Study on the extension evaluation of smart logistics distribution of fresh agricultural products based on the perspective of customers

Abstract

The rapid development of the Internet has accelerated the expansion of e-commerce sacle of fresh agricultural products. The actual audience of smart logistics distribution of fresh agricultural products is customers, and customers enjoy the process and results of distribution services. However, the current research mainly selects indicators from the aspects of enterprise performance, cost and technical level based on the perspective of managers and technicians, which make it difficult to truly reflect customers’ feelings in the evaluation results. At the same time, the evaluation methods mainly focus on the comprehensive evaluation method and fuzzy evaluation method. These evaluation methods are greatly affected by subjective factors in the evaluation grade distribution, and the assignment is often relatively complete and inaccurate. To solve these problems, this paper constructs the evaluation index system of intelligent logistics distribution of fresh agricultural products from the perspective of customers, so that the selection of indicators is more in line with the real wishes of customers. And we use the extension function to construct the correlation function for multi-level extension evaluation to ensure the accuracy of the evaluation results. Taking X logistics enterprise as an example, this paper verifies the scientificity of the evaluation index system of intelligent logistics distribution of fresh agricultural products through empirical research, which has reference significance for further improving the intelligent logistics distribution of fresh agricultural products.

Keywords

Smart logistics distribution fresh agricultural products customer perspective extenics

1 Introduction

With the development of the Internet, commodity transactions between people are more closer and efficient. The Internet of things, the Internet and cloud computing have changed the goal and direction of logistics [1]. And the logistics industry is developing towards intelligent logistics park and intelligent logistics platform [2]. “Digital earth” is gradually changing to “smart Earth” with the continuous improvement of high and new technologies. The progress of Internet of Things technology has brought opportunities to the development of all walks of life. The term “smart logistics” has also been brought into the public’s view by China Internet of Things. Samrt logistics is to use integrated intelligent technology to make intelligent logistics imitate human intelligence, and use the thinking ability of perception, learning, reasoning and judgment to make the logistics system solve some problems in logistics [3]. Smart logistics integrates the existing resources and technologies of the Internet of things, sensor networks and the Internet, and then realizes logistics automation, visualization, controllability, intelligence and networking through fine and dynamic scientific management, so as to improve resource utilization rate and productivity level [4].

With the globalization and large-scale development of economy, especially the rapid development of electronic commerce, smart logistics has become an important guarantee of the efficient operation of e-commerce, and plays an increasingly important role in the national economy. Smart logistics has become an important factor to improve logistics operation efficiency, reduce logistics costs and improve competitiveness [5]. It is also an effective way to reply the challenges of rapidly changing customer expectations, seize the opportunities brought by new technologies, and promote the development of new business models [6].

Smart logistics is first applied in the fresh agricultural products industry. Smart logistics distribution of fresh agricultural products is the product of the combination of fresh agricultural products distribution and Internet of Things technology on the basis of “smart logistics”. It is a logistics distribution management system for data processing and analysis of all links of fresh agricultural products logistics operation. With the characteristics and advantages of the Internet of things, it can improve the timeliness and accuracy of information decision-making in the circulation process of fresh agricultural products. At the same time, it can continuously adapt to the changes of the external environment in the supply and tracking of fresh agricultural products, and greatly improve the intensive and open distribution of fresh agricultural products, so as to make more full use of resources and improve the service level. Taking customer orientation as the service purpose can also benefit many parties. The development of smart logistics distribution of fresh agricultural products is the future development trend and the commanding point of competition of the fresh agricultural products logistics industry.

With the popularity of distribution centers and the requirement of fast response, logistics distribution scheduling becomes more and more complex, which puts forward higher requirements for scheduling algorithms and data processing capabilities. At present, the logistics scheduling problem is mainly studied from logistics mechanism [7] and cloud-based approach [8, 9]. For example, Wang et al. [10] developed an optimization model driven by real-time road traffic and connected vehicle conditions; Anderluh et al. [11] developed a multi-objective two-level model to improve the flexibility of intelligent city logistics. Among them, the Internet of Things technology is most widely used in logistics system [12]. Some specific problems of smart logistics based on the Internet of Things, such as vehicle routing problems, loading problems and storage distribution problems, can be solved by using advanced information and communication technology [13].

At present, relevant scholars have also made some research on the evaluation of smart logistics distribution. Liu et al. [14], Prasanna and Hemalatha [15] and Lai [16] took managers as the starting point to build the evaluation index system of enterprise logistics distribution from the aspects of management process, enterprise operation vitality and cost, turnover, personnel quality, timeliness of distribution, warehousing safety and so on. Montreuil [17], Govindan et al. [18], Guerrero-ibanez et al. [19] and Liu et al. [20] took technologists as the starting point to elaborate the construction of smart logistics distribution system and the influencing factors based on Internet of things, RFID, big data and other technologies. In the era of intelligent logistics, new generation information technologies such as Internet of things, big data and cloud computing are developing rapidly and gradually applied to all links of logistics. At the same time, online shopping and new retail have created a huge express market, and customers also put forward higher and more diversified requirements for express dellivery [21]. Therefore, improving the distribution efficiency and customer satisfaction is one of the important issues facing smart city distribution [22]. Through the analysis of the literature found that the current research on logistics distribution service is mostly based on the determination of relevant indicators from the perspective of technicians or managers, which focuses more on the performance, cost and technical level of the enterprise. However, the audience of intelligent logistics distribution is the vast number of customers, and the customers enjoy the process and the results of distribution service more. There are great differences in the concerns of the two when selecting indicators, so there may be deviation in the determination of indicators.

