Modeling Supply Chain Resiliency


In recent years, supply chains have been more susceptible to a variety of disruption—including natural disaster, technical failure and human errors. Hence, many supply chain enterprises are recognizing the importance of designing reliable and resilient supply chains in which these entities can withstand major disruptions within acceptable performance degradation and recover quickly from disruption with minimal cost and time.

Global supply chains have become more vulnerable to disruptions due to increasing their complexity and the rising frequency and severity of external risks such as geographic risks (e.g., political instability), economic risks (e.g., price volatility) and natural disasters. COVID-19 revealed the bottlenecks in our supply chain systems vividly. It disrupted supply chain of many systems including health care systems, causing stock outs of personal protective equipment, critical medical supplies and cleaning materials.

COVID-19 exposed the vulnerabilities of many organizations, especially those that have a high dependence on offshoring to fulfill their need for raw materials or finished products. A continual pursuit of lean systems to minimize costs, reduce wastes and drive up asset utilization has removed buffers and flexibility to absorb disruptions. In another example, the Japanese earthquake and tsunami in 2011 disrupted global supply chains including the automotive sector and retail supply chain in the United Kingdom. According to BBC News in 2011, the auto maker Nissan was forced to shut down its production in U.K. Sunderland plant for three days due to the shortage of engine parts that had been supplied by a Japanese supplier located in the earthquake zone.

A key challenge in the context of supply chain resilience is to understand the impact of different proactive and reactive mitigation strategies and assign resources—prior and during the aftermath of the disruption—in a timely and cost-efficient manner so the chance of supply chain operations being disrupted is minimized. To achieve this goal, it is necessary to quantify the resilience of supply chains and understand how different mitigation strategies can impact on that resiliency.

This article aims to quantify and improve the resilience of supply chain by developing Bayesian Networks (BN) models that are capable of measuring the reliability and resilience of two-echelon supply chains with buyer and supplier entities under single and multiple correlated disruptions. To bridge this gap, the BN model can assist supply chain decision-makers in measuring robustness and resilience with different mitigation strategies, taking into account the supplier-buyer dependency. The proposed metrics in this study quantify the causal relationship between local and global disruptions with suppliers and manufacturing using conditional probability theory.

Metric for measuring resilience of supplier and manufacturer

The resilience of a supplier can be calculated as the degree of not only robustness but also the recoverability of supplier. In the robustness metric, the supplier utilizes the pre-positioned inventory prior disruption. But in the resilience measure, in addition to pre-positioned inventory, the supplier may stock and utilize the surplus capacity to deal with the unexpected disruption scenario. Hence, a new parameter  is introduced that represents the capacity of supplier that will be utilized in the aftermath of disruption. The resilience of ith supplier can be calculated by Equation (1): 

In this case, we develop metric to measure the robustness of manufacturer in which the manufacturer can be disrupted due to three reasons; local disruption, global disruption or the failure of supplier. The robustness of manufacturer i denoted by  is defined as the capability of manufacturer i to meet its required demand from the relevant suppliers.  

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