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When supply disruptions occur, firms want to employ an effective pricing strategy to reduce losses. However, firms typically do not know precisely how customers will react to price changes in the short term, during a disruption. In this article, we investigate the customer's order variability and the firm's profit under several representative heuristic pricing strategies, including no change at all (fixed pricing strategy), changing the price only (naive pricing strategy), and adjusting the belief and price simultaneously (one‐period correction [1PC] and regression pricing strategies). We show that the fixed pricing strategy creates the most stable customer order process, but it brings lower profit than the naive pricing strategy in most cases. The 1PC pricing strategy produces a more volatile customer order process and smaller profit than the naive one does. Although the regression pricing strategy is a more advanced approach, it leads to lower profit and greater customer order variability than the naive pricing strategy (but the opposite when compared to the 1PC strategy). We conclude that (i) completely eliminating the customer order variability by employing a fixed pricing strategy is not advisable and adjusting the price to match supply with demand is necessary to improve the profit; (ii) frequently adjusting the belief about customer behaviors under imperfect information may increase the customer's order variability and reduce the firm's profit. The conclusions are robust to the inventory assumption (i.e., without or with inventory carryover) and the firm's objective (i.e., market clearance or profit maximization).

We study inventory optimization for locally controlled, continuous‐review distribution systems with stochastic customer demands. Each node follows a base‐stock policy and a first‐come, first‐served allocation policy. We develop two heuristics, therecursive optimization(RO) heuristic and thedecomposition‐aggregation(DA) heuristic, to approximate the optimal base‐stock levels of all the locations in the system. The RO heuristic applies a bottom‐up approach that sequentially solves single‐variable, convex problems for each location. The DA heuristic decomposes the distribution system into multiple serial systems, solves for the base‐stock levels of these systems using the newsvendor heuristic of Shang and Song (2003), and then aggregates the serial systems back into the distribution system using a procedure we call “backorder matching.” A key advantage of the DA heuristic is that it does not require any evaluation of the cost function (a computationally costly operation that requires numerical convolution). We show that, for both RO and DA, changing some of the parameters, such as leadtime, unit backordering cost, and demand rate, of a location has an impact only on its own local base‐stock level and its upstream locations’ local base‐stock levels. An extensive numerical study shows that both heuristics perform well, with the RO heuristic providing more accurate results and the DA heuristic consuming less computation time. We show that both RO and DA are asymptotically optimal along multiple dimensions for two‐echelon distribution systems. Finally, we show that, with minor changes, both RO and DA are applicable to the balanced allocation policy.