Logistics

Load carriers are essential for transporting manufactured parts in manufacturing companies. Despite their ‘simplicity’, they are usually expensive to purchase as they are manufactured expressly to fit purpose. While tracking methods such as GPS tracking can be used to prevent the loss of load carriers, this is associated with monitoring costs and presents challenges with regard to data protection as soon as the work performance of intralogistics employees is monitored. Assigning load carriers to designated clusters and monitoring these clusters provides an effective solution—without drawing conclusions about employee performance. Furthermore, artificial intelligence can optimize this approach whilst also deterring the theft of load carriers.

Due to the higher customization of products to customer groups and needs, body-in-white manufacturing industries are facing higher variant assembly at the later stages of the production line, thus increasing production costs per unit. Flexible production processes that involve flexible material flows, non-rigid manufacturing sequences, and the automatic reconfiguration of tools are regarded as the pillars of a resilient production system. This article presents a conceptual solution for flexible Body-in-White sheet metal production with autonomous collaborative robotic systems to make product costs affordable for a higher competitive advantage.

Manufacturing companies incorporate reverse logistics as a building block of the circular economy for greater sustainability. Case studies show that this can result in strategic opportunities. This article summarizes an analysis of expert interviews on the increase in supply chain resilience attributed to reverse logistics. Potential benefits are highlighted, and companies are encouraged to examine the approach and implement innovative solutions. The result is a hypothesis-based value model that serves as an orientation aid for decision-makers.

The constant advance of digitalization confronts companies with new challenges and opportunities. Immediate data processing is now ubiquitous and the advantages are obvious. However, broadband coverage in Germany is insufficient, which makes it difficult to improve processes. Mathematical approaches and machine learning enable timely optimization and smooth production.

More than 99% of German companies are small and medium-sized enterprises (SMEs), which therefore represent an important part of industrial supply chains. New regulations are increasing the pressure on companies to create transparency along the supply chain so that the role of SMEs is also coming into focus. However, they are often confronted with limited financial and human resources. Based on a quantitative survey and a literature review, this article deals with the question of what SME-friendly approaches could look like.

Routing flexibility enables a robust, resilient design of production. However, in highly available, decentralized controlled production systems with cyclic material flow, it can reduce efficiency due to undesired deterministic-chaotic behavior. An automated method for measuring such behavior is presented. It is tested with a double conveyor belt laboratory system. An embedded system simplifies data acquisition. Results indicate that the method is usable for manual and automatic production systems. It has the potential to recognize modeling deficiencies in Industry 4.0 control with IEC 61499. (Only in German)

The logistics industry offers numerous opportunities for data-driven solutions, such as improving the part feeding problem in assembly line industries. A data-based approach for will lead to an improvement of cost-effectiveness through optimized processes, resource utilization, and consistent supply to the assembly line. The generated approach is a mixed integer programming model which considers limited storage space, uses constraints, and various cost factors related to transport, replenishment, and picking.