A Multiple Adaptive Neuro-Fuzzy Inference System for Predicting ERP Implementation Success

Document Type: Research Paper


1 Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran

2 Faculty of Management, University of Tehran, Tehran, Iran


The implementation of modern ERP solutions has introduced tremendous opportunities as well as challenges into the realm of intensely competent businesses. The ERP implementation phase is a very costly and time-consuming process. The failure of the implementation may result in the entire business to fail or to become incompetent. This fact along with the complexity of data streams has led the researchers to develop a hierarchical multi-level predictive solution to automatically predict the implementation success of ERP solution. This study exploits the strength and precision of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting the implementation success of ERP solutions before the initiation of the implementation phase. This capability is obtained by training the ANFIS system with a data set containing a large number of ERP implementation efforts that have led to success, failure, or a mid-range implementation. In the initial section of the paper, a brief review of the recent ERP solutions as well as ANFIS architecture and validation procedure is provided. After that, the major factors of ERP implementation success are deeply studied and extracted from the previous literature. The major influential implementation factors in the businesses are titled as Change Orchestration (CO), Implementation Guide (IG), and Requirements Coverage (RC). The factors represent the major elements that guide the implementation project to a final success or to a possible failure if mismanaged. Accordingly, three initial ANFIS models are designed, trained, and validated for the factors. The models are developed by gathering data from 414 SMEs located in the Islamic Republic of Iran during a three-year period. Each model is capable of identifying the weaknesses and predicting the successful implementation of major factors. After this step, a final ANFIS model is developed that integrates the outputs of three initial ANFIS models into a final fuzzy inference system, which predicts the overall success of the ERP implementation project before the initiation phase. This model provides the opportunity of embedding the previous precious experiences into a unified system that can reduce the heavy burden of implementation failure.


Main Subjects

Article Title [Persian]

سیستم استنتاج فازی عصبی انطباقی چندگانه برای پیش بینی موفقیت پیاده سازی سیستم برنامه ریزی منابع سازمان

Authors [Persian]

  • ایمان رئیسی وانانی 1
  • بابک سهرابی 2
1 دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران، ایران
2 دانشکده مدیریت، دانشگاه تهران، تهران، ایران
Abstract [Persian]

پیاده سازی راهکارهای مدرن برنامه ریزی منابع سازمان فرصت ها و چالش های فوق العاده ای را در دنیای کسب و کارهای به شدت رقابتی فراهم آورده است. گام پیاده سازی سیستم برنامه ریزی منابع سازمان، فرآیندی هزینه بر و زمان بر است. شکست در پیاده سازی می تواند منجر به شکست تمام کسب و کار یا عدم رقابت پذیری آن شود. این حقیقت و پیچیدگی جریان های داده محققین را بر آن داشت تا یک راهکار چندگانه سلسله مراتبی به منظور پیش بینی خودکار موفقیت پیاده سازی راهکار برنامه ریزی منابع سازمان طراحی کنند. عوامل اساسی موثر بر پیاده سازی عبارتند از همراستاسازی تغییر، هدایت پیاده سازی و تامین نیازمندی ها. در همین راستا، سه مدل اولیه فازی عصبی طراحی، آموزش و اعتبارسنجی شدند. مدل ها به واسطه داده های گردآوری شده از 414 سازمان ایرانی در طی یک دوره سه ساله توسعه داده شدند. بعد از این گام، مدل نهایی توسعه یافت که موفقیت نهایی پیاده سازی را پیش بینی می نماید. این مدل تجارب حاصل از پروژه های پیشین را در یک سیستم آینده نگر یکپارچه می کند و قادر است تا بار سنگین شکست پیاده سازی راهکار برنامه ریزی منابع سازمان را کاهش دهد.

Keywords [Persian]

  • انفیس
  • برنامه ریزی منابع سازمان
  • موفقیت
  • پیاده سازی پایدار
  • پیش بینی
Akhgar, B., Rasouli, H., & Raeesi Vanani, I. (2012). Evaluation of knowledge-based competency in Iranian universities: A practical model. International Journal of Knowledge and Learning, 8(3-4), 282-297.

Amid, A., Moalagh, M., & Zare Ravasan, A. (2012). Identification and classification of ERP critical failure factors in Iranian industries. Information Systems, 37(3), 227–237.

Andersson, A., & Wilson, T. L. (2011). Contracted ERP projects Sequential progress, mutual learning, relationships, control and conflicts. International Journal of Managing Projects in Business, 4(3), 458-479

Ata, R., & Kocyigit, Y. (2010). An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines. Expert Systems with Applications, 37(7), 5454–5460.

