Since the basic ingredients of the cost analysis concept being promoted were (1) independence from program proponent pressures and (2) use of historical, parametric relationships, the phrase INDEPENDENT PARAMETRIC was appended to all future references to the new estimating approach. The SA [systems analysis] staff, with encouragement and support from ASD(SA) Dr. Gardiner Tucker, put together briefings laying out their cost growth findings and a list of recommendations…
On 2 December 1971, however, Deputy Secretary of Defense David Packard was given the briefing. Fortuitously, Mr. Packard had recently come under severe criticism from Congress because of continuing major cost growth problems. As a result, one of the charts illustrating the recent growth pattern of F-14 airframe cost per pound, especially caught Mr. Packard’s eye. It showed how official program estimates had grown by a factor of between 3 and 4 from early 1969 to the end of 1970, and now closely matched the earlier parametric estimates. Similar examples from the C-5A, F-111, and other programs convinced Mr. Packard to act immediately.
That was from Donald Srull’s excellent book, “The Cost Analysis Improvement Group: A History.“
David Packard signed a memo five days later that an independent parametric estimate would be performed for DSARC milestone reviews. SecDef Melvin Laird signed another memo in January 1972 that created the Cost Analysis Improvement Group in OSD, and is now the Cost Assessment side of OSD Cost Assessment and Program Evaluation.
The F-14’s independent estimate, of course, used actuals from the boondoggled F-111 program as an input. I suppose the fear is perpetuating poor outcomes that lead to higher costs — which was the impetus for should cost reviews.
The independent parametric technique also makes the most sense when you have plenty of data and where only a couple variables are changing at a time. The desire for bundling requirements into winner-take-all programs reduces the statistical power of the cost estimates.
Analysts are in the dark about causality when you have too little data. Military decision makers are in the dark about the value of a piece of equipment when they have no other alternatives to compare it to.
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