Thomas Kaiser · Oliver D. Doleski Advanced Operations Best Practices for the Focused Establishment of Transformational Business Models Advanced Operations Thomas Kaiser · Oliver D. Doleski Advanced Operations Best Practices for the Focused Establishment of Transformational Business Models Dr Thomas Kaiser Siemens AG München, Germany Oliver D Doleski Fiduiter Consulting Ottobrunn, Germany Translation: Global Translation Services (GTS) The translation costs from German into English were borne by Siemens AG ISBN 978-3-658-27584-6 ISBN 978-3-658-27585-3 (eBook) https://doi.org/10.1007/978-3-658-27585-3 Springer Vieweg © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 This work is subject to copyright All rights are reserved 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 Vieweg imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH part of Springer Nature The registered company address is: Abraham-Lincoln-Str 46, 65189 Wiesbaden, Germany This book Contains • Guidance concerning the structured selection of fields of application having a bearing on competition that will be critical for commercial success in the context of digital transformation • Assistance with the careful combination of use cases to achieve holistic business objectives • Description of a structured transition from the simple to the complex based on realistic expectations of outcomes • Presentation and explanation of an effective phase model for the focused establishment of digital business processes and models • Assistance with the development of competitive business models v Foreword This book originates from a chapter of the book “Herausforderung Utility 4.0Wie sich die Energiewirtschaft im Zeitalter der Digitalisierung verändert” (“The Utility 4.0 Challenge—How the Energy Industry is changing in the Age of Digitalization”), which was published by Springer in 2017 and appeared in the same year as a self-contained essential The book, which amounts to 40 chapters in all, contains contributions from prominent authors from the academic and hands-on spheres illuminating key digital transformation issues in the energy sector against the background of the fundamental transition from an analog to a digital energy business The authors not limit themselves to an abstract depiction of a theoretical digitalization concept for the energy sector, instead offering the reader a comprehensive insight into selected concepts, smart technologies and concrete business models for the digital energy system of tomorrow This text is a translation into English of the complete revised and updated version of the chapter “Digital transformation, but how?—from ideas to realization planning” written by Thomas Kaiser The original text had a particular focus on the energy industry, but this has been replaced with a broader, cross-sectoral perspective for publication in the Springer Vieweg The other main change to the original text, alongside this move to a wider focus addressing all sectors and industries, has been the addition of a substantial amount of new content covering relevant questions and factors in relation to business model development vii viii Foreword This book begins with a clear and concise introduction to the principles of the key term advanced operations Thanks to its convenient format, this book is able to set out the principal elements of the advanced operations concept in concentrated form over just a few pages Munich September 2019 Dr Thomas Kaiser Oliver D Doleski Contents 1 Introduction Target Scenarios for Digitalization 2.1 Commercial Motivation for Digital Change 2.2 Filling in the Detail of a Target Scenario Suitable as a Guide for Action 2.3 Organization of Digitalization Initiatives 10 Development and Management of the Digital Use Cases 15 3.1 Evaluation and Prioritization of Identified Use Cases 15 3.2 Standard Procedure for the Focused Establishment of Digital Initiatives 17 Implementation-Related Success Factors 25 The “advanced operations” Transformation-Capable Business Model 31 5.1 Starting Point: The Business Model 31 5.2 Advanced Operations as a Business Model 32 5.3 Pragmatic Hypotheses for Advanced Operations 40 Bibliography 45 ix About the Authors Dr Thomas Kaiser serves as Senior Vice President for Siemens IoT Consulting Group headquartered in Munich, Germany Based on his vast experience he became a trusted advisor for digital transformation to many management board members and across various sectors Having been Managing Director for an US based global consulting firm before he embraced the full management and operational consulting experience—from its very early stages of the Big Data hype to the professional routines of IoT and digital related use cases in the meantime Dr Kaiser earned his doctorate degree in Economics while he strives for the balance between the inspiration of conceptual learnings versus their pragmatic applications in operational business life xi xii About the Authors Oliver D Doleski is a management consultant active in a wide range of industries and founder of Fiduiter Consulting He studied economics in Munich and has held various senior positions in public service and with the German global market leader in the semiconductor industry Today Oliver D Doleski is particularly interested in digital transformation, process management and smart markets and his main area of research at the moment is business model development He shares the expertise accumulated from his hands-on experience and research as the publisher and author of numerous publications and specialist books 4 