Authors : Benjamin Yaw Kokroko; Joseph Kobi; Edmund Kofi Yeboah

Volume/Issue : Volume 10 - 2025, Issue 10 - October

Google Scholar : https://tinyurl.com/y989pm8c

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DOI : https://doi.org/10.38124/ijisrt/25oct1432

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Abstract : The modern global supply chains are more vulnerable to disruptions than ever due to their complex geographically distributed designs. This in-depth research evaluates three advanced analytical tools, scenario analysis, Monte Carlo simulation, and predictive analytics, and their application to the problem of supply chain disruption to prevent its development into an operational crisis. This study shows how organizations use these methods to quantify the disruption probabilities, vulnerability exposure and mitigation strategies by using systematic literature review, quantitative modeling, and empirical case analysis. Monte Carlo simulation can be used to give probabilistic quantification of risks in multi-tier supply networks and, through this approach, produce probability distributions of possible outcomes in the thousands of possible outcomes. Scenario analysis allows the strategic assessment of the possible disruption pathways based on systematic exploration of the possible what-if. Using machine learning algorithms and patterns of historical data, predictive analytics provides real-time functionality of risk detection. Findings have shown that hybrid methods which incorporate both these methods can have high predictive accuracy (76-86%), which is better than single-method methods. The analysis of the applications in the manufacturing, healthcare, and food supply chain shows that there are sector-related profiles of risks and mitigation needs. Results indicate the sensitivity of the lower-level visibility of suppliers since the disturbance of Tier 3 is transmitted upstream through supply chains with exponential impacts, worsening the average performance of 97.8% at the origin to 51.1% at the manufacturer stage. The study adds an elaborate framework of unifying these methodologies into organizational risk management procedures that would give the practitioners realistic guidelines to apply in implementing data-driven disruption prevention measures. This research study contributes to the theory on supply chain risk management by showing how quantitative modeling methodologies can convert reactive approaches to crises management to proactive resilience.

Keywords : Supply Chain Disruption, Monte Carlo Simulation, Scenario Analysis, Predictive Analytics, Supply Chain Resilience, Vulnerability Assessment, Probabilistic Modeling, Machine Learning Risk Detection, Multi-Tier Supply Networks, Disaster Recovery Planning.

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