Cold Chain Monitoring with IoT Sensors: A Data-Driven Approach to Reducing Spoilage in Temperature-Sensitive Supply Chains

Spoilage of temperature-sensitive goods such as food and pharmaceuticals remains a persistent challenge in cold chain logistics. Traditional monitoring methods often lack the granularity and responsiveness required to prevent quality degradation during transport and storage. This paper investigates the application of Internet of Things (IoT) sensors for real time monitoring and control within the cold chain. We develop a conceptual framework that integrates IoT enabled sensing, cloud-based analytics, and decision support systems to mitigate spoilage risks. Empirical data are drawn from logistics operations involving perishable foods and vaccines across multiple geographic zones. Using mixed methods that combine time series analysis, machine learning, and system modeling, the study demonstrates how IoT data streams improve visibility, responsiveness, and traceability. The paper identifies key implementation challenges, such as data integration and infrastructure limitations, and suggests pathways for future research. Findings offer actionable insights for logistics firms, policymakers, and technology providers seeking to build more resilient and responsive cold chain systems.