International Journal of Computational Intelligence Research (IJCIR)

Volume 2, Number 1 (2006)


Artificial neural network modeling in forecasting successful implementation 

of ERP systems

Se Hun Lim
Dept. of Management Information Systems, Sangji University, 660 Woosan-Dong Wonju-CIty Kanwon-Province, 220-702, South Korea

Kyungdoo Nam
Dept. of International Teade, Konkuk University, 1 Whayang-Dong KwongJin-Gu Seoul, 143-701, South Korea


Artificial Neural Network (ANN) is widely used in business forecasting. ANN is a powerful forecasting tool. It is suitable for solving complex problems. Recently, ANN has been applied in many varieties of business decision making, such as bankruptcy forecasting, customer churning prediction, stock price forecasting, business process innovations, and systems development. In this study, we investigated the usefulness of the ANN model in forecasting success when implementing Enterprise Resource Planning (ERP) systems. We used an ANN method to compare the performance of three different models: ANN, Multivariable Discriminant Analysis (MDA), and Case-based Reasoning (CBR). Experimental results show that the ANN approach is a promising method for forecasting successful ERP implementation.

Key words
ERP, ANN, MDA, CBR, forecasting, decision making.