[Retracted] A Prediction and Evaluation Model Analysis of Enterprise Economic Management Mode Based on Neural Network Strategy

Wang, Zijin; Han, Xiujuan

doi:https://doi.org/10.1155/2022/9639025

Security and Communication Networks

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Research Article | Open Access

Volume 2022 | Article ID 9639025 | https://doi.org/10.1155/2022/9639025

[Retracted] A Prediction and Evaluation Model Analysis of Enterprise Economic Management Mode Based on Neural Network Strategy

Zijin Wang¹and Xiujuan Han²

Academic Editor: Chin-Ling Chen

Received24 Feb 2022

Revised08 Apr 2022

Accepted18 Apr 2022

Published12 May 2022

Abstract

Intelligent finance is an inevitable product for continuous development of big data, which is also a weapon to improve the work efficiency of enterprise economic management. The significance and feasibility of an artificial intelligence technology in corporate management performance have been analyzed. Two types of neural networks are used, which one is the single BP neural network without LSTM and the other is a BP neural network with LSTM layer is used to capture corporate time characteristics of performance factors between cost-savings ratio and corporate performance factors. The results have shown that the two types of BP neural networks have good accuracy in predicting corporate performance and which the prediction errors are within 5%. And both training loss and test loss have good convergence for predicting corporate performance. However, the BP neural network with LSTM layer has better accuracy than a single BP neural network. The correlation coefficient reached 0.97, which shows that the BP neural network model established in this article has good accuracy in predicting corporate performance, which is sufficient for predicting corporate performance. The application prediction errors of BP neural network in enterprise performance are all within the acceptable range, and the maximum error is only 1.23%.

1. Introduction

Enterprise economic management is a process of continuously setting goals, checking goals, and finding countermeasures. The implementation of enterprise economic management requires the support of corresponding corporate culture [1]. Only a good performance management system cannot guarantee its effectiveness. The design of the performance management system only plays a role in platform construction. As a brand new information science technology, big data technology can sort out a large amount of valuable information from massive data, which improve the accuracy of decision-making [2]. In the era of big data, with the advancement of the management accounting system, management accounting has been paid more and more attention in corporate management, especially in corporate performance management [3]. However, there are still some problems for company performance management [4]. Not enough attention is paid to the application of management accounting in performance management, and the degree of integration between the performance management system of management accounting and the actual situation of the enterprise is not enough.

Artificial intelligence has strong application value in enterprise economic management [5]. There have been many examples of AI technology application in enterprise economic management, such as early warning of potential financial crises and assessment of financial crises. Enterprise economic management is the core content of an enterprise company [6]. The level of enterprise economic management not only can affects the financial status but also can affect the development and operation of the company. The traditional corporate enterprise economic management model generally converts the data of corporate business activities into useful accounting information in accordance with prescribed procedures and processes for the use and decision-making of relevant personnel [7]. At the same time, enterprise economic management also organizes and summarizes the business that occurs during the production and operation of the company to form a financial report [8].

For the first time, the DEA method had been used to analyze the changes trend for technical efficiency and decomposition indicators of listed companies in China's big data industry [9]. The factor analysis method is used to measure the business performance of listed big data companies and their relationship with the intensity of R&D investment [10]. The research results of scholars on the evaluation index of enterprise innovation performance, combined with the observation and analysis of the current technological innovation process of large data enterprises times, have constructed a large data enterprise innovation performance multi-index evaluation system [11]. Huang et al. discussed whether big data can bring benefits to corporate performance, and the results show that the implementation of big data by companies has a positive effect on financial performance improvement [12]. Caputo et al. believed that big data analysis methods can process all kinds of data into useful information for the business. This information is the enterprise's knowledge assets. The enterprise conducts knowledge asset management to realize the value of big data and improve enterprise performance [13]. Rehman analyzed the correlation between the application of corporate human resources big data and overall performance, which pointed out that corporate human resources departments use big data technology to manage personnel, which can improve corporate efficiency and performance [14]. Based on the AHP-DEA method, Mustafa et al. selected Bolsa to evaluate the performance of real estate trust investment funds, and which put forward relevant research conclusions that expanding scale and relying on economies of scale can improve performance [15]. Mao used the Todim method to study the application of performance evaluation in strategic emerging industries [16]. Md et al. combined fuzzy comprehensive evaluation and decision-making experiment and evaluation laboratory (DEMATEL) methods to resolve the interdependence between attributes in the corporate performance structure [17]. Tai found that there is actually a positive (negative) correlation (downgrade) between upgrades and abnormal returns when the company's financial performance is better. It further clarifies the correlation between corporate governance evaluation activities, abnormal returns, and the company's financial performance [18]. Jiang and Xue studied the impact of corporate environmental responsibility (CER) and ownership structure on the corporate performance. The results verify that CER has a positive impact on corporate financial performance [19]. Many researchers have done a lot of research on Business performance management, but the application of neural network methods in Business performance management is less studied. In this paper, the main combination of BP neural network technology is to assess the performance of enterprises.

