Fraunhofer Institute for Material Flow and Logistics

The increasing integration of the Internet of Everything into the industrial value chain has built the foundation for the next industrial revolution called Industrie 4.0. Although Industrie 4.0 is currently a top priority for many companies, research centers, and universities, a generally accepted understanding of the term does not exist. As a result, discussing the topic on an academic level is difficult, and so is implementing Industrie 4.0 scenarios. Based on a quantitative text analysis and a qualitative literature review, the paper identifies design principles of Industrie 4.0. Taking into account these principles, academics may be enabled to further investigate on the topic, while practitioners may find assistance in identifying appropriate scenarios. A case study illustrates how the identified design principles support practitioners in identifying Industrie 4.0 scenarios.

In response to the 2013 Update of the European Strategy for Particle Physics, the Future Circular Collider (FCC) study was launched, as an international collaboration hosted by CERN. This study covers a highest-luminosity high-energy lepton collider (FCC-ee) and an energy-frontier hadron collider (FCC-hh), which could, successively, be installed in the same 100 km tunnel. The scientific capabilities of the integrated FCC programme would serve the worldwide community throughout the 21st century. The FCC study also investigates an LHC energy upgrade, using FCC-hh technology. This document constitutes the second volume of the FCC Conceptual Design Report, devoted to the electron-positron collider FCC-ee. After summarizing the physics discovery opportunities, it presents the accelerator design, performance reach, a staged operation scenario, the underlying technologies, civil engineering, technical infrastructure, and an implementation plan. FCC-ee can be built with today's technology. Most of the FCC-ee infrastructure could be reused for FCC-hh. Combining concepts from past and present lepton colliders and adding a few novel elements, the FCC-ee design promises outstandingly high luminosity. This will make the FCC-ee a unique precision instrument to study the heaviest known particles (Z, W and H bosons and the top quark), offering great direct and indirect sensitivity to new physics.

Abstract: We review the physics opportunities of the Future Circular Collider, covering its e+e-, pp, ep and heavy ion programmes. We describe the measurement capabilities of each FCC component, addressing the study of electroweak, Higgs and strong interactions, the top quark and flavour, as well as phenomena beyond the Standard Model. We highlight the synergy and complementarity of the different colliders, which will contribute to a uniquely coherent and ambitious research programme, providing an unmatchable combination of precision and sensitivity to new physics.

Abstract: In response to the 2013 Update of the European Strategy for Particle Physics (EPPSU), the Future Circular Collider (FCC) study was launched as a world-wide international collaboration hosted by CERN. The FCC study covered an energy-frontier hadron collider (FCC-hh), a highest-luminosity high-energy lepton collider (FCC-ee), the corresponding 100 km tunnel infrastructure, as well as the physics opportunities of these two colliders, and a high-energy LHC, based on FCC-hh technology. This document constitutes the third volume of the FCC Conceptual Design Report, devoted to the hadron collider FCC-hh. It summarizes the FCC-hh physics discovery opportunities, presents the FCC-hh accelerator design, performance reach, and staged operation plan, discusses the underlying technologies, the civil engineering and technical infrastructure, and also sketches a possible implementation. Combining ingredients from the Large Hadron Collider (LHC), the high-luminosity LHC upgrade and adding novel technologies and approaches, the FCC-hh design aims at significantly extending the energy frontier to 100 TeV. Its unprecedented centre of-mass collision energy will make the FCC-hh a unique instrument to explore physics beyond the Standard Model, offering great direct sensitivity to new physics and discoveries.

Even though research has suggested that supply chain agility and supply chain adaptability are distinct capabilities, little is known about their performance effects and about the contextual conditions under which they are effective. Based on a sample of 143 German firms, we empirically investigate the effects of supply chain agility and supply chain adaptability on cost performance and operational performance using hierarchical regression analysis. We ground our investigation in the dynamic capabilities view and contingency theory. We find that supply chain agility and supply chain adaptability positively affect both cost performance and operational performance. We further find evidence for a mediating role of supply chain agility in the links between supply chain adaptability and performance. Product complexity positively moderates the links between supply chain adaptability and cost performance, and supply chain adaptability and operational performance. The results contribute to the literature by offering a more nuanced understanding of the performance implications of supply chain agility and supply chain adaptability, thereby addressing the crucial question of why their benefits may or may not materialise under varying levels of product complexity.