Implementation-Related Success Factors 29 The stabilizing phase in change management Finally, new routines should be brought to employees’ attention, so that they can be experienced and learned, in a stabilizing phase approximately two years later, by means of: • Incorporation of corresponding digital transformation target variables, milestones and degrees of fulfillment into target agreements combined with the relevant incentive systems • Development of a training curriculum to enhance employees’ knowledge and capabilities strategically—certifiable learning solutions should be established if scope and complexity so indicate • Complete communication routines to ensure that knowledge (primarily via digital channels), emotional appeals (primarily via campaigns and PR) and news of opportunities to become involved (primarily via expertise platforms and communities of practice) are always spread effectively Stabilization through application routines in day-to-day operations is not the end of the matter, however, and it is important also to mention certain overarching considerations of a fundamental nature for business So far, the focus has been “just” on a pragmatic decision-maker’s perspective of how an organization operating in a competitive environment should begin to tackle new opportunities (with risk in mind) and/or take on new competitors successfully This in no way relieves management of the fundamental duty to review, adapt and develop the overarching business model The next section, which is also the last, discusses two different ways to approach this aspect First of all, there is the seamless process of “telling the story” of how the strategic core of the business model should be optimized, after the initial learning curve, in line with the successful use cases This introduces further considerations into the digitalization target scenario procedure from Chap. 2 Alternatively, the framework introduced below can also be used to highlight the strategic train of thought when configuring a business model in respect of a new, “unconventional” competitor with expertise in the data sphere The “advanced operations” Transformation-Capable Business Model This section aims to introduce the reader to the basic idea of advanced operations as best practice for the focused establishment of transformational business models It begins with a brief look at the general concept of business models 5.1 Starting Point: The Business Model It seems advisable, prior to embarking on a more detailed discussion of commercial approaches for realizing new, digital business ideas in the context of advanced operations, to establish a common understanding of what the term “business model” actually means Defining the term “business model” A business model is a simplified and idealized description of the basic principle of how an organization’s business activities play out in practice It sketches out how a company creates value and places its wares in the relevant target markets Business models are applied, holistic outlines of all of a company’s value-creating workflows, functions and interactions that create customer value and thus generate revenue to secure the company’s economic existence Put another way, a business model fills in the detail of the business idea on which it is based (cf Doleski 2014, p. 652) © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 T Kaiser and O D Doleski, Advanced Operations, https://doi.org/10.1007/978-3-658-27585-3_5 31 32 5 The “advanced operations” Transformation-Capable Business Model The upheaval caused by the impact of digitalization in virtually every area of society and commerce is creating a pressing need for new and radical solutions Digitalization is making it all but infeasible to continue running a business on the basis of conventional analog operations to the extent that strictly traditional approaches are verging on the obsolete The advent of digitalization is both a blessing and a curse for businesses because while it opens up new opportunities with one hand, it threatens to kill off existing business models with the other (cf Schallmo 2016) The foresighted development of business models fit for the future is thus an existential priority Business models generally create the conceptual framework for the systematic realization of digital business ideas and the opening up of new business areas and therefore represent the practical toolkit available for implementation in advanced operations initiatives Economists and business practitioners have devised countless business model concepts capable of developing and growing a business Business models are of course explored in great detail in the literature and there is thus no need for a more detailed discussion of the relevant concepts in this book Readers who wish to pursue this aspect further and have a knowledge of German are advised to seek out Business Model Canvas by Osterwalder and Pigneur, Wirtz’s Business Model and Integrierte Geschäftsmodell iOcTen (cf Osterwalder and Pigneur 2011; Wirtz 2011; Doleski 2015).1 These approaches all stem from academic research in economics, but there are also other practical aids and tools for the development of business models available including the proven Siemens Business Model Framework BizMo (cf., once again, Mütze and Gerloff 2019) 5.2 Advanced Operations as a Business Model Every business model can in principle be characterized comprehensively with reference to ten elements These constituent modules together provide a seamless picture of how revenue is generated and consequently