Analysis of Perishable-Inventory Systems with Censored Demand Data

Authors:
Xiangwen Lu;Jing-Sheng Song;Kaijie Zhu
Affiliations:
Cisco Systems, San Jose, California 95134;Fuqua School of Business, Duke University, Durham, North Carolina 27708;Department of IELM, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
Venue:
Operations Research
Year:
2008

Citing 0
Cited 9

A Nonparametric Asymptotic Analysis of Inventory Planning with Censored Demand

Mathematics of Operations Research
Technical Note---A Note on “The Censored Newsvendor and the Optimal Acquisition of Information”

Operations Research
Bounds and Heuristics for Optimal Bayesian Inventory Control with Unobserved Lost Sales

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
Adaptive Data-Driven Inventory Control with Censored Demand Based on Kaplan-Meier Estimator

Operations Research
A Censored-Data Multiperiod Inventory Problem with Newsvendor Demand Distributions

Manufacturing & Service Operations Management
Weak aggregating algorithm for the distribution-free perishable inventory problem

Operations Research Letters
Salesforce Contracting Under Demand Censorship

Manufacturing & Service Operations Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider a multiperiod inventory system of a perishable product with unobservable lost sales. Demand distribution parameters are unknown and are updated periodically using the Bayesian approach based on the censored historical sales data. We develop an explicit expression of the first-order condition for optimality that demonstrates the key trade-off of the problem. The result generalizes partial characterizations of this trade-off in the literature. It shows that the myopic solution is a lower bound on the optimal inventory level. It also enables us to quantify the expected marginal value of information.