In recent years, the rapid development of information technology has affected the way the world economy operates. The emergence of e-commerce has greatly shortened the time and space distance between economic participants and maximized the sharing of resources. However, the financial management and risk assessment capabilities of the existing supply are insufficient to adapt to the rapidly developing new environment. This article uses a combination of normative analysis and empirical analysis to analyze the status quo of the supply chain of small and medium e-commerce companies. First, this article establishes an evaluation framework for the supply chain of e-commerce companies based on edge computing. Second, according to the distribution of the supply chain, this article adds the member’s predetermined quota, reputation, execution time, and other indicators as parameters to establish a fuzzy neural network model. On this basis, combined with the price regression model, the pricing plan is evaluated. The results show that the financing risk obtained by this model differs very little from the actual risk. The above-mentioned model constructs an e-commerce enterprise supply chain financing risk management model that adapts to the environment of the new era.
Nowadays, with the rise of the network economy environment, e-commerce companies have gradually become the mainstream of domestic and foreign business activities, and they are in a period of rapid development [
In the aspect of enterprise supply chain management, Hameed et al. [
However, the financial management and risk assessment capabilities of the existing supply are insufficient to adapt to the rapidly developing new environment [
In the field of intelligent computing, based on the development of cloud computing, edge computing is applied to the Internet service environment, and mobile edge computing (MEC) technology is born. MEC is a new type of network structure, running and providing information technology services and cloud computing capabilities [
The basic framework of mobile edge computing.
The actual value of edge computing continues to increase. With the explosive growth of the demand for Internet of Things device access, the demand for analysis and computing power on the MEC side will double [
The emergence of e-commerce has greatly shortened the time and space distances between economic participants and maximized the sharing of resources [
According to the characteristics of the supply chain financing of e-commerce enterprises and following the ideas and principles of the evaluation index system design, this paper will gradually carry out the relevant construction of the supply chain financing performance evaluation system under the background of e-commerce. In the context of e-commerce, the comprehensive evaluation of supply chain financing risks and the determination of a scientific and reasonable performance evaluation system are the most important. Consumers’ consumption habits have shifted from offline to online, which requires SMEs to adopt more e-commerce models for business operations. At present, there are still many problems and shortcomings, and they cause the relatively lagging development of the enterprise. Therefore, studying the financial management mode has important theoretical and practical significance in this period.
When goods or labor is treated as commodities and exchanged to meet our own needs, we need to set prices for them [
Use Euclidean distance to measure the closeness between indicators. Euclidean distance is the straight-line distance between two points in space [
In the above formula,
The specific process of cluster analysis is as follows: Combine the two closest units into one category to form a category, and calculate the distance between the newly generated category and the other categories to form a new distance matrix [ According to the same principle as the second step, merge the two categories with the closest distance. If the number of categories is still greater than 1, the model continues to repeat this step until all the data is merged into one category.
The task points in Annex 1 are firstly clustered [
A pedigree diagram of supply chain indicators for e-commerce companies.
As shown in Figure
The optimal clustering result is obtained through iterative optimization of the divided mean.
Algorithm steps are as follows:
Here, we substitute the data of the three classes obtained by the Q-type clustering analysis into the calculations and obtain the cluster centers of each class after 20 iterations [
The latitude and longitude coordinates of the cluster centers.
Final cluster center | |||
---|---|---|---|
Clustering | |||
1 | 2 | 3 | |
Latitude | 22.67 | 23.02 | 23.11 |
Longitude | 114.04 | 113.73 | 113.23 |
Next, use the data in Annex 1 to obtain the average value of task pricing in the 3 regions. Combine Annex 2 to obtain the distance from each member to the 3 cluster centers and then to obtain the average distance from all members to the 3 cluster centers. The distance from the member to the cluster center and the average price of various cluster tasks are shown in Table
The distance from the member to the cluster center and the average price of various cluster tasks.
Clustering | |||
---|---|---|---|
1 | 2 | 3 | |
Task pricing average | 69.07 | 68.04 | 68.11 |
Average distance between members and cluster center (km) | 1.546 | 1.602 | 1.825 |
The specific data of the number of corresponding members and the number of tasks in each cluster area is shown in Table
The number of corresponding members and the number of tasks in each clustering area.
Clustering | |||
---|---|---|---|
1 | 2 | 3 | |
Number of members | 538 | 451 | 988 |
Number of tasks | 202 | 190 | 443 |
According to Table
By pairwise comparison, the method of establishing a pairwise comparison matrix is used to compare the influences of factors [
Because the dimensions of
According to formula (
The relationship between comprehensive indicators and task pricing.
