This paper presents decision-analytic concept selection framework for a commercial system and an uncertainty modeling using objective data. The selection of a system concept for which a final system is designed and manufactured is a decision making process with incomplete information. Decision analysis is a prescriptive approach for decision making under uncertainty. While realizing that humans make decisions violating the expected utility axioms, decision analysis uses a set of tools to guide a decision maker toward an unbiased and rational decision making. The objective of this research is to propose a decision-analytic framework for commercial system concept selection, and an approach to utilize as much objective data as possible in the uncertainty modeling. Toward this objective, this paper construct cost distribution using case-based reasoning and market share distribution applying bootstrap to customers’ preference data obtained from conjoint analysis. The proposed approach is demonstrated in an illustrative example: a decision-analytic automobile concept selection.

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