The engineering of digital twins and their user interaction parts with explicated processes, namely process-aware digital twin cockpits (PADTCs), is challenging due to the complexity of the systems and the need for information from different disciplines within the engineering process. Therefore, it is interesting to investigate how to facilitate their engineering by using already existing data, namely event logs, and reducing the number of manual steps for their engineering. Current research lacks systematic, automated approaches to derive process-aware digital twin cockpits even though some helpful techniques already exist in the areas of process mining and software engineering. Within this paper, we present a low-code development approach that reduces the amount of hand-written code needed and uses process mining techniques to derive PADTCs. We describe what models could be derived from event log data, which generative steps are needed for the engineering of PADTCs, and how process mining could be incorporated into the resulting application. This process is evaluated using the MIMIC III dataset for the creation of a PADTC prototype for an automated hospital transportation system. This approach can be used for early prototyping of PADTCs as it needs no hand-written code in the first place, but it still allows for the iterative evolvement of the application. This empowers domain experts to create their PADTC prototypes. 

Process-aware digital twin cockpit synthesis from event logs

Change Management: Altering Mindsets in A Global Context

A Framework for the Classification of Human-Robot Interactions Within the Internet of Production

Actionable Artificial Intelligence for the Future of Production

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

Future Scenarios and the Most Probable Future for Next Generation Manufacturing

Nowadays, the importance of non-physical goods in traditional production companies is steadily increasing. Accompanying this, companies are confronted with challenges in the area of knowledge transfer and employee support during production processes. Particularly affected are conservative industries such as the textile industry, which rely heavily on implicitly available experience-based (tacit) knowledge. A promising approach to tackle these challenges presents the concept of an “Internet of Production (IoP)”. This contribution provides a framework on how the IoP can be conceived and incorporated within the textile industry. Focusing on five textile use-cases, it is described, how know-how transfer and support systems are imminent for the wide-spread introduction and perpetuation of the IoP.

Know-How Transfer and Production Support Systems to Cultivate the Internet of Production Within the Textile Industry

Digitalization in the production sector aims at transferring concepts and methods from the Internet of Things (IoT) to the industry and is, as a result, currently reshaping the production area. Besides technological progress, changes in work processes and organization are relevant for a successful implementation of the “Internet of Production” (IoP). Focusing on the labor organization and organizational procedures emphasizes to consider intra-company factors such as (user) acceptance, ethical issues, and ergonomics in the context of IoP approaches. In the scope of this paper, a research approach is presented that considers these aspects from an intra-company perspective by conducting studies on the shop floor, control level and management level of companies in the production area. Focused on four central dimensions—governance, organization, capabilities, and interfaces—this contribution presents a research framework that is focused on a systematic integration and consideration of human aspects in the realization of the IoP.

A Research Framework for Human Aspects in the Internet of Production – An Intra-company Perspective

Digital Shadows as the aggregation, linkage and abstraction of data relating to physical objects are a central vision for the future of production. However, the majority of current research takes a technocentric approach, in which the human actors in production play a minor role. Here, the authors present an alternative anthropocentric perspective that highlights the potential and main challenges of extending the concept of Digital Shadows to humans. Following future research methodology, three prospections that illustrate use cases for Human Digital Shadows across organizational and hierarchical levels are developed: human-robot collaboration for manual work, decision support and work organization, as well as human resource management. Potentials and challenges are identified using separate SWOT analyses for the three prospections and common themes are emphasized in a concluding discussion.

Human Digital Shadow: Data-based Modeling of Users and Usage in the Internet of Production

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Know-How Transfer and Production Support Systems to Cultivate the Internet of Production Within the Textile Industry

A Research Framework for Human Aspects in the Internet of Production – An Intra-company Perspective

Human Digital Shadow: Data-based Modeling of Users and Usage in the Internet of Production