A fuzzy case-based reasoning model for sales forecasting in print circuit board industries

Authors:
Pei-Chann Chang;Chen-Hao Liu;Robert K. Lai
Affiliations:
Department of Information Management, Yuan Ze University, 135 Yuan Tung Road, Chung-Li 32026, Taiwan, ROC and Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan Tung ...;Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan Tung Road, Chung-Li 32026, Taiwan, ROC;Department of Computer Science and Engineering, Yuan Ze University, 135 Yuan Tang Road, Chung-Li 32026, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

Reliable prediction of sales can improve the quality of business strategy. Case-based reasoning (CBR), one of the well known artificial intelligence (AI) techniques, has already proven its effectiveness in numerous studies. However, due to the uncertainties in knowledge representation, attribute description, and similarity measures in CBR, it is very difficult to find the similar cases from case bases. In order to deal with this problem, fuzzy theories have been incorporated into CBR allowing for more flexible and accurate models. This research develops a fuzzy case-based reasoning (FCBR) and explores its potential use in supporting a forecaster during the forecast process for forecasting the future sales of a printed circuit board factory. Numerical data of various affecting factors and actual demand of the past 5 years of the printed circuit board (PCB) factory are collected and input into the FCBR for future monthly sales forecasting. Experimental results show the effectiveness of the FCBR model when comparing it with other approaches.