or production sites to have a broad overview of the site selection process.

7.5

Best Practices and Future Prospects

As shown in this chapter, the keys to a successful production network are sufficient planning and the anticipation of environmental trends. Hereinafter, some best practices illustrate how companies have succeeded in optimizing their production network (see Fig. 7.9). Balancing complexity vs. capability

Universally applicable Observe the global competition

Best practices

On a regional planning level

Use bargaining power

Commission local agencies

From the beginning of planning to implementation Fig. 7.9 Best practices of site selection processes

Collaborate with local partners

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Balancing Complexity and Capability

It is important to maintain a balance between complexity and capabilities in the planning process. A company should not build up more complexity than it can handle, because otherwise, the results will not be satisfactory and the relocation as a whole may fail (Verhaelen et al., 2020). For this purpose, one has to consider the site roles of a production network. For example, one manufacturer of optical machines uses complexity-related site roles that differentiate the capabilities of different sites by considering the complexity of the manufactured products. By doing so, the companies can cluster their product portfolios and their site roles by means of complexity.

7.5.2

Use Bargaining Power

For example, a global chip manufacturer built a new production facility in Asia for one of its more labor-intensive and less sophisticated production steps. For this purpose, the executive board narrowed down a shortlist of three remaining possible sites and negotiated in parallel with representatives of each business park. The advantage of this competition between the locations resulted in achieving concessions with direct subsidies and tax exemptions, which amounted to around 30 percent of the total investment. This example demonstrates that bargaining is an appropriate and recommendable method to achieve cost advantages and simplify the decision for a final location.

7.5.3

Commission Local Agencies

One of the world’s largest automotive suppliers was able to reduce the decision process for a new production site in South Korea from usually 6–8 months to just 4 months by installing a closely monitored system. The choice of South Korea was based on the fact that five major automotive manufacturers have big production sites there, making it a good location for an automotive supplier. As a first step, the supplier commissioned a local agency to compile a list of suitable sites, which was then shortened by comparing the sites concerning their distance to automotive manufacturers. To finalize the decision, a detailed evaluation of all three remaining sites was done, which included visits by senior executives of the supplier to all of the sites to get a personal impression. This best practice is intended to illustrate that a systematic approach with the help of local agencies can speed up the site selection process, especially in countries where companies are unfamiliar with local conditions.

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Collaborate with Local Partners

Another common best practice by which a company can expand its production network is a collaboration with local partners. In some countries, it is even a legal requirement to have a local partner in the form of a joint venture. The advantages of local partners are obvious: They have in-depth knowledge of customers and procurement markets, can help to overcome bureaucratic hurdles, and have the necessary know-how in the procurement of land and personnel. But it is crucial to select these partnerships wisely and to ensure that they are legally safeguarded. In 1991, the automobile manufacturer Volkswagen AG entered into a joint venture with the Chinese automobile manufacturer FAW Group Corporation. The results of the cooperation were 330,000 vehicles, 300,000 engines, and 180,000 gearboxes produced annually in China. The model range included the VW Jetta, VW Bora, VW Golf, Audi A4, and the Audi A6. This cooperation with a local partner enabled Volkswagen AG to access the Chinese market and set up a branch based on legal regulations.

7.5.5

Observe the Global Competition

Incumbents should not just focus on market growth and potential, they should also have an eye on their competitive environment, especially on the rapid growth of low-cost competitors. Xiaomi, a Chinese electronics company, with its structural cost advantage, built her skills and improved her performance over time and hence became a real threat to incumbents such as Apple and Samsung. Xiaomi convinces with its low prices and a design that resembles that of top-notch companies. Additional success factors for new production sites that have proven to be effective are to prevent staff turnover by binding new employees in the country abroad through higher wages, social facilities, and events and also to encourage an exchange of knowledge and ideas between employees in the country of origin and the targeted country (Abele, 2006). Of course, all of the presented best practices involve a great deal of effort and money, but the results illustrate that they are worth all the work and resources. According to a study by the Boston Consulting Group, we are in the fourth phase of globalization. The reason for this change is the influence of digital growth on global trade. Digital technologies used in manufacturing might be able to decrease labor costs by around 30% in industrial countries like Germany, South Korea, or the USA. Furthermore, the output per worker will also increase by around 30%. Companies, which built their production networks and supply chains in the context of the third phase of globalization where outsourcing of manufacturing into low-cost countries to build up globally dispersed supply chains took place, should reconsider these networks based on the new circumstances (Boston Consulting Group, 2016). This prediction is supported by a survey by McKinsey, which examines the effects of the COVID-19 crisis in the fashion industry. Nearly one in two of the procurement managers surveyed stated that they would increasingly rely on near-shoring

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production in nearby countries such as Turkey for the European market or Mexico for the American market (McKinsey & Company, 2020). Future location planning processes will be based on changing, volatile conditions. Due to the hysteresis of the production network adaption after the event of a disturbance, the network needs to integrate changeability as an enabler for resilience. To create a resilient production network, footprint management has to be designed in the desire for constant change, even though the changes or events that might happen are yet unknown. Here, big data analysis is an enabler to shorten the time of perceiving a change by identifying outliers of influencing factors (Lanza et al., 2019). An enhancement of transparency and the right decision-making structure can decrease the time from identification of an event to the change of footprint and management to ensure competitive production networks (Lanza & Treber, 2019; Verhaelen et al., 2020).

References Abele, E. (Ed.). (2006). Handbuch globale Produktion. Hanser.. Retrieved from http://deposit.dnb. de/cgi-bin/dokserv?id¼2713912&prov¼M&dok_var¼1&dok_ext¼htm Boston Consulting Group. (2016). Three Paths to Advantage with Digital Supply Chains. Retrieved from https://www.bcg.com/publications/2016/three-paths-to-advantage-with-digital-supplychains Festo AG & Co. KG. (2019). One Location Project – Produktion in China. Hochdörffer, J., Buergin, J., Vlachou, E., Zogopoulos, V., Lanza, G., & Mourtzis, D. (2018). Holistic approach for integrating customers in the design, planning, and control of global production networks. CIRP Journal of Manufacturing Science and Technology, 23, 98–107. Kinkel, S. (2009). Erfolgsfaktor Standortplanung (2. Aufl.). Springe. Retrieved from http://site. ebrary.com/lib/alltitles/docDetail.action?docID¼10297053 Lanza, G., Ferdows, K., Kara, S., Mourtzis, D., Schuh, G., Váncza, J., Wang, L., & Wiendahl, H.-P. (2019). Global production networks: Design and operation. CIRP Annals, 68(2), 823–841. https://doi.org/10.1016/j.cirp.2019.05.008. Lanza, G., & Treber, S. (2019). Transparency increase in global production networks based on multi-method simulation and metamodeling techniques. CIRP Annals, 68(1), 439–442. https:// doi.org/10.1016/j.cirp.2019.03.011. Mahle. (2019). Location planning @MAHLE. Set up of a new plant in Mexico. McKinsey & Company. (2020). Modebranche: Jeder vierte Lieferant in finanziellen Nöten durch COVID-19. Retrieved from https://www.mckinsey.de/news/presse/2020-05-06-modelieferanten-corona Moser, E., Stricker, N., & Lanza, G. (2016). Risk efficient migration strategies for global production networks. Procedia CIRP, 57, 104–109. https://doi.org/10.1016/j.procir.2016.11.019. Mühlenbruch, H., Großhennig, P., & Nyhius, P. (2006). Produktionsstufen- und Logistikgestaltung im Globalen Varianten Produktionssystem. Wt-Online, 96, 405–410. VDI Fachausschuss Fabrikplanung. (2009). Handlungsempfehlung zur Gestaltung globaler Produktionsnetzwerke. Ein Leitfaden der Arbeitsgruppe “Standortplanung des VDI-Fachausschusses Fabrikplanung”. Verhaelen, B., Haefner, B., & Lanza, G. (2020). Methodology for the strategy-oriented distribution of decision autonomy in global production networks. Procedia CIRP. Wöhe, G., & Döring, U. (2010). Einführung in die allgemeine Betriebswirtschaftslehre (24., überarb. und aktualisierte Aufl.). Vahlen.

Design for X – Site-Specific Adaptation of Production Processes and Products

8

Shun Yang, Sina Peukert, and Gisela Lanza

When a new production facility is set up abroad, it is necessary to consider the specific requirements of the respective location in order to align the production facility to the local market conditions and thus fully exploit the potential of global production. This is basically possible in different ways: On the one hand, a pure adaptation of the production technology and the internal logistics (e.g., change of the degree of automation or adaptation of the material flow) can be carried out. On the other hand, the design of the product manufactured abroad also offers scope for adaptation to the local conditions. Such adaptations can be market-related or—as in this chapter—bring the product closer to the locally available labor force, skills, and production resources, thus leading to an optimized and cost-reduced production for the foreign location. In order to provide practitioners with guidelines for the location-specific adaptation of production processes and product design when opening a new production site abroad, this chapter gives an overview of the factors that need to be examined with regard to necessary adaptations. Besides, the chapter presents different design options being used to adapt products and production processes to local conditions.

8.1

Introduction

Both the adaptation of the product design and the adjustment of production processes can be summarized by the term Design for X (DfX) (Nelson, 2016). The X represents either a specific property or a specific life cycle phase of the product (Tichem, 1997). For instance, the Design for Manufacture and Assembly (DfMA) is one of the first developed and most widespread approaches which aim at an economic production and assembly by a suitable production process S. Yang (*) · S. Peukert · G. Lanza wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany e-mail: [email protected]; [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_8

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selection and at resulting in a component design adapted to the location-specific requirements (Anderson, 2008; Andreasen et al., 1985; Boothroyd et al., 2002; Pahl et al., 2005). In order to provide practitioners with guidelines for the location-specific adaptation of production processes and product design when opening a new production site abroad and thus to support the implementation of Design for X, this chapter gives an overview of the factors that need to be examined with regard to necessary adaptations. Besides, the chapter presents different design options that can be used to adapt products and production processes to local conditions (see Sect. 8.1). Section 8.2 furthermore illustrates the adaptation of these options using a practical example. Section 8.3 finally summarizes the findings.

8.2

Methodology for the Site-Specific Adaptation of Production Processes and Products

The objective of this chapter is the presentation of a methodology that allows for a systematic adaptation of production processes and products to site-specific production conditions. The methodology consists of two modules. In the first module, the location-specific requirements are analyzed, while the design options are subsequently developed in the second module.

8.2.1

Site-Specific Requirements

When opening a new production site abroad, multiple dimensions of adaptation have to be taken into consideration for the adjustment of production processes and product design. According to Weiler (2010), eight different necessary categories of adaptation have to be considered which result from the influencing factors of global production (Lanza et al., 2019). Altogether, eight site-specific requirements can be derived which are summarized in Fig. 8.1. As it can be seen, the adaptations exemplarily refer to the cost structure, to the available production resources, to transportation conditions, or to the qualification of the employees. In the following, the requirements illustrated in Fig. 8.1 will be explained and outlined by means of different examples which strengthen their understanding. For example, the adaptation to cost structures primarily refers to the fact that at the new location, constructive adjustments to the product may have to be made in order to take advantage of site-specific factor costs. A product design can thereby be called cost-structure-oriented if it enables an intensive use of favorable factor costs at the production site, thus replacing expensive costs. For example, a high manual workload may be justified by low labor costs, thus enabling savings in machine costs by using simpler, more cost-effective production equipment. In addition, care should be taken to ensure that labor-intensive activities, such as assembly, are always carried out in the country with the lowest wages in the case of cross-border production.

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Fig. 8.1 Requirements for production processes and product design

Moreover, auxiliary processes such as internal logistics or workpiece handling can be changed easily without the need for subsequent adjustments. Similarly, material costs might be substituted by increased manual labor at lower personnel costs, e.g., by joining parts manually instead of cutting components from the solid, thereby wasting a lot of material. In some cases, cheaper substitute materials can be used, which require a slower production process. Especially in low-wage countries, minimal material costs should be aimed for, since this results in a strong relative total cost reduction. For example, instead of expensive materials with a high surface quality, cheaper materials can be used which are then reworked (see Fig. 8.2). As a second dimension of adaptation, the adaptation to process capabilities does not only consist of an adjustment to the available production resources, but also of an adaptation to existing qualifications. While the former emphasizes the use of local resources, production technologies, and prevailing production conditions, the latter implies an adjustment to the local level of training and qualification. For example, as the use of complex production technologies and sophisticated automation may not be applicable in low-wage countries, standard procedures might be more suitable (see Fig. 8.3). Next to the adaptation to cost structures and process capabilities, the adaptation to coordination requirements builds the third dimension of adjustment, thereby referring to low coordination and support efforts. Normally, a large number of individual procurement objects from different suppliers result in a high effort in terms of coordination, support, and logistics. In order to work with only a few suppliers, but still obtain the same added value, entire modules for procurement in low-wage countries should preferably be adopted (see Fig. 8.4).

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Fig. 8.4 Optimizing product design through modularization

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Fig. 8.6 Translation of standards into clear-cut generalist specifications

Since logistics processes might also have to be adjusted to local requirements, the adaptation to transport conditions also forms part of the procedure and comprises a reduction of costs, transportation time, and damage through the use of standardized loading equipment, robust transportation, and the reduction of storage and carrying costs. For example, the use of standardized and normed load carriers simplifies handling along the transport, reduces costs, and avoids special equipment (see Fig. 8.5). To prevent misunderstandings resulting from different cultures, languages, or technical backgrounds, the fifth dimension of adaptation is related to an adaptation to communication requirements. When adjusting to local requirements, it is recommended to use local standards and rules. For instance, the technical documentation must be specified in an understandable and explicit way (see Fig. 8.6). Related to political and governmental influencing factors, an adaptation to the local taxes and duties might be suitable in order to, e.g., minimize customs-related

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costs. Therefore, when adapting to local characteristics, the selection of materials and production processes should be based on locally available materials, especially taking into account their prices, availabilities, and qualities (see Fig. 8.7, top). This might reduce costs by up to 50%, as advantageous local cost structures are used and import duties, transport costs, and exchange rate risks are eliminated. In addition to a targeted selection of production materials, the formal classification of the imported goods must be considered, since there are different duties for products and components (see Fig. 8.7, bottom). Since the newly opened site might be situated in a region with either a high risk of product piracy, little legal protection, or high staff fluctuations, there also is a need for adaptation to knowledge protection requirements. In this context, a segmentation of the product might, e.g., prevent imitations. As a last requirement, the adaptation to dynamic developments emphasizes the consideration of potential changes resulting from changing production factors. Here, the consideration of compatible thermoplastics might serve as an example of the adaptation to risks as they can be flexibly used on the same machine.

8.2.2

Design Options for Site Adaptation

In order to be able to meet the requirements for a successful site-specific adaptation (see Sect. 8.2.1), different design options might be deployed (see Fig. 8.8). In the first place, an adjustment of the production processes might be done without changing the product structure or product design. As shown in Fig. 8.8, several possibilities such as change of material flow, handling, quality assurance, tools or

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Fig. 8.8 Typical development paths for local adjustment. Reprinted from Handbuch globale Produktion, by E. Abele (Ed.), 2006, Hanser

instruments, equipment, machine productivity, manufacturing technologies, process chain, etc., could be considered here. As a second option, adjustments of the production processes to the newly established site might have to go hand in hand with a modified product structure. While this latter option is associated with higher adjustment efforts, it also offers higher adjustment potentials (Abele, 2006). Regarding a change of the product structure, several approaches are recommended, such as a change of the design or construction of secondary parts, product structure, or functionality. However, the design options cannot replace the individual analysis of the corporate strategy, the market requirements, and the site-specific conditions of the production site. Rather, they are intended to provide ideas. Costs and benefits must therefore always be compared. The typical development paths for local adjustment are illustrated in Fig. 8.8. Two concrete industrial use cases are presented in the following to explain the above design options in more detail. The first use case occurs in the assembly line for an electric engine of industrial plants and explains the adjustment of the production processes while at the same time not changing the product structure. One of the assembly processes is feeding of a slat package and caulking with a spindle. For the assembly line in Germany, the automated feed of the slat package out of blase is applied. The gripper is adapted to enable transmission of the slat package into the workpiece carrier. Then, the shaft is automatically provided. Subsequently, the spindle is caulked in a slat package. In China, on the contrary, the slat package and spindle are manually seized and inserted together, before the flip-switch is released. Similar site-specific adaptations of the production process can also be seen in other processes, such as assembling of the insulating mask on the guy anchor, and pressing of the ball bearing into a bearing cap. The second case focuses on a site-specific adaptation of the production processes which results from a modified product design. Typical design adaptations that imply

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Error-proof design instead of costly automatic tester for quality assurance Simple joining process instead of expensive connection technology

Fig. 8.9 Adapt manufacturing engineering by modified product design

changed production processes or technologies are, e.g., a differential design instead of an integrated design, an error-proof design instead of costly automatic testers for quality assurance, and simple joining processes instead of expensive connection technologies. The integral design thereby aims to combine as many functions as possible in one component, thus reducing the number of individual parts to be joined. The differential construction method, on the contrary, describes the division of a component into several individual parts that are favorable from a manufacturing point of view, thus corresponding to the design principle of the separation of functions or division of tasks. For a simple and inexpensive production, a greater degree of differentiation should be aimed for, i.e., many simple parts should be provided. For simple and inexpensive assembly, on the other hand, a higher degree of integration is usually aimed for (Ehrlenspiel et al., 2007). As a result, fewer parts have to be assembled, which may be more complex and therefore more costly to manufacture. A higher number of parts or differentiation contrasts with a higher integration or individual part complexity (see Fig. 8.9). Besides the presented reasons, a higher degree of differentiation in the building structure may also be appropriate based on other objectives: • Dividing a complex scrap-sensitive assembly or manufacturing operation into several simpler processes. • Reducing the variety of materials and manufacturing processes. • Using standard or repeat parts. • Dividing a large-volume element into stackable components.

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8.3

Application of the Methodology for Site-Specific Adaptation of Production Processes and Products

8.3.1

Initial Situation and Adaptation Task

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As an application example, the adaptation of a sensor product for production and assembly in Asia is presented in the following. The manufacturer is a German company that produces sensors and sensor solutions for industrial applications. Until recently, the entire production for the Asian market took place at the main plant in Germany (E1), but now, a new production site in Malaysia must be integrated in the value-added network. The innovative company has recognized the importance of localized production processes and products as the basis for a successful production. The aim is to develop a site-specific adaptation of production processes and products for the development of the Asian, above all the Chinese market (A1). For this purpose, it is necessary to decide on the appropriate level of site-specific adaptation. Therefore, the network was analyzed in detail and the production factors were recorded. In doing so, the concept of the eight site-specific requirements of global production was used (see Sect. 8.1). Overall, measures were designed not only for the in-house production parts, but also for the purchased parts, as these are also to be sourced almost exclusively from Asia. The resulting measures are presented below.

8.3.2

Development of Concrete Measures Resulting from the Requirements for Site-Specific Adaptation

A total of 15 measures for site-specific adaptation has been worked out. As an example, the measures resulting from an adaptation to the local cost structure are explained below: A cost structure-oriented sensor has high priority since the target costs are significantly lower than the costs of current products manufactured in Germany. The Asian sales market requires a cost structure that corresponds to the local competitive products and the existing purchasing power. Due to the low labor costs both at the in-house production location in Malaysia and at the supplier locations in Asia, it is expedient to achieve cost savings in other, more expensive factors by increasing the use of labor. At the future location, reducing the demand for manufacturing equipment is particularly suitable for this purpose in order to avoid investments in capital-intensive manufacturing technologies. Several possibilities have been found to adapt the production processes in such a way that the requirements for the production equipment are reduced: In the production of the sensors in Germany, a complex automated soldering process is used to connect two printed circuit boards. An adjustment has been made here for the Asian market by realizing the connection with individual cables instead of conductor tracks. The hand soldering process thus allows for a replacement of expensive automated systems by manual work.

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Germany

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Fig. 8.10 Simplify application of product label

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Adhesive bonding

Fig. 8.11 Simplify production processes through material change

A further reduction of the requirements for the production equipment can be achieved in the application of product labels (see Fig. 8.10). In Germany, the labeling is done by a thermal transfer printing process. In the case of the sensor manufactured in Asia, the expensive imprint can be replaced by an identification sticker that is applied manually. A small recess in the housing enables precise positioning. A third adjustment to the existing cost structure can be achieved by changing a connection between two components. In previous models, this tolerance-critical connection is by an automated caulking process since the complex positioning of the two components requires a precise capital-intensive workpiece conveying system. By using a connection design that can be realized with the help of a hand press, a further optimization of the cost structure is possible. Furthermore, by empowering the production processes for the use of different plastic materials, a less capital-intensive joining technique can be used. This allows two parts to be joined manually instead of using an ultrasonic welding process (see Fig. 8.11). All in all, it can be clearly seen that the manual portion of the work is deliberately increased for these products and that they must be also adapted to manufacturing inaccuracies and fluctuations in the manufacturing process. Tolerance chains could be reduced, for example, by adding manual adjustment steps to reduce requirements for manufacturing accuracy. In general, the production processes are characterized

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by a very simple design and very low complexity. For example, the manufacturing steps are simplified, and the simple manufacturing technologies are utilized.

8.4

Summary

Summarizing this chapter, it can be said that in order to obtain the full benefit of global production, the two options of adapting production processes and product design to locally given circumstances should be explored. Here, the recognition and analysis of local conditions form prerequisites for realizing the adaptation. Eight dimensions of site-specific requirements have been presented that might guide practitioners on their way to adaptation. In addition, two design options are established and illustrated to meet the requirements. These outputs serve as a source of ideas and support site-specific production processes and product designs by providing basic proposals for adaptation. Looking at the way forward, the digitalization of all areas of life increasingly finds its way into production and often implies disruptive changes. For example, new capabilities of smart automation technologies such as smart and connected machines are reshaping the operations of manufacturing plants themselves. As a result of these transformations, which are difficult to predict, manufacturing companies find themselves in a more and more dynamic environment. Approaches such as the regionalized implementation strategy hence need to be developed. By using these, the company could select the most suitable smart automation technologies for a sitespecific adaptation of production process in the future.

References Abele, E. (Ed.). (2006). Handbuch globale Produktion. Hanser. Retrieved from http://deposit.dnb. de/cgi-bin/dokserv?id¼2713912&prov¼M&dok_var¼1&dok_ext¼htm Anderson, D. M. (2008). Design for manufacturability & concurrent engineering: How to design for low cost, design in high quality, design for lean manufacture, and design quickly for fast production. Cambria, CA: CIM Press. Andreasen, M. M., Kähler, S., & Lund, T. (1985). Montagegerechtes Konstruieren. New York: Springer. Boothroyd, G., Dewhurst, P., & Knight, W. A. (2002). Product design for manufacture and assembly (2nd ed., rev. expanded., Vol. 58). M. Dekker. Ehrlenspiel, K., Kiewert, A., & Lindemann, U. (2007). Kostengünstig Entwickeln und Konstruieren (6., überarbeitete und korrigierte Auflage). New York: Springer. Lanza, G., Ferdows, K., Kara, S., Mourtzis, D., Schuh, G., Váncza, J., Wang, L., & Wiendahl, H.-P. (2019). Global production networks: Design and operation. CIRP Annals, 68(2), 823–841. https://doi.org/10.1016/j.cirp.2019.05.008. Nelson, D. (2016). The innovation tools handbook. Boca Raton, FL: CRC Press. Pahl, G., Beitz, W., Feldhusen, J., & Grote, K.-H. (2005). Konstruktionslehre (6. Aufl.). New York: Springer. https://doi.org/10.1007/b137606. Tichem, M. (1997). A design coordination approach to design for X (Dissertation, Delft university). Weiler, S. (2010). Strategien zur wirtschaftlichen Gestaltung der globalen Beschaffung (Vol. Bd. 158). Shaker.

9

Product-Mix Allocation Felix Klenk, Sina Peukert, and Gisela Lanza

9.1

Introduction

Enforced by continuously expanding production structures and production programs, original equipment manufacturers have faced an increasing complexity when dealing with product allocation decisions in recent years. Fluctuations in demand and other key components of the production network require adaptable and well-structured production mechanisms to maintain and enhance global competitiveness. Questions of where and when to produce certain products have risen in priority, especially in the context of globally operating production networks, due to extended factor costs and global market dynamics. Therefore, it is essential for manufacturers to further optimize and reconfigure their production networks as historically grown networks or isolated allocation decisions have led to efficiency losses. The problems of product allocation and network configuration are highly interdependent and hence need to be taken into account in an integrated manner when making decisions. A more detailed look at considering customer influence on production network decisions is presented by Hochdörffer et al. (2018a), and is also introduced in Chap. 10. This chapter proposes several approaches that particularly focus on product allocation decisions, whereas some integrated approaches also regard configurational aspects. The presented decision support methods in this chapter range from simpler tools that help comparing the most promising allocation and reconfiguration decisions (such as the portfolio approach) to more complex and elaborate methods that aim to provide an optimal solution for product allocation (e.g., mathematical optimization). After giving detailed insights into each approach, F. Klenk (*) · S. Peukert · G. Lanza wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany e-mail: [email protected]; [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_9

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the advantages and disadvantages of the methods are discussed under the viewpoint of their practical applicability. Lastly, an integrated approach that contains some of the discussed methods is presented and reviewed by a case study for the production network of an aviation manufacturer.

9.2

Upfront Approaches to Support Product-Mix Allocation

9.2.1

Checkbox Approach

The checkbox approach aims to identify and evaluate basic preconditions and minimal requirements of production sites and capabilities in order to allow for the allocation of product variants. Its main goal is to set a frame that eliminates unfitting product allocation possibilities. The process is useful when decision-makers face many unfiltered allocation possibilities. For the following description of the general structure, the model by Abele et al. (2008, p. 106) is used. Commonly used as a tool for solving site location problems, the method can also be adapted and used in a similar manner for allocation decisions. For a start, the approach is split up into multiple different stages of ever deeper assessment and evaluation. While the first one is defined as a global preselection, the second one consists of the module of target region or country. In a third step, more local variables build up the local preselection stage. The approach is concluded by the local shortlist and, lastly, the investment proposal and decision. These stages can differ and need to be defined anew for every case and application. It should be considered also that the given classification must not necessarily focus on geographical aspects and can rather be focused on a more specific technology or cost-related aspects. For every step of the decision process, minimal requirements for specific product allocations need to be defined for each product. At the starting level, these requirements, such as basic cost level or demand market, are the least detailed ones and are looking to exclude production entities in countries and regions. If a possible allocation candidate does not fulfill these checkbox requirements, it is not included in the further decision process. The process of finding fitting criteria is key in the analysis and should be done by expert survey or using other qualitative methods. Over the next iterations, more detailed and product-specific criteria are derived using similar methods. These detailed criteria can range from the technical feasibility of a specific product feature to the cost of procurement for particular intermediates of the product. The level of defined requirements and number of iterations are up to the decision-maker which allows for a flexible evaluation process. Over time, the iterating evaluation process generates a condensed shortlist of production entities fitting the product allocation requirements. The approach does neither include interdependencies of entities in a production network nor of multiple product variants. Hence, the character of minimal requirements should be maintained, as complex structures cannot be displayed.

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Portfolio Method

The portfolio method is suitable to assess and compare different product allocation options and to obtain a detailed and transparent overview of the network structures for single product variants. The selection process for the prioritized production entities and the evaluation methods used may vary and are dependent on the requirements of the decisionmaker. The portfolio approach therefore represents a versatile and adjustable way of evaluating the decision criteria of different allocation options. Abele et al. (2008, p. 108) provide a specific implementation of the portfolio approach as a decision tool for the footprint reconfiguration of production entities that will exemplarily show the practical application of this method. To follow common portfolio tools, the approach compares and evaluates possible investment options for production networks. The allocation and reallocation of product variants within a network can therefore be understood as such investment options. For the evaluation of different options, the approach mostly uses quantitative data like the relative net present value of the specific reconfiguration for the y-axis and savings on operational expenditure for the x-axis. The net present value includes all information about the short-term cost aspects like the costs of the production ramp-up. Data regarding long-term costs are aggregated into savings on operational expenditure using information like savings in expenses for materials or logistics. The third adjustable evaluation criterion for different allocation options can be shown by the size of each depicted data point. Thus, the size measures the net present value of the reconfigured product variant, illustrating the increased importance of larger variants with a greater impact on general profitability. These three evaluation components of the portfolio approach are mandatory but can also be expanded even more, e.g., through the coloring or shape of data points. As mentioned, the used criteria like net present value or operational expenditure are highly flexible and can be adjusted for individual requirements of the decisionmaker. However, it should be mentioned that the approach does not offer a specific evaluation, e.g., quantitative analysis, but rather provides a transparent tool for comparison of multiple options.

9.2.3

Clustering Analysis of Product Portfolios

The clustering approach aims to aggregate different product variants into featuresimilar clusters. These groups try to maximize homogeneity within clusters and heterogeneity between clusters in order to reduce complexity for more advanced decision approaches later. The application of clustering analysis is a useful tool to achieve an advantageous tradeoff between planning complexity and reality closeness. For further explanation, this section introduces the clustering algorithm by Hochdörffer et al. (2018b). The approach is divided into three separate modules. The general structure is introduced by Fig. 9.1. The first module defines and selects technology-related

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Fig. 9.1 Components of the clustering approach. Reprinted from Hochdörffer (2018)

product features. The second module is needed to characterize fitting similarity measures, on which basis the later clustering process performs. The last module contains the actual clustering algorithm. To achieve the goal of reducing complexity and maintaining an accurate product aggregation quality, it is important to define and select production-related product features that adequately characterize the products. Product variant requirements are represented by variables for process and capacity features. On the one hand, process features specify if there are any alternative production technologies for a process step that can be used to manufacture a product variant or not. On the other hand, capacity features show the needed capacities if a separate production technology is chosen. This is applied for all possible production steps and each product variant. After characterizing each product variant by key features, distance or similarity measures need to be defined to compare and cluster them. As both features differ, it is necessary to use different distance measures for both. For further details on which exact measures to use, see Hochdörffer et al. (2018b). However, the application of these measures results in a distance matrix which compares all product variants pairwise. The last section of the approach is the heuristic clustering algorithm itself. It uses the calculated distance matrix as input as well as an additional predefined input parameter k, which represents the number of wanted clusters. Then, the algorithm starts with randomly assigning k product variants as cluster centers. Based on these initial centers, the remaining products are assigned to the nearest cluster according to the distance matrix introduced above. This preliminary solution of clusters is then evaluated. The total cost of a cluster is understood as the sum of all distances between its product variants and the belonging cluster center. Based on minimizing the distance matrix and hence the cost of the cluster, new cluster centers are defined at each cluster to secure the most cost-efficient allocation. Afterward, it is necessary to reassign the non-center product variants to the closest cluster center. This process step is repeated until the overall cost situation does not change anymore. Concluding, the respective cluster solution with the assigned products is returned. The clustering itself also needs to be repeated several times to avoid locally optimal solutions. Furthermore, there are several extrinsic and intrinsic measurements to

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evaluate the accomplished clustering quality. One of the measures consists in using the silhouette coefficient for intrinsic validation. This technique measures a cluster objects’ cohesion, i.e., the similarity to its own cluster, and its separation from the object compared to other clusters.

9.3

Solution Approaches to Product-Mix Allocation in Global Production Networks

9.3.1

Mathematical Optimization for Product Allocation

This section proposes the application of the approach introduced by Hochdörffer (2018). The primary focus of this mathematical optimization is to formulate an accurate model which generates the best possible product allocation within a given production network. The solution contains a holistic result consisting of an optimal product variant allocation sequence in addition to an optimal network configuration sequence over time that minimizes the total cost of the production network. The model is also capable of dealing with and reflecting the occurrence of adaption and flexibility within the network. The mixed-integer linear program is split up into four different components. The basic optimization model sets the general frame for the allocation solution as it includes the holistic network representation. It is followed by several measures to depict the network’s ability for adaption and flexibility. The last two sections of the approach consist of a set of constraints and an evaluation function that includes all relevant cost aspects. The first part of the mathematical optimization contains the basic model of the network and of the products. Each product is depicted as a sequence of different process steps. For a specific process step, there are several alternative production technologies. Treber et al. (2019) use these technologies in a similar manner to optimize production networks through a reallocation approach. To represent these possible combinations, tuples of process steps and production technologies are introduced and implemented as specific segments that are localized at every production site. The number of different segments of production sites and their use is defined by each site’s size and technical capacity. Further restrictions arise from resource capacities for the consumption of resources needed to provide certain segments. Figure 9.2 illustrates this general structure. Additionally, suppliers and customers as well as the flow of goods are taken into consideration within the basic model. The core of the optimization approach is its possibility to feature multiple adaptability and flexibility aspects. The first introduced measure is the product-mix flexibility, referring to the property that single resources can be used to manufacture multiple products. It allows for a high degree of choice when allocating product variants inside a network without increasing costs. Implemented into the basic model, this is represented through tuples of resource, segment, and process-step-

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Fig. 9.2 Components of the optimized production network. Reprinted from Hochdörffer (2018)

technology combinations. The extent of possible flexibility is restricted by product segment-compatibility parameters. The option for routing flexibility measures is included into these restricting parameters. Depending on the assigned segments and mandatory process-step-technology combinations, product variants can be manufactured by using multiple routes throughout the production network. This allows for an agile and robust supply chain within the network that decreases risks and the chance of production shortages. Following, the need for consideration of short-term demand fluctuations is met by volume flexibility measures. Volume flexibility implies a cost-efficient adaption of the production volume. This is implemented by multiple variables that are all used to create a flexible production volume in addition to existing capacities. The variable volume is also called flexibility corridor. There are internal parameters, such as workforce-related factors like overtime and temporary work, or general aspects like temporary resources, but also external ones like outsourcing of process steps and production segments. The last addition is the application of network reconfiguration aspects to handle middle and long-term fluctuations in demand and other important external criteria like evolving costs. A given configuration limits the amount of flexibility in terms of the mentioned measures. Therefore, several possibilities for network adjustments are introduced. Sites and segments can be closed and opened, resources can be hired or dismissed permanently, unlike above where volume flexibility means short-term change, and capacity restrictions of segments can be expanded or decreased. Also, process settings can be variated by activation, deactivation, or adaption of new production technologies. Additionally, the utilization of transportation routes can be changed by closing, reopening, or opening completely new routes. Next to these above elementary components, there are several possibilities to further depict realistic applications. Strategic actions that have already been decided and affect network settings can be used to set a frame to certain variations of network modeling. Long-term contracts, for example, lead to constraints for the earliest and

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latest opening, as well as the closing of sites and segments. Another possible adjustment is the application of greenfield planning to the basic model. This is implemented by initializing the states of process settings, segments, and sites with set values. Fluctuations within network structures, caused by the ramp-up of production, induce efficiency loses that are considered. Learning effects and capacity reductions are regarded to allow for the realistic simulation of ramp-up production with increasing efficiency. The third section deals with the matter of basic constraints for the optimization model. They are used to model the above-stated characteristics. Here, we will spare these constraints due to their pure mathematical character. Completing the optimization approach, an evaluation function is introduced which consists of various cost aspects. By minimizing this expression of total cost of the production network, the optimal network configuration is derived. The mathematical evaluation function is clustered into four cost categories. Quantitydependent costs deal with transportation, inventory, and material cost, i.e., higher quantities equal higher cost. Contrasting to these, quantity-independent costs represent all cost factors that are fixed, e.g., cost for process settings, network sites, or segments and regular resources. The third group of costs arise due to flexibility measures such as the outsourcing of capacities and the use of temporary resources. Separated from this third group, the costs for the reconfiguration of network structures include all relevant investment costs that deal with the permanent restructuring of the network itself, but not the later ongoing fixed and variable costs of maintaining a structure. The introduction of qualitative criteria into the evaluation function in a similar manner is also addressed by Lanza and Moser (2014) through a multi-objective approach focusing more on future uncertainty.

9.3.2

Post-Optimality Analysis

The post-optimality analysis proposed by Hochdörffer (2018) targets the identification and evaluation of further improvement potential of an already existing solution which is, e.g., provided by the mathematical optimization. It is to point out that the mathematical solution is only optimal within a set of constraints. These restrictions will now be adapted to continue the optimization. Within the approach, improvement potential is solely understood as the adaption and use of structures and capacities that lead to a reduction of the objective value and, therefore, to cost reduction. The overall data representation of the post-optimization is directly derived from the original mathematical optimization and is therefore similar. Differences occur due to the fact that all variables of the mixed integer linear program are relaxed into a linear program. The solution method of the simplex algorithm is used to calculate an optimum. The analysis consists of three different modules. The first module is identifying promising optimization directions. Afterward, the approach analyzes shadow prices and slack variables within the network. The latter two analysis modules both use a parametric optimization, whereas module one is reiterating the solution algorithm

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Fig. 9.3 Post-optimality approach to integrated product allocation. Reprinted from Hochdörffer (2018)

presented in the mathematical optimization. This structure is illustrated in Fig. 9.3. It shows that all three modules of the approach are based on the solution of the mathematical optimization approach. Given the already existing solution space of the mathematical approach, further improvements that minimize the objective value need to be identified. Possible improvements are defined as optimization directions which expand the existing solution space by modification of adjustment possibilities. In a first step, these optimization directions need to be recognized by the application of different methods, such as benchmarking, creativity techniques, and expert surveys. For each identified direction, a specific working hypothesis is derived that is used to solve the optimization program again. The resulting objective value of the new solution space validates whether the corresponding optimization direction is supported, in the case of decreased objective value, or not, in other cases. At first, each working hypothesis is validated separately which sorts out cost-increasing optimization directions. After that, the same is done for combinations of the remaining hypotheses to examine possible interdependencies. The working hypothesis with the best objective value generated by this iterating process is transformed and implemented into a modified optimization program that is used for the following analysis of shadow prices. This is necessary as each modification of adjustment possibilities leads to new shadow prices which are introduced in the next paragraph. The next module is based upon the previously modified mathematical program. Using negative shadow prices of single constraints by enlarging underlining capacities through parametric optimization, the objective function can be further decreased. Negative shadow prices therefore represent current capacity bottlenecks where the shadow prices correspond to the amount of possible cost reduction, including factors like potential costs for capacity expansion or technical feasibility.

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As shadow prices can be affected by interdependencies through multiple constraints, every measure based on these must be implemented individually. Like the greedy algorithm, the approach implements the measure with the highest shadow price fist, then solving the optimization program again to validate that no other constraint is violated. After obtaining a new solution space, new shadow prices are calculated. The process is repeated until no further improvements are available. As the validity needs to be checked every time a capacity is adjusted, the approach in module two is similar to the approach in module one, using the basic optimization program in every iteration. Lastly, the third module deals with the analysis of slack variables and the resulting allocation adjustments. Similarly, to shadow prices, slack variables indicate a misallocation of capacities. A positive slack variable means that the current capacity is higher than the actual demand for the restricted resource and therefore adds unnecessary costs to the objective function. The allocation solution obtained by module two is not affected when cutting down unneeded capacities. A renewed solution to the optimization program is hence not necessary after the capacity adjustment. Any positive slack variable can be implemented into measures in parallel. Costs occurring with these measures are considered in the analysis. It is to mention though that positive slack variables can also be reduced by covering additional capacity needs, i.e., when extending production quantities.

9.4

Discussion and Application

9.4.1

Discussing the Advantages and Disadvantages of the Mentioned Approaches

After having outlined the general features and processes of the decision tools for product allocation problems, it is further important to discuss their practical characteristics when applied. It can be seen that the approaches have an enormous range of use case possibilities, as nearly every method has different outcome aims and prerequisites. Starting with the simplest methods of decision-making, the checkbox and the portfolio approach do not aim for directly identifying the cost-optimal allocation of products, but rather sequentially exclude the most unfitting allocation choices. The underlying evaluation of allocation choices of both methods can be individually modified by the decision-maker and is therefore highly flexible. In contrast to more mathematical approaches, they require limited effort and provide the planning party with a high amount of overview and transparency over possible and favorable allocation and reconfiguration options. Regarding complexity reduction and overviewing aspects, the clustering analysis also needs to be emphasized. Especially for allocation problems with an elevated number of product variants, it allows decision-makers to break down planning complexity to a manageable degree while simultaneously preserving a dense aggregation of information.

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This level of transparency and aggregation is barely achievable by the application of the mathematical or the post-optimality approach. Due to their complex mathematical structures, the resulting allocation solution is often complex to comprehend. The amount and quality of required data for both methods, ranging from the current production network configuration to detailed cost aspects like material and human work force cost, need to be far superior to the supportive decision tools mentioned above, as the value of the optimal solution otherwise heavily diminishes. In contrast to the other approaches, the mathematical method as well as the postoptimality analysis, however, generate a holistic solution set for all product variants at once for an extended time frame. This solution set represents the optimal configuration for every production entity in the network given all relevant information. Unlike the checkbox or portfolio approach, the mathematical formulation allows for a network-focused point of view. This leads to the consideration of interdependencies between different network sites and capabilities. Another advantage of the mathematical optimization is the fact that the method maps a variety of possible flexibility-adjustments, like routing, volume, and product-mix flexibility. This increases the quality of the resulting solution and the optimal network configuration. The room for even further optimization is filled by the implementation of shadow prices and slack variables when using the post-optimality approach. However, this approach can only be used when an already implemented network configuration is given.

9.4.2

Mixed Approaches and Their Practical Implications

The previous section dealt with the major characteristics of each approach when being applied. The question of which method a decision-maker should choose to fit his/her own individual purposes is, however, not fully answered yet and will remain so, as relevant decision variables may differ depending on the situation and preference. Nevertheless, the following sections will provide the reader with an idea of when to use and combine suitable approaches. When merging different approaches, the range of application possibilities increases drastically. Overall, the most versatile method that can be used in combination with any other approach is the clustering analysis for product variants. Data mining methods such as the clustering approach hold promising potential for future network decisions, as shown by Verhaelen et al. (2019). However, on its own, the clustering analysis does not give a proper allocation solution but can reduce complexity and increase transparency for any other approach. For general applications or single-choice allocation options, it can be easily operated as a condensing tool to then exert the checkbox or portfolio approach to further cut down and compare allocation solutions. The checkbox approach can be implemented in almost the same way as to aggregate information and then evaluate the information and decide over the shortened number of options with the portfolio approach.

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When dealing with more complex and network-centered allocation problems, the integrated approach of clustering analysis, followed by the mathematical optimization and the post-optimality method, is highly preferable. It can be used for nearly all practical cases of allocation and reallocation problems, as it is highly flexible in its mathematical formulation. What this integrated approach may look like will now be shown by a practical example of the aviation industry that has been conducted by Hochdörffer (2018). The analyzed subject of the practical case is the production network for the final assembly of a passenger aircraft manufacturer for one product family. This product family consists of four different product variants. Grown historically, the network includes important suppliers and four production plants with 33 localized and 22 potential production segments, where product variants and corresponding process steps can be allocated. For the planning horizon of 5 years, the production program as well as the demand forecast are given. The forecast consists of a fixed demand part and a scenario case for later and uncertain demand. The need for reconfiguration and optimization of the production network is clear, as the production entities already operate at full capacity. In a first step, the clustering algorithm is applied to partition the product portfolio into 10 product variant clusters. Selected criteria for similarity measures are based on their possible process sequences and their capacity consumption at the production segments. The clustering leads to an extensive diminishment of needed constraints and mathematical formulations. The computed solution set contains various openings and closings of production segments during the planning horizon to achieve the lowest-cost configuration. It also provides the decision-maker with detailed insight and transparency of the overall cost structure. The cost for flexibility and reconfiguration can be tracked for every segment and plant. Given the specific manufacturer network, they account for 4.4% of the total cost. Using this solution set, the last module of the integrated approach features the post-optimality analysis. Firstly, based on interviews with representatives of the manufacturer, four different working hypotheses for the modification of adaption possibilities are derived and used for the identification of feasible optimization directions. However, two of these hypotheses are rejected as they show no influence on total cost, yet the two remaining ones are implemented as they offer additional benefit. The first profitable hypothesis suggests that a new transportation route should be opened to connect two segments of consecutive process steps between two production sites. The other hypothesis implies the reduction of costs through expanding production capabilities by opening a new segment at plant one. Secondly, the improvement measures that are indicated by shadow price analysis are implemented. In this case, they are made up of the adaption of operating time of resources and adaption of temporary work at different production entities. Thirdly, the same is done with slack variable analysis. In consequence, segments and processes with unnecessary capacities are modified for a better capacity utilization.

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Summary

Concluding, this chapter aimed to support the search of network configuration decisions for product-mix allocation problems. Therefore, several approaches were introduced and illustrated, which differed in the amount and quality of required data as well as in their general goal for decision support. Firstly, three upfront approaches, which mainly try to simplify the decision process, were presented. Starting, the checkbox method uses basic allocation requirements to sort out unsuitable allocation decisions. By eliminating these options, the problem complexity decreases. The clustering analysis aims toward a similar goal of complexity reduction. Here, the product-mix is clustered into feature-related groups based on formal similarity measures. One tool for increasing transparency and the general understanding of network-related allocation decisions is shown by the portfolio approach. The method depicts each possible allocation decision as an investment option and graphically compares the respective options’ profitability and key features. Following these more supportive techniques, the chapter proposes two optimization-focused approaches. The mathematical optimization is a tool that maps every component of the global production network into a formal frame. An optimal solution which contains the allocation set for each product variant is generated by means of computing. Within the model, there are different possibilities for adaption, e.g., network reconfiguration, and flexibility, e.g., routing, product-mix, and volume flexibility. Composed of this solution, the post-optimality analysis tries to further improve the solution by consideration of possible optimization directions, shadow prices, and slack variables. The last section of the chapter discusses practical advantages and disadvantages of the mentioned approaches. Afterward, promising combinations of these are presented and exemplified. Then, an integrated approach based on Hochdörffer (2018) is introduced and shown by a practical case study for the global production network of an aviation manufacturer.

References Abele, E., Meyer, T., Näher, U., Strube, G., & Sykes, R. (Eds.) (2008). Global production. A handbook for strategy and implementation. Springer, New York Hochdörffer, J. (2018). Integrierte Produktallokationsstrategie und Konfigurationssequenz in globalen Produktionsnetzwerken (Dissertation, Karlsruhe Institute of Technology). Hochdörffer, J., Buergin, J., Vlachou, E., Zogopoulos, V., Lanza, G., & Mourtzis, D. (2018a). Holistic approach for integrating customers in the design, planning, and control of global production networks. CIRP Journal of Manufacturing Science and Technology, 23, 98–107.

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Hochdörffer, J., Laule, C., & Lanza, G. (2018b). Product variety management using data-mining methods — Reducing planning complexity by applying clustering analysis on product portfolios. In IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) 2017, 593–597. Lanza, G., & Moser, R. (2014). Multi-objective optimization of global manufacturing networks taking into account multi-dimensional uncertainty. CIRP Annals - Manufacturing Technology, 63(1), 397–400. Treber, S., Moser, E., Helming, S., Häfner, B., & Lanza, G. (2019). Practice-oriented methodology for reallocating production technologies to production locations in global production networks. Production Engineering - Research and Development, 56(2), 783. Verhaelen, B., Thomas, K., Häfner, B., Lanza, G., & Schuh, G. (2019). Potenziale datenbasierter Produktallokation. ZWF Zeitschrift Für Wirtschaftlichen Fabrikbetrieb, 114(3), 96–100.

Order Planning

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Florian Stamer, Sina Peukert, and Gisela Lanza

10.1

Introduction

In the context of managing global production networks, the way customer orders are planned and scheduled in the production network highly affects a company’s competitiveness. A well-defined and well-executed planning process enables manufacturing companies to satisfy their customers’ needs to a high degree while keeping production costs at a low level. In a company’s overall planning hierarchy consisting of long-term strategic, mid-term tactical, and short-term operational planning, order planning can be positioned between the mid-term and the shortterm planning processes. As it can be seen in the automotive industry, for example, the transition from mid- to short-term planning currently lacks consistency since the primary and secondary demand planned mid-term does not necessarily match the real customer demand arising in the short term. Customer order planning in this context can significantly improve consistency by bringing together demand forecast and real customer orders. In the first part of this chapter, the relevance of order planning is motivated. After that, a promising approach addressing order planning under uncertainty is presented in more detail. In the end, the key insights are summarized.

10.2

Motivation

Various well-known trends in recent years and decades have shaped the way products are designed, manufactured, and distributed today. On the demand side, the overwhelming driver is the radical focus on customer-specific needs and the regionalization of products and services. This leads to many product variants each F. Stamer (*) · S. Peukert · G. Lanza wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany e-mail: fl[email protected]; [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_10

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with smaller quantities which must be manufactured at a competitive price level. This “mass customization” enables customers to choose one product variant among many according to their specific preferences (Buergin et al., 2016). On the supply side, customer-individual mass production is realized by a modular product architecture and a variant-flexible flow production. These production systems are not anymore operated in a central world factory delivering to customers all over the planet. Rather, manufacturing companies went global successively in the past decades and most are operating in globally distributed production networks today (Lanza et al., 2019). Various advantages go hand in hand with this trend, and multiple motives still urge organizations to follow the global trend. The most important among them is the reduction of production costs, especially labor costs, creating proximity to the customer, and opening up new markets. The resulting key challenge nowadays is the manufacturing of customer-specific products at globally distributed and interconnected locations. Concerning this complexity inflation, both on the demand as well as on the supply side, the definition, and execution of a well-structured planning process come to the fore. Order planning is important in this context for several reasons. As already mentioned above, it closes the gap between mid-term and short-term planning. From a mid-term tactical point of view, manufacturing organizations have to forecast primary and secondary demand and communicate capacity needs of pre-products and parts with their suppliers months in advance. At this early stage, the specific customer requirements are not yet determined. Moreover, customers demand short lead times for their individualized products. From a short-term operational point of view, a fit between the forecasted planned demand and the upcoming real customer orders needs to be generated (Koren, 2010; Volling, 2009). This is all leveraged by the complexity of planning within a global production network, where limitations such as the buildability and capacity restrictions of individual factories increase difficulty. To sum it all up, manufacturing companies face the challenge to make optimal decisions in order planning under the uncertainty of order configurations within a global production network. From this practical perspective, the overall goal here is to bring customer demand and the optimal supply of capacities and materials together. Both sub-goals are best fulfilled at a different time in the product lifecycle so that a tradeoff under uncertainty results. Regarding the order planning process, two states of information uncertainty can be distinguished: (a) The date and quantity of customer orders are known, but the customer-specific order configuration is uncertain. (b) The customer orders are generally uncertain and order configurations are unknown as well. While the first situation is exemplary for the aircraft industry, the second one particularly applies to the automobile industry. To address the practical need for a meaningful order planning methodology, both cases are presented in more detail below (Buergin, 2018).

