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

Abstract

This paper examines the use of Big Data analytic techniques to explore and analyze large datasets that are used to capture information about DoD services acquisitions. It describes 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. It tests the application of Big Data analytic techniques by applying them to a dataset of CPARS ratings of acquired services, and it creates 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 that four variables exhibit the largest impact on the success/failure rates of services contracts: type of contract; awarded dollar value; workload per filled billets; of 1102 billets filled by contracting office.

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Document Details

Document Type
Technical Report
Publication Date
Sep 22, 2015
Accession Number
AD1014641

Entities

People

  • Michael Dixon
  • Rene Rendon
  • Uday M. Apte

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Space

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
  • Public Policy
  • Supply Chain

Readers

  • Government Contracting/Procurement.
  • Naval Personnel Management
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks