Bibliometric method combines mathematical and statistical methods to summarize the research status of related topics and a specific research field, and mainly analyzes the research status, research hotspots, and research trends. Zhou et al. used VOS viewer, Sci2 and Cite Space to explore the publication trend, author and journal distribution, research topics, and hot spots of the research topic of the green supply chain, and predict the research frontier. However, it can be seen from its research that the factors considered have certain limitations [30]. Fan and Stevenson conducted a systematic review of the existing literature on supply chain risk management and proposed a new and comprehensive conceptual framework for supply chain risk management [31]. Moosavi et al. found through bibliometric analysis that blockchain technology can not only improve the transparency and traceability of supply chain management, but also improve the efficiency and information security of supply chain management. The application of blockchain technology in supply chain management is relatively lacking, and there are great research opportunities in future research [32]. Iftikhar et al. used bibliometric analysis and systematic review methods to review the relevant literature on technology innovation, data innovation, and supply chain resilience, in order to timely review the key research clusters, the evolution of research over time, the knowledge trajectory and the research methodology development in this field. The bibliometric method can effectively analyze the current research situation in the research object field, but in this study, the research team's application of the bibliometric method is still not deep enough [33]. Roblek et al. applied bibliometric methods to explore the overall development trend and importance of agile related to management and organization [34]. In order to track the research frontiers and hot spots of closed-loop supply chains, Guan et al. used visualization software VOS viewer and Cite Space for analysis. A descriptive analysis was first conducted to identify trends in the number of publications, major journals, top authors, and regions. A thematic cluster analysis was then performed to identify the research areas. Subsequently, hot issues and research trends are summarized based on co-keywords, clustering, and co-citation analysis. "Game theory" and "remanufacturing" are new trends in closed-loop supply chain research. "Dual channel", "quality" and "circular economy" have become hot topics. Although this research has achieved the summary of the current situation of supply chain tracking research, it has not made effective prediction and put forward effective suggestions for its development trend [35].

In summary, many researchers have studied automotive supply chain disruption risk management from multiple perspectives, but it is not clear how the research on automotive supply chain disruption risk management develops. We do not know the evolution characteristics of the research topics in this research area, and these questions deserve our further exploration. These subjects are worth exploring in depth because they provide clues to how this field of research will develop in the future. In view of the lack of in-depth research on the risk management of automobile supply chain disruption, we designed the present study to identify the research hotspots and future development trends in this field. To do so, we conducted a bibliometric analysis of automotive supply chain disruption risk management and performed a qualitative analysis of the results.

This paper mainly discusses these research questions as follows: (1) What are the most representative articles, the number of articles published in top journals, the co-cited articles, and the authors in the field of automotive supply chain disruption risk research? (2) Which organizations, institutions, and countries contribute to the research of automotive supply chain disruption risk? (3) What are the main research clustering topics? (4) What are the future research trends and research hotspots in the field of automotive supply chain sustainability?

The rest of this paper is organized as follows: Section 2 presents the data source and search methodology. Section 3 presents the results of the bibliometric analysis and science mapping. The analysis includes the most representative scientific production, journals, authors, countries, institutions, and organizations. The science mapping shows the citation analysis, by articles, authors, and terms. Section 4 includes a discussion of the research, findings, analysis, and implications of the scientific research on the relevant aspects of automotive supply chain disruption risk management. Section 5 provides the conclusions, limitations, and future research concludes the paper.

There are two innovative points in the study. First, the study learned about the interruption management of the automobile supply chain in recent years through literature measurement, and then analyzed the interruption risk of the automobile supply chain through the results of literature measurement, without providing corresponding suggestions for the development and management of the automobile supply chain.

#### **2. Data Source and Methodology**

For the collection of article information, the source of data is very important. We used SCI, SSCI WOS and EI databases as sources of scientific literature. Web of Science (WOS) is one of the important database platforms for obtaining global academic information, including more than 20,000 international authoritative and influential academic journals, covering social sciences, humanities and arts, natural sciences, engineering and technology, and other disciplines. The database is considered the most trustworthy and thorough source of data [36,37] and is frequently used in bibliometric research on the progress and evaluation of various scientific fields [38].

