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International Journal of Performability Engineering  2020 , 16 (5): 775-783 https://doi.org/10.23940/ijpe.20.05.p11.775783

Orginal Article

Cooperative Quality Evaluation of Supply Chain using Structural Characteristics

Xu Aidi, Shang Yunfeng*

College of International Business, Zhejiang Yuexiu University of Foreign Languages, Shaoxing, 312000, China

通讯作者:  * Corresponding author. E-mail address: yfshang101@163.com* Corresponding author. E-mail address: yfshang101@163.com

版权声明:  2020 【-逻*辑*与-】#x000a9; 2020 Totem Publisher, Inc. All rights reserved.

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Abstract

There is a quality benefit error in the block collaborative supply chain that is affected by the environmental factors of the supply market. In order to improve the ability of collaborative quality evaluation, a supply chain collaborative quality evaluation method based using structural characteristics is proposed. The sampling model of collaborative quality characteristic information of block collaborative supply chain is constructed. According to the quantitative recurrent analysis results of the collaborative quality sample data, block chain information fusion is carried out, and the association rules fusion feature distribution parameter set of block collaborative supply chain collaborative quality panel data is extracted. The collaborative quality data fusion of block collaborative supply chain is carried out by using the method of random probability density feature detection. Combined with the method of piecewise linear estimation, the statistical feature quantity of supply chain collaborative quality evaluation is constructed. According to the prior sample quantitative recurrent analysis results of supply chain collaborative quality evaluation, the collaborative quality feature quantity is analyzed, and the cooperative quality feature quantity of block collaborative supply chain is extracted. The structure feature extraction and fusion clustering method are used for information clustering. According to the distributed fusion results of characteristics, the collaborative quality of supply chain is evaluated. The simulation results show that the proposed method has high accuracy and good confidence in the evaluation of supply chain collaborative quality, and it improves the ability of collaborative quality control of block collaborative supply chain.

Keywords: fuzzy C-means ; clustering ; block cooperative supply chain ; cooperative quality evaluation

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Xu Aidi, Shang Yunfeng. Cooperative Quality Evaluation of Supply Chain using Structural Characteristics[J]. International Journal of Performability Engineering, 2020, 16(5): 775-783 https://doi.org/10.23940/ijpe.20.05.p11.775783

© 2020 Totem Publisher, Inc. All rights reserved.

1.Introduction

Block collaborative supply chain is a network structure of supply chain systems that realize energy sales and transmission. With the continuous transmission link of block collaborative supply chain, the collaborative quality of block collaborative supply chain is getting higher and higher. It is necessary to construct the collaborative quality evaluation and monitoring model of block collaborative supply chain. The big data information fusion method is used to evaluate the collaborative quality of block collaborative supply chain [1]. To improve the ability of collaborative quality evaluation and prediction of block collaborative supply chain, we use thhe collaborative quality control of collaborative quality evaluation results of block collaborative supply chain combined with the statistical analysis results of collaborative quality characteristics of block collaborative supply chain. The collaborative quality evaluation model of supply chain is established, reduced, and improved. The research on collaborative quality evaluation model of supply chain has attracted great attention [2].

The prediction of collaborative quality of block collaborative supply chain is based on the detection and feature extraction of big data, and the discrete big data analysis model of collaborative quality of block collaborative supply chain is constructed. Big data mining and statistical analysis of collaborative quality characteristics of block collaborative supply chain are used to evaluate the collaborative quality. In reference [3], a supply chain collaborative quality evaluation model based on fuzzy subspace clustering and feature identification is proposed. The big data distribution model of block collaborative supply chain collaborative quality is established. Combined with feature space reconstruction technology, the cooperative quality feature extraction of block collaborative supply chain is carried out to improve the ability of collaborative quality evaluation, but the computational complexity of this method is high. In reference [4], a supply chain collaborative quality evaluation algorithm based on Lyapunov prediction is proposed. The Lyapunov feature quantity of block collaborative supply chain collaborative quality is extracted and evaluated according to the clustering results of feature fusion. However, the information fusion degree of this method is not high and the prediction accuracy is not high. In reference [5], a supply chain collaborative quality evaluation model based on matched filtering detection is proposed to classify collaborative quality samples of block collaborative supply chain to realize collaborative quality evaluation, but the adaptability of this method to collaborative quality evaluation is not good. Literature [6] considers that there are some differences in component quality due to factors such as the production technology level of suppliers. For manufacturers, high-quality components need to bear a large procurement cost, but the finished products also have high quality and lower the cost of product recall and rework. Considering this practical factor, combined with fuzzy demand and capital budget constraints, a new four level supply chain network mixed integer programming model is constructed to determine the number and scale of suppliers, manufacturers and distribution centers in the network to allocate reasonable product flow on each network path. In view of its NP hard characteristics, the corresponding genetic algorithm is designed. Large scale experiments show the effectiveness of the model and algorithm. However, the clustering results of this method in the process of supply chain integration are not ideal. For literature [7], in order to comply with the development trend of green supply chain, enterprises and suppliers establish green strategic partners to achieve win-win results. In this paper, the five factors of quality, service, environment, cost and technology are considered comprehensively, and a scientific and reasonable supplier evaluation index system is established. The FAHP method and entropy method are used to weigh each index subjectively and objectively. Grey relation TOPSIS is used to rank the suppliers and select the best suppliers as the green strategic partners, but the calculation time of this square is longer.

