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Manufacturing Cost Estimation Using Piecewise Function Approaches

Author

Listed:
  • Eren Sakinc

    (Bayer Pharmaceuticals, New Jersey, USA)

  • Alice E. Smith

    (Department of Industrial and Systems Engineering, Auburn University, Auburn, USA)

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.

Suggested Citation

  • Eren Sakinc & Alice E. Smith, 2023. "Manufacturing Cost Estimation Using Piecewise Function Approaches," Journal of Economic Analysis, Anser Press, vol. 2(3), pages 113-140, June.
    • Handle: RePEc:bba:j00001:v:2:y:2023:i:3:p:113-140:d:62
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    References listed on IDEAS

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    2. Valverde, Santiago Carbo & Humphrey, David B., 2004. "Predicted and actual costs from individual bank mergers," Journal of Economics and Business, Elsevier, vol. 56(2), pages 137-157.
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