Optimization of Inventory levels for the Air Force Commissary Service

Abstract

The purpose for this study was to find an optimal procedure for distributing a fixed allocation of inventory greastest constant aggregate availability possible. Regression techniques were employed to develop a set of equations which accurately predict inventory item availabilities. Items were classified by their review period (7, 14, or 30 days) and a response surface was fit from a simulation model for each of these review period values. These surface equations were used to optimize aggregate availablility using the marginal analysis technique. An alogorithm was created that generates a shopping list which prioritizes items according to their contribution to increasing the aggregate availability measure. It was generally observed that items having characteristics which produced unstable stock levels appeared nearer to the top of the list than those items having stable stock level characteristics. This study contributes to the achievement of the primary mission of the Air Force Commissary Service, Which is to provide a benefit to military members that promotes a positive attitude towards the Air Force and enhances the quality of life for Air Force members. This in turn improves morale and retention rates of quality personnel, providing contribution to the overall Air Force mission. Keywords: Inventory modelling. Theses.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1988
Accession Number
ADA205901

Entities

People

  • Richard J. Britt

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Air Force Facilities
  • Algorithms
  • Classification
  • Computational Science
  • Data Science
  • Experimental Design
  • Factorial Design
  • Inventory
  • Inventory Control
  • Mathematical Models
  • New York
  • Operations Research
  • Quality Of Life
  • Regression Analysis
  • Simulations

Readers

  • Logistics and Supply Chain Management.
  • Mathematics or Statistics
  • Organizational Psychology.