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Im Zuge der Globalisierung und einer steigenden Marktdynamik ge-
winnt die Optimierung der Lieferketten zunehmend an Bedeutung.
Die Untersuchung unterschiedlicher Beschaffungsstrategien im Wert-
schöpfungsnetzwerk unter Berücksichtigung des Einflusses verschie-
dener Produktionsparameter fällt in der unternehmerischen Praxis
zunehmend schwer. Hierbei können Simulationen von Lieferketten
Abhilfe schaffen, um Wertschöpfungsstrukturen entlang der Supply
Chain zu analysieren und zu verbessern.
Der FIR e. V. an der RWTH Aachen arbeitet mit anderen Instituten im Aachener Exzellenzcluster "Integrative Produktionstechnik für Hochlohnländer" daran, die Wettbewerbsfähigkeit von deutschen Unternehmen zu stärken. Ein Ziel ist es, die Qualität und die Effizienz der menschlichen Entscheidungen innerhalb der Disposition zu verbessern. Zur Steuerung und Optimierung von Lieferketten greifen Entscheider auf eine Vielzahl von Informationen aus der Logistik zurück, die sie schnell erfassen, verarbeiten und richtig interpretieren müssen. Menschen handeln aber nicht ausschließlich rational und die Qualität der Entscheidungen hängt von zahlreichen und
individuell unterschiedlichen Größen ab. Zur Unterstützung der Mitarbeiter und einer Verbesserung der Planung einer Supply-Chain wird im Rahmen des Teilprojekts die folgende Kernfrage untersucht: Welche Wechselwirkungen existieren innerhalb von Lieferketten zwischen Beschaffungsstrategie und übergeordneten Zielgrößen unter Berücksichtigung verschiedener Nachfrageszenarien?
Influenced by the high dynamic of the markets the optimization of supply chains gains more importance. However, analyzing different procurement strategies and the influence of various production parameters is difficult to achieve in industrial practice. Therefore, simulations of supply chains are used in order to improve the production process. The objective of this research is to evaluate different procurement strategies in a four-stage supply chain. Besides, this research aims to identify main influencing factors on the supply chain’s performance. The performance of the supply chain is measured by means of back orders (backlog). A scenario analysis of different customer demands and a Design of Experiments analysis enhance the significance of the simulation results.
Influenced by the high dynamic of the markets and the steadily increasing demand for short delivery times the importance of supply chain optimization is growing. In particular, the order process plays a central role in achieving short delivery times and constantly needs to evaluate the trade-off between high inventory and the risk of stock-outs. However, analyzing different order strategies and the influence of various production parameters is difficult to achieve in industrial practice. Therefore, simulations of supply chains are used in order to improve processes in the whole value chain. The objective of this research is to evaluate two different order strategies (t, q, t, S) in a four-stage supply chain. In order to measure the performance of the supply chain the quantity of the backlog will be considered. A Design of Experiments approach is supposed to enhance the significance of the simulation results.
Industry 4.0 and the consequent necessity of digitalization has also impli-cations to the field of procurement, resulting in the so-called term of Procurement 4.0. Digitalization can be a valuable tool to increase the efficiency of the procurement organization and to exploit new opportunities of growth. A mandatory requirement to perform the digital transformation is an increased transparency along the procurement process chain. This paper aims to conceptualize a digital shadow for the procurement process in manufacturing industry as a basis for advanced data analytics procedures. The term digital shadow stands for a sufficiently accurate, digital image of a compa-ny's processes, information and data. This image is needed to create a real-time eval-uable basis of all relevant data in order to finally derive recommendations for action. The formation of the Digital Shadow is thus a central field of action for Industrie 4.0 and forms the basis for all further activities.
This research area focuses on the management systems and principles of a production system. It aims at controlling the complex interplay of heterogeneous processes in a highly dynamic environment, with special focus on individualized products in high-wage countries. The project addresses the comprehensive application of self-optimizing principles on all levels of the value chain. This implies the integration of self-optimizing control loops on cell level, with those addressing the production planning and control as well as supply chain and quality management aspects. A specific focus is on the consideration of human decisions during the production process. To establish socio-technical control loops, it is necessary to understand how human decisions are made in diffuse working processes as well as how cognitive and affective abilities form the human factor within production processes.