Simulating Organizational Learning from Returns: Simulation of Closed Loop Supply Chains in Military Cases

Advances in new technologies and the management of complex supply networks have allowed firms to make their supply chains more flexible, responsive, and efficient. Organizational learning, improved IT capabilities, and new manufacturing technologies are among the drivers of these supply chain improvements. This study investigates the effectiveness of organizational learning in the context of a Closed-Loop Supply Chain (CLSC). We apply the Monte-Carlo simulation methodology to a case of military CLSC involving line-replaceable units (LRUs). Priority is put on minimizing downtime in the equipment caused by LRU failures. Additionally, we consider costs and the environmental footprint. We incorporate organizational learning into the simulation in two ways. Namely, improved failure rates and shorter lead times. This study presents a set of quantitative assessments on the effectiveness of several organizational learning interventions in a military CLSC. The results indicate that learning leading to product improvement has the largest impact on overall inventory cost reduction. This study contributes to the current research on CLSC value creation by quantifying the concrete implications of specific interventions using realistic data in a military CLSC. In addition, this study contributes to the growing literature on CLSC value creation in general, and in CLSC informational value research more specifically. However, this study focuses on a specific intervention, representing only a few ways in which value can be created in a CLSC. By providing managers with quantitative results regarding CLSC interventions, this research can aid managers in making better decisions regarding CLSC investments.