Forecasting Next Generation Manufacturing

Transferring the idea of the Internet to the manufacturing landscape—the Internet of Production (IoP)—fundamentally changes our understanding of how products are developed, produced, and utilized. A key concept of the IoP is digital shadows that connect data, products, and equipment and are shared in cross-organizational data spaces. These developments are also core ideas driving the evolution of the current Industry 4.0 paradigm into its next generation (“Industry 4.U”) and have far-reaching implications that go beyond mere technical issues. From a company-internal perspective, managers and workers need to deal with new forms of collaboration and cooperation between humans, robots, smart machines, and algorithms. From a company-external (network) perspective, data-based value creation and capture in platform-based ecosystems change the logic of many manufacturing business models. These changes have been reinforced by the COVID-19 pandemic, which acted as a catalyst for many transformation processes. Given the high uncertainty in the likelihood of occurrence and of the technical, economic, and societal impacts of these concepts, we conducted a technology foresight study in the form of a real-time Delphi analysis to derive reliable future scenarios featuring the next generation of manufacturing systems. This chapter introduces the conceptual and technical background of this study, defines important terms and frameworks, and provides an overview of the Delphi projections that are presented and analyzed in greater detail in the remaining chapters of this book.

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The Delphi method is a structured scientific approach used to organize and structure an expert discussion in order to gain insights about the future. In order to develop scenarios for the future of Next Generation Manufacturing, an innovative real-time Delphi survey was conducted with 35 experts from industry and academia. The survey involved evaluating a set of 24 projections on the future of Next Generation Manufacturing, and the results of the survey were used to develop reliable future scenarios. Our main objective was to create a picture of the elements of Next Generation Manufacturing in 2030, guided by developments in the context of Industry 4.0. By using an innovative real-time Delphi approach in the context of Next Generation Manufacturing, we extend this established tool of strategic technology management from predicting technological developments and their impact on firms and society to providing a strategic guide for decision-makers in times of high uncertainty. Our study thus serves as a template for further applications of forecasting studies in interdisciplinary settings with high degrees of technical uncertainty.

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In our real-time Delphi survey, we present 24 projections for Next Generation Manufacturing. An international set of experts from multiple fields, e.g., engineering, information systems, social sciences, and management, evaluated these projections regarding their likelihood and their impact on manufacturing firms by the year 2030. The experts predict that in the coming decade, we will see a significant increase in the use of production data in the form of digital shadows, which will in turn shape both internal and external processes of manufacturing companies. The quantitative results of the Delphi study show that there is significant disagreement among the experts about the likelihood and impact of several of the projections. The most likely projection is the increased importance of environmental sustainability, while the least likely is the emergence of a central platform provider for Next Generation Manufacturing. The most impactful projections are those related to the roles of digital services, data sharing, hybrid intelligence, and environmental sustainability.

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Next Generation Manufacturing describes a vision of an open network of sensors, assets, products, and actors which are not restricted to a focal organization or a closed supply chain. A core principle of the digital shadow is that it collects and shares data about the usage of products within and across organizations, allowing them to optimize operations, investment decisions, innovation processes, or the generation of new products. Sharing of usage data, however, requires new forms of governance, both internally and externally. Given the high uncertainty in the likelihood of occurrence and the technical, economic, and societal impacts of these concepts, we conducted a technology foresight study, in the form of a real-time Delphi analysis, to derive reliable future scenarios featuring the next generation of manufacturing systems. This chapter presents the governance dimension and describes each projection in detail, offering current case study examples and discussing related research, as well as implications for policy makers and firms. For example, according to the experts, subscription models for production machines will be the new industry standard by 2030. This is due to the changing needs of manufacturers and customers, as well as the impacts of digitization and Industry 4.0. Customers would benefit from guaranteed machine availability and lower investment costs, while manufacturers would benefit from increased customer satisfaction and longer-term business relationships.

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Next Generation Manufacturing promises significant improvements in performance, productivity, and value creation. In addition to the desired and projected improvements regarding the planning, production, and usage cycles of products, this digital transformation will have a huge impact on work, workers, and workplace design. Given the high uncertainty in the likelihood of occurrence and the technical, economic, and societal impacts of these changes, we conducted a technology foresight study, in the form of a real-time Delphi analysis, to derive reliable future scenarios featuring the next generation of manufacturing systems. This chapter presents the organization dimension and describes each projection in detail, offering current case study examples and discussing related research, as well as implications for policy makers and firms. Specifically, we highlight seven areas in which the digital transformation of production will change how we work, how we organize the work within a company, how we evaluate these changes, and how employment and labor rights will be affected across company boundaries. The experts are unsure whether the use of collaborative robots in factories will replace traditional robots by 2030. They believe that the use of hybrid intelligence will supplement human decision-making processes in production environments. Furthermore, they predict that artificial intelligence will lead to changes in management processes, leadership, and the elimination of hierarchies. However, to ensure that social and normative aspects are incorporated into the AI algorithms, restricting measurement of individual performance will be necessary. Additionally, AI-based decision support can significantly contribute toward new, socially accepted modes of leadership. Finally, the experts believe that there will be a reduction in the workforce by the year 2030.

