The Impact of Duplicate Orders on Demand Estimation and Capacity Investment
Management Science
A Partially Observed Markov Decision Process for Dynamic Pricing
Management Science
On "The Censored Newsvendor and the Optimal Acquisition of Information"
Operations Research
A Nonparametric Asymptotic Analysis of Inventory Planning with Censored Demand
Mathematics of Operations Research
Impact of Supply Learning When Suppliers Are Unreliable
Manufacturing & Service Operations Management
Dynamic Pricing for Nonperishable Products with Demand Learning
Operations Research
Dynamic Supplier Contracts Under Asymmetric Inventory Information
Operations Research
Online algorithms for the newsvendor problem with and without censored demands
FAW'10 Proceedings of the 4th international conference on Frontiers in algorithmics
Achieving a Long-Term Service Target with Periodic Demand Signals: A Newsvendor Framework
Manufacturing & Service Operations Management
A Censored-Data Multiperiod Inventory Problem with Newsvendor Demand Distributions
Manufacturing & Service Operations Management
Learning in stochastic machine scheduling
WAOA'11 Proceedings of the 9th international conference on Approximation and Online Algorithms
A practical inventory control policy using operational statistics
Operations Research Letters
Optimal learning of transition probabilities in the two-agent newsvendor problem
Proceedings of the Winter Simulation Conference
Biased Judgment in Censored Environments
Management Science
Salesforce Contracting Under Demand Censorship
Manufacturing & Service Operations Management
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Retailers are frequently uncertain about the underlying demand distribution of a new product. When taking the empirical Bayesian approach of Scarf (1959), they simultaneously stock the product over time and learn about the distribution. Assuming that unmet demand is lost and unobserved, this learning must be based on observing sales rather than demand, which differs from sales in the event of a stockout. Using the framework and results of Braden and Freimer (1991), the cumulative learning about the underlying demand distribution is captured by two parameters, a scale parameter that reflects the predicted size of the underlying market, and a shape parameter that indicates both the size of the market and the precision with which the underlying distribution is known. An important simplification result of Scarf (1960) and Azoury (1985), which allows the scale parameter to be removed from the optimization, is shown to extend to this setting. We present examples that reveal two interesting phenomena: (1) A retailer may hope that, compared to stocking out, realized demand will be strictly less than the stock level, even though stocking out would signal a stochastically larger demand distribution, and (2) it can be optimal to drop a product after a period of successful sales. We also present specific conditions under which the following results hold: (1) Investment in excess stocks to enhance learning will occur in every dynamic problem, and (2) a product is never dropped after a period of poor sales. The model is extended to multiple independent markets whose distributions depend proportionately on a single unknown parameter. We argue that smaller markets should be given better service as an effe ctive means of acquiring information.