Data assessment for effective regional factory location selection

Procedia CIRP, vol. 138, pp. 957–961

Abstract

Factory location selection is complex, uncertain and time-consuming while it entails long-term consequences and high costs. To enable faster and less subjective decisions we aim to develop a data-driven decision support system. This system contains suitable data sets representing important location selection factors preselected in this paper. The focus and main contribution of this paper is the regional focus of the developed holistic overview of factors and data sets. Finally, we give an outlook on how to analyse this data in the decision support system to be developed. Thereby, we enable more objective and sustainable factory location decisions.

Authors 4

1. Michael Riesener Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Aachen, Germany

2. Esben Schukat Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Aachen, Germany

3. Siyuan Wang Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Aachen, Germany

4. Carsten Engeln corresponding Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Aachen, Germany