In term of the evaluation contents and methods of intelligent logistics distribution, Nathanail et al. [23], Bozorgi-Amiri and Khorsi [24], Xiong et al. [25], etc. evaluates the intelligent logistics enterprise, logistics distribution center and path, and warehousing system through analytic hierarchy process and multi-objective planning model, and on this basis, they conducted an empirical study; Wang [26] and Zhang [27] used the fuzzy comprehensive evaluation method to evaluate the logistics distribution center and urban smart logistics distribution respectively; Li et al. [28] evaluated the grain logistics by using the artificial heuristic model; Pan and Nguyen [29] evaluated logistics enterprises form service capacity and customer perception by balanced scoring method. The actual situation is that due to the continuous strengthening of the application of Internet of Things and other technologies in logistics distribution, the relevant indicators of smart logistics distribution evaluation have changed accordingly. The research on the evaluation of smart logistics by the combination of qualitative indicators and quantitative indicators needs to be strengthened, and the traditional evaluation methods (comprehensive evaluation method, fuzzy evaluation method, etc.) are greatly affected by subjective factors in the evaluation process, such as hierarchy assignment, which may affect the accuracy of evaluation results.

As for the research on smart logistics, scholars mainly focus on the enterprise evaluation, the application of information technology, the selection and evaluation of distribution centers and routes, and there are few studies on the evaluation of logistics distribution effect. Therefore, in view of the above two aspects, this paper determines evaluation indicators from the perspective of customers, which can reflect the real needs, perception and satisfaction of customers. And the evaluation indicators is also conducive to the market promotion of smart logistics distribution of fresh agricultural products (closer to customers). At the same time, in view of the defect that the evaluation weight of indexs are easily affected by subjective factors and the assigned values are often integer defects, this paper uses the extension evaluation method to establish the correlation function between the evaluation grade and the index, and ensure the accuracy of the evaluation results. Our research enriches the evaluation system and research methods of smart logistics distribution of fresh agricultural products, and provides a basis for logistics distribution enterprises to improve sustainable development and market competitiveness.

2 Establishing the evaluation index system of smart logistics distribution of fresh agricultural products from the perspective of customers

2.1 Selection of evaluation indicators

Through the review of relevant literature such as smart logistics and fresh agricultural products logistics distribution, as well as in-depth interviews with relevant customers, combined with the actual situation of current fresh agricultural products distribution enterprises. We first select indicators through customer participation indicators, and then select indicators according to the process and results of fresh agricultural products distribution service. This is mainly reflected in determine the evaluation indicators from three aspects: the convenience, timeliness and accuracy of consumers enjoying the intelligent logistics distribution service of fresh agricultural products. To be specific, for example, in terms of warehousing, we select indicators that can avoid customer cargo errors; in terms of intelligent transportation, we select indicators that can show the real-time status of customer cargo; in terms of information control, we select indicators that can provide real-time services to customers; and in terms of orders and pick up, we select indicators that can provide convenience to customers. The selection of these indicators removes the enterprise cost, performance and other relevant indicators concerned by managers, and increases the proportion of customers enjoying distribution service indicators, so as to avoid the problem that technicians only pay attention to technical level and technology conversion rate. From the perspective of customers, we integrate the technical elements directly related to customers in the intelligent logistics distribution of fresh agricultural products into the index selection, and reflects the focus of customers from the process and results of intelligent logistics distribution service of fresh agricultural products. However, there may be multiple collinearity problem among the above indicators, so we use factor analysis method to deal with the indicators, and eliminate the evaluation indicators with low factor load. The questionnaire was designed according to 24 initial indicators to count the importance of smart logistics distribution service indicators of fresh agricultural products that customers think. It is mainly aimed at the target groups with online shopping habits and certain knowledge and ability, and conducted through questionnaire survey platform, social network and some paper questionnaires. We distributed 50 paper questionnaires, collected 50, and then received 120 responses from the survey platform. A total of 170 responses were received, of which 131 were valid. We input the questionnaire data into spss20.0 statistical software for reliability and validity analysis. The maximum variance orthogonal rotation method and principal component analysis method are used for factor analysis of the measured data. The kmo test coefficient is 0.900, greater than 0.7, indicating that the sample data is suitable for factor analysis. The observed value of Bartlett ball test is 1673.97, the degree of freedom is 276, and the significance is 0, indicating that there are common factors among the sample groups, which can be extracted by factor analysis. After eliminating the automorphic factor and the indicators (7 indicators) whose components are less than 0.5 and greater than 0.5 in all factors, a new dimension can be obtained by rotating the load matrix table. At the same time, four common factors were extracted, the characteristic root of the first factor was 9. 294, which explained 42.246% of the total variance of all the original variables; the characteristic root of the second factor was 1.622, which explained 7.373% of the total variance; the characteristic value of the third factor was 1.417, which explained 6.441% of the total variance; the characteristic value of the 4 factor was 1.059, which explained 4.816% of the total variance. These factors explain 60.876% of the total variance of all variables, which is generally well explained and can basically summarize the basic information of the original model. The analysis results are ideal, and the Cronbach’s α factor is 0. 933, so the reliability is very good.

Main factor 1 includes: automatic sorting capacity (the automatic sorting amount of goods after entering the warehouse), intelligent inventory count (the inventory is checked by the automatic inventory equipment), RFID hand-held machine management (the use of RFID hand-held machine), automatic goods in and out of the warehouse (the way of goods in and out of the warehouse), data mining and prediction (the control of inventory goods to reduce the shortage rate). These indicators are related to the intelligent equipment in the warehouse, so they are called intelligent warehousing system.

