In today’s fast-paced market, companies are challenged to meet increasing customer demands and shorter product life cycles. To successfully respond to these demands, companies must produce a wide variety of different products. This requires the determination of necessary processes and resources for each product, which can be difficult for process engineers due to the high manual effort and expertise involved. The current state of research has not yet provided explicit definitions of the necessary knowledge and has not fully achieved complete process planning automation. To address this challenge, a digital twin is a valuable tool for automating and understanding process planning. This paper presents a digital twin concept for process planning. It automatically analyzes the product, determines production processes, and selects appropriate resources by linking information about products, resources, and processes. The effectiveness of the digital twin concept is demonstrated through verified and validated use cases, including the production of a compressor element.

Producing companies are increasingly challenged by the complex and volatile environment they have to deal with. The collapse of supply chains due to the COVID-19 pandemic and the chip crisis are two examples that emphasize the relevance of handling global disruptions. Production planning and control (PPC) especially suffers from an uncertain environment, leading to frequent replanning and rescheduling activities. Several publications highlight the importance of innovative approaches from Reinforcement Learning (RL) to manage the challenges in PPC. The underlying structure of the PPC task essentially determines the contribution RL can make. While some selected tasks already show excellent potential regarding the applicability of RL (e.g., order dispatching), others are rarely supported by RL yet (e.g., order management). However, it is unclear how research and industry can identify tasks and problems in PPC that are well-suited for applying RL. To meet this research demand, this publication provides a methodology to identify the potential of RL for PPC tasks. The methodology was developed through literature research and expert workshops. It was applied to a real-world use case, indicating the applicability and success potential of the suggested methodology.

Small and medium-sized enterprises are under constant competitive pressure, forcing them to adopt a more proactive attitude towards efficiency and innovation. Emerging technologies are not only changing the manufacturing sector, but are also having a profound impact on society. One of the most notable technologies is artificial intelligence (AI). While the integration of AI in SMEs is complex and holds significant potential to improve decision-making processes along the value chain and across all operational functions, there are other exponential technologies such as the Internet of Things (IoT). Other exponential technologies such as the Internet of Everything and digital twins amplify these benefits when used in conjunction with AI. The aim of this study is to identify the application potential of AI in manufacturing SMEs. To achieve this, a literature review was conducted. Application potentials as well as application examples from manufacturing SMEs were identified and evaluated. Through analysing 69 relevant papers, it highlighted 10 potential application areas and 13 feasible applications fields, including forecasting, process optimization, and customer service automation. The findings suggest that AI can enhance productivity, cost reduction, flexibility, and decision making across various SME departments. In addition, the potential of AI is demonstrated using three illustrative application examples.

This chapter provides an overview of the current challenges and opportunities for SMEs in the context of the fifth industrial revolution using an international survey with industrial participants. The survey was developed based on the structure of the Industry 5.0 decomposition (see Chap. 1 of this book). The aim of the survey was to determine the importance of Industry 5.0 from a global perspective and to identify the concepts and methodologies that represent an obstacle to the implementation of Industry 5.0. To this end, an analysis of possible barriers within the three Industry 5.0 pillars resilience, human-centration, and sustainability was carried out. The survey was conducted with more than 200 international industry experts from more than ten countries. The results served as a basis for deriving key hypotheses regarding the implementation barriers of Industry 5.0 concepts. These hypotheses will be discussed in future focus group workshops to derive the key influencing factors for a successful transformation of manufacturing SMEs to Industry 5.0.