Complexity Assessment in Projects Using Small-World Networks for Risk Factor Reduction
Despite following standard practices of well-known project management methodologies, some projects fail to achieve expected results, incurring unexplained cost overruns or delays. These problems occur regardless of the type of project, the environment, or the project manager’s experience and are cha...
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MDPI AG
2024-12-01
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| author | Juan-Manuel Álvarez-Espada José Luis Fuentes-Bargues Alberto Sánchez-Lite Cristina González-Gaya |
| author_facet | Juan-Manuel Álvarez-Espada José Luis Fuentes-Bargues Alberto Sánchez-Lite Cristina González-Gaya |
| author_sort | Juan-Manuel Álvarez-Espada |
| collection | DOAJ |
| description | Despite following standard practices of well-known project management methodologies, some projects fail to achieve expected results, incurring unexplained cost overruns or delays. These problems occur regardless of the type of project, the environment, or the project manager’s experience and are characteristic of complex projects. Such projects require special control using a multidimensional network approach that includes contractual aspects, supply and resource considerations, and information exchange between stakeholders. By modelling project elements as nodes and their interrelations as links within a network, we can analyze how components evolve and influence each other, a phenomenon known as coevolution. This network analysis allows us to observe not only the evolution of individual nodes but also the impact of their interrelations on the overall dynamics of the project. Two metrics are proposed to address the inherent complexity of these projects: one to assess Structural Complexity (SC) and the other to measure Dynamic Complexity (DC). These metrics are based on Boonstra and Reezigt’s studies on the dimensions and domains of complex projects. These two metrics have been combined to create a Global Complexity Index (GCI) for measuring project complexity under uncertainty using fuzzy logic. These concepts are applied to a case of study, the construction of a wastewater treatment plant, a complex project due to the intense interrelations, the integration of new technologies that require R&D, and its location next to a natural park. The application of the GCI allows constant monitoring of dynamic complexity, thus providing a tool for risk anticipation and decision support. Also, the integration of fuzzy logic in the model facilitates the incorporation of imprecise or partially defined information. It makes it possible to deal efficiently with the dynamic variation of complexity parameters in the project, adapting to the inherent uncertainties of the environment. |
| format | Article |
| id | doaj-art-e6bcf74d70854c3e922190a25ab2ff6e |
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| issn | 2075-5309 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-e6bcf74d70854c3e922190a25ab2ff6e2025-08-20T02:00:29ZengMDPI AGBuildings2075-53092024-12-011412406510.3390/buildings14124065Complexity Assessment in Projects Using Small-World Networks for Risk Factor ReductionJuan-Manuel Álvarez-Espada0José Luis Fuentes-Bargues1Alberto Sánchez-Lite2Cristina González-Gaya3Department of Computer Science and Artificial Intelligence, Escuela Técnica Superior Ingeniería Informática, Universidad de Sevilla, Avda. de la Reina Mercedes s/n, 41012 Sevilla, SpainProject Management, Innovation and Sustainability Research Center (PRINS), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, SpainDepartment of Materials Science and Metallurgical Engineering, Graphic Expression in Engineering, Cartographic Engineering, Geodesy and Photogrammetry, Mechanical Engineering and Manufacturing Engineering, School of Industrial Engineering, Universidad de Valladolid, Paseo del Cauce, 59, 47011 Valladolid, SpainConstruction and Manufacturing Engineering Department, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 12, 28040 Madrid, SpainDespite following standard practices of well-known project management methodologies, some projects fail to achieve expected results, incurring unexplained cost overruns or delays. These problems occur regardless of the type of project, the environment, or the project manager’s experience and are characteristic of complex projects. Such projects require special control using a multidimensional network approach that includes contractual aspects, supply and resource considerations, and information exchange between stakeholders. By modelling project elements as nodes and their interrelations as links within a network, we can analyze how components evolve and influence each other, a phenomenon known as coevolution. This network analysis allows us to observe not only the evolution of individual nodes but also the impact of their interrelations on the overall dynamics of the project. Two metrics are proposed to address the inherent complexity of these projects: one to assess Structural Complexity (SC) and the other to measure Dynamic Complexity (DC). These metrics are based on Boonstra and Reezigt’s studies on the dimensions and domains of complex projects. These two metrics have been combined to create a Global Complexity Index (GCI) for measuring project complexity under uncertainty using fuzzy logic. These concepts are applied to a case of study, the construction of a wastewater treatment plant, a complex project due to the intense interrelations, the integration of new technologies that require R&D, and its location next to a natural park. The application of the GCI allows constant monitoring of dynamic complexity, thus providing a tool for risk anticipation and decision support. Also, the integration of fuzzy logic in the model facilitates the incorporation of imprecise or partially defined information. It makes it possible to deal efficiently with the dynamic variation of complexity parameters in the project, adapting to the inherent uncertainties of the environment.https://www.mdpi.com/2075-5309/14/12/4065risksproject managementcomplexityadaptabilitycomplex networkscoevolution |
| spellingShingle | Juan-Manuel Álvarez-Espada José Luis Fuentes-Bargues Alberto Sánchez-Lite Cristina González-Gaya Complexity Assessment in Projects Using Small-World Networks for Risk Factor Reduction Buildings risks project management complexity adaptability complex networks coevolution |
| title | Complexity Assessment in Projects Using Small-World Networks for Risk Factor Reduction |
| title_full | Complexity Assessment in Projects Using Small-World Networks for Risk Factor Reduction |
| title_fullStr | Complexity Assessment in Projects Using Small-World Networks for Risk Factor Reduction |
| title_full_unstemmed | Complexity Assessment in Projects Using Small-World Networks for Risk Factor Reduction |
| title_short | Complexity Assessment in Projects Using Small-World Networks for Risk Factor Reduction |
| title_sort | complexity assessment in projects using small world networks for risk factor reduction |
| topic | risks project management complexity adaptability complex networks coevolution |
| url | https://www.mdpi.com/2075-5309/14/12/4065 |
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