Can feature removal benefit the automotive manufacturers amid supply shortages? An analytical investigation

The automotive industry has been significantly impacted by the global semiconductor shortage since 2020. Traditional strategies, such as maintaining safety stock and sourcing from backup suppliers, have been proven insufficient in mitigating supply shortages. To address the global chip shortage and build supply chain viability, leading automotive manufacturers worldwide have adopted an innovative adaptation strategy known as the feature removal strategy. This strategy involves temporarily removing non-vital features and retrofitting them once supply shortages are alleviated, thereby mitigating disruptions. Given the increasing frequency, severity, and unpredictability of global chip shortages, it is crucial to investigate the potential benefits of the feature removal strategy for automotive manufacturers. This study aims to address this gap analytically. We develop a stochastic dynamic programming model to optimize pricing and production decisions under the feature removal strategy. We reformulate the model into an equivalent convex problem and propose structural properties to manage the complexity arising from the high dimensionality of state and action spaces. Comparative analyses with benchmark strategies underscore the efficacy of the feature removal strategy in enhancing profitability and sustaining supply chain viability, especially in prolonged supply shortage scenarios.