This research, motivated by a real-life case study in a highly competitive automobile supply chain, experimentally studies the impact of disruption on the competitiveness of supply chains. The studied supply chain faces two major risks: disruption of suppliers and tough competition from competitors. Any disruption in upstream level of the supply chain leads to an inability to meet demand downstream and causes market share to be lost to the competitors. For such a setting, a resilient topology is redesigned that can recover from and react quickly to any disruptive incidents. To this aim, we speculate there are three policies that can be used to mitigate the disruption risk, namely keeping emergency stock at the retailers, reserving back-up capacity at the suppliers, and multiple-sourcing. The problem is addressed using a mixed integer non-linear model to find the most profitable network and mitigation policies. We design a piecewise linear method to solve the model. Based on the data extracted from an automotive supply chain, practical insights of the research are extracted in a controlled experiment. Our analysis suggests that implementing risk mitigation policies not only work to the advantage of the supply chain by sustaining and improving its market share but also benefit customers by stabilizing retail prices in the market. Using the case study, we analyze the contribution of each risk strategy in stabilizing the supply chain's profit, market share, and retail price. Our analysis reveals that downstream "emergency stock" is the most preferable risk mitigation strategy if suppliers are unreliable.

Additional Metadata
Keywords Automotive industry, Competition, Disruption, Resilient supply chain, Supply chain management
Persistent URL dx.doi.org/10.1016/j.ejor.2016.11.041, hdl.handle.net/1765/95320
Journal European Journal of Operational Research
Citation
Rezapour, S. (Shabnam), Farahani, R.Z. (Reza Zanjirani), & Pourakbar, M. (2017). Resilient supply chain network design under competition: A case study. European Journal of Operational Research, 259(3), 1017–1035. doi:10.1016/j.ejor.2016.11.041