Capability Investment Strategy to Enable JPL Future Space Missions

The Jet Propulsion Laboratory (JPL) formulates and conducts deep space missions for NASA (the National Aeronautics and Space Administration). The Chief Technologist of JPL has responsibility for strategic planning of the laboratory's advanced technology program to assure that the required technological capabilities to enable future missions are ready as needed. The responsibilities include development of a Strategic Plan. As part of the planning effort, a structured approach to technology prioritization, based upon the work of the START (Strategic Assessment of Risk and Technology) team, was developed. The purpose of this paper is to describe this approach and present its current status relative to the JPL technology investment strategy. The JPL Strategic Technology Plan divides the required technological capabilities into 13 themes. The results reported here represent the initial analysis of seven themes: In-situ Planetary Exploration Systems, Survivable Systems for Extreme Environments, Precision Flying Systems, Deep Space Communication, Planetary Protection Systems, Utilization of High Capability Computing, and Engineering Systems. The remaining six themes will be included in the study planned for FY '06. Each theme is hierarchically decomposed into component capabilities, to a level where quantitative estimates can be ascribed. For example, in the In-Situ Exploration theme, the sub-theme of Mobility is broken down into Surface Mobility, which allows an estimate of the meters traversed per command, a specific and measurable quantity. This structure is repeated and data filled in for each mission. All of this information is analyzed using an optimization technique formulated to maximize total missions technologically enabled subject to overall cost constraints. Note that capabilities are given credit only if all capabilities needed to enable a particular mission are selected for funding. The recommended investments at each area of the capability hierarchy are plotted as a function of the total budget available to the sponsor. The robustness of the investment strategy is quantitatively analyzed as a function of potential variation (the uncertainty) of the input data. In on-going work we are looking at measures for relative mission value, dependencies among missions and capability areas, and time profiles of the recommended investments.