SWARM INTELLIGENCE-BASED AUTONOMOUS LOGISTICS FRAMEWORK WITH EDGE AI FOR INDUSTRY 4.0 MANUFACTURING ECOSYSTEMS

Abstract

This study presents a quantitative investigation into a Swarm Intelligence-Based Autonomous Logistics Framework integrated with Edge Artificial Intelligence (Edge AI) for optimizing performance in Industry 4.0 manufacturing ecosystems. The research aims to empirically evaluate how decentralized swarm coordination combined with edge-level inference enhances logistics efficiency compared to conventional centralized and cloud-based control architectures. Using a multi-site experimental design and statistical modeling, the study examined relationships among swarm coordination metrics (agent density, communication frequency) and edge-computing parameters (node density, inference delay) on key logistics indicators such as throughput, latency, cycle time, energy consumption, and fault tolerance. The data were analyzed using correlation, regression, and structural equation modeling (SEM), yielding significant results: swarm density (β = 0.41, p < .001) and communication frequency (β = 0.36, p < .01) were strong positive predictors of throughput, while edge-inference delay exhibited a negative effect (β = –0.32, p < .01). The overall model demonstrated robust explanatory power (R² = 0.78) and good structural fit (χ²/df = 2.23, CFI = 0.96, RMSEA = 0.045). Comparative analysis revealed that the hybrid swarm-edge system achieved a 45% latency reduction, 22% increase in throughput, and 19% improvement in energy efficiency relative to traditional architectures. These findings validate the hypothesis that distributed intelligence enhances operational responsiveness and sustainability in cyber-physical manufacturing environments. The study contributes a statistically verified model for real-time logistics optimization, aligning with previous works by Hamann (2018), Lu et al. (2023), and Iftikhar et al. (2022), and establishes a foundational quantitative framework for future research on autonomous, data-driven logistics systems under Industry 4.0.