Badawy, D. A. M. (2003). Managing IT as an Investment [Review of the book Managing IT as an Investment by K. Moskowitz &H. Kern]. Journal of Engineering and Technology Management, 20(4), 381–383.

Basoglu, N., Daim, T., & Kerimoglu, O. (2007). Organizational adoption of enterprise resource planning systems: A conceptual framework. Journal of High Technology Management Research, 18(1), 73–97.

Bernroider, E. W. N. (2008). IT governance for enterprise resource planning supported by the DeLone–McLean model of information systems success. Information and Management, 45(5), 257–269.

Blackwell, P. S., Esam, M., Kay John, M. (2006). An effective decision-support framework for implementing enterprise information systems within SMEs. International Journal of Production Research, 44(7), 3533–3552.

Boyacioglu, M. A., & Avci, D. (2010). An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange. Expert Systems with Applications 37(12), 7908–7912.

Bryson, J. (2017), Managing information services: A sustainable approach. Abingdon, UK: Routledge.

BuiaD. T.Pradhanc, B., Lofmana, O., Revhauga, I., & Dicka, O.B., (2012). Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Computers and Geosciences, 45[H1] [a2] , 199–211.

Cao, J., Calderon, T., Chandra, A., & Wang, L. (2010). Analyzing late SEC filings for differential impacts of IS and accounting issues. International Journal of Accounting Information Systems, 11(1), 189–207.

Chen, M. S. (2015). Neuro-fuzzy approach for online message scheduling. Engineering Applications of Artificial Intelligence, 38[H3] [a4] , 59–69

Chofreh, A. G., Goni, F. A., Shaharoun, A. M., Ismail, S. & Klemeš, J. J. (2014). Sustainable enterprise resource planning: Imperatives and research directions. Journal f Cleaner Production, 71[H5] [a6] , 139-147.

Chofreh, A. G., Goni, F. A., & Klemeš, J. J. (2017a). Development of a roadmap for Sustainable Enterprise Resource Planning systems implementation (Part II). Journal of Cleaner Production, 166[H7] [a8] , 425-437.

Chofreh, A. G., Goni, F. A., & Klemeš, J. J. (2017b). A roadmap for Sustainable Enterprise Resource Planning systems implementation (Part III). Journal of Cleaner Production, 174[H9] [a10] , 1325-1337.

Chofreh, A. G., Goni, F. A., & Klemeš, J. J. (2017c). Development of a Framework for the Implementation of Sustainable Enterprise Resource Planning. Chemical Engineering Transactions, 61[H11] [a12] , 1543-1548.

Costa, C. J., Ferreira, E., Bento, F., & Aparicio, M. (2016). Enterprise resource planning adoption and satisfaction determinants. Computers in Human Behavior, 63[H13] [a14] , 659-671

Delone, W., & McLean, E. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30.

Dokić, A. & Jović, S. (2017). Evaluation of agriculture and industry effect on economic health by ANFIS approach. Physica A, 479, 396–399

Doom, C., & Milis, K. (2009). CSFS of EPR implementations in Belgian SMES: A multiple case study. European and Mediterranean Conference on Information Systems (EMCIS2009), Crowne Plaza Hotel, Izmir.

Gholamzadeh Chofreh, A., Ariani Goni, F., & Klemeš, J. J., (2018). A roadmap for Sustainable Enterprise Resource Planning systems implementation (part III). Journal of cleaner Production, 174[H15] [a16] , 1325-1337

Gunther, L. C., Colangelo, E., Wiendahl, H. H., & Bauer, C. (2019). Data quality assessment for improved decision making: A methodology for small and medium-sized enterprises. Procedia Manufacturing, 29[H17] [a18] , 583-591.

Hakim, A., & Hakim, H. (2010). A practical model on controlling the ERP implementation risks. Information Systems, 35(2), 204–214.

Hicks, B. J., Culley, S. J., McMahon, C. A., & Powell, P. (2010) Understanding information systems infrastructure in engineering SMEs: A case study. Journal of Engineering and Technology Management, 27(1–2), 52–73.

Hosoz, M., Ertunc, H. M., & Bulgurcu, H. (2011). An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower. Expert Systems with Applications, 38(11), 14148–14155.[H19] [a20] 

Huang, C. L., & Dun, J. F. (2008). A distributed PSO-SVM hybrid system with feature selection and parameter optimization. Applied Soft Computing, 8(4), 1381–1391.

Hunton, J. E., McEwen, R. A., & Wier, B. (2002). The reaction of financial analysts to enterprise resource planning (ERP) implementation plans. Journal of Information Systems, 16(1), 31–40.

