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.

During strategic network design, not only strategic but also operational decisions must be made long before a production network is put into operation. These include determining the location and size of inventories within the production network and setting operational parameters for production lines, such as the shift model. However, the large solution space comprising a high number of highly uncertain design parameters makes these decisions challenging without decision support. Therefore, data farming offers a potential solution, as synthetic data can be generated via the execution of multiple simulation experiments spanning the solution space and then analyzed using data mining techniques to provide data-based decision support. However, data farming has not yet been applied to strategic network design due to the lack of adequate solution space management. To address this shortcoming, this paper presents a structured solution space management approach that integrates production network-specific requirements and Design of Experiment (DoE) methods. The approach enables converting the solution space in strategic network design into individual input data sets for simulation experiments, generating a comprehensive database that can be mined for data-based decision support. The applicability and validity of the comprehensive approach are ensured via a case study in the automotive industry.

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.

Modern production systems are increasingly challenged by volatile and complex market conditions. To maintain their competitiveness, the application of Reinforcement Learning (RL) in production planning and control has shown considerable promise. However, the state space in RL approaches is often excessively large and complex, which triggers the curse of dimensionality and hinders learning efficiency. A significant research gap exists regarding systematic methodologies for defining, evaluating, and iteratively optimizing the state space to improve both the transparency and learning performance. This paper addresses this gap by proposing a novel methodology consisting of two steps. First, an initial state space is systematically derived from corporate planning goals with the help of a feature map. Second, Shapley Additive Explanations (SHAP) values are utilized to quantify the influence of each state feature on the RL agent’s action selection and to iteratively improve the state space accordingly. The proposed methodology is validated using a real-world industrial use case from the machinery industry. The results indicate that the methodology successfully identifies dominant state features, leading to an improved learning behavior and enhanced transparency in the RL agent’s decision-making process.

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.