Risk management for optimizing management decision-making in the context of public-private partnership
DOI:
https://doi.org/10.32347/2707-501x.2023.52(2).110-121Keywords:
risk management, optimization, management decision-making, public-private partnership, construction enterprise, private sector, public sectorAbstract
The article reflects that in modern business conditions, public-private partnership (PPP) is gaining increasing popularity in the implementation of large-scale infrastructure projects. This cooperation format offers significant advantages for both public and private sectors but also carries certain risks. PPP risks can be divided into two main groups: project-related risks and partner-related risks. Types of risks include technical risks, financial risks, regulatory risks, political risks, and contractual risks. Partner-related risks include reputation risks and behavioral risks.
To optimize management decision-making process in the context of PPP, it is important to use methods of quantitative and qualitative risk analysis, as well as the principles of rational decision-making. It is necessary to consider the interests of all stakeholders since the success of PPP depends on effective cooperation and mutual understanding between the public and private sectors. Applying risk-informed management decision optimization methods allows minimizing negative consequences and maximizing positive project results. The authors of the article explore theoretical and practical aspects of risk management in public-private partnership (PPP) to optimize management decision-making. The authors propose an integrated PPP risk management model that considers both quantitative and qualitative factors. The method of quantitative risk assessment is used, which allows determining the probability and impact of risks on the project. A method of resilience analysis to risks is proposed, which allows determining how resilient the project is to various types of risks. Recommendations for PPP risk management are provided, aimed at optimizing management decision-making.
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