Dynamic inventory ordering under demand uncertainty with abrupt market shocks

Volatile market disruptions induce abrupt changes in demand and prices, substantially complicating inventory decisions in dynamic settings. This paper develops a lost-sales multi-period inventory model that explicitly incorporates stochastic market shocks. Demand is characterized by a location-scale distribution, while market shocks are modeled as a compound Poisson process that drives the intertemporal evolution of demand parameters. Within a profit-oriented newsvendor framework, we analyze how such shocks affect ordering decisions and inventory performance under both myopic and forward-looking perspectives. To address the resulting non-stationary dynamic program, we propose a shock-aware dynamic programming approach to the optimal shock-aware policy for real-time shock hedging. We benchmark the proposed policy against several commonly used alternatives, including myopic, Gaussian variance-inflated, and shock-observed reactive policies. Numerical experiments demonstrate that our proposed approach improves expected discounted revenues by from 4% to 6% on average, and yields substantial performance gains in certain scenarios. Overall, the results underscore the importance of modeling abrupt market shocks and provide actionable insights for inventory management in highly volatile environments.