Keywords

1 Integrated Usage

The Internet of Production increases the opportunities for gathering data in the development, production, and the usage cycle of companies. Within the user cycle, data about the usage of products is collected and shared between other domains to enable the development of products, processes, and even business models. However, usage data are seldom utilized across companies or departments to optimize operations, investment decisions, or innovation processes. Learning and analytics can take place faster and more efficiently if manufacturers not only utilize their own data but also can access data from similar contexts in other entities. Our work describes the interplay and trade-offs between governance, organization, capabilities, and interfaces (GOCI) from a company-internal and -external perspective to enhance sustainability and profitability in an Internet of Production (IoP, see Chap. 1, “The Internet of Production: Interdisciplinary Visions and Concepts for the Production of Tomorrow”). The vision is to foster IoP-based value creation during the usage of connected data, products, and equipment by the selection of a governance mode, a specific organizational structure, development of capabilities, and the design of interfaces. Both an internal perspective and an external perspective resulting from the connectivity and networking of data, assets, and users forming a business ecosystem are needed to realize this vision.

From an internal perspective, context-aware and user-adaptive interfaces between humans and machines are the enablers for realizing the operational benefits of the IoP. Task demands must correspond to human operators’ physical and cognitive ergonomic requirements to support efficient task execution and responsible decision-making. The external perspective covers the availability of data and capacity of third parties and how the resulting value is captured and shared among the actors. A third focus is on the interplay between the internal and the external perspective and the tradeoffs and frictions that evolve from different principles of sustainable value creation from both perspectives. For the realization of this vision, our research builds on the work of Gawer (2014) and Parker and van Alstyne (2018). The structure is guided by a set of four factors that govern the implementation of the IoP internally and externally: governance, organization, capabilities, and interfaces (see Fig. 21.1).

Fig. 21.1
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The GOCI-Framework

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The four factors are discussed in detail for the internal and external perspective as well as the interplay between the two.

2 Internal Perspective: Acceptance and Sustainability of the IoP Application Within a Socio-Technical System Approach

2.1 Research Direction and Issues

Irrespective of their role and background, humans will continue to be an essential part of the complex socio-technical system into which the IoP is embedded. However, their tasks, required qualifications, and work structures will change, as will the tools they use, how they exert influence in the overall work system, and the allocation of responsibility for decisions.

Novel forms of hybrid teamwork in production context, e.g., human-robot collaboration, AI-based support for decision-making processes, and an appropriate mapping of human skills and capabilities to the technical systems form the basis for an efficient, target-group specific interaction in cyber-physical systems. The integration of more and more technical support systems and progressive process automation, particularly in the area of production systems, will constantly increase the proportion of knowledge-intensive work, while the proportion of physically demanding work that must be performed by humans will constantly decrease. As a result, the focus, which has traditionally tended to be on the physical strain of employees, must shift to take cognitive strain into account to ensure sustainable working conditions. To this end, the approach already established in the production process of using digital shadows to analyze and prospect for future developments must be explicitly expanded to include relevant data from human actors. This will allow for enabling the socially sustainable design of the entire work system in all four GOCI dimensions, taking into account appropriate data protection, privacy, and personal self-determination in the form of a human digital shadow (Mertens et al. 2021). Finally, possibilities to establish operational and organizational structures to support collective production intelligence have to be identified to enable holistic knowledge building and management. The goals of the internal perspective are, therefore:

  1. 1.

    Create ergonomic, transparent, trustful, and responsible interfaces and decision support systems for production systems and the IoP.

  2. 2.

    Automate human knowledge and expertise for the human-centered design of hybrid teamwork and for the exchange of best practices.

  3. 3.

    Ensure professional profiles and qualifications of the working person to allow efficient, effective, and satisfactory interaction between all entities of socio-technical production system.

  4. 4.

    Support in work process design and strategy development to promote socially sustainable solutions and to take into account ethical, legal and social implications as an immanent part of change management.

