Technical Note—Managing Nonperishable Inventories with Learning About Demand Arrival Rate Through Stockout Times

Alain Bensoussan () and Pengfei Guo ()
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Alain Bensoussan: Jindal School of Management, University of Texas at Dallas, Richardson, Texas; and Department of System Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong
Pengfei Guo: Faculty of Business, Hong Kong Polytechnic University, Kowloon, Hong Kong

Operations Research, 2015, vol. 63, issue 3, 602-609

Abstract: We study a periodic review inventory model with a nonperishable product over an infinite planning horizon. The demand for the nonperishable product arrives according to a Poisson process. Lost sales are unobservable but the stockout times are observable. We formulate the problem as a dynamic programming model with learning on arrival rate according to stockout times and further simplify it by using unnormalized probabilities. We then compare the system performance with those under other two information scenarios where lost sales are observable or both lost sales and stockout times are unobservable. We show that the optimal inventory order-up-to level with observable stockout times is larger than the one with observable lost sales. We also show that more information improves the system performance.

Keywords: information updating; inventory management; Bayesian statistics; nonperishable products (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:63:y:2015:i:3:p:602-609

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