Competitiveness Evaluation of Express Delivery Enterprises Based on the Information Entropy and Gray Correlation Analysis

Abstract

With the rise of e-commerce in China, the express delivery industry has developed rapidly; however, in this stage of rapid growth, the development of express delivery enterprises is uneven. There are problems such as high loss, damage rates, and poor service attitudes. An evaluation of the competitiveness of express delivery enterprises can help these companies understand their shortcomings and learn from each other’s strengths, which can promote the long-term development of express delivery enterprises. In this study, the information entropy method and the gray prediction method were used to establish an index system and analyze the core competitiveness of four listed express delivery companies in China, using indicators such as service quality, price level, market position, guarantee ability, and comprehensive strength; the purpose was to study the competitive advantages of these express companies. By using the gray correlation analysis to calculate the degree of correlation and comparing the size of the degree of correlation, it can be concluded that among the four express delivery companies, ZTO Express shows the strongest competitiveness and that Shentong Express is the weakest. Through the evaluation and analysis of the competitiveness of express delivery enterprises, this study provides a reference basis for operation development and competitiveness improvement of express delivery enterprises in China.

1. Introduction

The rapid development of e-commerce in China is partly driven by the shopping frenzy of Double 11 and Double 12 (which are two major online shopping festivals in China). These festivals were originally created by the e-commerce giant Alibaba and have since become highly anticipated events for both consumers and retailers. During these festivals, consumers can enjoy significant discounts and promotions on various products available on online platforms. The festivals have gained immense popularity in China and have had a profound impact on the growth of the e-commerce and logistics sectors. Additionally, they have played a significant role in shaping consumer behavior and driving economic activities in the country. As a consequence, the volume of delivery parcels has also increased sharply, and the increase in the number of express deliveries has brought forth higher requirements for express delivery companies. These companies need to respond quickly when the number of parcels increases and deliver the goods to customers on time and with good quality and quantity [1]. All companies want to enjoy the dividends brought by e-commerce, which also means that the competition between express delivery companies is becoming increasingly fierce, and the market share of the Tongda Department (consisting of Zhongtong, Shentong, Yuantong, and Yunda) is increasing as a group of prominent express delivery companies in China. These companies have established extensive networks of delivery points and a nationwide distribution system. They are well known for their efficient and reliable express services and offer a wide range of products to meet the diverse needs of consumers. The rise of the Tunda system has positively impacted the express delivery industry, driving competition and development within the sector while providing consumers with convenient and high-quality express services. But who has more competitive advantages among the four companies of Zhongtong, Yuantong, Shentong, and Yunda Express? In this study, the four companies were compared using the information entropy and grey correlation methods to build competitiveness evaluation indicators. The most competitive express delivery company was determined through the evaluation. Based on the obtained results, the other express delivery companies can find and overcome their shortcomings to develop themselves on a long-term basis rather than relying on their previous achievements.

This study aims to establish an index system through systematic analysis and optimization methods and use the gray comprehensive evaluation model to evaluate the core competitiveness of Zhongtong, Yuantong, Shentong, and Yunda Express, which are listed express delivery companies in China, under the current Internet-based economic environment. The analysis of the gray correlation method was as follows: first, establish a model of influencing factors that affect the development of the express delivery industry; then, quantify the indicators of each influencing factor and establish their initial value; and, finally, use a numerical calculation method to find the weight of each influencing factor, and then according to the empirical analytical results, focus on analyzing the competitiveness between Yunda, Yuantong, Shentong, and Zhongtong Express, as well as the most critical factors affecting the development of the express delivery industry [2,3,4].

According to the data from the National Bureau of Statistics, the total retail sales of consumer goods in China in 2022 was CNY 43.97 trillion, of which online retail sales accounted for CNY 1.31 billion, showing an increase of 5.3% in year-on-year growth [5,6,7]. Online shopping has led to a significant increase in the number of parcels and business volume of courier companies. E-commerce platforms also pay more attention to the quality and timeliness of courier companies when considering courier partners, rather than just paying attention to prices. China’s express parcel volume has increased from 9.19 billion pieces in 2013 to 63.52 billion pieces, and the volume of online parcels due to the annual Double 11, Double 12, and other carnivals has increased sharply, which has also brought challenge to express delivery companies [8,9,10,11,12]. As the leading enterprises in the industry, how to obtain competitive advantage has become a problem for major express delivery companies. Many express delivery companies continue to increase their investment, including the construction of transshipment centers, the acquirement of more truck vehicles and automated sorting equipment, and the optimization of information technology platforms. The profitability of major express delivery companies has improved, the brand effect has gradually formed, and the available practical resources are also tilted toward these major express delivery enterprises.

There are many calculation methods to analyze the competitiveness of express delivery enterprises, and the most commonly used methods include analytic hierarchy, data envelopment analysis, and gray correlation analysis, according to Li [13]. To evaluate the competitiveness of express delivery enterprises in China, this study constructed a comprehensive index system from four aspects: market scale, service quality, guarantee ability, and financial strength. Combined with the gray correlation analysis method and the entropy weight method, an evaluation model was constructed, and the six most representative express delivery companies in China were selected for empirical analysis. According to the factors affecting the international competitiveness of China’s private express delivery enterprises and their current situation, and based on the connotation of the international competitiveness of these enterprises and the characteristics of the express delivery industry, Liu [14] constructed an evaluation index system to determine the shortcomings of China’s private express delivery enterprises and enhance their international competitiveness. By studying the factors influencing the low-carbon performance of express delivery enterprises, Gao [15] constructed a comprehensive evaluation index system for the low-carbon performance of express delivery enterprises from five aspects: packaging, warehousing, transportation, distribution, and recycling. The fuzzy comprehensive evaluation method was used to evaluate the low-carbon performance of express delivery enterprises, and finally, the low-carbon performance of these enterprises was improved through the PDCA cycle (which stands for Plan, Do, Check, and Act, a management method used for continuous improvement in various fields, including business, healthcare, and quality management. It is a cyclical process that guides organizations in effectively implementing change and achieving desired outcomes). Liu [16] took private express delivery enterprises as the research object, discussed the relevant theoretical knowledge, summarized the current situation and development of China’s private express delivery enterprises, and carried out a competitiveness analysis by establishing a gray correlation model. According to the factors and current situation affecting the international competitiveness of China’s private express delivery enterprises, and based on the connotation of the international competitiveness of these enterprises and the characteristics of the express delivery industry, Bu [17] constructed an international competitiveness evaluation index system of China’s private express delivery enterprises to determine the shortcomings leading to the lack of international competitiveness of China’s private express delivery enterprises and to improve their international competitiveness. In the study by Zhu [18], the grey correlation analysis method and the analytic hierarchy method were used to construct a competitiveness evaluation index system of listed traditional Chinese medicine enterprises in China. This study can provide a reference for understanding the competitiveness evaluation of listed enterprises of traditional Chinese medicine. In the study by Fan [19], a comprehensive evaluation method of retail e-commerce enterprises based on the analytic hierarchy method and the fuzzy theory was proposed, which not only could provide insights into the macro-trend of e-commerce development but also reflect the micro-state of each component of e-commerce competitiveness. The proposed method was applied to evaluate the competitiveness of four typical e-commerce companies, and the results verified the effectiveness and operability of the method. By studying the factors influencing the low-carbon performance of express delivery enterprises, Arslan [20] constructed a comprehensive evaluation index system from five aspects: packaging, warehousing, transportation, distribution, and recycling. The fuzzy comprehensive evaluation method was used to evaluate the low-carbon performance of express delivery enterprises, and, finally, the low-carbon performance of these enterprises was improved through the PDCA cycle. Nobar [21] took private express delivery enterprises as the research object, discussed the relevant theoretical knowledge, summarized the current situation and development of China’s private express delivery enterprises, and carried out a competitiveness analysis by establishing a gray correlation model. Based on the abovementioned studies, this paper discusses the feasibility of corresponding countermeasures.

The AHP method is a decision-making approach that combines the use of an analytic hierarchy process and fuzzy logic. It allows decision makers to incorporate subjective judgments and handle uncertain information when evaluating criteria and alternatives. By using linguistic variables and fuzzy membership functions, this method provides a flexible and comprehensive approach to decision making in situations where precise data is limited. The AHP method has many subjective analysis components, and the construction of the weight coefficient matrix is greatly affected by the choice of subjective factors, which is highly dependent on the experience of experts [22]. The data envelopment method is challenging to use when judging the importance of indicators. The weights solved based on a DEA model do not consider the differences between data indicators and dimensions. Thus, the weights are different, which cannot truly reflect the importance of indicators. In this study, the information entropy method and the gray association analysis method were combined to determine the weight of each evaluation index of four express delivery companies, which eliminates the influence of human factors when determining the weight and enables the evaluation result to be more objective and closer to the ground truth.

