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The efficient dealing with the dynamic environment of production industries is one of the most challenging tasks of Supply Chain Management in high-wage countries. Relevant and current information are still not used sufficiently, to handle the influence of the dynamic environment on intra- and inter-company order processing adequately. Among other things, the problem is caused by missing or delayed feedback of relevant data. As a consequence of that, planning results differ from the actual situation of production. High Resolution Supply Chain Management describes an approach aiming on high information transparency in supply chains in combination with decentralized, self-optimizing control loops for Production Planning and Control. The final objective is to enable manufacturing companies to produce efficiently and to be able to react to order-variations at any time, requiring process structures to be most flexible.
Production in high-wage countries can be made more efficient, cost-effective, and flexible by solving the conflict between planning and value orientation. A promising approach is to focus on planning and decision-making processes (production planning and control, design of production processes and machinery, etc.) and to aim to maximize overall planning efficiency. Planning efficiency can be expressed as the ratio between the benefit generated by preparing detailed process instructions to produce the parts or components and the corresponding planning efforts. Industrial companies wanting to gain a competitive advantage in dynamic global markets have to identify a set of non-dominated solutions with the most favorable effort–benefit ratio rather than a single solution. The optimum between detailed planning and the immediate implementation of value-adding activities (process steps) in the process chain needs to be found dynamically for each product.
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.