越览（143）——精读博士论文的3.4供应链网络结构指标分析

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“越览（143）——精读博士论文

《供应链网络结构视角下的产业链韧性研究》

的3.4供应链网络结构指标分析

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3.4 Analysis of supply chain network structure indicators

of "Yuelan (143)—— Intensive reading of

the doctoral dissertation

‘Research on the resilience of

the supply chain from the perspective of

supply chain network structure’".

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一、内容摘要（Summary of content）

本期推文将从思维导图、精读内容、知识补充三个方面介绍博士论文《供应链网络结构视角下的产业链韧性研究》的3.4供应链网络结构指标分析。

This issue of tweets will introduce 3.4 Analysis of supply chain network structure indicators of the doctoral thesis "Research on Industrial Chain Resilience from the Perspective of Supply Chain Network Structure" from three aspects: mind mapping, intensive reading content, and knowledge supplement.

二、思维导图（Mind mapping）

三、精读内容（Intensive reading content）

（一）网络节点指标分析（Network node indicator analysis）

1. 点度中心性（Degree centrality）

通过Gephi计算2018年与2021年汽车产业供应链网络中各企业的入度中心性、出度中心性和点度中心性，并按点度中心性大小排序，选取排名前十的企业进行分析（见表3-1和表3-2），并区分核心企业与非核心企业。

Gephi was used to calculate the in-degree centrality, out-degree centrality, and point degree centrality of each enterprise in the automotive industry supply chain network in 2018 and 2021, and the enterprises were sorted by the size of the point degree centrality. The top ten enterprises were selected for analysis (see Table 3-1 and Table 3-2), and core enterprises were distinguished from non-core enterprises.

（1）核心企业分析（Core enterprise analysis）

2018年与2021年核心汽车企业的最高点度中心性分别为244和305，表现出较强的网络连接能力。入度中心性普遍高于出度中心性，表明汽车企业依赖众多供应商、零部件来源复杂；出度中心性较低，客户较为集中。2021年点度中心性整体上升，但出度中心性略有下降，主要受疫情冲击、油价上涨、消费能力减弱及新能源汽车补贴退坡等因素影响，导致汽车销量减少。

The highest point degree centrality of core automobile companies in 2018 and 2021 was 244 and 305 respectively, showing strong network connectivity. In-degree centrality is generally higher than out-degree centrality, indicating that automobile companies rely on many suppliers and have complex sources of parts; out-degree centrality is low and customers are more concentrated. In 2021, point degree centrality increased overall, but out-degree centrality decreased slightly, mainly due to the impact of the epidemic, rising oil prices, weakened consumption capacity, and the decline in subsidies for new energy vehicles, which led to a decrease in automobile sales.

（2）非核心企业分析（Non-core business analysis）

除去核心企业后，点度中心性排名前十的企业中，2018年最高为46，2021年升至54，均为汽车制造或零部件企业，如吉利汽车、奇瑞汽车、上汽通用等。吉利汽车虽为上市公司，但因在香港上市未被归为核心企业。疫情背景下，企业主动拓展供应商与客户网络，增强供应链韧性，推动了点度中心性的整体提升。

Excluding core enterprises, among the top ten enterprises in terms of point centrality, the highest was 46 in 2018 and rose to 54 in 2021. All of them are automobile manufacturing or parts companies, such as Geely Automobile, Chery Automobile, SAIC General Motors, etc. Although Geely Automobile is a listed company, it is not classified as a core enterprise because it is listed in Hong Kong. Under the background of the epidemic, enterprises have actively expanded their supplier and customer networks and enhanced the resilience of the supply chain, which has promoted the overall improvement of point centrality.

2. 介数中心性（Betweenness centrality）

利用Gephi计算2018年和2021年供应链网络中企业的相对介数中心性，并对排名前十的企业进行分析。介数中心性衡量企业在供应链中控制资源与信息流的能力。

Gephi was used to calculate the relative betweenness centrality of companies in the supply chain network in 2018 and 2021, and the top ten companies were analyzed. Betweenness centrality measures the ability of companies to control resource and information flows in the supply chain.