2.2 Preliminary Work and Background

Earlier research has already considered human-centered design aspects for creating a socio-technical framework for the Internet of Things (Shin 2014). Taking the example of human-robot collaboration, previous work mainly focused on safe collaboration and technological solutions in order to avoid safety guards (Wang et al. 2017). In contrast, ethical and moral aspects have been investigated in social robotics or medical applications. For mastering the increasing complexity and information available in the IoP, decision support systems must be adapted to the requirements of human operators and their diverse needs (Keim et al. 2010). Despite the vast amount of research on visual, cognitive complexity, and interface design methodology, these findings are often neither transferable for the context of production, nor do they provide actionable guidelines for designing sustainable interfaces for socio-technical production systems.

Prior work in the context of previous funding phases has covered the iterative and user-centered design, development, and evaluation of support systems for working persons. Here, an empirical modeling of users’ needs considered factors like user diversity, acceptance, and compliance with interactive systems. Human-centered interaction design was applied, for instance, in contexts of intelligent decision support assistance systems for production planning and control, supporting the operator as a decision maker with appropriate information acquisition, data aggregation, and operation choice (Nelles et al. 2016). Further studies on information complexity (Ziefle et al. 2015) and the relations between trust, technology acceptance, human efficiency, and effectivity (Brauner et al. 2017) stressed the importance of a user-centered approach (Stiller et al. 2014). To enhance the conformity of semiautonomous robotic assembly processes with operator’s expectations, cognitive automation was applied by simulating human assembly and decision-making strategies (Faber et al. 2017). While higher degrees of automation and occupational safety have already been addressed, important ergonomic and moral questions are still open.

2.3 GOCI Dimensions

The internal perspective on usage in the socio-technical workings system IoP is mainly shaped by the involvement of the human in the underlying production processes. An economically and socially sustainable implementation of the IoP requires appropriate operational and organizational structures to enhance communication and knowledge transfer between working persons, digitalized production technology, and customers as well as the readiness to adapt to changing conditions in the sense of continuous human-oriented change management (see Fig. 21.2).

Fig. 21.2
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Main aspects of the internal perspective on the usage phase in the IoP (structured according to GOCI scheme)

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Governance. The IoP changes organizational processes, structures, and management strategies, yielding new requirements for the internal governance of these systems. To ensure broad acceptance, the diverse stakeholder perspectives must be continuously taken into account to allow holistic decisions. The ethical, legal, and social implications should be an intrinsic part of this decision-making process. To enable this in such a dynamic environment, there is also a need for appropriate methods that guide the people involved and consider the entire utilization phase. At the same time, decision-making by autonomous, human-like systems must ensure transparent processes, security, and privacy.

Organization. A sustainable socio-technical production system is characterized by a highly flexible organizational structure and hybrid team organization, which enables reacting to changing conditions in a short-cycle manner. The example of human-robot collaboration stresses that inter-team communication as well as ergonomics in the workflow are crucial for safe and effective collaboration. Hybrid team organization has to ensure acceptance by the working persons, flexible division of tasks, and mutual learning and adaption processes. Despite the elimination of mindless decisions, strategic decisions still depend on humans who have to perceive and process increasingly complex multi-dimensional data sets and to make decisions whose effects are increasingly difficult to forecast. In particular, this demands an organizational setup facilitating higher work productivity, the acceptance and willingness of the human actors to adopt and use novel technology, the ergonomic design of working and learning environments, and the promotion of mutual learning.

Capabilities. With raising amounts of available data, persons involved into production need to manage multiple production processes or collaborate with multiple robots simultaneously. Advanced decision support systems can reduce the cognitive load by analyzing, e.g., best practice examples with regard to the relevant success factors. Necessary qualifications will be deviated and concepts to enhance trust into this artificial intelligence investigated within the context of holistic change management to develop the future of production work in a participatory way. Aiming at intelligent support and control systems, ways of representing human experience and competence in solving indecisive and unstructured problems are of particular importance for a sustainable solution.

Interfaces. An increasing digitalization and connectivity of devices implies challenges by raising the amount of production data available, causing high cognitive and visual complexity to handle these data and associated cognitive strain. Although the data of cyber-physical production systems are generally preprocessed by the infrastructure to be understandable for people at all, there are multiple application scenarios which require context-specific data visualization. Especially with an increasing complexity of the operator’s task, appropriate decision support systems following context-sensitive design principles are required. In case of human-robot collaboration, for instance, both the interface for data visualization and the physical interaction design are of crucial importance.

