Quick Response (QR) is a movement in the apparel industry to shorten lead time. Under QR, the retailer has the ability to adjust orders based on better demand information. We study how a manufacturer-retailer channel impacts choices of production and marketing variables under QR in the apparel industry. Specifically, we build formal models of the inventory decisions of manufacturers and retailers both before and after QR. Our models allow us to address who wins and who loses under QR, and suggest actions such as service level, wholesale price and volume commitments that can be used to make QR profitable for both members of the channel, i.e., Pareto improving. Detailed discussions with a major retailer, and information from industry sources provide supporting evidence for the structure and conclusions of the model.inventory, channels, lead time, service level, Bayesian models

We focus on backup agreements between a catalog company and manufacturers—a scheme to provide upstream sourcing flexibility for fashion merchandise. A backup agreement states that if the catalog company commits to a number of units for the season, the manufacturer holds back a constant fraction of the commitment and delivers the remaining units before the start of the fashion season. After observing early demand, the catalog company can order up to this backup quantity for the original purchase cost and receive quick delivery but will pay a penalty cost for any of the backup units it does not buy. In representative contracts with five companies, the fraction held as backup varies from 20% to 33% and the penalty ranges from 0 to 20% of cost. We model this inventory problem and derive the optimal solution. We provide results from a retrospective parallel test of the model against buyer decisions in 1993 based on a data set from the women's fashion department at a catalog company (Catco). The results indicate that backup arrangements can have a substantial impact on expected profits and may result in an increase in the committed quantity. Also, these arrangements may maintain the manufacturer's expected profit for a wide range of parameters.

We focus on the problem of buying fashion goods for the “big book” of a catalogue merchandiser. This company also owns outlet stores and thus has the opportunity, as the season evolves, to divert inventory originally purchased for the big book to the outlet store. The obvious questions are: (1) how much to order originally, and (2) how much to divert to the outlet store as actual demand is observed. We develop a model of demand for an individual item. The model is motivated by data from the women's designer fashion department and uses both historical data and buyer judgement. We build a stochastic dynamic programming (DP) model of the fashion buying problem that incorporates the model of demand. The DP model is used to derive the structure of the optimal inventory control policy. We then develop an updated Newsboy heuristic that is intuitively appealing and easily implemented. When this heuristic is compared to the optimal solution for a wide variety of scenarios, we observe that it performs very well. Similar numerical experiments show that the current company practice does not yield consistently good results when compared to the optimal solution.

This paper develops and analyzes a principal-agent model for product specification and production motivated by Ücore buyingÝ decisions at an automobile manufacturer. The model focuses on two important elements of the ÜcoreÝ buyer's responsibility: (1) assessing the supplier's capability, and (2) allocating some or all of a fixed level of some buyer-internal resource to help the supplier. Under the contracting scheme we model, the buyer (principal) delegates the majority of product specification and production activity to the supplier (agent), but retains the flexibility to commit a given, observable amount of an internally available, limited resource (e.g., engineering hours) to help the supplier. The supplier, in turn, allocates his resource (e.g., engineering hours) to produce the finished product. As in the motivating scenario, both the supplier's resource allocation and capability are assumed to be hidden from the buyer. Hence, the principal's problem is to determine a menu of (resource-commitment, transfer-price) contracts to minimize her total expected cost. Our analysis demonstrates that if buyer resource and supplier capability are substitutes, then the buyer's second-best involvement in the supplier's production process will be greater than first-best. The opposite is true if they are complements. Further, when the opportunity cost for the buyer's resource is zero, then in the substitutes case the buyer will commit all of its resource, while in the complements case the buyer may withhold some resources to screen the supplier type. We describe two applications of the modelÔone in inventory management and one in pharmaceutical drug discoveryÔto illustrate its applicability and versatility. Finally, we use insights from the model to suggest hypotheses for empirical study.production outsourcing, hidden information, adverse selection, contract menu, complements, substitutes