A Forecasting Model for Procurement Administrative Lead Time

Abstract

The thesis objective is to develop a model to forecast the cost and the lead time in awarding a contract. All available, pertinent contract data was obtained and utilized from the Procurement Department of Naval Air Warfare Center Weapons Division, China Lake, California. The data was limited to the years 1989 through 1991. The actual cost of letting a contract has not been recorded, so a prediction model was fit only for the Procurement Administrative Lead Time (PALT). Cost is believed to be positively correlated with PALT. Explanatory data available for each contract were: contract amount, contract type, contract description, and competitive nature. A 'complexity score' was also available, which was determined by procurement personnel. Since many of the same variables used to compute complexity were also used to predict PALT, those variables were verified as possible predictors of cost by building a prediction model for complexity score. The following variables served as good predictors of PALT: contract amount, contract description and contract type. It was also determined that the competitive nature of the contract had little impact on PALT. With this data, it is difficult to forecast PALT precisely for a given contract. However, with the recommended collection of additional data, PALT and the cost of a contract should become predictable with increasing confidence.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1992
Accession Number
ADA258016

Entities

People

  • Douglas J. MacKinnon

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Aerial Warfare
  • California
  • Classification
  • Contractors
  • Contracts
  • Data Analysis
  • Databases
  • Department Of Defense
  • Governments
  • Lead Time
  • Procurement
  • Schools
  • Security
  • United States
  • United States Naval Academy
  • Word Processors

Readers

  • Computational Modeling and Simulation
  • Regression Analysis.
  • Technical Research and Report Writing.