SCI database is currently the most authoritative large-scale multidisciplinary comprehensive retrieval tool in the world, so it is regarded as one of the main source databases of foreign literature. In the SCI database, the search term is limited to "auto supply chain" and "supply chain interruption risk", the language is "English", the publication year is limited to "after 2000", and the document type is "Article". Under this condition, a total of 1024 documents are screened.

The SSCI WOS database mainly includes SSCI journal papers. The research takes WOS as the search source, takes "auto supply chain", "supply chain interruption risk" and "risk management" as the search keywords, limits the search language to "English", and sets the document type to "article" and "review". In order to obtain more effective supply chain information, the research selects the time node when the auto industry starts to develop as the initial search time, so set the time span = '2000–2022', Databases = 'WOS Core Collection'. A total of 1003 articles were retrieved.

The search time for this study began on 7 January 2023. After searching in the WOS database, a total of 1084 literatures were obtained. All the screening was conducted in one day to prevent data bias resulting from daily database updates. This is just a preliminary simple screening. Considering the defects in the retrieval technology, we will filter and clean the original literature data by means of manual screening to remove irrelevant and duplicate literature data. Finally, a total of 866 articles are selected as sample data for further detailed analysis.

The dataset we obtained includes important information such as author, title, year, journal source, affiliation, abstract, etc. The dataset was managed in Excel, and we used version 5.7R2 of the Cite Space software for bibliometric visualization analysis [39].

#### **3. Current Status of the Field of Automotive Supply Chain Disruption Risk Management**

This section may be divided into subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.

Next, this part focuses on the research status of the field of automotive supply chain disruption risk management, including the trend of the number of published articles, publication types, top journals, etc.

Figure 1 shows the number of publications and citations from 2000 to 2022 in the field of automotive supply chain disruption risk management. The overall trend of the publications follows an exponential growth (y = 0.2409x2 − 1.4415x + 8.9311; R2 = 0.9) after mathematical exponential adjustment. The result accords with a price curve and it demonstrates that the field of automotive supply chain disruption risk management has received increasing attention. Similar verifications are also presented in other literature [40,41]. In addition, from 2000 to 2011, the growth rate of the number of published articles was relatively flat. Since 2012, the number of articles published has increased significantly. Especially after 2015, the number of articles published showed a sharp upward trend. The reason may be that the Tohoku earthquake in March 2011 temporarily shut down Japan's auto industry, forcing European and North American manufacturers to halt production because their inventories in Japan had been depleted. Goldman Sachs estimates that Japanese carmakers are losing \$200 millionm a day. A one-third drop in global daily car production has led to a total global loss of five million vehicles, against a planned loss of 72 million in 2011 (a loss of about 7%), and months of stagnation in the global car industry's supply chain. As well, floods in Thailand disrupted supply chains for electronics and auto parts. In 2012, an explosion at a major Ford supplier, Evonik, disrupted production. In order to improve the competitive advantage of the automotive supply chain, the research topic of automotive supply chain management has been widely discussed by many researchers. Therefore, more and more scholars focus on how to reduce the disruption risk of the automotive supply chain after these emergencies. In terms of the number of citations of articles, the number of citations has continued to rise. Since the data statistics of this study are up to 26 August 2022, the number of citations in Figure 1 shows a downward trend in 2022. It can be seen that more and more researchers have been involved in the study of automotive supply chain disruption risk in recent years. *Processes* **2023**, *11*, x FOR PEER REVIEW 7 of 26

**Figure 1.** Number of publications and citations from 2000 to 2022. **Figure 1.** Number of publications and citations from 2000 to 2022.

**Table 1.** Top 20 journals in the field of Automotive Supply Chain Disruption risk management.

The number of citations of a paper can indicate the research contribution and importance of the paper. According to the results reported in Table 2, Vickery et al. published the title of the effects of an integrative supply chain strategy on customer service in the Journal of Operations Management in 2003 and financial performance: an analysis of direct versus indirect relationships, which has been cited 691 times [42]. Zhu et al. published in the Journal of Cleaner Production in 2007 titled Green supply chain management: pressures, practices, and performance within the Chinese automobile industry, has been cited 637 times [43]. In 2011, published in the Journal of Operations

**Number**

**Percentage (%)**

**Cumulative Percentage (%)**

2 International Journal of Production Economics 54 4.60% 9.37% 3 International Journal of Production Research 53 4.52% 13.89% 4 Supply Chain Management-An International Journal 37 3.15% 17.04% 5 Production Planning & Control 35 2.98% 20.02% 6 Sustainability 29 2.47% 22.49% 7 International Journal of Operations & Production Management 26 2.22% 24.71% 8 Computers & Industrial Engineering 25 2.13% 26.84% 9 Benchmarking-An International Journal 21 1.79% 28.63% 10 Industrial Management & Data Systems 17 1.45% 30.08% 11 Journal of Manufacturing Technology Management 16 1.36% 31.44% 12 Journal of Operations Management 15 1.28% 32.72% 13 International Journal of Logistics Management 14 1.19% 33.91% 14 European Journal of Operational Research 13 1.11% 35.02% 15 IFAC Papers OnLine 13 1.11% 36.13% 16 Business Strategy and the Environment 12 1.02% 37.15% 17 IFIP Advances in Information and Communication Technology 12 1.02% 38.17% 18 IEEE Transactions on Engineering Management 10 0.85% 39.02% 19 International Journal of Productivity and Performance Management 10 0.85% 39.87% 20 Procedia CIRP 10 0.85% 40.72%

According to the results reported in Table 1, the top 20 journals published about 40% of the total number of articles in the field of automotive supply chain disruption risk management. The Journal of Cleaner Production ranked first, with 56 papers published. This journal plays an important role in the display of research results in the field of Automotive Supply Chain Disruption risk management. Then, the top five remaining journals are International Journal of Production Economics, International Journal of Production Research, Supply Chain Management-An International Journal, Production Planning & Control. The top five journals account for about 20% of the total publications.

**Table 1.** Top 20 journals in the field of Automotive Supply Chain Disruption risk management.

The number of citations of a paper can indicate the research contribution and importance of the paper. According to the results reported in Table 2, Vickery et al. published the title of the effects of an integrative supply chain strategy on customer service in the Journal of Operations Management in 2003 and financial performance: an analysis of direct versus indirect relationships, which has been cited 691 times [42]. Zhu et al. published in the Journal of Cleaner Production in 2007 titled Green supply chain management: pressures, practices, and performance within the Chinese automobile industry, has been cited 637 times [43]. In 2011, published in the Journal of Operations Management, the contingency effects of environmental uncertainty on the relationship between supply chain integration and operational performance [44], Structural investigation of supply networks: A social network analysis approach published in the Journal of Operations Management [45], It can be seen that in the beginning, scholars mainly focused on the theoretical analysis of automotive supply chain operation. With the continuous changes and development of the automotive industry, scholars have also changed their research perspectives to supplier selection, green supply chain, supply chain optimization, and other aspects.

**Table 2.** Top 20 most cited articles in the field of Automotive Supply Chain Disruption risk management.

#### **4. Bibliometric Analysis** Chain Disruption risk management by Cite Space software. It shows the mutual

**4. Bibliometric Analysis**  *4.1. Influence of Authors*

on corporate practices Evidence from the automotive industry 2008

#### *4.1. Influence of Authors* cooperation and correlation between different authors. Among them, the authors with the

20Environmental management system certification and its influence

Figure 2 presents the analysis of co-authorship in the field of Automotive Supply Chain Disruption risk management by Cite Space software. It shows the mutual cooperation and correlation between different authors. Among them, the authors with the highest correlation are mainly the following groups: The first group is MB Frazer, MS Kerr, WP Neumanm, RW Norman, RP Wells; The second group is Lin Zhou/Fulin Zhou/Yandong He/Yun Lin/Xu Wang; The third group is LC Brown/Dm Gute/Hj Cohen/Amc Desmars; The fourth group is Fabio Sgarbossa/Alena Otto/Alessandro Persona/Martina Calzavara; The fifth group is Hamzeh Soltanali/Abbas Rohani/Jose Torres Farinha; The sixth group is Shukriah Abdullah/Nor Kamaliana Khamis/Jaharah Ghani. From the above results, it can be seen that a large number of scholars currently use space software to analyze the risk of disruption in the automobile supply chain and propose management strategies accordingly. In addition, according to the correlation evaluation in Figure 2, there is a significant correlation between a large number of authors, which indicates that most of the authors have a consistent main direction in the study of automobile supply chain disruption risk. highest correlation are mainly the following groups: The first group is MB FRAZER, MS KERR, WP NEUMANM, RW NORMAN, RP WELLS; The second group is LIN ZHOU/FULIN ZHOU/YANDONG HE/YUN LIN/XU WANG; The third group is LC BROWN/DM GUTE/HJ COHEN/AMC DESMARS; The fourth group is FABIO SGARBOSSA/ALENA OTTO/ALESSANDRO PERSONA/MARTINA CALZAVARA; The fifth group is HAMZEH SOLTANALI/ABBAS ROHANI/JOSE TORRES FARINHA; The sixth group is SHUKRIAH ABDULLAH/NOR KAMALIANA KHAMIS/JAHARAH GHANI. From the above results, it can be seen that a large number of scholars currently use space software to analyze the risk of disruption in the automobile supply chain and propose management strategies accordingly. In addition, according to the correlation evaluation in Figure 2, there is a significant correlation between a large number of authors, which indicates that most of the authors have a consistent main direction in the study of automobile supply chain disruption risk.

