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.
S. Takai and K. Banga, "Effects of Product Attributes in Case-Based Reasoning Methods for Cost Estimation and Cost Uncertainty Modeling," Journal of Mechanical Design, Transactions of the ASME, American Society of Mechanical Engineers (ASME), Jan 2014.
The definitive version is available at http://dx.doi.org/10.1115/1.4026869
Mechanical and Aerospace Engineering
Keywords and Phrases
Case-Based Reasoning; Concept; Cost Distribution; Hierarchical Clustering; Leave-One-Out Cross-Validation; Regression Analysis
Article - Journal
© 2014 American Society of Mechanical Engineers (ASME), All rights reserved.