The aforementioned challenges require placing the human directly into the loop of the development and production process, providing knowledge about human factor requirements in digitalized production environments.

3 External Perspective: Designing Mechanisms for Value Capture in Business Ecosystems for the IoP

3.1 Research Direction and Issues

The IoP is, by definition, not restricted to a focal company or value creation within a closed network of established partners. Instead, it resembles the vision of an open network of sensors, assets, products, and actors that continuously generate data. A core element hence is the (re-)use of data, digital shadows, and applications by other parties to facilitate faster and more efficient learning and analytics. Therefore, incentives, governance as well as new ways of user integration are necessary elements to make this vision a reality. The rise of platforms (business ecosystems) where these data is being exchanged and enhanced by dedicated “apps,” often offered by specialized third-party entities, is one of the largest current economic trends (Rietveld and Schilling 2021). To create value, ecosystems rely on complementary inputs made by loosely interconnected, yet independent stakeholders (Parker and van Alstyne 2018). In the case of the IoP, platform participants include the orchestrator of the platform, operators of production assets (users in form of factories), and providers of applications analyzing data and providing decision support (app programmers). In addition, the goods being produced can also become part of the platform in form of connected (“smart”) products. With this, end-users (customers) also become a participant. Among these participants, dedicated mechanisms governing data access and privacy are required. At the same time, the ability to implement the vision of the IoP is a question of setting the right incentives to align the different interests and priorities of the partners involved. Objectives of the external perspective are therefore:

  1. 1.

    Establish the IoP as an open ecosystem for industrial data of both machines (assets) and products produced in the usage stage

  2. 2.

    Managing the tension between openness and control in order to allow for value capture of all actors involved

  3. 3.

    Provide a set of managerial decision parameters when setting up an industry platform around the IoP

3.2 Preliminary Work and Background

Open platforms offer distinct economic advantages. They allow a firm to harness external inputs and innovation as a complement to internal innovation by facilitating an exchange between users who otherwise could not transact with each other (Parker and van Alstyne 2018). Theoretically, platforms (also: two-sided markets) have been investigated in the industrial organization literature. Essential to most economic definitions are the existence of “network effects” that arise between the participants (Gawer 2014; Allen et al. 2021). Platforms typically reside upon a layered digital infrastructure, where lower-level layers (e.g., physical components) enable and support functionalities at higher, user-facing layers. A recent stream of literature complements the economic analysis by studying distinct governance and orchestration challenges. Less work has addressed the situation of the IoP that depends on the concurrent commitment of complementary inputs from independent stakeholders towards a de novo ecosystem creation effort (Dattée et al. 2018). A core decision here is platform openness (e.g., Ondrus et al. 2015; Parker and van Alstyne, 2018). Dattée (2018) provides an analytical model for this situation, and Benlian et al. (2015) investigates complementors’ decision to join a platform based on its openness.

Jiang et al. (2017) investigated market structure effects and the rise of manufacturing platforms for Additive Manufacturing, highlighting the demand for appropriate governance mechanisms (IP protection, user integration, etc.). Platforms also represent a key concept in research on business models for Industry 4.0, where methodologies were developed to model BM alternatives (Adner and Rahul 2010; Kapoor and Nathan 2015; Wang and Miller 2019). The Fraunhofer Industrial Data Space initiative (Otto et al. 2017; Jarke 2017) has focused on requirements and rather technological challenges of inter-organizational data exchange. This requires novel conceptual information modeling and significant research for applications in production engineering. In conclusion, previous research has investigated the application layer, i.e., defining elements for value creation out of the digital shadow. Basic mechanisms of platform markets are well understood, too. However, dedicated research in the context of industrial data applications is missing, as well as on work on value capture, i.e., models to appropriate economic rents from the IoP. Platform openness has been derived as a key variable in this context. The larger the openness, the higher the likelihood of value creation (in terms of generating novel insights from data), but the lower the ability to capture value by one actor. Large openness also supports free-riding, i.e., participating at the fruits of data sharing while not contributing to the data stock.

3.3 GOCI Dimensions

Also, for the external perspective the four layers from the GOCI framework guide the research on value creation and capture through the integration of data based on data- and platform-based industrial ecosystems. (Gawer 2014) – see Fig. 21.3.

