Intelligent Risk Management and Decision-Making in Complex Offshore Engineering Projects (Published)
The offshore engineering industry stands at the confluence of extreme environmental conditions, intricate supply chains, and high capital intensity. Such projects—spanning from subsea infrastructure development to deep-water production systems—are inherently exposed to multifaceted uncertainties across technical, financial, environmental, and human domains. Traditional risk management frameworks, though methodologically sound, have proven insufficient in coping with the dynamic, data-rich, and uncertainty-dominated nature of modern offshore projects. The central research problem addressed in this study is the persistent limitation of deterministic and semi-quantitative risk management approaches in managing complex, uncertain, and interdependent risks throughout the offshore project lifecycle. This paper proposes an integrated decision-support model that leverages intelligent risk analysis, probabilistic simulation, and organizational learning principles to enhance decision-making under uncertainty in offshore project planning and execution. The primary objective of this research is to develop and validate a hybrid intelligent risk management model—referred to herein as the Intelligent Offshore Decision-Support Model (IODSM)—that integrates artificial intelligence (AI)-assisted analytics, probabilistic risk quantification (using Monte Carlo simulation), and qualitative reasoning (through fuzzy logic) within a unified decision-support framework. The model is designed to operate across the Front-End Engineering Design (FEED) and Engineering, Procurement, Construction, Installation, and Commissioning (EPCIC) phases, enabling continuous risk learning and adaptive decision optimization. The novelty of the IODSM lies in its multi-layered integration: combining data-driven insights from historical and real-time project data with probabilistic forecasting and human-centered learning systems to achieve both predictive precision and organizational adaptability. The methodology adopted in this study synthesizes three core components. The first component is data-driven risk identification and mitigation, enabled through advanced analytics and AI techniques. This includes the use of supervised learning algorithms for pattern recognition in failure and performance datasets, unsupervised clustering for anomaly detection in cost and schedule deviations, and Bayesian belief networks for dynamic probabilistic inference under uncertainty. These techniques transform disparate project data—ranging from engineering design parameters to procurement logistics—into actionable risk intelligence. The second component is advanced risk prioritization, integrating Monte Carlo simulation and fuzzy logic. Monte Carlo simulation provides quantitative insights into cost and schedule variability by propagating probabilistic input distributions through project models, generating a risk-informed range of possible outcomes. Fuzzy logic complements this by incorporating qualitative or incomplete risk information (e.g., expert judgment, environmental unpredictability, and human reliability factors) into a structured reasoning framework that supports more nuanced risk prioritization. The third component introduces Learning Organization principles, embedding continuous learning, feedback loops, and psychological safety within the risk management process. Through structured post-event reviews, digital knowledge repositories, and AI-assisted learning analytics, the organization evolves into a learning ecosystem capable of adaptive decision-making. This triadic integration—AI-driven analytics, probabilistic simulation, and learning-oriented culture—forms the methodological foundation of the proposed decision-support model. A simulated application of the IODSM was conducted using representative offshore project data, encompassing FEED cost estimation uncertainties, EPCIC schedule deviations, and marine installation risk profiles. The results demonstrate substantial improvements in predictive accuracy, decision robustness, and organizational learning outcomes. From a quantitative perspective, the integration of Monte Carlo simulation reduced cost estimation uncertainty ranges by approximately 25% compared to traditional deterministic models. The inclusion of fuzzy logic in qualitative risk scoring improved prioritization accuracy by 18%, as measured against post-project validation data. AI-assisted anomaly detection identified latent correlations between procurement delays and weather-related offshore installation risks that were previously overlooked in conventional risk registers. These findings underscore the value of intelligent risk analytics in enhancing the comprehensiveness and responsiveness of offshore risk management. From an organizational perspective, embedding the Learner Mindset into the IODSM yielded qualitative benefits in project culture and long-term resilience. The model’s learning feedback loops facilitated iterative improvement across successive projects, transforming historical “lessons learned” into predictive knowledge assets. Psychological safety—defined as the freedom to voice concerns or share errors without fear of reprisal—proved critical to fostering a data-sharing culture that feeds the AI analytics engine. Moreover, the structured reflection processes enabled by the model promoted a shift from reactive risk mitigation to proactive risk anticipation. In this sense, the IODSM not only enhances decision quality but also cultivates a sustainable organizational learning capability—a critical differentiator in an industry where the cost of error is measured in both capital and environmental terms. A key contribution of this research lies in the systemic integration of technology and human factors within a coherent risk management architecture. The IODSM transcends the conventional divide between quantitative risk modeling and qualitative human judgment by embedding both into a continuous learning cycle. Data from project execution phases feed back into the AI models, refining probabilistic parameters and fuzzy inference systems. Simultaneously, human feedback from project teams updates contextual understanding and calibrates the model’s learning algorithms. This dual-loop feedback mechanism—one digital and one cognitive—ensures that the system remains both technically accurate and contextually relevant. Such integration aligns with modern systems engineering principles and the emerging paradigm of intelligent infrastructure management. The implications of the research are multifaceted. At the strategic level, the adoption of the IODSM enables offshore project stakeholders—operators, contractors, and regulators—to make risk-informed decisions with greater confidence and transparency. The model supports scenario-based planning, enabling decision-makers to test the resilience of project strategies under varying uncertainty conditions (e.g., market volatility, supply chain disruptions, or environmental hazards). At the operational level, the model enhances the efficiency of project planning and control by dynamically updating risk profiles as new data emerge. This adaptive capability reduces the lag between risk detection and mitigation, thereby minimizing the likelihood of cascading project failures. At the organizational level, the IODSM provides a structural mechanism for institutional learning, allowing knowledge gained from one project to inform future endeavors. This represents a critical shift from static risk registers to dynamic, self-evolving knowledge systems. The research further highlights several practical enablers for implementation. These include the establishment of integrated data platforms capable of consolidating design, procurement, and construction data; the deployment of AI-driven risk dashboards for real-time visualization of uncertainty propagation; and the incorporation of learning analytics modules to measure the effectiveness of risk interventions over time. The study also emphasizes the importance of leadership commitment and governance structures in embedding learning organization principles. Without psychological safety, transparent communication, and an open feedback culture, the technological sophistication of the IODSM cannot yield its full benefits. Thus, successful implementation requires alignment across technology, process, and culture. In conclusion, this study advances the field of offshore project management by presenting a comprehensive, intelligent, and adaptive framework for risk-informed decision-making under uncertainty. The proposed IODSM embodies a paradigm shift from deterministic, reactive risk management to probabilistic, proactive, and learning-oriented governance. By coupling AI-assisted analytics with probabilistic modeling and embedding these within a culture of continuous learning, offshore organizations can achieve greater resilience, efficiency, and innovation. The findings suggest that intelligent risk management, when supported by a learner mindset, can transform uncertainty from a source of vulnerability into a strategic asset for organizational growth and sustainability. This integration marks a critical evolution in offshore engineering—where the convergence of data intelligence, probabilistic reasoning, and human learning forms the foundation for safer, more adaptive, and future-ready project ecosystems.
Keywords: Artificial Intelligence (AI), Decision Support Systems, Intelligent Risk Management, Monte-Carlo Simulation, Offshore Engineering Projects, Probabilistic Risk Analysis