（1）核心企业分析（Core enterprise analysis）

一汽解放、东风集团、长安汽车、比亚迪等企业介数中心性位居前列，分别代表了不同的发展模式，如合资吸收、一体化自主研发等。介数中心性整体呈下降趋势，反映出疫情背景下资源分散以降低产业链断裂风险的调整策略。

FAW Jiefang, Dongfeng Group, Changan Automobile, BYD and other companies are at the forefront of betweenness centrality, representing different development models, such as joint venture absorption, integrated independent research and development, etc. The overall betweenness centrality is on a downward trend, reflecting the adjustment strategy of dispersing resources to reduce the risk of industrial chain rupture under the background of the epidemic.

（2）非核心企业分析（Non-core business analysis）

去除核心企业后，企业介数中心性虽仍较低，但2021年有所上升（从0.0151升至0.0226），说明供应商与客户在供应链中的地位日益增强。疫情期间企业增加对供应商的依赖，同时消费者议价能力提升，带动这些节点的重要性上升。

After removing the core enterprises, the enterprise betweenness centrality is still low, but it has increased in 2021 (from 0.0151 to 0.0226), indicating that the status of suppliers and customers in the supply chain is increasing. During the epidemic, enterprises increased their dependence on suppliers, and consumers' bargaining power increased, driving the importance of these nodes to rise.

3. 离心度（Centrifugal）

通过Gephi计算2018年与2021年汽车产业供应链中各企业的离心度，并按降序排列结果如表3-5所示。离心度反映企业至供应链最远节点的距离，即下游供应链的“长度”。

Gephi was used to calculate the eccentricity of each enterprise in the automotive industry supply chain in 2018 and 2021, and the results were arranged in descending order as shown in Table 3-5. The eccentricity reflects the distance from the enterprise to the farthest node in the supply chain, that is, the "length" of the downstream supply chain.

（1）企业类型变化（Changes in business types）

2018年高离心度企业中既有国内非上市公司（如保锐科技）、也有上市公司（如佛山照明、上海汽车变速器有限公司）；而2021年高离心度企业则全部为国外企业，如DNA Pacifica Inc、GF乔治费歇尔集团等。

Among the high centrifugal enterprises in 2018, there were both domestic non-listed companies (such as Baorui Technology) and listed companies (such as Foshan Lighting and Shanghai Automotive Transmission Co., Ltd.); while in 2021, all high centrifugal enterprises were foreign companies, such as DNA Pacifica Inc, GF George Fischer Group, etc.

（2）影响因素分析（Analysis of influencing factors）

疫情期间，国内部分供应商抗风险能力不足，导致供应链中断；相较之下，国外企业凭借更强的风险管理与稳定的运营能力，在疫情冲击下仍能维持正常供给，从而在全球供应链中保持关键节点地位。

During the epidemic, some domestic suppliers lacked the ability to resist risks, resulting in supply chain disruptions; in contrast, foreign companies, with stronger risk management and stable operational capabilities, were able to maintain normal supply under the impact of the epidemic, thus maintaining their key node position in the global supply chain.

4. 接近中心性（Closeness centrality）

通过Gephi计算2018年与2021年汽车产业供应链网络中各企业的接近中心性，并列出两年接近中心性排名前十的企业（见表3-6）。接近中心性衡量企业与其他节点的平均距离，反映其获取资源与信息的效率。

Gephi was used to calculate the proximity centrality of each enterprise in the automotive industry supply chain network in 2018 and 2021, and the top ten enterprises in terms of proximity centrality in the two years were listed (see Table 3-6). The proximity centrality measures the average distance between an enterprise and other nodes, reflecting its efficiency in obtaining resources and information.

（1）核心含义（Core meaning）

接近中心性越高，企业与供应链伙伴关系越紧密、联系越频繁，能够更快速地获取并转化外部资源，同时更具信息优势，减少对其他企业的依赖，提升网络中的自主性和反应能力。

The higher the proximity centrality, the closer the partnership between the enterprise and the supply chain and the more frequent the contact. It can obtain and transform external resources more quickly, and at the same time it has more information advantages, reduces dependence on other enterprises, and improves autonomy and responsiveness in the network.

