Digital Transformation and Supply Chain Agility: A Post-Pandemic Assessment of Logistics SMEs

• Abbas Ahsun

  Texas University

  Author

The post-pandemic recovery of logistics small and medium-sized enterprises (SMEs) has intensified pressure to translate digital transformation investments into tangible supply chain agility. Yet, the specific pathways through which production-oriented versus consumption-oriented technologies predict agility remain underexplored, particularly for resource-constrained logistics SMEs in emerging economies. This study addresses this gap by developing and validating a machine learning-based predictive framework for supply chain agility using a cross-national dataset of 491,636 SME technology adoption records spanning 2017–2023. Methodologically, the research employs Random Forest, XGBoost, Gradient Boosting, Ridge, and Lasso regression models, benchmarked through an 80–20 train-test split and evaluated using R², RMSE, and MAE. The Random Forest model achieved the highest test R² of 0.6249, significantly outperforming linear baselines (R² ≈ 0.05) and demonstrating that production technologies—automation, cloud infrastructure, and digital supply chain integration—are substantially stronger predictors of agility than customer-facing consumption technologies. Feature importance analysis identified digital infrastructure investment and workforce digital literacy as the top two predictors, together accounting for 34.7% of model variance. The study concludes that logistics SMEs should prioritize foundational production-side digital capabilities over consumer-facing applications to build post-pandemic agility. The validated framework offers a replicable, interpretable tool for SME managers and policymakers to allocate digitalization resources more effectively, while extending dynamic capabilities theory into the logistics SME context.

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Copyright (c) 2026 Abbas Ahsun (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.