| Summary: | In recent decades, supply chains have become increasingly vulnerable as firms expand and globalize their operations. Consumer expectations, short product life cycles and the adoption of lean principles have also contributed to supply chains becoming less resilient. The COVID-19 pandemic highlighted that for many firms, more work is needed to understand where vulnerabilities exist and to mitigate and prepare for eventual disruptions. Supply chain risk management (SCRM) is one area of study focused on the identification, assessment, mitigation and recovery from risks. In particular, we focus on the identification of risk within a global retail supply chain. Traditional risk models are ill-suited for addressing low-probability and high-impact events, such as a global pandemic. Therefore, we explore the development and application of a risk model based on the principles of time-to-survive (TTS) and time-to-recovery (TTR). We examine two definitions for TTS. In a supply disruption scenario, TTS represents the time a supply chain can continue to operate before facing performance impact. In a demand disruption, TTS𝑑 measures the time a firm can operate before reaching storage capacity constraints. We also introduce an alternative TTR based on the weighted average lead time from a disrupted node to a distribution center. The key metric we examine is risk exposure time (RET), which enables us to quantify the relative vulnerabilities of nodes for use in prioritizing additional SCRM processes and resources.
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