（2）排名情况（Ranking）

2021年前十企业接近中心性均为1，整体水平较2018年更高，显示出疫情背景下供应链结构趋于紧密。相比之下，2018年前十中仍有部分企业（如湖州金泰科技与南京畅嘉科技）接近中心性仅为0.667，说明其在供应链网络中的位置相对边缘。

The proximity centrality of the top ten enterprises in 2021 is 1, and the overall level is higher than that in 2018, indicating that the supply chain structure has become tighter under the background of the epidemic. In contrast, some enterprises in the top ten in 2018 (such as Huzhou Jintai Technology and Nanjing Changjia Technology) still have a proximity centrality of only 0.667, indicating that their position in the supply chain network is relatively marginal.

（3）企业构成特点（Enterprise structure characteristics）

除核心汽车企业外，部分国内上市企业（如精达股份、华谊集团）与国外企业（如BPW、Hessapp）也拥有较高接近中心性，表明这些企业在资源协调与信息获取方面同样具有优势，是产业链中具有高联通性的关键节点。

In addition to core automobile companies, some domestic listed companies (such as Jingda Holdings and Huayi Group) and foreign companies (such as BPW and Hessapp) also have high proximity centrality, indicating that these companies also have advantages in resource coordination and information acquisition, and are key nodes with high connectivity in the industrial chain.

5. 特征向量中心性（Eigenvector centrality）

通过Gephi计算2018年与2021年汽车产业供应链网络中企业的特征向量中心性，并列出核心企业和非核心企业中排名前十的节点。特征向量中心性不仅反映企业的连接数量，更强调所连接节点的重要性，是衡量企业在网络中“影响力”的关键指标。

Gephi is used to calculate the eigenvector centrality of enterprises in the automotive industry supply chain network in 2018 and 2021, and the top ten nodes among core enterprises and non-core enterprises are listed. Eigenvector centrality not only reflects the number of connections of an enterprise, but also emphasizes the importance of the connected nodes. It is a key indicator to measure the "influence" of an enterprise in the network.

（1）核心企业分析（Core enterprise analysis）

2018年与2021年特征向量中心性最高的企业均为长安汽车（值为1.0），表明其处于产业链核心位置，连接的重要性和影响力最大。其他中心性较高的企业（如一汽解放、上汽集团、福田汽车）在两年中基本保持稳定。值得关注的是比亚迪汽车，其特征向量中心性在2021年升至0.588并进入前十，反映其因垂直一体化、自主研发及新能源战略，在产业链中地位迅速上升。

The company with the highest eigenvector centrality in both 2018 and 2021 is Changan Automobile (value is 1.0), indicating that it is at the core of the industry chain and has the greatest importance and influence in connection. Other companies with high centrality (such as FAW Jiefang, SAIC Group, and Foton Motor) remained basically stable in the two years. It is worth noting that BYD Auto, whose eigenvector centrality rose to 0.588 in 2021 and entered the top ten, reflecting its rapid rise in the industry chain due to vertical integration, independent research and development, and new energy strategies.

（2）非核心企业分析（Non-core business analysis）

去除核心企业后，一些合资或外资企业（如一汽大众、通用、福特）以及本土未上市企业仍拥有较高的特征向量中心性，说明它们通过关键客户或供应商群体形成了稳固的网络结构。尽管部分国外企业依然居于较高位置，但整体趋势显示本土汽车企业的中心性不断增强。

After removing the core enterprises, some joint ventures or foreign-invested enterprises (such as FAW-Volkswagen, GM, and Ford) and local unlisted enterprises still have high eigenvector centrality, indicating that they have formed a stable network structure through key customers or supplier groups. Although some foreign companies still occupy a high position, the overall trend shows that the centrality of local automobile companies is constantly increasing.