In response to the 2013 Update of the European Strategy for Particle Physics (EPPSU), the Future Circular Collider (FCC) study was launched as a world-wide international collaboration hosted by CERN. The FCC study covered an energy-frontier hadron collider (FCC-hh), a highest-luminosity high-energy lepton collider (FCC-ee), the corresponding 100 km tunnel infrastructure, as well as the physics opportunities of these two colliders, and a high-energy LHC, based on FCC-hh technology. This document constitutes the third volume of the FCC Conceptual Design Report, devoted to the hadron collider FCC-hh. It summarizes the FCC-hh physics discovery opportunities, presents the FCC-hh accelerator design, performance reach, and staged operation plan, discusses the underlying technologies, the civil engineering and technical infrastructure, and also sketches a possible implementation. Combining ingredients from the Large Hadron Collider (LHC), the high-luminosity LHC upgrade and adding novel technologies and approaches, the FCC-hh design aims at significantly extending the energy frontier to 100 TeV. Its unprecedented centre-of-mass collision energy will make the FCC-hh a unique instrument to explore physics beyond the Standard Model, offering great direct sensitivity to new physics and discoveries.

The Internet of Things (IoT) is an emerging network superstructure that will connect physical resources and actual users. It will support an ecosystem of smart applications and services bringing hyper

Human activity recognition (HAR) is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs).

This open access book provides a 360° view of the technological and economic aspects of data spaces and presents various implementations and use cases.

Typical logistics processes became significantly more complex and dynamic during the last decades. Among other things, this is driven by internationalization of supply chains and global competition, shorter product life-cycles, mass customization, and stricter quality requirements. However, the advent of new technologies may alleviate these challenges; here, RFID technology is particularly promising. This article describes a novel use for RFID in logistics: By storing additional information like routing or necessary processing steps directly on the tag, an 'Internet of Things' is possible. We describe the vision and a possible implementation for the 'Internet of Things in Logistics', evaluate effects of this approach to the efficiency of a material handling system, and describe additional services, which become possible by using RFID in logistics.

Human–computer interaction ( HCI ) is a cornerstone for the success of technical innovation in the logistics and supply chain sector. As a major part of social sustainability, this interaction is changing as artificial intelligence applications (Internet of Things, autonomous transport, Physical Internet) are implemented, leading to larger machine autonomy, and hence the transition from a primary executive to a supervisory role of human operators. A fundamental question concerns the level of control transferred to machines, such as autonomous vehicles and automatic materials handling devices. Problems include a lack of human trust toward automatic decision making or an inclination to override the system in case automated decisions are misperceived. This paper outlines a theoretical framework, describing different levels of acceptance and trust as a key HCI element of technology innovation, and points to the possible danger of an artificial divide at both the individual and firm level. Based upon the findings of four benchmark cases, a classification of the roles of human employees in adopting innovations is developed. Measures at operational, tactical, and strategic level are discussed to improve HCI , more in particular the capacity of individuals and firms to apply state‐of‐the‐art techniques and to prevent an artificial divide, thereby increasing social sustainability.

In this chapter we present our IoT Reference Architecture. This IoT Reference Architecture is, among others, designed as a reference for the generation of compliant IoT concrete architectures that are tailored to one’s specific needs. For other usages of the IoT Architectural Reference Model see Chap. 3 .

Accurate information plays an important role for the circulation of materials and products. It influences the economically and ecologically successful execution of processes such as reconditioning and the corresponding supply chain management. Digitization concepts, such as digital twins, enable the relevant information to be made available to the right actor at the right time in a decentralized manner. It is assumed that digital twins will play an important role in the future and can contribute, among other things, to the successful implementation of circular economy strategies. However, there is no uniform definition of the term digital twin yet and the exploration and use of digital twins in the context of circular economy products and supply chains is still in its infancy. This article presents potential contributions of digital twins to the circularity of products and the management of circular supply chains. To this end, the derivation and validation of a definition for the term digital twin is described. A stakeholder analysis with a special focus on the processes of the individual stakeholders results in an overview of potentials and information requirements of circular supply chains for a digital twin. The paper concludes that circular supply chains can benefit from digital twins, but that there is still a need for research and development, particularly regarding product and use case-specific implementations of the concept.