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Fig. 10.1 Uncertain information in mid-term order planning

Although customer orders are available under information status (a), there is planning uncertainty regarding the order configuration for each customer. Depending on the final configuration, necessary parts need to be ordered from the suppliers, and production capacities need to be arranged months in advance. The reason for uncertain order configurations of secure orders lies in the customers who demand having the flexibility to choose product options with the shortest possible lead time and the latest possible fixing date per option as just-in-time specification. As already mentioned above, this customer behavior is exemplary for the aircraft industry, where aircraft orders are planned years in advance, but their specification needs to be adjusted short-term based on current circumstances such as passenger demand and requirements. Regarding the mid-term production planning process, this means that companies have to deal with customer orders under uncertain order configurations (Buergin, 2018). Under information status (b), there is even higher planning uncertainty due to the lack of information on customer orders. In this case, plan orders can be generated to anticipate short-term incoming customer orders. Determining potential configurations of the plan orders aims for minimizing the deviation from the real customer demand. The more similar plan and real customer orders are in the end, the more the planning uncertainty can be reduced. As already mentioned above, the customer behavior under information status (b) is exemplary for the automotive industry, where customized cars are expected to be delivered with short lead times. It is also possible that customers compromise on the configuration to meet the desired delivery date. Based on the example of the automotive industry, this is often the case if customers buy their car at a local retailer instead of placing a customer-specific order. Again, if the pre-configured car is very similar to the customers’ expectations, the customer is highly satisfied and less “costs” for not meeting the real demand occur. Overall, regarding the mid-term production planning process, this means that companies must deal with plan orders under uncertain customer order assignment. Figure 10.1 summarizes the two states of information uncertainty described above (Buergin, 2018). Addressing information states, (a) and (b) as well as corresponding planning uncertainties, this chapter presents a practice-oriented methodology for mediumterm order planning in global production networks of variant-rich series production under uncertainty of the customer configuration.

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Approaches for Global Order Management

The presented methodology follows a three-step approach. Each step is related to specific planning tasks that build upon each other. Regarding the two situations of information uncertainty (a) and (b) described above, the procedure also distinguishes between the two corresponding use cases. Regarding the practical implementation of the procedure, examples from the mentioned industries are presented within the planning tasks and give important insights. The three planning tasks and the issues involved can be described as follows and will later be discussed in more detail: 1. Order generation (case a and case b) • How can orders with corresponding configurations be generated in the medium term? (a) Based on existing customer orders which are not specified yet, scenarios are generated to illustrate the uncertain order configurations. (b) Since no real customer orders exist at all, plan orders are generated based on the future probability of order configurations. 2. Order scheduling in the production network (case a and case b) • How can customer orders with uncertain order configurations and plan orders be scheduled in a production network in the medium term? • The scheduling in case (a) as well as in case (b) can be modeled as a binary optimization problem. 3. Sales order assignment (only case b) • Real customer orders need to be assigned to planned orders from step one. This step is only necessary in case (b). Figure 10.2 illustrates the planning procedure.

Fig. 10.2 Mid-term order planning tasks under uncertain configurations

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10.3.1 Order Generation The scheduling of orders within the production network is prevented by the fact that order configurations are either uncertain or orders are not known at all. To address this issue, there is a need to generate orders with real configurations, which are then plannable, but at the same time represent the uncertain structure of customer demand. For doing so, the planning step of order generation utilizes a variant tree for modeling product variants. In general, a tree consists of a root, several nodes, and multiple leaves at the end. Each connection between the root and a leaf is called a path. In a variant tree, every product variant and thus every possible order configuration is represented by a path to one leaf of the tree. According to this logic, every node on the corresponding path represents a decision between multiple options by the potential customer. Sticking to the example of the aircraft industry, the root of a variant tree may be the Airbus A320 model family. Nodes represent different options such as choosing the size of the fuselage or the engine. So, overall, the variant tree is used to represent all possible order configurations a customer can choose from. Rules for the buildability of a specific variant can also be integrated into the logic by allowing only buildable combinations of options (Ehinger et al., 2002; Rosenberg, 1996). Figure 10.3 shows an exemplary variant tree for the Airbus A320 family. Regarding the two use cases (a) and (b), the order generation step is executed equally by utilizing a variant tree such as the one described above. The only difference lies in the outcome. While the outcome in case (a) is the generation of scenarios per customer, the outcome in case (b) is the generation of plan orders per sales market. In the following, the creation and application of a variant tree are explained in detail and supported by examples. In case (a), the end customer and the ordered product model are known in advance. For example, it is clear that the customer will order a derivative from the Airbus A320 family, but due to market uncertainties, he or she cannot specify the finally desired fuselage size or engine yet and will make a decision as late as possible. Depending on when the customer order is finally specified, some of the options within the variant tree can also be considered known if the corresponding options are fixed before they are scheduled within the global production network in the next step. If the customer, for example, decides before scheduling that less than 120 seats are needed, smaller derivatives such as the A318 can be fixed in the variant

Fig. 10.3 Exemplary variant tree for the A320 family

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tree and the range of possibilities can be further reduced. According to the exemplary variant tree above, the V2500 engine from the International Aero Engines consortium is not available for the A318 derivative and thus need not to be considered further. For all the other unknown options, probabilities are assigned to each node within the tree. Since orders are directly matched to a specific customer, individual considerations for each customer are considered. The result is a variant tree per customer order that represents different scenarios based on the assigned probabilities of outstanding customer options. The overall probability for one variant can be derived by simple multiplications along the path since independence is generally assumed. Depending on the number of possible order configurations with positive probabilities and the number of orders considered, the number of relevant scenarios can be very high, so that a selection must be made to limit complexity. The reduced number of relevant scenarios should be as representative as possible to keep the planning process unbiased (Mißler-Behr, 2001; Scholl, 2001). To give an example, one single product model within the fleet of a big aircraft manufacturer can be built according to more than 500,000 scenarios regarding the different combinations of multiple options the customer can choose from. Exposed to more than 120 customer orders in a single quarter, the number of theoretically possible scenarios goes up to more than 500,000120, which is way too large to handle in the later steps. In application, the number of scenarios would be reduced to around 200 representatives, which also includes a worst-case scenario. The choice of representatives could be made by considering the scenarios’ individual workloads, for example. Scenarios with similar workloads could be clustered and represented by only one scenario among them (Buergin, 2018). As already noted, in case (b), the variant tree is used similarly. But other than in case (a), absolutely no a priori information regarding the customers’ orders is available. Based on market-specific probabilities of order configurations, so-called plan orders are generated per market and product model. Hence, the variant tree in case (b) does not model a range of different scenarios per customer, but a very large number of possible variants with corresponding probabilities per sales market. Under such circumstances, the variant tree can get even more complex. An automobile manufacturer with three sales markets, for example, has around 4.14*1024 possible variants due to many customer specification options. Therefore, the assigned probabilities for each variant are near zero. Again, it makes sense to also reduce the number of possible options within the variant tree to preserve a manageable complexity. It is thus necessary to prioritize the available options and option groups and only pick the most important ones (Buergin, 2018). In the automobile industry, for example, only single derivatives with corresponding motorization and transmission options could be considered. The number of electrically adjustable seats is too detailed and can be abstracted in this planning step. As a result, the variant tree then does not represent all possible order configurations but the most important ones. The remaining risk of not meeting the real customer demand is addressed in step three – sales order assignment.

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10.3.2 Order Scheduling The second step of the procedure focuses on the scheduling of the generated orders within the global production network. In case (a), the customer order configuration is not clear at this point and scheduling is executed based on the expected specifications; thus, it is a stochastic scheduling of customer orders within the production network. In case (b), customer order configurations are already determined by plan orders, thus leading to deterministic scheduling of customer orders within the production network. The uncertainty of meeting real customer orders in case (b) is outsourced to the process of order assignment (step three). Despite these different assumptions in cases (a) and (b), both problems can be modeled in a similar way as described in the following. The task of mid-term order scheduling in the production network consists of the allocation of an order i to a location l in a period t. It is an allocation problem that can be formulated as a binary optimization problem, where a binary decision variable Xilt 2 {0,1} determines if order i is allocated to a location l in period t or not (Buergin et al., 2016; Neumann & Morlock, 2002; Nickel et al., 2014). The target function of the problem minimizes different cost functions under the application of a certain allocation vector x – a subset of all valid allocations based on Xilt. The consideration of different cost functions depends on the specific application field, but the most important among them are order-related costs, order distance costs, costs of workload deviation, and level-scheduling costs. Order-related costs are characterized by the fact that, unlike the other cost elements, they are incurred specifically for each individual order and thus independently of other customer orders. They depend on the order configuration and are therefore calculated based on the expected values of the generated order scenarios. Order-related costs could, for example, be material or inventory costs. Order distance costs might be taken into account for individual customers for whom more than one order is scheduled within the same period, although the customers might demand a certain time gap in between the orders. This is often the case in the aircraft industry, where customers in general order multiple airplanes, but do not want them to be delivered at the same time. Since the optimization approach aggregates orders at the level of periods, orders that actually demand a time gap in between might be produced in the same period but cannot be delivered to the customer in the same period. Costs of workload deviation occur if the capacity demand of a production location in a certain period does not meet the capacity supply. Flexibility costs can be taken into account here as well as costs for conversion, which is the adoption of the upper flexibility limit. Levelscheduling costs in the target function are used to ensure that the materials used in the final assembly are as evenly distributed as possible by penalizing deviations from the even distribution of requirements through costs. The optimal solution of the target function is limited through the compliance with several constraints, for example, the sequencing of workload-intensive product models with upper limits (Buergin, 2018). Regarding the automotive industry, preventing the scheduling of multiple cars with sliding roofs in a row might be a classical example of such constraint due to the high assembly effort.

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This optimization problem can be modeled in practice with IBM’s CPLEX and be solved, for example, with a branch-and-cut algorithm (Buergin, 2018). After the successful scheduling of all orders within the production network, in case (a), the process of customer order planning is finished at this point. The uncertainty of order configuration was handled by stochastically scheduling the orders in the production network based on the expected probabilities per customer order and corresponding specification options. In case (b), configurations were determined by plan orders and deterministically scheduled within the production network. Uncertainty regarding the match with a real customer order still exists and has to be taken into account in the next and last step of order assignment.

10.3.3 Order Assignment This last step aims to finally dissolve the uncertainty of customer orders by bringing together short-term incoming customer orders and already scheduled plan orders in the global production network. As soon as a match is found, an order configuration can be directly assigned to a customer order, and a delivery date can be guaranteed. Since the range of possible variants can be quite large as stated in step one, the probability that a certain customer order directly matches an existing plan order is comparably low. Nevertheless, there are multiple possibilities to fulfill customer requirements, using the concept of reconfiguration flexibility (Brabazon & MacCarthy, 2004). Reconfiguration flexibility can be realized by changing the options of a plan order, exchanging options between plan orders, or by changing the options after final assembly (Brabazon & MacCarthy, 2004; Buergin et al., 2017, 2018a, 2018b). Besides realizing reconfiguration flexibility by adjusting the product itself, it can also be reached by using the customers’ flexibility to differ from its desired configuration if a later delivery date must be accepted instead (Buergin, 2018). In the automotive industry, for example, this is often the case if a customer buys an already manufactured car at the local retailer instead of ordering a totally customized car that will be delivered in some weeks or months. This utilization of customer flexibility is highly dependent on the customers’ preferences. In the US, for example, 94% of the customers favor a short lead time (build-to-stock) over a customized product (build-to-order), while in Germany, only 38% of the customers are willing to give up customization for a shorter lead time (Holweg & Pil, 2004; Volling, 2009). To sustain the buildability of all variants after reconfiguration is applied, the process of assigning customer orders to plan orders is divided into two stages: The first stage searches for the best-fitting plan order, given an arising real customer order. This can be modeled as an allocation problem similar to the one in stage two and is formulated with the help of a binary optimization problem that minimizes the corresponding reconstruction, changing, and penalty costs of the reconfiguration process (Neumann & Morlock, 2002; Nickel et al., 2014).

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Fig. 10.4 Assignment of customer orders and plan orders

If the selected plan order from stage one perfectly matches the customer order, the second stage is trivial, and the plan order can directly be delivered to the customer after production. In general, this chance is comparably low, and necessary reconfigurations have to be applied to the selected plan order in the first place. It is then checked if the buildability of all concerned configurations is still ensured. If buildability is not guaranteed and stage two does not release these changes, the chosen plan order is blocked and stage one is executed again. If stage two does release the changes, they are finally implemented, and the customer gets the assigned plan order. In the case that, after several runs through the loop, no good plan order can be found in stage one, the customer is offered an alternative configuration, or the order is scheduled as build-to-order. The procedure is summarized in Fig. 10.4. As a result of the last step, each real customer order is assigned to a plan order that is already scheduled within the production network. If necessary, reconfigurations are applied, and the corresponding product can be delivered to the customer.

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Manufacturing companies nowadays face several challenges regarding the mid-term order planning processes, especially the handling of uncertainty. Communication with suppliers about capacity needs, for example, needs to be worked out as early as possible, while customer needs regarding the exact order configuration are unknown at that point, resulting in a conflict. Driven by the need for a practical approach addressing these challenges, the objective of this chapter is the presentation of a methodology for medium-term order planning in global production networks of multi-variant serial production under uncertainty of customer order configurations. This can be realized by anticipating not yet available and thus uncertain order configurations in the medium term and taking them into account in order planning and scheduling. Thus, efficient and optimal planning can be carried out under uncertainty. Two particular cases of uncertainty at the mid-term planning date are outlined in this chapter. They are supported by two different corresponding use cases from

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industry, emphasizing the broad applicability and high practical relevance of the methodology. In case (a), real customer orders are available, but the specific order configuration is unknown (aircraft industry). In case (b), no customer orders are available at all (automotive industry). Three planning tasks are defined to fulfill the initial goal statement, namely: order generation, order scheduling, and sales order assignment. For case (a), the planning task of order generation consists of generating scenarios regarding order configurations for known customers and product models. For case (b), the step is carried out by generating plan orders regarding unknown orders as well as unknown order configurations for markets and product models. Now that concrete orders are generated, they are scheduled within the global production network. For both case (a) and case (b), the scheduling problem can be defined as a binary optimization problem that allocates a certain order to a location and a period. In case (a), the scheduling is stochastically optimized concerning the expected configuration for each order and the methodology ends here. In case (b), the scheduling is optimized based on deterministic plan orders which do not guarantee to meet the real customer orders. This is why the last step of sales order assignment needs to be applied after order scheduling. A real customer order is therefore allocated to an existing plan order, and buildability is checked. Applying this methodology to the aircraft industry means that customers can place orders months or years in advance while determining concrete configurations short-term as a just-in-time specification. The generation of scenarios in the first step allows planning concrete aircraft orders as early as possible in the mid-term, depending on the most likely scenarios per customer order. An expected increase in domestic flights, for example, increases the possibility that potential customers order airplanes with shorter fuselages. The resulting scenarios can then be scheduled in the production network. As time goes by in the planning process and customers determine their actual configurations in the short term, adjustments to the initial scenarios are most likely minimal and an optimal planning process under uncertainty can be carried out. Applying the methodology to the automotive industry, on the other hand, plan orders are generated in the first place. They are based on research into the demand in the single sales markets and can be treated and scheduled in the production network as if they were real customer orders. These plan orders can then be brought together with actual customer orders by either adjusting the specifications of an already scheduled car or by providing the customer another car with other specifications in exchange for a short lead time, as is the case in buying cars at the local retailer.

References Brabazon, P. G., & MacCarthy, B. (2004). Virtual-build-to-order as a mass customization order fulfilment model. Concurrent Engineering, 12(2), 155–165. Buergin, J. (2018). Robuste Auftragsplanung in Produktionsnetzwerken. Mittelfristige Planung der variantenreichen Serienproduktion unter Unsicherheit der Kundenauftragskonfigurationen (Dissertation, Karlsruhe Institute of Technology).

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Buergin, J., Beisecker, J., Fischer, S., Geier, B., Tutsch, H., Mercamp, S., & Lanza, G. (2017). A modular-based approach for just-in time specification of customer orders in the aircraft manufacturing industry. Procedia CIRP, 61, 499–504. Buergin, J., Blaettchen, P., Kronenbitter, J., Molzahn, K., Schweizer, Y., Strunz, C., Almagro, M., Bitte, F., Ruehr, S., Urgo, M., & Lanza, G. (2018a). Robust assignment of customer orders with uncertain configurations in a production network for aircraft manufacturing. International Journal of Production Research, 1–15. https://doi.org/10.1080/00207543.2018.1482018. Buergin, J., Blaettchen, P., Qu, C., & Lanza, G. (2016). Assignment of customer-specific orders to plants with mixed-model assembly lines in global production networks. Procedia CIRP, 50, 330–335. Buergin, J., Helming, S., Andreas, J., Blaettchen, P., Schweizer, Y., Bitte, F., Haefner, B., & Lanza, G. (2018b). Local order scheduling for mixed-model assembly lines in the aircraft manufacturing industry. Production Engineering, 12(6), 759–767. https://doi.org/10.1007/ s11740-018-0852-x. Ehinger, G., Eisenhart-Rothe, M., Hauck, C., Klostermann, F., Krugmann, R., Murtic, S., & Puri, W. (2002). Komplexitätsmanagement. Leitprojekt Integrierte, Virtuelle Produktentstehung [Abschlussbericht]. Holweg, M., & Pil, F. K. (2004). The second century. Reconnecting customer and value chain through build-to-order: Moving beyond mass and lean production in the auto industry. Journal of Product Innovation Management, 22. https://doi.org/10.1111/j.0737-6782.2005.116_2.x. Koren, Y. (2010). The global manufacturing revolution. Product-process-business integration and reconfigurable systems. Boca Raton, FL: Wiley. Lanza, G., Ferdows, K., Kara, S., Mourtzis, D., Schuh, G., Váncza, J., Wang, L., & Wiendahl, H.-P. (2019). Global production networks: Design and operation. CIRP Annals - Manufacturing Technology, 68, 823–841. https://doi.org/10.1016/j.cirp.2019.05.008. Mißler-Behr, M. (2001). Fuzzybasierte Controllinginstrumente. Entwicklung von unscharfen Ansätzen. Weisbaden: Deutscher Universitätsverlag. Neumann, K., & Morlock, M. (2002). Operations research. Munich: Carl Hanser Verlag. Nickel, S., Stein, O., & Waldmann, K.-H. (2014). Operations research. New York: Springer. Rosenberg, O. (1996). Variantenfertigung. In Handwörterbuch der Produktionswirtschaft. Stuttgart: Schäffer-Poeschel. Scholl, A. (2001). Robuste Planung und Optimierung. Grundlagen, Konzepte und Methoden, Experimentelle Untersuchungen. Heidelberg: Physica-Verlag. Volling, T. (2009). Auftragsbezogene Planung bei variantenreicher Serienproduktion. Eine Untersuchung mit Fallstudien aus der Automobilindustrie. Wiesbaden: Gabler-Verlag.

Adding an OPEX Perspective to Network Optimization

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A manufacturing network, carefully configured by choosing the right locations for its sites and their respective roles (see Chap. 2), aims to fulfill its network strategy (see Chap. 6). At the same time, each site on its own strives toward operational excellence, eventually turning into a continuously improving organization. While the interplay between these sites should be regularly refined and revised to guarantee the fulfillment of the network strategy, every single site should be frequently assessed regarding its Operational Excellence (OPEX) maturity (see Chap. 3). Metaphorically speaking, not only is it essential for a soccer coach to decide on the right formation and tactics, but it is also crucial to assess the players’ current performance and to develop their capabilities through training. In order to foster the improvement of their plants, companies set up and formalize company-wide improvement initiatives. Here, companies often follow the success of the Lean pioneer Toyota and design as well as deploy their company-specific Lean production system (XPS) (Netland, 2013). However, managing the deployment of their XPS within a network bears two challenges. First, sites from different countries or regions and sites of different types might vary in their speed to adopt the overall XPS, its elements, or its tools (Netland & Aspelund, 2014). Therefore, one site would find itself in a less or more mature state of XPS deployment than another site would. Secondly, since each site’s purpose and context differ within a network (Ferdows, 1997), a sole one-to-one performance comparison of internal sites would lead to wrong conclusions (Cua et al., 2001). Both challenges contribute to the same overarching questions: whether the XPS implementation yields performance improvements and how this XPS effectiveness

M. Grothkopp (*) · M. Ritz · T. Friedli Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland e-mail: [email protected]; [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_11

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can be measured in a network considering its different kinds of sites. Are the players developing their abilities, and does this, in turn, lead to better performance? The following case study will expound on how a globally operating and leading pharmaceutical company approached this question and how the monitoring of performance improvements and the XPS deployment was operationalized.

11.1

Theoretical Background

As stated in the introduction, sites are concerned with their performance and capabilities, often referred to as maturity. Maturity relates to the degree of a site’s implementation of OPEX tools, methods, and processes. On the other hand, performance relates to outcome measurements, e.g., financial or operational indicators (see Chap. 3). A conceptual framework had to be found that could serve as a theoretical basis combining both performance and maturity to address the questions raised above. The chosen framework dates back to the mid-1990s and stems from researchers who conducted several studies in European countries to review manufacturing competitiveness. In their study “Made in Britain: The True State of Britain’s Manufacturing,” Voss and Hanson (1993) introduced the world-class manufacturing model comprising and linking practices as well as performance. Practices imply processes that a company has in place to improve its operations, from both technical and organizational points of view, e.g., total quality, lean production, or culture. These practices are linked to performance, which encompasses operational performance, such as reliability or inventory turns, and business performance, such as market share (Voss et al., 1997). In their seminal publication, Voss et al. (1995) compiled the results from four country studies to compare the competitiveness of each manufacturing sector. The authors categorized the participating manufacturing sites, and thereby countries were compared based on their number of manufacturing sites within a particular category. These categories were derived based on a two-dimensional matrix (see Fig. 11.1). Voss et al. (1995) operationalized each axis based on interview questions ranking from one (low performance or low practice implementation) to five (high performance or high practice implementation). The aggregation of each question into a single performance score and practice score resulted in 0–100% scales (see Fig. 11.1). The dimensions “performance” and “practice” were used to differentiate five categories1 of plants depending on the respective score in both dimensions. “Punchbags” include sites that score below 50% in both dimensions. Diametrical to this, the “World Class” category is defined as scoring above 80% in both dimensions, and sites within this category are deemed to outperform other manufacturing sites worldwide. “Contenders” are close followers, scoring between

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50% and 80%, and are about to move into “World Class” if they keep improving. The categories mentioned above imply a linear logic: the higher the practice score, the higher the performance score. However, some manufacturing sites could not be assigned to these categories and, thus, do not follow this linear logic. The first category is the “Promising” one, which includes sites scoring above 50% in the practice implementation but below 50% in performance. Sites in this category appear to be promising since they have heavily invested in developing capabilities but still await the future pay-off in performance. Lastly, the category “Won’t go the distance” refers to sites that score below 50% in practice implementation but score higher than 50% in the performance dimension. These sites might have performed well in the recent past but missed to implement enduring practices, which puts them into a fragile position in the long-term (Voss et al., 1995). The framework from Voss et al. (1995) posed an appropriate conceptual basis to approach the challenge of assessing and monitoring sites’ performance as well as maturity progress over time within a network.

11.2

From Theory to Practice

The framework depicted in Fig. 11.1 served as a conceptual basis for a joint research project between one of the largest, globally operating pharmaceutical companies and the Institute of Technology Management. By the time of the project start, the abovesite organization, responsible for the deployment of the XPS within a network of more than 40 manufacturing sites, had a comprehensive maturity assessment in

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place. However, neither a tool nor a framework to consider both performance and maturity in a combined way within their network of sites was used. Consequently, the organization faced the question of how to measure the progress of both XPS deployment degree (maturity) and the resulting performance improvements in a combined way. This would enable them to identify sites that needed further support and to derive appropriate measures. For 1 year, the central, above-site function of the company’s XPS organization worked together with the Institute of Technology Management to transfer the framework from Voss et al. (1995) into practice. This project posed the challenge of operationalizing both axes to be reconsidered and adapted to the company’s specific requirements. This is due to two distinct reasons: the need for a close monitoring through frequent assessments and the requirement for a meaningful performance measurement. The former requirement meant the way of collecting data following the approach of Voss et al. was unpractical since conducting interviews to assess both performance and maturity is immensely time-consuming. A regular qualitative interviewbased assessment would have been an impossible effort for the OPEX organization of the company. The latter requirement, comparing performance in a meaningful way, was only insufficiently addressed by the initial operationalization. Firstly, translating qualitative interviews into a quantitative scale leaves room for subjectivity in the assessment. Secondly, comparing manufacturing sites across different industries based on qualitative interviews yields imprecision if implications are derived. The company-specific operationalization of both axes and their logic is explained in the following sections.

11.2.1 Operationalization of OPEX Maturity Voss et al. (1995) used the terminology “practice” for their x-axis and referred to it as having processes in place in order to improve the running operations. In the case of the company considered in this chapter, the focus is on deploying their companyspecific production system, their XPS. At heart, the implementation of certain tools and methods included in the XPS bears the same logic as the “practice” terminology. Yet, the x-axis of the company’s framework was renamed to “adherence-to-standard” due to the different, well-established operationalization. The company has a comprehensive maturity assessment in place, which follows a binary logic. Within its XPS, the company differentiates between different areas (e.g., logistics, quality, and manufacturing), and each area contains a different and thoroughly defined set of “operating standards”. These standards include the existence of tools like visual boards, the definition of specific teams like cross-functional teams, or the adherence to clearly defined processes like problem-escalation processes and are easily assessable, and therefore avoid subjectivity in a site’s assessment. Moreover, depending on the type of the production site, the overall set of operating standards to be implemented varies. Thus, a production site that only manufactures final products (drug products) has to implement more and different

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operating standards than a site that manufactures semi-finished (bulks) as well as final products (drug products). Therefore, each site works on implementing a predetermined set of operating standards that it should adhere to. Based on the thoroughly defined operating standards, each area within a site can be evaluated by checking if every single operating standard is being adhered to or not. To determine the site’s maturity, the ratio of adhered standards to overall standards (sum of adhered and not adhered standards), considering each area within the site, is to be calculated. This final score represents the overall “adherence-to-standard” (ATS) and thus the maturity of a single site regarding its XPS implementation. Thereby, the explained operationalization with thoroughly defined operating standards for various areas allows XPS coordinators to clearly identify a site’s maturity even though the sites might differ in their type.

11.2.2 Operationalization of Performance The y-axis of Voss et al. (1995) refers to the performance of a site. Like the x-axis operationalization, the operationalization of the y-axis needed to be adjusted to the specific needs and circumstances of the case study company: holistically measuring performance, benchmarking against the industry, aggregation to a single performance score, and meaningful comparison based on site characteristics. Opposing the qualitative approach from Voss et al. (1995), the company aimed for a more objective and quantitative measurement of performance by using metrics. In addition, and following the St.Gallen OPEX philosophy, performance should be measured holistically to avoid one-dimensional optimization. Therefore, four dimensions, namely Safety, Quality, Supply, and Finance, were defined and gave guidance to select the final set of assessment metrics. According to the dimensions above, a well-balanced set of metrics was defined among which sites were to be benchmarked. To do so, five benchmarking values would be defined for each metric. One value would represent the worst performance, another one the best performance. The remaining three values would represent the 25th percentile, the 50th percentile or median, and the 75th percentile. These benchmarking or percentile values reflect both an industry benchmarking and an internal benchmarking. Common industry benchmarking databases, such as the St.Gallen OPEX benchmarking and another consultancy benchmarking, were used to derive the benchmarking values for a certain set of metrics, providing an external comparison against the industry. In addition, the company derived values from its balanced score card (BSC) metrics that had been reported for several years, leading to an internal database. An advantage of choosing metrics, and the respective benchmarking values, from the BSC is the good accessibility of data. Sites were used to report their performance values for the respective metrics on a yearly basis anyway. Together, these sources posed a well-balanced set of metrics with benchmarking values, which sites could compare themselves with.

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Fig. 11.2 Linear interpolation to calculate the relative performance score. Adapted from Interpolation und Approximation, by T. Richter, T. Wick, 2017, Springer Spektrum

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A site would receive a relative performance score for each metric by comparing its performance with the benchmarking value set. As a simple example: If a site had a service level of 90% and the 50th percentile value would be 90%, the relative performance score for the site in the metric “service level” would equal 0.5. Since the sites’ reported performance values are not always precisely equal to either of the percentile values and are somewhere between them, the relative performance score needed to be determined with a calculation. For this purpose, a linear interpolation was used to calculate the estimated percentile rank of the reported performance value from the site, leading to the metric’s relative performance score. This interpolation is depicted in Fig. 11.2. Again, the metric “service level” is taken as an example, and Fig. 11.2 shows the five different percentile values on the x-axis. With a service level of 93%, the site performs between the 50th and the 75th percentile. A linear interpolation with both x-axis values (90 and 97) as well as the y-axis values (50 and 70) yields a 61st percentile rank and a relative performance score of 0.61. The above-explained percentile rank normalization and the resulting relative scores pose another advantageous feature of the operationalization: Metrics of different scales and units are transformed to the same unit and scale. This enables the aggregation of every metric with an equally weighted average to a single aggregated relative performance score that incorporates the holistic aspect of the metric set. A single aggregated relative performance score can thus be used to plot the sites on the y-axis. The last challenge was to ensure a meaningful comparison of sites based on their specific characteristics. Sites that manufacture one kind of product, often called technology, cannot be compared to sites that manufacture another utterly different

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kind of product. Comparing each site to the same set of benchmarking values for every metric, would thus be misleading. Therefore, the aforementioned percentile values of metrics were differentiated by different technologies. Hence, the five benchmarking values of a metric for one technology differ from the set of benchmarking values for another technology. Thereby, sites can be benchmarked against values that stem from sites that manufacture the same technology (kind of products). However, the transformation into relative performance scores, and eventually aggregated relative performance score, provides the possibility to plot different kinds of sites onto the same y-axis in a meaningful way. Additionally, some sites might manufacture more than one technology, making them so-called “multi-tech-sites”. Even though most sites struggle to distinguish certain metrics between their technologies, they could provide values for each technology and thus receive a relative performance score for the first and the second technology. However, to calculate an aggregated relative performance score for the entire site, these technology-specific relative performance scores for each metric needed to be aggregated beforehand. Instead of taking an unweighted average of two relative performance scores, the technologies’ absorption ratios were defined as a good estimate to reflect the site’s primary technology. Here, the absorption ratio was determined based on the technology’s occurred costs, and thus the more costintensive technology would be weighted higher than the other technology. Moreover, the operationalization of this technology-split allowed for a manual weight adjustment for each metric. In summary, the operationalization of the y-axis, the performance axis, considers the various aspects such as a holistic performance measurement, benchmarking against industry peers, the aggregation of several metrics into one aggregated score, and the specific site characteristics to allow for a meaningful comparison.

11.2.3 Practical Tool Implementation The conceptual operationalization of both axes set the basis to awaken the concept to life. Nevertheless, to be applied in practice, this logic needed to be implemented with a supplementing software. On the one hand, the tool should be easy to use and maintain. On the other hand, functionalities, such as longitudinal data storage and monitoring both performance and XPS implementation progress, should be implemented, automated, and stable. Microsoft Excel served as an appropriate software to handle the number of metrics, the number of sites as well as the expected amount of data for a yearly reporting frequency. Besides, the modular structure and the programming of the tool ensured to fulfill the above-mentioned requirements. The tailored software tool can be divided into a frontend and a backend, both containing two sheets. An input and an output sheet provide the interface to the user. As the names suggest, the input sheet contains an input form for the user to fill in data, and the output sheet presents information about the selected site within a particular year as well as a graphical output resembling the Voss et al. (1995)

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framework (see the detailed workflow and site performance measurement cockpit in the next sections). The backend can be separated into the calculation and database sheet. The calculation sheet, fed by data from the input sheet, contains the benchmarking percentile values for each metric per each technology and processes the calculations that were mentioned in Sect. 11.2.2. These calculated values, in turn, are to be transferred to the database sheet. On the other hand, the database sheet stores both absolute values and relative performance scores for each site over the years. Thereby, it provides the data that the above-site manager wants to monitor and analyze in the output sheet.

11.2.4 Standardized Usage Process Using the input sheet, the above-site organization is set to update the site’s performance data regularly. During the roll-out phase, the reporting frequency was initially defined as a 12-month period. However, the general performance measurement concept would also allow adjusting the reporting interval in case of changed requirements. Thanks to its modular structure, the software tool can easily be adapted accordingly. Figure 11.3 summarizes the standardized workflow for inserting the site’s performance data measured with the selected metrics at the end of the defined reporting interval. After completing the process by transferring the

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entries, all data points are automatically added to the database sheet and represented in the performance review output cockpit. Responsibilities for maintaining the site performance measurement system are clearly defined. As per an implemented standard process, local XPS coordinators are responsible for providing the performance data to the above-site XPS organization at the end of the reporting period. Subsequently, the performance measurement system owner incorporates the update following the workflow just outlined.

11.2.5 Performance Measurement and Scoring Across the Network Maintaining the tool on a regular basis is not a purpose in itself but a prerequisite to analyze the current status of outcome performance and capabilities across the sites of the company’s network. Relative scores are determined based on internal and external benchmarking against appropriate peer groups (see detailed description in Sect. 11.2.2) and allow XPS coordinators to comprehensively understand the current status per site. Indication of all relative performance scores and aggregated relative performance scores motivates a comprehensive performance review across the specified dimensions and considers trade-offs. Figure 11.4 shows the result scoring output: Besides the calculated relative performance scores, the output additionally displays the absolute performance data reported for the period. Thus, absolute and relative performance levels can be interpreted in direct comparison to each other. Furthermore, strategic network management can make use of the compiled performance database as well as the relative score system. Based on the available longitudinal performance development, target setting and resource allocation are informed by data-backed implications. Therefore, an integrated site performance measurement cockpit across the network allows visually analyzing a site’s progress

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over time, comparing different operating units or regions of the network, and filtering time periods (see Fig. 11.5). Priority areas for the subsequent period can be derived.

11.3

Benefits and Summary

The described approach for quantifying, monitoring, and interpreting different performance and maturity levels adds value to both continuous improvement efforts on-site and network management. While individual sites receive a clear assessment of their current position compared to dedicated peers and are enabled to track their progress over time, the above-site management can use the framework for defining priorities and allocate resources overseeing the entire company. Furthermore, the above-site organization owns a standardized tool for evaluating the impact of the deployed production system. Continuously tracking progression over time allows assessing both short-time improvements and sustainable increase of effectiveness over time. This also leads to insights about the degree of effectiveness of certain parts, categories, and practices of the production system. Additionally, the company decided to frequently use the methodology for recognition purposes and awards production sites based on their current performance and development over time. The theoretic framework of Voss et al. (1995) acts as a foundation of an integrated analysis of both performance and maturity across the network and provides guidance to the company how to comprehensively improve in both maturity and performance. Applied on a site level in the specific context, the basic idea

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might also be applicable to monitor the progress of entire production sub-networks in the future.

References Cua, K. O., McKone, K. E., & Schroeder, R. G. (2001). Relationships between implementation of TQM, JIT, and TPM and manufacturing performance. Journal of Operations Management, 19 (6), 675–694. https://doi.org/10.1016/S0272-6963(01)00066-3. Ferdows, K. (1997). Making the most of foreign factories. Harvard Business Review. Netland, T. (2013). Exploring the phenomenon of company-specific production systems: One-bestway or own-best-way? International Journal of Production Research, 51(4), 1084–1097. https://doi.org/10.1080/00207543.2012.676686. Netland, T., & Aspelund, A. (2014). Multi-plant improvement programmes: A literature review and research agenda. International Journal of Operations & Production Management, 34(3), 390–418. https://doi.org/10.1108/IJOPM-02-2012-0087. Richter, T., & Wick, T. (2017). Interpolation und approximation. In Einführung in die Numerische Mathematik (pp. 351–465). Berlin: Springer Spektrum. Voss, C., Åhlström, P., & Blackmon, K. (1997). Benchmarking and operational performance: Some empirical results. International Journal of Operations & Production Management, 17(10), 1046–1058. https://doi.org/10.1108/01443579710177059. Voss, C., Blackmon, K., Hanson, P., & Oak, B. (1995). The competitiveness of European manufacturing? A four country study. Business Strategy Review, 6(1), 1–25. https://doi.org/ 10.1111/j.1467-8616.1995.tb00169.x. Voss, C., & Hanson, P. (1993). Made in Britain: The true state of Britain’s manufacturing. London: IBM Consulting Group.

Process Quality Improvements in Global Production Networks

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Rainer Silbernagel, Tobias Arndt, Sina Peukert, and Gisela Lanza

A key challenge for manufacturing companies today is to ensure overall process quality within their production network while working in globally distributed and dynamic environments. In this chapter, a description model to systematically analyze process quality across locations and identify improvement measures using a value stream-based approach is presented. In order to holistically increase process quality in the network, two evaluation procedures based on a hierarchical key performance indicator system are discussed. This method is especially useful in production networks, where certain products are manufactured in several steps across multiple plants.

12.1

The Strategic Importance of Process Quality

In today’s production networks the importance of (process) quality increases steadily. The drivers for this development are—among the more obvious reasons of cost reduction or higher productivity—much more versatile. As manufacturing companies are becoming a part of increasingly large networks and are specializing in their core competencies even more, the companies’ individual influence on the quality of their products decreases (Friedli et al., 2014). Hence, resulting in a greater dependence on the quality of supplied goods and the challenge to sustainably guarantee high quality of the end product required by the customer (Bay & Schaal, 2012; Pfeifer et al., 2000). In order to face this challenge, it is vital to manage a

R. Silbernagel (*) · S. Peukert · G. Lanza wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany e-mail: [email protected]; [email protected]; [email protected] T. Arndt GAMI – Global Advanced Manufacturing Institute, Suzhou, China # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_12

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strategically designed production network on the operational level in the best possible way (Rautenstrauch, 2002). Within said production network, individual sites concentrate on their core competencies and therefore take on certain roles within the network. Although the more or less autonomously acting locations can thus contribute to a higher output of the overall network, this does not take into account the interdependencies between processes involved and their effects on quality and other partners within the network (Feldmann & Olhager, 2013; Lanza et al., 2013; Tapiero & Kogan, 2007). In order to address these challenges and guarantee the required product quality along the entire value stream, an integrated value stream-based approach considering the effects of certain measures on the whole production network, not just on individual plants, is necessary. The quality on the operational process level must come into focus and be monitored on the station, location, and network level. Moreover, as sites develop their own performance measurement systems, it is essential to account for their specialization and simultaneously maintain a holistic perspective on interdependencies within the network (Arndt et al., 2019). Since network coordination can be thought of as the actual management of the network itself (Hayes et al., 2005, p. 150ff.; Jacob et al., 2006, p. 274ff.), a decisive aspect is to ensure high-quality standards. Thus, the method mostly contributes to the Coordination layer of the St.Gallen Management Model for Global Manufacturing Networks (see Chap. 2). Further, the method also ensures a fit with the strategy and configuration levers by defining a hierarchic and rolespecific target system to ensure the strategic goals with regard to process quality. By following a value stream-based approach, the presented model captures the production process chain of the network on an operational level and combines individual quality characteristics to the overall process quality. Using the said approach in an exemplary network of a globally acting automotive supplier, we were able to implement measures leading to a huge decrease in quality cost for the whole network. Those measures would have never been implemented from a plant-level point of view, because of an increase in quality costs at the plant itself and a lack of compensation or interest alignment. For more details regarding the use case or the method as a whole see also (Arndt, 2018; Arndt et al., 2019).

12.2

Value Stream-Based Model for Improving Process Quality

The aim of the provided methodology is to systematically analyze process quality across locations and identify improvement measures using a value stream-based approach. The model contains five steps which build upon each other and respectively rely on the predecessor’s outcomes (see Fig. 12.1).

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Essential model characteristics

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Fig. 12.1 Five-step description model for process quality

First, within the essential model characteristics, a concept of measuring the products quality needs to be defined and the production networks in general have to be modeled (Arndt, 2018, p. 58). In the second step, a hierarchical target and performance indicator system that makes it possible to assess process quality bottom-up will be developed. It represents the manner in which quality relevant figures are measured and aggregated from the operational to the strategic network level (Reichmann et al., 2011). At this point, the individual site specializations are taken into account as well. Step 3, the value-stream-based network analysis and data acquisition, is the groundwork a big part of the model rests upon. All quality-relevant features of the production network are captured and linked to the individual processes. Quality costs, e.g., for inspection, rework and scrap are identified and also linked to the processes responsible (Arndt et al., 2019; Nyhuis et al., 2008). In order to identify the greatest quality “pain-points”, inspired by the method of a Process Failure Mode and Effects Analysis (FMEA) (Hering & Schloske, 2019), an extended risk priority index (RPIext) is calculated and assigned to the error source of the respective process (Arndt & Lanza, 2016). The fourth step deals with the identification of suitable measures that are able to improve process quality globally. Predefined measures are selected with regard to their contribution in minimizing the RPIext and their influence on the main objectives of the target system (e.g., quality, time, cost, flexibility, and sustainability) (Arndt, 2018, p. 98). As implemented measures are changing the complex network performance, it is crucial to somehow perform an evaluation of process quality improvements. Therefore, in the fifth step, two approaches are possible: a simulative approach to enable ex ante evaluation of the improvement and a business intelligence approach for a constant monitoring of the implemented measures, based on the given approach.

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Essential Model Characteristics

Production networks are versatile and complex. In order to successfully assess process quality, it is important to classify their fundamental characteristics through a standardized description model (e.g., a digital twin). It is important to mention that through a comprehensive description of the production network to be analyzed (e.g., certain product, subnetwork, etc.), usually all essential model characteristics are identified, already (Arndt, 2018, p. 114ff.) A key aspect to be modeled is the network’s output, i.e., the manufactured product and the quality associated with it. To describe the characteristics of an end product, a product model, which combines the product-oriented, manufacture-oriented, and value-oriented perspectives, can be used (Garvin, 1984). It ensures that a product, with its complex functional relationships between individual components, can be adequately described during the entire creation process in the production network (Book, 2015, p. 111). Hence, each product’s quality is measured through certain quality features Q1, Q2, . . ., Qn which account for quality requirements of different customers (e.g., physical dimensions or weight) as well as process-related characteristics (e.g., delivery date and quantity), ensuring a 100% traceability. If the quality characteristics deviate from their target value, the kind of defect (e.g., product, quantity, time) and its cause (e.g., technology, people, process organization) must be identified. In addition to the cause, the exact time of the defect should be recorded as well (Arndt, 2018, p. 60). Besides product characteristics, the model also describes the network itself and the way in which different plants, suppliers, and customers are linked and interact with one another. In order to express these diverse interdependencies in a generic way, a production network model is used. It differentiates between sites, suppliers, and customers which are connected through transportation, source, or distribution processes. Within each site, one can classify generic process (e.g., production, logistics, distribution, or quality processes). The quality of the respective process is evaluated through quality features of each product passing this process and is recorded in individual key figures (Arndt, 2018).

12.4

Hierarchical Target and Performance Indicator System

The overall degree of target achievement among all processes, stations, and sites defines the process quality of the entire network. As each site’s performance system is based on individual core competencies and strategic purposes (e.g., low-cost vs. high tech) (Arndt, 2018, p. 69; Ferdows, 1997; Friedli et al., 2014, p. 90), the significance of the main strategic objectives (e.g., quality, time, cost, flexibility, and sustainability) is evaluated differently for different site specializations. Accordingly, a hierarchical target system for each plant in the network, based on its specialization, is to be implemented (Arndt et al., 2019; Wiendahl et al., 2010).

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Fig. 12.2 Evaluation of exemplary site specializations

In practice, the Analytical Hierarchy Process (AHP) can be used for designing an overall hierarchical target and performance indicator system. For more details on the AHP see Arndt et al. (2017), Arndt (2018), Saaty and Vargas (2012). The AHP should be accompanied by targeted workshops including the plant managers of the focused plants as well as specific functional supervisors, such as network planers, quality managers, logistic planners, and process leaders to integrate different perspectives (Arndt et al., 2019). Figure 12.2 shows an exemplary evaluation of the strategic objectives for three different plant specializations resulting in a site-specific target system. This process can also be done for each involved plant in particular. The percentage-wise evaluation allows an adequate consideration of site-specific sub-objectives and interdependencies between the sites. From a management perspective, it is advisable to consider all site specializations in a mutual portfolio on the superordinate network level, in order to successfully assess and improve process quality within the entire production network (Friedli et al., 2014, p. 90).

12.5

Value-Stream-Based Network Analysis and Data Acquisition

In order to capture all relevant process-individual key figures within the production network, a global quality value stream-based approach can be used to analyze the process quality of the production network in a structured way most network planners are familiar with. For more details on the extended notation for quality value stream see: Haefner et al. (2014), Nyhuis et al. (2008). This provides a management perspective on process quality in the production network and enables the executives to identify interdependencies between critical processes causing quality issues and to derive suitable measures (Arndt, 2018, p. 86ff.) Accordingly, all relevant processes identified in the network model (step 1) and their corresponding quality features and potential defects of the product model are recorded and aggregated on station, process, or site level. With regard to the wellknown value stream method, the quality value stream especially captures testing,

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rework, and scrapping processes. The associated times (e.g., testing or rework times) and quality costs (e.g., labor costs, value of scrapped, and additionally needed parts) are analyzed and also linked to the processes the defects emerge from. Additionally, three monitoring curves to visualize the amount of defect parts, occurring quality costs, and resulting quality time are added below the value stream (see Fig. 12.3) (Arndt et al., 2019; Haefner et al., 2014). Negative effects on the dimensions quality, cost, and time are represented by positive surcharge on the curve, positive effects vice versa (see Fig. 12.3). If, for example, a quality process detects more product defects than planned or allowed, this would yield to a positive surcharge on the curve exactly at that point in the value stream where this quality process is located. Huge spikes in the respected curves are indicators for processes to focus on when searching for improvement measures (Arndt, 2018, p. 96ff.) In order to prioritize and identify the greatest opportunities for improvement, the RPIext as an extension of the FMEA is used. The FMEA evaluates a product defect based on its significance for the customer (S), its probability of occurrence (O), and its probability of detection (D) (Bertsche & Lechner, 2004). However, since between the occurrence and the detection of an error can be a big gap in terms of intermediate processes or time, the FMEA is not sufficient to comprehensively evaluate the impact of an error or the quality lacking process where the error occurs. For an evaluation of a product defect, the RPIext rather takes factors for the increase in value of a product between the occurrence and the detection of an error (V) and the replenishment time of an error-free product (R) into account as well. The indicators are rated from 1 to 10 (Arndt & Lanza, 2016; Bertsche & Lechner, 2004): RPIext ¼ S  O  D  V  R

ð1Þ

With these two additional factors, the error evaluation meets the requirements of decentralized, widespread production networks and allows for a comprehensive prioritization of processes to improve. By taking into account the unnecessarily added scrapping value due to late detection, for example, defects being detected at another plant then their error source will be prioritized higher leading to a networkwide problem solving (Arndt et al., 2019).

12.6

Identification of Suitable Measures

Through the value stream-based analysis of the production network, sources of quality issues can be traced back to specific processes that cause them. Depending on the type and extent to which certain key figures deviate from their specified target value and the resulting RPIext, processes to be improved can be prioritized and measures to reduce the RPIext can be developed. These measures are then again assessed in terms of their impact on minimizing the RPIext. If, for example, a certain process causes defects, which will be detected at another plant, additional testing

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devices at the source process will reduce the time between occurrence and detection and therefore the quality cost and time (Arndt, 2018, p. 67ff.). Each measure should be defined in a standardized way incorporating specific requirements, implementation-related information, and a detailed description of the impact on the target dimensions (Arndt, 2018, p. 98ff.). Once the decision is made to implement a certain measure, it will change the configuration of the production network itself within the given boundaries and thus affect the target systems of all plants involved. As key figures are dependent on multiple influential factors and therefore constantly change over time (even without any changes to the system), the evaluation of measures and their potential effects on the process quality is difficult and should be evaluated dynamically and monitored over time (Arndt, 2018, p. 102; Arndt et al., 2019).

12.7

Evaluation of Process Quality Improvements

Therefore, we introduce two evaluating approaches to predict and monitor the effects of quality measures in the dynamic environment of production networks to master process quality globally. The first approach deals with a multi-method simulation creating a digital twin of the production network based on the previously explained value stream-based analysis. Based on the target and performance indicator system, the effects of previously identified measures can be assessed on the network level, considering plant-specific target systems. Therefore, unforeseen interactions and consequences at the network level as well as the performance of the specific measure in a dynamic environment can be identified and analyzed, before implementation (Arndt et al., 2019; Arndt et al., 2016). The second approach comprehends the simulation approach by implementing a business intelligence system for real-time monitoring of the implemented measures to present insights on a detailed process level and therefore support executives, managers, and other end users in making informed business decisions not only regarding process quality (Williams, 2016). The data is used to create a virtual representation of the production network— analogously to the global value-stream assessed in step 3 of the model—that visualizes the real-time performance on the operational process level in conjunction with the underlying network configuration and coordination concepts. Since all essential processes are constantly monitored against their individual performance baseline, the effects of measures will influence the RPIext and therefore become visible, validating the results of the simulation.

