:::
Open Access Open Access  Restricted Access Subscription or Fee Access

An advanced tool for dynamic risk modeling and analysis in projects management

Afshin Jamshidi, Daoud Ait-Kadi, Angel Ruiz

Abstract


Risk is inherently present in all projects. Quite often, many projects fail to achieve their time, quality, and budget goals. Despite its high relevance to the success of megaprojects, risk management remains one of the least developed research issues. Therefore, advanced risk assessment is essential in minimizing losses and enhancing profitability. This paper proposes an advanced decision support tool using Fuzzy Cognitive Maps (FCMs) for dynamic risk assessment in project management. The proposed tool is able to predict the impact of each risk on the other risks or the outcomes of projects by considering uncertainties and complex interdependencies among risk factors. This tool could help project managers to manage the risks in a more effective and precise way and offer better risk mitigation solutions. The proposed tool could be undertaken by all organizations with the highest level of risk management maturity in the largest and most complex projects. In addition, it can be applied as an advanced decision support tool in variety of problems such as prioritization, failure analysis, etc. An academic numerical example related to outsourcing illustrates the applicability and simplicity of the proposed method.

Keywords


Project Management;Dynamic risk modeling;Risk assessment;Fuzzy cognitive maps;Prediction

References


Abdo, H., & Flaus, J.-M. (2016). Uncertainty quantification in dynamic system risk assessment: a new approach with randomness and fuzzy theory. International Journal of Production Research , 5862-5885.

Abdullah, L., & Jamal, N. (2011). Determination of Weights for Health Related Quality of Life Indicators among Kidney Patients: A Fuzzy Decision Making Method. Applied Research Quality Life, 349–361.

Ahmad, K., & Kumar, A. (2012). Forecasting Risk and Risk Consequences on ERP Maintenance. International Journal of Soft Computing and Engineering , 2(5), 13-18.

Aqlan, F., & S. Lam, S. (2015). Supply chain risk modelling and mitigation. Intrnation Journal of Production Research, 5640-5656.

Azadeh, A., Salehi, V., Arvan, M., & Dolatkhah, M. (2014). Assessment of resilience engineering factors in high-risk environments by fuzzy cognitive maps: A petrochemical plant. Safety Science, 99-107.

Baitheiemy, J. (2003). The seven deadly sins of outsourcing. Academy o/ Management Executive, 17(2).

Çelik, Y., & Yamak, S. (2013). Fuzzy soft set theory applied to medical diagnosis using fuzzy arithmetic operations. Journal of inequalities and applications, 1-9.

Dey, P. K. (2012). Project risk management using multiple criteria decision-making technique and decision tree analysis: A case study of indian oil refinery. Production Planning & Control, 903-921.

E.I. Papageorgiou, C. S. (2004). Active Hebbian learning algorithm to train fuzzy cognitive maps. International Journal of Approximate Reasoning, 37, 219-249.

Elpiniki I. Papageorgiou, K. E. (2005). Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization. Intelligent Information Systems, 25(1), 95-121.

Elpiniki I. Papageorgiou, K. E. (2005). Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization . Intelligent Information Systems, 25(1), 95-121.

Glykas, M. (2010). Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications. Springer.

Jamshidi, A., Abbasgholizadeh Rahimi, S., Ait-Kadi, D., & Ruiz, A. (2015). A new decision support tool for dynamic risks analysis in collaborative networks. In B. F. Camarinha-Matos L. (Ed.), Risks and Resilience of Collaborative Networks. IFIP Advances in Information and Communication Technology. 463, pp. 53-62. Albi, France: Springer.

Jamshidi, A., Abbasgholizadeh Rahimi, S., Ait-Kadi, D., & Ruiz, A. (2015). Dynamic risk modeling and assessing in maintenance outsourcing with FCM. the 6th IESM Conference- IEEE. Sevila.

Jamshidi, A., Abbasgholizadeh Rahimi, S., Ruiz, A., Ait-Kadi, D., & Rebaiaia, M. (2016). Application of FCM for advanced risk assessment of complex and dynamic systems. 8th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2016 (pp. 1910-1915). Troyes: IFAC-PapersOnLine.

Jimmy Gandhi, S., Gorod, A., & Sauser, B. (2012). Prioritization of outsourcing risks from a systemic perspective. Strategic Outsourcing, 5(1), 39-71.

Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man–Machine Studies, 24, 65–75.

Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man–Machine Studies, 24, 65–75.

Lopez, C., & Salmeron, J. (2012). Dynamic risks modelling in ERP maintenance projects with FCM. Information Sciences, 256, 25-45.

Lopez, C., & Salmeron, J. (2014). Dynamic risks modelling in ERP maintenance projects with FCM. Information Sciences, 256, 25–45.

Papageorgiou, E. I. (2014). Fuzzy Cognitive Maps for Applied Sciences and Engineering (From Fundamentals to Extensions and Learning Algorithms) . Springer.

Salmeron, J. (2010). Fuzzy Cognitive Maps-Based IT Projects Risks Scenarios. Studies in Fuzziness and Soft Computing , 247, 201-215.

Salmeron, J., & Lopez, C. (2012). Forecasting Risk Impact on ERP Maintenance with Augmented Fuzzy Cognitive Maps. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 439-452.

Siu, N. (1994). Risk assessment for dynamic systems: An overview. Reliability Engineering & System Safety, 43(1), 43–73.

Somayeh Alizadeh, M. G. (2009). Learning FCM by chaotic simulated annealing. Chaos, Solitons and Fractals, 41, 1182-1190.

Talon, A., & Curt, C. (2017). Selection of appropriate defuzzification methods: Application to the assessment of dam performance. Expert Systems With Applications, 160–174.

Welborn, C. (2007). Using FMEA to assess outsourcing risk. QUALITY PROGRESS, 40(8), 17-21.

Wu, D. &. (2008). Supply chain risk, simulation, and vendor selection. International Journal of Production Economics, 646-655.


Full Text: PDF

Refbacks

  • There are currently no refbacks.




______________________________________________________________________________

The Journal of Modern PM (ISSN: 2317-3963) | info@journalmodernpm.com