Data mining for customer service support
Information and Management
Multiperiod forecasting in stock markets: a paradox solved
Decision Support Systems - Special issue: Data mining for financial decision making
A new hybrid case-based architecture for medical diagnosis
Information Sciences—Informatics and Computer Science: An International Journal
Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors
Expert Systems with Applications: An International Journal
Global optimization of case-based reasoning for breast cytology diagnosis
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Expert Systems with Applications: An International Journal
Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry
Expert Systems with Applications: An International Journal
Introducing attribute risk for retrieval in case-based reasoning
Knowledge-Based Systems
A case-based knowledge system for safety evaluation decision making of thermal power plants
Knowledge-Based Systems
Journal of Intelligent Manufacturing
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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.