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Insights and Best Practices

Speaking from experience, already existing Value-Stream Maps (VSM) can be utilized to create the global value-stream in step three, making the implementation of the method much faster. Missing or conflicting data can be gathered easily by interviewing operators. The analysis of the RPIext might at first look like a very timeconsuming process, since it must be calculated for all processes that can affect certain quality aspects in some way. In fact, most companies already perform FMEAs to improve their process quality. Thus, focusing on RPIext provides a very detailed view on process quality when prioritizing measures by using the data already at hand (Arndt et al., 2019). As the evaluation of site-specific characteristics and their corresponding target objectives is done in workshops on site, they might be subjective and therefore distort the result. Since these evaluations have a great impact on the site-specific process quality—they are used as weighting vectors—the time for comprehensive evaluation workshops upfront and gathering a cross-functional group of participants is well spent. An advantage of the multi-method simulation is that unforeseen implications on the complex production network can be assessed beforehand and without investing in the implementation of measures. Moreover, through a dynamic modeling from the process up to the network level, it is more likely to discover interdependencies between certain processes or network entities that were not in the focus of a specific measure. Using the method in practice, we discovered that measures that benefit the whole network do not necessarily benefit the plant to be implemented in. On the contrary, some measures might lead to an increase in scrap and therefore quality costs in the focal plant due to early detection of the defect. Even though the measure increases the process quality globally, plant managers are not incentivized to implement such measures. We recommend to implement compensation measures between plants with their individual target systems to align interests with respect to global process quality (Arndt et al., 2019). To learn more about interest alignment in production networks see also (Silbernagel et al., 2019a, 2019b). Real-time monitoring through a business intelligence system requires high investments in advance. Once a business intelligence and reporting concept is in place, further synergies beyond an increase in process quality can arise. This includes more detailed value-stream insights and a real-time reporting of other key performance indicators that support executives in making crucial business decisions. Controlling departments can aggregate reports more easily and use them for advanced analytics on a production network level. Thus, the real-time monitoring approach can be quite sustainable in the long run.

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Summary

The goal of the presented method is to systematically analyze process quality within production networks from a value stream-based point of view. As proven in realworld cases, the model is mostly used in sub-networks or on certain product categories due to the high complexity of production networks today. Nevertheless, there are some key aspects to keep in mind when analyzing and improving process quality. As global process quality relies on the quality of all processes and the features of each product passing them, an in-depth analysis on the operational process level is essential. Furthermore, it is decisive to account for each individual site specialization since it determines the attitude and engagement toward certain processes or quality targets. Building upon the global value-stream analysis, the RPIext is a useful tool to identify critical processes and evaluate the most beneficial measures possible. Once certain measures are identified, it is important to pay attention for potential interdependencies during their implementation. An alteration of the network’s configuration in one spot or process can have an unexpected impact on other processes somewhere else within the production network. A comprehensive evaluation and implementation of process quality measures is often associated with high investments, regardless of the approach chosen. For this reason, an individual assessment must be made as to which evaluation alternative can more effectively and sustainably secure the long-term success of the company.

References Arndt, T. (2018). Bewertung und Steigerung der Prozessqualität in globalen Produktionsnetzwerken [Dissertation, Karlsruhe Institute of Technology]. Arndt, T., Buderer, C., Hofmann, M., & Lanza, G. (2016). Simulation-based evaluation of quality control strategies in global manufacturing networks. Advanced Materials Research, 1140, 473–480. https://doi.org/10.4028/www.scientific.net/amr.1140.473. Arndt, T., Kumar, M., Lanza, G., & Tiwari, M. K. (2019). Integrated approach for optimizing quality control in international manufacturing networks. Production Planning & Control, 30 (2–3), 225–238. https://doi.org/10.1080/09537287.2018.1534271. Arndt, T., & Lanza, G. (2016). Planning support for the design of quality control strategies in global production networks. Procedia CIRP, 41, 675–680. Arndt, T., Lemmerer, C., Biegler, C., Sihn, W., & Lanza, G. (2017). Steuerung globaler Produktionsnetzwerke. Entwicklung eines Standortrollenmodells zur dynamischen Bewertung von Gestaltungsmaßnahmen. Wt Werkstattstechnik Online, 107(4), 241–246. Bay, L., & Schaal, S. (2012). Pannen: Wie Rückrufe die Autobauer belasten. Retrieved November 18, 2020, from https://www.handelsblatt.com/unternehmen/industrie/pannen-wie-rueckrufedie-autobauer-belasten/6507146.html Bertsche, B., & Lechner, G. (2004). FMEA – Fehler-Möglichkeits- und Einfluss-Analyse. In Zuverlässigkeit im Fahrzeug- und Maschinenbau (pp. 106–159). New York: Springer. Book, J. (2015). Modellierung und Bewertung von Qualitätsmanagementstrategien in globalen Wertschöpfungsnetzwerken (Dissertation, Karlsruhe Institute of Technology).

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Feldmann, A., & Olhager, J. (2013). Plant roles: Site competence bundles and their relationships with site location factors and performance. International Journal of Operations & Production Management, 33(6), 722–744. https://doi.org/10.1108/IJOPM-03-2011-0077. Ferdows, K. (1997). Making the most of foreign factories. Harvard Business Review, 75(2), 73–88. Friedli, T., Mundt, A., & Thomas, S. (2014). Strategic management of global manufacturing networks. New York: Springer. https://doi.org/10.1007/978-3-642-34185-4. Garvin, D. A. (1984). What does ‘product quality’ really mean? MIT Sloan Management Review, 26 (1). Haefner, B., Kraemer, A., Stauss, T., & Lanza, G. (2014). Quality value stream mapping. Procedia CIRP, 17, 254–259. https://doi.org/10.1016/j.procir.2014.01.093. Hayes, R., Pisano, G., Upton, D., & Wheelwright, S. (2005). Pursuing the competitive edge. Boca Raton, FL: Wiley. Hering, E., & Schloske, A. (2019). Prozess-FMEA. In Fehlermöglichkeits- und Einflussanalyse (pp. 39–53). New York: Springer Vieweg. https://doi.org/10.1007/978-3-658-25763-7. Jacob, F., Meyer, T., & Leopoldseder, M. (2006). Management globaler Produktionsnetzwerke. In E. Abele, J. Kluge, & U. Näher (Eds.), Handbuch globale Produktion (pp. 274–323). Munich: Hanser. Lanza, G., Arndt, T., & Haefner, B. (2013). Qualitätssicherung in globalen Wertschöpfungsnetzwerken. Über alle Grenzen hinweg. QZ - Qualität Und Zuverlässigkeit, 58(12), 26–29. Nyhuis, P., Nickel, R., & Tullius, K. (2008). Globales Varianten-Produktionssystem. In P. Nyhuis, R. Nickel, & K. Tullius (Eds.), Globalisierung mit System. Garbsen: PZH-Verlag. Pfeifer, T., Geiger, E., Russack, T., & Rübartsch, M. (2000). Kooperationen schnell und sicher gestalten—Perspektiven des Qualitätsmanagements in Netzwerken. QZ - Qualität Und Zuverlässigkeit, 45, 153–154. Rautenstrauch, T. (2002). SCM-Integration in heterarchischen Unternehmensnetzwerken. In A. Busch & W. Dangelmaier (Eds.), Integriertes supply chain management (pp. 343–361). Wiesbaden: Gabler. Reichmann, T., Baumöl, U., & Kißler, M. (2011). Controlling mit Kennzahlen. Die systemgestützte Controlling-Konzeption mit Analyse- und Reportinginstrumenten. Munich: Vahlen. Saaty, T. L., & Vargas, L. G. (2012). Models, methods, concepts and applications of the analytic hierarchy process. New York: Springer. Silbernagel, R., Stamer, F., Häfner, B., Linzbach, J., & Lanza, G. (2019a). Kollaboration in globalen Wertschöpfungsnetzwerken. ZWF Zeitschrift Für Wirtschaftlichen Fabrikbetrieb, 114(5), 314–317. https://doi.org/10.3139/104.112085. Silbernagel, R., Wagner, R., Häfner, B., & Lanza, G. (2019b). Qualitätsregelstrategien in Wertschöpfungsnetzwerken. Wt Werkstattstechnik Online, 109, 802–806. Tapiero, C. S., & Kogan, K. (2007). Risk and quality control in a supply chain: Competitive and collaborative approaches. Journal of the Operational Research Society, 58(11), 1440–1448. https://doi.org/10.1057/palgrave.jors.2602420. Wiendahl, H.-P., Reichardt, J., & Nyhuis, P. (2010). Handbuch Fabrikplanung. Konzept, Gestaltung und Umsetzung wandlungsfähiger Produktionsstätten. Munich: Hanser. Williams, S. (2016). Business intelligence strategy and big data analytics. Morgan Kaufmann. https://doi.org/10.1016/C2015-0-01169-8.

From Plants to Network: Digitalization as an Enabler for Global Manufacturing

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Christoph Benninghaus

Nowadays, digital technologies are important drivers for the success of manufacturing companies. If digital technologies are applied, manufacturing activities are typically more efficient and reliable. Thus, offshoring activities from high-wage regions to foreign locations become less probable. However, technologies such as automated-guided vehicles, additive manufacturing, smart glasses, and related solutions like machine learning, Big Data analytics, or augmented reality are often only considered at the plant level and are not rolled out to other plants (or in an unsystematic, resource-inefficient way). This chapter examines which aspects help to make the right location decisions, what a structured technology transfer process looks like, and how technologies can be managed on a global level. These network decisions are related to the network configuration and coordination construct.

13.1

Introduction

Manufacturing conditions are changing substantially. In particular, plants in highwage regions are facing increasing competitive pressure from direct competitors within their industry, new competitors from other businesses (e.g., internet companies), and other plants within their own company’s production networks. As pointed out in the previous chapters, the strategic management of global manufacturing networks is an important lever to unlock opportunities for single plants and the whole network. A promising solution for better positioning high-wage

C. Benninghaus (*) Construction Company, Schaan, Liechtenstein # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_13

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locations is often seen in the field of digitalization1. It has become an essential element in daily life and various industries. In this chapter, the term “digital technologies” refers to all kinds of machines or systems that have a computing, performing, and communication ability that are discussed in the context of digitalization, such as augmented reality, automated-guided vehicles, collaborative robotics, smart glasses, cloud computing, machine learning, Big Data analytics, etc. Although many technologies and solutions of digitalization are not new, numerous use and business cases have proven that digitalization can have a significant impact on the success of manufacturing companies. In doing so, the value of a technology often lies not in the technology itself but in its ability to support and optimize (existing) processes. Managers of manufacturing networks must make strategic choices regarding network coordination and configuration (see Chap. 2). Although the importance of network management is outlined in various practical and theoretical publications, most networks are still managed fragmentedly, rarely planned, and not optimized (Cattaneo et al., 2010; Ferdows, 2014). Emerging “internal and external events, developments and opportunities” (Papakostas et al., 2015, p. 894) in particular ensure that structured management becomes necessary. Such developments and opportunities can be also seen in the latest developments of digital technologies. Similar to general network management decisions, the management of digital technologies is also an underdeveloped topic as most companies still manage their digital transformation in an isolated and uncoordinated manner and on the local level. The local management and unsystematic implementation of digital technologies typically result in isolated applications, waste of resources, and missing interlinks of digitalization projects. To overcome the issue of inconsequent and uncoordinated management of digital technologies in global manufacturing networks, the following chapter will introduce (1) recommendations regarding plant selection for implementing digital technologies, (2) a technology transfer process, and (3) concepts on how to structure and manage digitalization from a global perspective. More details, background information and research findings can be found in Benninghaus (2019).

13.2

Digitalization in the Right Place

Digitalization activities are typically driven by plants or specific units inside a plant. Characteristically, there is no specific “start date” as plants are trying, testing, and implementing first solutions on their own expertise and to the best of their own judgment. It must be understood that digitalization requires strategic commitment

1

Digitalization, (Industrial) Internet of Things, Industrie 4.0, smart manufacturing, and others are based on similar concepts. In the following, the term “digitalization” will represent all related wordings and constructs.

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From Plants to Network: Digitalization as an Enabler for Global Manufacturing

Fig. 13.1 Digitalization location factors. Adapted from Benninghaus (2019)

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Political, Economical, Social, Technological, Legal, Environmental (PESTLE) factors

Strategic level Strategy

Role

Investments

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Know-how & Experience

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and planning across the whole company’s manufacturing network, because the implementation of digital technologies does not inevitably result in an increase in productivity. Further, many digital technologies are only beneficial if they are scaled. Nevertheless, there are some aspects that promote the successful implementation of digital solutions. Every company must ask itself when, what, and where a new technology should be implemented. According to Hess et al. (2016, p. 123), “integrating and exploiting new digital technologies is one of the biggest challenges that companies currently face.” The location (where), timing (when), and the solutions (what) need a strategic review. For those companies that have overcome the “trial and error” phase of testing various digital technologies, a consideration of the main aspects according to the literature and successful practice companies is the next logical step. Figure 13.1 points out the most significant and supporting criteria for an efficient introduction and the appropriateness of digitalization activities in a company’s location (only the main variables will be considered to limit overall complexity). The gray boxes around the “factory” symbolize the external location factors. The upper and lower boxes build on the political, economic, social, legal, technological, and environmental (PESTLE) attributes. Political or economic attributes may include political stability, tax legislation, subsidies, trade barriers, logistics costs, and more. These factors are already impactful when considering the implementation of digital technologies. For example, high import taxes or low subsidies can hinder or even stop investments. In the same way, social, legal, or environmental factors have an impact. Additionally, technologies facilitate location decisions, because not all technologies are available everywhere or are strategically relevant in a dedicated factory (e.g., fixed automation technology for highly flexible plants and products). However, the ability to select, implement, and deploy technologies is a success lever of manufacturing companies (Gaimon, 2008; Porter, 1985). Several authors

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discussed the meaning of external location factors in their publications and can be considered for further reading (e.g., Colotla et al., 2003; De Meyer & Vereecke, 1994; Ellram et al., 2013; Meyer, 2008; Shi & Gregory, 1998; Vereecke & Van Dierdonck, 2002; Yip, 1992). Besides these location factors, proximity to other stakeholders is of high relevance. Universities, research institutes, providers, and suppliers are important partners when implementing new digital technologies. They can support R&D, identify suitable applications, derive concepts, implement solutions, and provide scalable technologies that cannot be built within the company or if internal efforts would be too high. Such business ecosystems can affect location decision as their existence is in general limited and tends to be higher in high-wage regions in Western Europe or selected Asian countries (Ferdows et al., 2016). The inside of the factory in Fig. 13.1 is detailed according to its strategic and operational areas. At strategic level, plants need to review their local strategy, structure, and role. Typically, each factory should have a described plant role, as pointed out in Chap. 2. Lead factories, or more advanced sites in general, are by definition required to implement new technologies first. These plants are responsible for developing, testing, piloting, and scaling technologies in their respective manufacturing network, as other plants might have limited abilities and resources. Furthermore, a final budget for investments is necessary to acquire and maintain a technology. Smaller and strategically less relevant factories tend to fail in this category. Additionally, cultural aspects need to be evaluated when deciding on a location. Culture comprises values, behavior patterns, and standards as well as artifacts (Wien & Franzke, 2014), and technology acceptance varies according to geography, generation, and gender. As training and acceptance is needed to implement digital technologies, this aspect varies not only from company to company but also from plant to plant. From an operational perspective, human resources, know-how, experience, and infrastructure must be reflected. As for innovation in general, the qualification and educational background of employees are indispensable characteristics to understand when deploying new technological innovations. Hitherto, there is still a tendency that qualification levels are higher in more developed countries (Billon et al., 2010). However, this share changes continuously, and less developed countries are closing the gap. The knowledge of each individual employee makes up the technological experience of a plant. It is empirically acknowledged that a firm’s past technological experience and capabilities path its future (Phene & Almeida, 2003; Teece et al., 1997). Lean management is particularly a perquisite here and is the basis for all digitalization activities (see Chap. 14). Experience is a critical indicator and often hinders successful implementation in “younger” locations. Finally, a minimum infrastructure is a basis for all technology decisions. Here, hard (e.g., energy, roads, and energy) and information-related infrastructure (e.g., access to internet, network speed, and server reliability) are of utmost importance. It

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is obvious that some technologies have a high demand regarding infrastructure (e.g., wastewater disposal, internet connectivity, and electricity) and that this factor can be understood as a grounding criterion. Nonetheless, compared to the other factors, infrastructure can be upgraded more easily. In contrast to qualification, it partly develops faster in less developed countries compared to high-wage locations in central Europe or North America (Park et al., 2016). A maturity assessment can be a good starting point to address the questions on an operation level. Related maturity models usually describe different development stages toward a target level and are offered by various consultancies and research institutions. Ketokivi et al. (2017, p. 20) summarize this concept, stating that “location decisions must be understood not just through the lens of economic attractiveness of one region or country over another, but also as a decision where many organizational and technological interdependencies become relevant.” Hence, a manufacturing company should consider the respective plant only if the described factors are positively addressed and indicate acceptance as well as implementation success of a new digital technology.

13.3

Successful Digital Technology Transfer

After piloting and scaling digital technologies in selected plants, the need arises to transfer and roll out these solutions. The right timing (when) depends highly on individual achievements and cannot be generalized. For the “what,” which means the selection of technology, various approaches exist. Among others, the analytical hierarchy process, rankings, scoring, utility models, fuzzy techniques, and mathematical or programming methods have been derived. In addition, a company and plant’s strategy determine what technologies should now be rolled out to other locations. As a systematic sharing of technological innovations requires higher operational performance, this topic is deeply linked to the location decision for pioneering digital technologies (von Krogh et al., 2018). Successful technology transfer to other locations is a vital capability of a manufacturing company as soon as a (digital) technology and related processes are running in a stable manner. Figure 13.2 briefly describes the main considerations for a systematic technology transfer. The first two steps (broken lines) were already covered in the last section. It was pointed out that the selected technology needs to fit the strategic and operational conditions of a site. Afterward, the planning concept deals with the appropriateness, robustness, and transferability of the technology (Grant & Gregory, 1997). While appropriateness and robustness define the degree of difficulty of a technology transfer, transferability is associated with economic factors such as timeline for transfer, logistics, costs, operative resources, or documentation completeness. For example, a 100% robust technology can be transferred to any other location without any adaptations or additional requirements.

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Strategic location fit

Operational location fit

(e.g. strategy, investments, culture)

(e.g. human resources, infrastructure)

Implementation concept

Planning concept

(e.g. risk and failure analysis, roadmap)

(appropriateness, robustness, transferability )

Process adjustment and implementation

Knowledge development

Continuous improvement

Technology performance improvement

Fig. 13.2 Digital technology transfer process (based on Benninghaus, 2019)

In a next step, a reliable implementation concept should be outlined. This includes, among others, a detailed risk and failure analysis, which can also reflect the willingness and acceptance in the receiving site. A roadmap is the outcome of this stage. Subsequently, processes need to be adjusted in the receiving plant (if required), and the pre-defined technology can be implemented. This goes hand in hand with the training, education, and knowledge development of the local employees. The last two stages—performance improvement and continuous improvement (dotted lines)—can be accompanied or are performed autonomously by the receiving site. Although this procedure for technology transfer has been proven in theory—in similar forms by Galbraith (1990); Grant and Gregory (1997); or Thomas et al. (2008)—and in practice, company-specific considerations and peculiarities can influence the process. It is only rarely the case that a technology can simply be adopted by a site. In particular, the iterations of single stages “planning concept” or “risk and failure analysis” are common. As technology selection and transfer is an important lever for manufacturing companies, an individual evaluation and implementation is crucial.

13.4

Structuring Digitalization in Global Manufacturing

Many companies manage digital technologies in a rather unstructured way and with focus on the single plant level, as some digital technologies already offer (limited) local benefits. However, consideration from global perspective unlocks further advantages. On the one hand, a manufacturing network’s configuration (see Chap. 2) can be changed. For example, digital technologies can promote the establishment of new sites—especially in high-wage regions. Even though digital technologies are not the main drivers for these developments, they typically function as “key enablers.” The access to, implementation, and operation of such technologies come into

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consideration to allow an efficient and productive manufacturing. Studies and reports show that the implementation of automation and digital technologies are expected to result in more than 18% productivity gains within the next several years (Koch et al., 2014). Further, network configuration can change based on new site specializations. Having implemented the latest technologies upgrades a site’s competence level and repositions it in the network. A shift in responsibility and competences induced by technologies is a phenomenon that can now be seen in many company subsidiaries in China. The typical “low-cost” sites get upgraded and are now as competitive and modern as the factories in the company’s home base. On the other hand, network coordination (see Chap. 2) can be changed and supported by digital technologies. Human-machine interfaces such as smart glasses and solutions like augmented reality can help to better cooperate within manufacturing networks. Communication becomes more convenient and interactive compared to phone calls or online meetings. Software solutions such as manufacturing execution systems (MES) are implemented to synchronize, coordinate, and steer manufacturing operations across single locations. It is foreseeable that this and other data solutions (e.g., cloud, professional data analytics) will extensively transform the management of networks and provide further savings. As the technological, structural, and organizational consequences for companies can be quite comprehensive (depending on the initial setup), systematic management and transparency about the respective activities are required. The following framework (see Fig. 13.3) can help to map technologies that go beyond single locations. It was tested in and implemented by several companies (cf. Benninghaus, 2019). The general form of the framework changes depending on the number of considered plants. A hexagon fits for six plants, a triangle for three factories, etc. The different forms symbolize production, supply chain, and (process and product) development competences. Scientific research has shown that production skills typically require the lowest level of competences, while development competences require the highest degree (Feldmann et al., 2013). For instance, production competence encompasses manufacturing and assembly activities, technical maintenance, or process improvement. Supply chain competences comprise logistics, procurement, supply, and supplier development. Last, development competences include R&D, the introduction of new technologies, product improvement, and knowledge management regarding process and product know-how. The symbols in the framework are used for better visualization, as they classify and bundle the technologies into several groups. The intention is to improve readability in cases of manifold technologies or larger networks: • Automation and manufacturing technology (including embedded systems) such as robotics, automated-guided vehicles, additive manufacturing, etc. • Data analytics and solutions such as MES, cloud, machine learning, Big Data analytics, etc. • Human-machine interfaces such as mobile devices, smart glasses, wearables, and related solutions like augmented reality, etc.

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U s U se i e in n P r De Supp oduc vel lyC tion o sta pmen hain g t

x rod e in P

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x Site Spain

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n uctio Prod Chain ly p p Su t men elop Dev ge sta

Technology X is developed or in operation at site

x Human-machine interfaces

x Dataanalytics&solutions

X

Site Mexico

Site USA

Site Russia

Site Brazil

Site India

x Automation & manufacturing

Site Switzerland

Site Canada

Site Thailand Site Germany

Development is performed for other sites in the manufacturing network

Fig. 13.3 Digital competence management framework (adapted from Benninghaus, 2019)

Further, a grey symbol demonstrates that an activity (production, supply chain, or development) is performed for other sites in the manufacturing network. The number in each symbol stands for a specific technology. For example, a small circle with a “1” could stand for collaborative robots, “2” for additive manufacturing, “3” for automated-guided vehicles, etc. That list can be created on behalf of each company and its specific needs. Likewise, new or other technologies can easily be added to the framework. Links between technologies can be added by arrows to describe dependencies (e.g., establishing WIFI infrastructure before implementing automated-guided vehicles). The framework seeks to help manufacturing companies in structuring their digital technologies more effectively on a global level. It should support operation mangers to plan, coordinate, and manage their technological setup and the utilization of technologies. Double work and unwanted activities can be identified quickly, and the waste of resources or extra efforts for standardization can be avoided at an early stage. However, another form of visualization might fulfill similar requirements. A table structure to cluster the technology portfolio would be especially more advisable for companies with large manufacturing networks to maintain readability and applicability.

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13.5

187

Summary

Digitalization is not a completely new trend, but as a technologically enabled solution, it has recently received great attention in research and practice. It is one of the most significant developments and transformations in our society, daily life, and most industries. To overcome the gap caused by the fact that digital technologies are predominantly managed on the local level in industry, this chapter provides recommendations and guiding frameworks to reach the next logical stage. In fact, the highest benefits and cost savings from digital technologies can only be realized after scaling it within a plant or the manufacturing network. This chapter has shown that careful location selection is a fundamental perquisite and important decision criteria regarding this have been outlined. Furthermore, a guideline to roll out digital technologies was introduced. Finally, the derived competence framework has revealed its strengths in visualizing and mapping digital technology activities. It should support operation managers in planning and managing digital technologies. Such a systematic network design is an important lever to get maximize the benefits of digitalization. Whether digital technologies are rolled out in parallel, step-by-step, or sequentially, this chapter supports the selection of the location, the systematic rollout, and its management from a global perspective. From the perspective of manufacturers with plants in high-cost regions, it is worth taking a closer look at this topic as the structured, global management of digital technologies is one of the best opportunities to limit further offshoring activities and keep manufacturing in highwage regions.

References Benninghaus, C. (2019). Impact of digitalization on the strategic management of international manufacturing networks: A configurations perspective (Dissertation, University of St.Gallen). Billon, M., Lera-Lopez, F., & Marco, R. (2010). Differences in digitalization levels: A multivariate analysis studying the global digital divide. Review of World Economics, 146(1), 39–73. Cattaneo, O., Gereffi, G., & Staritz, C. (2010). Global value chains in a postcrisis world: Resilience, consolidation, and shifting end markets. In O. Cattaneo, G. Gereffi, & C. Staritz (Eds.), Global value chains in a postcrisis world: A development perspective (pp. 3–20). Washington, DC: The World Bank. Colotla, I., Shi, Y., & Gregory, M. J. (2003). Operation and performance of international manufacturing networks. International Journal of Operations & Production Management, 23 (10), 1184–1206. De Meyer, A., & Vereecke, A. (1994). Strategies for international manufacturing. INSEAD working paper series 94/25/SM/TM. Ellram, L. M., Tate, W. L., & Petersen, K. J. (2013). Offshoring and reshoring: An update on the manufacturing location decision. Journal of Supply Chain Management, 49(2), 14–22. Feldmann, A., Olhager, J., Fleet, D., & Shi, Y. (2013). Linking networks and plant roles: The impact of changing a plant role. International Journal of Production Research, 51(19), 5696–5710. Ferdows, K. (2014). Relating the firm’s global production network to its strategy. In J. Johansen, S. Farooq, & Y. Cheng (Eds.), International operations networks (pp. 1–11). New York: Springer.

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Ferdows, K., Vereecke, A., & De Meyer, A. (2016). Delayering the global production network into congruent subnetworks. Journal of Operations Management, 41, 63–74. Gaimon, C. (2008). The management of technology: A production and operations management perspective. Production and Operations Management, 17(1), 1–11. Galbraith, C. S. (1990). Transferring core manufacturing technologies in high-technology firms. California Management Review, 32(4), 56–70. Grant, E. B., & Gregory, M. J. (1997). Adapting manufacturing processes for international transfer. International Journal of Operations & Production Management, 17(10), 994–1005. Hess, T., Benlian, A., Matt, C., & Wiesböck, F. (2016). Options for formulating a digital transformation strategy. MIS Quarterly Executive, 15(2), 123–139. Ketokivi, M., Turkulainen, V., Seppälä, T., Rouvinen, P., & Ali-Yrkkö, J. (2017). Why locate manufacturing in a high-cost country? A case study of 35 production location decisions. Journal of Operations Management, 49–51, 20–30. Koch, V., Kuge, S., Geissbauer, R., & Schrauf, S. (2014). Industry 4.0 - Opportunities and challenges of the industrial internet. Strategy& and PwC report. Meyer, T. (2008). Investments abroad: Using the right evaluation techniques. In E. Abele, T. Meyer, U. Näher, G. Strube, & R. Sykes (Eds.), Global production (pp. 102–139). New York: Springer. Papakostas, N., Georgoulias, K., Koukas, S., & Chryssolouris, G. (2015). Organisation and operation of dynamic manufacturing networks. International Journal of Computer Integrated Manufacturing, 28(8), 893–901. Park, S.-T., Im, H., & Noh, K.-S. (2016). A study on factors affecting the adoption of LTE mobile communication service: The case of South Korea. Wireless Personal Communications, 86(1), 217–237. Phene, A., & Almeida, P. (2003). How do firms evolve? The patterns of technological evolution of semiconductor subsidiaries. International Business Review, 12(3), 349–367. Porter, M. E. (1985). Technology and competitive advantage. Journal of Business Strategy, 5(3), 60–78. Shi, Y., & Gregory, M. (1998). International manufacturing networks - To develop global competitive capabilities. Journal of Operations Management, 16, 195–214. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. Thomas, A. J., Barton, R., & John, E. G. (2008). Advanced manufacturing technology implementation: A review. International Journal of Productivity and Performance Management, 57(2), 156–176. Vereecke, A., & Van Dierdonck, R. (2002). The strategic role of the plant: Testing Ferdows’s model. International Journal of Operations & Production Management, 22(5), 492–514. von Krogh, G., Netland, T. H., & Wörter, M. (2018). Winning with open process innovation. MIT Sloan Management Review, 59(2), 53–56. Wien, A., & Franzke, N. (2014). Grundlagen der Unternehmenskultur. In Unternehmenskultur (pp. 29–45). Wiesbaden: Springer Fachmedien. Yip, G. S. (1992). Total global strategy: Managing for worldwide competitive advantage. Englewood Cliffs, NJ: Prentice Hall.

Enabling Data-Based Applications in Manufacturing

14

Paul Buess

Lean manufacturing has enabled companies worldwide to increase productivity and quality by eliminating waste and was characterized as the most influential manufacturing paradigm. Besides lean management, IT technology-driven smart manufacturing, in the German-speaking area referred to as Industry 4.0, recently receives tremendous attention and will lead to the next fundamental paradigm shift in manufacturing. A key aspect of smart manufacturing is the exploitation of manufacturing data for more efficiency, process transparency, and quality. To support companies in gaining a competitive advantage by exploiting their manufacturing data, this chapter consolidates and briefly describes 14 data-based applications in manufacturing along with their key barriers and key enablers. The scope of the research and thus of the presented data-based applications is the site level.

14.1

Introduction

The history of the manufacturing industry is characterized by several paradigm shifts. Around 1900, the production of goods was dominated by craftsmanship. In 1913, Henry Ford introduced the first moving assembly line. This was the starting point of mass production, which was the dominating production paradigm until the emergence of lean manufacturing (LM) after World War II. Since then, LM has enabled companies worldwide to increase productivity and quality by eliminating waste and was characterized as the most influential manufacturing paradigm (Holweg, 2007). Besides LM, a new manufacturing concept recently receives increased attention from industry, academia, and government. The IT technologydriven smart manufacturing (SM), in the German-speaking area referred to as P. Buess (*) Dürr Systems AG, Bietigheim-Bissingen, Germany # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_14

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Industry 4.0, is heavily supported by governmental programs in many manufacturing countries (Thoben et al., 2017). Following Lasi et al. (2014), SM technology will lead to the next fundamental paradigm shift in manufacturing. In 2017, the Institute of Technology Management (ITEM) conducted a study to explore the future of LM, especially in the light of the emerging SM technologies. A large majority of the participants, mostly production and operation managers, has identified data analysis as a key enabler for further operational improvements. This understanding is supported by several scholars who emphasize the great potential of data utilization in manufacturing, e.g., to support the identification of hidden problems in complex manufacturing processes (Qi & Tao, 2018) and to significantly decrease operating costs due to data-driven preventive maintenance (O’Donovan et al., 2015a). In a nutshell, manufacturing is an industry where data exploitation will contribute significantly to companies’ competitiveness (Harding et al., 2006). This observation resulted in a deeper research of the potential of data-based applications, which subsequently resulted in the authors dissertation (Buess, 2020). As part of this dissertation, DBAs in manufacturing were collected and clustered, and the key challenges as well as key enablers to apply them successfully were identified. The results of this research are presented in this chapter in condensed form.

14.2

Data-Based Applications

The term data-based applications (DBAs) is used as an umbrella term for the use cases of data exploitation in manufacturing. DBAs include, for instance, Big Data applications (Åkerman et al., 2018), data-driven applications (Russom, 2015), and machine learning applications (Wuest et al., 2016). Table 14.1 lists the 14 DBAs, along with a brief description of their objectives and approaches. The DBA classification system shown in Table 14.1 is inspired by several existing classifications of data-driven methods (O’Donovan et al., 2015b), data mining (Choudhary et al., 2009; Harding et al., 2006), artificial intelligence (Meziane et al., 2000), and data analytics enabled I.40 application (Pilloni, 2018).

14.3

Key Challenges and Enablers to Apply DBAs

Despite the broad range of DBAs and the large potential, data utilization in manufacturing seems to be still in its infancy. In 2017, the ITEM-HSG and the RWTH Aachen conducted a joint study on manufacturing analytics among 100 manufacturing companies. As depicted in Fig. 14.1, the study revealed that only a small fraction of 5.5% of the available data is actually used.

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Table 14.1 Data-based applications Application DBA short description Category I: Planning and scheduling 1. Production scheduling Objective: Determination of an optimal production plan Approach: Mathematical optimization model using data (e.g., machine availability) to optimize a target value (e.g., cycle times) 2. Layout planning O: Determination of an optimal production layout A: Mathematical model optimizing a target value (e.g., minimal WIP, minimal material handling) under given constraints (e.g., the number and type of machines, space) Category II: Production control 3. Real-time control O: Real-time monitoring of the production process A: Permanent comparison between the expected behavior and the actual behavior of the production system, including real-time notification in case of deviations 4. System performance O: Transparent overview of the overall system performance and measurement visualization of trends A: Automatic collection of operation metrics (e.g., times, scrap rates) and calculation and visualization of KPIs (e.g., OEE) Category III: Maintenance 5. Condition monitoring O: Increase maintenance effectiveness and efficiency A: Monitoring equipment status to trigger maintenance only in case of unusual behavior 6. Predictive maintenance O: Increase maintenance effectiveness and efficiency A: Equipment condition monitoring and prediction of degradation to derive maintenance plans that ensure equipment availability but avoid unnecessary maintenance 7. Prescriptive O: Increase maintenance effectiveness and efficiency maintenance A: As predictive maintenance but in addition, the prescriptive maintenance application also suggests, or even starts, maintenance activities autonomously Category IV: Internal logistics 8. Track and trace O: Traceability of containers, materials, and products (e.g., position, cycle times) A: Track and trace of products, etc. by using unique identifiers such as RFID tags 9. Material flow O: Demand-oriented, automated control of the material flow management A: Two variations: (1) push approach: a central production control system calculates the optimal material flow to meet demands with little material handling and low WIP inventories (2) Pull approach: a digital Kanban system (e-Kanban) detects automatically if the available material falls below a minimum threshold and triggers the replenishment process 10. Inventory O: Smart inventory management to ensure material availability with management minimal inventory A: Accurate tracking of inventory enables low stocks and timely reordering from suppliers (continued)

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Table 14.1 (continued) Application DBA short description Category V: Quality management 11. Quality monitoring O: Identification of defective products (reactive quality control) A: Real-time comparison of quality data (e.g., geometrical dimensions) and reference values. 12. Quality improvement O: Systematic and preventive avoidance of errors A: The availability of accurate quality-related data enables failure root cause methods to identify and eliminate systematic error causes Category VI: Environment, health, and safety 13. Energy monitoring O: Reduction of energy consumption A: Measuring energy consumption to find saving potentials 14. Environmental O: Ensure healthy working conditions for employees monitoring A: Measuring environment conditions such as air quality 1. Vallhagen et al. (2017), 2. Kumar et al. (2018), 3. Hirmer et al. (2017), 4. Meissner et al. (2018), 5.Yunusa-kaltungo and Sinha (2017), 6. Åkerman et al. (2018), 7. Matyas et al. (2017), 8. Louw and Walker (2018), 9. Wan et al. (2018). 10. Saygin (2007), 11. Wuest et al. (2014), 12. Oliff and Liu (2017), 13. Lenz et al. (2017), 14. Pilloni (2018)

Fig. 14.1 Share of exploited data of available data (based on Wenking et al., 2017)

This observation raised the question why manufacturing companies struggle to create value from their manufacturing data. This research followed the approach of first consolidating key challenges real-world companies face when trying to exploit their production data and then derive key enablers to overcome these challenges. The primary source of information are in-depth case studies with three major companies which are leaders in their respective markets. Besides, interviews with Prof. Dr. Guido Schuster and Prof. Dr. Thorsten Wuest, both leading scholars in the field of manufacturing data analytics, have been conducted.

14.3.1 Key Challenges Table 14.2 lists 9 challenges of using DBAs in manufacturing, including 21 related sub-challenges. Discussing all identified challenges in detail would go beyond the

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Table 14.2 Key challenges of using DBAs in manufacturing Category Employees

Main challenge Initial resistance of employees

Employee qualification

Organization

Return on investment (ROI) Calculation of investment decisions Misuse of data Comparability of data Access to expert knowledge

Technology

Basic requirements

Distributed data

Sub-challenge Fear of job loss Fear of loss of job autonomy Fear of control Shop floor employees Middle management Top management Uncertainty of results Chicken and egg problem Internally Externally Within the plant Within the manufacturing network Limited internal resources Competition for data scientists IT system performance Data security Technical infrastructure Data integration Data access

scope of this chapter.1 However, Table 14.2 clearly illustrates that discussing the implementation of DBAs only from a technical perspective is not sufficient. The case companies have reported several situations in which a technologically flawless DBA did not deliver the expected value due to reservations by employees. For instance, maintenance employees did not like the idea of relying on the recommendation of a maintenance assist instead of their own experience and intuition and just ignored the system recommendation. The maintenance case indicated above is one example for the sub-challenge the fear of loss of job autonomy. Further employee resistance can be triggered by the fear of human labor being gradually substituted by more costeffective DBAs and the fear of control of individuals on the shop floor due to omnipresent data collection. Employee qualification refers to the need of a broad range of skills required for DBAs, including manufacturing know-how, IT know-how, and data analytics knowhow as well as the ability to overcome internal employee resistance. Companies have reported a shortage of people with these skills. Furthermore, the top management often lacks a basic understanding of data analytics. Hence, the decision for or against a DBA project is often “a question of faith” rather than an informed decision. Regarding shop floor employees, a major challenge is to ensure data quality of manual data input. From an individual point of view, more data collection implies more effort, but does not result in directly visible benefit. 1

For more details see Buess (2020).

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From an organizations point of view, the ROI calculation of investment decisions is a major challenge for two reasons. First, the result of any data analysis is not guaranteed; thus calculating an expected ROI as well as the breakeven of DBA projects is hardly possible. Second, a chicken and egg problem can be observed as managers ask for a proof of concept before granting project funds, but the proof of concept cannot be provided prior to the analysis. Misuse of data refers to the challenge to avoid the internal (e.g., control of individuals’ performance and behavior) and external (e.g., data theft) misuse of data. Whereas data security is mostly seen as a technological challenge, the practitioners revealed that it is often not the IT system but employees which are the weakest part of data protection. Comparability of data refers to the challenge to ensure consistent KPIs within the factory to allow meaningful comparisons and interpretations. A fourth organizational challenge is to ensure access to expert knowledge. Currently, companies report a lack of people combining manufacturing, IT, and data analytics skills. At the same time, those individuals are scarce at the market, and therefore companies observe a “war for data talents.” They also report difficulties to hire data talents as they often receive very lucrative offers from financial institutions and consultancies, while manufacturing companies are often bound by a collective wage agreement. The technology part has two main challenges. First, basic requirements comprise the basic technical IT infrastructure to allow the system to collect, process, store, extract, and analyze data in reasonable time, whereas reasonable time is defined by the actual DBA, hence can range from seconds (e.g., real-time control) to hours. It also includes the technical measures for data security. Finally, companies have indicated that without a sophisticated central data management (usually the manufacturing execution system (MES)), finding and merging distributed data causes enormous effort, thus making most data analytics uneconomical. Hence, companies need to establish a single source of truth of manufacturing data, including an easy and user-friendly access for authorized employees.

14.3.2 Key Enablers The term enabler is defined as “something or someone that makes it possible for a particular thing to happen or be done” (Cambridge University Press, 2019). In this context, an enabler is a measure that increases the likelihood of the successful implementation of DBAs. A majority of the following enablers address one of the key challenges described above. The key enablers are shown in the overview in Table 14.3 and briefly described in the following section. Human labor will continue to be of central importance in industrial production. Regarding the interaction of DBAs and employees, the challenges resistance of employees and employee qualification have been identified. The corresponding enablers to address these challenges are foster acceptance and role-specific training.

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Table 14.3 Key enablers of using DBAs in manufacturing Category Employees

Main enabler Foster acceptance

Role-specific training

Organization

Create favorable conditions

Data usage Transparency and awareness Standardization of metrics Internalize external expertise Technology

State-of-the-art technology

Sub-enabler Understand the value of DBAs Being part of the solution development Basic training of end users Citizen data scientists Advanced and holistic understanding Basic but holistic understanding Structured approach to select DBAs Ensure management Buy-in Put the right leader in place Maintain the motivation Data guidelines Data security officer Management responsibility for standardization Convince with nonmonetary benefits Benefit from cooperation Benefit from falling Component costs Cloud computing

Related sub-challenge Fear of job loss Fear of loss of job autonomy Shop floor employees

Middle management Top management Uncertainty of results and chicken and egg problem

Internal and external misuse of data and fear of control Comparability of data within plant and production network Limited internal resources and competition for data scientists

IT basic requirements and data access

1. Foster Acceptance To increase the acceptance of employees and reduce the fear of job loss and the loss of job autonomy and control, employees need to understand the value of DBAs and should be involved in developing new solutions. Understand the Value of DBAs (Sub-Enabler) From an individual worker perspective, it is not easy to see how DBAs will benefit them personally. Thus, the management has to communicate clearly that a DBA is beneficial for the company (e.g., by increased competitiveness) and for the worker (e.g., job security, higher individual productivity, or reduction of monotonies routine activities).

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Being Part of the Solution Development The willingness to use new data-based applications is significantly increased, if the end user was involved in the solution development process. The use cases have clearly shown that the individual contribution to the development of a DBA is a key driver of workers’ willingness to use it. End-user involvement should be fostered at the early phase of the solution development, e.g., to set the objectives from an end-user perspective as well as to integrate domain expertise. Later on, the end user must be able to provide feedback and should also serve as quality gate. 2. Role-Specific Training Basic Training of End Users Understanding the basic logic behind a DBA allows end users to use the application more effectively as well as to provide more qualified feedback to the solution developer. This not only improves the user-friendliness of a new DBA but also increases its acceptance among end users. So companies should not only train employees in using an application but also to understand their underlying logic. Advanced training for end users (e.g., to fully understand the machine learning foundation of the smart maintenance tool) often adds little value for the daily job and is therefore economically not practical on a large scale. Citizen Data Scientists To mitigate the lack of internal data analytics skills without hiring expensive external experts or consultants, the concept of citizen data scientists (CDS) is very promising. CDS are experienced technicians or engineers that already work in the company and are selected for extra training in data analytics skills. CDS candidates usually have a strong intrinsic interest in data but also have exiting IT skills, often from a private context. After intensive training—involving formal training, self-training (including YouTube), and exchange with data and IT experts—CDS combine manufacturing and data analytics expertise and are thus suited to act as internal consultants for DBA projects on the shop floor. Advanced and Holistic Understanding of Middle Management Due to the broad range of necessary skills of middle managers in charge of DBA projects, an all-encompassing enabler cannot be presented. However, one approach emerged in the case study analysis is to combine formal and informal training. One company offered a dedicated 150-hour data analytics training course and, in addition, encouraged employees to work part time in an IT or data analytics team for up to 40 weeks to apply the training content. Basic but Holistic Understanding of Top Management The tight schedule of top managers does not allow to attend a full-content training course. However, top managers can benefit from a course with reduced scope and depth, tailored to their requirements. By understanding the basic concepts of data analytics, top managers are better qualified to evaluate internal or external DBA proposals.

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3. Create Favorable Conditions The case studies have revealed four organizational enablers that increase the probability of successful DBA projects. Structured Approach to Select DBAs A structured approach facilitates the identification of the most promising DBA opportunities and ensures adequately resource endowment. The structured approach, called Analytic Business Case Review in a case company, includes intense discussions between the data expert and the process owner to evaluate the potential of DBAs for the specific context. Only if all stakeholders agree that the potential added value of the DBA justifies the initial investment, resources are allocated. A standardized DBA evaluation approach facilitates the comparison of DBA use cases across several production sites. Ensure Management Buy-In Like in every project, management support is a critical success factor. Management buy-in is critical at the start of the project to provide resources even if the ROI of these investments is highly uncertain. However, it is also critical after the initial phase, especially if the DBA project does not deliver the intended results in the first iteration. Data analytics is an iterative process, and finding useful results may require more than one iteration round. Therefore, a lack of management commitment can result in a hasty termination of the project. Companies with a high persistence against setbacks in the early phase of DBA projects have shown to be more likely to implement DBAs successfully. To foster ongoing management buy-in, Dr. Wuest proposes to built-in little success stories along the way: “Quick Wins keep people happy and management calm.” Put the Right Leader in Place The selection of the leader in charge of a DBA project strongly influences the project’s probability of success. Summarizing his personal experience from joint DBA projects with industry, Professor Schuster presents a profile of requirements for a good DBA project leader. First, as DBA projects are too time-consuming to be driven besides the daily business, a DBA project leader should have dedicated time to manage the project. Second, a certain level of seniority of the project leader does certainly help to have access to enough financial and personal resources. Furthermore, seniority is valuable to convince the top management for support as well as to overcome internal resistance against DBA. Third, persistence (“a certain terrier mentality”) and persuasiveness are necessary character traits of a leader, not only to overcome initial resistance but to keep the project running despite potential setbacks. Maintain the Motivation Similar to the management, project members may lose motivation and confidence in the project in case of setbacks. As described above, a persistent project leader and the use of “quick qins” are helpful to maintain the motivation of the project team. The small rewards of “quick wins” increase the motivation to invest additional effort to reach the defined objectives.

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4. Data Usage Transparency and Awareness Data guidelines and data security officers help to avoid the misuse of data and mitigate the fear of control of workers. Data Guidelines Guidelines are supposed to create transparency over the collection and use of data. They define clear limits for tracking individual behavior and performance. Creating transparency includes open discussions with employee representations, with the results of the discussion informing the guidelines. A strict and transparent data policy strongly reduces employees’ rejection of DBAs. Data Security Officer In addition to establishing data guidelines, the appointment of a data security officer is recommended. He or she is in charge to ensure full compliance with internal and external data security and privacy regulations. 5. Management Responsibility for Standardization of Metrics The standardization of metrics company-wide may require sites or departments to update their metrics definitions and calculations. Usually, the motivation to do so is very low as it takes much effort but also as new metrics are not compatible with legacy data anymore. Thus, setting and enforcing company-wide standards must be driven and ensured by the top management. 6. Internalize External Expertise Companies struggle to meet the internal demand for employees with data analytic skills. Besides the internal qualification of employees, two other options are available: to hire data experts externally or to cooperate with external partners. Convince with Nonmonetary Benefits To hire data professionals despite the intense competition of solvent financial companies and consultancies, manufacturing companies need to attract candidates with nonmonetary benefits. One key argument to convince potential candidates is the perspective to test and evaluate ideas and prototypes in the real world, close to the working space, and with instant feedback. For the case companies, a second argument was the high job security as well as the strong employee brand, which helped to convince coveted specialists. Benefit from Cooperation To benefit from external know-how and resources, companies can collaborate with external partners, for instance, with consultancy companies and research institutions. Universities may be attractive collaboration partners. Master and PhD students not only bring a high intrinsic motivation to complete a project successfully in time but also have, in contrast to company employees, the time to work on it almost full time. Furthermore, universities may have access to experienced experts as well as to software and to computing power. As their objective is progress in academic research, universities are eager to

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participate in projects not for financial reasons and are thus often more affordable than consultancies. 7. State-of-the-Art Technology The technical infrastructure to collect, transfer, process, store, and protect data remains a key prerequisite for DBAs. Benefit from Falling Component Costs Budget restrictions have been an important barrier for digital technologies in the past. However, this challenge is mitigated by the fact that the costs of components such as sensors (factor 2), bandwidth (40x) computing power (60x), and storage capacity (50x) have dropped in recent years (Taisch et al., 2018). Hence, the necessary IT infrastructure is getting affordable for more companies for the first time. Cloud Computing Cloud computing is a core technology of smart manufacturing. The key advantages of cloud solutions are minimal initial investment costs and high scalability in terms of computing power and storage. Companies pay only for the performance and services they actually need. In addition, Vogel-Heuser et al. (2017) highlight the high robustness of cloud computing. Data remain accessible, even in the case than one server is temporarily down as the data is stored at multiple servers at the same time.