Fig. 21.3
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Main aspects of the external perspective on the usage phase in the IoP (structured according to GOCI scheme)

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Governance. The central construct here is the degree of openness vs. desire for control of each actor in the ecosystem. This delicate tension balances the level of value creation for all ecosystem participants and the level of value capture for each participant, i.e., how actors can contribute to and profit from the ecosystem. Governance also determines the rules for data exchange across organizations. Possible governance modes (and patterns of platform governance) need to be identified and matched to the performance of observed use cases.

Organization. Organizational forms refer to the design patterns of a platform business model that structure value creation and capture. This also asks the question whether firms shall join an existing ecosystem (under which conditions) or try to orchestrate their own. The main question platform players need to ask is how they want to play and use an ecosystem. Further, organizational design deals with new forms of collaboration across organization and how to organize this within a focal organization.

Capabilities. Operating on an IoP platform demand new capabilities in firms, including business model innovation, mastering organizational change, or building an ecosystem. Actors in the ecosystem should all have the skills to contribute to the overall value creation. It is important to ask which capabilities are currently available and which need to be built up or provided by others. Platform-based ecosystems manage complementary capabilities to provide value that a single organization would not have access to. Further transparency can be generated across organizations which can lead to a more sustainable ecosystem. This leads to a re-interpretation of the central economic question of the boundaries of a firm.

Interfaces. With interfaces, a distinction must be made between interfaces on the platform and interfaces between platforms. From a platform perspective, the openness of an API is a signal of willingness to share data and knowledge, hence attracting third parties. At the same time, open interfaces can be a technical risk and reduce the ability to capture value. The central question for the platform orchestrator is how to achieve a competitive advantage through strategic openness. Interfaces need to be designed to enable these exchanges and access while maintaining privacy and security. Open machine-to-machine interfaces are thus being investigated as a key design factor for the Internet of Production.

4 Interplay Between Internal and External Perspective: A Delphi Study

The elaborated GOCI framework does not only provide a structure in which individual research efforts from the internal and external perspective of usage can be integrated, but it also highlights opportunities for interdisciplinary research that combines the two perspectives and spans across the four dimensions. Thereby, the interplay between the internal and external perspective can be investigated identifying areas where tensions between the corresponding visions and future developments might arise. As an example, the approach and results of a Delphi study on usage-centered developments in the manufacturing industry in the upcoming decade are presented. A full presentation of the obtained results and a detailed discussion of their implications have been published by Piller et al. (2022).

Forecasting the future implications of IoP technology and processes on the manufacturing industry is made difficult by the high uncertainty of the technological advancements. Here, the Delphi method provides a structured approach to derive reliable future scenarios based on expert assessments (Landeta 2006). In an anonymous and multi-stage format, the experts rate the likelihood and future impact of a set of projections, aiming for a consensus in their assessments. By ensuring a high degree of diversity in the selection of both projections and experts, the method enables the inclusion of the perspectives of different stakeholder groups (Linstone 1981).

For the IoP Delphi study on the next generation of manufacturing, projections were developed for the four dimensions of the GOCI framework and a fifth, added dimension of resilience (Van Dyck et al. 2022a). The projection development process included workshops with a first group of experts from different scientific fields as well as a complementary literature review. After systematic refinement and pre-testing, 24 projections were selected for the expert survey (see Fig. 21.4). Then, an international panel of experts from both industry and academia assessed the projections in a real-time Delphi format (Gnatzy et al. 2011). The obtained quantitative and qualitative responses formed the basis for developing future scenarios for the manufacturing industry (Van Dyck et al. 2022a).

Fig. 21.4
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The 24 projections investigated in the Delphi study on usage-centered developments in the manufacturing industry in the upcoming decade

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Overall, the experts agreed that the emergence of IoP concepts such as digital twins and digital shadows will shape the future manufacturing ecosystem (Pütz et al. 2022). Nevertheless, the expert assessments also highlight a high level of uncertainty about how exactly this digital transformation will look like. This observation emphasizes the need and opportunities for future research in this area. In the following, some of the key findings for each dimension of the GOCI framework are presented (Van Dyck et al. 2022b):

Governance. Introducing open data sharing into the manufacturing industry is expected to create new opportunities for the cooperation of business partners within the manufacturing ecosystem and facilitate the emergence of corresponding business models. Based on this forecast, the experts also predict that digital services for production machinery will offer decisive competitive advantages as the margin for improving physical efficiency diminishes. Simultaneously, adequate measures for data protection and data security were identified as central internal prerequisites for the acceptance of these forms of collaboration. Consequently, industrial data protection regulations may be necessary to manage the tensions between internal requirements and external opportunities.

