Research on Classification of Back-Propagation Neural Network and Decision Tree in Demand Forecasting Model of Spare Parts

• Main Authors: Chiu-Yang Yu, 尤秋揚
• Other Authors: Chih-Wen Yang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/xw539v

碩士 === 國立臺中科技大學 === 流通管理系碩士班 === 107 === Spare parts are not intermediate products or final products that are sold to consumers, spare parts are an essential part of maintaining smooth operation of production equipment during production activities. The function of the spare part is to ensure that the production equipment is kept in operation, and the analysis of the demand forecast is a key element of the inventory management of the spare parts. Most spare parts are expensive that too high spare parts inventory levels may lead to the company''s capital turnover problem. Therefore, effectively controlling the inventory level of spare parts is an important issue for enterprises. This study collects two-year historical data of spare parts of a manufacturing company specializing in the production of industrial raw materials. We propose classification model of Back-Propagation Neural Network and Decision Tree in demand forecasting model of spare parts. We analyze the influence of different period database on both Moving Average forecasting model and Moving Bootstrap model. The results show that the accuracy of both Back-Propagation Neural Network classification model and Decision Tree classification model are more than 70%, showing that both two classification models can be used to classify the optimal demand forecasting model for spare parts. This study also found that both the Moving Average forecasting model and Moving Bootstrap model performed best in the model with a period of twelve.