The prospect of technological advances still persuades planners and decisionmakers to seek increased performance, greater precision, added function capability, and thus more complexity, all of which tend to increase cost and reduce reliability. Such courses are frequently followed without proper regard to realistic operational needs. Also, there is often a misconception at technically naïve management levels that the promise of a new (or better) system can be validated solely through analysis, without recourse to actual experimental hardware or software. This has often proved to be an invalid assumption. As a result, budgets are often formulated without adequate provision for funds to permit realistic evaluation of alternatives or, even when budgeted, such funds are cut prematurely in favor of a “preferred” approach advanced by an articulate system advocate… To satisfy as many needs as possible, there often is reluctance to fund for contingencies or to support alternative options.
… At the same time there must be adequate provision for technology “push” in contrast to requirements “pull.” If R&D is completely constrained to respond only to specific requirements, the current trend toward guaranteed success as a criterion for initiating development programs will grow to the detriment of invention and innovation.”
That was a slice from the “Report of the Navy the “Marine Corps Acquisition Review Committee.” Volume 1, Office of the Secretary of the Navy, January, 1975.
Two complementary issues jump out to me. First, the fact that requirement-pull approaches invariably take existing designs and seek to add performance. Those are the only ones amenable to paper (or computer-aided) analysis. The problem is that most of the design’s optimization has already been squeezed out. So small advances take far more complexity, which costs money and reduces reliability. The real need is to jump onto a new military innovation s-curve, not squeeze out tiny advances on the existing one.
Second, the desire for economies of scale leads to large, multi-mission platforms. When one machine is asked to do everything, experimentation because extremely difficult and costly. So less alternatives are explored because they too are also assumed to be large. Ironically, the justification is to save money through single production lines, single logistical and training needs, etc.
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