<![CDATA[This study introduces the design and evaluation of an integrated intelligent wood management system aimed at improving operational efficiency within the forestry logistics industry. Traditional enterprise platforms in the timber sector typically function as isolated modules, separating forecasting, routing, and administrative operations, which leads to redundant processes and planning inaccuracies. The proposed system unifies three intelligent modules: a hybrid forecasting model for demand estimation, a two-stage route optimization algorithm combining nearest-neighbor and 2-opt heuristics, and a RESTful backend that enables synchronized administrative updates through periodic polling. The system architecture, developed using Node.js and MySQL, ensures high scalability and low response latency. Experimental validation on 15,000 timber delivery records achieved an average API response time of 142 ms representing a 68% improvement over conventional ERP systems alongside forecasting accuracy of 85.6% and an 18.2% gain in route efficiency. These results demonstrate that integrating artificial intelligence with heuristic optimization within a lightweight, deployable framework can effectively support real-time decision-making in industrial wood management.]]>
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