Abstract
In the event of a disruption at a primary supplier, the manufacturer’s sourcing and production operations are inevitably affected, necessitating a response strategy based on the estimated disruption duration. This paper derives the optimal production policy for each response strategy, aiming to minimize total costs over a planning horizon within a dynamic programming framework. We characterize these production policies across various model parameters, particularly focusing on the estimated disruption duration and initial inventory level. Our analysis identifies when the passive acceptance strategy and the backup strategy each hold an advantage, and how the cost benefits of each strategy vary with different model parameters. Additionally, we examine the consequences of overestimating or underestimating the disruption duration, providing insights into how the chosen response strategy and production policy, based on the estimated duration, diverge from the true optimal ones as the actual duration unfolds. We also highlight the cost implications of these discrepancies. Finally, in scenarios where the true duration follows a probabilistic distribution, our numerical experiments demonstrate how strategy selection and the cost of misestimation are influenced by the mean of the true disruption duration and the initial inventory level. These analyses offer decision-makers valuable guidance on when to act or wait in the face of disruptions.
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