| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 90 === At present, market management system is challenged by industry globally and variety products. An accurate prediction of order demand will increase the competitive ability for an enterprise. The closeness of the forecasted amount to the real demand will influence the production cost. The individual product demand having interaction effect on the demand of manufacture components when the several modular products are produced, meanwhile, fluctuation of the short-term products demand will decrease the precision of demand forecasting. Therefore, a multi-products short-term demand-forecasting model is important and necessary.
In this thesis, we take the merit of the neural network in high precision of the forecasting to the multi-products system and establish a demand-forecasting model. In this paper, we use the simulation software AweSim to simulate the orders data in production line for six months. Data for order types, arrival times, and numbers of product are collected and trained by the Back-Propagation neural network (BPN) to build a BPN multi-product demand-forecasting model. Simulation study shows that the BPN forecasting model has performance on the different order distributions, and the average mean square errors (MSEs) of the test samples are all under 0.15. In addition, this research brings up an order features vector also effective in reducing the forecasting error, which can take a reference for the future research.
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