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Rapid cost estimation of metallic components for the aerospace industry

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  • de Cos, Javier
  • Sanchez, Fernando
  • Ortega, Francisco
  • Montequin, Vicente

Abstract

This paper illustrates and compares the results of the application of two different approaches--non-parametric and artificial neural network techniques--for the rapid cost estimation of turbine components. This technique is a simple and automatic way for the estimation of the cost of a piece with no expert intervention. Three methods of estimation are compared: the projection pursuit method (PPR), the local polynomial approach (LOESS) and adaptive neural networks (ANNs). This comparative analysis serves to enhance current work that seeks to choose the optimum predictor model. The results confirm the validity of the neural network theory in this field of application, but not a clear superiority as compared with the non-parametric approach. The present research provides a new tool to avoid inadequate piece budgeting strategies. The use of these methods contributes to the minimisation of errors in the budgeting of new items.

Suggested Citation

  • de Cos, Javier & Sanchez, Fernando & Ortega, Francisco & Montequin, Vicente, 2008. "Rapid cost estimation of metallic components for the aerospace industry," International Journal of Production Economics, Elsevier, vol. 112(1), pages 470-482, March.
    • Handle: RePEc:eee:proeco:v:112:y:2008:i:1:p:470-482
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    References listed on IDEAS

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    Cited by:

    1. Julia Bendul & Vasile Apostu, 2017. "An Accuracy Investigation of Product Cost Estimation in Automotive Die Manufacturing," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 8(7), pages 1-15, November.
    2. Loyer, Jean-Loup & Henriques, Elsa & Fontul, Mihail & Wiseall, Steve, 2016. "Comparison of Machine Learning methods applied to the estimation of manufacturing cost of jet engine components," International Journal of Production Economics, Elsevier, vol. 178(C), pages 109-119.
    3. Bodendorf, Frank & Xie, Qiao & Merkl, Philipp & Franke, Jörg, 2022. "A multi-perspective approach to support collaborative cost management in supplier-buyer dyads," International Journal of Production Economics, Elsevier, vol. 245(C).

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