Knowledge Management in the Fourth Industrial Revolution: Mapping the Literature and Scoping Future Avenues

Due to increased competitive pressure, modern organizations tend to rely on knowledge and its exploitation to sustain a long-term advantage. This calls for a precise understanding of knowledge management (KM) processes and, specifically, how knowledge is created, shared/transferred, acquired, stored/retrieved, and applied throughout an organizational system. However, since the beginning of the new millennium, such KM processes have been deeply affected and molded by the advent of the fourth industrial revolution, also called Industry 4.0, which involves the interconnectedness of machines and their ability to learn and share data autonomously. For this reason, the present study investigates the intellectual structure and trends of KM in Industry 4.0. Bibliometric analysis and a systematic literature review are conducted on a total of 90 relevant articles. The results reveal 6 clusters of keywords, subsequently explored via a systematic literature review to identify potential stream of this emergent field and future research avenues capable of producing meaningful advances in managerial knowledge of Industry 4.0 and its consequences

State-of-the-Art Review and Synthesis: A Requirement-based Roadmap for Standardized Predictive Maintenance Automation Using Digital Twin Technologies

Recent digital advances have popularized predictive maintenance (PMx), offering enhanced efficiency, automation, accuracy, cost savings, and independence in maintenance. Yet, it continues to face numerous limitations such as poor explainability, sample inefficiency of data-driven methods, complexity of physics-based methods, and limited generalizability and scalability of knowledge-based methods. This paper proposes leveraging Digital Twins (DTs) to address these challenges and enable automated PMx adoption at larger scales. While we argue that DTs have this transformative potential, they have not yet reached the level of maturity needed to bridge these gaps in a standardized way. Without a standard definition for such evolution, this transformation lacks a solid foundation upon which to base its development. This paper provides a requirement-based roadmap supporting standardized PMx automation using DT technologies. A systematic approach comprising two primary stages is presented. First, we methodically identify the Informational Requirements (IRs) and Functional Requirements (FRs) for PMx, which serve as a foundation from which any unified framework must emerge. Our approach to defining and using IRs and FRs to form the backbone of any PMx DT is supported by the track record of IRs and FRs being successfully used as blueprints in other areas, such as for product development within the software industry. Second, we conduct a thorough literature review spanning fields to determine the ways in which these IRs and FRs are currently being used within DTs, enabling us to point to the specific areas where further research is warranted to support the progress and maturation of requirement-based PMx DTs.Comment: (1)This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Towards Transportation Digital Twin Systems for Traffic Safety and Mobility Applications: A Review

Digital twin (DT) systems aim to create virtual replicas of physical objects that are updated in real time with their physical counterparts and evolve alongside the physical assets throughout its lifecycle. Transportation systems are poised to significantly benefit from this new paradigm. In particular, DT technology can augment the capabilities of intelligent transportation systems. However, the development and deployment of networkwide transportation DT systems need to take into consideration the scale and dynamic nature of future connected and automated transportation systems. Motivated by the need of understanding the requirements and challenges involved in developing and implementing such systems, this paper proposes a hierarchical concept for a Transportation DT (TDT) system starting from individual transportation assets and building up to the entire networkwide TDT. A reference architecture is proposed for TDT systems that could be used as a guide in developing TDT systems at any scale within the presented hierarchical concept. In addition, several use cases are presented based upon the reference architecture which illustrate the utility of a TDT system from transportation safety, mobility and environmental applications perspective. This is followed by a review of current studies in the domain of TDT systems. Finally, the critical challenges and promising future research directions in TDT are discussed to overcome existing barriers to realize a safe and operationally efficient connected and automated transportation systems.Comment: 15 pages, 2 figures; corrected issue in author(s) fiel

Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions

Enabling the Development and Implementation of Digital Twins : Proceedings of the 20th International Conference on Construction Applications of Virtual Reality

Welcome to the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2020 is "Enabling the development and implementation of Digital Twins". CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry. This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants

ADOPTION OF DIGITAL TWIN WITHIN THE DEPARTMENT OF THE NAVY

Digital twins have the potential to support the decision-makers that design, build, operate, and maintain the platforms that the Department of the Navy (DON) relies upon to conduct naval operations. However, the thin body of knowledge on digital twins presents a challenge for the DON as the range of applications and risks associated with onboarding digital twins are still unclear. This thesis conducts a qualitative technology assessment to determine the effects that adopting digital twins has on the DON’s enterprise architecture. Analysis of an enterprise-wide adoption identifies opportunities and risks of digital twins within the context of the DON’s strategy, processes, people, technology, cyber security, and risk management. The business value provided by digital twins is principally dependent upon the aggregate risk value of the physical platform and the fidelity and frequency of the digital twin’s synchronizations.Captain, United States Marine CorpsCaptain, United States Marine CorpsApproved for public release. Distribution is unlimited

A Supportive Framework for the Development of a Digital Twin for Wind Turbines Using Open-Source Software Tiril Malmedal Mechanics and Process Technology

The world is facing a global climate crisis. Renewable energy is one of the big solutions, nevertheless, there are technological challenges. Wind power is an important part of the renewable energy system. With the digitalization of industry, smart monitoring and operation is an important step towards efficient use of resources. Thus, Digital Twins (DT) should be applied to enhance power output. Digital Twins for energy systems combine many fields of study, such as smart monitoring, big data technology, and advanced physical modeling. Frameworks for the structure of Digital Twins are many, but there are few standardized methods based on the experience of such developed Digital Twins. An integrative review on the topic of Digital Twins with the goal of creating a conceptual development framework for DTs with open-source software is performed. However, the framework is yet to be tested experimentally but is nevertheless an important contribution toward the understanding of DT technology development. The result of the review is a seven-step framework identifying potential components and methods needed to create a fully developed DT for the aerodynamics of a wind turbine. Suggested steps are Assessment, Create, Communicate, Aggregate, Analyze, Insight, and Act. The goal is that the framework can stimulate more research on digital twins for small-scale wind power. Thus, making small-scale wind power more accessible and affordable

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities