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.

The growing complexity and dynamism of industrial and organizational projects require management approaches that can effectively adapt to uncertainty and rapidly changing operational environments. In this context, this study proposes a methodology to identify the most suitable management approach—predictive, agile, or hybrid—in complex projects. Building on the “Approach suitability tool” of the Project Management Institute’s (PMI), the methodology integrates quantitative assessments of complexity and systemic risk. This is achieved through the analysis of stakeholder and risk networks, using metrics such as cyclomatic complexity and the coevolution parameter g, which allow for a deeper understanding of interactions and the evolution of project elements. The methodology was validated in three peacekeeping missions of the United Nations: UNMISS in South Sudan, MONUSCO in the Democratic Republic of Congo, and MINUSTAH in Haiti. The results confirm that the methodology accurately identifies the most appropriate management approach, emphasizing the effectiveness of hybrid approaches in complex and volatile environments. The proposed methodology serves as a valuable tool for optimizing project management in diverse contexts, enabling a quantitative and systematic evaluation of complexity and risk. It is adaptable and applicable to a wide range of complex projects, improving decision-making and planning in uncertain settings. Furthermore, by incorporating resilience as a cross-cutting principle, the methodology strengthens the ability of projects and their teams to maintain functionality and sustain learning even in highly volatile environments, where continuous adaptation becomes a critical success factor. In this sense, resilience emerges as the property that allows projects to absorb disruptions, reorganize, and preserve their core purpose without losing cohesion or direction.

The risk study, conducted by a project manager, especially in predictive approaches and depending on their experience, involves a thorough examination of risks in the tasks outlined in the Work Breakdown Structure (WBS). Risk assessment is based on correlating the likelihood of the risk occurring with the severity of its damage or benefit. However, in complex projects, project managers face broad, diverse, and often unknown risks. Technical aspects under development, involvement of different technologies, some of them innovative, multiple cultures, numerous stakeholders, etc., may fall outside the scope of a conventional risk assessment by the project manager. Probabilistic analysis has traditionally formed the basis for risk evaluation in conventional or complicated projects, which were the prevalent approaches until recently. Nevertheless, new approaches for managing complex projects require a different risk assessment: Bayesian networks or fuzzy cognitive maps are two ways to evaluate risks, but not the only ones.