It can be judged that the curve in Figure
If the tasks are concentrated and the members are also concentrated in this area, then, in this area, members will compete to choose the task, which may lead to malicious snatching, resulting in a low task completion rate. Therefore, we can consider a scheme of jointly packaged and released tasks; that is, several tasks are bundled and handed over to one user to complete. Since the task points are not very far apart, the neighboring task points can be packed [
Scatter diagram of the distribution of supply chain task points.
The neural network that uses nonlinear prediction is usually a backpropagation neural model (BP model). Each represents the impact task. There are 7 factors related to price. The output layer has a node to build the relationship with the model. The neurons in the middle layer are not connected to each other, while the neurons in the adjacent layer are connected by weights. The structure of the fuzzy neural network model is shown in Figure
BP fuzzy neural network model structure.
The task is packaged; that is, several tasks are bundled and released, so that the tasks that originally need to be assigned to multiple members are completed by the same member. The basic idea is to divide the data into several categories according to the distance, so that the “difference” of the data within the category is as small as possible, and the “difference” between the categories is as large as possible. The clustering of sample individuals is usually called type clustering, and the clustering of research variables is called type clustering. The packaged task package will be priced slightly lower than the sum of the single pricing when it is not packaged.
After packaging, the task with the smallest number in the package is a relative single-package task. The number of tasks in other packages is a multiple of this relative to the actual number of single-package tasks as the relative number. Suppose that
When formula (
In the second search, the parameter
At this time, the relative error squared sum of the relative pricing is
The basic idea is to divide the data into several categories according to the distance, so that the “difference” of the data within the category is as small as possible, and the “difference” between the categories is as large as possible. The clustering of sample individuals is usually called type clustering, and the clustering of research variables is called type clustering. The evaluation of the supply chain level of e-commerce companies is shown in Figure
E-commerce company supply chain rating evaluation.
The pricing obtained through the model is established by the fuzzy neural network. Therefore, it can be shown that the model established by this question is reasonable and can be used for packaging.
In this article, we will influence the new pricing parameters. Related parameters include the salary level of the employee where the task is located, the complexity of the task, the reputation value of the member, the member's scheduled task limit, and the member's scheduled task start time. For changes that affect the pricing parameters, 0 is not affected. The knowledge base of some rules of the e-commerce supply chain is shown in Table
Knowledge base of some rules of e-commerce supply chain.
If | Then | |||||||
---|---|---|---|---|---|---|---|---|
Serial number | ||||||||
1 | 1 | 1 | ||||||
2 | 1 | 1 | ||||||
3 | 1 | −1 | ||||||
4 | 1 | −1 | ||||||
5 | 1 | −1 | ||||||
6 | 1 | 1 | ||||||
7 | 1 | −1 |
E-commerce companies can realize information exchange faster and more conveniently and provide transaction parties with more detailed resource information as much as possible, which greatly shortens the time for transaction decision-making, improves the success rate of transactions, and also plays a significant role in the resources of the whole society. All possible combinations of rules are obtained from the given rules after repeated learning, and the factors that affect pricing and the data that lead to price changes are input into the system, and the learning rate is
At this stage, in the e-commerce environment, the core of financial management of SMEs in our country is the allocation of financial rights, capital operation, and information. It is disclosed that the three submodels can be the main content of the SME financial management model under the current environment. Therefore, the new model should integrate the above three submodels and make them an organic unity. At present, there are still many problems and shortcomings, and they cause the relatively lagging development of the enterprise. Therefore, studying the financial management mode has important theoretical and practical significance in this period. The three submodels restrict and cooperate with each other in function and can effectively play positive influence.
At present, there are still many problems and shortcomings, which hinder the good development of the enterprise and cause the relatively lagging development of the enterprise, and it is difficult to adapt to the current increasingly competitive market environment. Therefore, studying the financial management mode that is suitable under the e-commerce has important theoretical and practical significance in this period. In the context of e-commerce, the comprehensive evaluation of supply chain financing risks and the determination of a scientific and reasonable performance evaluation system are the most important. Consumers' consumption habits have shifted from offline to online, which requires SMEs to adopt more e-commerce models for business operations. E-commerce companies can realize information exchange faster and more conveniently and provide transaction parties with more detailed resource information as much as possible, which greatly shortens the time for transaction decision-making, improves the success rate of transactions, and also plays a significant role in the resources of the whole society. However, the financial management and risk assessment capabilities of the existing supply are insufficient to adapt to the rapidly developing new environment. Therefore, studying the financial management mode that is suitable under the e-commerce has important theoretical and practical significance in this period. This article uses a combination of normative analysis and empirical analysis to analyze the status quo of the supply chain of small and medium e-commerce companies. This paper conducts innovative research on the financial management mode under the environment of e-commerce enterprises and proposes a new financial management mode. Although some progress has been made, there are still some limitations in the research, and there are still many contents related to the paper which need to be studied.
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
The authors declare that there are no conflicts of interest.