Towards resilient supply chains: a quantitative approach to risk and resilience

Purpose The COVID-19 pandemic and a multitude of other crises caused by natural disasters, geopolitical tensions or technical failures in intertwined logistics networks have shown that a well-performing company depends on a strong supply chain. Despite the increasing need for resilient supply chains, companies are often reluctant to invest in resilience measures. This study aims to develop a quantitative framework that integrates risk and resilience assessment in supply chains by linking disruption-induced losses to measurable performance indicators. Design/methodology/approach The authors develop a mathematical framework to assess the risk and resilience of supply chains. This framework quantifies losses incurred due to disruptions in a supply network. It integrates risk and resilience and is demonstrated using a system dynamics (SD) model. Findings The authors simulate the domestic supply chain of a representative manufacturing company and derive resilience measures using the proposed framework. The simulation demonstrates, in a simplified example, how the framework can indicate opportunities to improve supply chain performance. This stylized example serves as a conceptual illustration of the proposed mathematical framework rather than a real-world case study. While risk mitigation commonly focuses on cost minimization, resilience enhancement typically increases operational expenditures. The proposed framework links resilience measures to expected loss reductions and provides measurable indicators for evaluating the cost–benefit ratio of resilience strategies, which can enable decision-makers to quantitatively weigh cost and resilience against each other. Research limitations/implications The SD model illustrates theoretical assumptions using a risk event. It should be extended to calculate production losses across multiple disruptions and test different resilience measures. Originality/value The framework provides a basis for quantifying potential losses due to supply chain disruptions. A simulation model validates the framework and contributes by capturing the dynamic interaction of disruption and recovery while interlinking risk and resilience in one model. The simulation serves as a conceptual example rather than an empirical case study and aims to illustrate the applicability of the mathematical model under stylized conditions.