The Implementation of Back-Propagation Network in Demand Forecasting Model ─ A Case for Modular Products
| Autor: | Yun-Hui Cheng, 鄭勻惠 |
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| Rok vydání: | 2004 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 92 In recent years, the business operating environment varies and consumer demanding follows the trend of globalization, the business must raise customer satisfaction and reduce the manufacturing cost in order to increase its market competitive ability. So the decision maker must process an adequacy production planning and reach safety stock, meanwhile reduce the shortage cost, delay cost and stock cost. However the accuracy of forecast usually influences the manufacturing cost and decision quality. When the decision maker is doing short-term production planning, the short-term demand fluctuation and product relativity may influence the demand forecast accuracy. Therefore, scheduling the modular product to fit the production line shot-term demand-forecasting model is important and necessary. In this thesis, we take the merit of the Back-Propagation neural network (BPN) in superiority ability of the forecasting to the modular products system and establish a demand-forecasting model, this model can provide the decision maker carries out the basis of production planning. In this research, we use the simulation software AweSim to simulate the orders data in six kinds and nine kinds of components for six months. Data for order types, arrival times, and numbers of product are collected and trained by the BPN to build a BPN modular product demand-forecasting model. The BPN demand-forecasting model is constructed by the moving windows method and average square error (MSE) is the evaluation index for demand-forecasting model. Simulation study shows that the BPN forecasting model performance good on the different order distributions, the average mean square errors (MSE) of the best model test samples are all under 0.2. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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