2. Materials and Methods

2.1. Construction of a Core Competitiveness Evaluation Index System of Express Delivery Enterprises

This study took Yunda, Yuantong, Shentong, and ZTO Express as examples to compare and evaluate the core competitiveness of express delivery companies. The data were obtained from the annual reports of these listed express delivery companies and the information published on the website of China Post. The quality and ability of different express delivery companies are different, and the evaluation of core competitiveness can be based on the following aspects: service quality, price level, market position, comprehensive strength, and guarantee ability [23].

By studying the core competitiveness of these express delivery companies, we not only could see which express delivery company has more investment prices but also allow the express delivery companies to understand where their shortcomings are, and where the gap is from their competitors in the same industry; such information could help the express delivery companies to gain competitive advantage. The core competitiveness evaluation index system of express delivery enterprises must be built taking into account the following aspects: first, the evaluation index system established should be comprehensive, scientific, and includes both qualitative and quantitative components, and secondly, it needs to be feasible and realistic.

2.1.1. Evaluation and Analysis of the Competitiveness of Express Delivery Enterprises

To establish the core competitiveness evaluation system of express delivery enterprises, the current resources, customer service capabilities, and market structure of the express delivery enterprises were analyzed. First of all, the advanced machinery and equipment of the express delivery enterprises, the number of transshipment centers, their operating outlets, etc., are company resources [24]. The second factor is customer service capability, which can be measured by the number of transit centers, the number of operating outlets, the number of operating vehicles, and the customer complaint rate. Finally, the position of the express delivery companies in the market was examined from the aspects of market share, such as total assets, business volume growth rate, and net profit.

2.1.2. The Construction of Competitive Evaluation Index System for Express Delivery Enterprises

There are many indicators of the competitiveness of express delivery enterprises, and the evaluation of enterprise competitiveness should select as few indicators as possible to reflect the most comprehensive information, and these indicators need to be representative. The first-level indicators have five dimensions: service quality, price level, market position, comprehensive strength, and guarantee strength. The secondary indicators include ten sub-indicators, such as appeal rate, ticket revenue, and market share [25]. When determining the first-level evaluation index and the second-level evaluation index, the comprehensive representative indicators were determined based on a review of the existing literature, and the information entropy method and gray correlation analysis method were used to analyze and evaluate the competitiveness of the express delivery enterprises through these index data. The information entropy method was used to calculate the weight of each index, and by comparing the importance of each element at the same level, a comparative judgment matrix was constructed. Finally, the comprehensive weight of each level element on the system goal was calculated. The evaluation indicators of the competitiveness of the express delivery enterprises are shown in

Table 1. Competitiveness evaluation index system of express delivery enterprises.

Competitive analysis	First Indicators	Secondary Indicators
	Quality of service $U_1$	Appeal rate $U_{11}$
	Price level $U_2$	Revenue per ticket $U_{21}$
	Market position $U_3$	Market share $U_{31}$
		Total assets/billion $U_{32}$
	Comprehensive strength $U_4$	Business growth rate $U_{41}$
		Operating income $U_{42}$
		Net profit $U_{43}$
	Guarantee capability $U_5$	Number of transit centers $U_{51}$
		Number of operating outlets $U_{52}$
		Number of operating vehicles $U_{53}$

.

2.2. Grey Correlation Analysis

Many comprehensive evaluation methods exist, such as the weighted average method, functional–coefficient method, analytic hierarchy, and expert evaluation. However, although these methods are easy to understand and utilize, they are usually subjective, and their evaluation accuracy is lower than the gray correlation analysis method. The gray association analysis method is more objective because it does not need to determine weights. This method has a low requirement in terms of the amount of sample data needed. It also does not need a specific distribution law. Thus, it is best to choose the gray association analysis method to evaluate the core competitiveness of the enterprises [26].

2.2.1. Standardization of Data

Consider a set of n express delivery enterprises with m core competitive evaluation indicators. Thus, there are m data columns, where (i = 1, 2,…, n) and (j = 1, 2, 3,…, m). Let Xi(j) denote the value of the jth indicator for the ith enterprise, with (i = 1, 2, 3, 4, 5) and (j = 1, 2,…, m). To eliminate the influence of different dimensions in the original data, it is necessary to replace the initial data with dimensionless, sibling, positive additive data. Furthermore, the initial data need to be standardized to obtain standardized data [27].

