Evaluation of Project Management Methodologies Success Factors Using Fuzzy Cognitive Map Method: Waterfall, Agile, And Lean Six Sigma Cases

Mehtap Dursun; Nazli Goker

doi:10.18201/ijisae.2022.265

Authors

Mehtap Dursun Galatasaray University
Nazli Goker Galatasaray University

DOI:

https://doi.org/10.18201/ijisae.2022.265

Keywords:

Decision making, fuzzy cognitive maps, project management

Abstract

A methodology for project management refers to a set of guidelines that defines how to work and communicate while working as a project member. Waterfall practice is the old methodology. As a response to dealing with the difficulty of software development, it has turned out to the most widely utilized methodologies of project management in the software and management industries. Oher software development focused project management method, Agile, has appeared as a response to the shortcoming of Waterfall tool for handling complex projects. Lean Six Sigma is the combination of the main strategies of Six Sigma and Lean. This paper aims to reveal success factors of these three project management methodologies employing Fuzzy cognitive map (FCM) technique, which combines fuzzy logic and neural networks. Presence of cause-and-effect relationships between pair of success indicators and unavailability of crisp data led us to use FCM method in order to determine the most significant criteria of these project management methodologies. This is the first study that considers multiple and conflicting criteria of success factors of waterfall, agile, and lean six sigma project management methodologies. There is no study that aims to provide success criteria evaluation of waterfall, agile, and lean six sigma project management methodologies. This assessment is crucial for companies that have to be managed effectively their project processes in increasing technology and market competition. FCM is a suitable tool to solve this problem since it considers positive and negative relationships, causal links among criteria with their direction, and it is applicable in the absence of crisp data.

