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Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study

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  • Verlinden, B.
  • Duflou, J.R.
  • Collin, P.
  • Cattrysse, D.

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  • 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.
    • Handle: RePEc:eee:proeco:v:111:y:2008:i:2:p:484-492
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    References listed on IDEAS

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    1. Wang, Song-Gui & Tse, Siu-Keung & Chow, Shein-Chung, 1990. "On the measures of multicollinearity in least squares regression," Statistics & Probability Letters, Elsevier, vol. 9(4), pages 347-355, 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. 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. 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.
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    Cited by:

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    2. Rosa Lombardi & Raffaele Trequattrini & Federico Schimperna & Myriam Cano-Rubio, 2021. "The Impact of Smart Technologies on theManagement and Strategic Control: A Structured Literature Review," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2021(suppl. 1), pages 11-30.
    3. 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.
    4. 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.
    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. Ahmed Abdulkareem Ahmed Adulaimi & Biswajeet Pradhan & Subrata Chakraborty & Abdullah Alamri, 2021. "Traffic Noise Modelling Using Land Use Regression Model Based on Machine Learning, Statistical Regression and GIS," Energies, MDPI, vol. 14(16), pages 1-19, August.
    7. Evgenii A. Konnikov & Olga A. Konnikova & Dmitriy G. Rodionov, 2019. "Impact of 3D-Printing Technologies on the Transformation of Industrial Production in the Arctic Zone," Resources, MDPI, vol. 8(1), pages 1-15, January.
    8. 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).
    9. Latif Onur Ugur & Recep Kanit & Hamit Erdal & Ersin Namli & Halil Ibrahim Erdal & Umut Naci Baykan & Mursel Erdal, 2019. "Enhanced Predictive Models for Construction Costs: A Case Study of Turkish Mass Housing Sector," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1403-1419, April.
    10. 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).

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