6. 网络节点指标差异性分析（Analysis of differences in network node indicators）

利用 SPSS 软件对2018年与2021年汽车产业供应链网络的节点指标进行独立样本 T 检验，分析各中心性指标的变化情况。

SPSS software was used to conduct an independent sample T-test on the node indicators of the automobile industry supply chain network in 2018 and 2021, and to analyze the changes in various centrality indicators.

（1）显著变化指标（Significant change indicators）

离心度与接近中心性在0.05显著性水平下呈现显著差异。具体来看，2021年离心度均值上升4.9%，接近中心性均值下降5.8%。这反映出企业在供应链中离市场的“距离”变长，企业之间的合作关系相对疏远。

The centrifugal degree and the closeness centrality show significant differences at the 0.05 significance level. Specifically, the mean centrifugal degree increased by 4.9% in 2021, while the mean closeness centrality decreased by 5.8%. This reflects that the "distance" of enterprises from the market in the supply chain has increased, and the cooperative relationship between enterprises has become relatively distant.

（2）解释与启示（Explanation and revelation）

突发事件背景下，产业链呈现出向更远距离、更松散连接演化的趋势。一方面，离市场更远的企业受风险影响的时间更滞后，便于采取应对措施；另一方面，企业间关系稀疏化，有助于降低依赖性、分散风险、增强整体韧性，从而维持供应链稳定。

In the context of emergencies, the industrial chain shows a trend of evolving towards longer distances and looser connections. On the one hand, enterprises that are farther away from the market are affected by risks later, making it easier to take countermeasures; on the other hand, the sparseness of relationships between enterprises helps reduce dependence, disperse risks, and enhance overall resilience, thereby maintaining a stable supply chain.

（二）网络整体指标分析（Overall network indicator analysis）

1. 平均距离和网络直径（Average distance and network diameter）

通过Gephi软件计算，2018年与2021年汽车产业供应链网络的平均距离均在5以下，但2021年略高于2018年，表明网络中企业之间的信息传递效率有所下降，资源与信息在节点之间的传递路径变长。

Calculated by Gephi software, the average distance of the automobile industry supply chain network in 2018 and 2021 was both below 5, but in 2021 it was slightly higher than in 2018, indicating that the efficiency of information transmission between enterprises in the network has declined and the transmission path of resources and information between nodes has become longer.

此外，两年网络的网络直径均为12，说明在极端情况下，企业之间最远需经过12个节点才能实现连接，反映出汽车产业供应链结构庞大、节点众多、网络复杂。总体来看，汽车产业供应链仍保持一定连通性，但2021年资源传递效率略有降低，可能与网络结构疏松化、企业联系间接化有关。

In addition, the network diameter of the two-year network is 12, which means that in extreme cases, enterprises need to pass through 12 nodes at most to achieve connection, reflecting the huge structure, numerous nodes and complex network of the automotive industry supply chain. Overall, the automotive industry supply chain still maintains a certain degree of connectivity, but the efficiency of resource transmission has slightly decreased in 2021, which may be related to the loosening of the network structure and the indirectness of enterprise connections.

2. 网络密度（Network density）

通过Gephi计算，2018年和2021年汽车产业供应链网络的密度分别为0.001658和0.001651，均处于较低水平，说明企业之间的联系较为稀疏，主要集中在核心企业与其直接供应商或客户之间，整体协同合作程度较弱。

Calculated by Gephi, the density of the automobile industry supply chain network in 2018 and 2021 was 0.001658 and 0.001651 respectively, both at a low level, indicating that the connections between enterprises are relatively sparse, mainly concentrated between core enterprises and their direct suppliers or customers, and the overall level of collaboration is weak.

