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Validation and Selection between Machine Learning Technique and Traditional Methods to Reduce Bullwhip Effects: a Data Mining Approach

Authors :
Mojaveri, Hamid R. S.
Mousavi, Seyed S.
Heydar, Mojtaba
Aminian, Ahmad
Publication Year :
2009
Publisher :
Zenodo, 2009.

Abstract

The aim of this paper is to present a methodology in three steps to forecast supply chain demand. In first step, various data mining techniques are applied in order to prepare data for entering into forecasting models. In second step, the modeling step, an artificial neural network and support vector machine is presented after defining Mean Absolute Percentage Error index for measuring error. The structure of artificial neural network is selected based on previous researchers' results and in this article the accuracy of network is increased by using sensitivity analysis. The best forecast for classical forecasting methods (Moving Average, Exponential Smoothing, and Exponential Smoothing with Trend) is resulted based on prepared data and this forecast is compared with result of support vector machine and proposed artificial neural network. The results show that artificial neural network can forecast more precisely in comparison with other methods. 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Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....2ba4fe29f5a4988831d3d1314b910201
Full Text :
https://doi.org/10.5281/zenodo.1060157