A Quantitative Approach to Assess the Likelihood of Supply Chain Shortages

Abstract

We define supply chains (SCs) as sequences of processes that link the demand and supply of goods or services within a network. SCs are prone to shortages in delivering their output goals due to several factors such as personnel undersupply, inefficient processes, policy failure, equipment malfunction, natural hazards, pandemic outbreaks, power outages, or economic crises. Recent notable supply-chain failures include the 2021 Texas power crisis, personal protection equipment shortages during the COVID-19 pandemic, and regional or global food chain shortages. The consequences of such shortages can range from negligible to devastating. The Texas power crisis resulted in the death of 70 people and left approximately 4.5 billion homes and businesses without power for multiple days. In this paper, we presented a methodology to quantify the failure probability of the throughput of a supply chain. We divided the methodology into two major categories of steps. In the first step, we converted the given or assumed supply chain data into fault trees and quantify them. In the second step, we iterated the quantification of the fault tree to build a supply chain shortage risk profile. We introduced the notion of success criteria for the output from a facility, based on which we included or excluded the facility for quantification. With the inclusion of relevant field data, we believe that our methodology can enable the stakeholders in the supply-chain decision-making process to detect vulnerable facilities and risk-inform prevention and mitigation actions. Applications for this methodology can include construction, inventory stocking, assessing manufacturing quantities, policy changes, personnel allocation, and financial investment for critical industries such as nuclear, pharmaceutical, aviation, etc.