尽管2021年企业数量和关联关系数量较2018年有所增加，但网络密度略有下降，反映出企业之间新增联系增长速度低于节点增长速度，合作关系未能同步扩展。该结果表明，当前汽车产业仍存在资源分散与协作不足问题。提升供应链韧性不仅需优化结构和规模，更应加强企业间的联动与资源整合，推动供应链系统高效协同与稳定运行。

（三）重要企业识别（Identification of important companies）

利用熵权-TOPSIS法对2018年与2021年汽车产业供应链网络中的企业进行重要性评价。六项节点指标经标准化与权重计算后，分别用于构建综合评价体系。结果显示，两年中排名前十的企业均为核心企业，一汽解放（2018）与比亚迪（2021）位居第一，国产品牌如福田、长安、吉利等表现突出，说明本土汽车企业的重要性持续提升。剔除核心企业后，中外合资与国外企业（如上汽通用、通用汽车）仍占有一席之地，反映出本土企业崛起的同时，仍对外资企业存在技术与管理上的依赖。

The entropy weight-TOPSIS method is used to evaluate the importance of enterprises in the automotive industry supply chain network in 2018 and 2021. After standardization and weight calculation, the six node indicators are used to construct a comprehensive evaluation system. The results show that the top ten enterprises in both years are core enterprises, with FAW Jiefang (2018) and BYD (2021) ranking first. Domestic brands such as Foton, Changan, and Geely have outstanding performance, indicating that the importance of local automobile companies continues to increase. After excluding core enterprises, Sino-foreign joint ventures and foreign companies (such as SAIC-GM and General Motors) still have a place, reflecting that while local companies are rising, they still rely on foreign companies in technology and management.

通过SPSS双变量相关分析发现，点度中心性和介数中心性与企业重要性评价值高度相关，聚类系数相关性较弱。2021年整体指标间相关性下降，说明疫情等外部冲击影响了企业合作密度与网络结构稳定性。

Through SPSS bivariate correlation analysis, it was found that point degree centrality and betweenness centrality are highly correlated with the enterprise importance evaluation value, and the clustering coefficient is weakly correlated. The overall correlation between indicators decreased in 2021, indicating that external shocks such as the epidemic have affected the cooperation density of enterprises and the stability of network structure.

四、知识补充（Knowledge supplement）

介数中心性是衡量网络中节点在资源传递路径中“中介”作用的重要指标，反映一个节点在其他节点之间最短路径中所处的位置及其对信息流通的控制能力。具体而言，介数中心性描述的是一个节点在所有其他节点对之间最短路径上出现的频率，其数值越高，说明该节点越多地充当着网络中信息或资源传递的“桥梁”角色。与度中心性不同，介数中心性不仅关注节点本身的直接连接数量，还强调节点在整个网络结构中的战略位置。

Betweenness centrality is an important indicator to measure the "intermediary" role of nodes in the resource transfer path in the network. It reflects the position of a node in the shortest path between other nodes and its ability to control the flow of information. Specifically, betweenness centrality describes the frequency of a node appearing on the shortest path between all other node pairs. The higher the value, the more the node acts as a "bridge" for information or resource transfer in the network. Unlike degree centrality, betweenness centrality not only focuses on the number of direct connections of the node itself, but also emphasizes the strategic position of the node in the entire network structure.

在供应链网络中，介数中心性高的企业通常处于上下游之间的关键位置，承担着连接多个节点的中转功能。这类企业不仅在资源调配和物流安排中发挥重要作用，也可能因其网络控制能力而对产业链的稳定性产生显著影响。一旦这些关键节点发生风险，如产能中断或管理失效，可能对整个供应链产生连锁反应。因此，识别高介数中心性的企业，有助于把握产业网络的核心通道与潜在脆弱点，为供应链韧性提升和风险管理提供决策依据。

In the supply chain network, companies with high betweenness centrality are usually in a key position between upstream and downstream, and they assume the transit function of connecting multiple nodes. Such companies not only play an important role in resource allocation and logistics arrangements, but may also have a significant impact on the stability of the industrial chain due to their network control capabilities. Once risks occur at these key nodes, such as production capacity interruptions or management failures, it may have a chain reaction on the entire supply chain. Therefore, identifying companies with high betweenness centrality helps to grasp the core channels and potential vulnerabilities of the industrial network, and provide a decision-making basis for improving supply chain resilience and risk management.

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