Jang, J. S. R., Sun, C. T., & Mizutani, E., (1997). Neuro-Fuzzy and Soft Computing. New York, USA: Prentice Hall.

Kazemifard, M., Zaeri, A., Ghasem-Aghaee, N., Nematbakhsh, M. A., & Mardukhi, F. (2011). Fuzzy Emotional COCOMO II Software Cost Estimation (FECSCE) using multi-agent systems. Applied Soft Computing, 11(2), 2260–2270.

Law, C. C. H., Chen, C. C., & Wuc, B. J. P. (2010). Managing the full ERP life-cycle: Considerations of maintenance and support requirements and IT governance practice as integral elements of the formula for successful ERP adoption. Computers in Industry, 61(3), 297–308.

Law, M. M. S., Hills, P., & Hau, B. C. H. (2017). Engaging employees in sustainable development – a case study of environmental education and awareness training in Hong Kong. Bus Strat Environ, 26(1), 84-97.

Liao, S. H., & Wen, C. H. (2007). Artificial neural networks classification and clustering of methodologies and applications – literature analysis from 1995 to 2005. Expert Systems with Applications, 32(1), 1–11.

Lin, C. T.,  Chen, C.  B., & Ting, Y. C. (2011). An ERP model for supplier selection in electronics industry. Expert Systems with Applications, 38(3), 1760–1765.

Mamdani E. H., & Assilian S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13

Mandal, P., & Gunasekaran, A. (2002). Application of SAP R/3 in on-line inventory control. International Journal of Production Economics, 75(1–2), 47–55.

Momoh, A., Roy, R., & Shehab, E. (2010). Challenges in enterprise resource planning implementation: State-of-the-art. Business Process Management Journal, 16(4), 537-565.

Moohebat, M. R., Asemi, A., & Jazi, M. D. (2010). A comparative study of Critical Success Factors (CSFs) in implementation of ERP in developed and developing countries. International Journal of Advancements in Computing Technology, 2(5), 99-110.

Moosa, I., & Ramiah, V.. (2018), Environmental regulation, financial regulation and sustainability. In  S. Boubaker, D. Cumming,  & D. K. Nguyen (Eds.), Research handbook of finance and sustainability (pp. 372-385). Cheltenham, UK: Edward Elgar Publishing.

Nah, F., & Delgado, S. (2006). Critical success factors for enterprise resource planning implementation and upgrade. Journal of Computer Information Systems, 46(5), 99–113.

Nicolaou, A. I. (2004). Quality of post implementation review for enterprise resource planning systems. International Journal of Accounting Information Systems, 5(1), 25– 49.

Nikookar, G., Safavi, S.Y., Hakim, A., & Homayoun, A. (2010). Competitive advantage of enterprise resource planning vendors in Iran. Information Systems,  35(3), 271–277.

Nunnally, J. (1978). Psychometric Theory. New York, NY: McGraw-Hill.

Oliveira, A. L. I., Braga, P. L., Lima, R. M. F., & Cornélio, M. L. (2010). GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation. Information and Software Technology, 52(11), 1155–1166.

Poon, P. L., & Yu, Y. T. (2010). Investigating ERP systems procurement practice: Hong Kong and Australian experiences. Information and Software Technology, 52(10), 1011–1022.

Poston, R., & Grabski, S. (2001). Financial impacts of enterprise resource planning implementations. International Journal of Accounting Information Systems, 2(4), 271–294.

Raeesi Vanani, I., & Jalali, S. M. J. (2017). Analytical evaluation of emerging scientific trends in business intelligence through the utilisation of burst detection algorithm. International Journal of Bibliometrics in Business and Management, 1(1), 70-79.

Raeesi Vanani, I., & Jalali, S. M. J. (2018). A comparative analysis of emerging scientific themes in business analytics. International Journal of Business Information Systems, 29(2), 183-206.

Raeesi, I., & Sohrabi, B.. (2011). Collaborative planning of ERP implementation: A design science approach. International Journal of Enterprise Information Systems, 7(3), 58-67.

Rose, J., & Kræmmergaard, P. (2006). ERP systems and technological discourse shift: Managing the implementation journey. International Journal of Accounting Information Systems, 7(3), 217–237.

Salmeron, J. L., & Lopez, C.  (2010). A multicriteria approach for risks assessment in ERP maintenance. The Journal of Systems and Software, 83(10), 1941–1953.

Sankar, C. S., & Rau, K. H. (2006). Implementation strategies for SAP R/3 in a multinational organization: Lessons from a real-world case study. United Kingdom: Cybertech Publishing.