14.4

Summary

This chapter has consolidated and briefly described 14 data-based applications in manufacturing (see Table 14.1) along with key barriers (see Table 14.2) and key enablers (see Table 14.3). The scope of the research and thus of the presented DBAs is the site level. Nevertheless, during the discussions with company representatives, the high potential for production network optimization based on DBAs was highlighted. By integrating the MES systems of several production sites, the production planning can be done based on a network perspective rather than an individual site perspective. For instance, in a production network, the sites A and B produce parts for site C. In case any deviations occur in site A, the network perspective MES constantly reevaluates whether the deviation has any impact on the planned schedule of site C. If that is the case, the MES performs a rescheduling of the production plan of site B to compensate for the missing parts of site A. If that is not possible, the plan of site C is adjusted to the expected availability of the intermediate parts from sites A and B. It is fair to assume that there are many more use cases of data utilization on a network level beyond the example above. Hence, dedicated research on DBAs for production network and supply chain optimization is recommended as a follow-up research on the identified DBAs on the site level. Independently from the perspective on a single site or a manufacturing network, there are at least two shared challenges of data utilization in manufacturing in the future. First, the standardization of metrics is tedious on a site level; however, it is

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even more challenging across several sites within a manufacturing network. Nevertheless, the finest IT architecture combined with advanced analytics software remains useless, as long as the input data is not standardized. As simple as it sounds, the complete standardization of key production figures proves to be difficult in reality. Second, daring a look into the future, it can be assumed that AI will play an increasingly important role in the manufacturing industry. If this assumption proves to be true, another challenge for managers and solution developers arises from the black box character of almost all AI applications. For the end user, the process of decision making based on AI is not transparent anymore. However, process understanding is key for the idea of lean management of shop floor employee’s involvement in continuous improvement activities. An increased application of AI thus may lower the ability and motivation of shop floor employees to strive for the improvements of their own workplace. In a nutshell, discussing the question of how to balance simplicity, promoted by lean management, and complexity that arise from advanced IT and software will be even more important than it is already today.

References Åkerman, M., Lundgren, C., Bärring, M., Folkesson, M., Berggren, V., Stahre, J., Engström, U., & Friis, M. (2018). Challenges building a data value chain to enable data-driven decisions: A predictive maintenance case in 5G-enabled manufacturing. Procedia Manufacturing, 17, 411–418. https://doi.org/10.1016/j.promfg.2018.10.064. Buess, P. J. (2020). Enabling manufacturing companies to implement data-based applications to support lean practices (Dissertation, University of St.Gallen). Cambridge University Press. (2019). Search term ‘enabler’. Retrieved September 6, 2019, from https://dictionary.cambridge.org/de/worterbuch/englisch/enabler Choudhary, A. K., Harding, J. A., & Tiwari, M. K. (2009). Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501–521. https:// doi.org/10.1007/s10845-008-0145-x. Harding, J. A., Shahbaz, M., Srinivas, S., & Kusiak, A. (2006). Data mining in manufacturing: A review. Journal of Manufacturing Science and Engineering, 128(4), 969. https://doi.org/10. 1115/1.2194554. Hirmer, P., Wieland, M., Schwarz, H., Mitschang, B., Breitenbücher, U., Sáez, S. G., & Leymann, F. (2017). Situation recognition and handling based on executing situation templates and situation-aware workflows. Computing, 99(2), 163–181. https://doi.org/10.1007/s00607-0160522-9. Holweg, M. (2007). The genealogy of lean production. Journal of Operations Management, 25(2), 420–437. https://doi.org/10.1016/j.jom.2006.04.001. Kumar, R., Singh, S. P., & Lamba, K. (2018). Sustainable robust layout using big data approach: A key towards industry 4.0. Journal of Cleaner Production, 204, 643–659. https://doi.org/10. 1016/j.jclepro.2018.08.327. Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242. https://doi.org/10.1007/s12599-014-0334-4. Lenz, J., Kotschenreuther, J., & Westkaemper, E. (2017). Energy efficiency in machine tool operation by online energy monitoring capturing and analysis. Procedia CIRP, 61, 365. https://doi.org/10.1016/j.procir.2016.11.202.

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Louw, L., & Walker, M. (2018). Design and implementation of a low cost RFID track and trace system in a learning factory. Procedia Manufacturing, 23, 255–260. https://doi.org/10.1016/j. promfg.2018.04.026. Matyas, K., Nemeth, T., Kovacs, K., & Glawar, R. (2017). A procedural approach for realizing prescriptive maintenance planning in manufacturing industries. CIRP Annals, 66(1), 461–464. https://doi.org/10.1016/j.cirp.2017.04.007. Meissner, A., Müller, M., Hermann, A., & Metternich, J. (2018). Digitalization as a catalyst for lean production: A learning factory approach for digital shop floor management. Procedia Manufacturing, 23, 81–86. https://doi.org/10.1016/j.promfg.2018.03.165. Meziane, F., Vadera, S., Kobbacy, K. A. H., & Proudlove, N. (2000). Intelligent systems in manufacturing: Current developments and future prospects. Integrated Manufacturing Systems, 11(4), 218–238. https://doi.org/10.1108/09576060010326221. O’Donovan, P., Leahy, K., Bruton, K., & O’Sullivan, D. T. J. (2015a). An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. Journal of Big Data, 2(1), 25. https://doi.org/10.1186/s40537-015-0034-z. O’Donovan, P., Leahy, K., Bruton, K., & O’Sullivan, D. T. J. (2015b). Big data in manufacturing: A systematic mapping study. Journal of Big Data, 2(1), 20. https://doi.org/10.1186/s40537015-0028-x. Oliff, H., & Liu, Y. (2017). Towards industry 4.0 utilizing data-mining techniques: A case study on quality improvement. Procedia CIRP, 63, 167–172. https://doi.org/10.1016/j.procir.2017.03. 311. Pilloni, V. (2018). How data will transform industrial processes: Crowdsensing, crowdsourcing and big data as pillars of industry 4.0. Future Internet, 10(3), 24. https://doi.org/10.3390/ fi10030024. Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access, 1. https://doi.org/10.1109/ACCESS.2018.2793265. Russom, P. (2015). Bringing modern data-driven applications to the enterprise. Retrieved February 6, 2019, from https://tdwi.org/research/2015/03/checklist-bringing-modern-data-drivenapplications-to-the-enterprise.aspx Saygin, C. (2007). Adaptive inventory management using RFID data. The International Journal of Advanced Manufacturing Technology, 32(9), 1045–1051. https://doi.org/10.1007/s00170-0060405-x. Taisch, M. et al. (2018). The 2018 World Manufacturing Forum report: Recommendations for the future of manufacturing. Retrieved June 4, 2019, from https://www.worldmanufacturingforum. org/report Thoben, K.-D., Wiesner, S., & Wuest, T. (2017). “Industrie 4.0” and smart manufacturing – A review of research issues and application examples. International Journal of Automation Technology, 11(1), 4–16. https://doi.org/10.20965/ijat.2017.p0004. Vallhagen, J., Almgren, T., & Thörnblad, K. (2017). Advanced use of data as an enabler for adaptive production control using mathematical optimization – An application of industry 4.0 principles. Procedia Manufacturing, 11, 663–670. https://doi.org/10.1016/j.promfg.2017.07. 165. Vogel-Heuser, B., Bauernhansl, T., & ten Hompel, M. (Eds.) (2017). Handbuch Industrie 4.0: Bd. 4: Allgemeine Grundlagen (2nd edn). Springer Vieweg (Springer Reference Technik). Wan, J., Yang, J., Wang, Z., & Hua, Q. (2018). Artificial intelligence for cloud-assisted smart factory. IEEE Access, 6, 55419–55430. https://doi.org/10.1109/ACCESS.2018.2871724.

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Wenking, M., Benninghaus, C. and Groggert, S. (2017). Die Zukunft von Manufacturing Data Analytics Implikationen für eine erfolgreiche Datennutzung im produzierenden Umfeld. Industrie Management, (4), 33–37. Retrieved from https://www.wiso-net.de/document/IM__ BBE6FCC66E27D2FB130F473E5BD95617 Wuest, T., Irgens, C., & Thoben, K.-D. (2014). An approach to monitoring quality in manufacturing using supervised machine learning on product state data. Journal of Intelligent Manufacturing, 25(5), 1167–1180. https://doi.org/10.1007/s10845-013-0761-y. Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23–45. https://doi.org/10.1080/21693277.2016.1192517. Yunusa-kaltungo, A. and Sinha, J.K. (2017). Effective vibration-based condition monitoring (eVCM) of rotating machines. Journal of Quality in Maintenance Engineering, 23(3), 279–296. Retrieved from https://search.proquest.com/docview/1933250984?accountid¼28962

Managing Manufacturing Network Performance

15

Dominik Remling

Due to the increasing availability of data as well as new digital technologies and tools, there are increasing opportunities for the performance management of international manufacturing networks. In order to approach the topic, current survey results on the implementation status, barriers, and success factors of global manufacturing companies will be presented first. Based on this, some of the barriers and success factors will be addressed, and possibilities will be shown how they can be eliminated in practice. The site comparison matrix and site profile methodologies enable the comparison of sites even if they exhibit a high degree of heterogeneity. The two frameworks are explained using practical examples. In addition, a concept is proposed that allows the design of a performance management system at the manufacturing network level and goes beyond the approaches for pure site comparison.

15.1

Introduction

Performance management (PM) in general describes a holistic system that provides the framework for performance measurement and further leverages the information generated from it for multidimensional steering (Fischer et al., 2015). This involves gathering, steering, and communicating tangible and/or intangible indicators within an impact-oriented linkage of inputs, processes, outputs, and outcomes to improve the degree of organizational goal achievement (Fischer et al., 2015; Gleich, 2011). The topic has been addressed in the general corporate context for several years already, and a variety of theories and models have been developed (Choong, 2013; Neely, 2005; Taticchi et al., 2012; Yadav & Sagar, 2013). The most popular D. Remling (*) Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_15

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approaches include the “balanced scorecard” (Kaplan & Norton, 1992), the “performance pyramid” (Lynch & Cross, 1991), and the “performance prism” (Neely et al., 2001). The topic has evolved from the world of finance with the premise that a company should not be managed purely based on financial data. Accordingly, the research field was particularly driven by the manufacturing sphere (Neely, 2005). The most widespread approaches within manufacturing stem from the lean management and operational excellence (OPEX) developments deploying well-established key figures at an operational level.1 However, the question now arises once again of how to deal with the topic when leaving the boundaries of the single manufacturing site and looking at a network as a whole. In this context, the approaches from the area of OPEX seem too “operational” and the classic approaches from PM at the overall company level too “holistic.” Accordingly, a balance is needed to complement the two levels. From a scientific point of view, the importance of the topic of PM in international manufacturing networks (IMN) has been raised for a long time (Liebetrau, 2015; Shi & Gregory, 1998) but has hardly been taken up so far (Cheng et al., 2015; Costa Ferreira Junior & Fleury, 2018). The systematic literature review by Remling (in press) provides an overview of the literature of PM in IMNs until 2021. The results of the systematic literature review were seized in the benchmarking survey “Managing Global Production Networks in Today’s Business Environment” to evaluate the current state of PM in IMNs in practice.2 The results are described in the following.

15.2

Current Status in Practice

Figure 15.1 shows to what degree the participants think that they have a welldeployed PM system in place. Most of the follower companies and all successful practice companies agree with this statement. However, during the verification of the data of the successful practice companies in the form of interviews, it became clear that only in exceptional cases are the systems designed specifically for the manufacturing network. To identify the specific pitfalls in the development of PM systems related to IMNs, we asked the companies about the intensity of the barriers identified based on

1 Please refer to Chap. 3 for the theoretical background on OPEX. A practical example from the field of OPEX can be found in Chap. 11. 2 The benchmarking survey had been conducted in the period between May 6 and July 30, 2020, and yielded in total a sample of 88 participants. Most commonly, the participants hold positions such as COO, CTO, Head of Manufacturing, Head of Global Operations, etc. The participating companies mainly have their headquarters in German-speaking countries and come from various industries (33% mechanical engineering, 13% electrical engineering, 11% automotive, 10% metal products, etc.). The total number of production sites within the international manufacturing network ranges between less than 5 (24 companies) and more than 50 (7 companies).

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Strongly disagree Sucessful Practice

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Fig. 15.1 Implementation level of performance management systems

Strongly disagree

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Heterogeneity of IT-systems Heterogeneity of production sites Other technical barriers Exploitation barriers Missing management commitment Missing employee commitment Implementation barriers Gap between site and network level Missing guidance and standards Sucessful Practice

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Fig. 15.2 Development barriers of performance management systems

the above mentioned systematic literature review (see Fig. 15.2). The results show clear differences between the follower and successful practice companies. While successful practice companies have to deal in particular with the heterogeneity of IT systems and manufacturing sites as well as technical barriers, follower companies do not only struggle with the heterogeneity of manufacturing sites but also with implementation issues and the gap between site and network level performance. Although both groups see the heterogeneity of production sites as a core barrier, this is even more pronounced among the follower companies. In principle, this suggests that this barrier can be overcome. In the following subsection, we will present two practical examples for the comparison of production sites based on indicators.

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Severe importance

Clear strategic orientation with regard to conflicting goals Engage skilled and trained employees Involve diverse types of metrics Adapt the system over the course of time Follow a systematic development process Draw up guidelines for implementation Include aggregable (site level) and nonaggregable (network level) metrics Depict structural / configurative levers Consider relationship between site and network level Consider heterogeneity of production sites Depict infrastructural / coordinative levers Sucessful Practice

Follower

Fig. 15.3 Success factors for performance management

Regarding the heterogeneity of IT systems, we encountered an interesting phenomenon in some successful practice companies during the verification process. In these companies, the system landscape has often been expanded for some time to achieve the greatest possible data transparency. These companies now face the luxury problem that excessive amounts of data are available and that there is a high degree of complexity in the data system landscape, making it difficult to identify the key indicators and to compare them. This problem is less common among follower companies due to their lower IT and digitalization maturity. Apart from the barriers, we also identified important factors for the design of PM systems in our systematic literature review. These factors were also integrated into our survey (see Fig. 15.3). The most important prerequisites for both groups are a clear strategic orientation avoiding conflicting goals (see Chap. 1) and the employment of qualified employees. Surprisingly, opinions differ between the groups when it comes to the inclusion of different metrics (e.g., quantitative as well as qualitative or financial as well as production-related, etc.). However, it can be said that for both groups all the criteria listed are rather important in the development of PM systems. Accordingly, the requirements proposed by the scientific community are in fundamental agreement with those from practice.

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15.3

207

Steering the Development of Multiple Sites

One of the major barriers listed above is the heterogeneity of production sites, which is an important factor to consider when developing PM systems. As a matter of fact, the sites play a major role in PM, as they are the main executing players in the production network. Further, it is important to remember that PM systems can be used as a communication tool and can therefore be suitable for promoting competition or collaboration between individual sites. Accordingly, we at the ITEM-HSG started to shed light on the topic by addressing the question of site comparison. We first developed a two-level system that maps the individual site level using so-called site target profiles and, at a higher level, the site comparison matrix. Since the site comparison matrix is the core element of the site target profiles, we start by explaining the former.

15.3.1 Site Comparison Matrix The starting point for the development of the site comparison matrix was a project at a German Automotive Supplier Company operating 3 business units and 50 production sites worldwide. Ferdows stated that network managers need easy-to-use tools that reduce the complexity of managing production networks (Ferdows, 2018; Ferdows et al., 2016). In this case, management was also looking for an easy-touse tool to better understand the composition and configuration of their network. The goal was to support investment allocations and to create a balance between cash generation and cash use, considering an overarching production strategy. To achieve this goal, a classification scheme was first developed. This is also reflected in the explanation of the site portfolio in Chap. 2, which also contains a site classification scheme. To support the allocation of investments to sites, the consideration of financial indicators is essential but not sufficient when considered in isolation. Therefore, we have chosen two perspectives, a financial and a strategic one. In this way, we meet the requirement of including different metrics. The two dimensions are described in the following. The financial perspective focuses on the profit margin, more precisely on the achievement of the target margin individually defined for each site. As several business units were often represented at one site, the profit margin was calculated individually for each business unit and weighted by the corresponding sales. The formula for calculating the plant-related financial performance is shown below: #Bu

Financial PerformanceðsÞ ¼ i

Revi ðsÞ Margi ðsÞ   Tmargi : T rev Revi ðsÞ

ð1Þ

i ¼ business unit; Rev ¼ revenue; Trev ¼ total revenue; Marg ¼ margin (absolute); Tmarg ¼ target margin (relative)

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In other projects, we have included additional financial perspectives such as total net sales in relation to total headcount. The integration of several key figures allows a more comprehensive view of the network according to an OLAP3 logic. The strategic perspective can include many different factors depending on the company context, as described in Chap. 2. In this practical case, we have chosen three components: site growth, knowledge outflow to other sites, and competence level based on a site role concept. Growth was calculated using the following formula: #BU

GrowthðsÞ ¼ i¼1

Revi ðsÞ  RGi : T rev ðsÞ

ð2Þ

i ¼ business unit; Rev ¼ revenue; Trev ¼ total revenue; RG ¼ regional growth The knowledge outflow is determined by a qualitative rating of the site on a scale of one to four. A distinction is made between no training carried out for other sites, training carried out for sites in the same country, training carried out for sites in the same region, and global training. The competence level is mapped via the four roles, one of which is assigned to each site. In this case, the four roles are differentiated according to the production steps, process development capability, the definition of standards, production start-ups, and innovation. A representation of the site comparison matrix can be found in Fig. 15.4. In addition, the size of the site is represented by revenue. For this, other indicators are also conceivable, such as value-added (conversion costs) or manufacturing hours.

15.3.2 Site Target Profiles The site comparison matrix is an easy-to-understand tool for managing many sites. However, it is often the case that strategic decisions by network management require a detailed point-by-point view of the site. On the other hand, the sites need a detailed roadmap to move in the individual intended direction. Site target profiles were developed to meet this requirement from a top-down and bottom-up perspective. In the following, we present two examples from past projects. Example 1: Automotive Supplier Company Figure 15.5 shows the site target profile containing various elements. First, the site comparison matrix is in the center of the profile, so that the individual site can always

3 An OLAP (online analytical processing) cube or data cube, also called a cube operator, is a term used in data warehouse theory to represent data logically. The data is arranged as elements of a multidimensional cube. The dimensions of the cube describe the data and allow access in a simple way. Data can be selected via one or more axes of the cube (Gluchowski & Chamoni, 2006).

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see how it compares to other sites. This is the central element for promoting cooperation or competition between sites in combination with incentives. The concrete values for the financial as well as the strategic perspective are also shown. Thus, beyond the aggregated value in the overall view, it becomes clear for which business segments the financial result should be increased and to what extent. On the other hand, the strategic perspective provides information on which competencies are required for which product groups at the site and to what extent. This gives the site two essential guidelines on which further development can be based. The whole process is further enriched by a SWOT (strengths, weaknesses, opportunities, threats) analysis for future implications at the site and the resulting measures. Thus, the map is also suitable for monthly or quarterly site reviews in which the progress of the sites can be queried. At the same time, the maintenance of the tool depicted here is associated with manageable effort, so that it is an effective and at the same time efficient tool for the steering of sites. Example 2: Specialty Chemical Manufacturer The next example shows another possibility of the direct comparison of sites based on selected data (see Fig. 15.6). The overview resembles a profile and was used in combination with the site portfolio approach (see Chap. 2). Thus, network managers were in a better position to make strategic decisions. In this example, both costrelated and competence-related indicators play a role. Instead of product groups, however, the focus here is on processes.

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Fig. 15.5 Site target profile (automotive supplier example)

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Threats - Increase in material and personnel costs - Demographical change - Competition from low-cost countries - High market volatility

Weaknesses - Brownfield plant: Material flow result of historical evolution - High amount of fixed capital - Personnel costs - Overall flexibility

- Highly motivated and educated employees - Growth of regional market - Competitive medium and large seriers - Digitalization and further automation production - Vertical integration - High degree of automation, state of the art machines - Permanent change in site structuring is standard

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Fig. 15.6 Site target profile (specialty chemical manufacturer example)

The two examples clearly show how diverse the steering of sites is and how it must be strongly adapted to the individual needs of the company. In principle, three key questions must be asked before developing such tools: 1. Who is supposed to use the tool (network management and/or site management)? 2. What is the intended use scenario for the tool (communication, decision support, steering, etc.)? 3. Which contextual factors must be considered (organizational structures, product types, external influences, etc.)?

15.4

Steering the Development of a Network as a Whole

To explore the topic of PM in IMNs more deeply, we have set up a research project with the following two main premises: • Elevate performance measurement to a network level beyond merely evaluating site KPIs. • Take the previously created MS Excel-based tools to a more professional level in the form of a dashboard connected to IT systems.

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In the following section, the development of this holistic network dashboard will be explained. The above mentioned research project was co-financed by Innosuisse,4 two global manufacturing companies, and a software provider for technical implementation. On the research side, the ACA-HSG5 and the POM ETHZ6 collaborated on the project. The project focused on the development of a strategic dashboard to steer the development of a manufacturing network as a whole over time and to implement it at the two participating global manufacturing companies. The development process was based on three main assumptions and ideas: 1. A corporate strategy is represented by the resources and capabilities available within a firm (Grant, 1996). 2. In the context of a manufacturing network, network capabilities fulfill customer requirements (manufacturing priorities) and thus ensure competitiveness (Friedli et al., 2014). 3. In the sense of the principal-agent theory, a distinction is made between outcomebased and behavior-based performance (Eisenhardt, 1989). These three basic principles ensured the necessary focus for the project. Accordingly, we have mapped resources in the dashboard via the site portfolio approach (see Chap. 2) on the one hand and capabilities in the form of specific KPIs on the other. Since we have already explained the site portfolio approach and its operationalization in detail in Chap. 2, we will focus on the mapping of capabilities in this chapter.7 The network capabilities and the manufacturing priorities were mapped in the form of KPIs. Finally, we have combined historical-based KPIs with actions to influence behavior in the network. In this way, we managed to adhere to the three main initial assumptions.

15.4.1 The Multilayer Performance Management Framework For the concept development process itself, we followed the St.Gallen PM model and the multilayer PM framework (Fischer et al., 2015). Figure 15.7 shows the multilayer PM framework. It consists of several steps, the central element being the IPOO (input process output outcome) logic. In this logic, KPIs are sorted according

4

Swiss Agency for Innovation Funding. Institute of Accounting Control and Auditing, University of St.Gallen. 6 Chair of Production and Operations Management, ETH Zurich. 7 If you are interested in learning how to apply the site portfolio and to track the progress of a manufacturing network over time in a practical context, please refer to Chap. 19. 5

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Fig. 15.7 Multilayer performance management model. Reprinted from «Controlling Grundlagen, Instrumente und Entwicklungsperspektiven» by T. M. Fischer, K. Möller, W. Schultze, 2015, p. 418

to four process-oriented fields. The context level enables external and internal context factors to be considered in the context of PM and measurement. External context factors include market dynamics and other environmental influences. In addition, internal factors such as the organizational structure, the strategy, and the business model must also be considered when designing a PM system. These factors determine the performance indicators that are the focus of performance measurement and management. At the capture level, the relevant performance elements are identified and measured. Here, the identification and selection of key performance indicators are in the foreground. Subsequently, the performance elements are correlated within the framework of the couple level, so that the reciprocal effects between the individual variables become visible. This holistic picture now enables the actual steering process. Accordingly, measures for steering are derived within the framework of the control level. Finally, the decision-makers need to communicate the performance information created and set incentives to influence behavior in a targeted manner (Fischer et al., 2015).

15.4.2 Configuration of Performance Management in IMNs As part of the adaptation of the framework to our project purposes, the input field describes key figures that reflect the context of the manufacturing network (e.g., factor costs). The processing system field includes the actions within the network as well as the sites. The output comprises the capabilities of the network, which are developed and improved over time by the contextual factors and actions. At the top level (outcome), the associated KPIs for meeting customer requirements are allocated, which ultimately set the direction for the development of the network. Thus, a logical framework forms the basis for the development of the multidimensional control system in the form of a network dashboard (see Fig. 15.8).

Transferability of processes

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Fig. 15.8 Network dashboard KPI dimension structure

*Commonly applied strategy frameworks: PESTLE (Political, Economic, Sociological, Technological, Legal, and Environmental); PARTS (Players, Added Value, Rules, Tactics, Scope); SWOT (Strengths, Weaknesses, Opportunities, Threats)

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Product allocation Relocation project status

Factor costs Average blue collar costs in best cost countries

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The attentive reader will notice that the network capabilities listed here differ slightly from the network capabilities listed in Chap. 2. This is due to the fact that, to establish the necessary interrelationships between the outcome and output levels, we conducted an expert study to evaluate which network capabilities are necessary to achieve the listed manufacturing priorities. The ideas came from 28 experts in the field of managing global production networks from industry (manufacturing executives, operations vice presidents, COOs, etc.). With the results of the study, the manufacturing priorities can now be assigned to the network capabilities, so that it is clear which network capability/ies have a positive influence on the manufacturing priorities to be achieved.

15.4.3 Exemplary Use Case The logic of the structure can be explained using the reference example in Fig. 15.8. One of the two global manufacturing companies involved in the project has noticed a drop in sales of a certain product and attributed this to a too low contribution margin. To increase the contribution margin of this product, the network capabilities’ “exploitation of best-cost factors” and “economies of scale” can be considered. In this case, the focus was on strengthening the “exploitation of best-cost factor” capability. The key figure to be influenced here is the share of production hours of sites located in best-cost countries. To ultimately improve this capability, relocations from high-cost sites were undertaken. The key indicator for relocations was the project progress. The basis for decision-making as a trigger for the actions was the average labor costs as part of the factor costs. After developing the basic structure, we started to operationalize all levels. In doing so, we were guided by the approaches according to Liebetruth and Otto (2006) and Röglinger et al. (2009). In the first step, we researched a comprehensive collection of network- and production-related KPIs. We compared this collection with the individual company context and the corresponding goals and decided on a maximum of three key figures per category. For reasons of clarity, only one manufacturing priority and the corresponding elements from the IPOO structure are displayed in the technical implementation. This allows the network manager to concentrate on the most important manufacturing priorities and to consider them in isolation. However, a network capability can be assigned to several manufacturing priorities. The operationalization of this capability is, in this case, identical.

15.5

Summary

In summary, we have embarked on a journey in the field of PM starting from the identification of the implementation maturity, key challenges, and key success factors for IMNs in practice. The heterogeneity of IT systems and sites, as well as technical barriers, is seen as a challenge in this context, while, on the other hand,

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clear strategic guidelines, qualified employees, and multidimensional approaches are seen as success factors. Maturity-wise, it makes sense to start at the site level according to our trajectory and to establish an initial site comparison based on well-thought-out criteria and key figures. However, the heterogeneity of the sites—as our survey underlines—must be taken into account here. Accordingly, our proposal of the site comparison matrix enables to pursue the fulfillment of individually specified targets. Next, the resources of the network can be mapped via the site portfolio approach, which provides excellent opportunities to track the physical (configurative) development of the network over time. On the network level, we propose a multidimensional KPI structure with the manufacturing priority and network capability approach in focus. The proposed structure helps to identify relationships between customer satisfaction and network capabilities and to manage these capabilities in a targeted manner by building and developing them. The underlying measures enable concrete actions to be implemented in order to ultimately positively influence the achievement of objectives. Further, context measures help to contemplate if the taken actions will lead toward the intended direction. Generally, it is illusory to develop a blueprint for the PM of a manufacturing network due to the high individuality of the business context. Nonetheless, we provide a suggestion on how to successfully navigate the path to an effective PM system. At the time of writing, the research project was not yet fully completed, so the implementation part has been left out in this chapter.

References Cheng, Y., Farooq, S., & Johansen, J. (2015). International manufacturing network: Past, present, and future. International Journal of Operations & Production Management, 35(3), 392–429. https://doi.org/10.1108/IJOPM-03-2013-0146. Choong, K. K. (2013). Are PMS meeting the measurement needs of BPM? A literature review. Business Process Management Journal, 19(3), 535–574. https://doi.org/10.1108/ 14637151311319941. Costa Ferreira Junior, S., & Fleury, A. C. C. (2018). Performance assessment process model for international manufacturing networks. International Journal of Operations & Production Management, 38(10), 1915–1936. https://doi.org/10.1108/IJOPM-03-2017-0183. Eisenhardt, K. M. (1989). Agency theory: An assessment and review. The Academy of Management Review, 14(1), 57. https://doi.org/10.2307/258191. Ferdows, K. (2018). Keeping up with growing complexity of managing global operations. International Journal of Operations & Production Management, 38(2), 390–402. https://doi.org/10. 1108/IJOPM-01-2017-0019. Ferdows, K., Vereecke, A., & Meyer, A. (2016). Delayering the global production network into congruent subnetworks. Journal of Operations Management, 41(1), 63–74. https://doi.org/10. 1016/j.jom.2015.11.006. Fischer, T. M., Möller, K., & Schultze, W. (2015). Controlling: Grundlagen, Instrumente und Entwicklungsperspektiven. Stuttgart: Schäffer Poeschel. Friedli, T., Mundt, A., & Thomas, S. (2014). Strategic management of global manufacturing networks. New York: Springer. https://doi.org/10.1007/978-3-642-34185-4.

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Gleich, R. (2011). Performance measurement: Konzepte, Fallstudien und Grundschema für die Praxis (2., völlig überarbeitete Auflage). Vahlen. Gluchowski, P., & Chamoni, P. (2006). Entwicklungslinien und Architekturkonzepte des On-Line Analytical Processing. In P. Chamoni & P. Gluchowski (Eds.), Analytische Informationssysteme (pp. 143–176). New York: Springer. https://doi.org/10.1007/3-54033752-0_8. Grant, R. M. (1996). Prospering in dynamically-competitive environments: Organizational capability as knowledge integration. Organization Science, 7(4), 375–387. https://doi.org/10.1287/ orsc.7.4.375. Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard—Measures that drive performance. Harvard Business Review, 70(1), 71–79. Liebetrau, F. (2015). Strategic Performance Measurement and Management in Manufacturing Networks—A Holistic Approach to Manufacturing Strategy Implementation (Dissertation, University of St.Gallen). Liebetruth, T., & Otto, A. (2006). Ein formales Modell zur Auswahl von Kennzahlen. Controlling, 18(1), 13–24. https://doi.org/10.15358/0935-0381-2006-1-13. Lynch, R. L., & Cross, K. F. (1991). Measure up! Oxford: Blackwell. Neely, A. (2005). The evolution of performance measurement research. International Journal of Operations & Production Management, 25(12), 1264–1277. https://doi.org/10.1108/ 01443570510633648. Neely, A., Adams, C., & Crowe, P. (2001). The performance prism in practice. Measuring Business Excellence, 5(2), 6–13. https://doi.org/10.1108/13683040110385142. Remling, D., & Friedli, T. (in press). Performance management in international manufacturing networks. Die Unternehmung. Röglinger, M., Reinwald, D., & Meier, M. C. (2009). Ein formaler Ansatz zur Auswahl von Kennzahlen auf Basis empirischer Zusammenhänge [Tagungsband]. Retrieved from https:// www.researchgate.net/profile/Andreas_Eckhardt/publication/221201326_Does_it_Matter_in_ Recruiting_-_Eine_landerubergreifende_Kausalanalyse/links/54b8fc630cf28faced626537. pdf#page¼329 Shi, Y., & Gregory, M. (1998). International manufacturing networks-to develop global competitive capabilities. Journal of Operations Management, 16(2–3), 195–214. https://doi.org/10. 1016/S0272-6963(97)00038-7. Taticchi, P., Balachandran, K. R., & Tonelli, F. (2012). Performance measurement and management systems: State of the art, guidelines for design and challenges. Measuring Business Excellence, 16(2), 41–54. https://doi.org/10.1108/13683041211230311. Yadav, N., & Sagar, M. (2013). Performance measurement and management frameworks. Business Process Management Journal, 19(6), 947–971. https://doi.org/10.1108/BPMJ-01-2013-0003.

Operations Research in International Manufacturing Networks

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Martin Benfer, Sina Peukert, and Gisela Lanza

16.1

Introduction

As the previous chapters have explored, production networks have become increasingly complex. The rising product variety, the fragmentation of value streams, and the increasing technological specialization further increase the difficulty of managing production networks. The increasing dynamics of markets and political landscapes around the globe shortens the available time to make management decisions regarding production networks. To overcome these challenges, the use of operations research (OR) methods such as mathematical modeling, simulation, and optimization has proven helpful. OR methods enable modeling, understanding, and solving problems that are too complex for the human mind alone. These methods enable better understanding, allow the prediction of outcomes, and offer decision-making support in the face of the above-described challenges (Lanza et al., 2019). The first part of the following chapter provides an overview of the relevant methods from operations research, their benefits, and limitations. The second part of the chapter explores the distinct applications of these methods and shows examples of the successful OR method implementation in practice.

16.2

Operations Research Methods

Although many of the mathematical and statistical foundations of OR methods have already been developed decades and centuries ago, the bundling of these methods achieved familiarity and practical application under the term of “operations research” approximately around the time of World War II. By now, it has evolved to be an indispensable set of tools in a broad range of management-related M. Benfer (*) · S. Peukert · G. Lanza wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany e-mail: [email protected]; [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_16

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disciplines, for example, in mathematics, engineering, economics, and especially in the production and manufacturing context (Domschke et al., 2015). OR is concerned with the representation of real problems in quantitative models to find ideal solutions. However, by their very nature, models represent reality in a simplified way to aid understanding and enable decision-making. Consequently, this simplification limits the precision and applicability of models (Briskorn, 2020). This subchapter starts with a discussion of general aspects of modeling problems in production networks, followed by a more detailed description of simulation and optimization methods. Finally, artificial intelligence and decision and game theory are discussed briefly.

16.2.1 Modeling Models are representations of the real world that capture the behavior of a part of the world within a set of conditions. As they are only simplified versions of reality, models allow us to understand, analyze, predict, and influence reality. In general, models support problem-solving. Thus, they need to accurately describe and examine the system’s behavior concerning the problem, while being as simple as possible to increase understanding. Thus, models should always be created and used with the problem in mind, ensuring their ideal usability. Model Basics and Taxonomy Models are the representations of the parts of the reality called systems. The description of a system contains a border limiting the system from its environment, and a set of inputs and outputs that let it interact with the environment and its behavior. A model replicates parts of the system’s elements while omitting others and thus only approximates a real system’s behavior. Generally, there are four types of models, which are distinguished according to their applications: • Descriptive models are used to describe a system, representing its properties without the actual behavior. Typical examples of this in production network management are data storage systems and performance measurement tools. • Diagnostic models or explanatory models allow the observation of causalities between the in- and outputs of a system. Analytic methods like regression analysis fall into that category. • Predictive models use knowledge regarding the abovementioned causalities to predict system behavior based on new inputs. Examples of this within this context are simulation models but also neural networks. • Prescriptive models allow the determination of inputs that lead to the desired output of the system. Optimization models and predetermined decision-making rules are common examples.

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OR methods are attributed mainly to the latter two categories as their purpose is to directly aid the decision-making process. Furthermore, OR focusses on quantitative models which explicitly describe states and relationships using mathematical equations and numerical values. Such models are characterized by high degrees of complexity and can only be used with modern computers (Domschke et al., 2015). Model Creation Process The creation of such models is a complex laborious process with a series of steps. Here, only the major aspects of the modeling process will be discussed. Problems in OR are typically solved by first creating a conceptual model that contains an abstract description of the system and the examined problem. That model is implemented as an executable model which can be used to experiment on the problem (Sargent, 2010). To aid the process of model creation, model frameworks can be applied which include general theoretical descriptions of relationships and need only to be specified for the examined problem (Benfer et al., 2019). Model Types There is a broad range of quantitative model types, which are distinguished by their mathematical formulations. They range from the most basic form, linear deterministic models, to time-variant simulation models through to artificial neural networks. The following sections give an overview of the models most commonly applied in production network management.

16.2.2 Simulation Simulation models are commonly understood to be complex time-variant models. In production networks, three types of simulation models are most commonly used to represent the examined systems. While system dynamics simulations enable the modeling of broad interactions in abstract continuous systems, discrete event simulation facilitates the tangible representation of many human-made systems, and agent-based simulations embody the interactions of multiple self-controlled actors. Before outlining these three most common model types, this section introduces the basics of simulations and their taxonomy and concludes with a discussion of the use of mixed models and the interpretation of simulation results. Simulation Basics and Taxonomy Simulation is a commonly used method to describe complex interactions that are not analytically solvable by approximation utilizing the calculating power of modern computers. The main idea is to discretize infinite systems into finite parts and calculate the interaction between them step by step. Based on this principle, a wide range of model types has evolved in the last 50 years. Generally, simulations can be divided into continuous and discrete simulation methods. In the continuous model, the model states and events change continuously with every time step,

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whereas the discrete model only takes on finite values due to sudden changes. Additionally, the models can be of deterministic or stochastic nature. Deterministic models always find the same outputs for the same inputs, whereas stochastic models contain probability theory components, introducing a degree of randomness (Law, 2015). System Dynamics System dynamics simulations allow the calculation of first-order differential equations. These simulations consist of stocks that describe the current state of a system and flows, which express the system’s change over time. Flows directly influence the state of the stocks while being regulated by specified relations to the stocks. The numerical calculation of these systems involves the alternating computation of stocks and flows. In terms of application, these models are particularly suitable when the interactions within a system are gradual and do not change based on sudden events. In production systems, they can be used for risk calculations and for the macro-level flow of materials, when individual items are not of interest (Tako & Robinson, 2012). Discrete Event Simulation In contrast to system dynamics, discrete event simulation (DES) models the changes in a system’s state based on specific events. These events are executed as a sequence of operations, starting from an initial time onward. Events can be initiated both deterministically and based on stochastic distributions, making discrete event simulation very suitable for stochastic problems. Discrete event simulations are used when the systems’ interactions are characterized by distinct changes in state, which is typical of technical human-made systems. Thus, DES is used in production networks to simulate the flow of individual orders through production systems and, for example, the delivery of individual items. DES is well suited to determine lead times, delivery reliability, and disruption robustness (Law, 2015). Agent-Based Simulation Agent-based simulation (ABS) describes a system’s behavior by means of a set of independent actors, i.e., agents, and their interactions. Each agent possesses a set of states that can change trough inputs from outside or based on time. Based on its current state, an agent acts and reacts differently to inputs. The primary use for ABS is to describe systems where their overall behavior is challenging to model, whereas individual actors and their interactive behavior within the system are well understood. In production networks, ABS is relevant whenever multiple independent actors make decisions based on cues from others, such as the behavior of suppliers within a supply chain, and decision-making in process control. In reality, the modeling objectives, the system itself, or the availability of data lead to problems that can often only be solved by using multi-method models. Furthermore, the need to include other simulation methods also arises during the modeling process (Macal & North, 2005).

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Result Evaluation and Interpretation One of the main challenges of modeling that particularly applies to simulation is the difficulty of evaluating results, as stochastic effects may mislead users. Depending on the intended application, different strategies can be used: • In order to ensure repeatability, different seed values are used. • Additionally, uncertainty regarding conditional parameters can be controlled using the design of experiments, which describes the targeted variation of input parameters. • As simulations require significant resources and the relations between input and output parameters are difficult to assess, more simplified models can be used to interpolate between base points determined using simulation. These meta models help to make general observations regarding relations between various parameters and allow for a quick calculation of specific conditions. Typical meta modeling methods are regression analysis and neural networks (Law, 2015).

16.2.3 Optimization Whereas the previously discussed methods concentrate on informing decisionmakers regarding the consequences of their actions, optimization models seek to determine optimal decisions, i.e., inputs given valuation of outcomes. The goal when designing optimization models is to approximate the global optimum as reliably and quickly as possible while accurately reflecting the real-world optimum. Depending on the type of descriptive model employed, different optimization methods can be applied. The most common methods, their advantages, and their limitations are discussed in this section. Optimization Basics and Taxonomy In optimization problems, the tradeoffs between accuracy, speed, and reliability are very apparent. An optimization model consists of a model describing the systems’ behavior given a set of inputs and an optimization method that adapts the inputs of the describing model to optimize the outcomes it produces. More simplistic models allow shorter and more reliable optimization but sacrifice accuracy, whereas overly complex models are very difficult to optimize. Depending on the structure of the predictive model, specific optimization methods are available to find the optimal inputs efficiently. While various optimization model types exist, only the ones commonly applied in production network management are discussed here. The most basic form of optimization is linear optimization. If the system cannot accurately be described using linear equations, more complex nonlinear optimization techniques are necessary. If the model and, thus, the output function, is highly complex, heuristics can significantly reduce the solution space. Some more recent approaches to solving complex optimization problems are simulated annealing and evolutionary algorithms.

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Linear Optimization Linear optimization is the most basic form of optimization. Here, the predictive model describes a linear dependency between inputs and outputs. The space of allowed inputs is restricted by a set of linear equations as well. The well-known and very tangible simplex algorithm searches for the optimal result, which is always found in one of the input spaces’ corners by jumping along the edges of the solution space, following the steepest gradient to a minimum or maximum of the output function. Several variations to extend the algorithm’s capabilities exist. A more efficient algorithm is the Karmarkars’ algorithm, an interior-point method, variations of which are common today. Linear optimization is strictly limited to systems that can be described using linear equations. However, due to its efficiency and speed, it is still successfully applied to problems with a broad portfolio of possible solutions (Eiselt & Sandblom, 2012). Nonlinear Optimization When the problem escapes a linear description, optimization becomes more challenging, and nonlinear optimization becomes necessary. Depending on the nature of the problems, different nonlinear optimization methods are applicable. These methods can optimize more intricate problems. However, specific knowledge of the problem structure is required to choose fitting algorithms, which excel at the specific type of examined problem. Thus, several more generalist approaches have been designed and used heavily in production management and other fields (Domschke et al., 2015). Heuristics, Evolutionary Algorithms, and Simulated Annealing Among the main difficulties for many optimization algorithms are the size of the solution space and local optima. Heuristics are methods that quickly assess a set of decision-making options and disregard options that seem futile. They thus allow finding solutions much more quickly but run the risk of missing beneficial but ostensibly unattractive solutions (Domschke et al., 2015). Evolutionary algorithms follow the principles of evolution and natural selection to facilitate the identification of beneficial solutions to complex problems (Michalewicz & Schoenauer, 2013). Simulated annealing is a method inspired by metal cooling processes. Simulated annealing algorithms can both find local optima efficiently and jump between them to not get stuck in them (Suman, 2013). These and some other methods have found increasing use in recent years. Multi-Objective Optimization An issue often found in optimization problems is that users pursue multiple objectives, for example, short lead times and low costs, leading to multi-objective optimization problems. There are two commonly applied solutions to this. The first one forms a one-dimensional objective function by valuing the importance of the different objectives (Eiselt & Sandblom, 2012). This approach requires specific a priori knowledge of the crucial characteristics. Another option is the use of Pareto optimal solutions. This method calculates all solutions where the improvement of

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one objective necessarily sacrifices performance in another objective (Tuy et al., 2013). This method gives decision-makers an idea of the range of possible solutions and allows them to define their priorities. Limitations and Result Interpretation As alluded to in the beginning, optimization is a potent tool that still comes with significant limitations. Firstly, solutions should be tested for their sensitivity, as optimization can sometimes lead to edge solutions that may not hold when encountering slight variations of the conditions. Depending on the degree of uncertainty, a more or less comprehensive sensitivity analysis needs to be performed. In some cases, optimization may just not be applicable due to a lack of data. Then, it is more important to advance understanding of the problem by using diagnostic or predictive models. Finally, optimization encourages the neglect of the nonquantitative aspects of problems. Decision-makers should make an effort to consider these aspects as well and include them in the models where possible.

16.2.4 Artificial Intelligence Artificial intelligence (AI) denotes the capability of computer-based systems to make intelligent decisions based on the given inputs. These methods are especially common when decisions have to be made very quickly. Historically, AI was designed symbolically, i.e., the decision-making rules where explicitly defined a priori. These types of AI still find application in systems where the decisions made need to precisely follow a set of regulations. In recent decades, however, with the emergence of self-learning methods enabled by more potent computers, statistical AI which learns information stored implicitly in data has become more common. The most prominent types of statistical AI are neural networks, which can learn very complex decision-making rules by training on existing data. These approaches have shown some intriguing successes recently, but they are limited in terms of deducting reasons for the decisions made (Russell & Norvig, 2016).

16.2.5 Decision and Game Theory One of the older fields of OR is decision theory, which is concerned with the consequences of decisions under uncertainty. The most common method is the utility function which describes the benefits of a decision considering a range of unknown influences. This and the comparative methods find value especially in strategic decision-making but can be applied wherever uncertainty is a concern (Schum, 2013). A closely related field is game theory, which describes decision-making in situations with multiple actors pursuing their own goals. As service-based business models have become more common, these methods have seen increased interest from researchers and practitioners alike. Research in this space offers both

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descriptive models capturing the behavior of actors and prescriptive models to aid decision-making under the circumstances of a “game” (Young & Zamir, 2015).

16.3

Application

The methods shown in the previous section are used in a multitude of scenarios. In this section, the most common applications for OR methods in production networks are illustrated and discussed. Firstly, general fields for application are shown, followed by different objectives that managers pursue with these methods. Subsequently, the section explores several perspectives for method application. The chapter closes with a discussion of current trends and active fields of research.

16.3.1 Fields of Application The fields of application of OR methods in production networks can be understood using the general distinction of the St.Gallen management model for global manufacturing networks in strategy, configuration, and coordination (see Chap. 2). As the following paragraphs and Fig. 16.1 show, most applications of OR methods in network management are part of the configurative aspect of production management. With some exceptions, OR methods are most applicable in problems with clearly measurable system parameters, as commonly found in configurative tasks. The configurative problems OR methods solve can be distinguished further into allocative problems, which assume a fixed structural framework of production sites and network design problems, which seek to find ideal structures. Strategic tasks Risk Assessment Problems

Strategic Tasks

Market Development Problems Configurative Tasks

Network Design Problem Network Adaption Problem Site Selection Problem Allocation Problem Product-Mix Allocation Problems Production Program Planning Problems Order Allocation, Routing, & Scheduling Problems

Coordinative Tasks

Cooperation Problems 1 Day

1 Month

1 Year

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Time Horizon

Fig. 16.1 Typical OR problems in manufacturing network management

30 Years

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address aspects like overall risks, uncertainty, and market developments, while coordinative tasks are focused on cooperation between multiple parties in the supply chain. In each of these categories, a range of problems with quantitative elements need to be solved. Allocative problems are characterized by shorter time horizons, less uncertainty, fewer variables, and a focus on objectively measurable but less comprehensive objectives compared with strategic problems. Consequently, solutionfocused methods like optimizations, which require a high degree of data quality and availability, are more commonly used for allocative problems, whereas strategic problems are often approached with more explorative approaches like simulation or even time-invariant modeling. The following paragraphs provide an overview of the typical applications for OR methods in production networks. Allocative Problems Allocative problems comprise the allocation, routing, and scheduling of orders in the network, production program planning, and product-mix allocation. In the former two cases, the users assume that structural parameters like the available sites, the number of employees, and the number of machines are unchangeable. This is shown in more detail in Chap. 10. The objective of these problems is to find the optimum solution based on a set of operative criteria, such as lead times, total landed costs, and overall capacity utilization. Both types of problems are usually well quantifiable, enabling the use of optimization methods. In many cases, these optimizations are combined with discrete event simulation to reflect the discrete nature of manufacturing. When decision-making is time-sensitive, heuristics and artificial intelligence can be used to select beneficial solutions while forgoing comprehensive optimization. Product-mix allocation problems focus on aligning forecasted production volumes of a variety of products with existing production facilities. This type of problem is discussed in Chap. 9. The objective is finding the ideal distribution of production volumes per product type restricted by the available capacity and competences of the sites. In their purest form, where only one production step is considered, they can be modeled as linear optimization problems. However, when more complex production sequences with multiple components and production processes at multiple sites are considered, more complex models have to be used. In some cases, capacities and competencies even possess a degree of flexibility, which leads to more complex decision-making problems. In addition to costs and lead times, risks and environmental aspects can be considered in product-mix allocation models. A specific variation of the allocative problems discussed may be extended to include suppliers. Suppliers appear as an addition to the company’s own network and can be used to extend the internal production capabilities and capacities. This approach is only relevant to situations where suppliers are able to quickly change production volumes and where the contractual situation permits it. In that case, optimization methods can be used for supplier selection.

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Network Design Problems Whereas the previous problems assumed a mostly fixed production structure, network design problems seek to find changes in existing structures. In order to allow structural changes, a larger time horizon, typically of multiple years, is necessary. These long-term examinations involve a higher degree of uncertainty. Furthermore, structural changes involve decision-making aspects that lie outside of most quantifiable models like market access, regulatory requirements, and complex trade barriers. Due to these factors, optimization is less prevalent in solving these problems, and for the application of optimization techniques, comprehensive restrictions have to be formalized and implemented. Two typical network design tasks in production network management can be distinguished. Network adaption problems examine the structure of the entire production networks and search for ideal solutions given a set of restrictions and expected production volumes. The objective is typically cost-based, even though carbon emissions, lead times, and risks also play a role. For these types of problems, both simulation and time-invariant models are used and sometimes combined with optimization approaches like nonlinear optimization and evolutionary algorithms. To examine multi-period decision-making problems in this context, Markov chains and Monte-Carlo simulation can be applied. Distribution network problems are a variation of the network adaption problem. Here, the number and location of distribution centers with an optimum of low costs and short delivery times are sought. For this problem, graph-based modeling and optimization approaches are typical. In some cases, this approach also allows for the determination of ideal final assembly structures, especially in products with high transportation costs such as heavy machinery. The second type of network design problem is site selection. In site selection problems, the decision for a new site has been made, but the ideal location has yet to be determined. These types of problems examine a selection of candidate sites and their suitability. In contrast to the more comprehensive and open network optimization problems, site selection problems typically assume the product mix and volume at the new site to be fixed. Instead, they focus more on the site evaluation over multiple periods and in multiple demand scenarios. Due to this more restrained problem structure, optimization methods can be used more easily for site selection, especially in the earlier stages, where the linear approximation of the behavior is reasonably accurate. In the later, more detailed stages, event discrete simulation and more complex optimization methods can be applied. Both types of network design problems are challenging to implement as an optimization, however, due to the difficulty representing all aspects of these decision-making processes in quantitative models. Additionally, they require significant data that needs to be gathered from outside of the company, e.g., wages and transportation costs at a multitude of locations. Strategic and Coordinative Tasks Outside of the previously discussed configurative problems, some strategic and coordinative problems can benefit from quantitative models. The most commonly

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modeled aspect is market development using system dynamics. The results of such forecasts can be used as input to network adaption or even product mix allocation. Although market developments are often only predicted based on external parameters, some models account for the influence of a company itself on a market by including parameters such as pricing, lead times, and proximity to the market as input factors. In these cases, optimization of achievable returns by adjusting these parameters is fruitful. In addition to market development, system dynamics simulations are used to model risks in complex supply chains. More recently, methods developed in game theory have been used to optimize service-based offerings as well as cooperative production problems.

