Selecting research and development projects is always difficult because data for assessment are seldom available. ICI Americas used simple decision trees successfully to screen new product ideas and decide on a major effort in new process research. The new process research model incorporated only 10 variables, making estimation feasible; a visual sensitivity analysis gave management confidence in the model conclusions. The screening model incorporated qualitative selection criteria (for example, quick development, low risk, patentable, low capital, high sales) into a single quantitative expression of expected net present value. ICI Americas used it to select projects from 53 new product ideas. These examples illustrate the value of a logical, simple model even when data are sparse.
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Sidney W. Hess (1993) studied this question.
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