A METHODOLOGY FOR LARGE-SCALE RESEARCH-AND-DEVELOPMENT PLANNING BASED ON CLUSTER-ANALYSIS

This research is a description of a decision support approach to large-scale R & D planning. A quantitative model is used based on three analytical tools: 1) the interaction matrix, 2) hierarchical cluster analysis, and 3) the Boston Consulting Group (BCG) strategic planning matrix. 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 judgment, experience, and insight of the decision maker(s). The decision support approach presented here supports, rather than replaces, the judgment 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.