Sarcouncil Journal of Applied Sciences Aims & Scope

Abstract: Real-time supply chain visibility has emerged as a critical enabler for operational excellence, risk mitigation, and rapid decision-making in modern enterprises. With global supply chains becoming increasingly dynamic, interconnected, and data-intensive, traditional batch-processing systems are no longer sufficient to handle the velocity and variability of supply chain events. Kafka-based data streaming architectures offer a scalable, fault-tolerant, and high-throughput foundation for continuously processing logistics movements, inventory fluctuations, transportation updates, and supplier interactions as they occur. This article examines the fundamental principles, architectural considerations, and practical applications of Kafka-driven real-time supply chain visibility. It further discusses advanced analytics, predictive modeling, and integration patterns powered by streaming data flows. Emphasis is placed on Kafka’s distributed log structure, pub-sub mechanisms, event streaming pipelines, and stream processing capabilities, and how these collectively address long-standing challenges such as data latency, fragmented visibility, silos across suppliers, inconsistent system updates, and limited orchestration across global supply chain networks. The article concludes by highlighting the transformative impact of Kafka-enabled architectures in enhancing resilience, transparency, and decision intelligence across end-to-end supply chain ecosystems.