Effects of Product Attributes in Case Based Reasoning Methods for Cost Estimation and Cost Uncertainty Modeling

Abstract

This paper presents casebased reasoning methods for cost estimation and cost uncertainty modeling that may help designers select a new product concept at the early stage of product development. The casebased reasoning methods without cost adjustment (CBR) and with cost adjustment (CBRA) are compared with analogybased cost estimation (ABCE) and multivariate linear regression analysis (RA). Under the conditions studied in the illustrative example of this paper (i.e., a single knowledge base, sport utility vehicle (SUV) concepts, and up to five concept attributes), leaveoneout crossvalidation results indicate that both CBRA and RA accurately estimate cost and reliably model cost uncertainty; and optimum attribute sets for the most accurate cost estimation and the most reliable cost uncertainty modeling are different in all methods. The results of this paper indicate that designers may need to carefully select attribute sets by analyzing tradeoffs between the accuracy of cost estimation and the reliability of cost uncertainty modeling when product cost is used as a criterion to select concepts.