A Simulated Single-Item Aggregate Inventory Model for U.S. Navy Repairable Items

Abstract

A readiness-based sparing (RBS) model for the repair and replenishment of repairable items is needed by the Navy which considers the aggregate inventory of repaired units and new ones. This thesis presents progress in the development of such a model. In contrast to other such current repairables models in the literature, it also allows for both batch repair and procurement. A theoretical model had been developed earlier at the Naval Postgraduate School for the probability distribution of inventory position for such a model. However, no theoretical model has yet been developed for the probability distribution of net inventory because the real-world inventory management of repairables is quite complex. Therefore, a simulation model was developed of the Navy's repairables management process to explore the nature of that distribution as a function of relevant system parameters. It was then run for a range of values of a subset of those parameters. The net inventory distribution appears to be Normally distributed with its mean and variance being a linear function of the product of carcass return rate and repair survival rate. The theoretical distribution for inventory opposition was not only validated, it was found to be quite robust. Further analyses, however, are required before the affects of all relevant parameters are well understood.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1993
Accession Number
ADA274858

Entities

People

  • Kevin J. Maher

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Attrition
  • Computations
  • Computers
  • Goodness Of Fit Tests
  • Inventory
  • Inventory Control
  • Lead Time
  • Mathematical Models
  • Operations Research
  • Probability Distributions
  • Procurement
  • Random Variables
  • Scheduling (Production)
  • Statistics
  • Steady State
  • Time Intervals
  • United States

Readers

  • Computational Modeling and Simulation
  • Logistics and Supply Chain Management.