Building Real-Time Monitoring Applications for Cold Chain Logistics Using Big Data and Resilience-Driven Disruption Management

Cold supply chains, particularly in the pharmaceutical and biotechnology sectors, are crucial for maintaining the quality and efficacy of temperature-sensitive products. However, these supply chains are increasingly vulnerable to disruptions caused by poor visibility, fragmented systems, and lack of real-time data [2][3]. This paper presents an integrated approach to building a resilient cold supply chain using advanced big data technologies and cloud services from Amazon Web Services (AWS) [1]. We examine how platforms like AWS Glue, S3, Lambda, EC2, and Athena, coupled with distributed computing frameworks such as Apache and data lakes formatted with Parquet files, can enhance real-time tracking and decision-making. A full-stack data architecture is proposed, from ingestion to advanced analytics, enabling disruption prediction and automated corrective action models. Visualization tools like Power BI and Tableau [6][7] empower stakeholders to derive insights and take timely actions, such as dispatching backup trucks or invoking emergency refrigeration protocols. The study concludes that embedding optimization models and heuristics into this ecosystem fosters resilience, minimizes losses, and ensures regulatory compliance [5].