Journal of Primeasia
Integrative Disciplinary Research | Online ISSN 3064-9870 | Print ISSN 3069-4353
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Volume 3 Number 1 2022
RESEARCH ARTICLE (Open Access)
Predicting Supply Chain Resilience with Machine Learning: A Comparative Analysis of Ensemble and Neural Approaches Across Logistics, Manufacturing, Healthcare, and Agriculture
Md Jahidul Islam Ridoy 1*
Journal of Primeasia 3 (1) 1-8 https://doi.org/10.25163/primeasia.3110776
Submitted: 02 May 2022 Revised: 13 July 2022 Accepted: 19 July 2022 Published: 22 July 2022
Abstract
Background: Modern supply chains operate under a level of complexity and volatility that conventional analytical tools were never designed to handle. Geopolitical disruptions, pandemic-driven demand shocks, and climate-related logistics failures have collectively exposed the fragility of systems still relying on static forecasting rules and historical averages. Machine learning (ML) has emerged as a compelling — though not uncomplicated — response to this challenge, offering the ability to model nonlinear relationships across high-dimensional operational data in ways that traditional methods cannot.
Methods: This study employed a quantitative cross-sectional design combining survey data from 200 supply chain professionals across logistics, manufacturing, healthcare, and agriculture with a supervised ML modelling framework. Respondents completed a validated five-point Likert-scale instrument measuring five constructs: Demand Forecasting (DF), Inventory Optimization (IO), Risk Prediction (RP), Operational Efficiency (OE), and Supply Chain Resilience (SCR). Five algorithms — Random Forest, Gradient Boosting, Artificial Neural Network, Support Vector Machine, and Multiple Linear Regression — were trained on an 80/20 stratified train-test split and evaluated using accuracy, R², RMSE, and error rate. Feature importance was extracted using both mean decrease in impurity and permutation-based methods.
Results: Random Forest achieved the strongest predictive performance (accuracy = 87%, R² = 0.70), with Gradient Boosting close behind (86%, R² = 0.69). All constructs were strongly and positively intercorrelated, with Demand Forecasting showing the tightest association with SCR (r = 0.74), followed by Risk Prediction (r = 0.72). Feature importance analysis identified DF (0.30) and RP (0.28) as the dominant predictors of resilience. Over 70% of respondents perceived high ML impact on forecasting and risk management, while only 8% reported low impact across all dimensions.
Conclusion: ML-powered anticipatory capabilities — particularly demand forecasting and risk prediction — are the most robust drivers of supply chain resilience, outweighing execution-layer efficiency in predictive importance. Meaningful adoption barriers persist, however, and the benefits of ML remain unevenly distributed across firm sizes and geographies.
Keywords: Supply Chain Resilience; Machine Learning; Demand Forecasting; Predictive Analytics; Ensemble Methods
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