ItemArticle

A statistically guided hybrid machine learning framework for predicting supply chain resilience in complex operational environments

Title
A statistically guided hybrid machine learning framework for predicting supply chain resilience in complex operational environments
Creator
Radhakrishnan, G.V.; Upreti, Kamal; Kshirsagar, Pravin R.; Krishnan, Sivaneasan Bala; Shankar, Uma; Jain, Rituraj; Tiwari, Akhilesh
Description
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.
Source
International Journal of Information Technology (Singapore);Volume;18;Issue;1;pp.629-644
Date
01-01-2026
Publisher
Springer Science and Business Media B.V.
Subject
Explainable AI; Operational efficiency; Predictive modeling; Supply chain resilience; Supply chain strategy
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 25112104;
Format
online
Language
English
Type
Article

Citation

Radhakrishnan, G.V.; Upreti, Kamal; Kshirsagar, Pravin R.; Krishnan, Sivaneasan Bala; Shankar, Uma; Jain, Rituraj; Tiwari, Akhilesh, “A statistically guided hybrid machine learning framework for predicting supply chain resilience in complex operational environments,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/22102.

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