Optimization of forecasting supply chain management sustainable collaboration using hybrid artificial neural network

Authors:
Sehun Lim;Juhee Hahn
Affiliations:
Department of Information System, Chung-Ang University, Ansung City, Kyunggi-Do, South Korea;Department of Business, Chung-Ang University, Ansung City, Kyunggi-Do, South Korea
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Abstract

Artificial Neural Network (ANN) is widely used in business to optimize forecasting. Various techniques have been developed to improve outcomes such as adding more diverse algorithms, feature selection and feature weighting in input variables, and modification of input case using instance selection. In this research, ANN is applied to solve problems in forecasting a Supply Chain Management (SCM) sustainable collaboration. This research compares the performance of forecasting SCM sustainable collaboration with four types of ANN models: COANN (COnventional ANN), FWANN (ANN with Feature Weighting), FSANN (ANN with Feature Selection), and HYANN (HYbrid ANN with Feature Weighting and Feature Selection). Using HYANN to forecast an SCM sustainable collaboration gave the best results.