Main factor 2 includes: intelligent delivery reminder (through APP, website platform or SMS to remind logistics delivery and arrival information), payment method (customer’s capital settlement method), emergency order processing capacity (whether the customer’s emergency order can be handled in time), intelligent pick-up (using intelligent equipment for customer self-service pick-up), business level of service personnel (whether the service personnel are skilled in the use of smart devices). These indicators are related to customer order processing, so they are called intelligent order processing system.

Main factor 3 includes: road condition reminders (using the Internet of things and GPS to provide real-time road conditions for distribution vehicle routes), GPS vehicle scheduling (using cloud computing and GPS to achieve rapid and effective scheduling between sites to provide convenience for customers), online information exchange on the portal (conduct two-way information communication with customers anytime, anywhere in the portal of distribution enterprises), arrival control of fresh agricultural products (specifically referring to the timeliness of goods arrival) and real-time update of logistics information (logistics status and information update). This category of indicators includes various road information and service information, so it is called intelligent information management system.

The main factor 4 includes: the use of electronic map (using GIS to store and transmit map data), vehicle line tracking and monitoring (using GPS technology to track and monitor the delivery vehicles to discover the cargo safety in time).These 2 indicators are called intelligent transportation system.

Table 1 is obtained from the above.

Table 1
Evaluation index factor analysis results of smart logistics distribution of fresh agricultural products

Primary indicators Secondary indicators Factor loading(s) Eigenvalues Cumulative contribution rate

Intelligent Storage System (C₁) Automatic sorting capacity (C₁₁) 0. 670 9. 294 42. 246%

Intelligent inventory count (C₁₂) 0. 584

RFID handset management (C₁₃) 0. 780

Automatic stock in and stock out quantity (C₁₄) 0. 753

Data mining and prediction (C₁₅) 0. 799

Intelligent order processing system (C₂) Emergency order processing capacity (C₂₁) 0. 590 1. 622 49. 619%

Methods of payment (C₂₂) 0. 539

Smart pick up (C₂₃) 0. 616

Business level of service staff (C₂₄) 0. 516

Intelligent delivery reminder (C₂₅) 0. 592

Intelligent information management system (C₃) Road condition reminders (C₃₁) 0. 708 1. 417 56. 060%

GPS vehicle dispatching (C₃₂) 0. 669

Online information exchange on Web portals (C₃₃) 0. 769

Cargo arrival control (C₃₄) 0. 661

Real time updating of logistics information (C₃₅) 0. 508

Intelligent transportation system (C₄) Use of electronic maps (C₄₁) 0. 619 1. 059 60. 876%

Vehicle line tracking and monitoring (C₄₂) 0. 802

Primary indicators	Secondary indicators	Factor loading(s)	Eigenvalues	Cumulative contribution rate
Intelligent Storage System (C₁)	Automatic sorting capacity (C₁₁)	0. 670	9. 294	42. 246%
	Intelligent inventory count (C₁₂)	0. 584
	RFID handset management (C₁₃)	0. 780
	Automatic stock in and stock out quantity (C₁₄)	0. 753
	Data mining and prediction (C₁₅)	0. 799
Intelligent order processing system (C₂)	Emergency order processing capacity (C₂₁)	0. 590	1. 622	49. 619%
	Methods of payment (C₂₂)	0. 539
	Smart pick up (C₂₃)	0. 616
	Business level of service staff (C₂₄)	0. 516
	Intelligent delivery reminder (C₂₅)	0. 592
Intelligent information management system (C₃)	Road condition reminders (C₃₁)	0. 708	1. 417	56. 060%
	GPS vehicle dispatching (C₃₂)	0. 669
	Online information exchange on Web portals (C₃₃)	0. 769
	Cargo arrival control (C₃₄)	0. 661
	Real time updating of logistics information (C₃₅)	0. 508
Intelligent transportation system (C₄)	Use of electronic maps (C₄₁)	0. 619	1. 059	60. 876%
	Vehicle line tracking and monitoring (C₄₂)	0. 802

2.2 Determination of the weight of evaluation indicators

Because the constructed evaluation indicators contain qualitative indicators that are difficult to measure accurately, analytic hierarchy process has great advantages in solving the problem of combining qualitative indicators with quantitative indicators in multi-objective analysis. Therefore, the evaluation objects and indicators in the evaluation system should be consistent with the hierarchical structure model required by analytic hierarchy process (AHP). Then we use the principle of analytic hierarchy process to determine the weight of each evaluation index, and the smart logistics distribution evaluation of fresh agricultural products from the perspective of customers as the overall target layer A of the hierarchical analysis, and the intelligent warehousing system C₁, intelligent order processing system C₂, intelligent information convention system C₃ and intelligent transportation system C₄ as the primary sub target layer; automatic sorting ability, vehicle route tracking and monitoring and other 17 indicators as the second-level sub-target layer. Through the evaluation of the relative importance of each index by 8 experts of the enterprise according to the scale 1–9, and constantly summarizing and unifying opinions, we finally determine the comparative value of indicators at all levels, establish the judgment matrix, calculate the maximum eigenvalue and corresponding eigenvector of each matrix, and then conduct the consistency test. Get the index weight in Table 3.

