مدل پس انتشار و فازی دلفی برای پیش بینی فروش در صنعت PCB

Reliable prediction of sales can improve the quality of business strategy. In this research, fuzzy logic and artificial neural network are integrated into the fuzzy back-propagation network (FBPN) for sales forecasting in Printed Circuit Board (PCB) industry. The fuzzy back propagation network is constructed to incorporate production-control expert judgments in enhancing the model's performance. Parameters chosen as inputs to the FBPN are no longer considered as of equal importance, but some sales managers and production control experts are requested to express their opinions about the importance of each input parameter in predicting the sales with linguistic terms, which can be converted into pre-specified fuzzy numbers. The proposed system is evaluated through the real world data provided by a printed circuit board company and experimental results indicate that the Fuzzy back-propagation approach outperforms other three different forecasting models in MAPE measures.

The printed circuit board (PCB) industry has grown up with the rapid development of the electronic, information and communication industries recently. During the past 30 years, the PCB industry in Taiwan has been striving to improve the manufacturing techniques, increase the production equipments, and strengthen the quality control, in order to integrate the developments of up-stream, middle stream and down-stream industries. This endeavor had successfully made Taiwan be ranked top 3 in the world for the total production amount of PCB. However, the overall accomplishment of PCB industry has been decreased recently by the influence of the profit conditions of the down-stream industries such as information, communication and consuming electronic industries. To decrease a cost means to increase a profit. Hence, in order to improve the enterprise's competitiveness, the executives should be able to make correct decisions using the available information, and ‘forecasting’ is viewed as an important part of decision making. Reliable forecasting of sales can help to make an effective inventory control and a proper scheduling process to increase the usage percentage of machines, which can avoid works being held up for lack of materials. To provide appropriate decisions and help the policy maker judge correctly is the basis of the production planning, with the end of decreasing the overall costs. Thus, all enterprises are working on the exploitation of prediction methods, which decide the success and failure of an enterprise. When dealing with the problems of sales forecasting, many researchers have used hybrid artificial intelligent algorithms to forecast, and the most rewarding method is the application integrating artificial neural networks (ANNs) and fuzzy theory. This method is applied by incorporating the experience-based principal and logic-explanation capacity of fuzzy theory and the capacity of memory and error-allowance of ANNs, as well as self-learning by numerical data. This research focuses on the monthly sales forecasting of PCB and applies the fuzzy Delphi to select variables with a better and more systematic way from expert experience. These input variables will be converted into pre-specified fuzzy numbers; aggregated and then fed into the FBPN for monthly sales forecasting, with the purpose of improving the forecasting accuracy and using this information to help managers in decision-making.

Recently, more and more researchers and industrial practitioners are interested in applying fuzzy theory and neural network in their routine problem solving. This research combines fuzzy theory and back-propagation network into a hybrid system, which will be applied in the sales forecasting of PCB industries. The major characteristics of this FBPN include the followings: 1. Data collections. The data applied in this research are derived from the historic data from a PCB company located in Chung-Li, Taiwan, ROC. 2. The input variables in traditionally BPN network are not processed at all and they are unconditionally input into BPN for further processing during the training procedure. However, this may come out with large training errors. To correct this flaw, this research applies the SRA, step-by-step to filter out the unrelated factors and keep only those factors, which have significant effects to the sales of PCB factory. Therefore the accuracy of the forecasting results can be further improved. 3. Through the introduction of FDM, the opinion of various experts can be elucidated and incorporated into the input variables. The linguistics structure of the input variables can be designed in the questionnaires and various experts will express their personal opinion through the questionnaires. It is a very useful method for collecting data and assigning weight to various variables. The experimental results in Section 5 demonstrated the effectiveness of the FBPN that is superior to other traditional approaches. In summary, this research has the following important contribution in the sales forecasting area and these contributions might be interested to other academic researchers and industrial practitioners: 1. Feature selections. To filter out significant factors from a series of input variables, the FDM is superior to the SRA method. FDM will collect the opinion from various experts and assign different weights to these variables according to their experiences in this field. Therefore, it is very easy to extract important factors from these various variables. In contrary, gradual regression analysis may come out with a combination of various variables, which is mutually correlated. However, the effect of these selected variables may not significant enough to be included in the final inputs. The errors for input from fuzzy Delphi is 12.88% and errors from SRA is 13.87%. It is obvious to see that FDM is more effective for applications. 2. The effect of tendency. When take tendency effect into consideration, the overall errors are decreased. Tendency and seasonality are included in the time series data and these two factors will affect the accuracy of the forecasting method dramatically. This research applies the Winters trend and seasonality exponential smoothing model to forecast the sales and then convert this data as an input to the BPN model. After the training procedure, the final errors, no matter it is from FDM or SRA, are decreased significantly. Errors from gradual regression analysis decreased from 13.84 to 7.15% and FDM from 12.88% down to 6.19%%. This shows the significance of including Winters trend and seasonality exponential smoothing model in the model. 3. Comparisons of different forecasting models. This research applies three different performance measures, i.e. encompassing test, forecasting errors and accuracy of forecasting to compare the FBPN with other three methods, i.e. GF, MRA and BPN. The intensive experimental results show the following: 1. In encompassing test, FBPN and BPN models are superior to GF and MRA. 2. As for MAPE, FBPN has the smallest MAPE and it is only 3.09%. Therefore, FBPN model by combining FDM and BPN model is a very powerful and effective forecasting tool that can be further applied in other field of applications since expert's opinion can be incorporated into the model.