Data-Driven Supply Chain Cost Synergistic Optimization for Small and Medium-Sized Manufacturing Enterprises

Abstract

Small and medium-sized enterprises (SMEs) constitute over 99% of all enterprises in China and serve as the fundamental backbone of the national economy. However, they face persistent challenges in supply chain management characterized by information silos, Cost out of control, rigid processes, and heavy reliance on manual labor—a predicament succinctly described as "no money, no personnel, and no technology." While commercial integrated software products have proliferated in the market, they predominantly offer feature-stacked solutions that require users to self-filter, failing to address the specific pain points of SMEs. This paper proposes a data-driven collaborative optimization framework tailored to the resource-constrained context of SMEs. Drawing on cloud-native architecture, mixed-integer linear programming, heuristic algorithms, and Robotic Process Automation (RPA), the framework addresses four core dimensions: information synergy, cost optimization, process reengineering, and intelligent decision-making. The research identifies that the primary competitive advantage of SMEs lies in their organizational agility—"small boats turn around easily" —which enables rapid adoption of lightweight, SaaS-based solutions without the complex system integration required by large enterprises. By focusing on the "inventory-routing" joint optimization problem and automated quotation systems as core algorithmic breakthroughs, this study demonstrates that significant cost reductions can be achieved through targeted, algorithm-centric interventions rather than comprehensive ERP overhauls. The findings contribute to both the academic literature on SME supply chain management and practical guidance for technology adoption strategies in developing economies.