Table 3
Weight of smart logistics distribution evaluation indicators of fresh agricultural products

Target level First level sub target level and weight Secondary sub target level and weight

Evaluation on intelligent logistics distribution of fresh agricultural products (A) Intelligent Storage System (C₁ 0. 1697) Automatic sorting capacity (C₁₁ 0. 3735)

Intelligent inventory count (C₁₂ 0. 3546)

RFID handset management (C₁₃ 0. 0353)

Automatic stock in and stock out quantity (C₁₄ 0. 1722)

Data mining and prediction (C₁₅ 0. 0644)

Intelligent order processing system (C₂ 0. 4724) Emergency order processing capacity (C₂₁ 0. 1804)

Methods of payment (C₂₂ 0. 0881)

Smart pick up (C₂₃ 0. 1664)

Business level of service staff (C₂₄ 0. 3694)

Intelligent delivery reminder (C₂₅ 0. 1957)

Intelligent information management system (C₃ 0. 2854) Road condition reminders (C₃₁ 0. 1098)

GPS vehicle dispatching (C₃₂ 0. 1665)

Online information exchange on Web portals (C₃₃ 0. 2382)

Cargo arrival control (C₃₄ 0. 4147)

Real time updating of logistics information (C₃₅ 0. 0708)

Intelligent transportation system (C₄ 0. 0725) Use of electronic maps (C₄₁ 0. 2500)

Vehicle line tracking and monitoring (C₄₂ 0. 7500)

3 Extension comprehensive evaluation method

When evaluating the smart logistics distribution of fresh agricultural products from the perspective of customers, the factors involved are often variable. With the change of the evaluation subject and the evaluation target, the evaluation index will also change when it is established. Even if the same enterprise is located in different cities, the evaluation factors will change. Therefore, as things are in a state of change and development, the evaluation indicators should also be changed appropriately. In addition, when determining the corresponding value of the evaluation grade, the traditional method often adopts integer assignment, which is not precise enough and easy to be affected by human factors. So the size of its value also affects the final evaluation result.

In view of the above problems, this paper uses the extension comprehensive evaluation method to evaluate the smart logistics distribution of fresh agricultural products from the perspective of customers. This method can expand research according to the increase or decrease or change of the characteristics of the items, and its dynamic variability is consistent with the change of the evaluation indicators from the customer’s perspective. In addition, the evaluation grade can be determined by establishing the correlation function between the evaluation indicators and the evaluation grade, avoiding the impact of subjective evaluation of the evaluation grade and ensuring the objectivity and comprehensiveness of the evaluation. Specifically, the extension evaluation method mainly includes the following 4 steps:

Table 2
Judgment matrix

A C₁ C₂ C₃ C₄ C ₁ C₁₁ C₁₂ C₁₃ C₁₄ C₁₅

C₁ 1 1/3 1/2 3 C₁₁ 1 1 7 4 5

C₂ 3 1 2 5 C₁₂ 1 1 9 3 4

C₃ 2 1/2 1 4 C₁₃ 1/7 1/9 1 1/5 1/3

C₄ 1/3 1/5 1/4 1 C₁₄ 1/4 1/3 5 1 7

C₁₅ 1/5 1/4 3 1/7 1

C ₂ C₂₁ C₂₂ C₂₃ C₂₄ C₂₅ C ₃ C₃₁ C₃₂ C₃₃ C₃₄ C₃₅

C₂₁ 1 3 1 1/3 1 C₃₁ 1 1/2 1/3 1/4 3

C₂₂ 1/3 1 1/2 1/2 1/3 C₃₂ 2 1 1/2 1/2 2

C₂₃ 1 2 1 1/3 1 C₃₃ 3 2 1 1/3 3

C₂₄ 3 2 3 1 2 C₃₄ 4 2 3 1 4

C₂₅ 1 3 1 1/2 1 C₃₅ 1/3 1/2 1/3 1/4 1

C ₄ C₄₁ C₄₂

C₄₁ 1 1/3

C₄₂ 3 1

A	C₁	C₂	C₃	C₄	C ₁	C₁₁	C₁₂	C₁₃	C₁₄	C₁₅
C₁	1	1/3	1/2	3	C₁₁	1	1	7	4	5
C₂	3	1	2	5	C₁₂	1	1	9	3	4
C₃	2	1/2	1	4	C₁₃	1/7	1/9	1	1/5	1/3
C₄	1/3	1/5	1/4	1	C₁₄	1/4	1/3	5	1	7
					C₁₅	1/5	1/4	3	1/7	1
C ₂	C₂₁	C₂₂	C₂₃	C₂₄	C₂₅	C ₃	C₃₁	C₃₂	C₃₃	C₃₄	C₃₅
C₂₁	1	3	1	1/3	1	C₃₁	1	1/2	1/3	1/4	3
C₂₂	1/3	1	1/2	1/2	1/3	C₃₂	2	1	1/2	1/2	2
C₂₃	1	2	1	1/3	1	C₃₃	3	2	1	1/3	3
C₂₄	3	2	3	1	2	C₃₄	4	2	3	1	4
C₂₅	1	3	1	1/2	1	C₃₅	1/3	1/2	1/3	1/4	1
C ₄	C₄₁	C₄₂
C₄₁	1	1/3
C₄₂	3	1

Step 1: Determining classical domain and node field

According to the concept of matter element, the comprehensive evaluation model of smart logistics and distribution of fresh agricultural products from the perspective of customers can be established. Set a total of M indicators for the comprehensive evaluation of smart logistics distribution of fresh agricultural products, namely C₁, C₂ . . . C_m. Based on this indicator, the evaluation indicators of smart logistics distribution of fresh agricultural products are divided into n levels, and described by the following qualitative and quantitative comprehensive evaluation matter element model (i.e. the classic domain Q_j). Q_j = (U_j, C, V_j), U_j is the level j, C is the level evaluation index, and V_j is the value range of the evaluation index, which is the classic domain. Section Q_u can be expressed as: Q_u = (U, C, V_u), U is the whole of the grade and V_u is the range of values U takes with respect to C, i.e. the section of U.