2.2.2. Constructing the Optimal Reference Series

Set the normalized indicator data column as follows:

$$\begin{array}{l}{X}_1^{\prime }\left(j\right)=\{X_1^{\prime }\left(1\right), X_1^{\prime }\left(2\right), \dots \dots , X_1^{\prime }\left(j\right)\}\\ X_2^{\prime }\left(j\right)=\{X_2^{\prime }\left(1\right), X_2^{\prime }\left(2\right), \dots \dots , X_2^{\prime }\left(j\right)\}\\ \dots \dots \hfill \\ X_n^{\prime }\left(j\right)= \{X_n^{\prime }\left(1\right), X_n^{\prime }\left(2\right), \dots \dots , X_n^{\prime }\left(j\right)\}\end{array}$$

$X_0\left(j\right)$ represents the optimal value of item j of an express delivery company. If the value of an indicator is as significant as possible, then $X_0\left(j\right)$ takes the value of 1. If the value of an indicator is smaller, which is better, then $X_0\left(j\right)$ takes the value of 0. Based on the above indicator data columns, the optimal reference data column is constructed for $X_0\left(j\right)$. The optimal reference data column consists of the best values in the above data columns for each indicator, denoted as $X_0\left(j\right)$ = {$X_0\left(1\right)$,$X_0\left(2\right)$,$X_0\left(3\right)$,……,$X_0\left(m\right)$} [28].

2.2.3. Constructing the Optimal Reference Series

Process the original data in advance and then calculate the absolute difference between the reference series and the comparison series at each point in time; the absolute difference between the reference series and the comparison series at a certain point in time ${∆}_i\left(j\right)$ can be calculated using the following formula: ${∆}_i\left(j\right)=|X_0\left(j\right)-X_i\left(j\right)|$ (i = 1, 2, 3,…, n; j = 1, 2, 3,…, m), where $X_0$ is the reference number. Then, two levels of minimum absolute difference are calculated: ${∆}_{min}=\frac{min}{i}\frac{min}{j}\left|X_0\left(j\right)-X_{i }\left(j\right)\right|$(i = 1, 2, 3,…, n; j = 1, 2, 3,…, m). Similarly, the two-stage maximum absolute difference can be calculated as follows: ${∆}_{max}$ = $\frac{max}{i}\frac{max}{j}|X_0\left(j\right)-X_{i }\left(j\right)|$ (i = 1, 2, 3,…, n; j = 1, 2, 3,…, m) [29].

2.2.4. Determining the Correlation Factor

The grey correlation analysis method was used to obtain the jth index value for the i-th express delivery company $X_i^{\prime }\left(j\right)$ and the jth optimal index value of the i−th express delivery company $X_0\left(j\right)$. The grey correlation coefficient is as follows:

The correlation coefficient of $X_i\left(\mathrm{j}\right)$ to $X_0\left(\mathrm{j}\right)$ on the jth element is calculated as follows. Among them, $\alpha$ is the resolution coefficient, which ranges between 0 and 1, and is usually 0.5.

$${\xi }_i\left(j\right)=\frac{\frac{min}{i}\frac{min}{j}\left|X_0\left(j\right)-X_i^{\prime }\left(j\right)\right|+\alpha \frac{max}{i}\frac{max}{j}|X_0\left(j\right)-X_i^{\prime }\left(j\right)|}{\left|X_0\left(j\right)-X_i^{\prime }\left(j\right)\right|+\alpha \frac{max}{i}\frac{max}{j}|X_0\left(j\right)-X_i^{\prime }\left(j\right)|}$$

