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.
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.
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.
Indicators of logistics demand forecasting
The demand forecasting process enhances forecasting accuracy in real-time, helps organizations manage their inventory levels and guides data-driven business decisions. Being aware of these buyer trends can prevent manufacturers from having static inventory and keep logistics operating costs to a minimum. These advanced forecasting methods provide logistics managers with powerful tools to make more informed, data-driven decisions, enhancing the efficiency and responsiveness of supply chain operations.
Without this approach, organizations risk overstocking or understocking inventory, which can lead to backorders or stockouts. The demand forecasting approach gives enterprises and their stakeholders more control and oversight into daily operations. With demand forecasting, organizations have the tools and datasets to predict future demand and drive smarter decision-making that can save an organization both time and money. The report also predicts that digital assistants will increase the volume of decision-making by 21% by 2026.
Research on the Prediction of Logistics Demand for Emergencies Based on BP Neural Network
In view of the extensiveness and reliability of the model in the field of prediction, this study will use this method to predict the logistics demand scale of Guangdong province. The purpose of this study is to predict the logistics demand scale of Guangdong province in the era of e-commerce. However, there are relatively few studies on logistics demand forecasting under the influence of e-commerce. However, considering the impact of e-commerce on logistics, it is necessary to reflect on the role of e-commerce in logistics demand forecasting indicators. Demand forecasting helps organizations maintain inventory management at the right time and mitigate fluctuations, stockouts and carrying costs.
- Therefore, factor analysis is used to reduce the dimension of these indicators.
- The approach can help increase long-term business value and optimize supply chain operations through strategic initiatives.
- Through the coupling analysis of logistics demand scale with F1, F2 and F3 by curve fitting again, it can be found that logistics demand scale presents significant non-linear positive correlation with these three principal components.
- The accuracy of these predictions is crucial for planning inventory levels, workforce requirements, warehouse space, and transportation logistics, which in turn helps minimize costs and improve service levels.
External Factors
The logistics demand scale of Guangdong province was taken as the output of the network. Based on the three-layer BP neural network for modeling and prediction, this paper determined that the BP network input was 3 (namely the 3 principal component scores calculated above). Therefore, by drawing the actual value of logistics demand scale and the predicted value by GM (1,1) model, it is found that there is a large error between the predicted value and the actual value as shown in Fig. The GM (1,1) method was used for modeling and predicted the logistics demand scale https://dailyscreak.com/ford-motor-company-case-study.html of Guangdong province from 2000 to 2019, as shown in Table 7.
Cloud based supply chain systems with demand forecasting capabilities have become standard for freight forwarders in 2026. Every one of these is a decision with a cash cost https://flarealestates.com/transforming-business-efficiency-with-acumatica-erp-real-world-success-stories.html when the forecast is wrong. This approach improves supply chain forecasting collaboration because the number based baseline forces a defensible starting point and the qualitative overrides force operators to say out loud what they think the market will do.
Third, this paper expands the application of GM (1, 1) model and BP neural network model in regional logistics demand forecasting. The benefits of logistics forecasting are enhanced efficiency and effectiveness of supply chain operations, reduced costs, improved service delivery, and optimized inventory levels. Understanding and implementing logistics forecasting significantly reduces operational costs and improves service delivery, making it a vital component for any business looking to maintain a competitive edge. By forecasting the logistics demand in Guangdong, this paper provides ideas and reference for solving the above problems, and embodies the value of regional logistics demand forecasting. Second, this paper enriches the literature of regional logistics demand forecasting. Also, in the previous literature on regional logistics demand forecasting, the relevant indicators of e-commerce are rarely considered (e.g., Nguyen ; Fan and Wu ; Han et al. ).