Step 2: Determining the material element to be evaluated

For the objects to be evaluated, the actual data or analysis results are expressed by matter element Q_i as follows:

$Q_{i} = (\begin{matrix} \begin{matrix} N \end{matrix} & \begin{matrix} c_{i 1} \\ c_{i 2} \\ . . . \\ c_{ip} \end{matrix} & \begin{matrix} v_{i 1} \\ v_{i 2} \\ . . . \\ v_{ip} \end{matrix} \end{matrix})$ (1)

In the formula: N represents the level of intelligent logistics and distribution enterprise of fresh agricultural products to be evaluated;

V_ik is the quantitative value of N about C_ik, i.e. the specific data obtained from the analysis of the object to be evaluated, where, k = 1, 2 . . . p, and p are the number of level 2 indicators.

Step 3: Establishing correlation function

In extension science, extension set is used to express the degree to which a thing has a certain nature. After the establishment of the matter element model for the intelligent logistics distribution of fresh agricultural products, in order to evaluate the logistics distribution status of the enterprise, it is necessary to calculate the “proximity” between the matter element to be evaluated and the classic domain of the matter element model. In the actual calculation, “proximity” needs to choose different calculation methods according to the characteristics of the indicators. In this paper, we choose to use the primary correlation function method in the extension theory.

Let: $ρ (v_{ik}, V_{j}) = | v_{ik} - \frac{a_{ji} + b_{ji}}{2} | - \frac{1}{2} (b_{ji} - a_{ji})$ (2) $ρ (v_{ik}, V_{u}) = | v_{ik} - \frac{a_{ui} + b_{ui}}{2} | - \frac{1}{2} (b_{ui} - a_{ui})$ (3)

Point out the “proximity” of V_ik and interval V_j and V_u respectively. For example, if P (V_ik, V_j)≥0, it means V_ik is not in the interval V_j, and if P (V_ik, V_u)≤0, it means V_ik is in the interval V_u, and different negative values indicate different positions of V_ik in the interval V_u.

Determine the secondary indicators of the object to be evaluated about the evaluation level j (j = 1, 2 . . . m):

$\begin{matrix} k_{j} (c_{jk}) = \\ {\frac{ρ (v_{ik}, V_{j})}{ρ (v_{ik}, V_{u}) - ρ (v_{ik}, V_{j})}, ρ (v_{ik}, V_{u}) \neq ρ (v_{ik}, V_{j}) \\ - ρ (v_{ik}, V_{j}) - 1, ρ (v_{ik}, V_{u}) = ρ (v_{ik}, V_{j}) \end{matrix}$ (4)

Among them, k_j (C_jk) is the K Level two index in the i level one index about the evaluation level j (j = 1, 2 . . . m).

k_j (c_jk)≥0 indicates that v_ik belongs to V_j, and the larger k_j (c_jk) is, the more attributes v_ik belongs to V_j; k_j (c_jk)≤0 indicates that v_ik does not belong to V_j, and the larger k_j (c_jk) is, the further away v_ik is from attributes of interval Vj.

Step 4: Multilevel extension evaluation

(1) First level evaluation

According to the weight line vector w_i = (w_ik) of each secondary indicator multiplied by the correlation matrix K (Ci_k) = (k_j(C_ik)) of each evaluation grade, the correlation matrix K (b_i) of each primary indicator for each evaluation grade can be obtained.

(2) Second level evaluation

According to the weight line vector w_i = (w_ik) of each level indicator multiplied by the correlation matrix K (B) = (k_j(b_i)) of each evaluation level, the correlation matrix K (N) of each evaluation level can be obtained.

(3) Determine the evaluation level

If k_j0 (N) = max k_j (N) is satisfied, and j = 1, 2 . . . m, it is said that the object to be evaluated N belongs to level j.

4 Evaluation and Analysis on intelligent logistics distribution of fresh agricultural products of X logistics enterprises

4.1 Development status of X logistics enterprise

The company is a comprehensive logistics distribution service provider integrating warehousing, transportation, order processing and information processing in Southwest China.In order to improve the timeliness of the company’s distribution in Southwest China and provide customers with convenient and efficient logistics distribution services, the company has established a large distribution center in Southwest China. It actively participates in the construction of intelligent logistics and distribution of fresh agricultural products, and also introduces advanced technology and equipment such as automatic sorting, intelligent inventory and comprehensive information data. Through the field survey of enterprises, we make quantitative statistics on quantifiable indicators, and then score the non quantifiable indicators after the interview. The actual measurement values of the enterprise are shown in Table 4 below:

Table 4
Index value of intelligent logistics distribution of fresh agricultural products of enterprise X

First level indicators Second level indicators Actual indicator value Unified indicator value

Intelligent Storage System (C₁) Automatic sorting capacity (C₁₁) 75% 75

Intelligent inventory count (C₁₂) 30% 30

RFID handset management (C₁₃) 95% 95

Automatic stock in and stock out quantity (C₁₄) 72% 72

Data mining and prediction (C₁₅) 82% 82

Intelligent order processing system (C₂) Emergency order processing capacity (C₂₁) 98% 98

Methods of payment (C₂₂) 94% 94

Smart pick up (C₂₃) 48% 48

Business level of service staff (C₂₄) 85 points 85

Intelligent delivery reminder (C₂₅) 82 points 82

Intelligent information management system (C₃) Road condition reminders (C₃₁) 73 points 73