2.2.5. Using the Information Entropy Method to Determine the Weight of Each Evaluation Index

Information entropy measures the value of information. When comparing the competitiveness evaluation index of a certain express delivery company with different enterprises in the same line, if the degree of difference between them is smaller, then this indicator is less important for measuring the competitiveness of express delivery enterprises. Through a comparison of the information entropy values, the importance of the competitiveness evaluation index of different express delivery enterprises is judged [12]. First, in the standard sample matrix $X_i^{\prime }\left(j\right)=\frac{X_i\left(\mathrm{j}\right)-{\mathrm{X}\left(\mathrm{j}\right)}_{min}}{{X\left(\mathrm{j}\right)}_{max}-{\mathrm{X}\left(\mathrm{j}\right)}_{min}}$; according to the formula $X_i^{″}\left(j\right)$=$\frac{X_i^{\prime }\left(j\right)}{{\sum }_{i=1}^n{X}_i^{\prime }\left(j\right)}$, the values of the second-level indicators of item j are normalized to obtain the normalized standard evaluation sample matrix $X_i^{″}\left(j\right)$. Secondly, according to the normalized standard sample matrix data, the formula $e_j=-\frac{1}{lni}{\sum }_{i=1}^n{X}_i^{″}\left(j\right)ln{X}_i^{\prime \prime }\left(j\right)$ is used to calculate the entropy size of the jth indicator. Thirdly, according to the formula $w_j=\frac{1-e_j}{\sum _{j=1}^m(1-e_j)}$, the weight size of the second-level index of item j is obtained.

2.2.6. Calculating and Sorting the Gray Correlation Degree

The grey correlation coefficient represents the degree of correlation between the ith express delivery company and the optimal reference series, and the degree of similarity between the ideal express delivery companies and actual express delivery companies is represented by the optimal reference series. The optimal correlation degree $r_i$ is the degree to which $X_i\left(j\right)$ is related to the optimal reference data column $X_0\left(j\right)$. To pool the information in each correlation coefficient for easy comparison, an averaging method is used to find the correlation degree $r_i$ of the curve, $r_i$ = ${\sum }_{j=1}^n{w}_j\ast {\xi }_i\left(j\right)$. The competitiveness of the express delivery enterprises is proportional to the gray correlation degree, and the obtained correlation degrees $r_i$ are sorted by size. Finally, the competitiveness of the express delivery enterprises is compared; the greater the gray correlation degree, the stronger the competitiveness of an express delivery enterprise. The smaller the grey correlation, the weaker the competitiveness.

2.2.7. Data Collection on Core Competitiveness of Express Delivery Enterprises

According to the data published by the four express delivery companies on the Internet, the evaluation index data showing the four express delivery enterprises’ competitiveness are shown in

Table 2. Data on core competitiveness evaluation indicators of express delivery enterprises.

Evaluation Indicators	Yunda	YTO	Shentong	ZTO
Appeal rate $U_{11}$	3.37%	5.06%	4.78%	2.7%
Revenue per ticket $U_{21}$	3.19	2.95	3.11	1.72
Market share $U_{31}$	15.79%	14.35%	11.6%	19.08%
Total assets/billion yuan $U_{32}$	224.9665	221.6097	138.5522	458.905
Business growth rate $U_{41}$	43.59%	36.78%	44.17%	42.2%
Operating income/billion yuan $U_{42}$	344.0405	311.5112	230.8894	221.0995
Net profit/billion yuan $U_{43}$	16.6770	16.8079	14.0831	56.7127
Transit centers $U_{51}$	59	78	68	91
Number of operating outlets/piece $U_{52}$	27,466	32,005	29,000	30,000
Number of trunk transport vehicles/unit $U_{53}$	8622	5500	5200	7350

.

According to the above steps of evaluating the competitiveness of express delivery enterprises using the information entropy method and the gray correlation analysis method, the index data columns can be obtained according to the index data in Table 2. Lists the initial matrix $X_i\left(j\right)$.

$$X_i\left(j\right)=\left(\begin{array}{cccccccccc}3.37& 3.19& 15.79& 224.97& 43.59& 344.04& 16.68& 59& 27466& 8622\\ 5.06& 2.95& 14.35& 221.61& 36.78& 311.51& 16.81& 78& 32005& 5500\\ 4.78& 3.11& 11.60& 138.55& 44.17& 230.89& 14.083& 68& 29000& 5200\\ 2.70& 1.72& 19.08& 458.91& 42.20& 221.10& 56.71& 91& 30000& 7350\end{array}\right)$$

