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Cost estimation support tool for vertical high speed machines based on product characteristics and productivity requirements

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  • Quintana, Guillem
  • Ciurana, Joaquim
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    Abstract

    This work concerns a machine tool selection problem, which consists of selecting the most suitable machine to satisfy manufacturing company requirements. The main goal of this work is to develop a cost estimation support tool for vertical high speed machining centres based on final part and productivity requirements of the company linked with machine tool characteristics available in the catalogues in order to apply the cost model and to calculate machine tool cost estimations. The cost model presented is based on multiple regression analyses and provides reasonably accurate market cost predictions. Applying the proposed cost model will help the user to determine the approximate market cost of the machine and can be especially interesting for decision makers in the preliminary stages of a selection process because it avoids long and costly studies.

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    Bibliographic Info

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

    Volume (Year): 134 (2011)
    Issue (Month): 1 (November)
    Pages: 188-195

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    Handle: RePEc:eee:proeco:v:134:y:2011:i:1:p:188-195

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

    Related research

    Keywords: Decision making Cost Multiple regression analysis Milling machine;

    References

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    1. Shtub, Avraham & Versano, Ronen, 1999. "Estimating the cost of steel pipe bending, a comparison between neural networks and regression analysis," International Journal of Production Economics, Elsevier, vol. 62(3), pages 201-207, September.
    2. Folgado, R. & Pe├žas, P. & Henriques, E., 2010. "Life cycle cost for technology selection: A Case study in the manufacturing of injection moulds," International Journal of Production Economics, Elsevier, vol. 128(1), pages 368-378, November.
    3. Cavalieri, Sergio & Maccarrone, Paolo & Pinto, Roberto, 2004. "Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry," International Journal of Production Economics, Elsevier, vol. 91(2), pages 165-177, September.
    4. Chou, Jui-Sheng & Tai, Yian & Chang, Lian-Ji, 2010. "Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models," International Journal of Production Economics, Elsevier, vol. 128(1), pages 339-350, November.
    5. Zhang, Yan & Xia, Guoping, 2010. "Short-run cost-based pricing model for a supply chain network," International Journal of Production Economics, Elsevier, vol. 128(1), pages 167-174, November.
    6. Ciurana, J. & Quintana, G. & Garcia-Romeu, M.L., 2008. "Estimating the cost of vertical high-speed machining centres, a comparison between multiple regression analysis and the neural networks approach," International Journal of Production Economics, Elsevier, vol. 115(1), pages 171-178, September.
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