Sawah, S. E., Tharwat, A. A. E. F., & Rasmy, M. H. (2008). A quantitative model to predict the Egyptian ERP implementation success index. Business Process Management Journal, 14(3), 288-306.

Sen, C. G., & Baraclı, H. (2010). Fuzzy quality function deployment based methodology for acquiring enterprise software selection requirements. Expert Systems with Applications, 37(4), 3415–3426[H21] [a22] .

Sen, C. G., Baraçlı, H., Sen, S., & Basligil, H. (2009). An integrated decision support system dealing with qualitative and quantitative objectives for enterprise software election. Expert Systems with Applications, 36(2) 5272–5283[H23] [a24] .

Shen, Y. C., Chen, P. S., & Wang, C. H. (2016). A study of enterprise resource planning (ERP) system performance measurement using the quantitative balanced scorecard approach. Computers in Industry, 75[H25] [a26] , 127–139

Sheu, A., Chae, B., & Yang, C. L. (2004). National differences and ERP implementation: Issues and challenges. Omega, 32(5), 361–371.

Siminski, K. (2017). Interval type-2 neuro-fuzzy system with implication-based inference mechanism. Expert Systems with Applications, 79[H27] [a28] , 140–152

Sohrabi, B., & Jafarzadeh, M. H. (2010). A method for measuring the alignment of ERP systems with enterprise requirements: Application of requirement modeling. Int. J. Management and Enterprise Development, 9(2), 158-178.

Sohrabi, B., Raeesi Vanani, I., & Baranizadeh Shineh, M. (2017). Designing a predictive analytics solution for evaluating the scientific trends in information systems domain. Webology, 14(1), 32-52.

Sohrabi, B., Raeesi Vanani, I., & Mahmoudian, P.. (2012a). A framework for improving e-commerce websites usability using a hybrid genetic algorithm and neural network system. Neural Computing and Applications 21(5), 1017-1029.

Sohrabi, B., Raeesi Vanani, I.,  Qorbani, D., & Forte, P. (2012b). An integrative view of knowledge sharing impact on e-learning quality: A model for higher education institutes. International Journal of Enterprise Information Systems, 8(2), 14-29.

Sohrabi, B., Raeesi Vanani, I., Gooyavar, A., & Naderi, N. (2019). Predicting the readmission of heart failure patients through data analytics. Journal of Information & Knowledge Management, 18(01), 1950012-1, 1950012-20[H29] [a30] 

Sohrabi, B., Raeesi Vanani, I., & Abedin, B. (2018). Human resources management and information systems trend analysis using text clustering. International Journal of Human Capital and Information Technology Professionals, 9(3), 1-24

Soja, P. (2008). Examining the conditions of ERP implementations: Lessons learnt from adopters. Business Process Management Journal, 14(1), 105–121.

Sommer, R. A. (2009). A planning solution for virtual business relationships. Industrial Management and Data Systems, 109(4), 463-476.

Subramanianh, G., & Hoffers, C. (2005). An exploratory case study of enterprise resource planning implementation. International Journal of Enterprise Information Systems, 1(1), 23–38.

Svalina, I., Simunovi´c, G., Sari´c, T., & Luji´c, R. (2017). Evolutionary neuro-fuzzy system for surface roughness evaluation. Applied Soft Computing, 52[H31] [a32] , 593–604

Tahmasebi, P., & Hezarkhani, A. (2010). Application of adaptive neuro-fuzzy inference system for grade estimation; Case Study, Sarcheshmeh Porphyry Copper Deposit, Kerman, Iran. Australian Journal of Basic and Applied Sciences, 4(3), 408-420

Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 116–132.

Tana, Y., Shuaia, C., Jiaoc, L., & Shenb, L. (2017). An adaptive neuro-fuzzy inference system (ANFIS) approach for measuring country sustainability performance. Environmental Impact Assessment Review, 65[H33] [a34] , 29–40

Tsai, W-H. (2019). Enterprise Resource Planning (ERP) and sustainability. Special issue of the Sustainability, 1-2[H35] [a36]  (ISSN 2071-1050).

Ubeyli, E. D., Cvetkovic, D., Holland, G., & Cosic, I. (2010). Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes. Digital Signal Processing, 20(3), 678–691

Van Zanten, J. A., & Van Tulder, R. (2018). Multinational enterprises and the sustainable development goals: An institutional approach to corporate engagement. Journal of International Business Policy, 1(3-4), 208-233.

