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An Expert System for Predicting ERP Post-Implementation Benefits Using Artificial Neural Network

An Expert System for Predicting ERP Post-Implementation Benefits Using Artificial Neural Network

Ahad Zare Ravasan, Saeed Rouhani
Copyright: © 2014 |Volume: 10 |Issue: 3 |Pages: 22
ISSN: 1548-1115|EISSN: 1548-1123|EISBN13: 9781466654051|DOI: 10.4018/ijeis.2014070103
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MLA

Ravasan, Ahad Zare, and Saeed Rouhani. "An Expert System for Predicting ERP Post-Implementation Benefits Using Artificial Neural Network." IJEIS vol.10, no.3 2014: pp.24-45. http://doi.org/10.4018/ijeis.2014070103

APA

Ravasan, A. Z. & Rouhani, S. (2014). An Expert System for Predicting ERP Post-Implementation Benefits Using Artificial Neural Network. International Journal of Enterprise Information Systems (IJEIS), 10(3), 24-45. http://doi.org/10.4018/ijeis.2014070103

Chicago

Ravasan, Ahad Zare, and Saeed Rouhani. "An Expert System for Predicting ERP Post-Implementation Benefits Using Artificial Neural Network," International Journal of Enterprise Information Systems (IJEIS) 10, no.3: 24-45. http://doi.org/10.4018/ijeis.2014070103

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Abstract

Implementing Enterprise Resource Planning systems (ERPs) is a complex and costly project which usually delivers only a few of expected benefits. Obtaining the expected benefits of ERPs is impressed by a variety of factors and variables which is related to an organization or project environment. In this paper, the idea of predicting ERP post-implementation benefits based on the organizational profiles and factors has been discussed. Regarding the need to form the expectations of organizations about ERP, an expert system is developed by using Artificial Neural Network (ANN) method to articulate the relationships between some organizational factors and ERP's achieved benefits. The expert system's role is in the preparation to capture the data from the new enterprises wishes to implement ERP and predict likely benefits might be achieved from the system. For this end, factors of organizational profiles (such as industry type, size, structure, and so on) are recognized and a feed-forward architecture and Levenberg-Marquardt (trainlm) neural network model is designed, trained and validated with 171 surveyed data of Middle-East located enterprises experienced ERP. The trained ANN embedded in developed expert system predicts with the average correlation coefficients of 0.745, which is respectively high and proves the idea of dependency of ERP post-implementation benefits on the organizational profiles. Besides, total correct classification rate of 0.701 shows good prediction power which can help firms in predicting ERP benefits before system implementation.

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