I n this paper, we consider a retailer adopting a "money-back-guaranteed" (MBG) sales policy, which allows customers to return products that do not meet their expectations to the retailer for a full or partial refund. The retailer either salvages returned products or resells them as open-box items at a discount. We develop a model in which the retailer decides on the quantity to procure, the price for new products, the refund amount, as well as the price of returned products when they are sold as open-box. Our model captures important features of MBG sales including demand uncertainty, consumer valuation uncertainty, consumer returns, the sale of returned products as open-box items, and consumer choice between new and returned products and possibility of exchanges when restocking is considered. We show that selling with MBGs increases retail sales and profit. Furthermore, the second-sale opportunity created by restocking returned products enables the retailer to generate additional revenues. Our analysis identifies the ideal conditions under which this practice is most beneficial to the retailer. Offering an MBG without restocking increases the new product price. We show that if the retailer decides to resell the returned items as open-box, the price of the new product further increases, while open-box items are sold at a discount. On the other hand, customers enjoy more generous refunds along with lower restocking fees. The opportunity to resell returned products also generally decreases the initial stocking levels of the retailer. Our extensive numerical study substantiates the analytical results and sharpens our insights into the drivers of performance of MBG policies and their impact on retail decisions.

In response to competitive pressures, firms are increasingly adopting revenue management opportunities afforded by advances in information and communication technologies. Motivated by these revenue management initiatives in industry, we consider a dynamic pricing problem facing a firm that sells given initial inventories of multiple substitutable and perishable products over a finite selling horizon. Because the products are substitutable, individual product demands are linked through consumer choice processes. Hence, the seller must formulate a joint dynamic pricing strategy while explicitly incorporating consumer behavior. For an integrative model of consumer choice based on linear random consumer utilities, we model this multiproduct dynamic pricing problem as a stochastic dynamic program and analyze its optimal prices. The consumer choice model allows us to capture the linkage between product differentiation and consumer choice, and readily specializes to the cases of vertically and horizontally differentiated assortments. When products are vertically differentiated, our results show monotonicity properties (with respect to quality, inventory, and time) of the optimal prices and reveal that the optimal price of a product depends on higher quality product inventories only through their aggregate inventory rather than individual availabilities. Furthermore, we show that the price of a product can be decomposed into the price of its adjacent lower quality product and a markup over this price, with the markup depending solely on the aggregate inventory. We exploit these properties to develop a polynomial-time, exact algorithm for determining the optimal prices and the profit. For a horizontally differentiated assortment, we show that the profit function is unimodal in prices. We also show that individual, rather than aggregate, product inventory availability drives pricing. However, we find that monotonicity properties observed in vertically differentiated assortments do not hold.dynamic pricing, revenue management, perishable products, consumer choice, vertical and horizontal product assortments, efficient algorithm

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In this paper, we propose a new greedy-like heuristic method, which is primarily intended for the general MDKP, but proves itself effective also for the 0-1 MDKP. Our heuristic differs from the existing greedy-like heuristics in two aspects. First, existing heuristics rely on each item's aggregate consumption of resources to make item selection decisions, whereas our heuristic uses the effective capacity, defined as the maximum number of copies of an item that can be accepted if the entire knapsack were to be used for that item alone, as the criterion to make item selection decisions. Second, other methods increment the value of each decision variable only by one unit, whereas our heuristic adds decision variables to the solution in batches and consequently improves computational efficiency significantly for large-scale problems. We demonstrate that the new heuristic significantly improves computational efficiency of the existing methods and generates robust and near-optimal solutions. The new heuristic proves especially efficient for high dimensional knapsack problems with small-to-moderate numbers of decision variables, usually considered as "hard" MDKP and no computationally efficient heuristic is available to treat such problems.

doi 10.1287/mnsc.1030.016