A Study Sales Forecasting for High-variety Low-volume Production

• Main Authors: Chi, Jui-Fu, 紀瑞傅
• Other Authors: Lee, Shan-Lin
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/95015706264660120595

碩士 === 國立屏東科技大學 === 工業管理系 === 93 === Artificial Neural Network is provided with the capability for highly-emorizing, calculating, learning and alternative, to fit the non-linear problem. This study uses the advantages of Artificial Neural Network to establish a sales forecasting model for high-variety low-volume production. For some submersible pump factory, the author uses back-progation network with 3 layers, namely input, hidden, and output layer. Eight factors affecting the demand are identified, and they are rain quantity, promotion, per monthly sales in last year and so on. Nine hidden neurons are used to form the hidden layer of the proposed model. Artificial Neural Network model can provide better demand forecast than it is used to be. The error rate decreases from 33.344% to 11.568%. In comparing to the exponential smoothing model, the proposed method shows that it results smaller error rate. It is able to provide a better forecast for high-variety low-volume production