Developing a Risk Management Model for Banking Software Development Projects Based on Fuzzy Inference System
محورهای موضوعی : Urban Planning
tooraj karimi
1
(Faculty of Management and Accounting,university of tehran college of farabi,iran ,ghom)
mohammadreza Fathi
2
(faculty of manmagment and accounting,university of tehran college of farabi,iran,ghom)
yalda yahyazade
3
(factualty of Managment and Accounting,university of tehran college of farabi,iran,ghom)
کلید واژه: Rough Set Theory, fuzzy logic, Project Risk Management, Fuzzy Inference System, Expert systems, Software Development,
چکیده مقاله :
Risk management is one of the most influential parts of project management that has a major impact on the success or failure of projects. Due to the increasing use of information technology (IT) systems in all fields and the high failure rate of IT projects in software development and production, it is essential to effectively manage these projects is essential. Therefore, this study is aimed to design a risk management model that seeks to manage the risk of software development projects based on the key criteria of project time, cost, quality and scope. This is presented after making an extensive review of the literature and asking questions from experts in the field. In this regard, after identifying the risks and defining them based on the dimensions and indicators of software development projects, 22 features were identified to evaluate banking software projects. The data were collected for three consecutive years in the country's largest software development eco-system. According to Rough modelling, the most important variables affecting the cost, time, quality and scope of projects were identified and the amount of risk that a project may have in each of these dimensions was shown. Since traditional scales cannot provide the accurate estimation of project risk assessment under uncertainty, the indexes were fuzzy. Finally, the fuzzy expert system was designed by MATLAB software that showed the total risk of each project. To create a graphical user interface, the MATLAB software GUIDE was used. The system can predict the risks of each project before each project begins and helps project managers be prepared to deal with these risks and consider ways to prevent the project from failing. The results showed that quality and time risks were more important than cost and scope risks and had a greater impact on total project deviation.
Addison, T. (2003). E-commerce project development risks: evidence from a Delphi survey. International Journal of Information Management, 23(1), 25-40.
Aqlan, F., & Lam, S. (2015). A fuzzy-based integrated framework for supply chain risk assessment. International Journal of Production Economics, 161, 54-63.
Arish, A. A. M., & Seyedesfahani, M. M. (2010). Model-based decision support in planning risk responses. International Journal of Industrial Engineering and Production Management, 20(3).
Arnuphaptrairong, T. (2011). Top ten lists of software project risks: Evidence from the literature survey. In Proceedings of the International Multi Conference of Engineers and Computer Scientists (Vol. 1, pp. 1-6).
Boehm, B. W. (1991). Software risk management: principles and practices. IEEE software, 8(1), 32-41.
Boehm, B. (1989). Software risk management. In European Software Engineering Conference (pp. 1-19). Springer, Berlin, Heidelberg.
Chapman, Ch., & Ward, S. (2011). How to manage project opportunity and risk: Why uncertainty management can be a much better approach than risk management. John Wiley & Sons.
Chen, W., Liu, K., Su, L., Liu, M., Hao, Zh., Hu, Y., & Zhang, X. (2014). Discovering many-to-one causality in software project risk analysis. In 2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (pp. 316-323). IEEE.
Chatterjee, S., Maji, B., & Pham, H. (2019). A fuzzy rule-based generation algorithm in interval type-2 fuzzy logic system for fault prediction in the early phase of software development. Journal of Experimental & Theoretical Artificial Intelligence, 31(3), 369-391.
Dokas, I. M., Karras, D., A., & Panagiotakopoulos, D. C. (2009). Fault tree analysis and fuzzy expert systems: Early warning and emergency response of landfill operations. Environmental Modelling & Software, 24(1), 8-25.
Faezy, F. (2015). A Grey-Based Fuzzy ELECTRE Model for Project Selection. Journal of Optimization in Industrial Engineering 17, 57-66.
Foong, K. Ch., Chee, Ch. T., & Wei, L. S. (2009). Adaptive network fuzzy inference system (ANFIS) handoff algorithm. In 2009 International Conference on Future Computer and Communication (pp. 195-198). IEEE.
Ford, F. N. 1985. Decision support systems and expert systems: a comparison. Information & Management, 8, 21-26.
Ghasemi, A., Golkar, M.J., & Eslami, M. (2015). A New Fuzzy Stabilizer Based on Online Learning Algorithm for Damping of Low-Frequency Oscillations. Journal of Optimization in Industrial Engineering 17, 1-10.
