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Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models

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  • Chou, Jui-Sheng
  • Tai, Yian
  • Chang, Lian-Ji

Abstract

Accurately and timely estimating product costs is extremely beneficial to corporate survival. This study assesses the reliability of multiple regression analysis (MRA), artificial neural networks (ANNs), case-based reasoning (CBR), and hybrid intelligence (Hi) to forecast costs of thin-film transistor liquid-crystal display (TFT-LCD) equipment. Newly completed equipment-development projects are provided by departments in a Taiwanese high-tech company, which is a top global producer of TFT-LCD equipment. The cross-fold validation method is applied to measure model performance, reliability, and prediction ease. Through comparison of various performance indices, the Hi method outperforms MRA, ANNs and CBR when used for cost prediction during conceptual stages. Although it is well developed in academia, artificial intelligence (AI) is rarely applied in practical project management. This study successfully describes an actionable knowledge-discovery process using a real-world data mining approach for the high-tech equipment manufacturing industry. Project managers (PMs) can benefit from applying the Hi approach to establish latent non-linear cost estimation relationships. The Hi approach is empirically proven an effective prediction technique for PMs considering overall evaluation criteria when determining the best selling prices of TFT-LCD manufacturing equipment to clients.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:128:y:2010:i:1:p:339-350
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    1. 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.
    2. Ben-Arieh, David & Qian, Li, 2003. "Activity-based cost management for design and development stage," International Journal of Production Economics, Elsevier, vol. 83(2), pages 169-183, February.
    3. Kee, Robert, 2010. "The sufficiency of target costing for evaluating production-related decisions," International Journal of Production Economics, Elsevier, vol. 126(2), pages 204-211, August.
    4. 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.
    5. Tsai, Kune-muh & Wang, Shan-chi, 2009. "Multi-site available-to-promise modeling for assemble-to-order manufacturing: An illustration on TFT-LCD manufacturing," International Journal of Production Economics, Elsevier, vol. 117(1), pages 174-184, January.
    6. Margaret Emsley & David Lowe & A. Roy Duff & Anthony Harding & Adam Hickson, 2002. "Data modelling and the application of a neural network approach to the prediction of total construction costs," Construction Management and Economics, Taylor & Francis Journals, vol. 20(6), pages 465-472.
    7. Baykasoglu, Adil & Kaplanoglu, Vahit, 2008. "Application of activity-based costing to a land transportation company: A case study," International Journal of Production Economics, Elsevier, vol. 116(2), pages 308-324, December.
    8. 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.
    9. Rogozhin, Alex & Gallaher, Michael & Helfand, Gloria & McManus, Walter, 2010. "Using indirect cost multipliers to estimate the total cost of adding new technology in the automobile industry," International Journal of Production Economics, Elsevier, vol. 124(2), pages 360-368, April.
    10. 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.
    11. Lin, James T. & Chen, Tzu-Li & Lin, Yen-Ting, 2009. "Critical material planning for TFT-LCD production industry," International Journal of Production Economics, Elsevier, vol. 122(2), pages 639-655, December.
    12. Tornberg, Katja & Jamsen, Miikka & Paranko, Jari, 2002. "Activity-based costing and process modeling for cost-conscious product design: A case study in a manufacturing company," International Journal of Production Economics, Elsevier, vol. 79(1), pages 75-82, September.
    13. Hwang, Yeong-Dong & Lin, Yi-Ching & Lyu Jr., Jung, 2008. "The performance evaluation of SCOR sourcing process--The case study of Taiwan's TFT-LCD industry," International Journal of Production Economics, Elsevier, vol. 115(2), pages 411-423, October.
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