Multi-period risk-aware procurement optimization under COVID-19 disruption

Abstract

Supply chain resilience has been a topic of active research in the operations research and AI communities for several years, but the COVID-19 pandemic threw the frailties of global supply chains into sharp relief. Disruptions and delays caused by fresh outbreaks leading to lockdowns, put severe strain on supply chains in many industries. In this work we develop lockdown-resilient procurement capabilities for a global technology company. First, through analysis of lockdown data from China we develop a logarithmic regression-based lockdown prediction method to complement a supplier risk metric for conventional risks. Second, we develop a multi-period stochastic optimization model that generates a medium-term risk-resilient procurement strategy through supplier diversification and carefully managed stock surplus. The strategy produced by this model is able to out-perform an earlier risk-constrained optimization by up to 50% expected cost when exposed to COVID-19 lockdown disruptions, and proves effective under sensitivity analysis of warehouse cost increases of up to 60%. The real-world viability of the approach is demonstrated by a real use case from IBM Manufacturing in Singapore.