基于灰色系统理论的辽宁省物流需求预测管理研究

Management Research on Logistics Demand Forecasting in Liaoning Province Based on Grey System Theory

摘要: 科学预测区域物流需求是物流系统规划与管理决策的基础性工作。以辽宁省为研究对象，构建了涵盖经济发展水平、产业结构、人口与消费水平、商业贸易、交通基础设施五个维度14项指标的需求预测指标体系。首先运用灰色关联度分析方法对影响因素进行筛选，识别出社会消费品零售总额(r = 0.781)、公路运输路线长度(r = 0.764)、城镇居民人均消费支出(r = 0.716)等6项关联度 ≥ 0.65的关键驱动因子；继而采用GM(1, 1)灰色预测模型对辽宁省2025~2027年货物周转量进行外推预测，模型后验差比值C = 0.427、小误差概率P = 1.0，精度等级为优。结果表明：辽宁省物流需求呈持续增长态势，2027年货物周转量预计达到139.82亿吨公里；消费市场规模与交通基础设施通达程度已取代传统经济总量指标成为物流需求的核心驱动力，反映出辽宁省物流需求驱动力由“生产型”向“消费型”转变的管理工程特征。研究为区域物流资源配置、基础设施投资决策与产业政策制定提供了可量化的管理决策参考框架。

Abstract: Scientific forecasting of regional logistics demand is a fundamental task for logistics system planning and management decision-making. Taking Liaoning Province as the research object, a demand forecasting indicator system was constructed, covering five dimensions—economic development level, industrial structure, population and consumption level, commercial trade, and transportation infrastructure—with a total of 14 indicators. First, the grey relational analysis method was used to screen the influencing factors, identifying six key driving factors with a correlation degree ≥ 0.65, including total retail sales of consumer goods (r = 0.781), highway route length (r = 0.764), and per capita consumption expenditure of urban residents (r = 0.716). Then, the GM(1, 1) grey prediction model was employed to extrapolate and forecast the freight turnover of Liaoning Province from 2025 to 2027, with a posterior error ratio C = 0.427 and a small error probability P = 1.0, indicating an excellent accuracy level. The results show that logistics demand in Liaoning Province is continuously growing, with freight turnover expected to reach 13.982 billion ton-kilometers by 2027. The scale of the consumer market and the accessibility of transportation infrastructure have replaced traditional economic volume indicators as the core driving forces of logistics demand, reflecting a shift in the logistics demand drivers in Liaoning Province from “production-oriented” to “consumption-oriented” management engineering characteristics. The study provides a quantifiable decision-making reference framework for regional logistics resource allocation, infrastructure investment decisions, and industrial policy formulation.