At this stage, with the continuous expansion of the scale of enterprises, the acceleration of capital flow, and the linear increase in the frequency of information, it is obvious that the traditional financial model can no longer adapt to the current rapid economic development [20, 21]. From the past to the present, one of the biggest problems existing in the enterprise economic management system in corporate companies is the disconnection between actual management and business [22]. At the same time, due to the advent of the era of big data, the traditional enterprise economic management model has been unable to efficiently and orderly complete the processing of data and filter out useful information in a timely manner. This drawback has hindered the rapid development of enterprises to a certain extent [23]. While smart finance brings development opportunities and advantages to mankind, it also faces many challenges and opportunities at present. The integration of artificial intelligence and enterprise economic management is an emerging field. Many development trends of intelligent finance have not been clarified, and many challenges will be faced. At this stage, intelligent finance combined with a rapidly development of the artificial intelligence method has obtained the best development opportunities, but at the same time it is also facing many challenges [24]. There are many data with strong correlation in the enterprise performance model, but it is very difficult to process these numbers manually and find the correlation among them. BP neural network is a model with forward propagation and back propagation. It can fit nonlinear data and find the correlation among them. This is suitable for the enterprise performance evaluation system [25]. There are a lot of data to be crunched in the business performance management, and it is a tricky task to rely on professionals to do it alone. The advantage of BP neural network is the processing of Galway nonlinear data, it can well handle the relevant complex data in the business performance management.

This article is mainly composed of five chapters. The first section is the development status of the combination of enterprise management performance and big data. The significance of corporate performance evaluation and large data sets are studied in Section 2. The third part introduces the theory and training process and testing process of two types of BP neural network. The fourth section describes the iterative process of the loss function of the BP neural network in the training phase and the test phase. Section 4 mainly introduces the feasibility and accuracy of BP neural network in business performance management by some statistical parameters. These statistical parameters mainly include error and correlation coefficient, prediction distribution curve, etc. At the same time, in order to more intuitively reflect the accuracy of the predicted value and the true value, it is reflected by the linear correlation coefficient. Finally, the applicability and accuracy of two different types of BP neural network models are verified. Finally, summary is given in Section 5.

2. The Significance of Corporate Performance Evaluation and Large Data-Sets

2.1. The Significance of Big Data for Performance Management

Effective performance evaluation is the key to employee participation, and it can also provide valuable feedback on skills and goals that are important to the success of corporate business [26]. Enterprise performance management is a kind of evaluation by supervisors of work performance of employees. In the evaluation process, the supervisor will determine the strengths and weaknesses of the employees, which will set goals and provide predictions and feedback on future performance of employees. The enterprise performance evaluation system consists of a series of evaluation systems, evaluation index systems, evaluation methods, evaluation standards, and evaluation institutions related to performance evaluation [27]. Companies should consider whether it has laid a good foundation for an establishment of a performance evaluation system in the process of their own development and reflect on whether they have the conditions to establish a value evaluation system. When the company is in a higher stage of development, the internal soft power will slowly grow and strengthen, the corporate culture will gradually become stable, and the quality of the selected employees will often be relatively high [28]. In the era of big data, the storage, collection, and processing of corporate financial information are more convenient, which provides the possibility for the full realization of the management accounting function. Management accounting adds value to the company through data analysis and has important advantages in corporate cost management, operation management, and performance management [29]. The application prospect is broader. Enterprises should pay attention to the innovative application of management accounting in the evaluation system and evaluation content in performance management, so as to make the performance evaluation at all levels of the enterprise more real and effective [30].

First of all, the large data sets technology has changed the traditional way of performance management. The purpose of performance management is not to evaluate, but to ensure that the direction of the organization's development is correct. Therefore, the target value in performance management should be dynamic and adjusted according to changes in the environment. Performance results are not used for employee appraisal, but only for goal correction. The performance goals are set according to influencing factors, and recommendations are given according to certain algorithms [31]. The performance goals are adjusted in real time, and it also can be updated according to each performance completion of staff. The large data sets technology is used to help companies comprehensively utilize massive amounts of data and which can quickly collect, manage, process, and organize data into helpful information to help companies make business decisions. The large data sets technology includes data processing tools (such as R language, etc.), data analysis theories and methods (such as regression, clustering, Bayesian, etc.), the large data set analysis tools, and data visualization [32]. The combination of corporate performance and big data is meaningful research, which will help companies better predict future development trends.