serve to present a complete description of companies’ business activities Figure 5.1 depicts the principal content of these ten business model elements as a guide and an aid to comprehension The business activity of a company is actually described with reference to ten model objects These model objects together map the normative, strategic and 1For a comprehensive overview of relevant business model approaches, see in particular Schallmo (2013) 5.2 Advanced Operations as a Business Model Financing Financing sources Cost structure Partners and partner channel Partner relationships Exchange of performance Customer requirements Performance Benefit 33 Customer segment Customer channel and communication Customer relationship Strategy Customers Finance Partners Value Market Revenue Processes Value creation Value chain Value chain configuration Personnel Knowledge/expertise Resources Strategic objectives Management and organizational structures Problem-solving behavior Elements and their content Market structure Market segmentation Competitors Enablers Normative dimension Strategic dimension Corporate philosophy Corporate objective/purpose Corporate culture Operational dimension Revenue strategies Pricing policy and price strategy Pricing Fig. 5.1 Ten model objects describing the business activity Doleski (2017, p. 630) operational dimensions of management in full, establishing the link between this approach and the St Gallen Management Model The first element, normative framework, represents the normative dimension of a business model, the value and strategy elements the strategic dimension The customer, market, revenue, enablers, processes, partners and finances elements are assigned to the operational viewpoint (cf Doleski 2015, p. 13) The aforementioned elements represent the generic basic modules of a business model and appear in principle in all models irrespective of sector and activity Taken together, these ten elements form the core of the iOcTen integrated business model (see again Doleski 2015 for details) The strategic core The operational elements having already been illustrated and described in the form of practical options in the preceding sections, the intention in this section is to look at the strategic core of the model in greater detail The sustainable success of an initiative (see Chap. 1 through 4) and, even more so, the (subsequent) longterm innovative capability of an organization will be found to correlate precisely with this combination of strategy reflected onto the customer value the company is capable of generating The term “advanced operations” is used with this very much in mind: it will not be sufficient in the long term simply to keep on initiating further waves of digitalization initiatives if the strategic core of the business (model) does not also undergo a transformation 34 5 The “advanced operations” Transformation-Capable Business Model Hypothesis concerning strategic differences between industries Readers are probably best advised to decide for themselves where their company sits in the following context and to bear in mind that not all examples will necessarily follow the very same pattern The distinctions addressed below should not on any account be mistaken for a degree of penetration and thus for competitiveness achieved through innovation per se, although a certain degree of correlation can be assumed Reference to and examination of current annual reports reveal some recurring patterns that enable a rough breakdown to be created as follows: • Group 1—“radically aligned with market and customer” A first group of companies already have data and IT elements integrated into their value strategy simply because this is an inherent part of operating in their particular sector Examples include, in particular, companies from the financial services, media, IT services, entertainment and communication sectors, which have implemented pioneering fields of application almost automatically simply in order to remain relevant The product or service concerned is very much the focal point here, with its presentation to the customer or user as such playing a secondary role (cf standards-based: Glass and Callahan 2015) Numerous use cases—often referred to as “applications”—have been embedded in the working and private lives of a digitally literate society for a long time now It seems immediately transparent how the operational elements of the business model must have changed permanently as a result Or consider the matter the other way around, as it were: how could the transformation of a bank possibly have succeeded if, for example, the same old procedures with the same old credit officers and unchanged customer relationships had been retained despite the managing board having adopted a digital SME strategy years ago Various studies show the profound longer-term changes in the working world (cf for an exhaustive report the Oxford study by Frey and Osborne 2013) It can be assumed that disruptive change will remain the order of the day in these sectors owing to the enormity of the effects on their value creation structures Nowhere does the blanket transformation of the business model appear more comprehensive than in the sectors mentioned, which have seen the core of their strategic value change significantly as a result—with further change assured • Group 2—“providing added value from within” A second group is distinguished by the unanticipated but now trail-blazing influence of data, information and application knowledge on the predominant business model The original