Optimizing Kafka-Based Supply Chain Architectures: A Comprehensive Performance Analysis

Abstract

This paper examines the impact of Apache Kafka parameter tuning on the performance and scalability of a microservices-based supply chain management system. The study evaluates the influence of key parameters including batch size, compression type, partition count, and replication factor on critical performance metrics such as throughput, latency, and fault tolerance. Experimental results demonstrate that fine-tuning Kafka configurations significantly reduces processing delays, improves throughput, and enhances system resilience under varying workloads. Notably, the trade-off between replication overhead and performance efficiency becomes evident at high message volumes, where lower replication factors yield better processing efficiency. The findings provide actionable insights and practical guidelines for optimizing Kafka performance in large-scale, event-driven supply chain systems.