2.2. The Preparation Process of Data-Sets

There are many sources of data, and it is necessary to select stable and testable data sources according to the needs of different indicators. However, it is generally necessary to analyze statistical calibers and statistical schemes for data discrepancies. If the discrepancies are caused by statistical means, it can be ignored. If a data source has large abnormal fluctuations, it can often use another data source for comparative analysis. If the two sides have fluctuations in the same direction and the same magnitude, then it needs to be analyzed from the performance indicators. If the two sides are very different, it is likely to be the data. Data sorting and cleaning are mainly to exclude dirty data sets and abnormal data, which are used to structure the data sets. Figure 1 shows the prediction process of BP neural network. First, the training data sets (such as employee performance, etc) are normalized, the training set is processed into normally distributed data to speed up the convergence speed more quickly, then continue to optimize the weight and bias through the back-propagation method. Once the model is trained, which the weights and biases are applied to the prediction of this research problem, and the test set can be quickly predicted. For the test set, once the BP neural network training completed the training set, the enterprise managers can pass unknown performance parameters to BP neural network, and it can be an efficient output.

3. Method and Theory

3.1. BP Neural Network

BP neural network is one of the most basic neural networks compared to deep learning methods, and it is mainly composed of fully connected layers. It has been successfully used in many fields. It also has strong nonlinear ability and dimensionality reduction ability. It is mainly composed of input layers, hidden layers, output layers, and etc., which the input layer allows multiple inputs. Weights and biases are the parameters that need to be learned. The directional propagation ways and the gradient descent ways are used to continuously fit the reversed error between the true value and the predicted value until the error function converges to find the optimal weight and bias. Figure 2 shows the structure of BP neural network. In this article, corporate performance value and employee personal performance are used as output and input of BP neural network, respectively, it can map the nonlinear relationship between input and output. In this study, a BP neural network with 4 hidden layers was selected, and the learning rate was set to 0.001.

The BP neural network also can be divided into two processes. The first process is a forward operation process. It performs matrix operations on the input, weight, and offset, and which then nonlinearizes the matrix through the activation function to obtain a certain matrix at a certain iteration step. The second step is backpropagation method, which first can calculate the predicted value and the true value through a loss function, and after determining the loss value, performs a derivative operation according to the backpropagation method and automatic differentiation technology to find the region of gradient descent. BP neural network continuously iteratively searches for the smallest gradient according to these two steps and then finds the optimal weights and bias parameters.

The study selects a network with three hidden layers as an introduction, the difference between the predicted value and the true value is the propagation error, which is an output error E of back-propagation, E can be defined in equation (1):where d is the predictive value of saving cost ratio in this study and O is a real value at every days. It can expand above error of equation (1) definition to the hidden layer and further expand to the input layer:where is the weight matrix. Obviously, reducing the error is actually looking for the inverse of gradient descent, and the error gradient descent method is as follows:

3.2. The LSTM Neural Network

In corporate performance evaluation, time characteristics are often also very important. Long and short memory neural networks are very suitable for extracting temporal information features. Corporate performance evaluation is not only the relationship between different influencing factors but also the relationship between the same factor at different times. Corporate economic performance management is often closely related to time. The long and short times memory neural network (LSTM) is used to extract the time characteristics of the enterprise economic data sets. Figure 3 shows the process steps of LSTM models structure. LSTM neural network has obvious advantages in dealing with temporal features, and enterprise performance management evaluation is a feature that is closely related to temporal features. LSTM is used to extract temporal features in enterprise performance management.

Compared to the single BP network without LSTM, the LSTM neural network has a structural change and has a memory function, which is mainly due to the existence of the gate structure, it is the reason why LSTM has the advantage of time memory. BP neural network is similar to convolutional neural network. It can effectively extract and map space-related features. It has strong nonlinearity, but it is difficult to learn time-related features. Due to the existence of forget gates, memory gates, and other structures, LSTM filters historical information, extracts useful historical information, and removes historical information with little relevance, thereby maintaining characteristic information with temporal characteristics. And each layer of LSTM has a strong connection, mainly to prevent useful historical information from being forgotten. The number of LSTM layers used in this study is 3, and the learning rate is 0.0001.

