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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
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Production Economics.

    Volume (Year): 112 (2008)
    Issue (Month): 1 (March)
    Pages: 470-482

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    Handle: RePEc:eee:proeco:v:112:y:2008:i:1:p:470-482

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    Web page: http://www.elsevier.com/locate/ijpe

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    1. Araz, Ceyhun & Ozkarahan, Irem, 2007. "Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure," International Journal of Production Economics, Elsevier, vol. 106(2), pages 585-606, April.
    2. H'mida, Fehmi & Martin, Patrick & Vernadat, Francois, 2006. "Cost estimation in mechanical production: The Cost Entity approach applied to integrated product engineering," International Journal of Production Economics, Elsevier, vol. 103(1), pages 17-35, September.
    3. Gallant, A. Ronald, 1981. "On the bias in flexible functional forms and an essentially unbiased form : The fourier flexible form," Journal of Econometrics, Elsevier, vol. 15(2), pages 211-245, February.
    4. James M. Hutchinson & Andrew W. Lo & Tomaso Poggio, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks," NBER Working Papers 4718, National Bureau of Economic Research, Inc.
    5. Kearns, P., 1993. "Volatility and the Pricing of Interest Rate Derivative Claims," Papers 47, Rochester, Business - Ph.D.,.
    6. Barria, J A & Hall, Stephen G, 2002. "A Non-parametric Approach to Pricing and Hedging Derivative Securities: With an Application to LIFFE Data," Computational Economics, Society for Computational Economics, vol. 19(3), pages 303-22, June.
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