16.3.2 Objectives The previously discussed problems feature a range of challenges and a variety of different objectives for OR methods. The chosen objectives dictate the available methods and are essential to finding the right solutions. The primary objective of all management activities is typically described as the company’s success while acting within a set of societal and moral norms. Because success is not well defined and is challenging to measure, more quantifiable objectives are commonly used for OR methods. Ideally, the objective that is most comprehensive but within the scope of the model’s description should be chosen. Typical objectives used in OR methods are profit maximization, cost minimization, capacity utilization maximization, and lead time minimization. Other objectives include the maximization of flexibility, resilience, and robustness, typically in terms of the previous objectives. Additional objectives include the minimization of environmental impact, resource consumption, and exposure to risks. The consideration of multiple objectives can improve the quality of the solutions but also complicates the modeling.

16.3.3 Perspective As with different objectives, it is crucial to consider the chosen perspective when applying OR methods in production management. The typical perspectives range from a single site, a sub-network of the company through to entire multi-stakeholder supply chains. While some methods may find beneficial solutions for some of these stakeholders, they might conflict with others. Thus, it is vital to carefully choose the right perspective and align solutions with the involved parties.

16.3.4 State-of-the-Art and Current Examples There are several fields of OR methods which currently enjoy particular interest of academia concerning application in production networks. First, the advent of industry 4.0 and the concept of digital twins have sparked interest in predictive and

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prescriptive methods that are more readily usable instead of requiring significant effort to gather data, set up the model, and validate it. With the increasing digitalization, more and more data is available to automatically update models and routinely use OR methods in production network management. Second, current crises have renewed interest in the field of resilience and sustainability, and an increasing number of models incorporate such aspects. Third, a lot of exciting advances have been made in game theory, helping to describe not just technical systems but human interaction. This field is likely to see further growth and application in production network management, especially considering the increasing importance of services. Finally, with increasing computational capacities, models have become more complex to reflect more aspects of the managerial decision-making space. In the future, these complex models could be coupled with artificial intelligence to support further or even substitute human decision-making in production networks.

16.4

Summary

OR methods play an essential role in managerial decision-making in production networks. They help to find beneficial solutions and describe tradeoffs in complex systems such as production networks. A multitude of methods exist, including several types of simulation and optimization methods. The use of suitable methods for the examined problem hence is critical. Furthermore, limitations of OR methods, especially with regard to nonquantifiable aspects of decision-making, need to be considered carefully. In the future, the use of OR methods will become even more commonplace, thus enabling production network managers to react quickly and precisely to future challenges in ever more complex and dynamic environments.

References Benfer, M., Ziegler, M., Gützlaff, A., Fränken, B., Cremer, S., Prote, J.-P., & Schuh, G. (2019). Determination of the abstraction level in production network models. Procedia CIRP, 81, 198–203. https://doi.org/10.1016/j.procir.2019.03.035. Briskorn, D. (2020). Operations research: Eine (möglichst) natürlichsprachige und detaillierte Einführung in Modelle und Verfahren (1st ed.). Berlin: Springer Gabler. https://doi.org/10. 1007/978-3-662-60783-1. Domschke, W., Drexl, A., Klein, R., & Scholl, A. (2015). Einführung in operations research (9th ed.). Berlin: Springer Gabler. https://doi.org/10.1007/978-3-662-48216-2. Eiselt, H. A., & Sandblom, C.-L. (2012). Operations research: A model-based approach (2nd ed.). Berlin: Springer. https://doi.org/10.1007/978-3-642-31054-6. Lanza, G., Ferdows, K., Kara, S., Mourtzis, D., Schuh, G., Váncza, J., Wang, L., & Wiendahl, H.-P. (2019). Global production networks: Design and operation. CIRP Annals, 68(2), 823–841. https://doi.org/10.1016/j.cirp.2019.05.008. Law, A. M. (2015). Simulation modeling and analysis (5th ed.). New York: McGraw-Hill Education. Macal, C. M., & North, M. J. (2005). Tutorial on agent-based modeling and simulation. In M. E. Kuhl (Ed.), Proceedings of the 2005 Winter Simulation Conference (pp. 2–15). New York: Association for Computing Machinery. https://doi.org/10.1109/WSC.2005.1574234.

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Michalewicz, Z., & Schoenauer, M. (2013). Evolutionary algorithms. In S. I. Gass & M. Fu (Eds.), Encyclopedia of operations research and management science (3rd ed., pp. 517–527). New York: Springer. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed., Global ed.). Harlow, UK: Pearson. Sargent, R. G. (2010). Verification and validation of simulation models. In B. Johansson (Ed.), Proceedings of the 2010 winter simulation conference (pp. 166–183). IEEE. https://doi. org/10.1109/WSC.2010.5679166. Schum, D. A. (2013). Decision analysis. In S. I. Gass & M. Fu (Eds.), Encyclopedia of operations research and management science (3rd ed.). New York: Springer. Suman, B. (2013). Simulated annealing. In S. I. Gass & M. Fu (Eds.), Encyclopedia of operations research and management science (3rd ed., pp. 1395–1404). New York: Springer. Tako, A. A., & Robinson, S. (2012). The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems, 52(4), 802–815. https://doi.org/10.1016/j.dss.2011.11.015. Tuy, H., Rebennack, S., & Pardalos, P. M. (2013). Global optimization. In S. I. Gass & M. Fu (Eds.), Encyclopedia of operations research and management science (3rd ed., pp. 650–658). New York: Springer. Young, P., & Zamir, S. (2015). Handbook of game theory with economic applications (Vol. 4). Amsterdam: North-Holland.

The Role of the Plant Leaders

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The management of manufacturing networks is considered to be high-level strategic management. As such, practitioners and scholars in the domain of network management show a tendency to overlook the role of individuals on the plant level. Namely, behavioral aspects of this microlevel are widely neglected. However, numerous examples from practice show how ignoring these behavioral aspects of individuals may be a major barrier for realizing network capabilities. The following chapter outlines why firms should—and how they can—include key individuals on the plant level. First, it explains why plant leaders are key individuals to consider not only for the plant but also for the network level. Subsequently, this chapter shows how plant leaders can be integrated into the strategic and coordinational aspects of network management. The insights provided in this chapter build on practical experience from numerous collaborations with manufacturing firms combined with the existing knowledge base from network management research1.

17.1

Why Consider Plant Leaders in the Context of Network Management?

Many scholars consider the plant level as an integral part of manufacturing network management (e.g., Thomas et al., 2015). However, the tendency to “viewing the plant as a black box” (Cheng et al., 2011, p. 1315) limits the understanding of dynamics within the network related to or caused by the plant-level perspective. The

Namely, findings from the author’s dissertation and a recent scholarly publication: Wiech and Friedli (2020).

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M. Wiech (*) SGL Carbon GmbH, Wiesbaden, Germany # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_17

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latter can help to explain why many firms fail to achieve network benefits like learning or flexibility, which are at least partly the result of behavioral aspects on the plant level. In particular, specific individuals inside the plants play an important role by influencing the whole manufacturing subsidiary’s willingness to cooperate. Ultimately, plant leaders are the key decision-makers to be considered in the context of network management as they can set the behavioral orientation of their entity and thereby contribute to or prevent network success (Wiech & Friedli, 2020). The job assignment of plant leaders includes responsibility for a manufacturing unit and the related operations (Smith et al., 2009). Among other things, plant leaders have to coordinate, supervise, and staff production (Hautaluoma et al., 1992). Furthermore, they represent the entire company locally and maintain relations with third parties (Feldman, 1988; Staughton & Johnston, 2009). As such, managing a manufacturing subsidiary is a challenging position requiring not only technical but also leadership and politics skills (Hum & Leow, 1992; Smith et al., 2009). In their daily work, plant leaders are confronted with a relatively high degree of autonomy due to spatial separation. Often, they need to take immediate decisions without consulting seniors in the headquarters, though today modern means of personal transport and fast-developing information and communication technology might allow for more headquarter control than 40 years ago. However, the current COVID-19 pandemic and the ramifications especially for intercontinental air travel have made many firms realize how difficult it is to control their foreign facilities. Network managers need to consider the local focus and devotion of their plant leaders. Walder highlights that plant leaders can become very attached to their entity and therefore are “akin to a village head, or the mayor of a small town or city. During his tenure of office, the manager develops an attachment to the unit, a vested interest in its growth and prosperity” (Walder, 1989, p. 249). Such behavioral aspects may impede the network-focused conduct of plant leaders. For example, the willingness of a plant leader to establish and mediate knowledge exchange with other subsidiaries in the network (Abdullah & Liang, 2013) will be limited if the plant leader fears weakening their own plant’s position by doing so (Cheng et al., 2011). Therefore, the following sections evince some ideas to consider plant leaders in network management practice.

17.2

Aligning Network Strategy with the Plant Level

One important aspect that brings plant leaders into prominence for network management is their task of translating and delivering strategic initiatives into the factory and to the shop floor level (Abdullah & Liang, 2013; Smith et al., 2009). Therefore, network managers should not only consider but include plant leaders early on when defining and implementing a network strategy. Many practitioners are unaware that a distinct network strategy is nothing less but the fundamental basis for the success of their network operations (Friedli et al., 2014). The following question proved to be a good entry point into the network strategy discussion: Why do we operate multiple, separated sites in a setup that clearly causes higher transaction costs due to spatial separation (Malmberg, 1995) than a single factory firm? By the late 1990s, scholars

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had already found an answer to this question. Shi und Gregory argued that firms can achieve distinct capabilities by operating multiple manufacturing entities in a network setup. Though notation and numbers vary between different authors, these network outputs or capability can be clustered in the following four categories: accessibility, thriftiness, learning, and mobility (Colotla et al., 2003; Friedli et al., 2014; Shi & Gregory, 1998; Miltenburg, 2009; Thomas et al., 2015). The network strategy is a clear target picture for each of these network capabilities (Thomas et al., 2015). Even though scholars found that plant capabilities are linked to network capabilities (Thomas et al., 2015), the exact mechanisms of how sites contribute to the network level and to what extent are equivocal. Nevertheless, it is an important exercise of network management to consider each plant’s current and target contribution to the network. To align the plant leader’s perception of their site’s contribution to the network strategy, network management should conduct distinct workshops. The following example demonstrates how these workshops can be set up and underlines the importance of this approach to avert uncontrolled orientation of plants not in line with network strategy. Example: The Perception of Own Plant’s Contribution During a workshop with six plant leaders and the network manager of the EMEA (Europe, Middle East, and Africa) subnetwork of a Germanheadquartered manufacturing firm, substantial differences between the centrally expected contribution of a plant and the respective perception of what plant leaders thought their plant delivered to the network were observed. This workshop was moderated by two researchers of the Institute of Technology Management (University of St.Gallen). Prior to the workshop, the operations management team prepared a target picture for the mentioned EMEA subnetwork and defined the contribution of each plant. First, during the workshop, network management and the participating plant leaders aligned on the general operations strategy and specifically on the subnetwork’s target capabilities. Here, it was the key task of the network manager to explain how the market requirements are translated into the network strategy. In doing so, network management not only met the approval of their plant leaders but also secured buy-in and consequently their support for the implementation of this new strategic orientation. Second, plant leaders were asked to explain the current and future contribution of their plant to the network strategy targets. This exercise revealed significant differences in the expectation of network management and the perception of their plant’s actual strategic contribution to the network. For example, Fig. 17.1 outlines the selfappraisal of one plant leader. He saw his own plant as having a growing knowledge-providing position and thus, contributed to the network learning capability by hiring several additional process engineers. In contrast, network management expected the site to be very cost-competitive without any knowledge-hub function. (continued)

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Very Low

Very High

Accessibility Thriftiness Learning Mobility Contribution of plant to network capabilities Plant self-assessment Network management intention for plant

Fig. 17.1 Perspectives on plant contribution to network capabilities (exemplified)

Though certainly simplified and not taking into account all details of scholarly research on the link between plant and network level2, the example above outlines a simple and straightforward approach to bringing headquarter expectations into the plants. The abovementioned workshop exercise also provides transparency to all plant leaders by developing their understanding of the role of their peers and, thus, equipping them to know, for example, who to get in touch with for help (i.e., the plant supposed to provide learning capabilities). Overall, it is an important task of network management to align each plant’s contribution with the responsible individuals and explain the overall network strategy. This process takes time and internal resources, but otherwise, plants will develop in unexpected ways, and network objectives won’t be met as predicted on some shiny PowerPoint slides. Plant leaders take a key role in this strategy alignment between plant and network level.

17.3

Network Coordination and the Plant Leader

Besides being integrated into network strategy, plant leaders can play a—for some maybe unexpected—role in managing manufacturing networks, in particular with regard to network coordination. Yet, theory tends to oversee the role of plant leaders for coordination in two ways (Wiech & Friedli, 2020): 1. The effect of particular network coordination mechanisms on the conduct of these key individuals 2. The phenomenon of selected plant leaders taking network coordination responsibility in some firms 2 For a comprehensive discussion on the mechanisms between the plant and network levels, see Thomas et al. (2015).

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Network coordination is particularly important to achieve network mobility, learning, and thriftiness (Colotla et al., 2003; Shi & Gregory, 1998). As such, by not considering the plant leader in their coordinational efforts, firms will struggle to achieve these important network outputs. Considering the importance of network coordination, it is somewhat surprising that the literature on network coordination is still limited. Network configuration and strategy topics have dominated the manufacturing network literature stream so far (e.g., Cheng et al., 2015). Noruzi, Stenholm, Sjögren, and Bergsjö (2018, p. 1607) even recognize that “literature around coordination of IMNs has still great potential to get improved.” Several definitions for the term network coordination exist.3 Ultimately, a common element among most of the existing definitions is that network coordination is about flows within the network and mechanisms to manage these. Typical mechanisms to coordinate manufacturing networks are centralization, standardization, incentivization, and the means of knowledge transfer (Wiech & Friedli, 2020). Historically, research on network coordination has been dominated by models to optimize physical intra-network flows or capacity planning (Cheng et al., 2015), but lately, several scholars have established new perspectives on network coordination (e.g., Norouzilame & Wiktorsson, 2018; Scherrer & Deflorin, 2017). Even though new areas of interest have been raised, network- and plant-level perspectives still dominate the manufacturing management literature (Cheng et al., 2015; Thomas et al., 2015). The individuals within the plants and their role for network coordination play only a subordinate role. However, some examples among studies that take a plant or network perspective as the level of analysis point out that the individuals within the plants should be considered. For instance, Luo (2005, p. 72) points to further research on subunit leaders and their influence on inter-unit cooperation. Therefore, Wiech and Friedli (2020) suggest considering the individual level. They state that the behavior of individuals within the network is affected by the network level and, at the same time, these individuals influence the network-level outcomes.

17.4

Coordination and Its Effect on Plant Leader Conduct

Network coordination-related decisions can have strong motivational effects on plant leaders. Vereecke et al. (2006) observed that plant leaders were intrigued by the higher autonomy of plant leaders in the lead plant. Other scholars see differences in the “willingness to participate in knowledge transfer” (Scherrer & Deflorin, 2017, p. 406) or characterize behaviors of decision-makers within the plants whereby they do not deliver the intended results but instead the superficial implementation of lean programs (e.g., Netland & Aspelund, 2014). In a recent study, scholars directly approached plant leaders to find out what these key individuals see as major barriers See Wiech (2020) on p. 39 for an overview of various definitions of the term “network coordination.”

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for inter-plant exchange. From interviews with 12 plant leaders, they identified the following recurring barriers limiting exchange and cooperation between plants (Wiech & Friedli, 2020): 1. 2. 3. 4. 5.

False incentives and inter-plant competition Lack of strategic orientation Intra-network heterogeneity Missing personal ties Limited resources and local focus

The first barrier is linked to the network coordination mechanism of incentivization. Plant leaders will refrain from cooperation with their peers if network management decides to foster competition between plants by setting incentives on plant level instead of setting shared objectives between multiple plants (Friedli et al., 2014). The latter has been reported to be very effective to foster interplant cooperation. For example, a plant leader from a German automotive supplier reported substantial improvement of inter-plant cooperation due to the implementation of shared inventory objectives with the downstream plant leader. Since then, they started working together to find a solution that did not just improve the inventory of one plant at the expense of the other, but instead determined the optimal inventory level throughout the whole value stream. In this example, network management found the right lever to ignite cooperation between at least two plants. On the other hand, in the study by Wiech and Friedli (2020), multiple plant leaders reported how network management stifled any form of cross-factory exchange by linking individual incentives to local performance indicators only. The second and third barriers are primarily linked to network strategy and configuration. A recurring theme among the interviewed plant leaders was the lack of common grounds with other plants. Many reported that they simply see too many differences in products and processes with some of their peer plants (Wiech & Friedli, 2020). Primarily, network management could address this by delayering the overall network of plants into several smaller subnetworks of plants with similarities (Ferdows et al., 2016). Please refer to Chap. 2 for a more detailed approach to delayering manufacturing networks into congruent subnetworks. Furthermore, network management could promote standardization within the entire network to create higher commonality between plants. The fourth barrier of missing personal ties between plant leaders can be addressed by organizing designated exchange meetings between plant leaders of the same subnetwork. Several practitioners from the plant and network level reported that regular exchange meetings help to overcome an initial hurdle and reluctance to call someone unfamiliar. These meetings are one important building block to put plant leaders into a position to trigger and mediate the exchange between their entities (Abdullah & Liang, 2013).

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Finally, plant leaders reported that a lack of resources and a strong focus on local challenges impede their willingness to engage in inter-plant exchange (Wiech & Friedli, 2020). This major constraint has also been identified by a German company with more than 20 plants around the globe. To enable the support of their main knowledge hubs4 in Germany for several less mature plants within the network, they introduced designated network resources in both hubs. Process and equipment engineering resources were created with a clear mission to allow the transfer of capabilities into other network entities. As a result, the affected plant leaders perceive support to and exchange with other network entities no longer as a burden but instead as an integral part of their job. Finally, the outlined barriers and examples above underline that listening to the key individuals on the plant level is a first important step. Based on this, network management can take various coordination measures to cater to their plant leaders, who eventually will expedite inter-plant exchange.

17.5

The Particular Role of Selected Plant Leaders in Network Coordination

The behavior of plant leaders is not only affected by coordination measures taken by network management. Some arrangements put plant leaders in the position to assume a network coordination role themselves. For instance, numerous articles discuss the lead plant concept (e.g., Cheng & Farooq, 2018; Deflorin et al., 2012; Ferdows, 1997), which puts the plant leader of these entities into a position that is not only focused on their own facility. The existing literature assigns plant roles based on overall site characteristics like capabilities and strategic site reasons. Such contextual factors strongly influence the plant leader’s attentional capacity (Ocasio, 1997). It determines the ability of plant leaders to assign at least a fraction of their efforts to network-related topics and not solely to running their plant. Wiech (2020) differentiates four plant situations based on plant capabilities and site performance to analyze which plant leaders could take over additional network-related duties (see Fig. 17.2). Thereby, plant capabilities are qualitative factors like experience with products or processes and the ability to cope with complexity5. Plant performance refers to the question of how smooth the operation of a factory is running. Therefore, typical operational performance indicators covering the dimensions of safety, quality, delivery, and cost can be used to assess this rather short-term perspective. The first lower left field in Fig. 17.2 presents a plant situation with low capabilities and low operational performance. The plant leaders of such facilities are internally focused. Typically, they manage relatively new plants. The receivers need to build capabilities and improve performance at the same time. Therefore, their 4

Two distinct plants, each for one subnetwork. Further reading related to plant capabilities: e.g., Demeter and Szász (2016), Ferdows (1997), Feldmann and Olhager (2013), Vereecke and van Dierdonck (2002), Vereecke et al. (2006). 5

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3. Provider and Seeker • Provide capabilities • Improve internal performance • Seek (performance) innovations • Own production responsibility • Mainly internal objectives

4. Network Player • Network performance • Interplant exchange • Own + cross-plant production responsibility • Coordinator of intranetwork exchange and cooperation • High share of network objectives and low share of internal objectives

1. Receiver • Improve internally • Receive and seek help

2. Keeper • Maintain internal performance • (build-up capabilities)

• Limited operational responsibility

• Own production responsibility

Focus Autonomy Incentivation

• Internal objectives

• Mainly internal objectives

Operational Plant Performance

Fig. 17.2 Plant context and implications for plant leaders (Adapted from Wiech, 2020, p. 192)

attentional focus should be on local topics, but help from more mature plants is appreciated. In the second lower right field, leaders of plants with high operational performance but low capabilities are sometimes trapped in a situation fixed by network management. Appreciated for their low-cost role within the network, these plant leaders are often not intended to build up more capabilities in their facility. The mission of these plant leaders is to maintain the performance position of the facility. As such, these keepers are also highly internally focused but need less support than less mature facilities. However, to maintain their high operational performance, these plant leaders need to widen their focus to the network to look for process improvements. Once their plant’s role changes to become more of a capability hub, their focus shifts even more to the network to build up more capabilities. The third upper left field in Fig. 17.2 outlines that managers of facilities with high capabilities and low operational performance also have an ambiguous focus. The low operational performance of their facility certainly keeps these individuals busy with local topics. On the other hand, due to their high capability level and the need to improve performance, they have to provide capabilities and search for performance improvements at the same time within the network. Examples of such plants that are relatively mature with high capabilities but lacking cost performance (e.g., profitability) can be found in Western Europe. Finally, plant leaders of facilities with both high performance and capabilities are best placed to assume a network-related authority. Focus on network-related topics can become an integral part of these individuals’ roles. For one, the network players

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should assume operational responsibility for plants with low capabilities and performance as well. For example, all major decisions related to production (e.g., changes to processes) need to be aligned between the receiver and the network player. The latter eventually confirms such changes or provides guidelines. Furthermore, these plant leaders also focus on the overall network performance and coordinate intranetwork exchange across plant boundaries. Network management needs to consider different plant situations as presented above. Specific measures are required to create plant leader behaviors in line with the expected plant role. Therefore, network management can provide specific role descriptions and differentiate autonomy and incentivization accordingly. Wiech (2020) outlines how autonomy and incentives should be designed for each of the four roles. Whereas plant leaders assuming a network coordinator role should assume a high degree of autonomy not limited to their own facility, the receivers enjoy much less autonomy that is linked only to their facility internally. The incentives reflect the degree of being internally or network focused (see Fig. 17.2). Receivers are mainly intra-plant focused. Therefore, their incentive bonus should mainly be linked to the performance and target achievement of their facility only. On the other hand, the targets of network coordinators relevant for their bonus remuneration should not only be linked to their own plant. Their bonus should instead be linked to aggregated network indicators that reflect the performance of the network as a whole. Keepers and providers and seekers should also have some targets linked to the network level. However, as their role descriptions put a clear focus on plant internal topics, their bonus-relevant targets and indicator should mainly be linked to plant internal performance. Nevertheless, firms striving for more exchange and cooperation between their plants should incorporate extrinsic rewards (Burgess, 2005), for example, they could link the bonus of their plant leaders to this objective. Finally, plant leaders assuming the network coordinator role for their subnetworks become—at least to some extent—part of network management. While comprehensive studies on the performance effects of this approach of making plant leaders part of network management are lacking, examples from practice prove this to be a promising strategy to improve network performance and intra-network exchange.

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Example: Improved Network Performance by Extending the Role of Plant Leaders The following example outlines how giving the reins to selected plant leaders can improve inter-plant exchange and eventually enhance network performance. The first step of the example company was to delayer their manufacturing network of more than 15 plants scattered across the globe into several subnetworks. Plants were clustered based on value streams, processes, or products to create more homogeneity within the newly formed subnetworks. By doing so, network management counteracted the barrier of having too much heterogeneity within one network so that plant leaders can recognize any benefit in exchanging with their peers (Wiech & Friedli, 2020). Second, for some subnetwork operations, management nominated one plant leader with additional network-related responsibility. The selection of these plant leaders with special salience was mainly based on plant context. As shown in Fig. 17.3, one of the created subnetworks consists of two European plants (A, B) and a facility located in Asia (C) with quite different capabilities and performance. Plant A has always been known as the unofficial lead plant. A similar level of capabilities can be found in Plant B, which struggles with performance due to a complex portfolio and aged equipment. Established for access to market reasons at the beginning of the twenty-first century, Plant C has not been able to build up many capabilities and still struggles with operational performance, especially to deliver the expected quality. The new role for the leader of Plant A was established in 2018. Since then, the tasks of organizing intra-network exchange and enhancing the performance of other network plants have been added to the job description of Plant A’s leader. Furthermore, personal targets also include these networkrelated topics. As a result, the nominated plant leader began to organize intranetwork exchange meetings between the plant’s management teams and process engineers. Furthermore, the key plant leader actively pushed support to solve issues in other facilities with local resources from Plant A, especially in Plant C. In 2020, a manager from the central operations team reassessed this strategic change and concluded that network performance had actually improved compared to 2018. Operational quality performance throughout the network especially benefited from stabilizing of production in Plant C thanks to the support of Plant A.

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Role description appended with: • Active support of other plants • Organize inter-plant exchange • Assumer responsibility for network performance Personal targets extension: • Network performance indicators (e.g. quality)

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Fig. 17.3 Nominated plant leader to assume additional network responsibility

Overall, this example underlines that selected plant leaders can play a key role in coordinating a manufacturing network. Plant leaders are not necessarily always solely focused on the operation of their own facility. Network management has to choose leaders from plants with particular contextual factors, assign responsibility, and design incentives accordingly. By doing so, firms can benefit from having plant leaders that partly assume network coordination-related tasks.

17.6

Summary

Network managers should consider the conduct of their plant leaders more often. They are not only key individuals for plant-internal topics but are also critical for successfully implementing network strategy. In order to ensure the implementation of initiatives in all facilities, even to the shop floor level, plant leaders need to be integrated into the strategy process. They need to understand the network and plant mission to behave correspondingly. Central management should use workshops to explain the network mission and work out jointly with the plant leaders each unit’s contribution to the overall mission. A self-assessment provides a suitable starting point for this alignment process between network and plant management. Each plant leader should explain how their facility contributes to the network mission. Subsequently, network management can review their own ideas and concentrate on those plant leaders whose feedback significantly deviated from the intended role. Besides giving a clear strategic orientation, several measures can be taken by network managers who intend to improve inter-plant cooperation and exchange. Namely, they can create homogeneous subnetworks by clustering plants with similar characteristics; create personal ties between plant leaders belonging to the same subnetwork through regular meetings and an established exchange platform;

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promote personal bonus targets linked to network performance instead of fostering inter-plant competition; and provide resources to plant leaders that allow broadening the focus from local plant to overall network topics. Finally, network management can surrender some of their authority to selected plant leaders with the objective of improving network exchange and eventually performance. One plant leader per subnetwork can be selected based on plant capabilities and performance. The nominated plant leaders should shift focus, no longer concentrating only on their own facility but also on coordinating exchange between plants and to drive network performance. This special role needs to be backed by additional autonomy, a clear role description, and bonus incentives linked to overall network success. Ultimately, plant leaders will become a driving factor for the network concept, giving central operations management more time to concentrate on other aspects of network strategy, coordination, and configuration.

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Vereecke, A., Van Dierdonck, R., & De Meyer, A. (2006). A typology of plants in global manufacturing networks. Management Science, 52(11), 1737–1750. https://doi.org/10.1287/ mnsc.1060.0582. Walder, A. G. (1989). Factory and manager in an era of reform. The China Quarterly, 118, 242–264. Wiech, M. (2020). Coordination of inter-plant exchange. (Dissertation, University of St.Gallen). Wiech, M., & Friedli, T. (2020). Using plant leaders’ perspectives to overcome barriers to interplant exchange. Journal of Manufacturing Technology Management. https://doi.org/10.1108/ JMTM-09-2019-0331.

Part III Practitioner Contributions

Strategic Transformation and Operations Management at Bühler AG: A Holistic Approach

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Holger Feldhege, Xue-Zhi Liu, and Dominik Remling

This chapter outlines the holistic approach to managing the global manufacturing network at Bühler. After a short introduction about the company, the chapter is divided into three parts. First, the foundation for the strategic transformation along the eagle icon is outlined. Second, key concepts for aligning the footprint, logistics, and supply chain with the group strategy are presented. In particular, the management of complexity and risks are addressed. Third, pragmatic concepts are described to enable strategy deployment and success control within the Bühler manufacturing network. Finally, the chapter concludes that strategic transformation is an ongoing journey; it is important to have open communication and supportive company culture to empower and engage the employees to join and embrace this journey together.

18.1

About Bühler AG

Founded in 1860 in Uzwil, Switzerland, Bühler AG is a family-owned global business that operates in more than 140 countries. The company develops industrial process solutions along complete value chains for food and mobility. Bühler technologies are found in smartphones, solar panels, diapers, lipsticks, banknotes, human nutrition, animal nutrition, and vehicles. Each day, two billion people consume foods produced on Bühler equipment, and one billion people travel in vehicles manufactured with parts produced with Bühler machinery. H. Feldhege (*) · X.-Z. Liu Bühler AG, Uzwil, Switzerland D. Remling Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_18

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Bühler has three business divisions: Grains and Food, Consumer Foods, and Advanced Materials. The Grains and Food solutions contribute to the global production and processing of wheat, corn, oats, rye, rice, pasta, cereal, and pulses. The Grains and Food solutions help manufacturers make safe and healthy finished products for human and animal nutrition. Consumer Foods offers solutions, including equipment for roasting cocoa beans, machines for preparing doughs, ovens for baking wafers, and enrobing machines. The Advanced Materials division provides solutions for die-casting, wet-grinding, and thin film technologies for high-volume application areas, including the automotive industry, precision optics, paints, electronics, packaging, inks, and ophthalmic. As a 100% family-owned business, the management philosophy of Bühler is highly influenced by 160 years of family business history. For the three Bühler family-owners of the company, business continuity, sustainability, and integrity and respect are the top priorities. At the same time, the family also insists on continuing research and development, as innovation has been the distinctive differentiator for Bühler over generations. Therefore, they maintain optimal general conditions for the company to operate in a stable shareholder structure, a long-term orientation, and steady company management that is not subject to the constraints of quarterly reporting, yet a management style that pursues business success. With the vision of “Innovations for a better world” and the mission to “Engineer Customer Success,” Bühler is a purpose-driven company that wants to become a part of the solution to more sustainable and efficient global value chains. The market driver for Bühler has always been innovations. Bühler’s unique proposition is its competence in delivering complete solutions, such as new plants or new processing lines, for its customers. From the whole plant’s engineering design to the complete installation of the new plant or process lines, Bühler’s scope entails all processing machines in the production line range, starting with raw material processing and finishing with the packaging of the end products. Bühler sells new plants and new process lines as “customer projects.” Bühler also sells standardized single machines to resellers or directly to customers as add-on machines or replacement machines. Bühler’s customer-centric approach means it focuses on addressing each customer’s needs individually and creating customized and differentiated processing solutions. These solutions also include world-class automation, digitalization, and service offerings, all based on the individual customer’s specific needs. In addition to customer projects and standardized single machines, customer service is a growing business driver for Bühler. With its extensive global coverage of service stations worldwide, the company strives to be its customers’ long-term business partner. The comprehensive range of service offerings includes but is not limited to spare and wear parts, on-site technician services, maintenance inspections, digital services, trainings, retrofit and revisions, product testing, and more. All services are designed to boost its customers’ production performance and extract the most value using Bühler processing technologies. In the Bühler Annual Report 2019, the company reported employing 12,767 employees and a global presence consisting of 85 sales offices, 98 service stations,

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32 manufacturing sites, and 25 application centers. Bühler’s turnover was CHF 3.3 billion on the group level, with an EBIT margin of 7.6% in 2019 (Bühler AG, 2020). Bühler pursues profitable growth through a local setup of all customer-facing functions and a global production network and supply chain. To be “in the region for the region” is key for Bühler’s success. Bühler has systematically localized its resources and has manufacturing sites, service stations, sales offices, R&D facilities, and application and training centers across 140 countries. This localized setup enables the company to be closer to the market and to deliver its solutions with Swiss quality standards and precision. This market-oriented business approach requires close alignment of business strategy and operations management to stay competitive in global markets. The operations arm of Bühler includes all aspects of manufacturing, logistics, and supply chain (MLS), also known internally as the “MLS” function. The operations function manages a network of 32 manufacturing sites worldwide and more than 11,000 suppliers. The MLS function’s task is to fulfill customer orders on time and quantity, quality, and on costs as defined by the order contracts. The key to running a successful global production network is to ensure that the MLS operations strategy actively incorporates the needs of the business division strategies and the regional market development strategy. As a result, this aligned business approach is anchored into the operations so that daily operations are in sync with the overall strategy. Managing Bühler’s global production network, which manufactures highly customized process solutions for a diverse range of market segments, requires strong leadership with a crystal-clear vision to guide the network and exceptionally high coordination between strategy implementation and operational management.

18.2

Foundation for the Strategic Transformation of the Bühler Manufacturing Network

The following paragraphs will outline how the global production network’s strategic transformation is executed at Bühler and the guidelines used to support the process. As a group, Bühler has a well-executed strategy alignment process that is strongly coordinated internally between the different functions to formulate its 5-year strategy. The process is a comprehensive and rigorous exercise that involves top management, middle management, and internal experts. To build a solid foundation for the 5-year strategy, new macroeconomic trends, long-term market developments, and scenario analyses are reviewed extensively by external and internal experts. Relevant major trends are defined and prioritized, which then set the base for innovation strategy. Following that, the business segments conduct detailed market assessments and develop product-market fit strategies. Then all the market segment strategies are consolidated bottom-up to the business division level and then group level. Finally, the business strategy is aligned, coordinated, and fine-tuned across functions on the group level and disseminated top-down to the entire organization.

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Furthermore, every subsequent year, the top management team conducts a strategy review cycle. In this review, the team actively evaluates the strategy’s success, the strategy implementation progress, upcoming new trends, and the consequences of the new trends for the company’s development. The team also reviews the market development to ensure that the initial market assumptions are still valid. If the assumptions need to be corrected, then actions are defined and implemented to make the necessary adjustments. This vigorous strategy alignment process and continuous review of market developments allow the group to closely monitor the markets to minimize market surprises and avoid falling into strategic gaps. The ability to swiftly correct course and make operational adjustments in a company with a worldwide footprint requires the management team to always anticipate and be ready for potential changes in the market strategy that could impact operations. This active anticipation for relevant changes becomes readiness for change, and thus sets in place a continuous transformation process. To facilitate a continuous transformation process, flexibility is one of the cornerstones that must be built into the global network setup. The Bühler manufacturing network is undergoing continuous transformation. The continuous nature of this strategic transformation reduces the probability of sudden costly restructuring because the transformation process is managed in parallel with the operating business as changes unfold over five to seven years. This practice of actively avoiding a strategic gap that would require short-term scale down or site closures is a management philosophy that is deeply rooted in the values of a familyowned business and the family business’s responsibility for its employees. This continuous transformation is a long-term journey. Therefore, a crystal clear vision, a “True-North,” is necessary to keep the target in sight and to ensure that all actions are directed toward achieving this vision. This vision in the operating function of Bühler is called the “MLS True North.” The True North becomes the guiding principle for MLS operations and also serves as the linkage to the business strategy. MLS True North is defined into three main parts: (1) deliver efficiency, (2) increase flexibility, and (3) detach growth from fixed assets. The combination of these three parts creates the perfect future vision state. Efficient operations ensure that the company stays ahead of the competition and stays strong in competitive environments. With high flexibility, Bühler will be able to react faster to market volatilities and changing customer demands. Finally, the company wants to grow without building up fixed assets. Fixed assets equal high fixed costs that negatively impact flexibility if the markets change. Incorporating high efficiency, high flexibility, and decoupling growth from fixed assets, the company can adapt much faster to market changes, even with a global production footprint. It was essential to formulate this True North vision and the MLS strategy into simple terms so that everyone working in the operations and the businesses could easily understand the operations strategy and its linkage to the group strategy. The COO’s team created a strategic icon, MLS strategy icon (see Fig. 18.1), to visualize the strategy into a simple-to-understand yet emotional and engaging image.

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Fig. 18.1 MLS strategy icon

The team chose the golden eagle as the representation because four eagle characteristics strongly resonate with the MLS organization members. The eagle has a sharp vision that allows it to see far and wide. The team also has a clear vision of what they want to achieve, and they plan ahead based on their vision. The eagle is a reliable partner for its mate as the pair builds nests and hunts together. The MLS team also aims to be a reliable partner for the business divisions by fulfilling the orders as agreed. The eagle is focused and persistent when it hunts. The MLS team is also focused and persistent in achieving their daily, weekly, monthly, and yearly targets. The eagle builds its nest using available resources from its environment; it adapts its nest material to the natural environment. The MLS team is also adaptable to changing environments, so that the company stays competitive in the market. The strategy icon provides clarity for everyone within the operational functions to understand why they do what they do and how their actions contribute to the strategic transformation. More importantly, the Strategy Eagle also functions as a

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simple guiding icon for the transformation process. The strategy icon consists of five elements, each representing an important aspect of the strategy. Profitable and Sustainable Growth One element of the eagle is profitable and sustainable growth. It is the Bühler Group’s target to achieve profitable and sustainable growth. The group’s mission is to engineer customer success and thus enables a profitable and sustainable future. The operations function contributes to this group target by working profitably and efficiently and by manufacturing top-quality products for customers. True North Naturally, the MLS True North is also an element of the Strategy Eagle icon. True North is the vision of the operational function. Having this defined vision ensures that the whole organization pulls in the same direction together. All actions and strategic initiatives must contribute and lead the organization closer to True North. Customer Success Engineering customer success is the group’s mission. The MLS function contributes to customer success by making sure that all factories follow the defined set of operational targets: safety, quality, costs, delivery, and people. By managing this set of operational targets, the company ensures that its customers receive the products in the quality, cost, and time that they required. The operation principles of SQCDP are the common denominator for all operations and are broken down into yearly targets to assure a common focus in operations and a link to the strategy. Hence, operations are a part of and supportive of the group strategy. People and Sustainability The company’s people are the main driver in the MLS transformation journey toward the True North. To thrive in the changing digital world, new competencies need to be developed. Embracing the growth mentality of continuous learning and being open to change is critical to succeeding in the modern age. Moreover, protecting the environment is vital for the company to ensure long-term sustainable development. The operational function has committed to becoming carbon neutral by 2030. People and sustainability are placed at the heart of the eagle. This element’s placement also reflects that a family-owned business’s values are focused on the people working for the company. Strategic Thrusts Five strategic thrusts make up the wing element of the strategy icon. These thrusts are to support and accelerate the transformation process. The thrusts are digital and innovation, operational excellence, logistics, supply chain, and manufacturing footprint. Under each thrust, strategic initiatives are initiated globally and regionally and coordinated across the thrusts. The Strategy Eagle is a simplified representation of the essential elements of the strategy, and it provides an overview of the MLS organization structure. The

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Strategy Eagle makes it easy for employees who are far removed from the strategy to connect with the long-term vision and understand the activities and initiatives that are necessary to get closer to the True North. The strategy is embedded into the eagle icon to ensure a deeply established transformation process. This visual representation becomes the common understanding that employees recognize and internalize; thus, all employees in the operations organization are united on the same transformation journey.

18.3

Aligning the Manufacturing Footprint, Logistics, and the Supply Chain Toward the True North

The following paragraphs will delineate the tools Bühler uses to align its global sites to perform effectively as a network and thus move closer toward its True North. Three essential elements must be addressed to develop a sustainable global network that is highly flexible and able to react to market development: (1) define a clear vision so that the whole network pulls in the same direction, (2) manage complexity in the global network, and (3) mitigate risks in the global production network. These three elements ensure that the network of different individual sites is aligned as one in the pursue of one True North.

18.3.1 Define a Clear Vision to Align the Network The MLS True North as described in the above section is the vision for the network transformation. The Strategy Eagle icon (see Fig. 18.1) and the different elements of the eagle, as summarized above, translate this vision into a clearly understandable and easily relatable concept for the employees in the MLS organization.

18.3.2 Manage Complexity in the Global Network The second element is managing complexity in the network to enable the company to remain competitive in the market. High complexity not only causes inefficiencies in the process, but it also impedes the network’s flexibility. More importantly, highoperational complexity can significantly increase operating costs. But the cost level must be managed within a competitive range to ensure competitiveness in the market. On an annual basis, in addition to handling all standardized single machine orders and customer service parts deliveries, the manufacturing network also manages more than 2500 customer projects, resulting in more than 125,000 components needed. These components generate more than 37,500 manufacturing orders for the Bühler network and over 87,500 project purchase requisitions for suppliers. The 37,500 internal manufacturing orders alone create over 1.5 million purchase orders to be processed on time and in full. These internal manufacturing orders result in 4.5

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million single work orders or tasks for workers in the network. Ensuring the fulfillment of customer orders on time and in full is an extraordinary challenge in this complex environment. All the parts need to be coordinated to arrive at the predetermined timing and in the correct sequence for a smooth on-site installation of the equipment. To manage complexity in its global network and keep costs within a competitive range, Bühler uses various tools to simplify its operations. Core vs. Non-Core Classification Process The classification of products and parts into core or non-core is the first step to determining what must be produced in-house and what can be outsourced (see Fig. 18.2). Generally, core products are produced in-house, and non-core products can be sourced through the supply chain. Sourcing externally through the supply chain reduces the complexity that arises from in-house production. The core versus non-core classification process is a three-level comprehensive process that first starts from the product portfolio, then from machines to components, and finally from components to parts. The process is a cross-functional endeavor that includes active participation from the customer-facing business divisions, the supply chain management team, and the internal manufacturing management team. The aim of this comprehensive classification process is to focus on the core value-add activities of assembly within the company and reduce non-core activities; consequently, complexity in the network is reduced. The first level of classification starts with the market perspective to determine the relevant product portfolio. The market’s view of Bühler’s core competencies is taken

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Table 18.1 Parts classification Category Core parts A

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Criteria
Intellectual property
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Medium lot size
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High repetition
Big lot size

Decision In-house

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In-house Partially outsource to supply chain Outsource to supply chain

into account in order to break down the whole processing solution technology into a machine product portfolio that the market considers as Bühler’s core technology. The defined core portfolio of machines then undergoes a second level of classification as the machines’ individual components are evaluated to be core vs. non-core technology components. These defined core technology components then undergo a third level of classification as the individual parts that make up the component are further classified as core or non-core parts. In Table 18.1, there are five categories of parts. Generally, the degree of strategic importance descends from A to D. When a component or a part is classified as non-core, Bühler must find suitable suppliers who can meet its requirements. If no suitable supplier can meet the criteria of time, cost, quality, and complexity as defined by Bühler, then the company will have to produce this non-core component or part in-house to ensure the integrity of the whole process line technology. Within the Bühler manufacturing network, the sites differ significantly in terms of capability in manufacturing processes, products to be manufactured, business segments covered, and markets served. Thus, no two Bühler sites produce an identical range of products. Based on local market requirements, available competencies in the in-house and external supply chain, as well as resources, each site has its own factory ID. The factory ID determines the core processes performed and the assigned range of parts and products to be manufactured at that site. After the core vs. non-core classification, if in-house production is chosen, the products or parts are then allocated to the appropriate production sites worldwide. The sites are also responsible for securing qualifying suppliers, in order to focus on their own core competencies, thus acting according to the True North “detach growth from fixed assets” and performing according to their defined factory ID.

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Bühler Event Chain A customer order in Bühler can range from a single machine to a complex greenfield project. A greenfield project would require machines and components from multiple Bühler sites and parts from hundreds of suppliers worldwide. It is quite common to have to coordinate more than 100 logistic routings to deliver all project components to a single customer location. Complexity in customer projects is high and must be managed to avoid missteps in the customer order fulfillment process. The Bühler Event Chain is introduced to manage complexity in fulfillment of customer project orders and to ensure that customer orders are delivered on-time as promised (see Fig. 18.3). A customer project order can be divided into three parts: (1) engineering work, (2) manufacturing, project procurement, and logistics, and (3) site installation. The Bühler Event Chain covers manufacturing, project procurement, and logistics. The Event Chain is an integrated, data-driven scheduling process based on lead times. There are 21 events defined in the Event Chain. Each customer project follows the same series of events as specified in the Event Chain. The scheduling of the customer project is based on the project delivery date. It starts from Event 00, where the project manager is responsible for ensuring that Event 00: order specification is completed, and documentation is handed over to the project support supply chain (PSSC). The PSSC function is responsible for the Event Chain. Therefore, the PSSC function is the single point of contact in the company when it comes to planning and executing the manufacturing, project procurement, and logistics of customer project orders. The last event on the chain is Event 20: arrived on site. With 21 clearly defined events, the Event Chain can provide full transparency on a customer project at any point in time, thus ensuring on-time delivery of the customer order. Also, each event in the chain is clearly defined, as is the required documentation needed to proceed to the next event. The PSSC is responsible for the integrity of the Event Chain; therefore, any changes to the planned dates trigger an approval and alignment process. The PSSC, the factory, and the project managers must align and agree on the changes. If an agreement cannot be reached, then the event conflict is escalated to management for a review. Consequently, the Event

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Chain allows the company to see deviations from the scheduled plan early on and plan adjustments accordingly. All customer projects are scheduled with the same principles on the Event Chain by the PSSC; this consolidated view provides a more accurate turnover forecast for the company. Moreover, since all customer orders are scheduled based on delivery dates, this consolidated data provides a clear overview of the demand for Bühler manufacturing sites. For manufacturing, the dates on the Event Chain are used for the relevant scheduling of all customer orders, which serves as the basis for sales and operations planning. 15-Month Sales and Operations Planning With a global network of sites, each with different capacities and producing different products, capacity planning on a global scale is necessary to ensure high utilization to keep factory hourly rates manageable. To overcome this challenge, the sales and operations planning (S&OP) process was developed to align demand and capacity with the consideration to achieve the turnover targets. S&OP uses information from the Event Chain. The challenge in operations planning at Bühler is to maintain flexibility to meet market requirements and yet ensure a high utilization rate to cover the costs. There are industries with high margins providing a lot of flexibility, and there are costdriven industries, which need to utilize capacities to a high degree. Bühler sees itself as mainly on the cost-driven side, but the company must operate according to the pull principle at the same time. This is due to its broad portfolio of highly individualized machines. Maintaining a pull-like operation but keeping capacity utilization high at the same time is a challenge. One important aspect of overcoming this trade-off is to estimate the future demand and then to balance the orders according to customer delivery dates with the available capacity. This was the reason for the company to introduce the sales and operations planning (S&OP) process with a clear structure that links the business sales to factory capacity planning. The global PSSC function provides a clear overview of the demand based on customer orders scheduled as per the Event Chain. The global S&OP planner then translates the demand into capacity requirements for the factories based on the factory product allocation matrix guidelines. The guidelines set the rules on which product order should be manufactured in which factory. The S&OP process provides a 3 + 12 ¼ 15-month forecast based on customer orders. This forecast allows future workloads to be planned and balanced in advance. The Bühler manufacturing network includes two S&OP hubs. Both hubs report to the business division heads and the COO. All production sites and related planning are guided by the S&OP forecast. The process has to follow specific rules to avoid unstable production programs and the risks of increasing inventory. Thereby, S&OP provides a full preview of the demand and committed workload in production hours for the upcoming 3 months (100% of hours on hand must be confirmed for the upcoming month, 90% hours on hand for the month after, and 80% of hours on hand for the third month). Following the 3-month forecast, the percentage of secured hours on hand for the 12-month rolling forecast is more fluid.

