Capacity Allocation Using Past Sales: When to Turn-And-Earn
Management Science
Neuro-Dynamic Programming
Customer Service Competition in Capacitated Systems
Manufacturing & Service Operations Management
Customer Loyalty and Supplier Quality Competition
Management Science
The Linear Programming Approach to Approximate Dynamic Programming
Operations Research
Dynamic Competitive Newsvendors with Service-Sensitive Demands
Manufacturing & Service Operations Management
Asymmetric Consumer Learning and Inventory Competition
Management Science
Dynamic Catalog Mailing Policies
Management Science
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Relaxations of Weakly Coupled Stochastic Dynamic Programs
Operations Research
Selecting a Portfolio of Suppliers Under Demand and Supply Risks
Operations Research
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We study the problem faced by a supplier deciding how to dynamically allocate limited capacity among a portfolio of customers who remember the fill rates provided to them in the past. A customer's order quantity is positively correlated with past fill rates. Customers differ from one another in their contribution margins, their sensitivities to the past, and in their demand volatilities. By analyzing and comparing policies that ignore goodwill with ones that account for it, we investigate when and how customer memory effects impact supplier profits. We develop an approximate dynamic programming policy that dynamically rationalizes the fill rates the firm provides to each customer. This policy achieves higher rewards than margin-greedy and Lagrangian policies and yields insights into how a supplier can effectively manage customer memories to its advantage. This paper was accepted by Martin Lariviere, operations management.