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Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 5 (2026): Volume 10, Issue 5, May 2026 | Pages: 687-699
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 31-05-2026
Supply chain disruptions have become a major challenge for modern logistics systems due to supplier failures, transportation delays, fluctuating customer demands, and various operational risks. Traditional forecasting and rule-based approaches often struggle to handle the complexity and dynamic nature of supply chain operations. To address these challenges, this paper presents an AI-powered Supply Chain Disruption Detection and Decision Support System designed to improve disruption prediction, demand forecasting, shipment monitoring, and supplier performance analysis using advanced machine learning techniques.
The proposed system utilizes historical supply chain data, including shipment records, inventory information, supplier performance metrics, and operational parameters, to train and evaluate multiple machine learning models such as Random Forest, XGBoost, LightGBM, and CatBoost. Advanced feature engineering techniques and hyperparameter optimization using Optuna were applied to enhance prediction accuracy and model performance. Among the evaluated models, LightGBM achieved the best forecasting and disruption prediction performance.
To improve transparency and interpretability, the system incorporates SHAP-based Explainable Artificial Intelligence (XAI) techniques, which help identify the most influential operational factors contributing to supply chain disruptions. The complete framework is implemented as a web-based application featuring interactive dashboards, real-time monitoring, analytics visualization, and automated reporting capabilities. Experimental results demonstrate that the proposed system significantly improves forecasting accuracy, disruption detection, and decision-making efficiency, making it a suitable solution for intelligent and smart supply chain management applications.
Supply Chain Disruption Prediction, Machine Learning, Demand Forecasting, SHAP Explainability, LightGBM, Decision Support System, Logistics Analytics, Supplier Risk Analysis, AI-Based Supply Chain Management.
Allakonda Vaishnavi, Kanigeri Anjali, M.Mamatha, & M.Sandhya Rani. (2026). AI-Powered Supply Chain Disruption Detection and Decision Support System. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(5), 687-699. Article DOI https://doi.org/10.47001/IRJIET/2026.105092
This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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