Name
Title | ||
|---|---|---|
Forecasting of manufacturing cost in mobile phone products by case-based reasoning and artificial neural network models |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s10845-010-0390-7 | J. Intelligent Manufacturing |
Keywords | Field | DocType |
Mobile phone,Product cost prediction,Case-based reasoning,Artificial neural networks | Manufacturing cost,Unit cost,Phone,Artificial intelligence,Engineering,Mobile phone,Case-based reasoning,Artificial neural network,Hybrid system,Machine learning | Journal |
Volume | Issue | ISSN |
23 | 3 | 0956-5515 |
Citations | PageRank | References |
15 | 0.84 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pei-Chann Chang | 1 | 1752 | 109.32 |
| Jyun-Jie Lin | 2 | 150 | 7.31 |
| Wei-Yuan Dzan | 3 | 36 | 4.06 |