The dynamic and rapidly evolving business environment poses numerous complexities for production logistics. The increasing frequency of product and model changes, coupled with the growing variability of components, underscores the urgency for adaptive measures to address ever-shortening product life cycles and advancing customer demands. Technological advancements have enhanced logistical productivity, but it is important to comprehensively tackle these challenges. To overcome these limitations, the concept of "transformability" is explored as a central cornerstone of the solution.

A design framework is proposed to increase the transformability. This paper systematically captures and models the production logistics system to achieve the goal. Key change enablers are identified and aligned with the production logistics system to develop specific change enablers for each production logistics area. These serve as the foundation for formulating transformation measures that can be incorporated into logistics planning. The results guide companies for successful implementation and long-term competitiveness by offering potential users a wide range of possibilities to increase their transformability within planning activities. This work contributes to raising awareness of the importance of transformable production logistics and offers practical recommendations for action. By embracing this approach, companies can proactively and effectively respond to dynamic fluctuations in the production environment, ensuring long-term competitiveness and sustainability

This study set out to develop a Reinforcement Learning (RL) agent for solving an extended Permutation Flow Shop Scheduling Problem (PFSSP). From the domain perspective, we see a lack of realistic constraints for synchronized, human-centered assembly lines. Moreover, objective functions must be provided to enable stress-reducing as well as robust planning under uncertainty. From a methodical perspective, RL has received more and more attention for problems of this type. However, we cannot identify applicable RL concepts for our extended PFSSP with multicriteria objectives. We propose a generic RL agent, which operates on an abstract representation of the schedule and with an objective-independent reward function. Our numerical experiments demonstrate that the agent successfully generalizes a policy and achieves better scores than a Simulated Annealing (SA) metaheuristic.

Manual work is a significant cost driver in manufacturing and logistics. However, research on the methods for analyzing manual processes utilizing sensor technologies, apart from technical feasibility, is scarce. Motion-Mining® is a technology that uses motion sensors, Bluetooth, and pattern recognition to enable highly automated process mapping and analysis of manual work.

The aim of this paper is to evaluate the benefits and limitations of applying this technology in manual production and logistics processes. To this end, Motion-Mining® is compared with traditional and low-tech Lean management tools for capturing and analyzing manual activities.

Ten semi-structured expert interviews as well as case studies in four companies were conducted. The results indicate that Motion-Mining® differs from Lean tools for process analysis mainly in terms of the effort required for data collection, the amount of data obtained, the representativeness of the data, the level of detail, and the insights gained.