strategic benefit—and with it the tangible or intangible product— remains the primary differentiating factor, but success is increasingly going to 5.2 Advanced Operations as a Business Model 35 depend on digital solutions (cf with emphasis on the trend: Davenport 2013) Typical sectors for this group include: automotive (personal mobility, but now with telematics-based real-time optimization), logistics (supply of goods, but now with multimodal environmental optimization), retail (mutually beneficial exchange, but now with increased transparency efforts and uptake of options on both sides), industrial and systems engineering (manufacturing and assembly, but now with life-cycle-optimized early warning system) and tourism (recreation and experience, but now with context-sensitive attractiveness boost) This is of course just a very rudimentary survey of selected examples There is in fact no need for greater detail, as these examples on their own illustrate how great the strategic benefit from transformation can be The advanced operations component is just as prominent in this second group but is evident only to a limited extent in the competitive overprovision of data analytics application innovations and is (should be) generally used to complement the business model concerned in a balanced and selective manner to enhance strategic benefit • Group 3—“tactical-selective” A third group appears on the surface to operate in a more peaceful setting in which oligopolistic or even natural monopolistic structures (still) dominate Examples include the various primary and utility sectors, the construction industry and fundamental public sector services The obvious (that is to say inherent in the nature of the business) value strategies are certainly not wholly isolated from innovations in data analytics, but tend not to be challenged disruptively by new competitors or technologies Pressure to adapt business models in this group comes (has come) primarily from legislation and regulation or in the form of shocks of external origin Sudden significant increases in value are uncommon too, with use cases and fields of application generally advancing in a more incremental manner with a predominantly internal focus This initial breakdown created on the basis of rough sector groupings has concentrated on the speed of model transformation so far, using the operational combination of benefit and strategy as the natural entry point to the interior workings of the model Advanced operations can pursue different perspectives The phenomenon of the internal perspective versus the external perspective seems though to be setting the trend The simplified business model chart (Fig. 5.2) is intended to help illustrate this brief more detailed examination Value Trigger of transformaƟon DirecƟon of transformaƟon Fig. 5.2 Perspectives for advanced operations Legend: Revenue Market Customer Enablers Processes Partner Finance Strategy For example: Industry 4.0 producƟon integraƟon Benefit models supported by data (for example mechanical engineering): upgraded product ideas Efficiency and/or in-house innovation dominates Internal perspective For example: markeƟng management Benefit models that revolve around data (for example the media): new product ideas Market and customer dominate External perspective 36 5 The “advanced operations” Transformation-Capable Business Model 5.2 Advanced Operations as a Business Model 37 The model, as can be seen in the right-hand side of the octagon, emphasizes the external perspective concerned in operational terms with the organization’s position in respect of the market, customers and revenue The left-hand side covers the internal relationship between the operational elements plus the efficiency-side of business operations Interestingly, there seems to be one clear tendency that sets apart the first group mentioned above—the business models that have traditionally focused on data information (the financial sector, for example)—from the other sector groups and that is the triggering element Target scenarios for the other groups are triggered by efficiency considerations or a desire to optimize costs, whereas in this first group it is new product and solution ideas that lead the way (see Chap. 2) Refer for examples to the broad-based BARC study for the German-speaking region (cf BARC study, Bange et al 2015), which provides evidence to substantiate this theory These sector representatives consequently take a closer interest in fields of application associated with sales success, marketing, customer segmentation, customer relationships and customer retention The transformation of the business model (to reach advanced operations) is triggered from the righthand side of the diagram and the operational elements on the left-hand side—in particular the value chain—have to comply with the change taking place all the way to a state of disruption The industries of the second group, in contrast, tend to favor a balanced alignment This is particularly true of retail and mobility sectors, which are quite distinct from those in the same second group (mechanical and industrial engineering, for example) and, even more prominently, the third group that concentrate on internal digitalization In this latter instance it is innovative process optimization efforts in manufacturing and logistics or controlling and risk management that dominate the agenda In this case too, the two sides of the model can be seen to interact, although the impact is less disruptive and has a less radical effect on the speed of transformation