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Fig. 18.4 Example of daily delivered order monitor

The S&OP translates demand into the capacity for the factory and slot plans afterward. A slot plan contains the date and time of a customer order assigned to a specific machine in a factory. Even with a well-scheduled monthly slot plan, there could be deviations from the scheduled plan during the month due to unexpected causes such as customer requests, machine breakdowns, and other reasons. Deviations could also be unscheduled production completed during the month or scheduled production shifted to later months. These deviations are monitored through the daily delivered order (DDO) cockpit (see Fig. 18.4). The organized demand and capacity planning and monitoring of deviations through S&OP enable Bühler to plan and balance factory capacities more effectively, improve on-time delivery, lower inventory, and enhance turnover forecast accuracy. The process is still a manual process; the company is currently working on digitizing the process to make it more efficient and to be able to track progress and deviation in real time. Digital Transformation to Become a Smart Production Network Around 90% of all Bühler sites run on an SAP ERP system template. Even though the sites all use the same ERP system, the underlying architecture and functions reflect, to a good degree, the heterogeneity of the products and related processes. Several projects are in progress to further harmonize the systems to bring about higher cost-savings and efficiency increase through digitization. Digital transformation is also an enabler to reduce and manage network complexity. Several digital projects are underway to create vertical integration of information from the machine and operator level to sales and operations planning (see Fig. 18.5). By linking the relevant data from different systems and sources, meaningful

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Fig. 18.5 Vertical integration through digital transformation

analytics can be derived from the data to better manage operations. Moreover, digital management solutions can bring full transparency and more efficiency to all aspects of operations in manufacturing, supply chain, and logistics. One active digital project is the integrated business planning project, which will further enhance and digitize the S&OP process. The aim is to establish one worldwide platform for network planning and forecasting. Through automatic system assignment of work, the manual effort for planning and reporting is significantly reduced. Deviations to plans also become more visible and can be tackled in realtime. More importantly, the system can analyze data for a better alignment of demand and supply in real-time and provide different simulations of scenarios for balancing workload in the whole network. Another digital project that would reduce operational complexity is the mCORE project. MCORE integrates the SAP manufacturing execution system (MES), linking the slot plan of individual machines and operator tasks in the factory into a powerful shop floor planning tool. With mCORE, paperless factories become a reality by embedding work order papers, drawings, quality inspection papers, etc. into the system. This could significantly reduce muda in the process as well as human errors. Moreover, with digital planning and sequencing of machine slots and worker tasks, all activities are transparent; and the system provides a high degree of granularity of manufacturing data, which can be used to enhance shop floor management. While vertical integration of information from machine and operator level to sales and operations planning is the first step to build the foundation needed for smart manufacturing, Bühler is also looking into horizontal integration of information from supply chain and logistics operations. Digitalization will significantly

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contribute to streamlining the operations of the whole production network. The building up of vertical and horizontal integration of data sources is necessary to take further steps toward applying industry 4.0 technologies.

18.3.3 Mitigate Risks in the Global Production Network Risk mitigation in the global setting is the third element in aligning the Bühler global production network to transform into an optimized, highly flexible network that can adapt to market changes in an agile way. Risk mitigation is actively designed into the whole network. There are multiple tools that Bühler utilizes to mitigate risks, including breathing, balancing, and product transfer; geopolitical consideration in network footprint; and resilient regional supply chain. There are additional costs associated with measures to mitigate risk, which the company needs to budget in advance. Breathing-In and Breathing-Out of the Supply Chain The breathing concept is in place for risky situations such as market volatility. The concept of breathing is differentiated between breathing-in and breathing-out. In this sense, breathing-in means shifting manufacturing volumes from the supply chain back into the company, and breathing-out means shifting manufacturing volumes into the external supply chain. Breathing increases flexibility by keeping the fixed cost low while retaining the option to fill the in-house hours to a pre-agreed level with the suppliers if demand decreases. Under normal conditions, Bühler maintains at least 30% of its capacity volumes in the external supply chain. This allows the company to keep its fixed cost low and provides flexibility. In the event of declining demand, it is possible to reduce supplier volumes based on respective agreements, so that Bühler’s internal capacities can be utilized more effectively as a result. Once the situation returns to normal, the opposite principle, breathing-out, will be applied. The aim is to shift capacities out of the company to be able to pursue the True North “detach growth from fixed assets” in the long term. Moreover, the breathing-in principle stipulates that a few so-called anchor factories, which are the key factories in the larger regions, can map the entire value chain so that they are able to supply in the event of supplier failure or overload. Balancing Between Different Manufacturing Sites The approach of balancing involves shifting manufacturing capacity between Bühler sites. Within the network, people and machine hours can be shifted to balance out the workload demand. Balancing is primarily encouraged within a region and is applied internationally only in critical conditions because it is more probable to have high differences in cost structures internationally. The additional cost effect of balancing must always be lower than the total idle cost effect when utilizing balancing as a tool. Balancing people allows the factory with a high workload and inadequate labor to get highly skilled temporary labor from another Bühler site quickly without

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compromising on product quality and spending time training new people. On the other hand, the factory with a low workload and too many idle employees can benefit from lending out qualified employees to another Bühler site. Furthermore, balancing people fosters exchanges of best practices, develops new skills for the employees, and unites the people with a sense of togetherness and passion for supporting each other in difficult situations. Balancing machines between sites allows the factory with increasing demand but inadequate machine capacity to retain core competencies in-house and still fulfill orders. Conversely, the factory with low utilization of the machine can benefit from reducing its fixed cost by leasing out the machine to the other site or selling the machine directly to the other site. To make these balancing decisions, Bühler has predefined decision models for which it is important that fixed and variable costs per manufacturing hour in each factory are made transparent. Thus, this sets the base to enable balancing between factories. Product Transfer Between Different Manufacturing Sites Bühler’s product transfer matrix shows the allocation of products to the markets, to the manufacturing sites, and to the suppliers on a global scale. Furthermore, each product’s manufacturing hours are broken down into single manufacturing processes such as welding, bending, painting, etc. This detailed information is needed when making management decisions on the long-term shift of manufacturing volumes from one site to another to generate cost-savings or to mitigate risk. Usually, a single site is the main production site for a specific product, serving the regional and export markets. However, depending on the factory export ratio, supplied countries, importance of the product across businesses, risk mitigation measures, and lead time to market, volumes can also be served from a second production site. From a risk mitigation perspective, each product’s importance for a business line is analyzed. If the product only serves one business line, the associated risk is rather low compared to a product serving multiple business lines. This is because risk accumulates from varying anticipation of market developments for each business line. Consequently, critical products are then allocated to more than one site within one region. A product stays in one region to comply with the “in the region for the region” principle. Within this region, the local risk profile is assessed to avoid barriers in shifting of orders between the sites. Market development also has an impact on product allocation. Products can be transferred from one site to another site when the critical volume is met or when there is an economic advantage to be gained from producing at a certain location. Product transfer is used as a risk mitigation tool as well as a cost-saving tool. Geopolitical Risk Considered in Network Footprint The global production network must have flexibility built in to mitigate risk in the increasingly VUCA and bi- or multi-polar world. Risk mitigation measures are

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implemented on an international level in the footprint setup to reduce risks from changes in the geopolitical landscape. The company cannot become too dependent on producing in one region; instead, its footprint design must allow it to serve as many markets as possible, even as the world becomes more polarized. This anticipation of geopolitical risk mitigation built into the network ensures that the network remains resilient. Bühler Group’s approach of “in the region for the region” is also applied to manufacturing, logistics, and supply chain, which means regional sites are empowered to become regionally self-sufficient to serve customers in the region. The company examines its own production export statistics to gauge the resilience of the regional footprint. High imbalances in Bühler manufacturing export statistics could indicate a heavy dependency on another regional manufacturing sites. The imbalance then prompts a more in-depth analysis of the causes and effects and further optimizes the network. The company prepares the secondary manufacturing site in the region to be ready to take up production should the geopolitical environment change. Resilient Regional Supply Chain Network to Mitigate Risk In addition to implementing risk mitigation measures on an international level to be prepared for geopolitical changes, risk mitigation measures on the local supply chain level are also applied. It is important to have a regional network of suppliers who are able to deliver to all Bühler manufacturing sites within a region or a country in the event that regional or district borders are closed, as was the case with COVID-19. A strong regional supply chain network ensures that production can continue, even when there is a disruption in intranational transport. In summary, the continuous transformation of the Bühler global network is a long-term process. The MLS True North is its operational vision that the whole operations organization, including manufacturing, logistics, and supply chain, follows as a guiding compass in its activities and initiatives. The organization is aligned under this clear vision, and the company has developed various tools and processes to reduce complexity in managing its network. It has also implemented measures to mitigate risks on an international and regional level to ensure its network remains resilient in the VUCA environment. In 2020, the resilience of the Bühler manufacturing and supply chain network was tested by the COVID-19 global pandemic. The company has already established a good base of global manufacturing footprint and supplier network to enable the company to make necessary flexibility adjustments using balancing and breathing to ensure delivery of all customer orders without interruption. It is evident that this aspect of risk mitigation in network design has to be maintained and further fine-tuned going forward to keep operations running at all times. Bühler’s tools and processes are considered key design components of the strategic transformation journey. As a result, its global network of sites is optimized to compensate each other instead of functioning as individual isolated sites.

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Strategy Deployment and Success Control

A major factor for successful deployment of strategy is the engagement of the people. The Bühler corporate values of trust, ownership, and passion are translated into the global operations community to mean fostering a culture of open communication, working transparently to avoid surprises, taking responsibility and ownership of one’s work, and supporting each other as a team. These behavioral guidelines foster a culture of “togetherness” and empower individuals in the team. Hence, the people are not driven by fear of not meeting performance targets; rather, they are encouraged to find solutions to challenges with the team and take ownership to move the initiative forward and ask for support when needed. This open culture plays a big part in ensuring that people are motivated to lead change, and they are motivated to contribute to the team. The Hoshin Kanri strategy deployment methodology is partially applied to guide the individual sites in their strategic transformation and operational management. The approach forms the bridge between strategy and strategy deployment. The methodology distributes targets across hierarchical levels down to the operational levels annually, assigning each site both strategic and operational targets. All sites then use the standardized Hoshin Kanri action plans and bowler charts to track and monitor the progress of both strategic and operational actions monthly. The strategic network objectives are broken down into annual targets. Strategic targets are based on projects, which can be individualized to specific sites depending on the site’s positioning. All strategic projects fall under one or more of the predefined strategic thrusts as seen in the Strategy Eagle (see Fig. 18.1). The operational targets are based on the defined standard of SQCDP (safety, quality, cost, delivery, and people) (see Fig. 18.6). Although SQCDP is the global standard and the KPI set is standardized, there is no global standardized target level for all

Fig. 18.6 Standard factory targets

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sites. Instead of comparing heterogeneous sites, all sites must aim for continuous improvement to ensure performance improvement compared to prior years. The central hub of the entire coordination process is the COO team. Monthly jour-fixe meetings are held by the COO with all production sites to review the progress. Site performances are regularly reviewed at the monthly jour-fixe to ensure they are on the right track. The review sessions cover predefined regular topics as well as various special focus topics that require deep-dives. In general, efficiency savings in material, value-add, and design to cost are the sites’ main indicators. Furthermore, managing ahead of the coverage difference is key for all sites. If a site fails to meet its operational targets for 2 months in a row, additional bilateral discussions are held with the COO team, and the corresponding site remains in focus. When a site shows a trend in declining performance, then a defined standard of actions can be applied to support the site to restabilize its performance or if necessary, to turn around its performance. The COO’s team is actively supporting the site to revert the declining trend. If problems arise regarding the strategic projects, the COO will also be involved directly. The ongoing digital project, digital factory management, will also contribute significantly to operational management and monitoring of success. The tool aims to aggregate data from the worker level to the team level, to the value stream level, to the factory level, and all the way to the COO level. The aggregated data is presented in a defined standard set of leading and lagging KPIs for each level. Thus, the decision-makers at each level can visualize the performance data that is relevant for them to take necessary corrective actions to get performance back on track. This cascading of information from the shop floor to the COO provides transparency on multiple levels, and the impact of actions taken or not taken becomes apparent. Digital factory management can be a powerful tool that brings transparency to operational management and, as a result, increases stability and efficiency. Bühler also makes a conscious effort to monitor fixed costs development in the overall footprint. To monitor fixed costs development, it is necessary to define its elements in terms of depreciation, buildings, machinery, etc. Instead of setting an arbitrary target of fixed cost to be reduced annually, the goal is to not increase fixed costs even with projected growth. Thus, every operational investment, including replacement of machines, must be carefully thought through and well justified. All operational investments must be approved by the COO function, which triggers the necessary discussions concerning make-or-buy decisions and ensures investments align with the True North strategic direction and the market development and business division strategy. In summary, the True North targets of delivering efficiency, increasing flexibility, and detaching growth from fixed assets are actively pursued by the network as a whole and by all individual sites. The COO’s team firmly steers the network transformation through the global deployment of strategy using Hoshin Kanri methodology and active monitoring of the network’s transformation progress. In addition to fixed cost monitoring, development in risk mitigation, breathing and balancing, and product transfers are closely monitored to ensure the network is on the right track.

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Conclusion

Steering a 160-year-old family-owned business through a long-term strategic transformation from excellent individual sites to become a resilient, strong global network requires strong leadership, a clear vision, remarkable global coordination, and resolve for the long transformation journey. Through constant transformation that aligns closely with business strategy and market development, the production network becomes more agile and can stay competitive in the market. For the alignment of the manufacturing footprint, supply chain, and logistics with the MLS True North, it is necessary to manage the high degree of complexity and as well to mitigate risks in the global environment. The first step to dealing with a high degree of complexity is to classify products and parts based on the company’s core competencies. This enables the initial and essential make-or-buy decisions in a meaningful way. A further fundamental element for mastering operational complexity is advanced planning. The Bühler Event Chain and the 15-month sales and operations planning are unique Bühler processes that enable the company to simplify its operations and manage deviations from plans more efficiently. Moreover, with its digital transformation initiatives to harmonize data globally through vertical integration and horizontal data integration, the company is on the way to building a solid digital foundation that will enable it to take further steps toward applying industry 4.0 technologies. For risk mitigation, Bühler applies the concepts of breathing and balancing in the network’s daily operations. On top of that, product transfers provide additional flexibility to ensure that the network can be agile yet regionally sufficient. Further risk mitigation measures embedded into the footprint setup with anticipation of geopolitical changes prepare the company to navigate the potential bipolar world. Even though having risk mitigation measures in place equates to having higher operational costs, it is necessary to ensure the long-term sustainability of the network and Bühler’s core competencies from the group level. A trust-based leadership style that fosters open communication in the teams and a supportive environment is necessary to guide this continuous network transformation. This openness culture influences the behavior of the people in the organization and supports them, especially in difficult situations, while providing them with a reasonable degree of autonomy. With an open culture and a clear True North vision, which is understood by everyone in the operations organization, the people can concentrate their energy and momentum toward implementation. They feel supported in what they are doing, and they know what is important even in the boss’s absence. A consistent leadership style combined with empowered individual team members collaborating in a global network are the basis of trust, respect, engagement, and ultimately succeeding together. All sites are guided on this transformation journey through both the strategic initiatives derived from the strategic thrusts and operative KPIs from the SQCDP standards. Regular monthly jour-fixe meetings, where all sites report and discuss their progress with the COO office, ensure that progress trends are closely tracked and that issues are identified early on.

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It is also important that all operational decisions are carefully considered and weighted to ensure that all activities and their future implications are aligned and contribute to the True North strategic direction. Since operational management decisions are aligned with the strategy, the outcomes of operational actions can provide valuable feedback to validate strategic assumptions. This feedback loop between strategy and operational management helps minimize market and operational surprises and prevent the formation of strategic gaps. Like many other companies, Bühler’s operational function still has many challenges to resolve on its transformation journey. Some challenges can also be opportunities, such as the challenge to simplify logistics operation for inbound and outbound logistics. The aim is to create efficient and sustainable logistics operations that provide customers with delivery reliability and provide delivery transparency in real-time. This challenge could also significantly contribute to Bühler’s sustainability target of carbon-neutrality at all manufacturing sites by 2030. Sustainability is another important topic that is gaining more momentum in Bühler’s operations functions. The transformation journey continues as the world moves toward digitalization; changes in manufacturing, logistics, and supply chain will only accelerate further. Companies that can adapt better and faster will be the market winners, while those that cannot adapt will fade away. In this light, Bühler’s operational function is actively looking into digital transformation and innovation to stay competitive in the market.

Reference Bühler AG. (2020). Annual Report 2019: The future is now! [Company Report]. Retrieved from https://www.Bühlergroup.com/content/Bühlergroup/global/en/media/annual-report-2019.html

Global Manufacturing at CLAAS: From a Local-for-Local Structure Toward Network Excellence

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Hendrik Schellmann and Christian Köbke

This chapter highlights the adaption of the St.Gallen Management Model for Global Manufacturing Networks for the CLAAS manufacturing network. The chapter starts with a short introduction about the characteristics and challenges of the company. Next, the adaption of the model along the three layers of strategy, configuration, and coordination is described. Adaption in this context means that CLAAS deliberately aligned the underlying concepts and frameworks to the specific needs of the company. This demonstrates the broad applicability of the approach in practice. Further, additional concepts and frameworks are described that enrich the model and helped the company to find their individual path toward a more effective and efficient global manufacturing network. Finally, the chapter is completed with a summary and an outlook on digitalization ambitions which support the network strategy.

19.1

Characteristics and Challenges of the CLAAS Group

CLAAS is one of the world’s leading manufacturers of agricultural machinery and among the biggest companies in the German mechanical engineering sector. The company, which has its corporate headquarters in Harsewinkel, Westphalia, is the European market leader in combine harvesters. CLAAS is also the global market leader for self-propelled forage harvesters, its second main product group. The CLAAS product portfolio includes tractors, round and square balers, forage harvesting machinery, tele-handlers, and efficient agricultural systems (EASY products) featuring integrated CLAAS electronics expertise. CLAAS manufactures at four German and eight international sites. Overall, the CLAAS Group has experienced an extraordinary growth phase in the last two decades. Turnover has increased from approximately €500 million at the H. Schellmann (*) · C. Köbke CLAAS KGaA, Harsewinkel, Germany e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_19

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Coordination / Network level

Global network US

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Factory for the world

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1995: DE Combines for the world

1999 - 2005: US, RU Local assembly, SKD

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Time

Fig. 19.1 Development of the BU Grain’s global manufacturing network

start of the 1990s to more than €4 billion in 2020. CLAAS employs around 11,400 employees all over the world. The company earns more than 75% of its revenue from international markets (CLAAS Group, 2020). The family-owned company was founded in 1913 and is now in the hands of the third family generation. The ten fully owned production plants and two joint venture sites are organized in three operative business units (BU), which are responsible for engineering and production. These comprise the BU Tractor, with two plants in France and Germany; the BU Forage, with two plants in France and Germany; and the BU Grain, operating six international plants in six countries. The production companies hand over the products to a fourth BU Sales and Service, which is operating a global sales distribution network including after-sales service, academy, and spare part centers. How CLAAS has developed from a single factory at the headquarters to an international manufacturing network is characteristic of a growing company with a significant export share. It follows strongly the typical globalization trend in the industry, but with the competencies to produce semi-knocked down (SKD) combines, it was possible to create very early a strong link between offshore plants and the headquarter factory. In the following, we describe this development for the BU Grain and its six factories today (see Fig. 19.1). “Factory for the World” Until 1995, CLAAS produced all products in three plants dedicated to certain product groups. Usines CLAAS France, located at Metz Woippy, France, manufactured balers. Hay and forage equipment was produced at Bad Saulgau, in

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southern Germany. Combines and self-propelled forage harvesters were produced only at Harsewinkel, Germany. From all three locations, CLAAS shipped farming equipment to all relevant markets in the world. “Local for Local” At the end of the last century, CLAAS started to expand its global footprint to enter new markets, which partly had local content requirements, as well as to realize cost savings. In 1997, CLAAS acquired a plant in Hungary, which was used as an extended workbench for Harsewinkel in the beginning. In the following years, CLAAS expanded to North America and Russia to produce combines closer to these markets with local content. The plants in Omaha, Nebraska, and Krasnodar were designed as SKD assembly plants. The big main components of the locally assembled combines were produced at Harsewinkel. This approach lowered the capital expenditures because the painting process, as well as all fabrication, remained at Harsewinkel. From 2008, CLAAS built a new plant in Chandigarh, India, with a fabrication and paint shop, and moved from a joint venture plant near Faridabad into the new, fully owned facilities. Since the acquisition of a factory in Gaomi, China, in 2014 and the plant extension in Krasnodar, Russia, in 2015, CLAAS also operates plants with the full manufacturing process chain including fabrication (welding and sheet metal technology) and painting facilities in Russia and China. Due to its vertical integration and thanks to a special investment contract that was signed in June 2016, the Russian site has now got the official status of a “Russian manufacturer” and receives the same state financial support for its combine harvesters as local producers. Four of the six production sites of the BU Grain are mainly focusing their processes on the production of products to be distributed on the local market. Exceptions are the plants in Harsewinkel and Hungary. These plants are producing SKD components for the USA and Russia as well and are shipping their products to all other regions of the world. “Global Manufacturing Network” In late 2018, CLAAS decided to strengthen the manufacturing network activities. The BU Grain introduced a new matrix organization with more focus on products and functions. This was, alongside the introduction of the new modular platform architecture for combine harvesters, the basis to connect production plants and make better use of cost advantages, capacities, and plant capabilities. The next chapters will give some insights as to how CLAAS continued the journey from a market-driven scope of single production plants toward a global manufacturing network. We will show some examples and principles about our motivation, concept, and methodology to design and operate the sites within the CLAAS manufacturing network.

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19.2

Applying the St.Gallen Management Model for Global Manufacturing Networks

Even before the recent organizational changes, CLAAS had started to conduct studies and projects internally to develop the manufacturing network. One example is the global operation footprint project with a strong focus on the calculation of factor costs and logistic costs optima. The St.Gallen Management Model for Global Manufacturing Networks (Friedli et al., 2014) was identified as suitable for CLAAS, adapted to the specific needs, and finally helped: • To identify missing initiatives in the existing project scope • To align the individual initiatives that have been already started • To broaden the perspective from a pure cost orientation The three levers of the framework—strategy, configuration, and coordination— define a clear focus on what a company needs to organize within its manufacturing network. Hence, the BU Grain with its six globally located factories applied the framework within the strategic initiative named manufacturing network. This initiative consists of three projects that directly refer to the three framework levers (see Fig. 19.2). In the project Manufacturing Strategy, the elements of network priorities and network capabilities were located. Besides, a strong focus was set on the site roles and their contribution to the network. Of course, this project did not start from scratch. It integrates all of CLAAS’s existing footprint strategies.

Strategic Initiative Manufacturing Network (Steering Committee on Executive Board Level)

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(Project Lead on Network Level)

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• Allocation of products, components & parts to sites

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• Allocation of capacities

• Global fixture management

• Development of capabilities • Site strategy and roles

• Global ramp-up management

• Spare parts manufacturing

• Multi languages on drawings • Definition of standards • Design guideline

Fig. 19.2 Structure of the strategic initiative “manufacturing network”

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The project Network Configuration allocates the products, components, and parts to the sites. Furthermore, it deals with the monitoring of the site utilization and drives necessary capability developments. Internally, we also call this project Manufacturing Service as we are striving to establish the best and most efficient manufacturing system for our products, which are no longer in the responsibility of the sites but in the responsibility of so-called product units. Hence, the sites just do the service of manufacturing the products for the product units, which is a new paradigm for the company. The project Network Coordination is responsible for creating the processes, organizing the knowledge transfer, and supporting all operational network activities. Of course, the decision-making process including how to calculate and compare is part of this project, as well as the global design guideline for manufacturing within the network. The three projects have strong interdependencies. They interact on all organizational levels. The site-level executes and defines requests; the network level makes decisions and monitors; and the corporate level supports with, e.g., calculation guidelines, IT solutions, or functional strategy input. In the following chapters, we will highlight some examples, use cases, and important elements of these three projects and show their contribution to the network design and operation.

19.3

Network Strategy: Network Priorities and C3 Approach as a Basis for the Network Design

The network strategy is the most important lever for the design of CLAAS’s global manufacturing network. It defines which objectives the company wants to achieve with its network, and hence it needs to fit the company’s overall strategy. Following mainly a local-for-local approach at CLAAS for many years, the different sites had quite clear ideas of their individual objectives. However, these objectives were not harmonized between the sites and sometimes turned out to be even contradictory. For example, we realized that, on the one hand, we tended to increase capacities at some locations due to localization reasons, but on the other hand, we were far away from a good capacity utilization looking at the sum of our sites. As a foundation for our network strategy, we formulated the C3 approach, which simply challenges us to find the right balance of three major dimensions describing the characteristics of our plants in the network. These dimensions are capabilities, capacities, and costs (see Fig. 19.3). However, different from the approaches of other industries, which simply move or copy plants to locations where manufacturing is cheap, we found the capability dimension to be the important one, taking into account what we already can perform in existing plants or what we can achieve with reasonable effort. In a first step, we strived for good transparency of the three dimensions to reveal short-term potentials for optimizing our activities in the network and find operations that could be suitable to be transferred to other sites in the network.

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Fig. 19.3 C3 approach to balance capabilities, capacities, and costs

However, a sustainable design of the manufacturing network cannot be based just on short-term considerations but must also follow long-term objectives. Hence, one of the main questions is how to develop the capabilities and capacities of the sites in the right way. Looking at it from a cost perspective, it seems to be reasonable to increase the capabilities of low-cost sites to make use of the cost advantage on a much broader scale of products or components manufactured there. However, increasing the site capabilities is limited trying to keep a constant cost level at the same time. Furthermore, it was clear for us that besides site capabilities there are also

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Fig. 19.4 Adapted manufacturing priorities (only exemplary values)

capabilities arising from the network as a whole and cannot directly be derived from the single site’s competencies. Hence, there are many more relevant factors for the design of a manufacturing network than just costs. In the St.Gallen Management Model for Global Manufacturing Networks (Friedli et al., 2014), we found convincing approaches to look at this topic. Among others, we have adapted the concept of manufacturing priorities for our purposes based on the St.Gallen approach. It defines the customer’s expectations toward a company’s products. Hence, it is not only addressing the manufacturing function but also serves other functions in a company as a basis for discussion. The qualitative method to define the manufacturing priorities is easy to understand and to use within workshops without major preparation of the participants. Furthermore, it provides a strong basis of the information, which can be easily adapted to the specific requirements of a company. Figure 19.4 depicts our adapted version of the manufacturing priorities, which defines the criteria in a slightly different way as proposed in the framework. The values in Fig. 19.4 do not show the real situation at CLAAS as this is confidential information. We experienced that the discussion about manufacturing priorities quickly leads to a common understanding of customer requirements (for different product groups) and on the other hand directly reveals week points, where people from different functions would give different priorities to certain requirements. Solving these disaccords from the beginning is from our point of view a key to a consistent network strategy.

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A clear set of manufacturing priorities, as well as the C3 approach, serves alongside others as strong inputs for the network configuration, as they define the requirements and conditions. Different configuration scenarios can now be challenged by the grade of fulfilling these boundary conditions and requirements. Additionally, we can also discuss objectives of other functions within the company’s network such as sales, quality, or R&D based on the manufacturing priorities. This way, they also serve as a bridge between the manufacturing network strategy and the other functional strategies.

19.4

Network Configuration: Define Site Competencies, Allocate Products, and Align Capabilities

Fig. 19.5 Classification of sites by product and process complexity

No. of product families

Within the network configuration, the network strategy is put into reality by assigning components and products as well as activities to the sites in the company’s network. This assignment eventually also defines the necessary capabilities and capacities of the sites. This sounds consequent and easy from a greenfield perspective, but as there are major differences between the present and the desired future state of a site, the network configuration mainly deals with the continuous development of the sites. We also adapted the site portfolio approach (see Chap. 2). It is a very powerful tool to depict the present configuration as well as the development path of the manufacturing network as it shows various dimensions of the network in one portfolio and reveals weaknesses at first glance. Furthermore, it provides an excellent basis for a structured discussion about highly relevant parameters of the network design. As there are many possibilities for how to operationalize the portfolio dimensions, we adapted the site classification criteria for the axis leading from the inside to the outside of the hexagon. In our case, we emphasize the competencies and capabilities, which also show the complexity a site can handle. We identified two factors that mainly drive the complexity (see Fig. 19.5):

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1. The number of different product families at a site 2. The number of different manufacturing processes that the workers need to perform for these products and that the site needs to manage overall For the second factor, we simply count the number of defined standard process chains that we provide at our plants, for example, the sheet metal fabrication (consisting of laser cutting, bending, welding, and painting of sheet metal parts). As further examples of process chains, we divide between the completely built unit (CBU) assembly, the SKD outbound process (where we assemble only components or pre-assemblies that are shipped to another site), and the SKD inbound process (which takes place at that site and leads to the final assembled product). Each of these process chains requires different capabilities as well as different ways of working on the shop floor but also in other departments like shipping, goods receipt, customs, or manufacturing engineering. In this logic defined by ourselves, a basic site, which we would rather denominate a specialist site, is simple from a process and product point of view. The number of different products is very limited. Thus, the site has to deal with a limited amount of part numbers leading, for example, to less variety in fabrication, fewer changeovers, as well as less necessity for worker guidance and detailed work instructions. The next level is a site, which has a process scope, meaning that there are different product families produced, but the variety of process chains is very limited. The site has to deal with more part numbers and changeovers, but the type of orders stays the same, and there are not many additional capabilities needed in indirect departments. A site with a product scope again has limited complexity due to part numbers and changeovers but needs to deal with many kinds of orders. The higher complexity is mainly in indirect functions due to the necessity of many different capabilities to perform all different processes and the effort for the coordination. An all-rounder site is one with the highest capabilities, as it needs to deal with the full product and process complexity. Based on this analysis, we can use the site portfolio to get an overview of the configuration of our whole network and check whether we have addressed the main requirements from the strategy in the network configuration, as was the original idea of Friedli et al. (2014). Furthermore, we are also able to depict the development of our sites in the network to double-check whether the future development is reasonable and realistic. Figure 19.6 illustrates the development of a German and a Hungarian site in the network. It provides the basis for further discussion of the capability development in the plants. Site Capability Evaluation and Development The site capabilities mainly describe skills and competencies that exist internally at a site and empower the site to perform defined manufacturing operations in the network. However, there is a strong influence of external factors promoting or restricting the development of certain capabilities. To assess and develop the capabilities of our plants, we developed a methodology that directly refers to skills

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Fig. 19.6 Site portfolio showing the development toward the future state

and competencies but also to limiting or promoting factors. We defined the following seven capability clusters to be evaluated in our manufacturing sites: 1. 2. 3. 4. 5. 6. 7.

Workforce Technology Infrastructure Sourcing Processes Complexity handling and flexibility External factors

Each of these clusters is divided into subcategories to concretize the evaluated criteria; for example, the cluster workforce has the three subcategories education, qualification, and availability. For each of the clusters and subcategories, we collected capabilities that we typically find in our sites. Sticking to the example of workforce and the subcategory of education, we evaluated the public education of workers as well as internal training of trainees and apprentices or even the participation of the site in dual study programs. In a workshop, a dedicated team with members from the site as well as from central network departments rates each capability. The rating covers a scale from one representing a very low capability level to nine representing the highest level (see Fig. 19.7). For transparency and comprehensibility, the team also needs to document the reasons for the rating, noting down strengths and weaknesses as well as further rating criteria for each capability. To achieve a minimum level of comparability between the different sites, rating criteria are given for most capabilities. Further,

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Laser Cutting Technologies, cutting gas, level of automation, quantity machines, age, state of the art, breakdowns

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Fig. 19.7 Site capability assessment (extract)

experts from central network departments bring in an external view knowing also the background of other sites in the manufacturing network, which helps to validate the rating from the network perspective. We do not intend to develop every site toward the highest level of all capabilities. Therefore, in a second step, a target value for each capability is defined considering the desired future role of the plant in the network configuration. Comparing the target value and the current rating reveals gaps that the site needs to close to fulfill all requirements within the network. However, to close these gaps, dedicated projects need to be defined to achieve the targets. Based on this, the needed resources and consequences, e.g., for the cost situation, can be derived, and a final decision is taken again from a network point of view. To give an example of an improvement project derived from the capability assessment, we identified poor welding capability at our plant in China for the

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components we intended to weld there. Conducting a root cause analysis, we found that the know-how of the welders was not on a sufficient level. We decided to set up a training with an examination of the welders, which finally lead to documentation of the welders’ qualification in a qualification matrix. Additionally, we provided a monetary incentive for higher qualification to motivate the welders to participate attentively in training. Having the required training available at other sites, the effort to perform this measure was rather low and led to a significant improvement of this capability, enabling the site to produce the required quality.

19.5

Network Coordination: Organizing the Operation of the Network

The network coordination as the third lever of the St.Gallen Management Model for Global Manufacturing Networks deals with the organization of daily work within the network (see Chap. 2). It must be clear who takes decisions for what and which methods or working processes need to be applied for the preparation. In the following, we will describe some of the components of our network coordination approach. The basis for the network coordination at CLAAS forms the organizational chart. When the new structure of the BU Grain was created, a matrix organization was set up referring to three dimensions: product units (PUs) as the leading structure, service units (SUs) representing certain functions, and sites. For the manufacturing network, the service unit manufacturing represents the leading function directing the manufacturing operations at the sites (see Fig. 19.8). Hence all Directors of Manufacturing at the sites directly report to the Senior Vice President (SVP) Manufacturing. Additionally, the SVP Manufacturing is leading two service functions (SFs), manufacturing engineering and supply chain management, which serve as central network functions for all relevant sites. The organization of the manufacturing network initiative shown in Fig. 19.2 is directly linked to this matrix organization, as the SVP Manufacturing as well as the

SVP SU Manufactuing

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Director Manufacturing Site A

Director Manufacturing Site B

Director Manufacturing Site C

Director Manufacturing Site D

Head of SF Manufacturing Engineering

Head of SF Supply Chain Management

Fig. 19.8 Matrix organization of the service unit manufacturing

Director Manufacturing Site E

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heads of the service functions are each responsible for one of the three projects within the initiative. Hence, the central network departments coordinate all major decisions concerning the manufacturing network. In the blueprint of the organization, it is clearly defined which decision the sites may take on their own and which topics are subject to the network functions or at least need their approval. At CLAAS, we call this the delegation of authority catalog. “Where-to-Make Decision-Making” as an Essential Process One of the main decisions within the network is what we call the where-to-make decision. It deals operationally with the question at which site a component should be produced. Hence, it is strongly connected with make-or-buy decisions, dealing with the question of whether to make a component inside the company’s manufacturing network or sourcing it from an outside supplier. Consequently, the decisions are strongly influenced by the network configuration, but as every decision needs to be reasonable from an economical point of view, it vice versa also has an influence on the network configuration. Coming back to the earlier described C3 approach, the reassignment of products and components to the sites was the first implemented approach to improve the output of the manufacturing network. Hence, the where-to-make decision-making was the first operational process we needed to organize systematically. Leaving the decisions to the local site management did not lead to the desired changes as evaluation methods and assumptions were often quite different. We achieved a breakthrough by assigning this task to the central service function supply chain management, which consequently developed an evaluation method to figure out parts that are suitable for being re-allocated to a best-cost country (BCC) site with free capacity. The method consists of five steps: 1. In a first step, we figure out parts and components, which are generally suitable for a re-allocation. By doing this, we are already considering the current or future capabilities of the sites as well as significant volumes. 2. With a quick-check method, we sort out all components, where a re-allocation does obviously not provide an economic benefit. 3. For the remaining components, we request quotations from the sites, which are prepared based on a standard cost calculation method. 4. Based on the quotations, we compare the different sourcing scenarios (often also taking into account buy options) through a sophisticated calculation scheme resulting in total landed costs. 5. Finally, we take the decision also taking into account strategic criteria that are not part of the calculation model. For the second step, we developed a simple method based on the relation of savings in manufacturing costs and additional freight costs due to long-distance logistics (see Fig. 19.9). It results in the comparison of one easy to understand value. Assuming that the prices for raw materials are more or less the same all over the world, cost advantages mainly come from lower labor expenses in the BCC. The

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Cost rate delta

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Fig. 19.9 Quick check approach

difference in the hourly cost rates we put in relation to the transport costs of a 40-ft. container from the BCC to the European site. The result is the minimum added value in hours that we must get into one container to achieve a cost breakeven. By counting the number of parts that fit into the container and multiplying it with the standard time for production, we directly can compare the added value with the minimum added value and see whether the result of the quick check is positive or negative. To estimate the number of parts fitting into the container, we use the 3D models of our parts and components to check how to pack them with a high density. For this, we even have software available by now, which can do this check and return the most appropriate packing solution within seconds. Guideline to Ensure Design for Manufacturing in the Network The guideline for design for manufacturing (DfM) is another element, which we added to CLAAS’s coordination mechanisms in the manufacturing network. One of our plants established this as a local guideline several years ago to foster the DfM approach. The guideline comprehends the description of the most important manufacturing technologies with their available features at the CLAAS site. Taking the bending technology as an example, it describes for all relevant sheet metal thicknesses the resulting bending radii or special form features that manufacturing can achieve based on the available bending machines and tools. So far, the guideline served mainly as information from manufacturing for the development engineers, to make sure they stick to available tools at the site to avoid investments in new manufacturing equipment as well as changeovers in production just because the designers use unfavorable parameters for their design (see Chap. 8 for further theoretical considerations on DfM in production networks).

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With the modular platform for combine harvesters being established, R&D, as well as manufacturing managers, more and more claimed to declare this DfM guideline as a network-wide standard for manufacturing. This way it should ensure that each modular component can be produced at any location in the network as it is most likely to be used in different products for different markets produced at different sites. However, this way we also open up the possibility to create economies of scale by bundling the manufacturing of the same components at one site and sending those to all relevant sites producing the final products. Hence, we enhanced the DfM guideline with technology features from the other sites in the network. Of course, it is not possible to standardize all manufacturing features from one day to the other, and it is not economical to provide always the same manufacturing capabilities and features at all sites. But, for some core technologies that we apply in nearly every plant, the DfM guideline now supports us in taking the right investment decision and opens up the ability to reassign parts and components to a site with much less effort than in the past. For the future, we expect to further increase the benefits we can derive from the harmonized DfM guideline.

19.6

Summary and Outlook

To sum up, we at CLAAS consider the St.Gallen Management Model for Global Manufacturing Networks a suitable approach for designing our network. The three levers strategy, configuration, and coordination very much help us to focus on the relevant design elements and to find out interdependencies of these levers. In this article, we gave some spotlights on how we adapted methods from the framework to our needs and showed some additional tools that we developed ourselves and added to the toolset. Besides the mentioned topics, there are many more methods and tools available—either from the framework or from CLAAS experience—which we additionally use already or which we are discussing for use in the future to further enhance our network management approach. However, it is not our intention to fully describe all our activities here in detail. Nevertheless, it is worth giving our readers at least a glimpse of our digitalization strategy that we are pursuing in the area of manufacturing as it is strongly supporting our network management approach. One major pillar of this strategy is an initiative that we call 3D-supported industrialization. Within this initiative, we develop and establish tools and processes to make use of 3D product data in the manufacturing engineering processes. In this way we can, for example, prepare manufacturing bills of material using graphical representations of the product. Based on this, we can create dedicated work instructions or perform assembly simulations at a very early stage of the product development process. More importantly in the context of this article, the big advantage of these new working processes for the manufacturing network is that they ease the collaboration over long distances and even language barriers. Three-dimensional pictures can be available at many locations at the same time in the same perspective. They clearly show an assembly context and do not leave much room for interpretation especially when you add 3D data of

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manufacturing facilities and equipment. This finally will enable us to do speed up the knowledge transfer between the sites and act flexibly in the network. This clearly shows how our digitalization activities are being driven hand in hand with our network activities and that these are both continuous engagements. With this strategy, we are positioning ourselves competitively and effectively for the challenges of the future.

References CLAAS Group. (2020). Impact, 2020 Annual Report [Company Report]. Friedli, T., Mundt, A., & Thomas, S. (2014). Strategic management of global manufacturing networks. Berlin: Springer. https://doi.org/10.1007/978-3-642-34185-4.

Applying a Regional Manufacturing Network Analysis for PALFINGER

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Martin Friedl

This chapter highlights the application of the St.Gallen Management Model for Global Manufacturing Networks for the PALFINGER manufacturing network. After an introduction of the company and its products, the chapter outlines the procedure and intentions for a reorganization of a regional part of the manufacturing network. The procedure is in line with the St.Gallen Management Model for Global Manufacturing Networks and demonstrates its applicability in practice. Afterward, two of the 25 derived measures and their specific implementation are described. Finally, the chapter is completed with an outlook and the implications for the PALFINGER manufacturing network of the future.

20.1

About PALFINGER Today

PALFINGER is an international technology and mechanical engineering company and the world’s leading provider of innovative crane and lifting solutions. With over 11,000 employees, 35 manufacturing locations, and a worldwide sales and service network with over 5000 service points, PALFINGER guarantees customers immediate and optimal proximity (Palfinger AG, 2020). With its future-proof product and complete solutions, PALFINGER, as a technology leader, puts benefits reaped by the customer at the center of its innovation efforts. Consistent digitalization and the use of the latest technologies increase operator safety and comfort, operational capability, and service life, thus ultimately contributing to the corporate success of its customers and partners. As a global company with strong regional roots, PALFINGER knows that sustainable thinking and behavior contributes significantly to economic success. M. Friedl (*) Palfinger AG, Bergheim, Austria e-mail: m.friedl@palfinger.com # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_20

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For this reason, the company assumes social, environmental, and economic responsibility along the entire supply chain. PALFINGER AG has been listed on the Vienna Stock Exchange since 1999 and generated sales of €1.75 billion in 2019 (Palfinger AG, 2020). The core product is the loader crane. The company is the world market leader in this segment with more than 100 models. In timber and recycling cranes on- and off-road as well as Hooklifts, PALFINGER is also the world’s biggest manufacturer. Over the years, the product portfolio has been steadily expanded with products such as the truck-mounted forklifts, tail lifts, and the access platforms. With our railway systems and bridge inspection units, we are European technology and market leaders in this high-tech sector. PALFINGER Marine is the global leading manufacturer of highly reliable, innovative, and customized deck equipment and handling solutions for the maritime industries. The product portfolio includes cranes, lifesaving equipment, winches, and handling equipment. Our worldwide service network includes the supply of spare parts with fast and professional onsite support. PALFINGER Marine operates in all major maritime segments, including offshore, marine, cruise, navy and coast guard, and wind.

20.1.1 Company Development During the Past 10 Years PALFINGER realized early that sustainable growth can only be granted through diversification, on the one hand by entering new markets and on the other by expanding the market offering and serving different customer segments. In order to quickly gain these benefits, extensive M&A activities were part of the company’s strategy in the years 2008–2018. With over 20 acquisitions and joint ventures, PALFINGER expanded in markets like CIS but also entered ones with new products, like in the maritime business. The company was organized in independent business units in all world regions. The business units were held accountable for the complete business model from defining the market offering, engineering, production throughout sales, service, and after sales. The headquarters acted like a financial holding but also incorporated corporate functions with guideline authority to the regions and business units. The strategy worked out well. With fast sales growth and earnings above industry average, PALFINGER developed into a company with €1.5 billion in turnover. With the given structure, it became apparent that for further growth, a change in how the company was led and steered was needed. The biggest organizational change in PALFINGER history so far was heralded at the end of 2018 with the Global PALFINGER Organization (GPO). The change from independent business units to a global functional organization was one of the key elements besides the refinement of the leadership principles in the whole company. Starting with January 1, 2019, the company was led by global functions (e.g., Sales and Service, Operations, Procurement, etc.) across all regions and countries. The complete product portfolio was split into global product lines that were accountable for the market offering and engineering in all world markets. The company’s leadership principles—drive, focus, inspire, empower, develop, and deliver—ensure that

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Applying a Regional Manufacturing Network Analysis for PALFINGER

LOADER CRANES

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Fig. 20.1 PALFINGER product overview

PALFINGER delights customers and defines how it cooperates internally and with external partners.

20.1.2 Product Overview As introduced above, PALFINGER is known worldwide for producing the most innovative, reliable, and cost-effective lifting solutions for use on commercial vehicles and in the maritime field. With our technological expertise and experienced staff, we set quality benchmarks in the industries in which we operate. PALFINGER is regarded as the leader in technology and innovation in its sectors. PALFINGER is number one worldwide for loader cranes, marine cranes, wind cranes, and container handling systems. Moreover, the company is a leading specialist in timber and recycling cranes, tail lifts, truck-mounted forklifts, and high-tech railway systems (see Fig. 20.1). Overall, PALFINGER aspires to be the service champion in the industry.

20.1.3 Market Segment Overview Today’s working world is more diverse than ever. Every market is unique, and every application is challenging. User requirements are highly individual and there’s no “one-size-fits-all.” In addition to making customers work more efficient in terms of costs and time, the portfolio also offers uncompromising product quality. No matter

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what, PALFINGER solutions offer maximum reliability and safety for operators and the environment. Thanks to compact dimensions and weight optimization, they can easily be integrated into the customer’s work routines. Advanced assistance systems and a dense service network offer the highest user comfort and make PALFINGER products the number one industry choice for many of the following segments: • Construction such as building material handling, roofing, scaffolding, and glass works • Forestry and agriculture such as fertilizer handling, landscaping, or timber processing and transport • Industry like in hydraulic engineering, mining, or machinery building • Infrastructure like for bridge inspection, industrial cleaning, or power plant maintenance • Railway such as bridge inspection and repair, railroad construction, maintenance, and intervention • State institutions as municipalities, military, or emergency services • Transport and logistics covering everything from heavy load cargo to containers and fast-moving consumer goods • Waste management and recycling like professional garbage removal, container discharge, and bulk waste handling

20.2

Reshaping the PALFINGER Manufacturing Network in Russia Starting in 2016

20.2.1 Initial Situation Until 2014, PALFINGER served the Commonwealth of Independent States (CIS) market, especially Russia, only by importing from central Europe. Further developing the market presence in the region was only possible with a dedicated product portfolio and local production footprint. With the acquisition of market leaders in the crane business as well as by signing two strategic joint ventures with a leading truck manufacturer, PALFINGER gained access to the market and a regional production footprint in Russia. During the post-merger integration process, next to financial reporting integration, the optimization of single sites, consolidating product designs, and using the sourcing power of PALFINGER were set as top priorities. The CIS production network grew substantially but was not planned from a holistic perspective. Leveling different standards and maturity levels of the sites as well as balancing the European engineering philosophy with local customer requirements was a demanding task. It quickly became apparent that a holistic view on the role of the sites in the PALFINGER manufacturing network was needed to lift potentials. As described in Sect. 20.1.1, at this point, independent business units acted in the region. Consequently, the scope of analyzing and reshaping the manufacturing network was reduced to the region CIS. The focus on one region has allowed gaining experience with the applied methodology and the subsequent roll-out of the project

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to other regions. The analysis was backed by the St.Gallen Management Model for Global Manufacturing Networks (see Friedli et al., 2014).

20.2.2 Key Questions In order to structure the project, the following key questions were defined: • What is the current manufacturing network capable of and what are its weaknesses and strengths? • What would an optimized network of CIS manufacturing sites look like in order to achieve the best possible delivery performance (cost, time, quality) by our assembly sites within the CIS region but also to other regions and third parties? • What know-how needs to be transferred? What does it take to establish the knowhow needed? What needs to be organized centrally on a regional or group level? What is under local site’s responsibility? • What opportunities do we have to operate the network, even if there are no or limited ERP systems in place that are not integrated? • What does the network configuration and coordination look like (processes, organizational structure, etc.)? • Which actions need to be taken to achieve the target network?

20.2.3 Targets Further, specific targets were defined to ensure meaningful outcomes from the manufacturing network analysis: • Analyze the current CIS manufacturing network and overall business setup including environment (PESTLE1) and stakeholder (customer, supplier, competitor) analysis • Support the development of the CIS manufacturing network strategy. • Redefine the CIS manufacturing network with regard to optimal fit between strategy, configuration, and coordination • Derive improvement measures for CIS sites and network and the (supply) interaction with the European network • Improve the effectiveness and efficiency of the CIS network through better allocation of responsibilities, know-how, and resources among network players • Define measures of how to manage and steer the CIS network including organizational structure and processes 1 Strategy framework for reflecting political, economic, sociological, technological, legal, and environmental influences.