Although the fourth industrial revolution is already in pro-gress and advances have been made in automating factories, completely automated facilities are still far in the future. Human work is still an important factor in many factories and warehouses, especially in the field of logistics. Manual processes are, therefore, often subject to optimization efforts. In order to aid these optimization efforts, methods like human activity recognition (HAR) became of increasing interest in industrial settings. In this work a novel deep neural network architecture for HAR is introduced. A convolutional neural network (CNN), which employs temporal convolutions, is applied to the sequential data of multiple intertial measurement units (IMUs). The network is designed to separately handle different sensor values and IMUs, joining the information step-by-step within the architecture. An evaluation is performed using data from the order picking process recorded in two different warehouses. The influence of different design choices in the network architecture, as well as pre- and post-processing, will be evaluated. Crucial steps for learning a good classification network for the task of HAR in a complex industrial setting will be shown. Ultimately, it can be shown that traditional approaches based on statistical features as well as recent CNN architectures are outperformed.

The first major contribution of the IoT Architectural Reference Model (IoT ARM) is the IoT Reference Model itself. Besides models, the IoT Reference Model provides the concepts and definitions on which IoT architectures can be built. This Chapter introduces the IoT Reference Model as a precondition for working with the Reference Architecture that is introduced in Chap. 8 .

The International Data Space (IDS) offers an information technology architecture for safeguarding data sovereignty within the corporate ecosystem. It provides a virtual space for data where data remains with the data owner until it is needed by a trusted business partner. When the data is shared, terms of use can be linked to the data itself. Analysis of six use cases from the first phase of the prototype implementation of the IDS architecture shows that the focus lies on the standardized interface, the information model for describing data assets, and the connector component. Further use cases are planned for the next wave of implementation that are based on the broker functionality and require the use of vocabularies for simple data integration. In addition, companies need to standardize the principles that are translated into the terms of use. These principles need to be shaped, described, documented, and implemented in a simple and understandable way. They also need to be understood in the same way by different actors in the corporate ecosystem, thus requiring semantic standardization. Furthermore, the IDS Reference Architecture Model needs to be set in context with respect to related models. In the F3 use case, an OPC UA adapter is used. Additional use cases for integration with the Plattform Industrie 4.0 administration shell and Industrial Internet Reference Architecture are pending. The IDS Architecture is also increasingly being utilized in so-called verticalization initiatives, in healthcare and in the energy sector for example. These kinds of initiatives – like the Materials Data Space – demonstrate the crossdomain applicability of the architectural components and provide information about further development needs. Finally, in anticipation of the future development of the use cases and utilization of the IDS, work on the economic valuation of data and on the settlement and pricing of data transactions must be accelerated.

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects.

Event-discrete simulation is a key method for decision support systems in planning and control of logistics systems. The ability to start a simulation based on the current situation of the system in real-time is central for these systems. In this paper, we present our system architecture that combines a real-time digital twin of logistics systems with simulation logic in a single (modularized) model. This combination not only reduces offline work creating and maintaining such a model and decision support system but also reduces runtime in this critical real-time use-case. The approach is demonstrated in two industrial use cases.

Supply chain risk management (SCRM) has become a popular topic over the past decade. It is not a surprise that the automotive industry has been a motivating arena for research within this field; however, the few existing empirical studies reveal that SCRM practices within this industry are still in their infancy. Because the identification of risks can be viewed as the trigger for SCRM, attempts to develop a risk profile for this industry that could serve as a guide to start the SCRM process are needed. This research identifies the main risks along the automotive supply chain by investigating their manifestation in three supply chains in Brazil and offers an initial risk profile for the Brazilian automotive industry. Although the importance of SCRM has been recognised by all analysed companies, the research findings underline the lack of preparedness regarding either identifying risk or considering risk-mitigation strategies and risk assessment. In this context, this study identifies the main risk in which a supply chain can be exposed, through the analysis of real-life manifested risks along different supply chains, as a way to help the supply chain start a SCRM process.

The German manufacturing industry is forced to evolve its processes, techniques, and organizations due to increasing global competition and progressive sustainability requirements. In this context, the soaring possibilities of bio- and information technology have recently let few authors develop the vision of a biological transformation of manufacturing, a concept that to date has been barely concrete to politicians, scientists, and managers. In this paper, we present results of the first systematic assessment of the biological transformation of the German manufacturing industry. We chose a combination of the Delphi method and scenario planning in order to assess key technologies, determine the status quo of Germany and provide a forecast of potential developments. Thereupon, we identify ten fields of action for setting the course for a sustainable industrial value creation. We conclude with a summary and recommendations for decision makers in politics, industries and research.