AIoT-Driven Business Intelligence and Supply Chain Optimization: Real-Time Insights for Strategic Decision-Making and Sustainable Operations

This research introduces a comprehensive framework for integrating Artificial Intelligence of Things (AIoT) into modern supply chain management. We examine five AIoT-enabled applications: dynamic fleet routing, supplier risk monitoring, autonomous inventory replenishment, Just-in-Time (JIT) delivery optimization, and warehouse robotics coordination. Synthetic datasets and machine learning models—ranging from regression to classification and reinforcement learning—simulate predictive scenarios for each domain. Our findings underscore measurable improvements in delivery efficiency, risk mitigation, and sustainability. This study demonstrates how AIoT facilitates real-time decision-making, enhances resilience, and fosters eco-conscious operations in complex supply chain ecosystems.

本研究提出了一套将人工智能物联网（Artificial Intelligence of Things, AIoT）融入现代供应链管理的完整框架。我们研究了五类基于AIoT的应用场景：动态车队调度、供应商风险监测、自主库存补货、准时制（Just-in-Time, JIT）配送优化以及仓储机器人协同。本研究构建了涵盖回归分析、分类任务与强化学习等多种类型的合成数据集与机器学习模型，用于模拟各应用领域的预测场景。研究结果证实，该方案可在配送效率、风险防控与可持续性层面实现可量化的提升。本研究阐明了AIoT如何助力复杂供应链生态系统实现实时决策、提升韧性并推动绿色环保运营。