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• Successful proof of the St.Gallen Management Model for Global Manufacturing Networks for an application across the global manufacturing network

20.2.4 Analysis Approach The analysis approach was divided into several steps as follows: Environment Analysis • Goal: Evaluate a holistic picture of the network environment • Approach – Stakeholder analysis comprising Strategic priorities of different business units (order winners/order qualifiers assessment) Supplier structure/analysis of real net output ratio Competitor analysis (positioning/differentiation) – Trends and environment analysis (using PESTLE1 framework)

Plant State, Improvement, and Maturity Report • Goal: Capture the current manufacturing site setup and performance • Approach – Plant visits – Interviews with local management – Analysis of plant tasks and development roadmap – Deriving plant maturity and improvement potential

Network Analysis: Configuration • Goal: Depict and understand the current network capabilities in terms of configuration • Approach – Organizational and cost structure of plants – Technology matrix – Plant capacity – Product portfolio and competences/capability matrix – Internal supply chain (material flow)

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Network Analysis: Coordination • Goal: Depict and understand the current network capabilities in terms of coordination • Approach – Site autonomy (standardization vs. centralization) – Site role discussion (lead factory concept) – Knowledge and information exchange – Resource sharing – Incentive system

Strategy Review/Formulation • Goal – Refine operations and network strategy – Identify gaps between current setup and future strategy – Formulate network mission • Approach – Review of global and local operations strategy – Define network priorities – Develop and refine strategy on the basis of analysis results – Define financial targets (revenue/profit)

Network Alignment • Goal: Define adaption plan for the CIS manufacturing network • Approach – Create list of implementation measures – Derive specific actions for the further development of CIS manufacturing network – Develop future footprint, plant characteristics, and organizational setup including coordination – Define implementation roadmap and prioritization of measures

20.2.5 Plant and Network Analysis Market At the time of the analysis, PALFINGER only served the B2B market in the CIS region, ranging from small businesses ordering single units to state institutions maintaining their fleet. On the one hand, customers were very technology- and

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performance-driven but less price sensitive; on the other hand, there were also customers asking for standard solutions in the low-end price segment. In general, individual customers didn’t ask for individualization, special customized, or even engineered-to-order solutions. Those were mostly requested by governmental organizations. This habit was reflected in the product portfolio and available options as well as in the make-or-buy strategy of sellable products. Thus, the high-end portfolio was ordered from plants in Europe. The production network in Russia focused on the low- and middle-end portfolio and special solutions with local content requirements. In particular, meeting the quality requirements as well as delivery on-time and in-full were central capabilities to ensure competitiveness. Serial business accounted for the highest fraction in revenue and therefore determined the strategic direction setting. The extensive effort for engineer-to-order products was valued and reimbursed by customers. Existing market segments were significantly addressed by the applied network configuration. Entering new markets would have required the enlargement of the footprint through an additional plant. Suppliers Access to the sourcing market in the region was an elementary contributor in realizing the tight cost targets. Besides that, short lead times, low risk of supply interruption, and avoidance of foreign exchange risk offered competitive advantage. However, due to quality constraints, a local supply base could not be established for all components at this point. For example, hydraulic components, high-quality steels, and precision valves were purchased in central Europe and then distributed to the plants in Russia. Therefore, the vertical integration in the plants was kept to a very high level. Of course, scale effects from purchasing these components centrally needed to compensate for higher logistics costs in the distribution. Even though supply quality had been increasing year over year and costs of nonquality in deliveries have been significantly reduced, supplier quality and delivery reliability remained an issue. The advantages of low inventories and stocks usually given by short supply chains couldn’t be fully realized. The following implications were derived from the analysis: • The access to key sourcing groups was considered a major benefit the network could contribute to. • Developing and managing of local suppliers was vital to reduce vertical integration while maintaining quality levels. • Further appropriate local suppliers could balance between regional and global supply for the region and allow the exploitation of exchange rate fluctuations. Competitive Priorities In all product lines, the product offering was targeting different customer segments and customer groups. Selling through dealers was the usual channel to the end customers. In many cases, those dealers also functioned as service and truck installation partners. Order qualifier and order winner priorities differed from product line to product line, but price was certainly among the highest priorities for

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winning orders. High market shares, well-perceived brands, and performance advantages provided very good competitiveness. Competition with local brands was relatively low, whereas competitors entering the market, especially from Asia, were an increasing challenge. Production Network Configuration The post-merger status of the plants was characterized by relatively old machinery and infrastructure, limited IT and ERP integration, and highly integrated vertical value creation. As there was no business connection between the entities before the acquisitions, there was also no production network in place. Nevertheless, similar product design concepts, materials used, and manufacturing technology and processes applied offered a big potential in procurement and also production network design. Because of the applied business unit management concept, a focused factory principle was kept as the baseline for further network development. The focus points had two dimensions: 1. Supply of sellable products like equipment (e.g., cranes) and turnkey solutions (e.g., truck + crane) to the market 2. Supply of components in the production network The network configuration could further be described with the allocation of the four main value creations steps—production, painting, assembly, and installation— for certain product lines within a site. An additional perspective was the lead, respectively, support from global or regional functions in product management, design, and engineering, as well as procurement. The PALFINGER network configuration is depicted in Fig. 20.2. The revenue as well as profit contribution of individual sites varied significantly, which was an indication that the configuration of the network was not properly addressed, as demonstrated in Fig. 20.3. The following key drivers were identified to improve the network configuration: • The mobility of production volume is a substantial necessity to improve delivery capability and improve plant utilization. The focus is placed on the fabrication of single components, since unique equipment is usually the cause of bottle necks. Moreover, capital intensive machinery needs to be run in maximum planned operating time to reduce product costs. • The over- and underutilization of assembly lines can be balanced by shifting workforce between lines. • Component plants are primary fulfilling internal orders but additionally obliged to fill excess capacities with third party customer orders and provide capacities for other regions if available. • For an improved collaboration between assembly and component plants, a proper sales and operation planning (S&OP) needs to be implemented, which also includes the provision of accurate forecasts from assembly plants to component plants.

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• Realizing the economies of scale is important to achieve cost-efficient production within the network. • Restructuring facility sizes and optimizing material flow and production layout will increase space productivity. • As the baseline is focused factory principle, realizing economies of scope is not an essential driver addressed by the network. • Reduction of duplications by establishing shared service yields high potentials for cost savings in the network (center of excellence/lead teams, shared service centers). Production Network Coordination The CIS region was managed by a regional management that was leading the business unit managers in the region as well as some regional support functions. This management team was accountable for the entire business model of the product lines in the region from market offering, via engineering and production, to sales and service. The group was acting as a holding and support organization. Quarterly steering committees consisting of one board member, the regional management team, and the business unit heads were fulfilling a control function and supervising strategic decisions. Besides that, the general decision competence was structured in order of hierarchy from group, region to business unit and location. Operations development was heavily driven by post-merger integration initiatives using expats onsite and involving experts from other plants in the group. Introducing

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PALFINGER manufacturing standards and processes was done step by step and concentrated on a single plant level. A performance measurement system provided a basis for the steering of individual plants. Besides existing standardized financial reporting, a set of operational KPIs was in place but not fully utilized to drive improvement. Knowledge sharing was mainly done in one direction from group level to the plants and not between plants in the region. The following key drivers could be identified to improve the network coordination: • Measuring manufacturing performance needs to be standardized within the region and combined with clear shop floor management structures in order to facilitate the daily management and increase transparency of operations. • External learning (local market needs and customer expectations, buying behavior, cultural aspects) and internal learning (local improvements, best practices, and technology improvements) are of utmost importance for the network. • The exchange between sites is even more important than the exchange between sites and area management. In both categories, the network still yields significant improvement potential. • The introduction of a lead team approach would change the organizational structure substantially and foster collaboration and the diffusion of knowledge in the organization. Especially the teamwork across plants on the shop floor level could be enhanced. • The motivation, personal involvement, and qualification of the employees forms the basis for long-term success. • Due to remote locations, attraction and retaining of management staff is emphasized. In particular, a strong second management level is vital to improve plants’ performance levels. In addition, a risk assessment for key functions needs to be implemented to ensures a long-term proactive personnel planning.

20.2.6 Derived Improvement Measures Out of the analysis results, 25 improvement measures were identified (see Fig. 20.4), and these were further narrowed down to 10 key aspects, out of which 2 are explained in the following sections. Lead Teams Knowledge sharing and harmonizing capabilities and skills within the network were ranked with significant impact and medium realization time. To identify the quick win functions, a detailed look into manufacturing processes and support functions applied in the network was taken. Along the manufacturing processes, welding is a key driver for productivity and quality. On the group level, a “Welding Center of Excellence” was already established inhibiting leadership, guideline, audit, best practice sharing, research, consultancy function, and a problem-solving responsibility, which requires specific expertise. Bringing guidelines and specific know-how

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down to the shop floor requires the continuous attention of a dedicated team who also drives the improvement process. Thus, a regional welding lead team was introduced. The team consist of specialists taken from several CIS plants but with the lead in one plant. The lead team is responsible for implementing group guidelines, roll out, and improving welding processes and technology, as well as support in troubleshooting. In parallel, other lead teams bearing responsibilities such as “lean deployment” were initiated. Each lead team follows the same basic idea but is tailored to the regional requirements and general existing organizational structures, especially on the group level. Mobility of Production Volume Flexibility in terms of sharing production capacity for either specific components or in certain production processes is key for efficient resource utilization. As the plants in the network used similar manufacturing processes, technology, and machinery, it was obvious that this should be leveraged. First, the range of capacity had to be identified if a need arose. The selection of relevant components was performed by comparing applied manufacturing processes, raw material used, and yearly demand. Next, to clarity the portfolio and volume to be exchanged, the specification of the identified components, especially related to quality and cost, is of utmost

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importance. With target cost and quality, the production part approval process was kicked off, and, after approval, parts were put into a pool of items which potentially can be shifted from one plant to another. The production allocation process follows the S&OP process in a 12-month rolling planning modus and includes phase in and phase out rules.

20.2.7 Conclusion Strategic footprint development initiatives often have the tendency to slide into discussions on specifics about either the right product offering, sales order generation, or production capacity or capabilities. The St.Gallen Management Model for Global Manufacturing Networks helped significantly in guiding through the process and further aids to find the right depth of discussion with the right set of stakeholders at the right point in time. The framework walks users through the key network aspects in a very systematic way and initially provides a transparent as-is situation as a baseline for the design phase defining the to-be situation.

20.3

The Manufacturing Network at PALFINGER Today and in the Future

The company’s aspiration is to offer the best-in-class solutions, to sustain its technology and innovation leadership in the core business and to drive digital transformation accompanied with new business models. The responsiveness of the production network to volatile market environments (production volume, order specifications, lead time requirements, cost pressure, etc.) or even crisis situations such as the COVID-19 pandemic must be much faster than in the past. Trends toward customized solutions or single point of contact for the end customer/user create a challenge for the overall organizational and process design. These circumstances demand a flexibilization of the production network and to allocate defined value creation steps to the sites on the one hand as well as to allocate product lines and models to the sites on the other hand. The manufacturing network tackles the manifold requirements with the following directions for the network configuration and coordination: • Reduce the overall footprint complexity • Produce in the region for the region to shorten lead times make use of material and labor cost arbitrage, and mitigate exchange rate risks • Harmonize production processes, technologies, and capabilities in the network to be able to share global production capacities when demands are fluctuating in different markets • Implement lead-follower concepts with the aim of having focused teams working on production process and technology improvement, development, and implementation

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• Have a dedicated process organization (F2F “forecast-to-fulfill” end-to-end process) in place that defines the process and tool chain setup A main driver for the network configuration is still to have access to low-cost production factors, but the importance of automation is increasing in order to create independencies from labor restrictions. The mobility of volume between plants is extremely important to avoid capacity limits and plant bottleneck-driven capital expenditure decisions. The mobility of products is done to a limited extent, whereas a bundling of identical products at one location in the global network is not planned. The approach here is rather to bundle similar products in one location, which can be manufactured on the same machine park. The characteristics of the network coordination are basically derived out of the general functional organization. The production network is led top-down from a global level, through to a regional and down to a plant level. This is executed the same way across the global product lines as well as regions. The global center of excellence structures for the main value creation steps—component fabrication, component painting, product assembly (e.g., crane), product installation (e.g., crane on truck)—together with lead functions in defined plants push manufacturing and industrial engineering process performance and actively contribute to new product implementations/launches. Lately, product innovation on mechanical or hydraulic components as well seem to be exhausted as basic electronics applications. Digital transformation, not only on the product side, has finally arrived in all functions across the company. New holistic business models require digital data continuity over the whole product life cycle. Features or performance on demand are only a few of the things that customers in the future will take for granted. Capturing data from solutions during real operations provides enormous value to optimize product engineering, manufacturing process engineering, or after sales and service offerings. Common technologies like virtual reality, augmented reality, or artificial intelligence are shared among disciplines and complemented by specific technologies like 3D printing in the production environment. This elementary digital transformation process is accompanied by a change process. Capabilities today are not necessarily the ones needed tomorrow. Largely vertical integrated plants will find it difficult to master performance challenges in the future. Solid partnerships with system, technology, and know-how providers are required to encounter this. All digital production initiatives need to strive for loss reductions and therefore underlay the basic lean philosophy. Although the triangle consisting of cost, quality, and delivery is still the key criterion for successful digitalization projects, it is not the only one anymore. Worker assistance systems push employer attractiveness; smart manufacturing processes reduce energy consumption and avoid waste and pave the way toward a green company. The development, implementation, and continuous improvement of standardized process templates are the subject of a global end-to-end process organization/ownership. Process standards are defined based upon group-wide valid business models and are realized in defined IT system and tool chain architecture. Within these, the prerequisites for process cost savings and the digital transformation in the value

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chain are provided. A business process collaboration platform provides access to as-is process information and training material and triggers change requests. A low-code platform enables the organization to realize customized or plant-specific applications of plants or departments without touching the process templates. Those applications are shared in the network; thus redundant application development can be reduced or even avoided. Lean management and Industry 4.0 are complementary topics for PALFINGER and are pursued with the approach of first establishing stable processes before automating and digitalizing them. Humans, machines, and data form a network with considerably better transparency of operational performance. This enables continuous improvement within the entire value chain to be pursued even more effectively. Center of excellence functions along the four value creation steps— fabrication, painting, assembly, and installation—pursue the goal of homogenizing manufacturing and business processes in the network, raising the maturity level in industrial manufacturing and accompanying product developments in the sense of simultaneous engineering.

References Friedli, T., Mundt, A., & Thomas, S. (2014). Strategic management of global manufacturing networks. Berlin: Springer. https://doi.org/10.1007/978-3-642-34185-4. Palfinger AG. (2020). Integrierter Geschäftsbericht 2019 [Company Report].

Network Optimization in 5-Year Cycles at Lapp Group

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Georg Stawowy, Boris Katic, and Dominik Remling

This chapter outlines the transformation of the Lapp manufacturing network along 5-year cycles. The centralization and standardization framework of the St.Gallen Management Model for Global Manufacturing Networks serves as a foundation to depict the transformation. The chapter starts with a short introduction about the characteristics and challenges of the company. Next, the five strategic initiatives of the past “know-how exchange system,” “site missions and strategies,” “make-or-buy decisions,” “lead buyer concept,” and “product cost calculation” are described. Afterward, the five strategic initiatives for the future “manufacturing IT and digitalization,” “product allocation decisions,” “manufacturing technology decisions,” “strategic logistics,” and “internal SC planning/order allocation” are also described. Finally, the chapter is completed with a summary.

21.1

About Lapp

The Lapp Group is a German provider of integrated solutions and branded products in the field of cable and connection technology. The group’s portfolio includes cables, industrial connectors, individual assembly solutions, automation technology, robotic solutions, and technical accessories. The company’s core market is the machinery and plant engineering industry. Further sales markets include the food G. Stawowy (*) Lapp Holding AG, Stuttgart, Germany e-mail: [email protected] B. Katic U.I. Lapp GmbH, Stuttgart, Germany D. Remling Institute of Technology Management, University of St.Gallen (ITEM-HSG), St. Gallen, Switzerland e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_21

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and beverage industry, the energy sector, and mobility. The products can be found in manufacturing machines, industrial robots, buses, and trains; food processing equipment, wind turbines, photovoltaic, charging systems for electric cars; oil drilling platforms; and much more. Today the name of the company stands for competence, quality, and innovation. High continuity and stability, guaranteed by the owner’s family, combined with strict customer orientation, innovative strength, and a consistent brand policy, are decisive for this success. The company delivers to every corner of the world, mostly from stock and with short delivery times. For this purpose, they have development and manufacturing capacities as well as logistics centers all over the world. The group operated in 2019 worldwide with 44 of its own sales companies, 18 production sites, and over 100 foreign representatives according to the company report. The company is divided into a regional organization pattern, comprising the three main regions Asia-Pacific; Latin-America, Europe, the Middle East, and Africa; and North America. For each region there is a dedicated management team. The C-level (CxO) functions are duplicated for the regions and a central function coordinate issues that need to be solved globally. Within a region, the factories are clustered regarding their product focus. Such clusters exist around standard cables (make-to-stock), customized cables (make-to-order), harnesses, and discrete manufacturing (e.g., connectors, cables glands). The business environment is characterized by high time-to-market and delivery speed, as the company must constantly adapt to new requirements and local standards, and competitors can launch similar substitutive products on the market within a very short time. The company deals with a very complex product portfolio rather than complex products. The portfolio of products comprises around 40,000 globally standardized articles and another 100,000 articles to serve local requirements. Despite the dynamic and fast market environment, the product life cycle is comparatively long in comparison to other industries. This embodies a huge challenge of not constantly growing the overall portfolio. This portfolio complexity leads to a respective “high mix” regime in all factories involved. Therefore, third party sourcing of finished goods is a necessary strategy to cope with this complexity.

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Revisiting the Network Strategy

Historically, the company followed a quite decentralized setup and local-for-local strategy. Considering local norms and customer requirements along with their own development capabilities, factories expanded their production scope and increased utilization to ensure economies of scale at the same time. However, this led to a constant increase of complexity for each factory regarding the variety of designs, compounds, process technologies, supplies, and the number of suppliers. Nevertheless, this development was accepted by the leadership team, if the increase in plant utilization and overall equipment effectiveness (OEE) delivered enough profitability. However, at some point, the hidden costs of managing this complexity (e.g., scrap rate, training needs, etc.), weak standardization among the facilities, and strong

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decentralized authority suggested the need to shift the paradigm of the existing production network. Thus, the CTO/COO, together with his regional counterparts and plant managers, initiated a strategic change. This shift was based on the belief that a stronger focus on product clusters, stronger standardization, and a higher degree of centralization would deliver additional productivity and lead to stronger synergies in the network. In a 5-year process, preceding the release of this book chapter, the leadership team pushed toward the right equilibrium between autonomy, centralization, and standardization. The initiatives are illustrated in Fig. 21.1 following the St.Gallen Centralization and Standardization framework (Friedli et al., 2014). Respecting the fact that Lapp’s success was built on local entrepreneurship, the strategic actions were guided by two major perceptions: 1. Decentralized decision-making rules (as much as possible at site level, as little as necessary at corporate level): A regional management is overall the right level to find synergies, to cope with complexity, and to govern the network without becoming “bureaucratic.” 2. Standards should allow local adaptations in the best sense of a described standard. For example, the shop floor management should be based on clearly defined KPIs and should follow the same basic structure. However, local adaptions in the shop

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floor management process or boards are accepted and welcome to stimulate local learning procedures. Standardization should not be an end in itself. Overall, the strategic changes have led to greater centralization at the regional level or even, depending on the added value for the entire network, at the global level. Furthermore, the intensified standardization has led primarily to better exploitation and improvement of the existing knowledge within the manufacturing network. As a result, the plants can better manage their complexity and avoid reinventing the wheel. To achieve this, the strategic change was constituted by five major strategic initiatives: A.1 Know-how exchange system: Improve cooperation within the network and stimulate corporate learning B.1 Site missions and strategies: Develop site missions and strategies in a broader context of the network B.2 Make-or-buy decisions: Implement a clear make-or-buy structure to strengthen the site missions C.1 Lead buyer concept: Introduce a lead buyer concept to effectively relieve the network from complexity C.2 Product cost calculation: Standardize product costing to enable fact-based allocations within the network The top five strategic initiatives will be described in the following section. A.1 Know-How Exchange System: Improve Cooperation within the Network and Stimulate Corporate Learning The evolution of the production footprint has evolved in a typical fashion: one factory after the other has been built up to support the local market penetration. To improve profitability, each plant manager was focused on loading their own factory and using local entrepreneurship. The implementation of the Lapp operation system (LOS) for the first time embodied a standardized framework and language for continuous improvement. While the LOS implementation has initiated a new dynamic in continuous improvement, it showed at the same time the limitations of the overall learning curves. When critically reviewing the numerous Kaizen activities in the factories, the company found frequent 5S activities and technical improvement projects that lagged behind the secured technical knowledge of other factories. The need for cross-fertilization was evident, although personal networking across the different entities has always been of specific interest to the shareholders and the corporate culture. However, the organization needed to move from “forcing” Kaizens top-down to a bottom-up dynamic and “hunger” for continuous improvement. The professional experience of the leadership team suggests that lean management is a strong playground for young professionals to create a culture of sportsmanship, competitiveness, and comradery that lead to strong bottom-up dynamics. Accordingly, the respective local resources and multipliers are a precondition.

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After defining the LOS on the central level, the company implemented and sustained the system by moving away from a centralized LOS team to decentralized experts and invested in networking, training, and competitive spirit among the factories. A mixture of young lean experts, experienced plant managers, and engineers assure the most mature and balanced improvement activities. As the COVID-19 pandemic has led to a new era of virtual meetings and learnings, the organization remains convinced that personal networking with mutual professional, cultural, and personal experiences has laid the foundation for accelerated continuous improvement dynamics and a spirit of “belonging” and “being in the winning team.” Learning from each other and to “copy with pride” has overcome the “not invented here” dilemma. Strong investments in networking and mutual learning have been accompanied by regional cultural change initiatives. Whereas the APAC region committed to a Great Place to Work program, the EMEA region initiated a change initiative “We run as ONE” and systematically challenged every factory to work on a change story and explicit change activities. Although every factory chose different metaphors (“ready for Champions League,” etc.), the region was strongly committed to one cultural umbrella. Today the spirit of ONE Lapp is a shared culture, and networking across regions has become a standard practice without central initiation. While in the political arena, we today complain about fading tolerance, increasing segregation, and nationalism, it appears important to point out that the professional experience in such an international network leads to different experiences for the people involved. International workshops, training, and shop floor projects are appreciated as enriching personal experiences. Regarding learning and personnel development, the CTO believes that “irrigation of plants, first requires a strengthening of the seeds.” Thus, the fertilization of technical knowledge and experience was focused. This was achieved by a production excellence training program driven by corporate HR. The program put a strong focus on technical and practical learnings (e.g., 1-week extrusion training but also systematic problem solving or supply chain related) and involved employees from around the globe and of different levels. Additionally, universities further supported the process and assured a strong academic background and quality that has justified a strong value of the training certificates. Lapp experts and senior production managers deliberately took over the role of trainers to lead by example and to make sure that the incorporated exercises and examples are the best possible to be derived from the Lapp context, e.g., the Operations Manager of Lapp India taught about six sigma. Travel efforts have been high but supported a learning experience across sites, ranks, and titles. Even though the programs overall involved high levels of investment, the effort has undoubtedly paid off. B.1 Site Missions and Strategies: Develop Site Missions and Strategies in a Broader Context of the Network Part of the strategic change is a selective reorientation of the plants as well as other third-party suppliers to strengthen process specialization and to achieve additional productivity gains. As a starting point for the redefinition of the site missions, the leadership team followed a roadmapping exercise to exchange thoughts on major

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trends and developments for the upcoming years (the entire process of developing the production network has been inspired by Christodoulou et al. (2008)). Despite the uncertainties of this exercise, the benefit should not be underestimated. An explicit consensus on assumptions builds the fundament for further decision making. The predefined categories, which serve as a basis for the assumptions, are: • • • • •

Market: Customer, competition, policies, regulations, and norms Applications and products Services Capabilities: Logistics, technologies, business processes, and partnerships Resources: Funds, skills, and workforce

Out of a set of up to 50 mapped assumptions, the team agreed on the top 10 major trends for the manufacturing network. Based on those trends, the team formulated ten business imperatives. Among others, it was determined that there was a need to improve control of capacity, to assure R&D and production competence in all three regions, and to stronger segment the supply chain. Therefore, the overall product portfolio of around 100,000 articles has been clustered according to production process similarities. In collaboration with product management and sales, these clusters have then been categorized within the product classification framework (Christodoulou et al., 2008). The categorization has been based on the strategic importance of own production expertise and competence as well as supplier performance (see Fig. 21.2). To develop investment needs for additional floor space and equipment for the next 3–5 years, the clusters were broken down into the regions and sites. The clustering and classification have resulted in continuous and significant changes in the positioning of the sites. Without the new logic, this change could have only been implemented with high investments in the short term. Thus, the transformation is carried out iteratively. Further, a cross-functional council and governance are in place to steer and control the allocation/transfer of products to either the company’s own factories or third-party suppliers (“portfolio management team”). In such a comparison, transparency and, ultimately, competition are crucial to define a clear site mission. Regarding the production network, a site mission can also be understood as “the purpose”: Why do we need this site in the network? Ideally, each site can contribute a uniqueness that already reveals where to allocate articles in the network. This uniqueness will never be achieved because of redundancies in order to improve the network’s resilience. Nevertheless, each site needs to work on a clear profile of strengths. The first clear positioning needs to consider whether the setup should be a low-mix/high-volume or a high-volume/low-mix. Both directions require different success factors. Further, a site mission needs to describe the complexity in the as-is status as well as the targeted stage. At Lapp, each site has described this in a stipulated one-pager format that covers the following criteria:

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Fig. 21.2 Product classification matrix

• • • • • • •

Compound types Copper strand types Machinery/process steps, including capacity Product types Cross section range SWOT Clear mission statement (purpose/mission)

The clearly defined site missions give respective guidance and therefore serve as a strategic filter in the make-or-buy decision. B.2 Make-or-Buy Decisions: Implement a Clear Make-or-Buy Structure to Strengthen the Site Missions To implement a new make-or-buy process that strengthens the site missions, the following principles were set:

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• Purchasing shouldn’t be the exclusive decision-maker in the make-or-buy process. • The product portfolio of Lapp should be strategically classified. • There should be transparency regarding the end-to-end margin. • The same calculation approaches should be applied for external suppliers as for internal sites. • The capacity usage of the internal production sites should be considered. From an organizational perspective, make-or-buy decisions are taken within the portfolio management team and must be signed by the strategic production and performance manager, as well as the purchasing director. This is further backed by financial transparency provided by the controlling department. As described in the previous subchapter, a product portfolio classification has been elaborated, which serves as a baseline for the make-or-buy process. Further, for each quadrant within the product classification matrix, a make-or-buy target quota has been set. The quota in case of low strategic importance and Lapp outperforms the supplier performance is 50% make and 50% buy. In this scenario, the supplier should be developed. If the supplier reaches a better performance than Lapp and the strategic importance is still low, around 20% should be made in-house. If the strategic importance is high and Lapp outperforms its suppliers, 80% should be made in-house. In case of high strategic importance and better supplier effectiveness, 100% should be sourced externally. This is because the company’s resources should focus on products that it manufactures better than its suppliers. Further, the product classification matrix has been enlarged by the dimensions of availability and flexibility, as well as the growth potential of external suppliers and internal sites. Taking the dimensions into account helps to source from the cheapest external supplier in periods of growth and fully utilized internal sites. In times of lower demand, Lapp can insource to utilize the available capacities. These dimensions also helped to allocate investments for new machines and equipment and to maintain the right cost base for the expected growth of a new product. In Fig. 21.3, you can find an exemplary visualization of the classification. Figure 21.3 shows four bubbles, each reflecting a product in one of the different quadrants. The bubble size represents the purchasing volume for each product. To increase transparency, the company made sure that all product-relevant costs in all internal factories as well as the cost calculations of the major suppliers were known, analyzed, and understood. By doing this, it is possible to align the calculation approaches and to incorporate the effect of the underutilization of a respective factory into the decision. The product cost calculation was a further strategic initiative and is described in the following subchapter. C.1 Product Cost Calculation: Standardize Product Costing to Enable Fact-Based Allocations Within the Network Although the organization has agreed on “responsiveness and flexibility” as the utmost objective for the supply network, the economic improvements played an important role for make-or-buy decisions and in the selection of specific suppliers.

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Therefore, a reliable and transparent comparison of all landed costs to a distribution hub is an undisputable “conditio sine qua non.” As one symptom of a grown production network, the company had to face the challenge that product costing methods differed from plant to plant. Even if costing structure and logic could be considered similar on a high level, a deep dive on specific assumptions suggested the strong need to revisit and implement a detailed scheme to calculate transfer prices. To assure compliance to the most restrict tax regulations within the network, the scheme has been based on the “Cost+” logic (i.e., a fixed margin is added to the variable unit costs to ensure the targeted profitability). Street prices (actual retail prices) for the company’s products should be purely determined by salespeople and not be predetermined by transfer prices including “hidden margins” in the standard costing. Furthermore, the understanding of standard costing and the strategic relevance of making the right assumptions for calculations needed to be reemphasized and implicitly trained. Assumptions that are too aggressive lead to short-term advantages in a make-or-buy decision but leave the factory behind with a high risk

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of losses. If assumptions appear to be “comfortable,” a factory earns money but may lose its competitive edge. It is easy to agree on a standard cost structure, but the devil is hidden in the details. Major aspects that should be clearly defined are depreciation costs, hourly machine rate, setup time, allocation of administrative surplus charges, standardization of cost centers, and a surcharge for scrap. Comparing OEEs to stipulate the hourly machine rate drives defining the appropriate OEE measure. More important is the discussion on which depreciation value to consider: Should the calculation be based on the legal depreciation (as is in the books) or an imputed depreciation that considers a realistic utilization period and replacement value? Controversial arguments occur over many of these assumptions. In the end, the benefit is to achieve a common knowledge and understanding of the costing standard. Finally, the company harmonized the process across the fiscal year before budget planning for recalculating the products, checked the major deviations as to the prior year, and agreed with purchasing in the portfolio meeting for in- or outsourcing needs. C.2 Lead Buyer Concept: Introduce a Lead Buyer Concept to Effectively Relieve the Network from Complexity Purchasing within the Lapp Group was organized according to the premise “be as decentral as possible, and only as central as necessary.” The purchasing volume is divided according to finished goods (40%), production material (80%), and indirect material and services (90%). To leverage the advantages of centralized purchasing, Lapp decided to set up a lead buyer organization (LBO). The LBO is part of the purchasing strategy and an own management function. Figure 21.4 shows the defined strategic, tactical, and operational initiatives. Apart from the organizational change, it was a major change of mindset for the purchasing department, for the sales companies, and production facilities. Therefore, the company also set up a cultural change project as part of ONE LAPP. Specifically,

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Lapp defined the slogan “we buy as ONE” and focused the mindset of everyone toward “best of Lapp instead of local optimization.” Within the new structure, the responsibility of a lead buyer is assigned to the country with the highest expertise in terms of market and supplier environment. A decision matrix consisting of product types and countries supports the selection process. The responsibility enables the lead buyer to act within the principle of one face to the supplier. Thus, the previous setup consisting of purchasers per product group has been fundamentally changed. The former setup resulted in suppliers being contacted by several purchasers. The new setup enables the company to define strategies as well as goals for suppliers and to develop suppliers into potential partner suppliers based on the overall performance instead of measuring the performance within different product groups. Furthermore, the lead buyer is the focal contact for diverse functions such as quality and product management. In this way, the lead buyer can bundle all requirements of the supplier development. Also, maverick buying (procurement by internal departments without the involvement of the purchasing department) from the sales companies is not possible anymore. All in all, the increased volume as well as consistent terms and conditions enabled vastly improved prices transparency for taking the right decisions. Further, the introduction of a portfolio management function, consisting of production, lead buyer, controlling, product management, and material planning, is a major benefit. Besides, the leading principle for the Lapp manufacturing network is “the best possible product for the best possible source.” This makes the sourcing process a lot clearer and enables the identification of the right source to always depend on the current status in the market (growth, availability, recession, etc.)

21.3

The Way Forward Toward Network Excellence

These developments in recent years have delivered remarkable progress in productivity in factories but also in overall organizational efficiency. Profitability from the sourcing side of the business has been substantially improved. It has also awakened the need for further corporate development. The more factories cooperate, the more clearly it becomes evident that the company must work on a holistic understanding of the supply chain. For the upcoming strategic cycle until 2027, respective emphasis will be put on the missing pieces of the puzzle (see Fig. 21.5): A.2 Manufacturing IT and digitalization B.3 Product allocation decisions B.4 Manufacturing technology decisions C.3 Strategic logistics C.4 Internal SC planning/order allocation A.2 Manufacturing IT and Digitalization The company devised a digitalization strategy in accordance with their vision of a digital supply chain and factory. It entails that all sites are connected to the same ERP

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systems that know the capacity status of machinery online. When a customer order appears, the customer would get the product from the nearest physical inventory and leave the option open for deliveries of partners. This high-level vision of the supply chain needs to be reflected in the future development of different business models in the Lapp Group. At the current time, the group has commenced activities for charging solutions in the e-mobility sector, which differs in market structure and expectations from the core business of the group. Specific customer needs from different business models will increasingly ask for a more segmented supply chain with respective processes and digitalization support. In such a scenario, the replacement order would be routed to the factory with the required technical competence and capacity. This means that the integration of suppliers is also needed. A further vision of a smart factory is to automatically set the parameters for machines. Therefore, an identical manufacturing execution system (MES) is needed in each factory. Furthermore, the company relies on a cable construction tool that includes the bill of material (BOM) of a product and needs to be connected to the ERP and MES system to allow online parameter settings and material consumption monitoring. The company took some time to clearly define the characteristics. The starting point to develop its vision of a smart factory were two pilot projects in different

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locations and with different technical approaches. One factory implemented a standard MES tool of one of the market leaders. The other factory waived the installation of an MES system and directly connected programmable logic controllers with a server to feed a tool for digital shop floor management. The pros and cons of both approaches have been revisited with all plant managers in a global conference to assure a direction based on the huge commitment of the people involved. It is worth noting the importance of also agreeing on standardized network infrastructure and extensive considerations of cybersecurity in the IT and operations technology (OT). The next step involved selecting an appropriate MES system for the entire company. The selection process was supported by external partners that meanwhile navigated through countless MES offerings. Five people from different factories were involved in the selection and jointly conducted a 1-week reference to evaluate the short-listed tools in the field. The core functionalities of different MES systems, such as online monitoring of OEE, did not serve as differentiating factors for the selection. As a major conclusion—and maybe a little surprisingly—the criteria “connectivity to the system architecture,” “adaptability,” and “graphical user interface (GUI)” were identified as the decisive factors. The company finally decided to implement a well-established tool with proven connectivity to the ERP system. After decades of working on the Toyota Production System, European companies learned about the power and need for visualization. Although today’s MES systems allow user attractive visualization, they did not fulfill the organization’s specifications regarding a digital SFM. Neither workflow functionalities nor practical support, such as documenting minutes of meetings, are supported by the vast majority of MES tools. Accordingly, Lapp decided to implement an additional tool for the digitalization of SFM. The process from first vision to the definition of specifications, selection of tool architecture, and finally rollout took much longer than expected and required patience and understanding from the plant managers’ community, which wanted to get started and follow their local spirit and motivation to go digital. Digitalization asks for the standardization of processes and tools. It is part of the digital evolution to also manage a change from the experience of local entrepreneurship toward being part of a highly standardized, digitalized network. A new equilibrium of freedom, rules, centralization, and governance will need to be developed without leaving disappointed change agents behind. B.3 Product Allocation Decisions As the past few years have demanded massive efforts to redefine the focus of factories, purchasing, and cooperation, it is of utmost importance to assure clear governance in allocating new products to the right sources based on clear criteria. This is required to define and assign a global role to balance local, regional, and global interests. This global role has developed objective decision criteria. The next several years will prove that this strategic filter for the network will lead to further specialization of factories.

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B.4 Manufacturing Technology Decisions Further standardization needs to be considered for the selection of production technologies for the group. The autonomy of sites has led to a certain complexity of brands and technologies in the installed asset base. The local authority for selecting production equipment has led to a strong buy-in of local teams, but the downsides are becoming more and more evident: Technical support across plants and spare part logistics are limited, and reliability tends to be underestimated for investment decisions. Every site complies with the concept of total cost of ownership (TCO) for investments. However, when new equipment is introduced with no reference experience in the group, it is impossible to estimate the TCO due to the lack of experience with the reliability in the field. Therefore, the company has stipulated certain equipment brands for core technologies (especially extrusion). As this alone is not enough given an increasing number of machine suppliers with large price differences, the company will work on better guidelines for investments. A respective global role has been installed to strengthen technical standardization. Standardization in this respect should not only be understood as the selection of a brand for major machine groups. A huge potential is even seen in auxiliary units such as printers or dryers. The strong growth of the group of 8–9% Compound Annual Growth Rate (CAGR) during the past 5 years demands investment funds significantly above the depreciation level for the upcoming years. Stronger than in the past, the organization will focus on the return on capital employed (ROCE). An increased technological focus is appreciated and accepted as a basis for the strong growth and market success but has increased pressure on the current ROCE level. To increase awareness, the KPI ROCE will be implemented more strongly into the company’s controlling scheme. Future investments will not only need to undergo a standard return on investment (ROI) calculation as in the past, but a checklist is also being developed to better understand the investment’s consequences regarding the working capital. Further, the organization will need to better reflect the consequences of the entire value chain. C.3 Strategic Logistics The logistical footprint of the company has grown along with regional growth. A network of three regional hubs (Singapore, Stuttgart, New Jersey) serves local warehouses that—like the factories—have been build up according to a local-forlocal rationale. The Lapp Group assumes a general trend of increasing customer expectations in logistical service comprising speed, reliability, and flexibility. Two major risks are forcing the organization to rethink its logistic footprint: • Increasing customer expectations cannot be fulfilled by purely increasing availability in customer’s proximity because simulations show massive risk for over proportional increase of working capital. • The company serves very different customer types with different service needs. If logistics are geared up for the most demanding customer, the logistical processes will overfulfill for other customers at additional costs. For example, whereas

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maintenance, repair, and overhaul (MRO) customers require a maximum of 24 h, this is not true for many customers of the OEM business that instead demand accuracy on the point of time rather than speed. For the further development of the logistical footprint, it is crucial to balance costs and working capital demands with customer expectations on delivery time. Therefore, intensive customer interviews are currently being conducted to better understand customer’s basic expectations and price sensitiveness regarding service improvements. Furthermore, the stronger differentiation of business models (project business in infrastructure, harnessing, catalog sales, customized products, etc.) will be challenges to strengthening the supply chain segmentation. C.4 Internal SC Planning/Order Allocation Finally, the new strategic period will ultimately lead to a strong increase in working capital and investment funds. To cope with increasing logistical demands, the factories will need to focus more strongly on a reduction of replenishment times. Whereas the major lean focus of the past five years was on OEE and scrap rate reduction, the company will need to redirect toward improved and increased flow of material. The focus on replenishment time should be respected, as should technical competence on detailed planning and scheduling based on smart factory functionalities.

21.4

Summary

Beginning from a predominantly decentralized manufacturing network structure, many things have changed at Lapp. The starting point was the redefinition of the factory’s missions and strategies, which enabled the continuous development of the sites toward their given direction over time through clear guidelines. Combined with a knowledge exchange program, the foundation for a higher level of network excellence was laid. The critical issues at the beginning of the value chain were first addressed by classifying the complex product portfolio. Here the added value of scientific frameworks becomes clear. The reduction of complexity in the product portfolio and a concentration on core competencies lead the company to a structured make-or-buy process and a lead buyer structure. The make-or-buy process has also required a revision of the product cost calculation, which not only leads to a higher transparency but also to more profound make-or-buy decisions, more reasonable product allocations, and the possibility of benchmarking. The lead buyer concept enables significantly improved communication with suppliers, which also results in several positive effects on performance. In the future, the company will push the allocation of products to locations based on the previously defined site strategies. This is a logical next step. The topic of manufacturing IT and digitalization will be also pushed forward by the introduction of MES systems and will bring the company closer to its defined vision of the smart

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factory and supply chain. On the other side of the value chain, customer requirements will be improved by optimized planning and logistics. The company follows a “do it now” logic in its quest for higher manufacturing network excellence, whereby only a selected number of topics are driven simultaneously. The processing of work packages in regular cycles, in turn, generates new work packages for the future. From this approach, companies can learn that the development of roadmaps along a predefined strategic direction is important but that sufficient flexibility should be granted so that the roadmap can be adapted along the way according to the upcoming logical next steps. Acknowledgment This case study is dedicated to all contributors within the Lapp Production Network. You’ve all done a great job!

References Christodoulou, P., Fleet, D., & Hanson, P. (2008). Making the right things in the right places: A structured approach to developing and exploiting “manufacturing footprint” strategy. Institute for Manufacturing, University of Cambridge. Friedli, T., Mundt, A., & Thomas, S. (2014). Strategic management of global manufacturing networks. Berlin: Springer. https://doi.org/10.1007/978-3-642-34185-4.

Holistic Manufacturing Network Management Approach at Jenoptik AG: Light and Production Division

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Richard Hummel

This chapter outlines the holistic approach of managing the global manufacturing network at Jenoptik. The chapter starts with an introduction about the background and historic development of the company. In the following section, the design and implementation of site roles and support functions are outlined in detail. This is followed by a mixed-integer optimization model to evaluate and compare different future configuration scenarios. This enabled the company to consider possible reconfigurations resulting from political reasons. Further, an approach to monitor the performance of sites and to use the information for future footprint considerations based on simulations are shown. Finally, the chapter is completed with a summary and an outlook.

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Introduction

22.1.1 Market-Driven Motivation Driven by progressive globalization, we at Jenoptik are continually working on a global production network tailored to our product and our market requirements. With the increase in the number of customer markets to be served, the number of regionally adapted and individual products, product variants, and customer-specific solutions is also multiplying at Jenoptik. Simultaneously, product life cycles are becoming shorter and shorter, and cost pressure is increasing. This multitude of influencing factors and requirements results in high complexity regarding the allocation of products to plants, the so-called product allocation (see Chap. 9), and the design of agile and resilient global production networks (see Chaps. 7 and 16). In particular, small- and medium-sized companies are reaching R. Hummel (*) Jenoptik AG, Jena, Germany e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_22

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their limits due to limited planning capacities and management resources and a lack of experience in the development and optimization of their production networks. The light and production division of Jenoptik AG can be understood as such a small- and medium-sized company. Uncoordinated, not comprehensive, holistic, and sustainable decisions were often the result in the past. This insight implies that decision models for the design and allocation of production can increase planning reliability and support medium- and long-term planning.

22.1.2 Trends and Self-Motivation “To bring production to the top of the change process!” This was both motive and goal of the company. A new cross-divisional, future-critical change process began in 2018, as markets and customer requirements have changed. Organizationally, several business units were combined into one department or newly assigned or expanded through acquisition. The product portfolio and the value creation stages were realigned with requirements. Securing competitiveness and being a driving force behind the change was the impetus. To achieve these goals, the requirements for an agile, resilient production network needed to be translated into a mathematical model for decision support. This decision support tool would allow future decisions based on a validated mathematical model, fast and resilient compared to production allocation decisions in the past. This path from local production to the global value creation network is explored in detail in the following sections. In addition to discussing the production network’s characteristics, product portfolio changes are highlighted, and the value creation network’s control and management using key performance indicators are displayed.

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Technological Background

22.2.1 Special Machine Manufacturer The light and production division of Jenoptik is a globally active specialist in the optimization of production processes (see Fig. 22.1). With our numerous years of experience and know-how in the fields of industrial metrology and optical inspection, modern laser-based material processing, and highly flexible robot-based automation, we develop customized manufacturing solutions for our customers in the automotive, aerospace, medical technology, and other manufacturing industries. As a firmly established key supplier in the global automotive market, we face the modern challenges of flexibility, productivity, and increasing variant diversity on a daily basis, thus meeting the growing demand for complex and turnkey industrial solutions with a focus on machine integration and process automation.

Holistic Manufacturing Network Management Approach at Jenoptik AG: Light. . .

Fig. 22.1 Global production network of Jenoptik light and production

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As a development and optimization partner, we jointly face our customers’ market-specific challenges and, by intelligently combining our key technologies, take customer-specific systems to a new, forward-looking level. With our entire Portfolio, we primarily supply the automotive market. Also, we are also active in the mechanical engineering, the medical technology, electrical engineering, and aerospace. Our customers include leading global automotive manufacturers and their suppliers and manufacturers of machine tools, plants, and medical technology and manufacturers, users, and integrators of laser machines.

22.2.2 Industrial Products To realize the customer’s wishes, fulfilling the functions and features desired and required in the respective market segment leads to a vast product variety; thus, we chose batch size one as a solution. As light and production division, we found that our product portfolio precisely matches the theoretical description. The portfolio includes laser welding and laser cutting machines from the laser processing business unit and high-precision measuring and inspection equipment and machine for production-related applications in the metrology business unit. Besides supplying markets and customers with the listed products, components and products are also used in the integrated, automated system. The performance spectrum of the project planning and realization of complex automated systems is also part of our service offering (see Fig. 22.2). Competencies and solutions in laser processing are efficient, precise, and safe 3D laser machines for perforation, cutting, and welding of plastic, metals, and sensitive materials and best in class laser robot trimming systems for the body in white. Metrology offers a complete range of high-precision metrology instruments and machines for optical, pneumatic, and tactile dimensional measurement and solutions for optical inspection of all machined surfaces.

Fig. 22.2 Competence and solution for light and production

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In the business unit automation, we realize specialized solutions focused on designing and constructing automated manufacturing production systems for original equipment manufacturers (OEM) and global tier-1 companies.

22.2.3 Strategic Success Factors The strategic success factors, also called competitive advantages, refer to the condition consciously created by a company by developing necessary and dominant skills, allowing it to achieve above-average results over a sustained period. There are different kinds of competitive advantages that determine the valueadding network management strategy. Some critical success factors for the valueadding network of the division light and production are: • Classic strategic success factors, including the triangle of cost, delivery time, and quality, as well as other goals like delivery reliability or flexibility • Location-specific factors, such as access to customers, markets, labor-force, suppliers, competitors, and resources • Sociopolitical factors, such as exchange-rate safety, tax benefits, and subsidy programs • Efficiency goals, including the bundling of products and processes and achieving flexibility through available capacities and access to heterogeneously distributed knowledge

22.3

Historical Development

22.3.1 Development of the Company After the takeover by Jenoptik AG in 2001, years of inorganic growth to supplement the product portfolio followed (see Fig. 22.3). This growth was associated with the acquisition of new production sites and the expansion of the existing ones. Each of these sites had its supply chain and was completely autonomous. Most of the production sites were organized as a workshop production. In a first time optimization step accompanied by economic crisis 2009–2010, a review of the own depth of value-added with the consequence of outsourcing the drawing parts’ mechanical production and the consolidation to a reduced number of production sites. The steep growth that followed the crisis had to be served by the few central locations. Quickly, capacity bottlenecks and supply problems occurred in the predominantly regional supplier network. At that time, this also led to the initiation of the capacity-driven further establishment of production sites. The premises for the location decisions were the access to qualified personnel resources and a system that meets the requirements of supplier network. The initial situation can be summarized as follows:

Fig. 22.3 Historical development of the company

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• The global production network is a result of historical developments and numerous decisions. • Currently, there is no holistic concept with a precise determination of tasks and roles. • Products and production processes are partially standardized and harmonized. Cost-reducing potentials due to economies of scale, local assembling, and standardization of components and products are partly utilized. • The production network is not focused on the customer. The production network is only partially adjusted to the sales markets. The resulting production site structure was no longer adequate to meet future challenges due to intensifying competitive conditions and shorter product life cycles. The task of industrial metrology is to ensure that the instruments in production processes function correctly and that the finished products meet desired characteristics. The devices were developed and produced as high-precision, production-related measuring instruments. Most of the measuring instruments were designed for a customer-specific application and are accepted based on a basic measuring equipment capability test. In addition to the fact that the measuring device was designed as a complete system and not modular, this aspect meant that it could only be produced at locations where the technological know-how was available. As a technology facility, the sites describe all essential functions of the product development process and the order fulfillment process. As one of three business units in the light and production division, metrology offers six major product lines concentrating on different production environments’ measuring tasks. It results from the merger of the former metrology manufacturers “Hommelwerke” and the “Etamic & Movomatic” group. While sectors like aerospace and electronics are growing and require tactile, pneumatic, and optical metrology, most of the measuring applications are in the automotive sector. The business unit laser processing’s products enable preparation, pitching, scoring, translucent, remote welding, and tire carving of parts. Both business units collaborate in offering integrated automation solutions. In particular, the desire for integrated solutions led to the far-reaching requirement that the individual products should be available as solution components in addition to stand-alone devices.

22.3.2 Product Portfolio Derived from the requirements described in the previous section, the product portfolio, on the one hand, adjusted and, on the other hand, adapted to the new requirements with new developments. The result was a product portfolio built on a modular system of components. The building set was developed across technologies and product lines. A high share of common parts was produced, and a product variant tree was created. These product variant trees are a suitable method to visualize the product variety through various assembly process stages. The primary purpose is to create transparency about the internal number of variants. Based on the

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bill of materials (BOM), the product variant tree maps the assembly stages to manufacture customer-specific products. In each stage, the type and quantities of all required components are displayed to include possible product configurations. The basic principle is to determine the variant origin as late as possible in the assembly process to make the production process as lean and unify as possible. The variant origin point, or decoupling point, is the point in the assembly process at which customer-specific parts and components are installed. Before this point, customer-neutral preproduction of standard assemblies and basic modules takes place. The introduction of this approach was a crucial prerequisite for developing a resilient, global production network.

22.3.3 Production Locations The second requirement was met with the geographical and content-related analysis of the sites. Geographic links to markets and customers were established by acquiring other companies and expanding existing sites. Additionally, we enabled individual plants to accept and, if necessary, expand requirements for each of the defined roles if appropriate. Change proposals are made based on the mathematical model. Figure 22.4 shows the production sites of metrology within the division light and production.

22.3.4 Organization Another important, preparatory step toward establishing a global production network was changing the organizational structure from strictly local, hierarchical organizational structures into a matrix organization structure. An important aspect

Fig. 22.4 Production locations of metrology in light and production

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was the separation of disciplinary and operational responsibility, which remained local, from the tactical and strategic responsibilities, which were centralized. Associated with this was installing a process and methods team that centrally controlled the definition of the individual roles in the production network and carries out their roll out and the monitoring of the conditions. The foundation for the house of products consisting of culture, organization, and communication was created. What follows is the production strategy. The production strategy is described by the strategy success factors and contributes to the overall company strategy. As the next section explains, a location role concept was developed using expert workshops.

22.4

Network Strategy Development: Modular Products and a Stacked Hub-and-Spoke Production Network

22.4.1 Use Case Jenoptik Division Light and Production The production strategy is based on the described strategic success factors and contributes to the overall company strategy (see Fig. 22.5). A workshop was held regarding the success factors in which the various factors were sorted by relevance and ranked for Jenoptik division light and production. In this workshop, which was conducted with the new way of working (WOW) method, the location role concept was developed. This concept is the basis for the global production network of the Jenoptik light and production division. The global network definition was carried out from the center of value creation, from production, and gradually developed into a valueadding network. As the first step, the business unit metrology was established. Another primary goal besides developing and establishing a global value network was to obtain a mathematical model for future agile planning of product allocation in

Fig. 22.5 Production strategy of division light and production

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the global network. This goal was pursued and implemented by the research project ProdAlloPlan.Net parallel to the global network development.