In order to solve the above problems, this paper proposes a supply chain collaborative quality evaluation method based on structural features. Firstly, the statistical model of collaborative quality feature information is established, and combined with big data fusion and structural feature extraction and fusion clustering method, the collaborative quality evaluation and evaluation is realized, and finally, the validity conclusion is obtained by simulation test and analysis.

2.Supply Chain Collaboration Quality Characteristic Information Sampling and Block Chain Information Fusion

2.1. Information about Quality Characteristics of Supply Chain Cooperation

The essence of supply chain is to provide valuable products or services for customers. By controlling material flow, capital flow and information flow, raw materials are supplied until products or services are delivered to final customers. The business operation gathered by relevant parties is simply the business combination around the core enterprises. The supply chain node enterprises involve different spans, and the supply chain is often composed of many different types of enterprises, even multinational enterprises. The node enterprises need to constantly update to adapt to the changes of market demand. The operation of information flow, logistics and capital flow in the supply chain is driven by user demand. The node enterprises in the supply chain can exist in different supply chains, so the supply chain has the characteristics of complexity, dynamic, user-oriented and cross.

In order to meet the needs of internal and external customers, supply chain management is a kind of connection between customers and suppliers through a series of activities such as plan, obtain, store, distribution and service. Supply chain management emphasizes that the supply chain is a whole, focusing on the integration and coordination of the whole. At the same time, it requires each node enterprise to share the logistics, information flow and capital flow, so as to realize the stability and durability of the supply-demand relationship. The goal is to deliver the right product required by customers to the right place (6R) at the right time, in the right quantity, in the right quality and in the right status, and to minimize the total cost. The basic idea of Supply Chain Management is to regard supply chain as a complete operation process. Through the use of modern enterprise management technology, information technology, network technology, integration technology and other modern technologies, the entire supply chain is integrated, so that customers, suppliers, manufacturers, distributors and service providers and other cooperative enterprises become a whole and form a highly competitive strategic alliance. Supply chain management has the following characteristics: information sharing, system integration, rapid response and interest coordination.

Relationship quality is a kind of intangible value produced by both sides of the transaction, that is, trust, satisfaction, commitment and other intangible values in the process of cooperation. The formation of the intangible value is closely related to the tangible part of the physical product or service process, which is formed with the process of realizing the value of the tangible entity. This kind of intangible value has a great impact on the trading results of both parties (Levitt, 1983), and its impact on value creation is direct and intangible. The relationship quality measurement system is shown in Figure 1.

   

Figure 1.   Relationship quality measurement system

2.2. Sampling of Collaborative Quality Characteristic Information in Supply Chain

In order to realize the collaborative quality evaluation of supply chain, it is necessary to construct the sampling model of collaborative quality characteristic information of block collaborative supply chain at first. According to the quantitative recurrent analysis results of collaborative quality sample data, the information fusion of block chain information is carried out, and the information collection of the characteristic quantity of quantitative evaluation of collaborative quality of block collaborative supply chain is carried out. The distribution state equation of collaborative quality feature is described as follows:

In that process of the evaluation of the cooperative quality of the chain, a block co-supply chain distribution structure is represented by a connected non-directed graph , wherein V is a cooperative quality characteristic attribute set of the cooperative supply chain of the block and . According to the energy load volatility, the estimated result of the cooperative quality characteristic of the cooperative supply chain of the block is as follows:

