IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v115y2008i1p171-178.html
   My bibliography  Save this article

Estimating the cost of vertical high-speed machining centres, a comparison between multiple regression analysis and the neural networks approach

Author

Listed:
  • Ciurana, J.
  • Quintana, G.
  • Garcia-Romeu, M.L.

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:115:y:2008:i:1:p:171-178
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925-5273(08)00155-2
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Deng, S. & Yeh, Tsung-Han, 2011. "Using least squares support vector machines for the airframe structures manufacturing cost estimation," International Journal of Production Economics, Elsevier, vol. 131(2), pages 701-708, June.
    3. Ramanathan, Ramakrishnan & Ramanathan, Usha & Zhang, Yubo, 2016. "Linking operations, marketing and environmental capabilities and diversification to hotel performance: A data envelopment analysis approach," International Journal of Production Economics, Elsevier, vol. 176(C), pages 111-122.
    4. Quintana, Guillem & Ciurana, Joaquim, 2011. "Cost estimation support tool for vertical high speed machines based on product characteristics and productivity requirements," International Journal of Production Economics, Elsevier, vol. 134(1), pages 188-195, November.
    5. Carlos F. A. Arranz & Caleb Kwong & Vania Sena, 2023. "The effect of consumption and production policies on circular economy business models: A machine learning approach," Journal of Industrial Ecology, Yale University, vol. 27(4), pages 1089-1104, August.
    6. Arroyabe, Marta F. & Arranz, Nieves & Fdez. de Arroyabe, Juan Carlos, 2015. "R&D partnerships: An exploratory approach to the role of structural variables in joint project performance," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 623-634.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Caputo, Antonio C. & Pelagagge, Pacifico M., 2008. "Parametric and neural methods for cost estimation of process vessels," International Journal of Production Economics, Elsevier, vol. 112(2), pages 934-954, April.
    2. Verlinden, B. & Duflou, J.R. & Collin, P. & Cattrysse, D., 2008. "Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study," International Journal of Production Economics, Elsevier, vol. 111(2), pages 484-492, February.
    3. Quintana, Guillem & Ciurana, Joaquim, 2011. "Cost estimation support tool for vertical high speed machines based on product characteristics and productivity requirements," International Journal of Production Economics, Elsevier, vol. 134(1), pages 188-195, November.
    4. Deng, S. & Yeh, Tsung-Han, 2011. "Using least squares support vector machines for the airframe structures manufacturing cost estimation," International Journal of Production Economics, Elsevier, vol. 131(2), pages 701-708, June.
    5. Johnson, Michael D. & Kirchain, Randolph E., 2009. "Quantifying the effects of product family decisions on material selection: A process-based costing approach," International Journal of Production Economics, Elsevier, vol. 120(2), pages 653-668, August.
    6. Eklin, Mark & Arzi, Yohanan & Shtub, Avraham, 2009. "Model for cost estimation in a finite-capacity stochastic environment based on shop floor optimization combined with simulation," European Journal of Operational Research, Elsevier, vol. 194(1), pages 294-306, April.
    7. Wang, Qing, 2007. "Artificial neural networks as cost engineering methods in a collaborative manufacturing environment," International Journal of Production Economics, Elsevier, vol. 109(1-2), pages 53-64, September.
    8. Qian, Li & Ben-Arieh, David, 2008. "Parametric cost estimation based on activity-based costing: A case study for design and development of rotational parts," International Journal of Production Economics, Elsevier, vol. 113(2), pages 805-818, June.
    9. Kwon, He-Boong, 2017. "Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 159-170.
    10. Lee, Jooh & Kwon, He-Boong, 2017. "Progressive performance modeling for the strategic determinants of market value in the high-tech oriented SMEs," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 91-102.
    11. Antonio Armillotta, 2021. "On the role of complexity in machining time estimation," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2281-2299, December.
    12. Duffner, Fabian & Mauler, Lukas & Wentker, Marc & Leker, Jens & Winter, Martin, 2021. "Large-scale automotive battery cell manufacturing: Analyzing strategic and operational effects on manufacturing costs," International Journal of Production Economics, Elsevier, vol. 232(C).
    13. Yochanan Shachmurove & Doris Witkowska, "undated". "Utilizing Artificial Neural Network Model to Predict Stock Markets," Penn CARESS Working Papers cae679cdc2e020f74d692ae73, Penn Economics Department.
    14. Johnson, Michael & Kirchain, Randolph, 2009. "Quantifying the effects of parts consolidation and development costs on material selection decisions: A process-based costing approach," International Journal of Production Economics, Elsevier, vol. 119(1), pages 174-186, May.
    15. Ibrahim, Awad Elsayed Awad & Elamer, Ahmed A. & Ezat, Amr Nazieh, 2021. "The convergence of big data and accounting: innovative research opportunities," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    16. Nasser Amaitik & Ming Zhang & Zezhong Wang & Yuchun Xu & Gareth Thomson & Yiyong Xiao & Nikolaos Kolokas & Alexander Maisuradze & Oscar Garcia & Michael Peschl & Dimitrios Tzovaras, 2022. "Cost Modelling to Support Optimum Selection of Life Extension Strategy for Industrial Equipment in Smart Manufacturing," Circular Economy and Sustainability,, Springer.
    17. Duffner, F. & Wentker, M. & Greenwood, M. & Leker, J., 2020. "Battery cost modeling: A review and directions for future research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    18. Marcin Relich & Arkadiusz Gola & Małgorzata Jasiulewicz-Kaczmarek, 2022. "Identifying Improvement Opportunities in Product Design for Reducing Energy Consumption," Energies, MDPI, vol. 15(24), pages 1-19, December.
    19. Kinyua, Johnson D. & Mutigwe, Charles & Cushing, Daniel J. & Poggi, Michael, 2021. "An analysis of the impact of President Trump’s tweets on the DJIA and S&P 500 using machine learning and sentiment analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:115:y:2008:i:1:p:171-178. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.