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


This paper presents case-based 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 case-based reasoning methods without cost adjustment (CBR) and with cost adjustment (CBR-A) are compared with analogy-based 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), leave-one-out cross-validation results indicate that both CBR-A 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 trade-offs between the accuracy of cost estimation and the reliability of cost uncertainty modeling when product cost is used as a criterion to select concepts. Copyright © 2014 by ASME.


Mechanical and Aerospace Engineering

Keywords and Phrases

Case-Based Reasoning; Concept; Cost Distribution; Hierarchical Clustering; Leave-One-Out Cross-Validation; Regression Analysis

Document Type

Article - Journal

Document Version


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© 2014 American Society of Mechanical Engineers (ASME), All rights reserved.

Publication Date

01 Jan 2014