Manufacturing Cost Estimation Using Piecewise Function Approaches

Eren Sakinc and Alice E. Smith
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Eren Sakinc: Bayer Pharmaceuticals, New Jersey, USA
Alice E. Smith: Department of Industrial and Systems Engineering, Auburn University, Auburn, USA

Journal of Economic Analysis, 2023, vol. 2, issue 3, 113-140

Abstract: This paper describes two novel approaches to cost estimation of manufactured products where a data set of similar products have known manufactured costs. The methods use the notion of piecewise functions and are (1) clustering and (2) splines. Cost drivers are typically a mixture of categorical and numeric data which complicates cost estimation. Both clustering and splines approaches can accommodate this. Through four case studies, we compare our approaches with the often-used regression models. Our results show that clustering especially offers promise in improving the accuracy of cost estimation. While clustering and splines are slightly more complex to develop from both a user and a computational perspective, our approaches are packaged in an open-source software. This paper is the first known to adapt and apply these two well-known mathematical approaches to manufacturing cost estimation.

Keywords: Artificial intelligence; cost estimation; non-parametric modeling; clustering; splines; cross-validation; dissimilarity index (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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