2.2.8. Data Processing

Normalize the data in Table 2. $X_0\left(j\right)(\mathrm{j}=\mathrm{1,2},\dots ,10)$ represents the optimal value of the j-th index of the four express delivery companies. For an express delivery company, the larger the value of an indicator, the more competitive it is, and $X_0\left(j\right)$ takes a value of 1. If a metric is smaller and the company is less competitive, then the value of $X_0\left(j\right)$ is 0. The smaller the value of the appeal rate, the better it is for express delivery companies, whereas the larger the values of revenue per ticket, market share, total assets, business growth rate, operating income, net profit, number of transshipment centers, number of operating outlets, and number of operating vehicles, the better it is for express delivery companies. This allows the construction of the optimal reference data columns $X_0\left(j\right)$ = {0,1,1,1,1,1,1,1,1,1}, where the value of j is the same as the number of index items used for evaluation. According to the common practice of grey correlation analysis, a value of 0.5 is used, and the gray correlation coefficient matrix $X_i^{\prime }\left(j\right)$ and ${\xi }_i\left(j\right)$ are obtained.

$$X_i^{\prime }\left(j\right)=\left(\begin{array}{cccccccccc}0.284& 1& 0.560& 0.270& 0.922& 1& 0.061& 0& 0& 1\\ 1& 0.837& 0.368& 0.259& 0& 0.735& 0.064& 0.594& 1& 0.088\\ 0.881& 0.946& 0& 0& 1& 0.080& 0& 0.281& 0.337& 0\\ 0& 0& 1& 1& 0.733& 0& 1& 1& 0.558& 0.628\end{array}\right)$$

$${\xi }_i\left(j\right)=\left(\begin{array}{cccccccccc}0.638& 1& 0.532& 0.407& 0.865& 1& 0.347 & 0.333& 0.333& 1\\ 0.333& 0.754& 0.442& 0.403& 0.333& 0.654 & 0.348 & 0.552 & 1& 0.354 \\ 0.362& 0.903& 0.333& 0.333& 1& 0.352 & 0.333& 0.410 & 0.430 & 0.333\\ 1& 0.333& 1& 1& 0.652& 0.333& 1& 1& 0.531 & 0.573 \end{array}\right)$$

3. Results

3.1. The Information Entropy Method Determines the Weights of the Secondary Indicators

Information entropy can be used to measure the value of information, and the information entropy method is used in this study to determine the weight of each secondary index. According to the size of the information entropy value, the importance of the competitiveness index of the four express delivery enterprises is determined in reverse. Each measurement index is given a corresponding weight, and then the competitiveness of the express delivery enterprises is measured and evaluated. If the competitiveness evaluation index of a specific express delivery enterprise is different from the other express delivery enterprises in the same industry, then the greater the importance of this index in measuring the competitiveness of these express delivery enterprises [30]. The specific operation steps are as follows:

First, the values in the standard sample matrix are normalized to obtain $X_i^{″}\left(j\right)=\frac{X_i^{\prime }\left(j\right)}{{\sum }_{i=1}^4{X}_i^{\prime }\left(j\right)}$, for the resulting new matrix $X_i^{″}\left(j\right)$.

$$X_i^{″}\left(j\right)=\left(\begin{array}{cccccccccc}0.131& 0.359& 0.290& 0.177& 0.347& 0.551& 0.054& 0& 0& 0.583\\ 0.462& 0.301& 0.191& 0.169& 0& 0.405& 0.057& 0.317& 0.527& 0.051\\ 0.407& 0.340& 0& 0& 0.377& 0.044& 0& 0.150& 0.178& 0\\ 0& 0& 0.519& 0.654& 0.276& 0& 0.889& 0.533& 0.295& 0.366\end{array}\right)$$

Secondly, according to the normalized standard sample matrix data, the formula $e_j=-\frac{1}{lni}{\sum }_{i=1}^n{X}_i^{″}\left(j\right)ln{X}_i^{″}\left(j\right)$ is used to calculate the entropy size of the jth indicator. If $X_i^{″}\left(j\right)=0$, ${\sum }_{i=1}^n{X}_i^{″}\left(j\right)ln{X}_i^{″}\left(j\right)$ = 0), the calculation result is as follows: e = ($e_1$, $e_2$, …, $e_{10}$) = (0.713, 0.791, 0.733, 0.638, 0.787, 0.600, 0.307, 0.710, 0.725, 0.602). Then, the resulting result is $w_j=\frac{1-e_j}{{\sum }_{j=1}^m(1-e_j)}$, and the weight size of each metric is obtained as W = ($w_1$,$w_2$,…,${ w}_{10}$) = (0.084, 0.062, 0.079, 0.107, 0.063, 0.118, 0.204, 0.085, 0.081, 0.117). The specific data of the weights of each indicator are summarized in

Table 3. List of weights for each indicator.