GPS vehicle dispatching (C₃₂) 76 points 76

Online information exchange on Web portals (C₃₃) 92 points 92

Cargo arrival control (C₃₄) 90% 90

Real time updating of logistics information (C₃₅) 95 points 95

Intelligent transportation system (C₄) Use of electronic maps (C₄₁) 70% 70

Vehicle line tracking and monitoring (C₄₂) 93% 93

First level indicators	Second level indicators	Actual indicator value	Unified indicator value
Intelligent Storage System (C₁)	Automatic sorting capacity (C₁₁)	75%	75
	Intelligent inventory count (C₁₂)	30%	30
	RFID handset management (C₁₃)	95%	95
	Automatic stock in and stock out quantity (C₁₄)	72%	72
	Data mining and prediction (C₁₅)	82%	82
Intelligent order processing system (C₂)	Emergency order processing capacity (C₂₁)	98%	98
	Methods of payment (C₂₂)	94%	94
	Smart pick up (C₂₃)	48%	48
	Business level of service staff (C₂₄)	85 points	85
	Intelligent delivery reminder (C₂₅)	82 points	82
Intelligent information management system (C₃)	Road condition reminders (C₃₁)	73 points	73
	GPS vehicle dispatching (C₃₂)	76 points	76
	Online information exchange on Web portals (C₃₃)	92 points	92
	Cargo arrival control (C₃₄)	90%	90
	Real time updating of logistics information (C₃₅)	95 points	95
Intelligent transportation system (C₄)	Use of electronic maps (C₄₁)	70%	70
	Vehicle line tracking and monitoring (C₄₂)	93%	93

4.2 Evaluation steps for intelligent logistics distribution of fresh agricultural products of enterprise X

4.2.1 Establish the matter element to be evaluated and determine the classical domain and the node domain

This paper intends to divide the evaluation of intelligent logistics distribution of fresh agricultural products into 5 levels: U = {u₁,u₂,u₃,u₄,u₅} ={Excellent, good, medium, average, poor} corresponds to {80∼100, 60∼80, 40∼60, 20∼40, 0∼20}. According to the matter element Q = (R,c,v) is the basic element describing things in extenics, the matter elements of the four first-order evaluation indicators are:

$Q_{1} = (\begin{matrix} \begin{matrix} R_{1} \end{matrix} & \begin{matrix} c_{11} \\ c_{12} \\ c_{13} \\ c_{14} \\ c_{15} \end{matrix} & \begin{matrix} 75 \\ 30 \\ 95 \\ 72 \\ 82 \end{matrix} \end{matrix}) Q_{2} = (\begin{matrix} \begin{matrix} R_{2} \end{matrix} & \begin{matrix} c_{21} \\ c_{22} \\ c_{23} \\ c_{24} \\ c_{25} \end{matrix} & \begin{matrix} 98 \\ 94 \\ 48 \\ 85 \\ 82 \end{matrix} \end{matrix})$

$Q_{3} = (\begin{matrix} \begin{matrix} R_{3} \end{matrix} & \begin{matrix} c_{31} \\ c_{32} \\ c_{33} \\ c_{34} \\ c_{35} \end{matrix} & \begin{matrix} 73 \\ 76 \\ 92 \\ 90 \\ 95 \end{matrix} \end{matrix}) Q_{4} = (\begin{matrix} \begin{matrix} R_{4} \end{matrix} & \begin{matrix} c_{41} \\ c_{42} \end{matrix} & \begin{matrix} 70 \\ 93 \end{matrix} \end{matrix})$

Through the formulation of the evaluation level, the classic domain of the intelligent storage system and its subordinate indicators can be obtained:

$Q_{1} (b_{1}) = (\begin{matrix} \begin{matrix} U_{1} \end{matrix} & \begin{matrix} c_{11} \\ c_{12} \\ . . . \\ c_{15} \end{matrix} & \begin{matrix} < 0, 20 > \\ < 0, 20 > \\ . . . \\ < 0, 20 > \end{matrix} \end{matrix})$ $Q_{2} (b_{1}) = (\begin{matrix} \begin{matrix} U_{1} \end{matrix} & \begin{matrix} c_{11} \\ c_{12} \\ . . . \\ c_{15} \end{matrix} & \begin{matrix} < 20, 40 > \\ < 20, 40 > \\ . . . \\ < 20, 40 > \end{matrix} \end{matrix})$

$Q_{3} (b_{1}) = (\begin{matrix} \begin{matrix} U_{1} \end{matrix} & \begin{matrix} c_{11} \\ c_{12} \\ . . . \\ c_{15} \end{matrix} & \begin{matrix} < 40, 60 > \\ < 40, 60 > \\ . . . \\ < 40, 60 > \end{matrix} \end{matrix})$

$Q_{4} (b_{1}) = (\begin{matrix} \begin{matrix} U_{1} \end{matrix} & \begin{matrix} c_{11} \\ c_{12} \\ . . . \\ c_{15} \end{matrix} & \begin{matrix} < 60, 80 > \\ < 60, 80 > \\ . . . \\ < 60, 80 > \end{matrix} \end{matrix})$ $Q_{5} (b_{1}) = (\begin{matrix} \begin{matrix} U_{1} \end{matrix} & \begin{matrix} c_{11} \\ c_{12} \\ . . . \\ c_{15} \end{matrix} & \begin{matrix} < 80, 100 > \\ < 80, 100 > \\ . . . \\ < 80, 100 > \end{matrix} \end{matrix})$

Joint domain: $Q (b_{1}) = (\begin{matrix} \begin{matrix} U \end{matrix} & \begin{matrix} c_{11} \\ c_{12} \\ . . . \\ c_{15} \end{matrix} & \begin{matrix} < 0, 100 > \\ < 0, 100 > \\ . . . \\ < 0, 100 > \end{matrix} \end{matrix})$

Similarly, the classic domains and sections of intelligent order processing system, intelligent information management system and intelligent transportation system can be obtained.

