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Inventory inaccuracy refers to the discrepancy between the actual inventory and the recorded inventory information. Inventory inaccuracy is prevalent in retail stores. It may result in a higher inventory level or poor customer service. Earlier studies of inventory inaccuracy have traditionally assumed risk-neutral retailers whose objective is to maximize expected profits. We investigate a risk-averse retailer within a newsvendor framework. The risk aversion attitude is measured by conditional-value-at-risk (CVaR). We consider inventory inaccuracy stemming both from permanent shrinkage and temporary shrinkage. Two scenarios of reducing inventory shrinkage are presented. In the first scenario, the retailer conducts physical inventory audits to identify the discrepancy. In the second scenario, the retailer deploys an automatic tracking technology, radiofrequency identification (RFID), to reduce inventory shrinkage. With the CVaR criterion, we propose optimal policies for the two scenarios. We show monotonicity between the retailer’s ordering policy and his risk aversion degree. A numerical analysis provides managerial insights for risk-averse retailers considering investing in RFID technology.

Abstract:With increasing concern over the environment, shipment consolidation has become one of a main initiative to reduce CO 2 emissions and transportation cost among the logistics service providers. Increased delivery time caused by shipment consolidation may lead to customer's order cancellation. Thus, order cancellation should be considered as a factor in order uncertainty to determine the optimal shipment consolidation policy. We develop mathematical models for quantity-based and time-based policies and obtain optimality properties for the models. Efficient algorithms using optimal properties are provided to compute the optimal parameters for ordering and shipment decisions. To compare the performances of the quantity-based policy with the time-based policy, extensive numerical experiments are conducted, and the total cost is compared.