Venugopal, C., Devi, S. P., & Rao, K. S. (2010). Predicting ERP user satisfaction ― an Adaptive Neuro Fuzzy Inference System (ANFIS) Approach. Intelligent Information Management, 2(7), 422-430

Wagner, E. L.,  Moll, J., & Newell, S. (2011). Accounting logics, reconfiguration of ERP systems and the emergence of new accounting practices: A socio-material perspective. Management Accounting Research, 22(2), 181-197.

Wan, Y., & Si, Y.W. (2017). Adaptive neuro fuzzy inference system for chart pattern matching in financial time series. Applied Soft Computing, 57[H37] [a38] , 1–18

Wang, E. T.G., Lina, C. C. L., Jiang, J. J., & Klein, G. (2007). Improving enterprise resource planning (ERP) fit to organizational process through knowledge transfer. International Journal of Information Management, 27(3), 200–212.

Wei, C. C., & Wang, M. J. J. (2004). A comprehensive framework for selecting an ERP system. International Journal of Project Management, 22(2), 161–169.

Wu, J., & Wang, Y. (2007). Measuring ERP success: The key-users’ viewpoint of the ERP to produce a viable IS in the organization. Computers in Human Behavior, 23(3), 1582–1596[H39] [a40] .

Wu, J. H., Shin, S. S., & Heng, M. S. H. (2007). A methodology for ERP misfit analysis. Information and Management, 44(8), 666–680.

Wu, L. C., Ong, C. S., & Hsu, Y. W. (2008). Active ERP implementation management: A Real Options perspective. The Journal of Systems and Software, 81(6), 1039–1050[H41] [a42] .

Wu, W. W. (2011). Segmenting and mining the ERP users’ perceived benefits using the rough set approach. Expert Systems with Applications, 38(6), 6940–[H43] [a44] 6948.

Yan, H., Zou, Z., & Wang, H. (2010). adaptive neuro fuzzy inference system for classification of water quality status. Journal of Environmental Sciences, 22(12), 1891-1896[H45] [a46] 

You, C. J., Lee, C. K. M., Chen, S. L., & Jiao, R. J. (2012). A real option theoretic fuzzy evaluation model for enterprise resource planning investment. Journal of Engineering and Technology Management, 29(1), 47–61.

Yusuf, Y., Gunasekaran, A., & Abthorpe, M. (2004). Enterprise information systems project implementation: A case study of ERP in Rolls-Royce. International Journal of Production Economics, 87(3), 251–266.

 [H1]Issue number?

 [a2]It is correct. The journal has no issue number

 [H3]Issue number?

 [a4] [a4]It is correct. The journal has no issue number

 [H5]Issue number?

 [a6] [a6] [a6]It is correct. The journal has no issue number

 [H7]Issue number?

 [a8] [a8] [a8] [a8]It is correct. The journal has no issue number

 [H9]Issue number?

 [a10] [a10] [a10] [a10] [a10]It is correct. The journal has no issue number

 [H11]Issue number?

 [a12] [a12] [a12] [a12] [a12]It is correct. The journal has no issue number

 [H13]Issue number?

 [a14] [a14] [a14] [a14] [a14] [a14]It is correct. The journal has no issue number

 [H15]Issue number?

 [a16] [a16] [a16] [a16] [a16] [a16] [a16]It is correct. The journal has no issue number

 [H17]Issue number?

 [a18] [a18] [a18] [a18] [a18] [a18] [a18] [a18]It is correct. The journal has no issue number

 [H19]Page numbers are too large. Are they true?

 [a20]The page numbers are correct

 [H21]Page numbers too large. Please recheck.

 [a22] [a22]The page numbers are correct

 [H23]Page numbers too large. Please recheck.

 [a24] [a24]The page numbers are correct


 [H25]Issue number?

 [a26]It is correct. There is no issue number

 [H27] [H27]Issue number?

 [a28] [a28]It is correct. There is no issue number


 [H29]Please mention the page numbers correctly

 [a30]Page numbers are correct. They are provided in the same way in the website and also the original PDF file

 [H31] [H31]Issue number?


 [a32] [a32] [a32]It is correct. There is no issue number


 [H33] [H33]Issue number?

 [a34] [a34] [a34] [a34]It is correct. There is no issue number


 [H35]Journal name, vol(isse), pp.

 [a36]It is the only information provided for the special issue on the website since it is separated from normal issues

 [H37] [H37]Issue number?

 [a38] [a38] [a38] [a38] [a38]It is correct. There is no issue number


 [H39] [H39]Page numbers are too large. Please recheck

 [a40]They are correct

 [H41] [H41]Page numbers are too large. Please recheck

 [a42]They are correct

 [H43]Page numbers are too large. Please recheck

 [a44]They are correct

 [H45]Page numbers are too large. Please recheck

 [a46] [a46]They are correct