## 1 Introduction  
Cost forecasting means different things at different stages of the product life cycle. In product design, the designer needs to know whether the product will be economically produced. After a product goes into mass production, forecasting the unit cost is the basis of financial and production planning activities. When a product enters the market, the follow-up customer service and maintenance costs must also be taken into account.  
Accurately predicting the unit cost of each product type is a very important task in any factory. If the unit cost is less than expected, then the efforts and investment of cost reduction are not necessary. Conversely, if the unit cost is more than expected, then the profitability of the product will be over-estimated, resulting in the wrong investment and production decisions. However, forecasting the unit cost is not an easy task because of the uncertainty of the unit cost, mainly due to the cost of human operations in the production of products, which is sometimes unstable. In addition, there is not much relevant literature in the unit cost forecasting. On the other hand, several recent studies (e.g. [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14])showed that fuzzy collaborative forecasting has great potential for the prediction of processes with uncertainty, such as yield learning, changes in price, fluctuations in the cycle time, and others. For these reasons, the application of fuzzy collaborative forecasting methods to improve the performance of the unit cost forecasting is worth a try. Therefore, some fuzzy collaborative forecasting models are proposed in this study, in order to enhance the accuracy and precision of the unit cost forecast.  
In the proposed methodology, the stakeholders are a group of domain experts, such as the product engineer, factory managers and accounting department staff. These experts apply fuzzy linear regression methods to predict the unit cost of a product. A fuzzy linear regression equation can be converted into a linear or nonlinear programming problem in a variety of ways. Furthermore, within the conversion process some parameters need a subject setting. As a result, forecasts obtained by the experts may be very different and therefore requires a collaborative mechanism to deal with the following issues:  
(1) How to integrate these forecasts?  
(2) How experts can refer to the forecasts of others to modify their own?  
In response to this issue, the methods presented in this study are as follows:  
(1) Some linear fuzzy regression models for the unit cost forecasting are proposed and compared.  
(2) Development of dedicated software to pass the forecast of each expert to other experts for their reference. In the meantime, the software can integrate different forecasts using the hybrid fuzzy intersection and back propagation network approach.  
(3) In reference to the forecasts of others, each expert subjectively modifies the parameters in the fuzzy linear regression method.  
The objectives of this study are as follows:  
(1) To enhance the accuracy of the unit cost forecast. In other words, the forecasts obtained must be very close to the actual values.  
(2) To improve the precision of the unit cost forecasting. Namely, a very small range containing the actual value can be estimated.  
(3) The application of an instance to compare the advantages and disadvantages of different linear fuzzy collaborative forecasting models.  
The organization of this study is described as follow. Section 2 first reviews the literature related to fuzzy collaborative forecasting and the unit cost forecasting. The problems faced by the existing methods are also discussed. Then, in Section 3, some fuzzy collaborative forecasting models are proposed to predict the unit cost of a product. An example is given to illustrate the applicability of these models. Section 4 makes conclusions and suggests some directions for future research.