Most durable products have two distinct types of customers: first-time buyers and customers who already own the product, but are willing to replace it with a new one or purchase a second one. Firms usually adopt a price-discrimination policy by offering a trade-in rebate only to the replacement customers to hasten their purchase decisions. Any return flow of products induced by trade-in rebates has the potential to generate revenues through remanufacturing operations. In this paper, we study the optimal pricing/trade-in strategies for such durable, remanufacturable products. We focus on the scenario where the replacement customers are only interested in trade-ins. In this setting, we study three pricing schemes: (i) uniform price for all customers, (ii) age-independent price differentiation between new and replacement customers (i.e., constant rebate for replacement customers), and (iii) age-dependent price differentiation between new and replacement customers (i.e., age-dependent rebates for replacement customers). We characterize the roles that the durability of the product, the extent of return revenues, the age profile of existing products in the market, and the relative size of the two customer segments play in shaping the optimal prices and the amount of trade-in rebates offered. Throughout the paper we highlight the operational decisions that might influence the above factors, and we support our findings with real-life practices. In an extensive numerical study, we compare the profit potential of different pricing schemes and quantify the reward (penalty) associated with taking into account (ignoring) customer segmentation, the price-discrimination option, return revenues, and the age profile of existing products. On the basis of these results, we are able to identify the most favorable pricing strategy for the firm when faced with a particular market condition and discuss implications on the life-cycle pricing of durable, remanufacturable products.trade-in rebates, pricing, remanufacturing, durable products, product-age profile

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.

We consider a market with two competing supply chains, each consisting of one wholesaler and one retailer. We assume that the business environment forces supply chains to charge similar prices and to compete strictly on the basis of customer service. We model customer service competition using game‐theoretical concepts. We consider three competition scenarios between the supply chains. In the uncoordinated scenario, individual members of both supply chains maximize their own profits by individually selecting their service and inventory policies. In the coordinated scenario, wholesalers and retailers of each supply chain coordinate their service and inventory policy decisions to maximize supply chain profits. In the hybrid scenario, competition is between one coordinated and one uncoordinated supply chain. We discuss the derivation of the equilibrium service strategies, resulting inventory policies, and profits for each scenario, and compare the equilibria in a numerical study. We find that coordination is a dominant strategy for both supply chains, but as in the prisoner's dilemma, both supply chains are often worse off under the coordinated scenario relative to the uncoordinated scenario. The consumers are the only guaranteed beneficiaries of coordination.

An increasing number of companies have been implementing comprehensive recycling and remanufacturing programs. These endeavors typically involve the operation of joint manufacturing and remanufacturing systems. One of the major challenges in managing such hybrid systems is the stochastic nature of product returns. In particular, there is significant variability in the condition of the returns. This paper presents an approach for assessing the impact of quality-based categorization of returned products. Through extensive numerical studies on a continuous-time Markov chain model, we show that incorporation of returned product quality in the remanufacturing and disposal decisions can lead to significant cost savings. We find that these savings are amplified as the return quality decreases, and as the return rate increases. We also show that prioritizing higher quality returns in remanufacturing is, in general, a better strategy.

This paper investigates a capacity planning strategy that collects commitments to purchase before the capacity decision and uses the acquired advance sales information to decide on the capacity. In particular, we study a profit-maximization model in which a manufacturer collects advance sales information periodically prior to the regular sales season for a capacity decision. Customer demand is stochastic and price sensitive. Once the capacity is set, the manufacturer produces and satisfies customer demand (to the extent possible) from the installed capacity during the regular sales period. We study scenarios in which the advance sales and regular sales season prices are set exogenously and optimally. For both scenarios, we establish the optimality of a control band or a threshold policy that determines when to stop acquiring advance sales information and how much capacity to build. We show that advance selling can improve the manufacturer's profit significantly. We generate insights into how operating conditions (such as the capacity building cost) and market characteristics (such as demand variability) affect the value of information acquired through advance selling. From this analysis, we identify the conditions under which advance selling for capacity planning is most valuable. Finally, we study the joint benefits of acquiring information for capacity planning through advance selling and revenue management of installed capacity through dynamic pricing.