Risk management in predictive projects with dichotomous variables: Consulting Project Case

Main Article Content

Juan Sebastián Dugarte Mendoza
Fabio Augusto Niño Liévano
Cesar Augusto Silva Giraldo
Yohanna Milena Rueda Mahecha
David Andrés Suarez Suarez
Tatiana Guadrón Porras
Erika Patricia Ramírez Oliveros
José Alonso Caballero Márquez
Claudia Consuelo Pinzón Velasco
Diana Alexandra Rodríguez Quiñónez
Nelson Javier Hernández Bueno

Keywords

Risk Management, Project Management, Dichotomous Variables, S Curve

Abstract

The article presents the application of dichotomous variables as a tool for risk management in predictive projects and proposes using the Integrated S Curve as a graphical representation that includes the variables cost, time, scope and quality. The methodology uses risk management and applying the “What happens if?” technique from where work scenarios in the project are. The paper exposes a case related to a strategic consulting project for a company in the metalmechanic sector in Colombia; from the case, it is possible to identify that the Integrated S Curve presents a better adjustment to the final result of the project compared to the S Curve generated at the time of planning.

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References

Alonso, P., Linares, J. P., & Palomo, J. (2019). Bayesian networks in project management: A survey of the state of the art. IEEE Access, 7, 96156-96176.

Badruzzaman, F. H., Fajar, M. Y., Rohaeni, O., Gunawan, G., & Harahap, E. (2020). CPM and PERT technique efficiency model for child veil production.

Chin, C., Spowage, A., & Yap, E. (2012). Project management methodologies: a comparative analysis. Journal for the advancement of performance information and value, 4(1), 106-106.

Cicmil, S., Williams, T., Thomas, J., & Hodgson, D. (2019). Rethinking project management methodologies: towards creative and contextually appropriate approaches. International Journal of Project Management, 37(1), 120-130. https://doi.org/10.1016/j. ijproman.2018.11.009

Ghosh, S. K., & Piplani, R. (2014). Project risk management using the project risk FMEA. International Journal of Engineering Research and Applications, 4(1), 197-203. https:// www.dropbox.com/s/sbqarx7aa6fbxym/ Project%20Risk%20Management%20 Using%20the%20Project%20Risk%20 FMEA.pdf?dl=0

Project Management Institute. (2017). A guide to the project management body of knowledge (PMBOK guide) (6th ed.). Project Management Institute.

Serrador, P., & Pinto, J. K. (2015). Does agile work? - A quantitative analysis of project success. International Journal of Project Management, 33(5), 1040-1051. https://doi.org/10.1016/j. ijproman.2015.02.001

Sharma, V., Soni, G., & Bansal, P. (2020). Earned green value management for project management. Journal of Cleaner Production, 267, 122104.

Shehabuddeen, N. (2019). View of Analysis of the Available Project Management Methodologies. Journal of Information Technology and Economic Development, 10(1), 68-81. https://doi. org/10.35632/ITED.2019.10.1.68-81

Stuckenbruck, L. C. (2018). PM methodologies: a practical comparison. Journal of Modern Project Management, 5(1), 87-97. https://doi.org/10.19255/ JMPM01905

Wang, M., Cui, Q., Du, X., & He, M. (2021). A classification and review of approaches and methods for modeling project risk. Engineering, Construction and Architectural Management, aheadof-print(ahead-of-print).

Yisa, S. B., & Oyedele, L. O. (2018). A review of risk management in construction: Opportunities for improvement. Journal of Construction Engineering and Management, 144(11), 04018104. https://doi.org/10.1061/ (ASCE)CO.1943-7862.0001559