Big Data Analysis of Contractor Performance Information for Services Acquisition in DoD: A Proof of Concept

Abstract

This paper explores the use of Big Data analytic techniques to explore and analyze large datasets that are used to capture information about DoD services acquisitions. We describe the burgeoning field of Big Data analytics, how it is used in the private sector, and how it could potentially be used in acquisition research. We test the application of Big Data analytic techniques by applying them to a dataset of CPARS (Contractor Performance Assessment Reporting System) ratings of acquired services, and we create predictive models that explore the causes of failed services contracts using three analytic techniques: logistic regression, decision tree analysis, and neural networks. The report concludes with recommendations for using Big Data analytic techniques in acquisition.

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Document Details

Document Type
Technical Report
Publication Date
Apr 30, 2016
Accession Number
AD1016815

Entities

People

  • Mike Dixon
  • Rene Rendon
  • Uday M. Apte

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Big Data
  • Business Administration
  • Data Analysis
  • Data Mining
  • Data Science
  • Databases
  • Information Retrieval
  • Information Science
  • Information Systems
  • Management Personnel
  • Neural Networks
  • Organizational Structure
  • Predictive Modeling
  • Procurement
  • Statistical Analysis
  • Supply Chain

Readers

  • Distributed Systems and Data Platform Development
  • Government Contracting/Procurement.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy