Strategic topics

The requirements for the design, configuration and operation of supply chains are constantly growing. In addition to a cost-effective and customer-oriented design of value networks, the aspects of resilience and sustainability are increasingly coming into focus. Particularly considering recent events such as the energy crisis, the Corona pandemic, or the Ukraine conflict in combination with a decreasing availability of fossil fuels, global warming, and a generally increasing frequency of global crises, the strategic work of the Supply Chain Engineering department is primarily designed to provide its customers with innovative solutions to these challenges.

The detailed solution is individual for each of our customers, as no two supply chains are identical. At the same time, a holistic mapping of all dynamic interactions within the supply chain is required for the precise improvement of processes. With the help of a specially developed tool for the discrete-event simulation of all planning and material flow processes in supply chains, OTD-NETWORK, the creation of a digital twin (Network Simulation) of a supply chain using object-oriented building blocks is a manageable process that our customers can understand.

The digital twin provides an essential basis for evaluating a wide range of configurations. The Supply Chain Engineering department is researching the development of comprehensive databases and key performance indicators in order to comprehensively enrich classic economic perspectives and quantifications. In this context, energetic and generally sustainability-oriented indicators play an important role (research project E²-Design). Furthermore, components from risk management and resilience management (Reskriver, CoVersatile) can be integrated through the possibility of scenario-based decision making by integrating random-based disruptive events or operational failures into the digital twin, making resilience and recovery time testable. In particular, the interplay of partially synergetic and partially conflicting target components in terms of generating so-called trade-off solutions is of great importance.

A digital supply chain twin enriched with this data in the sense of a digital twin of the supply chain that fulfills the requirements offers the ideal "playground" for innovative machine learning methods. A specific establishment and further development in this area is a main strategic focus of the Supply Chain Engineering department. The initially exclusively evaluating component of the material flow simulation is currently being expanded by artificial intelligence components within the framework of several parallel research projects (MOVE, Datenfabrik.NRW) in order to obtain a "self-learning simulation". In this process, a large number of simulation runs are triggered automatically, through which a machine learning algorithm iteratively examines promising areas in the solution space of the supply chain parameter settings and avoids deficient solution areas. This constellation makes time-consuming human "trial and error" on simulation parameters obsolete. The management of the complexity of the combination of efficiency, resilience and sustainability in supply chains is significantly advanced by the vision of the self-learning supply chain image.

The use and collection of data is an aspect that is indispensable for the previous strategic topics in order to derive promising recommendations for action. To ensure the future viability of our solutions, we as a department strive to make an active contribution to the development of end-to-end platform economies. Sharing data in collaborative and federated data spaces beyond one's own company boundaries while maintaining data sovereignty will be a significant component of value creation in the near future. As a department, we are one of the drivers of this progress with our extensive involvement in the development of the first open data ecosystem for the automotive industry, Catena-X. In addition, these data spaces can also be used to drive further AI applications, which achieve better performance through data enrichment. Finally, the ERP systems of tomorrow are also closely linked to this collaboratively generated progress, so that the requirements for ERP selection must integrate the new potentials.