The Necessity of Deduction for Efficient and Effective System Concept Formulation

The purpose of the NASA Pre-Phase A project life cycle phase is, “To produce a broad spectrum of ideas and alternatives for missions from which new programs/projects can be selected. Determine feasibility of desired system, develop mission concepts, draft system-level requirements, assess performance, cost, and schedule feasibility; identify potential technology needs, and scope.”There are, however, limited resources (time, money, and workforce) with which to conduct this trade space exploration. The question therefore is how to conduct this trade space exploration most efficiently, and produce a broad spectrum of ideas and alternatives for missions from which new programs/projects can be selected.Too often, Pre-Phase A efforts produce ideas and alternatives that are not selectable because they violate the project sponsor’s cost ceiling. These concepts often linger in an inefficient “churn” of negotiations to reduce production costs and/or minor (e.g., component level) design feature changes, all undertaken in an attempt to achieve consistency with the cost ceiling.More often than not, the root cause of “the churn” is a set of requirements derived through circular logic based on the desired capabilities of the design advocates, and not deductively from the objectives of the project sponsor. In Team-X studies at JPL, we have discovered that a successful first step in breaking “the churn” is deductively derived tests known as customer validation. The deductive identification of the minimum requirements on the system features identified through these tests is often enough to relieve enough pressure on the design that it is possible to find a design solution that fits under the cost ceiling. This avoids “the churn” that results from a sole focus on features of the design (“product focus”) that attempts to preserve the maximum capabilities of system features, irrespective of sponsor value.One benefit of these deductively derived tests is an increase in formulation efficiency (the amount of total work that results in ideas and alternatives for missions from which new programs/projects can be selected). Another benefit of these deductively derived tests is an increase in design efficiency (the amount of resources utilized, both technical and financial, to produce the sponsor’s desired result). And finally, Another benefit of these deductively derived tests is that they allow quantification of all margins in the system (performance, technical, and programmatic) which enables the system architect to identify and conduct useful trades.This paper provides lessons learned in this approach from design studies for planetary, earth science, and astrophysics missions in Team-X at the Jet Propulsion Laboratory.