Governing the dynamics of multi-stage production systems subject to learning and forgetting effects: A simulation study

Abstract

Managing production systems where production rates change over time due to learning and forgetting effects poses a major challenge to researchers and practitioners alike. This task becomes especially difficult if learning and forgetting effects interact across different stages in multi-stage production systems as rigid production management rules are unable to capture the dynamic character of constantly changing production rates. In a comprehensive simulation study, this paper first investigates to which extent typical key performance indicators (KPIs), such as the number of setups, in-process inventory, or cycle time, are affected by learning and forgetting effects in serial multi-stage production systems. The paper then analyses which parameters of such production systems are the main drivers of these KPIs when learning and forgetting occur. Lastly, it evaluates how flexible production control based on Goldratt’s Optimised Production Technology can maximise the benefits learning offers in such systems. The results of the paper indicate that learning and forgetting only have a minor influence on the number of setups in serial multi-stage production systems. The influence of learning and forgetting on in-process inventory and cycle time, in contrast, is significant, but ambiguous in case of in-process inventory. The proposed buffer management rules are shown to effectively counteract this ambiguity.