Propuesta de un modelo de Redes Neuronales Artificiales para la predicción del éxito o fracaso de proyectos basados en el enfoque PMBOK

Abstract

Globally, companies and organizations are increasingly implementing methodological tools or best practice standards for project management, such as PMBOK; however, it is important to recognize that, given the robustness of these approaches, a high level of expertise is required from project managers. In this context, the use of predictive tools can contribute to informed decision-making across the different stages of the project life cycle. Thus, this study proposes as its main contribution a novel model based on neural networks for predicting the success or failure of a project managed under PMBOK, using project features such as the resources involved, the project group according to PMBOK, and project indicators. For the development of this work, an adaptation of the CRISP-DM methodology into four phases was carried out. As a result, the model achieved consistent fitting over 100 training epochs, reaching an accuracy above 95% after 60–70 epochs, which suggests excellent fitting and generalization capabilities in predicting project outcomes. In conclusion, the model represents a key contribution to project management under PMBOK, with its differential factor being the inclusion of resources, the project group attribute, and the vector representation of qualitative project features identified within the indicator attribute.