Han, W. M., & Huang, S. (2007). An empirical analysis of risk components and performance on software projects. Journal of Systems and Software, 80(1), 42-50.
Hadjimichael, M. (2009). A fuzzy expert system for aviation risk assessment. Expert Systems with Applications, 36(3), 6512-6519.
Jamshidi, A., Yazdani-Chamzini, A., Yakhchali, S. H., & Khaleghi, S. (2013). Developing a new fuzzy inference system for pipeline risk assessment. Journal of loss prevention in the process industries, 26(1), 197-208.
Lee, E., Park, Y., & Shin, J. G. (2009). Large engineering project risk management using a Bayesian belief network. Expert Systems with Applications, 36(3), 5880-5887.
Makui, A., Gholamian, M.R., Mohammadi, S.E. (2016). A Hybrid Intuitionistic Fuzzy Multi criteria Group Decision Making Approach for Supplier Selection. Journal of Optimization in Industrial Engineering 20, 61-73.
Mohagheghi, V., & Vahdani, B. (2017). An Assessment Method for Project Cash Flow under Interval-Valued Fuzzy Environment. Journal of Optimization in Industrial Engineering, 10(22), 73-80.
Mohammadipour, F., & Sadjadi, S. J. (2016). Project cost–quality–risk tradeoff analysis in a time-constrained problem. Comput. Ind. Eng. 95, 111–121.
Mousavi, S. M., Raissi, S., Vahdani, B., & MOJTAHEDI, SEYED MOHAMMAD HOSSEIN. (2011). A fuzzy decision-making methodology for risk response planning in large-scale projects. Journal of Optimization in Industrial Engineering, 4(7), 57-70.
Muriana, C., Vizzini, G. (2017). "Project risk management: A deterministic quantitative technique for assessment and mitigation" International Journal of Project Management 35 (2017) 320–340.
Nourian, R., Mousavi, S. M., and Raissi, S. (2019). A fuzzy expert system for mitigation of risks and effective control of gas pressure reduction stations with a real application. Journal of Loss Prevention in the Process Industries, 59, 77-90.
PMI. (2013). Project Management Body of Knowledge (PMBOK). 5thEd. Project Management Institute (PMI) Pub., USA.
Pimchangthong, D., & Boonjing, V. (2017). "Effects of Risk Management Practice on the Success of IT Project"Procedia Engineering 182 (2017) 579 – 586.
Paré, G., Sicotte, C. Jaana, M., & Girouard, D. (2008, January). Prioritizing clinical information system project risk factors: a delphi study. In Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008) (pp. 242-242). IEEE.
Pourjavad, E., & Shahin, A. (2018). The application of Mamdani fuzzy inference system in evaluating green supply chain management performance. International Journal of Fuzzy Systems, 20(3), 901-912.
Ropponen, J., & Lyytinen, K. (2000). Components of software development risk: How to address them? A project manager survey. IEEE transactions on software engineering, 26(2), 98-112.
Hossain, Md. S. & Mahmud, Sh. (2016). Fuzzy Multi-Objective Linear Programming for Project Management Decision under Uncertain Environment with AHP Based Weighted Average Method. Journal of Optimization in Industrial Engineering, 9(20), 53-60.
Song, H., & Jiang, J. (2016). Risks Identification in Embedded Software Development: Evidence from MVBC Project Survey. Procedia Computer Science, 91, 798-806.
Sonchan, P., & Ramingwong, S. (2014, May). Top twenty risks in software projects: A content analysis and Delphi study. In 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (pp. 1-6). IEEE.
Sumathi, S., & Paneerselvam, S. (2010). Computational intelligence paradigms: theory & applications using MATLAB. CRC Press.
Shrivastava, S. V., & Rathod, U. (2017). A risk management framework for distributed agile projects. Information and software technology, 85, 1-15.
Schmidt, R., Lyytinen, K., Keil, M., & Cule, P. (2001). Identifying software project risks: An international Delphi study. Journal of management information systems, 17(4), 5-36.
Wallace, L., & Keil, M. (2004). Software project risks and their effect on outcomes. Communications of the ACM, 47(4), 68-73.
Zhang, Y., & Fan, Z. P. (2014). An optimization method for selecting project risk response strategies. International Journal of Project Management, 32(3), 412-422.
