A methodology for large-scale R&D planning based on cluster analysis

A decision-support approach to large-scale R&D planning is described. A quantitative model based on three analytical tools, the interaction matrix, hierarchical cluster analysis, and the Boston Consulting Group (GCG) strategic planning matrix, is used. Results of the model are used to determine the number of R&D program areas, the technological focus of each R&D program area, and the relative allocation of resources to the R&D program areas. Traditional optimization techniques for R&D planning often generate solutions without allowing for the judgement, experience, and insight of the decision maker. The decision-support approach presented supports, rather than replaces, the judgement of the R&D planner by using a graphic display of the relative position of technology clusters, and by using an interactive and iterative approach to problem solving. An application to R&D program planning for Virginia's Center for Innovative Technology's Commercial Space program is presented.<
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