Abstract
With the development of technology, the data stored by humans is growing geometrically. Especially in the logistics industry, the rise of online e-commerce has created a huge data flow in the informatized logistics network. How to collect, analyze, and organize this information in time and analyze the meaning of this information from it is a difficult problem. The paper aims to learn the management of logistics systems from the perspective of statistics. This article uses random analysis of 1,000 customers’ logistics records from the logistics enterprise information system, uses mathematical analysis and matrix theory to analyze the correlation among them, and analyzes customer types and shopping. The information on habits, daily consumption patterns, and brand preferences is classified and summarized using mathematical statistics. The experimental results show that the results of the study can well reflect customers’ daily habits and consumption habits. The experimental data show that mining effective and accurate information from massive information can help companies to quickly make decisions, formulate scientific logistics management programs, improve operating efficiency, reduce operating costs, and obtain good benefits.
Highlights
With the development of technology, the data stored by humans is growing geometrically
Analyzing customers’ consumption habits from their daily consumption is a problem currently being solved. is article uses random analysis of 1,000 customers’ logistics records from the logistics enterprise information system, uses mathematical analysis and matrix theory to analyze the correlation among them, and analyzes customer types and shopping. e information on habits, daily consumption patterns, and brand preferences is classified and summarized using mathematical statistics. e experimental results show that the results of the study can well reflect customers’ daily habits and consumption habits
Study the group’s shopping patterns and brand preferences. e study found that most consumers like to buy big brands and shop online. e general shopping patterns of customers are shown in Table 1 and Figure 3. e customer’s brand style is shown in Table 2 and Figure 4
Summary
With the development of technology, the data stored by humans is growing geometrically. In the logistics industry, the rise of online e-commerce has created a huge data flow in the informatized logistics network. Erefore, GPS technology, GIS technology, and logistics industry need to be organically combined [6, 7] Scholars such as Xuegin aim to use modern information technology to manage the medical traceability of implants. Methods: collect and analyze the problems existing in the traceability management of implantable medical devices, Mobile Information Systems combine their own work practices, draw on the advanced methods of other industries and the research and analysis process of traceability management in key foreign countries to find out the network traceability model, and plan basic requirements of sexual management. Xuegin has designed and developed an implantable medical device traceability management information system, including logistics, supervision, traceability, and use traceability. Analyzing customers’ consumption habits from their daily consumption is a problem currently being solved. is article uses random analysis of 1,000 customers’ logistics records from the logistics enterprise information system, uses mathematical analysis and matrix theory to analyze the correlation among them, and analyzes customer types and shopping. e information on habits, daily consumption patterns, and brand preferences is classified and summarized using mathematical statistics. e experimental results show that the results of the study can well reflect customers’ daily habits and consumption habits
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