Digital Transformation and AI-Driven Logistics Optimization for Small and Medium Enterprises: A Scalable Operations Perspective

Abstract

Executing digital transformation within small and medium-sized enterprises involves managing a profound tension between legacy organizational architectures and the computational demands of AI-driven logistics optimization. While macro-level institutional frameworks establish long-term pathways for structural integration, practical execution at the micro-operational level frequently encounters severe scalability constraints, particularly during real-time inventory and resource matching maneuvers. To address these systemic bottlenecks, this paper introduces a scalable operations framework that integrates decentralized edge intelligence with lightweight, real-time collaborative protocols designed specifically for resource-constrained logistics networks. Throughout our iterative implementation phase, unexpected data-synchronization anomalies across heterogeneous legacy enterprise systems forced a vital structural re-calibration toward asynchronous optimization topologies to prevent systemic communication collapses. Empirical simulations suggest that distributing heavy algorithmic burdens to localized edge nodes may, to some extent, reduce core processing latencies and enhance multi-enterprise synchronization, though underlying tracking discrepancies caused by hardware variations could partially bias long-term operational metrics. Considering the above factors, this leads us to further thinking regarding how decentralized technological standardization might ultimately redefine small-scale industrial empowerment, although further empirical research is explicitly needed to evaluate structural resilience under severe system disruptions.