Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Radhakrishnan, G.V.; Upreti, Kamal; Kshirsagar, Pravin R.; Krishnan, Sivaneasan Bala; Shankar, Uma; Jain, Rituraj; Tiwari, Akhilesh Title A name given to the resource A statistically guided hybrid machine learning framework for predicting supply chain resilience in complex operational environments Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of Information Technology (Singapore);Volume;18;Issue;1;pp.629-644 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s41870-025-02794-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s41870-025-02794-5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019658226?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019658226?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Radhakrishnan G.V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Kshirsagar P.R., Electronics and Telecommunication Engineering, J D College of Engineering and Management, Maharashtra, Nagpur, India; Krishnan S.B., Engineering Cluster, Singapore Institute of Technology, Singapore, Singapore; Shankar U., Faculty of Management and Social Sciences, Qaiwan International University, Kurdistan, Sulaimanyah, Iraq; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India; Tiwari A., Department of Business and Management, Christ University, Delhi NCR Campus, Ghaziabad, India Description An account of the resource This study proposes a hybrid machine learning framework to predict supply chain resilience by integrating principal component analysis, K-Means clustering, and ensemble learning models. The approach captures firm-level heterogeneity, enabling context-specific resilience prediction and interpretability using SHAP values. The findings demonstrate that ensemble models, particularly XGBoost, outperform traditional regression models, and reveal distinct resilience drivers across operational clusters. The framework offers actionable insights for improving resilience strategies and contributes a scalable, explainable approach for data-driven supply chain risk management. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. Subject The topic of the resource Explainable AI; Operational efficiency; Predictive modeling; Supply chain resilience; Supply chain strategy Publisher An entity responsible for making the resource available Springer Science and Business Media B.V. Relation A related resource ISSN: 25112104; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online