An Intelligent Model for Enterprise Resource Planning Selection Based on BP Neural Network

  • Amine Elyacoubi
  • Hicham Attariuas
  • Noura Aknin
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 37)


Enterprise resource planning (ERP) is the managing business system that allows an enterprise of any organization to utilize a collection of integrated applications to manage its business and automate many back office functions related to technology. The selection itself of a suitable ERP is one of the most important parts in the implementation. This paper attempts to use artificial neural networks to choose an ideal ERP for any enterprise. This paper constructs a three-level BP neural network to analyze the principle and model of a suitable ERP. By using the samples to train and inspect the BP neural network, we conclude that the application of BP neural networks is an effective method to forecast suitable ERP. Thus the purpose of this study is to requite mainly three factors among the many others that influence the choice of a suitable ERP. By using statistics in several investigation-filled samples, we can collect a database for many cases that can in return help us create a model that manages the choice of an ideal ERP for the company and reduces the costs of failure.


Enterprise resource planning ERP ERP implementation BP neural network 



This research was supported by the University ABDELMALEK ESSAIDI Faculty of Science, Tetouan Morocco supervised by Mrs. Noura Aknin and Atariuass Hicham.


  1. 1.
    ERP Selection: The SMART Way MoutazHaddaraWesterdals - Oslo School of Arts, Communication and Technology, 0185 Oslo, Norway. Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology (LTU), Porsön, SE-971 87 Luleå, SwedenGoogle Scholar
  2. 2.
    Zhang, P., Wang, S.: A study of neural networks in forecasting of logistics system. Wuhan University of Technology (2002)Google Scholar
  3. 3.
    Tavel, P.: Modeling and Simulation Design. AK Peters Ltd., Natick (2007)Google Scholar
  4. 4.
    Zhang, L., Wang, D., Chang, L.: A Model on Forecasting Safety Stock of ERP Based on BP Neural NetworkGoogle Scholar
  5. 5.
    Reuther, D., Chattopadhyay, G.: Critical Factors for Enterprise Resources Planning System Selection and Implementation Projects within Small to Medium Enterprises, Micreo Ltd. Queensland, Australia Mechanical, Manufacturing and Medical Engineering. Queensland University of Technology, Queensland, AustraliaGoogle Scholar
  6. 6.
    Lim, S.H., Nam, K.: Artificial Neural Network Modeling in Forecasting Successful Implementation of ERP SystemsGoogle Scholar
  7. 7.
    Razmi, J., Sangari, M.S.: A hybrid multi-criteria decision making model for ERP system selectionGoogle Scholar
  8. 8.
    Sun, H., Ni, W., Lam, R.: A step-by-step performance assessment and improvement method for ERP implementation: action case studies in Chinese companiesGoogle Scholar
  9. 9.
    Velcu, O.: Strategic alignment of ERP implementation stages: an empirical investigationGoogle Scholar
  10. 10.
    Chen, G., Sai, Y., Zhang, J.: ERP Implementation Based on Risk Management Theory: Empirical ValidationGoogle Scholar
  11. 11.
    Gupta, L., Wang, J., Charles, A., Kisatsky, P.: Prototype selection rules for neural network training. Pattern Recogn. 25–11, 1401–1408 (1992)CrossRefGoogle Scholar
  12. 12.
    Feng, W.: The Brief Introduction of Main Function Modules in ERP. Technol. Dev. Enterp. 5, 61–62 (2006)Google Scholar
  13. 13.
    Longo, F., Mirabelli, G.: An advanced supply chain management tool based on modeling & simulation. Comput. Ind. Eng.
  14. 14.
    Bansalk, K., Vadhavkar, S., Guptaa, A.: Neural networks based forecasting techniques for inventory control applications. Data Min. Knowl. Disc. 2(1), 97–102 (1998)CrossRefGoogle Scholar
  15. 15.
    Partovi, F.Y., Anandarajan, M.: Classifying inventory using an artificial neural network approach. Comput. Ind. Eng. 41, 389–440 (2002)CrossRefGoogle Scholar
  16. 16.
    He, Y., Li, F., Song, Z., Zhang, G.: Neural Networks Technology for Inventory ManagementGoogle Scholar
  17. 17.
    Zhang, P., Wang, S.: A study of neural networks in forecasting of logistics system. Wuhan University of Technology (2002)Google Scholar
  18. 18.
    Rumelhart, D.E., McClelland, J.D.: Parallel and Distributed Processing I, II. MIT Press, Cambridge (1986)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Amine Elyacoubi
    • 1
  • Hicham Attariuas
    • 2
  • Noura Aknin
    • 2
  1. 1.Science FacultyUniversity Abdelmalek EssaidiTangierMorocco
  2. 2.Science FacultyUniversity Abdelmalek EssaidiTétouanMorocco

Personalised recommendations