A hybrid cost estimation framework based on feature-oriented data mining approach |
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| Affiliation: | 1. University of Alberta, Edmonton, Canada;2. 4-9 Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta T6G 2G8, Canada;1. Department of Building, Civil & Environmental Engineering, Concordia University, 1515 Ste-Catherine Street West, Montréal, Quebec, H3G 2W1, Canada;2. Concordia Institute for Information Systems Engineering, Concordia University, 1515 Ste-Catherine Street West, Montréal, Quebec H3G 2W1, Canada;1. University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria;2. Infineon Technologies, Königsbrücker Straße 180, 01099 Dresden, Germany;1. Laboratoire ERPI/ENSGSI, Université de Lorraine, 8 rue Bastien Lepage, 54000 Nancy, France;2. Essilor International, 81 Boulevard Jean-Baptiste Oudry, 94046 Creteil, France;1. School of Mechanical Engineering, The University of Adelaide, SA 5005, Australia;2. Eka Australia, Level 5, 185 Victoria Square, Adelaide, SA 5000, Australia;1. Innovative Information Industry Research Center (IIIRC), School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town Xili, Shenzhen 518055, PR China;2. Shenzhen Key Laboratory of Internet Information Collaboration, School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town Xili, Shenzhen 518055, PR China;3. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, ROC;4. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC;5. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, ROC |
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| Abstract: | This paper presents an informatics framework to apply feature-based engineering concept for cost estimation supported with data mining algorithms. The purpose of this research work is to provide a practical procedure for more accurate cost estimation by using the commonly available manufacturing process data associated with ERP systems. The proposed method combines linear regression and data-mining techniques, leverages the unique strengths of the both, and creates a mechanism to discover cost features. The final estimation function takes the user’s confidence level over each member technique into consideration such that the application of the method can phase in gradually in reality by building up the data mining capability. A case study demonstrates the proposed framework and compares the results from empirical cost prediction and data mining. The case study results indicate that the combined method is flexible and promising for determining the costs of the example welding features. With the result comparison between the empirical prediction and five different data mining algorithms, the ANN algorithm shows to be the most accurate for welding operations. |
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| Keywords: | Cost estimation Feature modeling Data mining ERP Welding feature |
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