Applying Moving Bootstrap and Back-Propagation Neural Network for the Optimization Demand Forecasting Model of Spare Parts

• Main Authors: Hao-Wei Li, 李皓瑋
• Other Authors: Cheng-Chih Chang
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/64z45d

碩士 === 國立臺中科技大學 === 流通管理系碩士班 === 105 === Inventory control of spare parts has been an essential to many organizations since it is one of the most expensive assets. Most of the spare parts are to belong to intermittent demand and Bootstrapping has been claimed to be of great value for forecasting. While a small proportion of spare parts are regard to regular demand, Moving Average, frequently used to deal with this type of demand. We address combination forecasting model by the Moving Bootstrap based on Bootstrapping and Moving Average to classify the appropriate method. Then use the Back-Propagation Neural Network to construct the classification model which can be used to automatically select the better approach of forecasting. We find that the main explanatory variables about consumption of daily average, the ratio of days with zero consumption and standard deviation of daily consumption can exact classify the demand forecasting approach. In the future, enterprise arranges to purchase new spare parts, this combination model will assist in concluding the forecasting method and reducing the forecast error. Moreover, it leads to lower stock costs and improves operational performance.