22.4.2 Description of Roles in the Network The types of plants and principles of interaction need to be defined before making decisions about product allocation. We, as Jenoptik division light and production, have concretized the production structure in a location role model, inspired by a huband-spoke network configuration that combines economies of scale and customer proximity. The location roles are defined as follows. Leading Factory The leading factory is responsible for the product. The place of the leading factory is independent of the production locations. Each product or product line was assigned a leading factory as part of the production network’s development. The leading factory is defined as follows: • Mission: assumes ownership of a product or product lines globally over the entire life cycle • Functions: product management, research and development, sales, services, and procurement • Obligations: market requirement specification, operations, training SCM, certification, technology roadmap, and transfer kits Component Assembly Center (CAC) for Core Components All CACs in the division are shaped in U-lines according to the principles of singlepiece flow production. The lines are equipped with all materials for all variants. Any variant can be produced at any time. This configuration represents a make-to-stock production. The planning of the CAC production is done by a worldwide sales inventory and operations planning (SIOP). The produced components are delivered to the system assembly centers (SACs) and project execution centers (PECs) controlled by a pull mechanism. All components are tested and monitored as part of a central production stability evaluation. There is only one component assembly center per technology or component family (e.g., the optical measurement unit). A CAC is defined as follows: • Mission: provide core components for products or product lines to affiliates in time, cost, and quality and drive continuous improvement processes (CIP) • Functions: operations, SCM, and procurement process and method team • Obligations: demand planning, frame contracts, procurement, operations, inventory control, warranty claims, testing, and qualification

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System Assembly Center (SAC) for Configurable Products All SACs in the division are built and equipped in the same way and form a one-piece flow production. The defined components are delivered from the respective CACs. Additional parts required for the order are procured locally. Wherever the market for a specific product line reaches an economically viable size, a CAC is installed. The production is based on make-to-order (MTO) or configure-to-order (CTO) logic. Besides the goal of customer proximity, the SACs also fulfill the requirement for a resilient production network. The same contents and methods allow a level of capacity at every point in time. In the current version, we have correspondingly pronounced SACs for our product families in the critical regions of North and Central America, Asia, and Europe. A SAC is defined as follows: • Mission: fulfill orders of products for a defined market without or within a given scope • Functions: order management, operations, SCM procurement, manufacturing, engineering, and appropriate shop floor environment • Obligations: build in time, cost, and quality; execute to gross primary productivity (GPP) and incoterms, local sales and service; and comply with local laws and regulation Project Execution Center (PEC) for Customer-Made Products SACs supplemented by the functions necessary for project realization define the PECs. PECs also uses the components from the CACs, as far as technically reasonable and logistically available, and supplements them by local procurement. These engineer-to-order (ETO) processes are synchronized to such an extent that projects can be carried out in segments at several locations. The goal is always to carry out the final assembly at the customer’s site using the available resources. The locations for the PECs in the division ight and production are therefore geared to the markets and customers. The largely identical conditions that require a non-territorial area as a starting point allow a highly flexible production location. A PEC is defined as follows: • Mission fulfills individual customer requirements and projects for a defined market with engineering within a given scope. • Functions proposes (concept/cost/quote) design and engineering, product management, order entry management, project operation, installation and acceptance, and appropriate shop floor environment. • Obligations build in time, cost, and quality; execute project plan and get final acceptance certificate (FAC); best practice sharing; comply with producing technology roadmap; support local sales and service, comply with local laws and regulation to GPP and incoterms; and support local sales and service. Figure 22.6 shows the locations of the division light and production as well as their specific role in the global production network for the different product lines.

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Fig. 22.6 Site roles in the division light and production

22.4.3 Support Functions in the Network As described, the goal was to bring the production into the lead. That means the global production network needed to be developed from the inside out. In this context, it should also be mentioned that the global production network includes only those functions that are directly and indirectly assigned to production. Business areas, such as sales or service, were not considered. However, the global production network is structured so that the necessary data and information is provided accordingly. An example is the SIOP process, which ensures that all requirements on the world route are consolidated centrally and then made available to the production plants in an appropriately prepared form. This also applies to the material requirements for service. Our global production network’s essential support functions are the process, method, and technology (PMT) team; the quality assurance team; the material management team; and the SCM team. Using the developed methodology, the PMT team leads the product allocation in the global production network and sets the planning consideration of the defined processes (e.g., MTS/MTO/ETO) and the available methods (e.g., one-piece flow). The quality assurance team is responsible for the test planning and quality control of the individual plants. This applies particularly to the CACs and the SACs, where selected characteristics are recorded in the course of the defined final inspections and made available to the central quality assurance department for production stability monitoring. Another important support function in the global production network is materials management. Starting with the SIOP mentioned above, the materials management department records all requirements worldwide. They prepare the requirements and schedule the demand coverage in the defined production units. The local material

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management units then load the requirements and monitor the delivery. A further task of materials management is designing the parameters for pull control across the plants and production units. In the global production network, it must be ensured that there is a vertical supply between the sites and a horizontal supply between the value creation levels (i.e., CAC, SAC, PEC). The last team to be mentioned here is the SCM team. Its task is to control the flow of goods in the global network and to manage inventories.

22.5

Quantified Optimization of the Production Network

For further optimization and product allocation in the global production network, a mixed-integer optimization model was created to evaluate and compare different future configuration scenarios. The implementation of the product portfolio in the model was carried out, taking the defined roles into account. The implementation also included the definition of suitable production/assembly segments and transportation connections (i.e., supplier-plant/plant-plant/plant-customer region). Since the employees’ know-how is a decisive factor for determining the personnel resources, this fact was reflected through the formation of resource groups. Each plant has a qualification matrix, and the plant-segment-resource-group-product-combinations are based on these matrixes. For simplification, the assumption was made that suppliers in the global network can supply every plant at any time and in infinite quantities. To keep the validation and application in the described use case as transparent as possible, further assumptions were made regarding the described structures of the Jenoptik division light and production. For example, each selected segment’s fixed costs at the selected locations were estimated based on historical data. A further simplification in validation also concerns the procurement of raw materials. Here only costly materials that are important for product quality and difficult to obtain are considered. Costs for small parts and auxiliary materials are included in the processing of the plant-segment-resource group-product combinations.

22.5.1 Validation of the Network Model The suitability of the developed optimization model for a realistic representation of the designed production network was validated based on a recent business case. A constant production demand was assumed for the validation period. This use case resulted in a valid plant-segment-resource-group-product combination of the plants in Germany, the USA, and China. Derived from the BOM, the following material supply/material provisioning restrictions resulted: • Three components are delivered from one CAC. • Four components are purchased and provided centrally.

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• All other positions in the parts lists are matched locally/with more favorable prices. The possible adaptation aspects for flexibilization and reconfigurability were not limited. Further strategic decisions were not considered. The main focus was on material, processing, and resource costs to analyze the results and compare them to the business case’s target values. Compared to the variable costs for a finished product, the numerical value deviates less than 5% from the costs estimated in the business case. Regarding the network configuration, a match between the optimization model results and reality is given, and the model can be applied to future cases.

22.5.2 Roll Out Following the successful validation of the optimization model for production allocation in global production networks, the division’s product portfolio was now examined step by step, and the production allocation was reviewed carefully. An initial review was carried out, taking into account the objective (i.e., economies of scale) for the existing CACs, to reduce CACs and focus them on the respective core technology. The result of the focusing was, among other things, the specification of three CACs for the products of the metrology business unit. One each for optical, pneumatic, and tactile components and the kinematics and electronics belongs to the respective product family. Connected to this is the constant check of the depth of value creation in our own production. The review for the implementation or dissolution of SACs is also a continuous process, mainly driven by the changes in the markets and the product portfolio. With the optimization model and based on the effectively implemented changes, we can now implement appropriate decisions to allocate PECs. The number, location, and size of the PECs depend solely on the customer projects. The created production network and the agile, resilient decision support provided by the optimization model enable us to act proactively.

22.5.3 Agile Adaption and Optimization After the validation showed that the optimization model could realistically map the product allocation and network configuration for our division, the model’s application was extended. Now different planning horizons from 3 to 10 years were considered. Various scenarios were also run through, for example, if a CAC is omitted or additional PECs are added. In this phase of the optimization model’s application, not only costs but also the customer-plant relationship of a plant to the customer region was also considered. The following lexicographic problem was formed from both target criteria (i.e., total costs and customer proximity). The total cost of ownership remains

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the top priority in the design and adaptation of the production network. Customer proximity is also considered. During the optimization process, no more than 10% deviation from the previously determined optimal total costs is allowed with increased customer proximity. From the modeling of different scenarios, it was possible to recognize that the CAC for optical components in Germany can produce the entire product range and volume over the entire planning horizon and is suitable and sufficient as a CAC. In contrast to the monocritical optimization, the modeling also shows that the plant in the USA as SAC for a selected product line only makes economic sense when a particular local sales volume is reached. Before that, the supply from an existing SAC is feasible and economically better. The optimization model could also be used to show possible reconfigurations resulting from political reasons. If, for example, a delivery from the SAC in China results in an economic deterioration due to the changed import duties, a change to a delivery from the SAC in Germany can be realized in the short term. This also applies if, for example, due to strikes or a pandemic, capacities in a plant are temporarily unavailable.

22.5.4 Flexibility Aspects The development of a flexible production network is a strategic task. Product mix and route flexibility are as much a part of the model as capacity and qualification flexibility. These and other flexibility requirements were taken into account in the design of the model. The segmentation of products (i.e., product tree) and production (i.e., segments) enables an agile and fast adaptation to a changed product mix. Besides, it is also possible for the production network to “breathe” quickly and flexibly either by capacity adjustments (i.e., production time per day) or using free capacities (e.g., SAC 1 supports SAC 2).

22.6

Continuous Network Monitoring and Improvement Process

22.6.1 Monitoring the Production Sites The monitoring is divided into two areas. On the one hand, each site’s current performance in relation to the other sites and site development is monitored. On the other hand, it is equally important to monitor the global production network’s performance and continuously examine it for optimization. Figure 22.7 summarizes these two aspects. The site-related monitoring is based on a relative consideration of the change in KPIs defined for all locations. This comparative approach is chosen to take into account the different characteristics of the locations. In order to get a holistic picture

Fig. 22.7 Monitoring of production sites

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of the performance status, the 12 selected KPIs are divided into four subject areas, namely, performance, cost, time, and employees. For each topic area, three key figures were defined. The relative deviation between the target and actual value is evaluated monthly. With the selected KPIs, it is ensured that only key figures were selected, which are available in the monthly reporting and therefore do not require a separate query or a specification of the content. The sum of the 12 evaluated characteristics results in the individual plant’s performance level and the associated classification of each site compared to the other sites. From the selected monitoring, it is also possible to derive which role (CAC/SAC/PEC) assigned to the site via the product allocation model has a performance-enhancing or performance-reducing effect.

22.6.2 Continuous Development Derived from the described monitoring model and often driven by new events, the methodology for designing and continuously improving the global production network in the light and production division was developed. Connected to this is also the simulation possibility of the network. This simulation enables us to check the effects of parameter changes in the network reliably. Examples are the addition or removal of SACs, CACs, or PECs; the change in market demand; or changes in the product portfolio. With the simulation option, situations can be evaluated, and future situations are modeled.

22.7

Summary and Outlook

22.7.1 Summary The developed model for product allocation in global production networks enables the design of a global production network for the light and production division, including flexibility and reconfiguration measures over a long planning horizon. The model has a clear network focus in which nodes, edges, and attributes of the network are equally considered. In product allocation, several products and product variants can be considered simultaneously. The multicriteria formulation of the objective function makes it possible to optimize several objectives that might conflict with each other. The production process is multistage, involving internal and external resources and capacities modeled. The ability to act is also given for long-term planning horizons of several years by the dynamic reconfiguration possibilities. Small fluctuations in the production network are compensated and balanced by internal and external capacity expansions. Route- and product-mix-flexibility are core components of both the model and the modeling methodology. The methodology is mathematically implemented as a mixed-integral, linear optimization program and can be solved using exact or heuristic solution procedures. The formulation of the evaluation model as a lexicographic optimization model leads to more

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transparency and traceability of the determined solution. The model is implemented in Python and thus offers interfaces for the integration of further modules. The developed and partially implemented model comprehensively meets the objectives and requirements of the operation. The created solution approach provides more security in everyday planning and, in contrast to the previous procedure, an objective, and through use cases, validated the decision proposal for strategic and tactical questions.

22.7.2 Outlook From the successful implementation and testing of the product allocation and network configuration models, numerous points of action for the further development of the optimization model and also for the further development of the production network of the light and production division result. A post-optimal analysis can be directly linked to the solution finding approach. Reconfiguration decisions made can be critically questioned and, if necessary, adapted for realistic implementation. Besides, used capacities can be reexamined and optimized to improve solutions by integrating slip variables. A user interface for the developed methodology will be developed to improve its usability. Furthermore, a graphic implementation is pursued for the presentation of solutions. Connected to this is also the increase of transparency and traceability of the solution.

Global Traceability as a Competitive Advantage: The Model-Based Approach of a Tier-1 Automotive Supplier

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Fabian Liebetrau

This chapter presents a model-based approach for global traceability at a tier-1 automotive supplier. After outlining the company background and an overview about today’s automotive supplier industry, the vision and key challenges for traceability are described. This is followed by an internally developed model to guide the implementation steps of traceability along three layers. The first two layers enable traceability for the production of parts within the company’s value chain as well as supplier information and ensuring the distinctiveness of parts. Further, the third layer supports in evaluating the potential recall risk from a supplier, defining countermeasures to reduce the risk, and developing suppliers from a long-term perspective.

23.1

Introduction

23.1.1 Company Background The Automotive Supplier Company is a global supplier of safety-critical parts for passenger vehicles. With annual revenue of roughly €2 billion and 8000 employees in more than 16 plants and development centers globally, it supplies parts to all major automotive original equipment manufacturers (OEM) globally. The company has been growing rapidly with an average 10% increase per year for the last 20 years.

F. Liebetrau (*) Automotive Supplier, Eschen, Liechtenstein # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6_23

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23.1.2 Today’s Automotive Supplier Industry The development of global manufacturing and sourcing networks has enabled many manufacturing companies to realize levels of cost-optimal manufacturing and customer proximity that were previously unreachable (Cheng et al. 2015). All this happened while further advantages of dispersed manufacturing such as access to local markets, talents, etc. were realized (Liebetrau, 2015). However, as the complexity of manufacturing networks and supply chains increased, the harmonized management of standardization and global alignment of processes (both in IT and for operating procedures), systems, data structures/storages, quality requirements, process parameters, etc. became a significant challenge for companies. This is especially true for companies that grow rapidly and as a result of this might not have strong central departments that focus on global alignment and standardization. While such a deficiency in global standards and alignments might not lead to issues in day-to-day operations, as workarounds are usually developed to ensure the running of existing processes, it yields a risk for errors and information losses along the value chain and the product development process. This risk then proves to be especially critical when extraordinary events test the stability of existing processes and systems. In the case of a supplier in the automotive industry, a critical defect on the supplied part at a manufacturing facility of an OEM1—or worse, a defective part in the field—usually challenges the ability of the supplier to supply the data of potentially affected parts.2 A defective part in a vehicle usually triggers extensive investigations as the defect might lead to casualties. The capability of a supplier to determine the number of affected parts in such a case as quickly and correctly as possible can be crucial to the further existence of the supplier as the costs connected to claims and recalls might significantly exceed the financial reserves of the supplier. A prominent case of such a recall is the Japanese airbag manufacturer Takata. After a defect in their airbags was identified that affected at least 26 million manufactured units, Takata was forced to pay for the recall and replacement of all affected airbags. The connected recall costs, as well as additional legal costs connected to the death of an estimated 17 vehicle passengers, finally forced Takata to file for bankruptcy. In the event of a defect in the field, the target of the OEM and the supplier must be the correct determination of affected parts and the rapid exchange of those parts. In such a scenario, economic aspects are quickly overshadowed by potential legal pressure, connected costs, and even ethical considerations. Although initially “only” 26 million units were supposed to be affected, Takata was forced to exchange airbags in more than 100 million vehicles Here: final manufacturer of cars. In the automotive industry, two scenarios in claims from OEM to supplier can be distinguished: (a) 0-km case: The defective part(s) were identified at the OEM plant before the vehicle was shipped to the dealer network. (b) A field case: The defect was identified at the dealer network or during the daily operations of the vehicle by the customer. The latter part is especially critical as the number of affected parts might be high and the connected recall costs and image loss are also high. 1 2

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to eliminate any risk of defective parts in the field. The increase of total vehicles to be exchanged might be attributed to a lack of data availability or traceability along the value chain of Takata. Although OEMs usually protect themselves against costs connected to recalls or casualties by holding the suppliers accountable through extensive contractual frameworks, the perception of their brand is sustainably damaged, and an economic loss is inevitable. This results in increased requirements by OEMs toward their suppliers regarding data transparency and availability. In some cases, OEMs request data such as process data, capacity data, and output data not only from their suppliers but also from sub-suppliers. Suppliers can leverage those requirements and mitigate the risk connected to recalls by developing their capability to ensure traceability along the value chain and promote it as a competitive advantage. If implemented properly, this capability both protects the supplier itself and increases the perceived value of the supplier in the perspective of the OEM as risks are decreased. Traditionally, traceability refers to the ability to trace back the history of an item through documentation (International Organization for Standardization, 2015). In the context of the automotive industry, this means that a manufacturing company needs to be able to trace back the history (shipping dates, production dates, batch data, process parameters, data of quality-relevant tests, component batches, component delivery dates, etc.) of their products along the value chain. As digitalization progresses, the question, however, is not whether or not a company can ensure traceability, but to what degree and availability a company can realize traceability. Today, the challenge is not to generate the data but to manage it in a way that, if an issue occurs, the number of affected parts can be narrowed down as much as possible. This requires a company not only to generate, store, and archive data in a way that it is readily available and easily accessible but also to employ strategies to mitigate the risk of large recalls. To realize traceability as a competitive advantage, therefore, means to be able to generate and manage large amounts of relevant manufacturing and supply chain-related data as well as continuously decreasing the risk of high-impact recalls by improving existing processes and value chains.

23.2

Vision and Key Challenges in Achieving Traceability

23.2.1 Idealistic Vision Behind Traceability Coming from the perspective of an OEM or a data analyst tasked with identifying factors that influenced the failure of a claimed part and limiting the scope of potentially affected parts, the vision for traceability is as simple as it is hard to achieve: Based on a unique ID3 per shipped part, the complete manufacturing history is available in an analysis system. This includes batch information, process parameters and information from all manufacturing steps, results from all quality 3

Ideally automatically scanned (e.g., a QR code).

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measurements, etc. not only for the final assembly but also for its subassemblies and components including data of equal level of detail from all suppliers and sub-suppliers.

23.2.2 Key Challenges in Implementing Traceability Creating traceability across a value stream that incorporates multiple suppliers, internal manufacturing sites, and machines from different vendors results in organizational challenges as well as challenges from an IT and process perspective. From an organizational perspective, one key challenge is the cross-department integration in defining and implementing the traceability concept. Achieving traceability and reducing the size of potentially impacted parts requires the participation of many departments such as IT, quality, engineering, supply chain, and production. As creating traceability is a cross-department endeavor where benefits and implementation effort do not affect the same departments,4 there needs to be a clearly defined overall responsibility and then further a combined cross-department approach both for implementation and follow-up. Additionally, a limitation of financial resources in automotive suppliers usually does not allow for a big bang rollout of a detailed new traceability concept including IT-system, data structure, and traceability solutions. Instead, in a step-by-step approach, interlinked improvements can be realized systematically. This is naturally also true, as the economic target of any producing company is the long-term usability of existing manufacturing assets. These preexisting manufacturing assets often do not fulfill the latest and highestlevel data collection, storage, or management. Therefore, a potentially high machinespecific investment must be evaluated against the potential further lifespan of the asset and the received benefit. However, determining a return of investment on a traceability concept is not possible, as traceability only reduces opportunity costs. The organizational challenges are, however, the smaller of the two. Implementing traceability does not only involve cross-departmental integration for concept development but also the very detailed linking of data created by different systems, processes, machines, etc. across different plants and countries. This complexity increases when information from suppliers and sub-suppliers is to be integrated into the traceability concept.

4

The main profiteers of a functioning traceability concept are usually the supply chain and quality departments in a central function, as they might be tasked with analyzing data whenever a claim from a customer is created that affects multiple manufacturing sites. The effort of entering data and applying any type of systems, standards, and procedures, however, does not lie with those departments but with IT and local plants and their departments.

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Fig. 23.1 Model for traceability implementation

23.3

Model-Based Approach

To tackle both organizational as well as IT and process challenges at the Automotive Supplier Company, a model-based approach was used to illustrate the responsibilities for traceability aspects on the various levels of the organization. Further, the model served for improving the interconnectivity between the different organizational layers to ensure seamless traceability across the value chain. The model is supported by tools and methodologies to apply the concept of traceability in the product creation process. This is especially important as for an automotive supplier, major investments are usually tied to new customer projects.5 Such an investment can include new machinery and other goods used for production as well as flanking or supporting investments such as plant build-ups, plant extensions, or investments for additional measurement or scanning equipment. However, some decisions in product and process design with impact on traceability have to be evaluated on a case-by-case basis for each new product (e.g., what are critical components, when and where is measurement data tied to which component, etc.), while others are more tied to the manufacturing site where the part is produced (e.g., how is material managed in the ERP6 system, what data is maintained for each product, etc.). The model in Fig. 23.1 is structured to clarify the tasks, responsibilities, and decisions to be made on the different levels. Depending on the content of the levels, decisions and setup of the content have a higher or lower impact on traceability. The

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A customer project is a customer (OEM)-specific design of a part or assembly that is granted by the OEM for a given timeframe (usually the series of a vehicle). 6 Tied to this is an estimated volume curve and a sales price so that the connected investments can properly be estimated.

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next sections will illustrate the details of the different layers. Roughly the layers can be described as follows: • Layer 1: Ensuring process traceability and eliminating the impact of internal manufacturing errors. This bottom layer is focused on collecting and matching manufacturing process data to the parts produced. • Layer 2: Ensuring component traceability and creating the link between final parts and component batches. This middle layer is focused on material management and supplying the correct information about the material provided for future traceability cases. • Layer 3: Limiting supplier impact. The top layer focuses on the impact suppliers have on traceability and potential recalls.

23.3.1 Layer 1 Layer 1 describes the traceability along the overall value chain of the parts to be produced within the company. This value chain is characterized by a series of machines located in one or more manufacturing sites. For a new product to be produced, the responsibility for defining the series of production steps and machines rests with the manufacturing engineering department. This department is also responsible for the definition of the process failure mode and effect analysis (PFMEA) and the implementation of control mechanisms to ensure the manufacturing of parts according to customer specifications. Based on this definition, a series of quality control procedures (e.g., the control of process parameters, visual control, and control of parameters from customer requirements) is conducted, and the created data is stored—mostly locally. This layer is probably the most important and most challenging as a multitude of different software7 is involved, not only by their function but also by potentially different manufacturers. Looking from a traceability perspective, the manufacturing data is sometimes available per batch—without part allocation—in decentralized databases. Additionally, the process data created here is often not linked to an ERP or manufacturing execution system (MES) as the data is often not managed in those systems. The target of this layer is the definition of a product-specific traceability concept. To do this, the following questions must be answered: • Which process/quality data is collected where (input are usually customer requirements, control plan, PFMEA, etc.)?

7 For example, programmable logic controller (PLC), Maschinendatenerfassung (MDE, machine data acquisition), Betriebsdatenerfassung (BDE, operational data acquisition), Qualitätsdatenerfassung (QDE, quality data acquisition), supervisory control and data acquisition (SCADA)

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• What level of traceability granularity is needed (none, batch traceability, handling unit (HU) traceability, part traceability)? • Where along the value chain is the desired level of traceability realized (e.g., for part traceability, does a QR code, serial number, RFID chip, etc. need to be allocated on the part)? This might be limited by: – The manufacturing process (e.g., if material is surface treated, a QR code or serial number might be destroyed by the surface treatment) – The possibility to collect data (no component-specific data is collected, or the part-specific data is of little analysis value, no part-specific traceability might be needed) – The possible benefit to be achieved (detailed process data can be available per component or part, and the process greatly influences the application and is needed to be evaluated for further process steps) – The cost for the implementation (applying a part-specific identifier and subsequently reading it costs money and might influence the process, e.g., increase in takt time) • For which component is traceability data needed (in many cases, manufacturing and assembly processes are not always linear and sometimes have branch-like structures. For these structures, it needs to be defined per branch and sub-assembly which traceability level is needed. Additionally, once two or more sub-assemblies are joined to a leading identifier and part to continue carrying the traceability, information needs to be defined)? To evaluate the points above, a detailed checklist for the bill of material (BOM) and the process flow of each new product is to be evaluated as part of the product development process. This way, it is ensured that a proper traceability concept is in place that fits the requirements of the business and is potentially priced into the original product calculations. Depending on the manufacturing infrastructure, it needs to be ensured that the link of generated data can be created between multiple machines. For this, many strategies exist: • The generated process data is stored in machine-specific databases, and analyses can be conducted based on the product identifier per machine. • The data might be directly handed over between machines or a connecting identifier is created for any component or sub-assembly handed over. This way a link is created along the value chain (machine-to-machine communication). • The data is archived in a central database and can be linked manually. • An MES acts as a link between multiple machines and manages the material flow and part identifiers between machines. This way an overarching link between machine-specific part identifiers can be created. This enables data storage, archiving, and indexing in a central database.

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While the latter option of using an MES is the most beneficial one, it is also the most complex to implement. Nonetheless, it is the preferred solution as the implementation of an MES has the potential to make the management of machine data across machines independent from the software of the machine supplier. Additionally, MES often come with material management functionalities. These functionalities can be used to better: (a) Manage the transfer of components and sub-assemblies between the machines and therefore ensuring first-in-first-out (FIFO) (b) Handle the feeding of supplier components into the machine and connecting batch information of those components to the production order or the partspecific identifier on the line As described earlier, however, ensuring complete traceability for an automotive supplier is a step-by-step process, and the granularity of traceability is not the same along the entire manufacturing value chain. This means that, depending on the overall infrastructure (both machines and IT) and manufacturing processes, the level of traceability granularity varies. Normally, the granularity decreases (from part-specific data to HU or batch-specific data) the further back we progress toward the beginning of the value chain. This also varies from product to product,8 but as soon as more traditional manufacturing processes are conducted (e.g., forming or surface treatment), the granularity and level of detail of traceability date decrease. This is also one of the limitations of a traceability strategy. Whatever the target state of the overall traceability strategy is, its realization stands and falls with the setup of the existing processes and manufacturing infrastructure. If, for example, component-specific data is desired, but the manufacturing process is designed in such a way that neither component-specific FIFO can be realized nor a componentspecific identifier can be applied, the maximum traceability that can be realized is per batch. And the size of a given batch can be up to tens of thousands or even a hundred thousand parts depending on the manufacturing planning of the company. Therefore, increased traceability in the later steps of a value chain needs to be checked in comparison to the actual benefit it achieves considering the level of traceability detail of the earlier manufacturing steps.

23.3.2 Interface Between Layers 1 and 2 As depicted in our model in Fig. 23.1, the MES acts as a link between Layer 1, which is the machine layer and generates manufacturing process-relevant data, and Layer 2—which focuses on material management in the plant. The MES does this by gathering data from the manufacturing process and linking it to information and tasks it receives from the ERP. The target for the Automotive Supplier Company was 8 Electronic components tend to have more detailed traceability data available as, for example, forged parts.

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to have a MES as the main interface for the shop floor operator. In the target state, an operator would not need to have an ERP interface for day-to-day operations. All confirmations at the line are either done in MES or machine-specific software. This includes material management. The material can be ordered, confirmed, deleted, blocked, moved, etc. in the MES. Naturally, the ERP remains the leading system for inventory management, etc. Therefore, a functioning interface between the two systems needs to be created. This is also important as the MES can link component-specific information (e.g., supplier batch, time in storage, expiration date, etc.) to the specific production order or even part information of the produced part. The MES thus has a critical position in creating traceability as it can unite traceability information from internal manufacturing processes with information about the components utilized in the process.

23.3.3 Layer 2 Layer 1 is focused on securing data regarding internal traceability and is thus especially safeguarding customer rejects based on defects that are caused by internal processes. However, a significant number of claims can also come from defects caused by supplier parts. In the case of a tier-1 automotive supplier, more than 70% of the parts assembled in a given product may come from external suppliers. While the costs of customer recalls due to defects caused by suppliers can in theory be charged back to the suppliers, they nonetheless result in an image loss for any automotive suppliers. Furthermore, if the supplier simply goes bankrupt due to the size of the recall, this will also affect the tier-1 supplier. Additionally, defects can also be caused by part characteristics that were not specified (properly). In such a case, the automotive supplier will be responsible for connected costs. Thus, it is important to mitigate the risk connected to potential recalls that are caused by supplier parts. Layer 2, therefore, has two main tasks. Firstly, it needs to ensure that important information regarding supplier parts (supplier batch, HU or part identifier, etc.) is managed in the system and shared with adjacent systems that require this information. Secondly, the layer has the operational task to ensure FIFO and reduce the risk of mixed/wrongly used components (e.g., similar components for different versions of a product). This matches with the traceability definition in the introductory section of this chapter. The task is not only to generate and connect relevant data but also to proactively limit the potential reach of defects caused by suppliers. Since the majority of tasks on this layer are connected to internal logistics or material/warehouse management, the responsibility of this layer lies within the supply chain management or logistics department. Starting with incoming material in a manufacturing plant, in this layer, it needs to be ensured that all incoming material is complete and does not have any visible damages. All material needs to be entered into the ERP system. The target state here is to have an HU or multilevel HU of detail. Besides the obvious information contained in an ERP regarding material in an HU, additional traceability data must be included (e.g., supplier batch identifier, arrival and potential expiration date, etc.).

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These HUs need to be managed by a warehouse management solution that allows gathering detailed information of the storage location. This enables the supply chain department to ensure detailed FIFO coming from the warehouse to the machines. As soon as the material is supplied to a machine, it needs to be ensured that this material is used in FIFO sequence; this includes partially used HUs. This layer, therefore, needs to have a process to manage partially used HUs. In reality, there are several ways to ensure from simple-operating procedures (e.g., storing partially used HUs that have been deleted in ERP directly at the line) or managing part removal from HU part specifically. For the latter, there needs to be either part-specific scanning or some sort of removal calculation with connection to the BOM and the number of produced parts. In reality, a mixture of those procedures is often seen at the manufacturing machine. Usually, some form of FIFO rack is located at the line that allows the extraction of HUs or components in a fixed order. Ideally, each HU is scanned whenever a new HU is used. For critical components, possibly part-specific scanning is also implemented. However, as part-specific scanning takes time and thus influences the takt time of an assembly machine,9 the decision of how and if a part-specific scan needs to be realized is a joint decision with manufacturing engineering in Layer 1. The next important task of this layer is to ensure that any sort of work in progress (WIP)10 material is also: (a) Managed in a material management system (ERP/MES/separate warehouse management solution) to ensure traceability of the created HUs (b) Also fed into the following process step in the FIFO sequence Again, this can be either ensured by a system-driven approach (HU information contains date and time of manufacturing) or by the creation of dedicated FIFO storages. In practice, this task is easier said than done, also depending on the definition of FIFO. Generally, batch-specific FIFO can be realized more easily since it just means that the produced volume of a production order needs to be consumed before the material of the next production order. Therefore, in reality batch-specific FIFO is realized. However, if batches are rather large, a part by part FIFO might be desirable. Yet, the practical application of this is much more challenging, as it requires (a) the feeding of HUs in the correct order to the lines,11 which is difficult if HUs are stored in, e.g., block storages, and (b) the removal of

9

Depending on the machine, the takt time might be between 3 and 40 s. A scanning operation that lasts for 3–5 s per part therefore decreases the output of the machine by up to 50% or might require a separate operator for scanning. In a price-sensitive industry, this is a significant impact. However, automatic and fast solutions for scanning can also be implemented and designed at a price, naturally. 10 Used for material that is not finished and will be consumed or worked on in further manufacturing processes. 11 HUs need to have information regarding their creation in a way that it can be handled by logistic personnel.

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parts from the handling unit in the order that they were placed into the HU, which would require special packaging design, as the oldest parts would normally be at the bottom of a HU and the packaging personnel would be committed to stick to a specific order for putting parts into a handling unit. The challenges and tasks in the previous paragraph also apply when finished goods are shipped to a customer (OEM). Normally, the level of detail to which traceability and FIFO needs to be realized is specified by the customer. These customer requirements clearly define whether part-specific traceability or batch traceability is realized. Customer-specific packaging might also enable the removal of parts in FIFO order. The application of those requirements to the manufacturing site-specific infrastructure is the task of the supply chain department. While the description of an idealistic target status of traceability with part-specific FIFO is easy to do, there are operational limitations or factors that influence traceability and that are not easy to eliminate. Table 23.1 contains some relevant limitations but is by no means extensive or complete: As described in the previous paragraphs, the main target of Layer 2 is to link traceability information from supplier part internal manufacturing data. Additionally, this layer has the task to reduce the occurrences of FIFO violations to limit the number of affected parts during a recall. In a corporate setting, it is the task of the central supply chain department to define processes and systems in a way that the plants can fulfill this responsibility.

23.3.4 Layer 3 Layers 1 and 2 focus on standards and activities that can be implemented within a company. However, as previously discussed, a supplier and its process stability and capability to ensure traceability may have a significant impact on potential recalls. The target of Layer 3 is therefore threefold: • Evaluate the potential recall risk from a supplier • Define countermeasures to reduce the risk • Develop the supplier from a long-term perspective Due to the nature of these tasks, the responsibility for Layer 3 lies with the purchasing and supplier quality management departments. Depending on the corporate setup of those departments, the influence of supplier quality in selecting certain suppliers is smaller or bigger. In many cases, the past quality performance of a supplier is at least evaluated in purchasing decisions. A traceability component or the risk for callbacks is, however, seldom included. For calculating the potential recall impact of a supplier for a given component purchased from the supplier, the following calculation can be used:

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Table 23.1 Overview of influencing factors for part traceability Influencing factors Bunker storage of parts

Packaging of parts

FIFO racks

Management of unfinished HUs

Management of WIP

Block storage

Storage locations/warehouse management

Operational issues with components in the manufacturing process

Detailed description For some parts or materials, a bunker storage concept might be chosen where new material is filled from the top, while material is removed from the bottom. This ensures roughly that FIFO is realized. However, it usually cannot be checked whether a given component batch is completely removed before the next batch is taken out For part-specific FIFO, the packaging of a material needs to be designed in a way that placing and removal of parts from a container in the same sequence is possible While FIFO racks ensure the removal of parts/ containers in the order that they were placed in the rack, it is important that (a) they are filled correctly because there will be most likely no checking of the correct sequence on the shop floor and (b) are designed in a way that similar parts or parts from different batches cannot be mixed Whenever an HU of components is not completely used after a production order is finished, it needs to be ensured that it will be the first one to be used for the next production order. This will decrease the number of potentially affected parts when a recall is based on a defect from those components The desired level of traceability (batch, HU, or part) needs to be ensured between machines or manufacturing steps as well. Various implementation scenarios exist: part-specific FIFO flows, part/material management in MES/ERP, etc. Block storage is a relatively cheap way of storing material as no dedicated rack is required. However, managing FIFO or having clear transparency where which HU is stored is difficult and might lead to a mix-up Depending on the level of detail how warehouses and storage locations are managed in the production plant, ensuring traceability is easier or more difficult to ensure. If the specific location of each HU is available in the system on storage bin level, it is easy to ensure the delivery of HUs to the manufacturing line in the correct order. If, however, HUs are just roughly managed in large storage locations, mix-ups might easily happen A variety of operational issues during a manufacturing operation might be tied to components. In such a case, the operational decision to test components from a different batch of the supplier might be taken. If this test succeeds, the parts of the new batch will be used. Naturally, the components of the other batch are often not discarded but used in a later production run if the (continued)

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Table 23.1 (continued) Influencing factors

Detailed description

Mixing of batches/parts by suppliers

components are in specification. If this happens, batches are mixed, and the number of potentially affected parts is increased While the general requirement toward a supplier is to deliver components in FIFO order, errors might happen at the supplier site as well. The failure to apply FIFO might lead to a potentially increased number of affected parts in a recall scenario

Table 23.2 Variables for calculating recall potential for given supplier part Variable Precall Vannual

Description Potential number of recalls per year for a specific component [#] Annual volume of the component procured from the supplier [#]

ppm

The number of defective parts per million delivered parts from the supplier (estimate or based on past performance) Probability to detect a defective component delivered by the supplier in internal processes (estimate) [%]

pdetection

Precall ¼ V annual  ppm  ð1  pdetection Þ

Exemplary data 2,000,000 pcs 2 ppm 80%

ð1Þ

From this calculation, the annual volume, as well as past ppm performance, is usually given. The ability to detect a defect depends on the employed processes and safeguards. If, for example, the part purchased from the supplier is fulfilling a clearly defined characteristic that is also mostly safeguarded by 100% in process control, there is no or very low risk of not detecting the defect. If no safeguard is employed, the detection percentage is rather low. In such a case, additional countermeasures can be implemented. Utilizing the exemplary data in Table 23.2, the following calculation can be done: Precall ¼ 2, 000, 000  2 ppm  ð1  0:8Þ ¼ 0:8 This means that almost one part per year might slip through our internal processes and might cause a recall at the supplier. If such a defect is non-systematic, a recall might only affect one part. However, if a systematic error is detected, the volume to be recalled increases to the size of one or more batches. Using the average batch size of the supplier as well as average recall costs per part, the overall costs for a recall can be calculated as follows (Table 23.3): C recall ¼ Batch sizesupplier  Precall  crecall part

ð2Þ

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Table 23.3 Variables for calculating potential recall costs Variable Precall Batch sizesupplier Crecall crecall part

Description Potential number of recalls per year for a specific component [#] Annual volume of the component procured from the supplier [pcs] Approximate total costs for recalls per year [€] Average costs for a field recall of a given part—might vary per OEM [€/pcs]

Exemplary data 0.8 60,000 pcs

500–1000 €/ pcs

Given the above exemplary values (Table 23.3), the approximate total costs for recalls (for one component of one supplier) per year can be calculated as follows: C recall ¼ 60, 000pcs  0:8  500

∖€ ¼ 24, 000, 000 ∖€ pcs

In this fictional case, defects caused by one supplier part might lead to costs of up to €24,000,000 per year. This is a fictional value, but the calculation has two main purposes: • It illustrates that to decrease this value suppliers can improve their ppm, produce in smaller batches,12 or enable part-specific traceability and data availability. Both activities fall in the area of supplier development. • In a discussion of whether or not additional safeguards are needed (100% control, sorting actions, safe launch, process poka-yoke, etc.), this value helps to address potential cost discussions. Compared to the other two layers, Layer 3 aims more at supplier risk detection than actually ensuring traceability. Nonetheless, suppliers need to be developed to a level where they can ensure FIFO and collect relevant data to enable substantial traceability analyses.

23.4

Summary

In this chapter, the traceability challenges and targets for the Automotive Supplier Company were outlined. To address these challenges, a model-based approach was introduced. In this approach, the different challenges are addressed along three layers which can be anchored in a company’s organization. By doing so, the relevant tasks are segmented into different departments making the reaching of the target level of traceability feasible. However, in achieving the target level of traceability, Naturally, a defect can also be found in multiple batches. However, the first reflex in a recall scenario is always the blocking/recalling of the batch where the defective parts were found.

12

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not only organizational challenges but also IT system and architecture-specific challenges need to be solved. Furthermore, it was illustrated that realizing traceability does not end with ensuring proper data availability and connection. Instead, measures need to be proactively taken to reduce the risk of batch mix-ups and adhere to FIFO as strictly as possible. Additionally, measures to reduce the risk of internal and supplier-related recalls should be implemented. The decision on doing so should include risk-based evaluation as well as financial feasibility. As the challenges in implementing traceability are significant, improved traceability can only be realized with significant effort and investments. To focus the effort and spread out investments, an approach with two levers is proposed: (a) closing significant gaps in the traceability landscape through the implementation of required software and hardware frameworks as part of major projects and (b) incrementally improving the levels of traceability along customer projects and the cross-plant implementation of best practices. The idealistic target state of 100% part-specific traceability, however, is easier defined than realized. Besides the previously described challenges, there are also clear limits that cannot be eliminated. Mostly, those limits are tied to existing manufacturing processes in which the improvement of traceability is either not possible (e.g., application of part-specific identifier not possible), does not yield a benefit (e.g., only process-specific data for forming processes is available), or the benefit of a higher level of traceability is clearly offset by the effort/investment required. Nonetheless, it is important that a company creates a specific traceability strategy that addresses its situation, plus its internal and external requirements.

References Cheng, Y., Farooq, S., & Johansen, J. (2015). International manufacturing network: Past, present, and future. International Journal of Operations & Production Management, 35(3), 392–429. https://doi.org/10.1108/IJOPM-03-2013-0146. International Organization for Standardization. (2015). Quality management systems— Requirements (ISO standard no. 9001:2015). Retrieved from https://www.iso.org/obp/ui/#iso: std:iso:9001:ed-5:v1:en Liebetrau, F. (2015). Strategic performance measurement and management in manufacturing networks (Dissertation, University of St.Gallen).

Index

A Artificial intelligence (AI), 68, 70, 190, 200, 220, 225, 227, 230

C Centralization and standardization, 27, 42, 43, 45, 50, 68, 70, 71, 73, 75, 303 Classification, 41 Complexity, 66, 113, 255–262 drivers, 15, 66, 67 handling, 255, 278 networks, 16, 27, 28, 36, 48, 66, 67, 88, 113, 138, 140, 144, 176, 205, 207, 249, 255–262, 264, 303, 304, 306, 315, 317, 336 processes, 28, 36, 50, 67, 75, 107, 113, 123, 132, 140, 144, 176, 200, 206, 221, 255–258, 261, 264, 267, 277, 302, 315, 336, 338 products, 27, 28, 50, 67, 75, 88, 92, 113, 123, 131, 132, 137, 138, 144, 176, 256, 267, 277, 302, 315, 317 reductions, 36, 137, 140 systems, 4, 58, 107, 204, 206, 261, 336, 338 Configurations, 17, 18, 25–27, 34–41, 48, 50, 68, 80, 87, 88, 92, 94–96, 101, 104, 122, 133–135, 138–140, 144–152, 168, 174, 176, 179, 180, 184, 185, 207, 213–215, 226, 237, 238, 244, 269, 273, 276–281, 289, 290, 292–294, 298, 299, 317, 324, 326, 329, 330, 334 Coordination, 13, 17, 18, 25–28, 42–48, 50, 51, 68, 80, 87, 88, 92, 94, 96, 119, 168, 174, 179, 180, 185, 226, 236–244, 251, 266,

267, 269, 273, 277, 280–283, 289, 291, 295, 296, 298, 299 Cost calculations, 48, 281, 308, 310 factors, 33, 36, 65, 66, 78, 92, 95, 98, 106, 118, 121, 125, 135, 136, 215, 239, 275 fixed, 135, 139, 262, 263, 266 improvements, 7, 9–12, 135, 139, 240, 299 overhead, 79 pressure, 1, 5, 7–9, 67, 75, 298, 317 reduction, 119, 135, 136, 139, 167, 295, 315 transfer, 74, 79, 183, 262, 263, 266, 267, 309 Cost pressure, 7–9 COVID-19, 63, 67, 112, 114, 234, 264, 298, 305

D Decision-support, 34, 65, 68–70, 75, 107, 129, 140, 211, 318, 330 Design for manufacturing (DfM), 117, 282 Digital Factory Management, 266 Digitalization, 5, 12, 15, 16, 18, 68, 127, 179–187, 206, 230, 250, 260, 261, 268, 269, 283–285, 299, 311–313, 315, 337

E Environments, 7, 17, 30, 38, 49, 53, 55, 63–76, 81–84, 88, 90, 91, 93–95, 103, 108, 109, 114, 127, 167, 174, 192, 204, 220, 230, 252–254, 256, 264, 267, 288–290, 298, 299, 302, 311, 323, 327 Event Chain, 258, 259, 267

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 T. Friedli et al. (eds.), Global Manufacturing Management, Management for Professionals, https://doi.org/10.1007/978-3-030-72740-6

351

352 F Failure Mode and Effects Analysis (FMEA), 169, 172, 340 Flexibility, 11, 12, 27, 29, 31, 55, 58, 63, 68, 73–75, 90, 92, 95, 101, 104, 106, 133–135, 138–140, 145, 149, 150, 169, 170, 227, 229, 234, 252, 255, 259, 262–264, 266, 267, 278, 297, 308, 314, 316, 318, 321, 331, 333

G Game theory, 220, 225–226, 229, 230 Globalization, 3, 5, 91, 114, 270, 317

H Hoshin Kanri, 265, 266

I Incentives, 27, 40, 42, 45–48, 50, 209, 213, 238, 241, 243, 244, 280, 291 Incentive system, 45–48 Industry 4.0, 1, 5, 12–15, 18, 189, 229, 262, 267 Information and Knowledge Exchange, 44, 45, 50, 304 International Manufacturing Networks (IMN), 5, 10, 14, 15, 17–19, 25–51, 63–76, 101, 219–230

J Just-in-Time (JIT), 52–58, 145, 152

L Lead Buyer Concept, 310 Lead Factories, 182 Lead teams, 295–297 Lean manufacturing (LM), 189, 190 Local-for-local, 104, 271, 302, 314

M Make/buy, 70, 266, 267, 304, 307–309, 315 Manufacturing Execution System (MES), 185, 194, 199, 261, 312, 313, 315, 340–344, 346 Manufacturing priorities, 8, 26, 29–31, 33, 49, 50, 89, 95, 215, 275 Mathematical optimization, 133–137, 223–225, 329–331

Index Modeling, 134, 147, 219–223, 227–229, 331, 333

N Network capabilities, 26, 31–35, 37, 49–51, 77–84, 87, 90, 92, 94, 96, 215, 233, 235, 272, 290, 291

O Offshoring, 8, 9, 102, 179, 187 Operational Excellence (OPEX), 5, 14, 16, 17, 51–54, 57, 155–164, 204 Operationalization, 36–40, 52, 53, 158–161, 212, 215 Operations research, 52, 219–230 Optimization, 14–16, 18, 27, 28, 42, 49, 51–59, 63, 68, 75, 77–84, 96, 126, 129, 133–140, 146, 149, 150, 152, 155–165, 191, 199, 219, 220, 223–225, 227–230, 288, 301–318, 320, 321, 329–331, 333, 334 Order allocation, 32, 315 Order assignment, 150, 151 Order shifting, 262 Organizations, 2, 4, 14, 28, 34, 36, 42, 43, 47, 51–53, 58, 64, 66, 90, 96, 97, 144, 155, 157, 158, 162–164, 170, 193–195, 251, 253–255, 264, 267, 271, 280, 281, 286, 292, 295, 296, 299, 300, 302, 304, 305, 308, 313, 314, 324–325, 337, 339, 348

P Performance, 159–161, 170, 171, 203, 266, 331–333 barriers, 238 indicators, 65, 156, 167, 169–171, 174, 175, 203, 213, 238, 239, 241, 318 measurement system, 163, 168, 296 networks, 14, 15, 18, 34, 40, 46, 65, 81, 83, 84, 155–157, 163, 164, 167–171, 174, 175, 211, 220, 238–243, 265, 289, 299, 317, 318, 331 sites, 34, 40, 46, 47, 52, 58, 155–164, 168–170, 184, 207, 211, 239, 265, 289, 308, 317, 331, 333 Prioritization, 30, 49, 55, 172, 291 Product allocation, 133–135, 281, 313 Product classification, 256, 306–308 Product cost calculation, 308, 315

Index Production system, 2, 14, 51, 54, 55, 79, 144, 155, 158, 164, 191, 222, 313, 321 Product transfers, 262, 263, 266, 267

353

R Resilience, 35, 115, 229, 230, 264, 306 Resource sharing, 39, 45, 46, 50, 291 Risk, 65, 74, 169, 262–264, 345 exposure, 67, 229 mitigate, 76, 262–264, 337, 343

Site comparison matrix, 207–208 Site managers, 18, 31, 34, 45 Site missions, 304–307 Site portfolio, 16, 34–41, 48, 104, 212, 278 Site roles, 12, 13, 27, 34, 113, 208, 272, 291, 317, 326–327 Site selection, 101–115 Site target profiles, 208–211 St.Gallen management model, 16, 18, 25–50, 68, 101, 168, 226, 269, 272, 275, 280, 283, 285, 289, 290, 298 Strategy, 27–34, 87–99, 234–236, 251–255, 272, 302–311, 325 contents, 87–89, 93, 94, 96, 292 context, 83, 88, 90, 93, 94, 96, 283 deployment, 90, 249, 265, 266 development, 87, 97, 251, 252, 266, 267, 277, 283, 312, 325–329 gap, 27, 33–34, 252, 268, 291 Subnetworks, 27–29, 31, 33, 36, 48, 79, 87–99, 170, 235, 238, 239, 241–244 Support Functions, 328–329

S Sales & Operations Planning (S&OP), 259 Sand cone model, 10, 12, 15, 95 Simulations, 68, 75, 135, 174, 175, 219–223, 227–230, 261, 283, 314, 317, 333 Site capabilities, 92, 94, 274, 277 Site classification, 37, 38, 207, 276

T Technology transfer, 18, 179, 180, 183, 184 Total Productive Maintenance (TPM), 52–58 Total Quality Management (TQM), 14, 52–58 Traceability, 18, 170, 191, 334–349 Trade Off sand cone model, 10–12

Q Quality, 2, 7, 9–11, 17, 27, 29–31, 46, 48, 51, 54, 55, 57, 58, 63, 67, 75, 81–83, 90, 92, 95, 103, 119, 122, 124, 132, 133, 138, 140, 156, 158, 159, 167–176, 189, 192, 193, 196, 227, 229, 239, 242, 251, 254, 257, 261, 263, 280, 287, 289, 292, 296–299, 302, 305, 311, 321, 326–329, 336–338, 340, 345