One notable example is the Industry 4.0 concept (cf Brödner 2015 for a balanced presentation), which strategically taps new benefit categories (such as ML and RPA) with pacesetter technologies (advanced analytics applications) and is based on the organization’s own value creation and technology expertise The result, from the provider angle, is a new pattern of market and customer segmentation, extended revenue strategies and strategic forward integration that represents a genuine transformation of an organization’s business model and can therefore also amount to the same, over time, for representatives of the customer market 38 5 The “advanced operations” Transformation-Capable Business Model As a prima facie conclusion this demonstrates: • Business model transformations and the recognition of their necessity by management can have endogenous or exogenous origins • The speed of transformation correlates positively with advanced analytics applications directed from and to the outside • It feels (and this can probably even be measured) that these applications impacting directly in the customer markets clearly dominate in terms of awareness, popular legend and admiring recognition in the literature—and the stronger the “digital” element of the customer relationship experience, the more powerfully this effect comes to bear • It should therefore come as little surprise to realize that the more communicative and enthusiastic approach to spreading the word among US stakeholders combined with the apparently inexhaustible advanced analytics lab that is the West Coast has made the US a dominant ecosystem of opinion leaders (cf Keese 2014) Particularly popular applications from US-based companies like Amazon, Tesla and Alphabet consequently occupy the limelight • While they certainly merit this status, it would be a mistake to equate profile and popularity with the quality and adequacy of the business model transformation: advanced operations as an innovation-friendly digital transformation model does not necessarily have to be spectacular, it just—and this goes for all sectors—needs perfect integration Advanced operations are perfectly integrated A competitive aspect not examined in any detail in the preceding discussion, perfect integration can also serve as a quality seal for successful business model transformation and thus for advanced operations The real winners from digitalization are and will be those organizations that understand how to integrate the possibilities perfectly Perfection is achieved by focusing the best technologies of advanced analytics— using the model nomenclature chosen here—on the “core strategic benefit” This is why, in fact, the authors considered it so essential to include the illustration of the concept in Chap. 2 and the explanation regarding methods for ensuring its realization in Chap. 3 An additional phenomenon that was already a familiar success-critical factor under the old (analog) regime now comes into the picture alongside this design flexibility, which is aligned with the competitive strategy—and thus takes account of the normative framework 5.2 Advanced Operations as a Business Model 39 Advanced operations create an exclusive technology element This process ultimately yields an exclusive right of disposal—a module of what is now a digital value chain—starting with the recipient of the value and returning to the recipient of the value Advanced operations have thus understood how to create a “place”, a “section”, a “bottleneck” and/or an “exclusive territory”, permission for entry to or participation in which is subject to self-created and enforced rules These rules are themselves defined and protected technologically—either disruptively withheld from the traditional “players” or successfully incorporated into the existing business model The following examples are intended to provide an illustration, at least, of advanced operations in practice: • Amazon is degrading bricks-and-mortar retail with its own always-open global ordering platform of unprecedented size, reach and purchasing power • The elite of the research-driven pharmaceutical manufacturers use the fastest self-learning algorithms in perfectly selected test phases of clinical trials (for the parts not outsourced) • Google’s application assuredly needs no further explanation • Leading insurers and re-insurers are able anticipate which tangible risks will predominate faster than their competitors thanks to their exclusive investment in learning models It is the space constraints of the present format rather than the lack of suitable examples that prevents this list going on and on Advanced operations use advanced data technologies (advanced analytics) There is thus a common characteristic both in the action and in the result: an integrative way of looking at the existing or targeted business model facilitates the conquest, defense and development of the perfect mechanism, which leaves the rest of the players to let them not opt out for this new game—which usually means to let them pick up a share of the costs In our model, it is accordingly the changing of the revenue strategy that so often serves as a quality seal for advanced operations To reiterate, this trademark has been sought-after forever in every competitive sector—it is “just” that the dynamism of the change and resources involved have become disruptive The scale effects here easily become incommensurable at the relevant scales: the sheer volume of data involved—we talk about “big data” for a reason—opens up new benefits, new