As shown in equation (4), the first step in LSTM is to decide what information to discard from the cell state. The forget gate acts on the LSTM state vector to control the impact of the memory of the previous time stamp on the current time stamp. Parameters such as weights and biases are solved by automatic differentiation technology. When the gate control is equal to 1, the forgetting gates are all open, and LSTM receives all the information of the previous state. When the gate control is equal to 0, the forgetting gate is closed, and LSTM directly ignores and outputs a zero vector. Where is the activation function, is the weight matrix, and the is the output value at the last moment.

As shown in equations (5) and (6), the input gate is used to control the LSTM acceptance of historical information. First, a new input vector is obtained by nonlinear transformation of the input of the current time stamp and the output of the previous time. The input gate controls the amount of input accepted. The control variables of the input gate also come from the input and output. Tanh nonlinear function normalizes the input to between −1 and 1.

After passing the forget gate and memory gate to get the current time variable, which can refresh the variable by the following formula, as shown in equation (7).

As shown in equations (8) and (9), when the output gate is equal to 0, the output is closed, and the internal memory of LSTM is completely cutoff and cannot be used as an output. When the output gate is equal to 1, the output is fully opened, and the state vector of LSTM is all output:

3.3. Normalized Method

Because the input of enterprise performance factors is in different forms, there are certain differences in the form and magnitude of the input, which is unfavorable for the training of BP neural network, and there is a large distribution difference in the amount of input. Employee performance and labor are normalized into a data set conforming to a normal distribution, and its value remains between 0 and 1. Normalizing the input data with better distribution characteristics and correlation can speed up the convergence speed and improve the prediction accuracy. Figure 4 shows the normalized and without normalized methods. The left side of Figure 4 shows the distribution before data normalization, and the right side shows the distribution after data normalization. It can be seen that the data set has better correlation after being normalized, which is beneficial to the training process. In this study, the business performance management data were data preprocessing using a standard normalization method.

3.4. Loss Function and Activation Function

In the process of forward propagation, the input, weight, and bias need to be subjected to activation function for nonlinearization after matrix operation. If the activation function is not processed, the network will lose the nonlinear ability of fitting. The activation function is the source of nonlinearity in the neural network. If the activation function is removed, then the entire network will only have linear operations. In this study, the Sigmoid function is adopt. The Sigmod function is simple to implement and the derivative is easy to obtain; its output is within the interval of [0, 1], so it can be used as the output layer to represent the probability; and it is less affected by noise data. The expression of the Sigmoid function is as shown in equation (10).

The expression of MSE is shown in equation (11). This is a more commonly used loss function. Where MES is the average loss function, the is the real value of employee value, and the is the predicted value of employee value.

The training data sets, testing data sets, and predicted value can be described as equations (12)–(14). The “Train” is the mean of training data sets and the “Test” is the mean of testing data sets. The y is the predicted value.

4. Result Analysis and Discussion

After the BP model is established, the process of iterative training will begin. This study compares the accuracy of two deep learning predictions, the single BP neural network without LSYM layers and a BP neural network with LSTM layers. Figures 5 and 6 show the training loss and test loss under two models conditions. From Figures 5 and 6, it can be seen that the training and test losses of the BP neural network with LSTM are relatively small, which shows that the neural network with LSTM captures the temporal characteristics of enterprise performance very well.

In general, these two models have good convergence, whether it is training loss or testing loss. The two types neural network model have been adopt, which will be used to study the prediction accuracy of cost-saving rate. Meanwhile, it also could be seen from Figure 5 that the training data set and the testing data sets reach the convergence level within 500 steps, which shows that the neural network model can better fit the nonlinear relationship between corporate performance factors and the cost-saving rate very well. It can be seen from Figure 6 that the BP neural network with LSTM layer converges faster than a single BP neural network without LSYM layers, which is mainly due to the time dependence of the cost-saving rate. Moreover, the loss of a single BP neural network fluctuates in the initial stage of the testing data sets and which reaches a stable convergence level in the later stage. From the above, it can be concluded that the BP neural network can have better learning and predictive capabilities in fitting the relationship between the cost-saving rate and the enterprise performance factors. At the same time, it is necessary to fully consider the time dependence between the enterprise performance factors. The neural network will also have better learning and predicting abilities.

