Analysis of a production/inventory system subject to random disruptions
Management Science
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
A Production-Inventory System with Markovian Capacity and Outsourcing Option
Operations Research
Quantifying the Value of Leadtime Information in a Single-Location Inventory System
Manufacturing & Service Operations Management
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Ports-of-entry are critical components of the modern international supply chain infrastructure, particularly container seaports and airfreight hubs. The potential operational and economic impact resulting from their temporary closure is unknown but is widely believed to be very significant. This paper investigates one aspect of this potential impact, focusing specifically on the use of supply chain inventory as a risk mitigation strategy for a one supplier, one customer system in which goods are transported through a port-of-entry subject to temporary closures. Closure likelihood and duration are modeled using a completely observed, exogenous Markov chain. Order lead times are dependent on the status of the port-of-entry, including potential congestion backlogs of unprocessed work. An infinite-horizon, periodic-review inventory control model is developed to determine the optimal average cost ordering policies under linear ordering costs with backlogged demand. When congestion is negligible, the optimal policy is state invariant. In the more complex case of nonnegligible congestion, this result no longer holds. For studied scenarios, numerical results indicate that operating margins may decrease 10% for reasonable-length port-of-entry closures, that margins may be eliminated completely without contingency plans, and that expected holding and penalty costs may increase 20% for anticipated increases in port-of-entry utilization.