Figure 2 presents the analysis of co-authorship in the field of Automotive Supply

International Journal of Operations & Production Management

183

*Processes* **2023**, *11*, x FOR PEER REVIEW 9 of 26

#### **Figure 2.** Author cooperation network in the field of automotive supply chain disruption risk **Figure 2.** Author cooperation network in the field of automotive supply chain disruption risk management.

#### management. *4.2. Affiliation Statistics and Analysis Countries, Institutions, Organizations*

According to the national distribution of the number of papers published, the country with the largest number of papers is the United States (Figure 3). So far, a total of 162 papers have been published on the subject of automotive supply chain disruption risk management. It accounted for 18.7% of the total number of published papers. The second is Germany, which has published a total of 77 articles on the subject of automotive supply chain disruption risk management, accounting for 8.9%. Next is China. So far, a total of 62 articles have been published on the subject of automotive supply chain disruption risk management, accounting for 7.2%. The top ten countries accounted for 67.2% of the total number of published papers. In addition, from the distribution of papers published in Figure 3, it can be seen that the research related to the risk of automobile supply chain

disruption is mainly concentrated in some countries. As shown in Figure 3, the number of papers published in the United States, Germany, China, India, Canada, and other countries accounts for a large proportion, which further indicates that the automobile industry in the above countries has developed more rapidly, increasing the number of scholars' research samples. Therefore, in this study, we will focus on the contents of papers from the United States, Germany, China, India, Canada and other countries, and use the method of literature measurement to summarize the risk of automobile supply chain interruption, so as to put forward universal risk management opinions. Figure 3, it can be seen that the research related to the risk of automobile supply chain disruption is mainly concentrated in some countries. As shown in Figure 3, the number of papers published in the United States, Germany, China, India, Canada, and other countries accounts for a large proportion, which further indicates that the automobile industry in the above countries has developed more rapidly, increasing the number of scholars' research samples. Therefore, in this study, we will focus on the contents of papers from the United States, Germany, China, India, Canada and other countries, and use the method of literature measurement to summarize the risk of automobile supply chain interruption, so as to put forward universal risk management opinions.

According to the national distribution of the number of papers published, the country with the largest number of papers is the United States (Figure 3). So far, a total of 162 papers have been published on the subject of automotive supply chain disruption risk management. It accounted for 18.7% of the total number of published papers. The second is Germany, which has published a total of 77 articles on the subject of automotive supply chain disruption risk management, accounting for 8.9%. Next is China. So far, a total of 62 articles have been published on the subject of automotive supply chain disruption risk management, accounting for 7.2%. The top ten countries accounted for 67.2% of the total number of published papers. In addition, from the distribution of papers published in

*Processes* **2023**, *11*, x FOR PEER REVIEW 10 of 26

*4.2. Affiliation Statistics and Analysis Countries, Institutions, Organizations*

**Figure 3.** Number of countries' publications in the field of Automotive Supply Chain Disruption risk management. **Figure 3.** Number of countries' publications in the field of Automotive Supply Chain Disruption risk management.

