ADVANCED STRATEGIES FOR RISK ASSESSMENT IN PROJECT MANAGEMENT APPLIED IN THE FIELD OF NONCONVENTIONAL TECHNOLOGIES

Abstract

In the rapidly evolving landscape of nonconventional technologies, effective project management depends on risk assessment strategies that can address high uncertainty, complex interdependencies, and limited historical data. Traditional approaches often underperform in innovative environments such as renewable energy systems, where technical novelty and operational variability increase the likelihood of schedule, cost, and performance deviations. This paper presents an applied perspective on advanced risk assessment in project management through a case study involving the integration of Internet of Things (IoT) sensors in a wind turbine and the deployment of software that combines IoT data streams with artificial intelligence (AI) to enable preventative maintenance. The approach integrates structured qualitative techniques (expert elicitation, risk workshops, and risk registers) with quantitative and data-driven methods, including scenario analysis, probabilistic reasoning, and continuous monitoring using machine learning-based anomaly detection. The case study illustrates how real-time risk intelligence supports earlier identification of degradation patterns, improved prioritization of maintenance actions, and more adaptive response planning across the project lifecycle. As a result, the analyzed implementation demonstrates improved operational quality through higher asset availability and more predictable service delivery, while also contributing to increased customer satisfaction through enhanced transparency and reliability. The findings support the conclusion that embedding advanced, data-driven risk assessment methodologies into project governance strengthens resilience and improves outcomes in nonconventional technology projects.