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. >

[1]  James E. Gentle,et al.  Finding Groups in Data: An Introduction to Cluster Analysis. , 1991 .

[2]  M. Aldenderfer.  Cluster Analysis , 1984 .

[3]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[4]  G. W. Milligan,et al.  An examination of procedures for determining the number of clusters in a data set , 1985 .

[5]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[6]  Robert Frumerman,et al.  R&D Programs With Multiple Related Projects—II , 1987 .

[7]  J. DOUGLAS HILL,et al.  Unified Program Planning , 1972, IEEE Trans. Syst. Man Cybern..

[8]  Wm. E. Souder,et al.  Management decision methods for managers of engineering and research , 1980 .

[9]  M. Aldenderfer,et al.  Cluster Analysis. Sage University Paper Series On Quantitative Applications in the Social Sciences 07-044 , 1984 .

[10]  James R. Freeland,et al.  Recent Advances in R&D Benefit Measurement and Project Selection Methods : Management Science , 1975 .

[11]  James R. Freeland,et al.  Recent progress in modeling R&D project-selection processes , 1992 .

[12]  R. C. Lenz,et al.  Technological forecasting : a practical approach , 1970 .

[13]  G. W. Milligan,et al.  An examination of the effect of six types of error perturbation on fifteen clustering algorithms , 1980 .

[14]  Paul J. Schweitzer,et al.  Problem Decomposition and Data Reorganization by a Clustering Technique , 1972, Oper. Res..

[15]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[16]  J. King,et al.  Machine-component group formation in group technology: review and extension , 1982 .

[17]  A. F. Czajkowski,et al.  Selecting interrelated R & D projects in space technology planning , 1986, IEEE Transactions on Engineering Management.

[18]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[19]  Jonathan F. Bard,et al.  An interactive approach to R&D project selection and termination , 1988 .

[20]  Ralph H. Sprague,et al.  A Framework for the Development of Decision Support Systems , 1993 .

[21]  G. A. Hazelrigg,et al.  Radsim — A methodology for large-scale R&D program assessment , 1985, IEEE Transactions on Engineering Management.

[22]  Robert S. Ledley,et al.  Methodology to aid research planning , 1967 .

[23]  Matthew J. Liberatore,et al.  The Practice of Management Science in R&D Project Management : Management Science , 1983 .

[24]  John E. Gibson,et al.  Managing research and development , 1981 .