Forecasting of manufacturing cost in mobile phone products by case-based reasoning and artificial neural network models

The mobile phone manufacturers in Taiwan have made great efforts in proposing the rational quotations to the international phone companies with the ambition to win the bids by out beating other phone manufacturers. However, there are a lot of uncertainties and issues to be resolved in estimating the manufacturing costs for mobile phone manufacturers. As far as we know, there is no existing model which can be applied directly in forecasting the manufacturing costs. This research makes the first attempt to develop a hybrid system by integrated Case-Based Reasoning (CBR) and Artificial Neural Networks (ANN) as a Product Unit Cost (PUC) forecasting model for Mobile Phone Company. According to the cost formula of the mobile phone and experts’ opinions, a set of qualitative and quantitative factors are analyzed and determined. Qualitative factors are applied in CBR to retrieve a similar case from the case bases for a new phone product and ANN is used to find the relationship between the quantitative factors and the predicted PUC. Finally, intensive experiments are conducted to test the effectiveness of six different forecasting models. The model proposed in this research is compared with the other five models and the MAPE value of the proposed model is the smallest. This research provides a new prediction model with high accuracy for mobile phone manufacturing companies.

[1]  Kazuo Asakawa,et al.  Stock market prediction system with modular neural networks , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[2]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[3]  C. Ou-Yang,et al.  Developing an integrated framework for feature-based early manufacturing cost estimation , 1997 .

[4]  Elisabet Golobardes,et al.  Automatic diagnosis with genetic algorithms and case-based reasoning , 1999, Artif. Intell. Eng..

[5]  S. C. Hui,et al.  Data mining for customer service support , 2000, Inf. Manag..

[6]  Phillip Burrell,et al.  Case-Based Reasoning System and Artificial Neural Networks: A Review , 2001, Neural Computing & Applications.

[7]  Kyungsup Kim,et al.  The cluster-indexing method for case-based reasoning using self-organizing maps and learning vector quantization for bond rating cases , 2001, Expert Syst. Appl..

[8]  E. A. Lotfy,et al.  Applying Neural Networks in Case-Based Reasoning Adaptation for Cost Assessment of Steel Buildings , 2002 .

[9]  Pei-Chann Chang,et al.  A case-based expert support system for due-date assignment in a wafer fabrication factory , 2003, J. Intell. Manuf..

[10]  Jih-Chang Hsieh,et al.  A neural networks approach for due-date assignment in a wafer fabrication factory , 2003 .

[11]  Cheng-Seen Ho,et al.  A new hybrid case-based architecture for medical diagnosis , 2004, Inf. Sci..

[12]  T. Warren Liao,et al.  Fuzzy reasoning based automatic inspection of radiographic welds: weld recognition , 2004, J. Intell. Manuf..

[13]  Hans-Martin Krolzig,et al.  Multiperiod forecasting in stock markets: a paradox solved , 2004, Decis. Support Syst..

[14]  T. Warren Liao,et al.  An investigation of a hybrid CBR method for failure mechanisms identification , 2004, Eng. Appl. Artif. Intell..

[15]  Bo-Suk Yang,et al.  Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis , 2004, Expert Syst. Appl..

[16]  Bart Wyns,et al.  Prediction of arthritis using a modified Kohonen mapping and case based reasoning , 2004, Eng. Appl. Artif. Intell..

[17]  Sergio Cavalieri,et al.  Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry , 2004 .

[18]  T. Radhakrishnan,et al.  Milling force prediction using regression and neural networks , 2005, J. Intell. Manuf..

[19]  Pei-Chann Chang,et al.  Evolving fuzzy rules for due-date assignment problem in semiconductor manufacturing factory , 2005, J. Intell. Manuf..

[20]  Pei-Chann Chang,et al.  A hybrid system combining self-organizing maps with case-based reasoning in wholesaler's new-release book forecasting , 2005, Expert Syst. Appl..

[21]  Pei-Chann Chang,et al.  A hybrid system by evolving case-based reasoning with genetic algorithm in wholesaler's returning book forecasting , 2006, Decis. Support Syst..

[22]  Pei-Chann Chang,et al.  Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry , 2006, Expert Syst. Appl..

[23]  Mansour A. Karkoub.  Prediction of Hydroforming Characteristics using Random Neural Networks , 2006, J. Intell. Manuf..

[24]  Jian S. Dai,et al.  Product Cost Estimation: Technique Classification and Methodology Review , 2006 .

[25]  Chee Peng Lim,et al.  An intelligent decision support system for manufacturing technology investments , 2006 .

[26]  Se-Hak Chun,et al.  A new hybrid data mining technique using a regression case based reasoning: Application to financial forecasting , 2006, Expert Syst. Appl..

[27]  Vadlamani Ravi,et al.  Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review , 2007, Eur. J. Oper. Res..

[28]  A. J. Dentsoras,et al.  Case-DeSC: A system for case-based design with soft computing techniques , 2007, Expert Syst. Appl..

[29]  Jian-Wei Wang,et al.  A model for case retrieval based on ann and nearest neighbor algorithm , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[30]  Pei-Chann Chang,et al.  Evolving CBR and data segmentation by SOM for flow time prediction in semiconductor manufacturing factory , 2009, J. Intell. Manuf..

[31]  Pei-Chann Chang,et al.  Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[32]  Hui Li,et al.  Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors , 2009, Expert Syst. Appl..

[33]  Kyoung-jae Kim,et al.  Global optimization of case-based reasoning for breast cytology diagnosis , 2009, Expert Syst. Appl..