First Indicators	Weight	Second Indicators	Weight
Quality of service	0.084	Appeal rate	0.084
Price level	0.062	Revenue per ticket	0.062
Market position	0.185	Market share	0.079
		Total assets/billion yuan	0.107
Comprehensive strength	0.385	Business growth rate	0.063
		Operating income/billion yuan	0.118
		Net profit/billion yuan	0.204
Guarantee capability	0.284	Transit centers	0.085
		Number of operating outlets/piece	0.081
		Number of trunk transport vehicles/unit	0.117

.

The comprehensive evaluation results are obtained by substituting the gray correlation coefficient value and the weight of each index into the following formula: $r_i$ = ${\sum }_{j=1}^m{w}_j\times {\xi }_i\left(j\right)$, R = (r₁, r₂, r₃,…, r₁₀) = E × W = (0.617, 0.491, 0.429, 0.770).

3.2. Preliminary Analysis of the Calculation Results

The index data corresponding to each express delivery company is listed as a comparison data column, and the resolution coefficient value is 0.5. Then, all indicators (10 items) are taken as a comprehensive evaluation index system to determine the competitiveness of each express delivery enterprise based on four aspects: service quality, market position, guarantee ability, and comprehensive strength [27]. According to the calculated weight of the impact of each measure on competitiveness, the results are compiled into the following table to calculate the correlation degree and ranking of each express delivery company in various situations. The comprehensive evaluation results are shown in

Table 4. Comprehensive evaluation results of Zhongtong, Yuantong, Shentong, and Yunda Express Company.

Enterprise Indicator Data		Yunda	YTO	Shentong	ZTO
Quality of service	Appeal rate	0.054	0.028	0.031	0.084
Price level	Revenue per ticket	0.062	0.047	0.056	0.021
Market position	Market share	0.042	0.035	0.026	0.079
	Total assets/billion yuan	0.043	0.043	0.035	0.107
Comprehensive strength	Business growth rate	0.054	0.021	0.063	0.041
	Operating income/billion yuan	0.118	0.077	0.041	0.039
	Net profit/billion yuan	0.071	0.071	0.068	0.204
Guarantee capability	Transit centers	0.028	0.047	0.035	0.085
	Number of operating outlets/piece	0.027	0.081	0.035	0.043
	Number of trunk transport vehicles/unit	0.117	0.042	0.039	0.067
Total		0.617	0.491	0.429	0.770

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4. Discussion

Constructing a comprehensive evaluation index system and using the information entropy method and gray correlation analysis method to analyze and evaluate the competitiveness of express delivery enterprises to improve their core competitiveness is a process that fully reflects the planning, implementation, inspection, and waste disposal of circular management—the PDCA cycle management. The PDCA cycle, also known as the mass loop, improves the quality of managed objects. PDCA stands for Plan, Do, Check, and Act. In this study, the PDCA cycle was used to establish the competitiveness indicators of four express delivery companies, and the process is shown in Figure 1.

After completing the first cycle, a second cycle continues from the source. Through the above PDCA cycle, each cycle will impact the discovery of problems and their solutions in express delivery enterprises, resulting in an increase in business volume and improving the performance of express delivery enterprises.

5. Conclusions

Based on field research, qualitative analysis was used to conduct systematic analysis, and a comprehensive evaluation index system to determine the competitiveness of express delivery enterprises was constructed. Each evaluation index to determine the competitiveness of express delivery enterprises was obtained using the information entropy method and the gray correlation analysis method. Then, the indicators affecting the competitiveness of the express delivery enterprises were clarified. The PDCA cycle was used as a theoretical framework to continuously improve the core competitiveness of express delivery companies. The feasibility of the comprehensive evaluation index system was demonstrated via examples, and the results can provide a reference for future competitiveness evaluations of express delivery enterprises. At the same time, the indicators of the evaluation system have dynamic characteristics, and there are problems such as professionalism and scientific selection. This is also an essential direction for further in-depth study of the indicator system. It is feasible to evaluate the competitiveness of express delivery enterprises using the information entropy method and gray correlation analysis method, and these methods can simplify the evaluation index to solve complex problems, which is helpful to evaluate the competitiveness of express delivery enterprises. Attention must also be paid to how express delivery companies can improve their operational efficiency and enhance competitiveness.