Engineering Change Orders and their Impact on DoD Acquisition Contracts

Abstract

Cost growth is a problem DoD wide. Cost Estimators attempt to remedy this problem by accounting for uncertainty in the estimates they complete. They use tools such as Engineering Change Orders (ECOs) to account for the uncertainty, by applying a percentage to the final amount estimated. The following research gives the acquisition community a more precise tool to predict whether a DoD Acquisition Contract will have an Engineering Change Order, which can then be used also during programmatic cost estimating, and also a method for predicting the proper amount of ECO to apply when certain variables are present. The study used both logistic and multiple regression to accomplish this. For both types of regression a stepwise approach was adopted for the response. For the Logistic Regression the Y variable was that an ECO was present and the significant predictor variables were: UAV, >500M (dollars), Navy, Army, Aircraft, Firm Fixed Price (FFP), Cost Plus Fixed Fee (CPFF) and <5M (dollars). The final model was 85 predictive. The multiple regression modeled the expected ECO percent change (less than 100 of baseline). Predictive variables included: <5M, FFP, Munition, Electronics and Missiles, along with a base amount of 22 ECO. This model was more exploratory in nature due to the extreme variability present in ECO percent changes.

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Document Details

Document Type
Technical Report
Publication Date
Mar 23, 2017
Accession Number
AD1051568

Entities

People

  • Ian S. Cordell

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Advanced Electronics
  • Air Platforms
  • Ground and Sea Platforms
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Air Force
  • Aircrafts
  • Business Administration
  • Computational Science
  • Contracts
  • Cost Analysis
  • Cost Estimates
  • Data Science
  • Databases
  • Department Of Defense
  • Engineering
  • Information Science
  • Procurement
  • Statistical Algorithms
  • United States
  • War Colleges

Readers

  • Energy Conservation and Renewable Energy Engineering.
  • Government Contracting/Procurement.
  • Regression Analysis.

Technology Areas

  • Microelectronics