Search

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2008.05.002 Byline: Alper Sen (a), Alex X. Zhang (b) Keywords: Pricing; Dynamic pricing; Revenue management; Demand learning Abstract: For many industries (e.g., apparel retailing) managing demand through price adjustments is often the only tool left to companies once the replenishment decisions are made. A significant amount of uncertainty about the magnitude and price sensitivity of demand can be resolved using the early sales information. In this study, a Bayesian model is developed to summarize sales information and pricing history in an efficient way. This model is incorporated into a periodic pricing model to optimize revenues for a given stock of items over a finite horizon. A computational study is carried out in order to find out the circumstances under which learning is most beneficial. The model is extended to allow for replenishments within the season, in order to understand global sourcing decisions made by apparel retailers. Some of the findings are empirically validated using data from U.S. apparel industry. Author Affiliation: (a) Department of Industrial Engineering, Bilkent University, Bilkent, Ankara 06800, Turkey (b) Hewlett Packard Laboratories, 1501 Page Mill Road, MS 1U 2, Palo Alto, CA 94304, USA Article History: Received 11 October 2005; Accepted 5 May 2008

A method for tackling the multiple lot sizing problem in inventory management, by using dynamic programming, is presented.

Numerical results of a periodic-review model for joint demand fulfillment probability in a multi-item inventory system indicated that even if optimal combination order-up-to levels relies on the relation of item demands, a heuristic which disregards the connection is extensively quite effective, given that demand averages and variances are properly accounted for. One advantage of the model is that only the correlations of aggregated item demands in a time is needed, instead of individual orders.