4.2.2 Calculation of correlation

According to the above formula of correlation degree, the correlation degree of indicators at all levels can be calculated. Therefore, the correlation degree of indicators at different levels after calculation is shown in Table 5:

Table 5
Correlation statistics of various indicators under different levels

k₁ k₂ k₃ k₄ k₅

c₁₁ –0. 167 0. 250 –0. 375 –0. 583 –0. 688

c₁₂ –0. 625 –0. 500 –0. 250 –0. 500 –0. 250

c₁₃ 4. 000 –0. 750 –0. 875 –0. 917 –0. 938

c₁₄ –0. 222 0. 400 –0. 300 –0. 533 –0. 650

c₁₅ 0. 125 –0. 100 –0. 550 –0.700 –0. 775

c₂₁ 1. 000 –0. 900 –0. 950 –0. 967 –0. 975

c₂₂ 5. 000 –0. 700 –0. 850 –0. 900 –0. 925

c₂₃ –0. 400 –0. 200 0. 200 –0. 143 –0. 368

c₂₄ 0. 500 –0. 250 –0. 625 –0. 750 –0. 813

c₂₅ 0. 125 –0. 100 –0. 550 –0. 700 –0. 775

c₃₁ –0. 206 0. 350 –0. 325 –0. 550 –0. 663

c₃₂ –0. 143 0. 200 –0. 400 –0. 600 –0. 700

c₃₃ 7. 000 –0. 600 –0. 800 –0. 867 –0. 900

c₃₄ 9. 000 –0. 500 –0. 750 –0. 833 –0. 875

c₃₅ 4. 000 –0. 750 –0. 875 –0. 917 –0. 938

c₄₁ –0. 250 0. 500 –0. 250 –0. 500 –0. 625

c₄₂ 6. 000 –0. 650 –0. 825 –0. 883 –0. 913

	k₁	k₂	k₃	k₄	k₅
c₁₁	–0. 167	0. 250	–0. 375	–0. 583	–0. 688
c₁₂	–0. 625	–0. 500	–0. 250	–0. 500	–0. 250
c₁₃	4. 000	–0. 750	–0. 875	–0. 917	–0. 938
c₁₄	–0. 222	0. 400	–0. 300	–0. 533	–0. 650
c₁₅	0. 125	–0. 100	–0. 550	–0.700	–0. 775
c₂₁	1. 000	–0. 900	–0. 950	–0. 967	–0. 975
c₂₂	5. 000	–0. 700	–0. 850	–0. 900	–0. 925
c₂₃	–0. 400	–0. 200	0. 200	–0. 143	–0. 368
c₂₄	0. 500	–0. 250	–0. 625	–0. 750	–0. 813
c₂₅	0. 125	–0. 100	–0. 550	–0. 700	–0. 775
c₃₁	–0. 206	0. 350	–0. 325	–0. 550	–0. 663
c₃₂	–0. 143	0. 200	–0. 400	–0. 600	–0. 700
c₃₃	7. 000	–0. 600	–0. 800	–0. 867	–0. 900
c₃₄	9. 000	–0. 500	–0. 750	–0. 833	–0. 875
c₃₅	4. 000	–0. 750	–0. 875	–0. 917	–0. 938
c₄₁	–0. 250	0. 500	–0. 250	–0. 500	–0. 625
c₄₂	6. 000	–0. 650	–0. 825	–0. 883	–0. 913

4.2.3 Multi-level extension evaluation

Step 1: First level extension evaluation

According to the weight line vector w_i = (w_ik) of each level two indicator multiplied by the correlation matrix K (C_ik) = (k_J (c_ik)) of each evaluation grade, the correlation matrix K (b_i) of each level one indicator for each evaluation grade can be obtained, then it can be calculated that:

K(c₁) = [–0.173,–0. 048,–0.261,–0.564,–0.540]

K(c₂) = [0.763,–0.369,–0.551,–0.691,–0.770]

K(c₃) = [5.636,–0.331,–0.666,–0.777,–0.833]

K(c₄) = [4.437,–0.362,–0.681,–0.787,–0.841]

Step 2: Secondary extension evaluation

According to the weight line vector w_i = (w_ik) of each level indicator multiplied by the correlation matrix K (B) = (k_J (b_i)) of each evaluation level, the correlation matrix K (N) of each evaluation level can be obtained, then it can be calculated that:

K(N) = [2.261,–0.303,–0.544,–0.700,–0.754]

It is found that the maximum value of 2.261 is in the optimal level, so the evaluation level of intelligent logistics distribution of fresh agricultural products of the enterprise is excellent.

4.3 Result analysis

From the evaluation results, it can be seen that the evaluation grade of the fresh agricultural products logistics distribution enterprise is excellent, but there are also divergences in different systems of smart logistics distribution of fresh agricultural products. It can be seen that enterprises are in an advantageous position in intelligent information management system and intelligent transportation system, and in the second level of relative backwardness in intelligent storage system. Although the intelligent order processing system is in a good position, its relevance is relatively small.

(1) In the intelligent warehousing system, there are two aspects to be improved:

Automatic sorting capability. The biggest advantage of automatic sorting is that it can be fast and efficient. Besides, it can reduce the number of cargo handling times, cargo damage and the error rate of sorting. Therefore, for the enterprise, automatic sorting should be adopted in the processing of single orders as far as possible to give full play to the advantages of automatic sorting. It is understood that manual sorting is also one of the important reasons for cargo damage. Therefore, the enterprise needs to increase the amount of automatically sorted goods to improve the sorting capacity of the distribution enterprise.

Intelligent inventory counting. At present, the intelligent inventory equipment owned by enterprises is obviously insufficient, and a large number of personnel are required for regular inventory, which undoubtedly increases the workload. At the same time, the deficiency in replenishment also seriously restricts the development of intelligent logistics distribution of the company. Therefore, we suggest that enterprises increase intelligent inventory equipment and combine it with computer system to share inventory data. In addition, the company also needs to find the shortage problem in time and replenish it. The timeliness of delivery is also an important indicator in the process of meeting customer needs.