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Industrial production systems are facing radical change in multiple dimensions. This change is caused by technological developments and the digital transformation of production, as well as the call for political and social change to facilitate a transformation toward sustainability. These changes affect both the capabilities of production systems and companies and the design of higher education and educational programs. Given the high uncertainty in the likelihood of occurrence and the technical, economic, and societal impacts of these concepts, we conducted a technology foresight study, in the form of a real-time Delphi analysis, to derive reliable future scenarios featuring the next generation of manufacturing systems. This chapter presents the capabilities dimension and describes each projection in detail, offering current case study examples and discussing related research, as well as implications for policy makers and firms. Specifically, we discuss the benefits of capturing expert knowledge and making it accessible to newcomers, especially in highly specialized industries. The experts argue that in order to cope with the challenges and circumstances of today’s world, students must already during their education at university learn how to work with AI and other technologies. This means that study programs must change and that universities must adapt their structural aspects to meet the needs of the students.

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With the advent of Next Generation Manufacturing, information and communications technologies have become an essential part of the production process, creating and providing data for all stakeholders. Given the high uncertainty in the likelihood of occurrence and the technical, economic, and societal impacts of associated transformations in production, we conducted a technology foresight study, in the form of a real-time Delphi analysis, to derive reliable future scenarios featuring the next generation of manufacturing systems. This chapter presents the interfaces dimension and describes each projection in detail, offering current case study examples and discussing related research, as well as implications for policy makers and firms. Interfaces play a major role in the provision of information. We discuss the trend of implicit user interfaces and the benefits of working from home. Implicit user interfaces are based on user inputs that are not directly aimed at giving a command, but are nevertheless captured, understood, and used by the computer system to provide a richer user experience. Working from home has many benefits, including reducing costs and dependencies. However, experts disagree on whether plant directors will manage multiple factories centrally via telework due to complete and real-time transparency of all operations in a digital system by 2030. The COVID-19 pandemic has shown that it is important to have such an infrastructure even if working from home may not be considered appropriate in many manufacturing companies. Mobile apps that support production management are one key issue in this context.

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Given the high uncertainty in the likelihood of occurrence and the technical, economic, and societal impacts of digital transformations in the manufacturing industry, we conducted a technology foresight study, in the form of a real-time Delphi analysis, to derive reliable future scenarios featuring the next generation of manufacturing systems. This chapter presents the resilience dimension and describes each projection in detail, offering current case study examples and discussing related research, as well as implications for policy makers and firms. The current COVID-19 pandemic and its impact on human health, the biosphere on which we depend, and our need for certain commodities demonstrate the importance of developing global resilience. In 2030, supply chains are expected to be more decentralized, with production and sourcing moving closer to the end customer. Centralized production networks have been shown to be vulnerable to disruptions, and this trend is likely to continue. The majority of the experts do not expect production costs to rise substantially as a result of more regional production and higher inventory levels in order to cope with global crises. Some experts see reshoring, which is characterized by flexibility and resilience despite supposedly high costs in high-wage regions as a key long-term driver. In the future, production costs, while still important, will only be one factor taken into consideration by customers. The experts predict that AI-based decision-making systems will not be able to significantly increase production resilience by 2030. Factors such as lack of acceptance and the complexity of production networks are hindering the widespread implementation of such systems. However, companies that are already investing in AI see significant potential of this technology to help them overcome the challenges posed by global crises.

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Based on the results of a rigorous Delphi study, we present scenarios that portray a most probable future of Next Generation Manufacturing in 2030, enabled by connected data (digital shadows) shared in cross-organizational data spaces. We provide individual scenarios for the dimensions governance, organization, capabilities, interfaces, and resilience, as well as one aggregated scenario for the future development of the manufacturing ecosystem. Our analysis identifies two fundamental changes: a shift from the current focus in many Industry 4.0 use cases on operational efficiency toward more ecologically and socially sustainable production and an anthropocentric perspective complementing techno-centric production. We discuss emerging tensions resulting from these changes.

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The text discusses the concept of hybrid intelligence, which is a form of collaboration between machines and humans. It describes how this concept can be used in manufacturing to help improve productivity. The text also discusses how this concept can be used to help humans learn from machines. There is a debate in the intelligence community about the role of humans vs. machines. Machine intelligence can do some things better than humans, such as processing large amounts of data, but is not good at tasks that require common sense or empathy. Augmented intelligence emphasizes the assistive role of machine intelligence, while hybrid intelligence posits that humans and machines are part of a common loop, where they adapt to and collaborate with each other. The text discusses the implications of increasing machine involvement in organizational decision-making, specifically mentioning two challenges: negative effects on human behavior and flaws in machine decision-making. It argues that, in order for machine intelligence to improve decision-making processes, humans and machines must collaborate. The chapter argues that hybrid intelligence is the most likely scenario for decision-making in the future factory. The chapter discusses the advantages of this approach and how it can be used to improve quality control in a production system. The transformer-based language model called GPT-3 can be used to generate summaries of text. This task is difficult for machines because they have to understand sentiment and meaning in textual data. The model is also a “few-shot learner,” which means that it is able to generate a text based on a limited amount of examples. Transformer-based language models are beneficial because they are able to take the context of the processed words into consideration. This allows for a more nuanced understanding of related words and concepts within a given text.

[Abstract generated by machine intelligence with GPT-3. No human intelligence applied.]