RISK-ORIENTED MANAGEMENT OF REGIONAL REGENERATION PROJECTS IN UKRAINE

Abstract

The article examines risk-based project management in the field of restoration of regions that have suffered systemic destruction as a result of military actions, economic instability, or institutional collapse. The importance of managerial forecasting in the context of post-crisis restoration of territories, where high uncertainty requires risk prediction, is studied in detail. The features of integrating risk management into all stages of the project cycle are revealed, with an emphasis on the complexity of accounting for uncertainty in the context of post-crisis entropy. In the post-crisis period, when regions are in a state of deep economic, social and institutional instability, traditional project management models are failing to ensure the integrity, coherence and sustainability of recovery initiatives. The lack of a proper view of the risks associated with such projects leads to schedule delays, budget overruns, and reputational damage. The need to implement risk-based approaches is not only a managerial expedient, but also a prerequisite for the effectiveness of public investment in an environment of uncertainty. The authors propose an approach to scenario modeling using digital technologies and dynamic decision trees, which allows taking into account different trajectories of events. The need to combine quantitative analysis, behavioral models, and a normative approach is substantiated. Analytical approaches to forecasting risks and the formation of strategies for their minimization are proposed. In today's environment, the priority is to correctly identify priorities and search for approaches, levers, methods and tools that would ensure maximum efficiency in managing the development of territories and ensure proper coordination of all government institutions, taking into account the challenges of today. The timeliness of management decision-making should be based on scientific evidence and well-defined priorities. An example of classifying regions by the level of infrastructure development, which is key for planning smart technologies, is also considered.