Research Papers

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

[+] Author and Article Information
Shun Takai

Department of Technology,
Northern Illinois University,
DeKalb, IL 60115
e-mail: stakai@niu.edu

Karan Banga

Cummins, Inc.,
Whitakers, NC 27891
e-mail: karan.banga@cummins.com

1Corresponding autor.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received May 19, 2013; final manuscript received February 10, 2014; published online March 21, 2014. Assoc. Editor: Bernard Yannou.

J. Mech. Des 136(5), 051005 (Mar 21, 2014) (12 pages) Paper No: MD-13-1215; doi: 10.1115/1.4026869 History: Received May 19, 2013; Revised February 10, 2014

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.

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Fig. 1

Cost estimation using case-based reasoning

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Fig. 5

Portion of the modified knowledge base

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Fig. 4

Portion of the complete knowledge base

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Fig. 2

Example dendrogram

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Fig. 6

Portion of the standardized knowledge base

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Fig. 7

Dendrograms for automobile retrieval

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Fig. 8

Automobile selection criteria graphs

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Fig. 9

Cost distributions

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Fig. A1

Comparison of various approaches

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Fig. A2

Leave-one-out cross-validation result




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