Planning and Optimization of Changeable Production Systems by Applying an Integrated System Dynamic and Discrete Event Simulation Approach

Today, the planning and optimization of changeable production systems (CPS) has become a top priority item for the strategic management in most industries. Research has already provided a whole bunch of supposedly effective, mainly technical solutions that enhance the changeability of production systems. However, to actually select solutions for CPS, decision-makers do also need tools for their ex-ante evaluation. These are still missing – especially to assess the impact of organizational measures. Further, it appears that decision-makers have difficulties in coping with the dynamic complexity implied by CPS in planning activities. CPS are complex socio-technical systems – both in terms of their structure and their dynamic behavior – and so must be the underlying planning problems. Thus, any planning approach and in particular its related decision support tools must not only fit the dynamic complexity of the planning problem as a formal requirement, but must at the same time foster the decision-maker's understanding of the underlying managerial problem, i.e. it must foster managerial insight.Therefore, this paper aims at closing this gap and proposes an integrated planning approach based on a hybrid Discrete Event Simulation (DES) and System Dynamics (SD) framework. Both simulation methods are interlinked by an integrated planning approach sharing the same conceptual model and a common set of parameters and key performance indicators (KPI). The DES is applied (1) for an in-depth analysis of the effects of any measures to improve the changeability, based on KPIs relevant to the decision-maker, and (2) to verify the major parameters utilized in the SD model. However, the DES can only be applied for a given structural state of the system. Consequently, SD is utilized to investigate its dynamic behavior when it changes from one structural state to the other. The planning approach was successfully applied in an industry context, where it proved its ability to actually leverage the decision quality in current practice to manage CPS.