Organization. Experts project that the advancement of AI technology will have a significant impact on decision-making processes in the manufacturing industry, both on the shop floor and in production management. On the shop floor, the high level of standardization and large number of repetitions of individual work steps provide the optimal data basis for supporting production workers via AI-based assistance systems. In production management, managers will use similar assistance systems for short-term decision-making, improving multi-criteria optimization. However, experts also expect that when these forms of hybrid intelligence are used, humans will retain the responsibility and final decision-making, emphasizing the need for a highly skilled workforce.

Capabilities. The experts’ assessments highlighted improving the environmental sustainability in the manufacturing industry as a major opportunity enabled by IoP capabilities. Similar to offering digital services for production machinery, providing solutions for making products and processes more environmental sustainable is expected to bring competitive advantages, as the corresponding demand from both customers and employees rises. In addition, the introduction of digital shadows and the associated increase in the transparency of production processes can facilitate production planning and forecasting, which benefits resource efficiency. Thus, the internal use of production data and the external demands imposed on the company can work hand in hand to push companies toward environmentally sustainable manufacturing.

Interfaces. The global use of production data in the form of digital twins and digital shadows will require the development of new interfaces both within and between companies. From the external perspective, the experts project a demand for regulatory requirements ensuring open and standardized data interfaces between organizations. However, they are doubtful that such standards can be reached in the next 10 years. From the internal perspective, the ongoing automation of production processes, shifting the focus more from repetitive manual work to cognitive tasks like automation supervision and decision-making, creates the need for new human-machine interfaces. Again, the experts doubt, however, that it will be possible to develop reliable implicit interfaces in the next decade, placing the focus instead on assistance systems such as cobots and AI-based decision support systems.

To conclude, the IoP Delphi study on future usage-centered developments in the manufacturing industry is an example for the methodical implementation of the elaborated GOCI framework. Specifically, the study demonstrated the feasibility of including research questions from all four dimensions of the framework into a joint research approach. Moreover, the analysis combined the internal and external perspective on usage, enabling the investigation of their interplay. The study, thereby, offered a holistic perspective on the usage dimension in the future manufacturing ecosystem.

5 Further Research

Current research results and a holistic view on the internal role of human actors within the socio-technical system of the IoP can be found in the Chap. 22, “Human-Centered Work Design for the Internet of Production.” Specifically, measures across different levels of human-centered work design are presented to highlight the range of design dimensions that must be considered when aiming for a human-centered transformation of production work systems. For the work task level, guidelines for enabling efficient collaboration and cooperation of humans, robots, and smart agents in digitalized production systems are presented. A new framework for the classification of human-robot collaboration workplaces is introduced for the working condition level, and approaches for using corporate data to facilitate the knowledge transfer in global production networks and the implications of the IoP for new leadership models are discussed for the organizational level. Finally, the supra-organizational level is addressed in form of ethical considerations of how the IoP affects the understanding of responsibility and normative values in the work context.

An extensive literature overview of platform-based ecosystems and a holistic process model for platform-based ecosystems which builds on the four GOCI factors and the external perspective can be found in the Chap. 23, “Design Elements of a Platform-Based Ecosystem for Industry Applications.” The process model bundles most relevant findings of 130 papers and classifies them into 4 phases and 16 design elements for a process-oriented approach. Further, four industrial use cases for specific phases and design elements are shown for an exemplary application in an Industry 4.0 context. It highlights the importance of specific data and outlines what data can be shared from an external perspective. Further, the research deals with the strategic modeling of platform-based ecosystems and the research addresses control points that platform actors can proactively establish in order to adapt their business models and to jointly create and capture value. Both researchers and practitioners benefit from a holistic framework for platform-based ecosystems and from concrete examples that provide insight into this emerging research area.