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References

[1] H. Lei, F. Ganjeizadeh, P. K. Jayachandran, and P. A. Ozcan, “Statistical analysis of the effects of Scrum and Kanban on software development projects,” Robot. Comput. Integr. Manuf., vol. 43, pp. 59-67, 2017.
[2] R. D. Snee, “Lean six sigma–getting better all the time,” Int. J. Lean Six Sigma., vol. 1, pp. 9-29, 2010.
[3] E. V. Gijo, and J. Antony, “Reducing patient waiting time in outpatient department using lean six sigma methodology,” Qual. Reliab. Eng. Int., vol. 30, no. 8, pp. 1481-1491, 2014.
[4] A. Cockburn, “Selecting a project's methodology,” IEEE softw., vol. 17, no. 4, pp. 64-71, 2000.
[5] A. Lova, and P. Tormos, “Analysis of scheduling schemes and heuristic rules performance in resource-constrained multiproject scheduling,” Ann. Oper. Res., vol. 102, no. 1-4, pp. 263-286, 2001.
[6] D. M. Raffo, “Software project management using PROMPT: A hybrid metrics, modeling and utility framework,” Inf. Softw. Technol., vol. 47, no. 15, pp. 1009-1017, 2005.
[7] L. A. Vidal, F. Marle, and J. C. Bocquet, “Using a Delphi process and the analytic hierarchy process (AHP) to evaluate the complexity of projects,” Expert Syst. Appl., vol. 38, no. 5, pp. 5388-5405, 2011.
[8] J. Varajão, and M. M. Cruz-Cunha, “Using AHP and the IPMA competence baseline in the project managers selection process,” Int. J. Prod. Res., vol. 51, no. 11, pp. 3342-3354, 2013.
[9] D. Petković, S. H. Ab Hamid, Z. Ćojbašić, and N. T. Pavlović, “Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect,” Nat. Hazards, vol. 74, no. 2, pp. 463-475, 2014.
[10] U. Asan, A. Soyer, and E. Bozdag, “An interval type-2 fuzzy prioritization approach to project risk assessment,” J. Mult.-Valued Log. Soft Comput., vol. 26, no. 6, pp. 541-577, 2016.
[11] R. Joslin, and R. Müller, “The impact of project methodologies on project success in different project environments,” Int. J. Manag. Proj., vol. 9, no. 2, pp. 364-388, 2016.
[12] M. García-Melón, and R. Poveda-Bautista, “Using the strategic relative alignment index for the selection of portfolio projects application to a public Venezuelan power corporation,” Int. J. Prod. Econ., vol. 170, pp. 54-66, 2015.
[13] B. H. Tabrizi, S. A. Torabi, and S. F. A. Ghaderi, “A novel project portfolio selection framework: An application of fuzzy DEMATEL and multi-choice goal programming,” Sci. Iran., vol. 3, no. 6, pp. 2945-2958, 2016.
[14] M. K. Ghorabaee, M. Amiri, J. S. Sadaghiani, and E. K. Zavadskas, “Multi-criteria project selection using an extended VIKOR method with interval type-2 fuzzy sets,” Int. J. Inf. Technol. Decis. Mak., vol. 14, no. 5, pp. 993-1016, 2015.
[15] P. Serrador, and J. K. Pinto, “Does Agile work?—A quantitative analysis of agile project success,” Int. J. Proj. Manag., vol. 33, no. 5, pp. 1040-1051, 2015.
[16] N. Prascevic, and Z. Prascevic, “Application of fuzzy AHP for ranking and selection of alternatives in construction project management,” J. Civ. Eng. Manag., vol. 23, no. 8, pp. 1123-1135, 2017.
[17] Y. S. Chen, C. Wu, H. H. Chu, C. K. Lin, and H. M. Chuang, “Analysis of performance measures in cloud-based ubiquitous SaaS CRM project systems,” J. Supercomput., vol. 74, no. 3, pp. 1132-1156, 2018.
[18] K. Chatterjee, E. Zavadskas, J. Tamošaitienė J, K. Adhikary, and S. Kar, “A hybrid MCDM technique for risk management in construction projects,” Symmetry, vol. 10, no. 2, Article no. 46, 2018.
[19] A. Petrillo, G. Di Bona, A. Forcina, and A. Silvestri, “Building excellence through the Agile Reengineering Performance Model (ARPM) A strategic business model for organizations,” Bus. Process Manag. J., vol. 24, no. 1, pp. 128-157, 2018.
[20] T. Yaghoobi, “Prioritizing key success factors of software projects using fuzzy AHP,” J. Softw. Evol. Process, vol. 30, no. 1, Article No. e1891, 2018.
[21] S. L. Song, S. Ang, F. Yang, and Q. Xia, “A stochastic multiattribute acceptability analysis-based method for the multiattribute project portfolio selection problem with rank-level information,” Expert Syst., vol. 36, pp. 1-13, 2019.
[22] L. Zheng, C. Baron, P. Esteban, R. Xue, Q. Zhang, and S. L. Yang, “Using leading indicators to improve project performance measurement,” J. Syst. Sci. Syst. Eng., vol. 28, pp. 529-554, 2019.
[23] A. Eshghi, S. M. Mousavi, and V. Mohagheghi V, “A new interval type-2 fuzzy approach for analyzing and monitoring the performance of megaprojects based on earned value analysi,” Neural. Comput. Appl., vol. 31, pp. 5109-5133, 2019.
[24] X. Y. Wu, W. Y. Zhao, T. S. Ma, and Z. Y. Yang, “Improving the efficiency of highway construction project management using lean management,” Sustainability, vol. 11, pp. 1-27, 2019.
[25] Y. S. Chen, H. M. Chuang, A. K. Sangaiah, C. K. Lin, and W. B. Huang, “A study for project risk management using an advanced MCDM-based DEMATEL-ANP approach,” J. Ambient Intell. Humaniz. Comput., vol. 10, pp. 2669-2681, 2019.
[26] R. Axelrod, “Structure of Decision,” Princeton University Press Princeton, 1976.
[27] T. J. Ross, “Fuzzy logic with engineering applications,” third edn., 2010.
[28] B. Kosko, “Fuzzy cognitive maps,” Int. J. Man-Mach., vol. 24, pp. 65-75, 1986.
[29] K. Y. Kwahk, and Y. G. Kim, “Supporting Business Process Redesign Using Cognitive Maps,” Decis. Support Syst., vol. 25, no. 2, pp. 155-178, 1999.
[30] G. A. Papakostas, D. E. Koulouriotis, and V. D. Tourassis, “Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems,” Expert Syst. Appl., vol. 39, pp. 10620-10629, 2008.
[31] E. I. Papageorgiou, A. Markinos, and T. Gemptos, “Application of Fuzzy Cognitive Maps for Cotton Yield Management in Precision Farming,” Expert Syst. Appl., vol. 36, pp. 12399-12413, 2009.
[32] O. Soner, U. Asan, and M. Celik, “Use of HFACS–FCM in fire prevention modelling on board ships,” Saf. Sci., vol. 77, pp. 25-41, 2015.
[33] N. Goker, and M. Dursun, “A fuzzy scenario-based approach to analyzing neuromarketing technology evaluation factors,” Soft Comput., vol. 23, pp. 12295–12304, 2019.
[34] A. Büyükavcu, Y. E. Albayrak, and N. Goker, “A fuzzy information-based approach for breast cancer risk factors assessment,” Appl. Soft Comput., vol. 38, pp. 437-452, 2016.