Migration planning of global production networks – methodological support for the determination of robust migration paths and risk-efficient enablers of change

• Applicant: Professorin Dr.-Ing. Gisela Lanza
• Subject Area: Production Systems, Operations Management, Quality Management and Factory Planning
• Term: from 2019 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 418891325

Today’s production networks are often the result of uncoordinated decisions and opportunistic developments over time. Hence, manufacturing companies very often produce in complex, inefficient and rigid networks lacking strategical focus. Simultaneously, modern globalization challenges companies with disruptive changes of the business environment as well as increasing competition and cost pressure. For this reason, manufacturing companies must migrate their historical grown production networks and, therefore, continuously adapt the network configuration to changing conditions.The objective of this proposal is the development of a model based methodology for migration planning of global production networks. Based on the formulation of a focused global production strategy, which has to support the competitive strategy of a company, migration paths of the network configuration have to be identified under consideration of volatile influencing factors of the business environment and an adequate level of changeability for network objects has to be created.To achieve this, objective mathematical models for modelling and optimization of the structure and objects of a global production network will be formalized. Firstly, a resource based network model that enables the formalization of different network configurations will be developed. Applying this model, configurations which operationalize a global production strategy will be derived. The resulting configurations will be integrated into a stochastic-dynamic optimization model in order to identify an optimal migration strategy/policy of the network configuration. Furthermore, drivers of change of the business environment will be parametrized and integrated as stochastic model parameters. Lastly, a stochastic decision model will be developed which enables the selection of risk-efficient enablers of change for the network objects to enable their pro-active preparation for each planning period in the planning horizon. The developed models will be tested and validated in case studies.

• DFG Programme: Research Grants