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First, this paper constructs the Guangdong logistics demand forecasting indicator system by considering the development background of e-commerce. Through the above analysis, this paper finds that the existing research has provided a wealth of literature on logistics demand forecasting indicators and forecasting models. The fourth part is data analysis and regional logistics demand forecasting. It polls and gathers data from employees, stakeholders and customers to help forecast future decisions within an organization.<\/p>\n<\/p>\n
This approach to demand forecasting looks at external factors through both a macro and micro lens. They each cover many different approaches, models and formulas, depending on the size and scope of the demand forecasting strategy. A quantitative approach to demand forecasting is at the core of the entire process.<\/p>\n<\/p>\n
Finally, logistics companies should encourage experimentation with new distribution patterns (e.g., crowdsourcing logistics, and logistics alliances). The BP neural network model has better predictive effect than GM (1, 1) model. Second, e-commerce platforms and logistics enterprises should choose scientific forecasting methods when they make regional logistics demand predictions. During peak delivery periods and shopping carnivals, there are often too many orders to make the logistics work 13, 14. From a practical point of view, the insights provided by our study can provide recommendations for logistics enterprises and relevant e-commerce platforms.<\/p>\n<\/p>\n