High-Influence Factors for the Timeliness of Project Award for Navy Military Construction

Abstract

The current process that Naval Facilities Engineering Command (NAVFAC) uses to bring Military Construction (MILCON) projects from concept to contract award fails to provide reliable and timely results. This poor performance leads to the untimely delivery of critical facilities, which directly impacts warfighting and power projection capabilities. It erodes the professional reputation of NAVFAC, leaving supported commanders to question whether an essential product or service will arrive on time. Today, there are many internal NAVFAC teams exploring process-improvement opportunities across the entire construction timeline, of which pre-award is only one piece. However, many of these efforts are limited interms of number of projects or project factors considered. The focus of this thesis is to analyze projects from initial documentation up to contract award. To accomplish this, we linked two widely used but independent databases to capture the complete documented life cycle of hundreds of Navy-executed MILCONs. Once linked, dozens of project factors were collected, analyzed, and used in the development of various machine-learning models to assess their influence on project award performance. Our analysis highlights several factors among existing and newly constructed project metrics that appear to greatly influence the timeliness of project award. This collection of potentially high-influence factors can then help NAVFAC further focus its ongoing process improvements.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2020
Accession Number
AD1126649

Entities

People

  • Robert J. Thompson

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Acquisition
  • Algorithms
  • Artificial Intelligence
  • Contracts
  • Cycles
  • Data Analysis
  • Data Mining
  • Data Science
  • Databases
  • Department Of Defense
  • Engineering
  • Geographic Regions
  • Information Science
  • Life Cycles
  • Machine Learning
  • Operations Research
  • Predictive Modeling
  • Standards
  • Statistical Analysis
  • Statistics
  • United States

Fields of Study

  • Engineering

Readers

  • Organizational Process Management (OPM).
  • Systems Analysis and Design

Technology Areas

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