Designing an Adaptive Nuero-Fuzzy Inference System for Evaluating the Business Intelligence System Implementation in Software Industry

Document Type : Research Paper

Authors

1 Assistant Prof., Industrial Management, Allameh Tabataba’i University, Management and Accounting College ,Tehran, Iran

2 MSc., Information Technology Management, University of Tehran, Management College, Tehran, Iran

Abstract

The main goal of research is designing an adaptive nuero-fuzzy inference system for evaluating the implementation of business intelligence systems in software industry. Iranian software development organizations have been facing a lot of problems in case of implementing business intelligence systems. This system would be helpful in recognizing the conditions and prerequisites of success or failure. Organizations can recalculate the neuro-fuzzy system outputs with some considerations on various inputs to figure out which inputs have the most effect on the implementation outputs. By resolving the problems on inputs, organizations can achieve a better level of implementation success. The designed system has been trained by a data set and afterwards, it has been evaluated. The trained system has reached the error value of 0.08. Eventually, some recommendations have been provided for software development firms on the areas that might need more considerations and improvements.

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References

Aghajani, H., Samadi Miarkolaei, H., Khanzadeh, M. & Samadi Miarkolaei, H. (2013). Feasibility Study of ERP System Implementation (Case Study: National Oil Product Distribution Company Sary District). Journal of Information Technology Management, 6(2): 161-186. (in Persian)
Arnott, D. (2008). Success factors for data warehouse and business intelligence systems. ACIS 2008 Proceedings, 19th Australasian Conference on Information Systems, 3-5 Dec 2008, Christchurch.
Bosilj Vukšić, V., Indihar Štemberger, M. & Kovačič, A. (2008). Business process management and business intelligence as performance measurement drivers. 10 (1): 338-343.
Boyer, J., Frank, B., Green, B., Harris, T., & Van De Vanter, K. (2010). Business Intelligence Strategy: A Practical Guide for Achieving BI Excellence. MC Press, USA.
Chee, T., Chan, L. K., Chuah, M. H., Tan, C. S., Wong, S. F. & Yeoh, W. (2009). Business intelligence systems: state-of-the-art review and contemporary applications. In Symposium on Progress in Information & Communication Technology, 2 (4): 16-30.
Fotache, M. V. & Fotache, G. (2012). The Economic Recovery of the SME’s by Implementing BI Technologies. Economy Transdisciplinarity Cognition, 15 (1): 273-278.
Guillaume, S. (2001). Designing fuzzy inference systems from data: an interpretability- oriented review. Fuzzy Systems, IEEE Transactions on, 9(3): 426-443.
Hribar Rajterič, I. (2010). Overview of business intelligence maturity models. Management: Journal of Contemporary Management Issues, 15(1): 47-67.
Khanlari, A. & Kafaei, O. (2013). Surveying Effect of Structural Demotions on ERP System Success in Iranian Organization using this System, Journal of Information Technology Management, 6(1): 47-70. (in Persian)
Kia, Seyed Mostafa. (2010). Fuzzy Logic in MATLAB, Tehran: Kian Rayaneh Sabz publication. (in Persian)
Paswan, A. (2010). Business intelligence success: an empirical evaluation of the role of BI capabilities and the decision environment. Doctoral dissertation, University of North Texas.
Petrini, M. & Pozzebon, M. (2009). Managing sustainability with the support of business intelligence: Integrating socio-environmental indicators and organisational context. The Journal of Strategic Information Systems, 18(4): 178-191.
Popovič, A., Hackney, R., Coelho, P. S. & Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54(1): 729-739.
Presthus, W., Ghinea, G. & Utvik, K. R. (2012). The More, the Merrier: The Interaction of Critical Success Factors in Business Intelligence Implementations. International Journal of Business Intelligence Research (IJBIR), 3(2): 34-48.
Ranjan, J. (2008). Business justification with business intelligence. Vine, 38(4): 461-475.
Ranjan, J. (2009). Business intelligence: concepts, components, techniques and benefits. Journal of Theoretical and Applied Information Technology, 9(1): 60-70.
Rouhani, S., Ghazanfari, M. & Jafari, M. (2012). Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS. Expert Systems with Applications, 39(3): 3764-3771.
Shafia, M., Manian, A., Raeesi Vanani, I. (2013). Designing Fuzzy Inference System for Predicting Success of ERP Solution, Journal of Information Technology Management, 5(1): 89-106. (in Persian)
Tajfar, A.H., Mahmoodi Meimand, M., Rezasoltani, F. & Rezasoltani, P. (2014). Scoring ISMS Implementation Obstacles & Surveying Exploration Management Readiness, Journal of Information Technology Management, 6(4): 551-566. (in Persian)
Bosilj-Vuksic, V., Indihar-Stemberger, M.. (2009). Business Process Management and Business Intelligence as Performance Measurement Drivers, University of Zagreb, Croatia.
Vieira, J., Dias, F. M. & Mota, A. (2004). Neuro-fuzzy systems: A survey. In 5th WSEAS NNA International Conference on Neural Networks and Applications, Udine, Italia.
Vodapalli, N. K. (2009). Critical success factors of BI implementation. IT University of Copenhagen. Retrieved from: http://www.itu.dk/~navvod/ CSFs OfBIimpl.pdf.
Yan, S. L., Wang, Y. & Liu, J. C. (2012). Research on the comprehensive evaluation of business intelligence system based on BP neural network. Systems Engineering Procedia, 4: 275-281.
Yeoh, W., Koronios, A., Gao, J. (2007). Critical Success Factors for Implementation of Business Intelligence Systems: A study of Engineering Asset Management Organizations, University of South Australia, Mawson Lakes, 5095 Australia.
Yeoh, W. & Koronios, A. (2010). Critical success factors for business intelligence systems. Journal of computer information systems, 50(3): 23-32.
Yeoh, W., Koronios, A. & Gao, J. (2008). Managing the implementation of business intelligence systems: a critical success factors framework. International Journal of Enterprise Information Systems (IJEIS), 4(3): 79-94.
 

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