The (s,S.) Inventory Model Under Low Demand.

Abstract

Inventory managers often rely on cost-based rules to control their stock. Unfortunately, many of these rules do not recommend inventorying infrequently demanded items due to their assumed cost structures. This research provides a better understanding of the impact of low mean demand on the (s,S) periodic review inventory model. An unexpected result from applying the (s,S) model to low mean demand is that negative reorder points are commonly recommended. In other words, the firm does not reorder stock until a designated number of backorders are on-the-books. For firms that cannot tolerate lengthy delays in ordering, the (s,S) model may be constrained to allow only nonnegative reorder points. While constraining reorder points increases the inventory service, it also increases total costs. Fortunately, the (s,S) model is relatively insensitive to misspecifying the mean demand per period. It is also shown that approximately optimal (s,S) inventory policies are feasible alternatives to computationally burdensome optimal (s,S) policies under low demand. In fact, there is very little cost degradation from using approximately optimal (s,S) policies rather than optimal (s,S) policies when demand is Poisson distributed. This is true whether the demand parameters are known or estimated from historical demand data.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1987
Accession Number
ADA186151

Entities

People

  • David K. Peterson

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Airframes
  • Algorithms
  • Business Administration
  • Computational Science
  • Computations
  • Computer Programs
  • Computers
  • Estimators
  • Lead Time
  • Literature Surveys
  • Military Aircraft
  • Probability
  • Simulations
  • Statistical Estimation
  • Time Intervals

Readers

  • Logistics and Supply Chain Management.
  • Operations Research