In the intelligent order processing system, intelligent pick up is needed to be improved. At present, the main smart pick up methods in the market include APP pick up and intelligent pick-up container pick up. The advantage of intelligent pick-up container mainly lies in its ability to meet the time demand of customers, which solves the problems of some office workers by allowing customers to pick up goods at any time. The biggest advantage of APP picking up is that it can pick up goods directly by scanning the code, which saves customers’ time. The company has relatively few applications in this aspect, so it can increase the investment in intelligent pickup methods to meet the personalized needs of customers.

4.4 Suggestions

Based on the above analysis, the following suggestions are put forward for the smart logistics industry:

Strengthen the application of blockchain technology. Logistics and supply chain are dominated by information flow. Blockchain technology can closely connect various industries through digitization without too much interference from other factors. And it can provide reliable information about shared products through public blockchain, ensure the security, reliability, traceability and authenticity of data, and increase the trust between supply chain participants and end consumers.

Strengthen the construction of information systems and information sharing platforms. With the rapid development of information technology, information system will be more simple and quick real-time monitoring. The government should speed up the construction of market-oriented information platform and vigorously build logistics information sharing platform from the perspective of industrial benefits. In the process of construction, the government should take the establishment of a national unified information exchange system as the basis, pay attention to the integration of various resources and information, mobilize the enthusiasm of enterprises, and encourage enterprises to improve innovative and efficient operation modes. At the same time, the market mechanism should introduce smart logistics, improve relevant supporting service institutions, and build an open market-oriented smart logistics promotion service system. In addition, advanced information technologies such as Internet of things, cloud computing and big data should be used to deeply mine and develop various data and technologies to build a logistics park platform.

Formulate and improve relevant laws and regulations, and build unified standards. Standardization can not only reduce logistics costs, but also ensure the coordinated development of logistics technology and improve the level of logistics development. Firstly, the government should simultaneously plan and implement policy support and financial support, planning, design, development, construction and operation of public service content and communication facilities in intelligent logistics, create logistics information interconnection. It can also use direct bidding or strategic guidance to promote the research and development of smart logistics technology. Secondly, the government should pay attention to the construction of standards, summarize the commonalities and characteristics of logistics operation in different industries and fields, learn from advanced experience and combine with national conditions to formulate logistics standards and information standards suitable for national conditions. Finally, coordination needs to be strengthened. The government should rely on the standardization coordination mechanism across regions, departments and industries to coordinate and promote the construction of intelligent logistics standard system and the formulation of various professional standards, promote the formulation and improvement of relevant laws and regulations, and gradually build a scientific, systematic, advanced and open logistics standard system framework. At the same time, standardization should be guided by encouraging enterprises.

Focus on personnel development and training. There are a large number of employees and lack of unified training for employees. We should make full use of various resources to develop education, encourage schools and enterprises to carry out multi-directional and multi-level learning work, and learn advanced experience and deficiencies of enterprises to cultivate compound talents with practical ability.

5 Conclusion

From the perspective of customers, this paper constructs the evaluation system of intelligent logistics distribution of fresh agricultural products by using the extension method. The main research conclusions are as follows:

To determine the importance of each indicator from the perspective of customers can improve the accuracy of indicator selection. At the same time, it determines the evaluation indicators of smart logistics distribution of fresh agricultural products from the perspective of customers, which is more closely connected with customers’ enjoying logistics distribution services and more acceptable. And it is also conducive to the construction and promotion of smart logistics distribution of fresh agricultural products.

In terms of evaluation methods, the qualitative indicators are formally described by matter elements in order to effectively combine with quantitative indicators, and qualitative indicators are quantified by objects, features and characteristic values, which play a role in the evaluation system of qualitative indicators, and also provide reference for the introduction of qualitative indicators in other evaluation fields.

Footnotes

Acknowledgments

This study was supported by Guangxi Philosophy and Social Science Research Project: Research on the transformation and upgrading path and countermeasures of Guangxi manufacturing industry under the Internet business ecological environment (21FYJ055). We would like to express our sincere gratitude to the anonymous reviewers, and the editors for their truly valuable comments.

Conflicts of interest

We declare no conflict of interest. Additionally, the funders had no role in the design of the study, data collection, data analyses, data interpretation, writing of the manuscript, and decision to publish the results.

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Target level	First level sub target level and weight	Secondary sub target level and weight
Evaluation on intelligent logistics distribution of fresh agricultural products (A)	Intelligent Storage System (C₁ 0. 1697)	Automatic sorting capacity (C₁₁ 0. 3735)
		Intelligent inventory count (C₁₂ 0. 3546)
		RFID handset management (C₁₃ 0. 0353)
		Automatic stock in and stock out quantity (C₁₄ 0. 1722)
		Data mining and prediction (C₁₅ 0. 0644)
Intelligent order processing system (C₂ 0. 4724)	Emergency order processing capacity (C₂₁ 0. 1804)
		Methods of payment (C₂₂ 0. 0881)
		Smart pick up (C₂₃ 0. 1664)
		Business level of service staff (C₂₄ 0. 3694)
		Intelligent delivery reminder (C₂₅ 0. 1957)
Intelligent information management system (C₃ 0. 2854)	Road condition reminders (C₃₁ 0. 1098)
		GPS vehicle dispatching (C₃₂ 0. 1665)
		Online information exchange on Web portals (C₃₃ 0. 2382)
		Cargo arrival control (C₃₄ 0. 4147)
		Real time updating of logistics information (C₃₅ 0. 0708)
Intelligent transportation system (C₄ 0. 0725)	Use of electronic maps (C₄₁ 0. 2500)
		Vehicle line tracking and monitoring (C₄₂ 0. 7500)