strategies, new customers and markets and also new revenue strategies to which the competition has no option 40 5 The “advanced operations” Transformation-Capable Business Model but to respond Technological progress on the path to artificial intelligence appears unstoppable and will soon produce new mechanisms and new scale effects through self-referential learning A very interesting overview and outlook regarding the machine learning-artificial intelligence continuum can be found in the study “Maschinenlernen im Unternehmenseinsatz” (cf Böttcher et al 2017), which the authors recommend Readers are also recommended to ponder on the hypotheses concerning the principles of advanced operations derived in the concluding Sect. 5.3 and check them rigorously against the situation with their own decision-making and applications 5.3 Pragmatic Hypotheses for Advanced Operations A few clear-cut hypotheses formulated on the basis of the many factors raised in the wide-ranging discussion above, which in this particular publication format are often derived from experience and feedback rather than being comprehensively underpinned by science and theory, are set out in the following They augment the guidance on operational implementation at the level of a digitalization initiative presented in the preceding sections with long-term considerations regarding corporate strategy aspects of the realization of advanced operations as the concept has been introduced here Specifically, and not necessarily in any chronological order or order of priority: The speed of change and shifts in customer expectations vary from sector to sector, but the sectors that (still)—wrongly—believe changes driven by digitalization or data analytics are someone else’s concern need to be very sensitive to their situation and the developments affecting it Establishing advanced operations is not about the fastest possible penetration of all organizational areas (breadth) nor purely about leading-edge technologies (depth), but rather about accurate choices, based on situations and predictions, in relation to all original aspects of value generation and their efficient transformation through the organization’s own value chains The identification, definition, defense and/or conquest of revenue-generating mechanisms—which stand at the core of advanced analytics—have top priority Corporate return on investment thus comes before the play instinct 5.3 Pragmatic Hypotheses for Advanced Operations 41 The focus on internal efficiency gains that predominates at the moment (the left-hand side of our model in Fig. 5.2) appears advantageous in operational risk minimization: setbacks are not really accompanied by disappointed customer expectations On the other hand, conceptually underilluminated market- and customer-side applications/focuses are marked by a strategic negligence in terms of risk that could become toxic Balanced portfolio management and constant data science tracking integrated with the organization’s own innovation pipeline is therefore recommended Advanced operations never stop and are always ready for (measured) action if customers, competitors or exogenous factors change the game (advanced requirements) Organizations should strive for the leading edge—in the form of artificial intelligence—sooner rather than later because volume, speed and intelligence as such mutually reinforce each other The implementation recommendations (in terms of legal, organizational and practical decision-making factors) concerning effectiveness and efficiency presented in the operational sections must not be neglected Strategy papers and universally understood business models and their transformation not in any way reduce the gravity of elementary implementation errors 10 Anyone troubled by the novel term “advanced operations” should feel free to replace it with their own better alternative: this modest compendium makes no claim to authority in the matter Takeaways from this book • A good understanding of fields of application that have a bearing on competition and are likely to be critical for commercial success in the context of digital transformation • Assistance with the structured transition from the simple to the complex with reference based on realistic expectations of success • An explanation of an effective phase model for the focused establishment of digital business processes and models • Structured instructions for digital business model transformation © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 T Kaiser and O D Doleski, Advanced Operations, https://doi.org/10.1007/978-3-658-27585-3 43 Bibliography Aichele, C., & Doleski, O D (2013) Smart Meter Rollout—Praxisleitfaden zur Ausbringung intelligenter Zähler Wiesbaden: Springer Vieweg Bange, C et al (2015) Big Data Use Cases 2015—Getting real on data monetization Study Würzburg: Business Application Research Center (BARC) https://www.sas 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ISBN 97 8-3 -6 5 8-2 758 4-6 ISBN 97 8-3 -6 5 8-2 758 5-3 (eBook) https://doi.org/10.1007/97 8-3 -6 5 8-2 758 5-3 Springer Vieweg © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 This work is... Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 T Kaiser and O D Doleski, Advanced Operations, https://doi.org/10.1007/97 8-3 -6 5 8-2 758 5-3 _1 1 Introduction • The possibility that now exists... Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 T Kaiser and O D Doleski, Advanced Operations, https://doi.org/10.1007/97 8-3 -6 5 8-2 758 5-3 _2 2 Target Scenarios for Digitalization 2.1 Commercial