• Jubin Thomas
• Kirti Vinod Vedi
• Sandeep Gupta

E-commerce offers unparalleled opportunities to enhance the efficacy and efficiency of supply chain management (SCM), since it represents a revolutionary leap in technology. In developing nations, retail and consumer product merchants have a behavioural desire to use e-commerce for SCM. This research attempts to investigate this intention. This study investigates the impact of cloud-based SCM systems enhanced by advanced machine learning (ML) techniques on supply chain resilience (SCR). Utilising data from a prominent Chinese home appliance shipping firm, the research evaluates the performance of ML models—Multi-Regression, LSTM, and ANN—in improving demand forecasting and inventory management. Results demonstrate that LSTM had the greatest predictive accuracy among a models evaluated, with RMSE values average 231.98 across Local Transshipment Centers (LTCs), reducing supply chain interruptions and improving operating efficiency. These findings underscore the efficacy of integrating ML with cloud SCM for bolstering supply chain resilience amidst operational uncertainties.

Cloud-based supply chain management, Supply chain resilience, Machine learning, Demand forecasting, Inventory optimisation, Multi-Regression, LSTM, ANN (Artificial Neural Networks), SCM systems

"Enhancing Supply Chain Resilience Through Cloud-Based SCM and Advanced Machine Learning: A Case Study of Logistics ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 9, page no.e357-e364, September-2021, Available :http://www.jetir.org/papers/JETIR2109554.pdf

"Enhancing Supply Chain Resilience Through Cloud-Based SCM and Advanced Machine Learning: A Case Study of Logistics ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 9, page no. ppe357-e364, September-2021, Available at : http://www.jetir.org/papers/JETIR2109554.pdf