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

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.