• The business about industrial robots is not a substantive argument. SPS hardware production processes for the Earth-manufactured reference case must be highly automated to achieve the reference cost estimates. There is, at least as yet, no reason to believe that automation can be more readily applied in space — industrial robots will also (and do) work on Earth. If one assumes that the economically- optimal level of automation is applied to Earth-based manufacture, then the economically-optimal level of space manufacture automation can at best reduce the space/Earth cost differential, but not reverse it. That the quantitative estimates of space manufacturing costs give a different result than what is qualitatively expected is puzzling but there is a plausible explanation having to do with estimation techniques. Two basically different estimating procedures are in common use. Enumerative Estimating (sometimes called detailed, “grass-roots,” or “bottoms-up” estimating) predicts costs from the basic components. To do this, one must prepare a detailed plan, apply labor estimates to each element of the plan, develop materials estimates, determine the facilities and equipment capital costs, and amortize these. Detailed estimating is ordinarily applied when the nucleus of a design, development, and production team is in place, i.e., during or following a Phase B study and prior to a full-scale development bid submittal. During a concept study, the information, skills, and resources to support an accurate detailed estimate are not available. Parametric Estimating. This technique operates on historical analogy, i.e., cost is predicted based on historical costs of analogous hardware. Any parametric technique is keyed to the physical attributes of the thing being estimated . . . usually mass but occasionally other attributes such as area, power, capacity, etc. Parametric models operate at many levels. For familiar types of systems such as aircraft, high-level models exist that roughly predict cost on the basis of total aircraft weight for given aircraft type. One correlation has been published that purports to estimate the cost of any vehicular system, be it tank, truck, airplane, or ship, given only [1] mass, |2] installed power, and [3] quantity to be produced. The Boeing Parametric Cost Model (PCM) used in the SPS systems study operates at the subsystem or subsystem element level. Typical estimating relationships represent computers, receivers, cabling, sensors, primary structure, etc. This allows the PCM to be used for unfamiliar systems such as an SPS provided that the system is made up of elements for which historical correlations exist. PCM estimates in labor hours. Cost Predictability; Comparison of Parametric and Detailed Estimating It is clearly much easier to observe that computers, for example, follow a certain historical trend of DDT&E and unit cost versus mass (i.e., a parametric estimate) than it is to lay out all of the tasks, materials, and facilities required to design, develop, and produce a computer (i.e., an enumerative estimate). This is especially true if the trend data are incorporated in an automated estimating model, as is the case. It is sometimes stated that a parametric estimate is a “will-cost” result, whereas a detailed estimate is a “should-cost” result. This is an excellent summary of the difference: [1] The parametric estimate reflects a historical average of experienced costs including technical problems and other overrun factors. Given an adequate design definition, a parametric model properly used will predict an expected actual cost,
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