Workload Measures for Navy Inventory Control Points

Abstract

The Operations and Maintenance, Navy (O&MN) budget for the two Navy Inventory Controls Points (ICP's) has shown an overall increase over the past 15 years. However, the numerous outputs or workload measures being used at the ICP's do not seem to show the same trend as O&MN. The Naval Supply Systems Command (NAVSUP) wants to relate the budget to the various workload measures. In fact, NAVSUP would like a single measure of workload applicable to the two ICP's which could explain most of the behavior of O&MN. This measure of workload could serve as a simple but useful predictive tool for budget requests. This thesis examined data for O&MN and workload indicators representing the major functions performed by each ICP. The data covered the time interval from 1973 to 1987. Models using single and multiple variables were then developed through exploratory data analysis and regression analysis in an attempt to describe how O&MN is related to or can be explained by the workload indicators. The models using only a single workload measure did not do very well at explaining the behavior of O&MN, although if a single variable model must be chosen, the number of repairable line items appeared to be the best of O&MN predictor. The multivariate models were too data limited to be useful immediately. However, the potential for developing accurate models using multiple variables appears to be very good.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1988
Accession Number
ADA201860

Entities

People

  • Edgardo T. De Guia

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Coefficients
  • Computer Programs
  • Correlation Analysis
  • Data Analysis
  • Data Mining
  • Data Science
  • Information Science
  • Intervals
  • Inventory
  • Inventory Control
  • Maintenance
  • Power
  • Regression Analysis
  • Standards
  • Statistics
  • Time Intervals

Readers

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