Improving Earned Value With Statistics
I attended a very interesting session presented by Eric Druker and Dan Demangos of Booz Allen Hamilton and Richard Coleman of Northrop Grumman Information Systems, at the Department of the Navy Cost Analysis Symposium (DONCAS) last week covering improving Earned Value (EVM) analysis with statistics. The speakers covered many of the common points regarding EVM weaknesses and showed some work they had done in helping solve some of these issues.
I found it interesting that SEER-SEM’s Parametric Progress analysis solves the same problems by looking at EVM type data and parametrics in concert.
The Problem Statement from the briefing included:
- Currently, the traditional Earned Value Management calculations suffer from several shortcomings that lessen their viability as a cost estimating tool
Estimates developed using most EVM equations are subject to tail-chasing whenever the CPI changes throughout the life of a program
- Tail-chasing is when the EAC for an over running program systematically lags in predicting the overrun, and vice-versa
- This occurs because these equations are backwards looking in regards to CPI; they lack the ability to predict changes in the CPI looking forward, and fail to perceive trends
- Tail-chasing is thus inevitable because “in most cases, the cumulative CPI only worsens as a contract proceeds to completion.”
Since the traditional EVM equations are simple algebra, and not based on statistical analysis, estimates developed using them are not unbiased, testable or defensible
- Testable estimates are those which can be subjected to decisions based on measures of statistical significance
- Quantitative cost risk analysis can not be performed on EVM data without subjective inputs
They pointed out that statistics are rarely used with EVM data because it generally falls under program management or financial control, not cost estimating. This makes the data difficult for cost estimaters to acquire. They provided several other reasons as well.
Their conclusions were that statistical EVM analysis of programs of a similar nature, or performed by a similar contractor, can be used as a basis to project patterns in the CPI over time.
They actually showed how, in the particular environment and commodity they were able to accurately predict where the program would end up based on its progress to “time now”
Excellent paper and worth a look when the papers are published on the DONCAS web site.
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