A randomized pricing decision support system in electronic commerce
Decision Support Systems
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We focus on a logistics system where inventory is held at three levels: the customers, the retail store, and the warehouse. Retail customer segments are heterogeneous and differ in their reservation prices for product as well as their holding costs. They purchase product from a retail store managed by a retailer. The retailer chooses a retail pricing scheme to maximize his expected profit given a model of customer temporal response to retail pricing. This retailer is supplied product from a warehouse managed by a manufacturer.The manufacturer is responsible for maintaining inventory level at the warehouse and providing 100% service level for retailer orders. The manufacturer uses all available information to generate an inventory policy that maximizes expected profit subject to the service-level requirement. We evaluate the manufacturer's optimal expected profit under two possible schemes: (1) no information regarding the timing of retail promotion plans, and (2) full information regarding the timing of retail promotion plans.We show: (1) as the predictability of the sales impact of a promotion decreases, it may be optimal for the retailer to eliminate retail promotions; (2) increased stockpiling tendency of customers increases retailer profits and decreases manufacturer profits; and (3) retail-promotion information sharing can make retail promotions change from being less profitable than no promotions to being more profitable than no promotions for the manufacturer. We show the impact of fitting the model to a grocery store data set that provided data regarding retail sales (and associated prices) of canned tomato soup over two years. We also explore managerial insights suggested by the model.