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Flow time and product cost estimation by using an artificial neural network (ANN): A case study for transformer orders

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  • Aslan Deniz Karaoglan
  • Omur Karademir

Abstract

In electromechanical industrial corporations, determining the production cost of the orders according to the technical specifications demanded by the customer has great importance in giving an accurate price offer. Labor cost is one of the important and most variable cost components that must be estimated in order to give an accurate price offer. In this study, a feed-forward back-propagation artificial neural network (FF-BPN) is used to predict the flow times of power transformer orders of a transformer producer according to the technical specifications given by the customer. The results of this study show that the prediction capability of an artificial neural network is very good for this type of problem and results in better cost estimation than current company practice. A case study is carried out for a manufacturer of electrical transformers in Turkey.

Suggested Citation

  • Aslan Deniz Karaoglan & Omur Karademir, 2017. "Flow time and product cost estimation by using an artificial neural network (ANN): A case study for transformer orders," The Engineering Economist, Taylor & Francis Journals, vol. 62(3), pages 272-292, July.
    • Handle: RePEc:taf:uteexx:v:62:y:2017:i:3:p:272-292
    DOI: 10.1080/0013791X.2016.1185808
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    Cited by:

    1. Zbigniew Leszczyński & Tomasz Jasiński, 2020. "Comparison of Product Life Cycle Cost Estimating Models Based on Neural Networks and Parametric Techniques—A Case Study for Induction Motors," Sustainability, MDPI, vol. 12(20), pages 1-14, October.

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