Figure 7 shows the predicted value and the true value of business performance of the cost-saving rate within one year. It can also be seen that the difference between the predicted value and the true value was relatively small, and the error is within an acceptable range. The predicted value of cost savings cannot only match the overall trend better with the true value but also that predict the change trend of the cost recovery rate with the number of days. For these two types of networks, the prediction error is within 5%, and the main error occurs where the cost-saving rate changes greatly. The minimum error is only 0.8%, which is an approximately negligible error. Other errors are between 1% and 2%, and the prediction accuracy has been greatly improved. It is due to that the distribution of data set is uneven, which can be sampled in a denser place. As time goes by, the cost-saving rate fluctuates greatly, which is due to the increasing influence of enterprise performance factors, and it shows that the cost-saving rate has a clear correlation with time characteristics. It can also be seen from Figure 8 that the prediction accuracy rate of BP neural network with LSTM layers are slightly higher than the prediction performance of a single BP neural network where the cost-saving rate changes greatly.

The linear correlation coefficient curve can more intuitively reflect the fitting performance of the predicted value and the true value. The linear correlation curve reflects the distribution of the predicted value and the actual value of the enterprise performance. The closer its data value is to the y = x curve, the closer the values of x and y are, which further indicates that the prediction effect is better. It can be seen from Figures 9 and 10 that the data are well distributed on both sides of the linear straight line, which shows that the predicted value is well matched with the true value. At the same time, it can also be seen that the large error of the performance prediction value is the place that deviates from the linear straight line. The value of the correlation coefficient is generally a value between 0 and 1. The closer to 1, the better the predicted value fits. Generally speaking, if the linear correlation coefficient exceeds 0.9, it means that the prediction effect is relatively stable, and if it exceeds 0.95, it means that it has better prediction performance. Most of the correlation coefficients exceed 0.9, which can indicate that the prediction performance can meet the requirements of prediction performance. The closer the predicted value on both sides of the linear fitting straight line indicates the more accurate the employee value prediction in this part. Overall, the correlation coefficients are all over 0.95, which further shows that the BP neural network has a better fitting ability to the cost-saving rate. The correlation coefficient of the BP neural network with LSTM layer exceeds 0.97, which has better accuracy than a single BP neural network. This shows that there is an obvious time correlation between enterprise performance factors and cost-saving rates. When predicting the cost recovery rate in the future, the influence of time characteristics can be fully considered, which can improve the predictive readiness and generalization ability.

5. Conclusion

In the fourth section of this article, the accuracy and feasibility of BP neural network in predicting enterprise performance are shown in detail. Although the data set of corporate performance is highly nonlinear and the fitting relationship between them is complex, the BP neural network also predicts the future development trend of corporate performance very well, which is meaningful for corporate performance management. It is also a good model for reference.

In this research, the BP neural network has been used to predict the future trend of enterprise performance management. From the perspective of training and testing loss functions, the BP neural network model can learn the nonlinear relationship between corporate performance factors and cost-saving rates. The convergence of the loss function is faster and reaches a smaller convergence value. After considering the time characteristics, the learning ability and prediction ability of the BP neural network are improved, that is, the BP neural network has an LSTM layer. This shows that it is necessary to fully consider the influence of time characteristics when predicting the cost-saving rate through multiple factors of enterprise performance. And the learning and forecasting capabilities of the cost-saving rate will be improved. For the prediction of cost-saving rate, the BP neural network can not only match the cost-saving rate of each day well but also can better match the change trend of the cost-saving rate with the number of days. Where the cost-saving rate varies greatly with the number of days, the BP neural network with LSTM layer has better prediction accuracy than the BP neural network, which shows that there is a strong time correlation between multiple factors of corporate performance and the cost-saving rate. The overall prediction error of the cost-saving rate is within 5%, whether it is a single BP neural network or a BP neural network, this is an acceptable prediction range. This model shows good predictive ability and generalization ability. From the correlation coefficient distribution diagram, it can be seen intuitively that the BP neural network model has good predictive performance, and the correlation coefficient values are all over 0.95. The correlation coefficient R of a BP neural network with LSTM layer exceeds 0.97, which shows that this model can better fit the time characteristics between enterprise performance factors and cost-saving rates. And it can improve the predictive readiness and generalization ability. The BP neural network shows the ability to fit between the enterprise performance factors and cost-saving rate, which can provide certain reference value for enterprise performance management. Compared with BP neural network, BP neural network with LSTM layer has better performance in predicting enterprise performance characteristics, which can provide certain reference value for subsequent enterprise managers.

Data Availability

The data used in this article can be reasonably requested by readers and researchers.

Conflicts of Interest

The authors declare that there are no conflicts of interest in the study.

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Copyright © 2022 Zijin Wang and Xiujuan Han. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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