,

Wherein denotes the steady-state characteristic quantity of the block cooperative supply chain and is the length of the cooperative quality data of the block cooperative supply chain. According to the correlation between different indexes, the cooperative quality characteristics of the block cooperative supply chain are reorganized, and the discrete data set of the supply chain cooperative quality evaluation is obtained [8]. The distributed detection of the cooperative quality state of the block cooperative supply chain is carried out by using the random probability density model:

In the above formula, represents the IMF component of the collaborative quality characteristic quantity of the block collaborative supply chain [9], represents the harmonic state set of big data distribution of the collaborative quality of the block collaborative supply chain, and represents the ambiguity factor of the collaborative quality assessment of the block collaborative supply chain.

The cooperative quality characteristic quantity fusion model of the building block and the supply chain is as follows:

The ambiguity function of collaborative quality characteristic information sampling in supply chain is as follows:

The sampling model of collaborative quality characteristic information of block collaborative supply chain is constructed, and the panel data quantitative recurrent analysis is carried out according to the data sampling results.

2.3. Block Chain Information Fusion

According to the quantitative recurrent analysis results of collaborative quality sample data, block chain information fusion is carried out, and the feature distribution parameter set of association rule fusion feature distribution of collaborative quality panel data of block collaborative supply chain and the feature distribution set of collaborative quality statistical distribution of block collaborative supply chain are extracted as:

Where is a multivariate regression component, the cooperative quality factor is evaluated by the piecewise pre-whitening matching method. The association rule fusion feature distribution parameter set includes five variables. The cooperative quality fluctuation of block cooperative supply chain can be represented by . The association rule set of block cooperative supply chain cooperative quality is expressed by . So, the panel data linear programming function of supply chain cooperative quality evaluation is described as follows:

Wherein and represent the panel coefficient of supply chain collaborative quality evaluation and the correlation dimension of supply chain collaborative quality evaluation, respectively [10-11]. is the principal component of collaborative quality characteristic quantity of block collaborative supply chain. Collaborative quality evaluation is carried out according to the results of linear programming.

3.Evaluation and Optimization of Collaborative Quality in Supply Chain

3.1. Feature Extraction of Cooperative Quality based on Fuzzy C-Means Clustering

In this paper, a collaborative quality evaluation model of the supply chain based on the fuzzy C-means is proposed. Fuzzy c-means clustering combines the essence of fuzzy theory. Compared with K-means hard clustering, fuzzy C provides more flexible clustering results. Because in most cases, the objects in the data set cannot be divided into distinct separated clusters, assigning an object to a specific cluster is a bit stiff and may also cause errors. Therefore, each object and each cluster are given a weight, indicating the degree of the object belonging to the cluster. Of course, the method based on probability can also give such weights, but sometimes it is difficult to determine a suitable statistical model, so it is a better choice to use the fuzzy c-means with natural and non-probabilistic characteristics. The cooperative quality data fusion of the block and the supply chain is carried out in combination with the method of the random probability density feature detection, and the piecewise linear estimation method is combined [11-12]. The statistical analysis model for obtaining the cooperative quality evaluation of the supply chain is as follows:

According to the market environmental factors, the self-adaptive prediction of the cooperative quality of the block and the supply chain is carried out to obtain an association rule fusion characteristic distribution parameter set:

The cooperative quality feature fusion scheduling set is as follows:

By adopting the segment quantitative recursive analysis method, the cooperative quality characteristic prediction of the block collaborative supply chain is carried out [12-13], and the kernel function of the cooperative quality evaluation is obtained. The fuzzy C-means clustering function of the block collaborative supply chain cooperative quality characteristic fusion is as Equation (11).

According to the fuzzy C-means clustering result, the collaborative quality feature extraction and prediction of the block collaborative supply chain are carried out.

3.2.Output of Cooperative Quality Evaluation

According to the results of prior sample quantitative recurrent analysis of supply chain collaborative quality evaluation, the collaborative quality characteristics of block collaborative supply chain are extracted [14-15]. The principal component model of block collaborative supply chain analysis and prediction is obtained as follows:

According to the characteristic distributed fusion result, the cooperative quality evaluation of the block collaborative supply chain is carried out, and the statistical average value is obtained as follows:

Variance of collaborative quality evaluation:

Combined with the method of piecewise linear estimation, the statistical feature quantity of supply chain collaborative quality evaluation is constructed, and the feature distribution matrix of supply chain collaborative quality evaluation is obtained as:

Based on the comprehensive analysis, the structure feature extraction and the fusion clustering method are used for the information clustering, and the quality evaluation of the supply chain is carried out according to the distributed fusion results of the feature. The implementation process is shown in Figure 2.

