COVID-19 automotive supply chain risks: A manufacturer-supplier development approach

Abstract

Epidemic outbreaks pose a significant risk to supply chain and logistics, particularly in the global automotive industry. These risks are characterized by long-term disruptions that can have ripple effects, making them challenging to predict. Contingency plans must be developed to address these risks effectively, with simulation tools being a crucial component. These tools should be used as data integration tools from multiple sources to improve risk analysis quality, and preemptive measures should be taken when possible instead of only reacting to disruptions as they occur. This paper focuses on epidemic breakouts as a distinct category of disruption risk in automotive supply chains (ASCs) and logistics. It formulates a multi-objective nonlinear model to determine the optimal selection strategy for functional supply chains and logistics providers under the impact of epidemic outbreaks. The model uses a realistic nonlinear investment return model for supplier development and order allocation strategy, considering COVID-19′s impact. Nonlinear return models for both single and multiple manufacturers and suppliers are developed. A series of sensitivity tests for various scenarios illustrates the behavior of the model and its decision-making value. Numerical experiments and results show that supplier selection and order allocation strategies are mutually influential. Facility closures and reopenings' timing can have more significant impacts on the ASC performance than upstream disruptions or epidemic propagation speed. Lead-time, epidemic spread speed, and interruption durations are also vital factors to consider in optimizing overall performance and allocating investment funds across multiple suppliers to avoid risks and maximize returns. Decision-makers should consider all relevant factors simultaneously when designing such plans to ensure optimal performance and minimize risk.