AbstractsEngineering

In the past decade, internet and flexible manufacturing have revolutionized some of the basic principles of retailing. Two such aspects relate to product assortment and return policies. With the aid of advanced production technology, companies continuously increase their product assortments to reach more customers and satisfy their specific needs better. Higher product variety, however, typically raises operational complexity and costs. In addition, these costs can even be more significant when product returns are considered. We integrated return policies into a multiproduct model, where assortment, inventory, and/or pricing decisions were made in an integrated manner. Our research agenda focused on an expected-profit-maximizing firm that offers a set of horizontally differentiated products. The firm accepts product returns that are in resalable condition. We characterized the firm's return policy by the money refunded to the customer in case of return. We have a demand model that is based on individual consumer behavior, conceptualized to fit a well established two-stage utility maximization framework (nested multinomial logit model). Consumers decide which product (if any) out of a given assortment to buy in the first stage, and then decide to keep or return the item in the second stage. Our study shows an interesting interaction between product assortment and return policy. We explored the implications of return policies on product assortment planning. We showed that the structure of the optimal assortment fundamentally changes depending on the amount refunded and/or operational mode (make-to-order versus make-to-stock). Surprisingly, there are situations where a retailer is better off by offering eccentric products (i.e., those that are least likely to be purchased by a typical consumer). We also explored return policies for customized products. We determined that customizing firms should aim for product returns that are neither a net cost nor a net benefit. This can be achieved by partial refunding when designing their consumer return policies. In addition, we were the first to investigate return policies in a multiperiod environment. Restricting return policies to a single period analysis only, as other authors do, may lead to wrong conclusions. We show, for example, that firms can reduce their price when they consider multiple periods. In this multiperiod setting, we found that a salvage-down-to level inventory policy is optimal.