   

Figure 2.   Implementation process of collaborative quality evaluation

4. Simulation Test Analysis

In order to verify that application performance of the method in the evaluation of the cooperative quality of the supply chain, a simulation experiment analysis is carried out. The length of the cooperative quality information sample of the cooperative supply chain of the block is 2000. The scale of the training feature set is 20. The number of steps of the adaptive iteration is 120. The minimum predicted window coefficient is , the maximum predicted window coefficient , the minimum correlation dimension , the maximum correlation dimension , the region of the cooperative quality characteristic detection of the block cooperative supply chain are [0, 1.34] and [0.32, 30], and the supply chain cooperative quality evaluation is carried out according to the above-mentioned parameter setting. Obtaining the fuzzy C-means clustering result of the cooperative quality characteristic of the cooperative supply chain of the block is shown in Figure 3.

According to the characteristic results, the cooperative quality evaluation and the test prediction accuracy are carried out, and the comparison result is shown in Figure 4.

The analysis of Figure 3 shows that the accuracy ratio of the proposed method is close to 100% when the given minimum expected support threshold is in the range of 15.0 ~ 13.0. When the given minimum expected support threshold is in the range of 13.0 ~ 10.0, the accuracy of the comparison method is declining, and the minimum accuracy is 60%. The accuracy of the proposed method is still 100%. In order to further verify the advantages of the proposed method in time cost, it is compared with the other four methods. The comparison results of specific test operation time cost are shown in Figure 4.

   

Figure 3.   Cluster analysis results of collaborative quality evaluation of collaborative supply chain in block

   

Figure 4.   Comparison of accuracy of collaborative quality evaluation in supply chain

   

Figure 5.   Time comparison of collaborative quality assessment

Analysis of Figure 5 shows that the running time of all methods increases gradually within the given minimum expected support threshold. The time cost of the proposed evaluation method is lower than that of other methods, and the highest time cost is less than 0.8s. It shows that the proposed method has advantages in running time cost.

5. Conclusion

Big data information fusion method is used to evaluate the collaborative quality of block collaborative supply chain and improve the ability of collaborative quality evaluation and prediction of block collaborative supply chain. In this paper, a collaborative quality evaluation method of supply chain using structural characteristics is proposed. The feature quantity of block collaborative supply chain collaborative quality quantitative evaluation is collected, the association rules fusion feature distribution parameter set is extracted, the panel data linear programming function is constructed, and the piecewise quantitative recurrent analysis method is used to predict the collaborative quality characteristics of block collaborative supply chain. It is found that the proposed method has high accuracy, high confidence level and short running time cost, improving the ability of collaborative quality evaluation.

There are some limitations in the research of this paper. Further research can be carried out from the following aspects:

(1)Most of the existing studies are based on the results of cooperative relationship to measure relationship quality. On the basis of reading a large number of literature, it is found that although the theory of proximity based on the characteristics of cooperative relationship is proposed, it is not understood how to use the theory to measure relationship quality. In this paper, the specific indicators of each dimension are not comprehensive enough to be discussed in the future.

(2)When choosing the method to determine the index weight, due to the lack of research and the limitations of thhe analysis, the selected method may have problems in the study of relationship quality. In the future, further research can be done in the selection method to make the determination of index weight more reasonable.

(3)From the perspective of life cycle, it is a new direction to study the relationship quality between supply chain enterprises. At the same time, because this is a new attempt, there will inevitably be a lack of research ideas, approaches or theories.

(4)In different stages of the supply chain life cycle, the relationship quality measurement index system is different, and the management implications of improving the quality and stability of cooperation are also different. Although the paper puts forward the corresponding methods, these methods are not comprehensive and specific, nor put forward specific measures.

(5)The declining stage is the last step in the development of cooperation between supply chain enterprises, but the relationship between supply chain enterprises in this stage has great uncertainty. In the process of cooperation, the cooperative relationship will enter into a declining period at any time, which needs to be discussed in different situations. Due to the limited space, this part of research is ignored in this paper, which needs to be improved in future research.

Acknowledgments

The authors have declared that no competing interests exist.


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