۱- Introduction

۲- Literature review

۳-  Optimization model

۴- Sample average approximation method

۵- Computational results

۶- Managerial implications

۷- Conclusion

References

Abstract

The supply chain (SC) ability to return quickly and effectively to its initial condition or even a more desirable state after a disruption is critically important, and is defined as SC resilience. Nevertheless, it has not been sufficiently quantified in the related literature. This study provides a new metric to quantify the SC resilience by using the stochastic programming. Our metric measures the expected value of the SC’s cost increase due to a possible disruption event during its recovery period. Based on this measure, we propose a two-stage stochastic program for the supply chain network design under disruption events that optimizes location, allocation, inventory and order-size decisions. The stochastic program is formulated using quadratic conic optimization, and the sample average approximation (SAA) method is employed to handle the large number of disruption scenarios. A comprehensive computational study is carried out to highlight the applicability of the presented metric, the computational tractability of the stochastic program, and the performance of the SAA. Several key managerial and practical insights are gained based on the computational results. This new metric captures the time and cost of the SC’s recovery after disruption events contrary to most of previous studies and main impacts of these two aspects on design decisions are highlighted. Further, it is shown computationally that the increase of SC’s capacity is not a suitable strategy for designing resilient SCs in some business environments.

Introduction

Resilience is the ability of a system or firm to recover after a disruption event effectively and quickly, and in supply chain management, this ability is affected by SC’s design decisions and resources. As emphasized by Chopra & Sodhi (2014), Simchi-Levi et al. (2014), and, Ivanov et al. (2016) from 2000 to 2015, disruption events such as economic crises, earthquakes, terrorist attacks, and strikes occurred in SCs in more frequency and severity, and hence such an ability is crucial for SCs. The Business Continuity Institute reports that one-third of 408 surveyed companies experienced at least one SC disruption in 2017 and one-fifth of the corresponding disrupted companies stated cumulative losses of at least one million euros because of disruption events (Alcantara et al., 2017). Recently, quantitative models have received significant attention to optimize design and planning decisions in SCs with consideration of disruption events, and many companies such as IBM and Ford Motor used these methods (Simchi-Levi et al., 2014; Lu et al., 2015; Hosseini et al., 2019).