A multiechelon inventory model with fixed replenishment intervals
Management Science
Clearance Pricing and Inventory Policies for Retail Chains
Management Science
Heuristic Methods for Centralized Control of One-Warehouse, N-Retailer Inventory Systems
Manufacturing & Service Operations Management
Salesforce Incentives, Market Information, and Production/Inventory Planning
Management Science
Association Between Supply Chain Glitches and Operating Performance
Management Science
Dynamic Assortment with Demand Learning for Seasonal Consumer Goods
Management Science
Bounds, Heuristics, and Approximations for Distribution Systems
Operations Research
Incentive compatible regression learning
Journal of Computer and System Sciences
Clearance Pricing Optimization for a Fast-Fashion Retailer
Operations Research
Cue consistency and page value perception: Implications for web-based catalog design
Information and Management
Clearance Pricing Optimization for a Fast-Fashion Retailer
Operations Research
Analysis of cross-price effects on markdown policies by using function approximation techniques
Knowledge-Based Systems
A decision support system for mean-variance analysis in multi-period inventory control
Decision Support Systems
Fast fashion sales forecasting with limited data and time
Decision Support Systems
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Working in collaboration with Spain-based retailer Zara, we address the problem of distributing, over time, a limited amount of inventory across all the stores in a fast-fashion retail network. Challenges specific to that environment include very short product life cycles, and store policies whereby an article is removed from display whenever one of its key sizes stocks out. To solve this problem, we first formulate and analyze a stochastic model predicting the sales of an article in a single store during a replenishment period as a function of demand forecasts, the inventory of each size initially available, and the store inventory management policy just stated. We then formulate a mixed-integer program embedding a piecewise-linear approximation of the first model applied to every store in the network, allowing us to compute store shipment quantities maximizing overall predicted sales, subject to inventory availability and other constraints. We report the implementation of this optimization model by Zara to support its inventory distribution process, and the ensuing controlled pilot experiment performed to assess the model's impact relative to the prior procedure used to determine weekly shipment quantities. The results of that experiment suggest that the new allocation process increases sales by 3% to 4%, which is equivalent to $275 M in additional revenues for 2007, reduces transshipments, and increases the proportion of time that Zara's products spend on display within their life cycle. Zara is currently using this process for all of its products worldwide.