According to the statistics of the number of papers published by each country, it can be seen from the results reported in Figure 4 that the United States ranks first with 162 papers published and shows the strongest centrality, with a centrality index of 0.38. In second place is Germany with a centrality index of 0.22, and in third place is China with a centrality index of 0.18. In addition, the number of published papers in the United Kingdom, France, Italy, Canada, Iran and other countries is above 35, and their centrality is above 0.15. The cooperative research among these countries is relatively sufficient, and they can use each other's resources to conduct cooperative research and jointly contribute to the research on automotive supply chain disruption risk. At the same time, according According to the statistics of the number of papers published by each country, it can be seen from the results reported in Figure 4 that the United States ranks first with 162 papers published and shows the strongest centrality, with a centrality index of 0.38. In second place is Germany with a centrality index of 0.22, and in third place is China with a centrality index of 0.18. In addition, the number of published papers in the United Kingdom, France, Italy, Canada, Iran and other countries is above 35, and their centrality is above 0.15. The cooperative research among these countries is relatively sufficient, and they can use each other's resources to conduct cooperative research and jointly contribute to the research on automotive supply chain disruption risk. At the same time, according to the published statistical results of the papers in Figure 4, the development status of each country can be further analyzed in depth, and the risk factors of automobile supply chain interruption can be proposed from the national level. However, from the differences in the central evaluation results of various countries, it can be seen that most of the current research is still based on the United States. Therefore, if it is necessary to put forward the risk management strategy for domestic automobile supply chain disruption, the follow-up research still needs to focus on the domestic development status.

up research still needs to focus on the domestic development status.

**Figure 4.** Countries working together in the field of automotive supply chain disruption risk management.

to the published statistical results of the papers in Figure 4, the development status of each country can be further analyzed in depth, and the risk factors of automobile supply chain interruption can be proposed from the national level. However, from the differences in the central evaluation results of various countries, it can be seen that most of the current research is still based on the United States. Therefore, if it is necessary to put forward the risk management strategy for domestic automobile supply chain disruption, the follow-

**Figure 4.** Countries working together in the field of automotive supply chain disruption risk management. The United States, Germany, the United Kingdom, and China represent the study groups with the highest correlations. The institutions or universities with the most published articles are all located in the United States. At the top of the top 10 is Univ Michigan, which has published 11 papers. Univ Michigan's research on automotive supply chain risk management is relatively mature and widely recognized. Tied for second place are Islamic Azad Univ, Tech Univ Munich, and Chinese Acad Sci, all of The United States, Germany, the United Kingdom, and China represent the study groups with the highest correlations. The institutions or universities with the most published articles are all located in the United States. At the top of the top 10 is Univ Michigan, which has published 11 papers. Univ Michigan's research on automotive supply chain risk management is relatively mature and widely recognized. Tied for second place are Islamic Azad Univ, Tech Univ Munich, and Chinese Acad Sci, all of which have published seven papers. Among the remaining top 10 institutions, Univ Waterloo, Politecn Torino, and Urmia Univ Technol each published six papers. Chongqing University, Charles Univ Prague, and Tech Univ Kosice each published five papers. The number of articles published by the top 10 universities and institutions accounts for 10% of the total number of articles published by all institutions worldwide. The remaining 90% are publications with between one and four articles per institution.

which have published seven papers. Among the remaining top 10 institutions, Univ Waterloo, Politecn Torino, and Urmia Univ Technol each published six papers. Chongqing University, Charles Univ Prague, and Tech Univ Kosice each published five papers. The number of articles published by the top 10 universities and institutions accounts for 10% of the total number of articles published by all institutions worldwide. The remaining 90% are publications with between one and four articles per institution. Among the many research institutions, Univ Waterloo, Univ Toronto, McMaster Univ, Univ Western Ontario, Inst Work and Health and NIOSH have the strongest cooperative research ability and the strongest correlation. By sharing teaching resources and scientific research results, they promote research collaboration between institutions and achieve significant research results in the field of automotive supply chain risk management. This was followed by Univ Tehran Med Sci, Islamic Azad Univ and Cranfield Univ, Also available are Urmia Univ Technol, Allameh Tabatabai Univ, Univ New Haven, Birla Inst Technol & Sci, Michigan State Univ. Further analysis from Figure 5 shows that the cooperation network of research institutions is relatively simple, only Among the many research institutions, Univ Waterloo, Univ Toronto, McMaster Univ, Univ Western Ontario, Inst Work and Health and NIOSH have the strongest cooperative research ability and the strongest correlation. By sharing teaching resources and scientific research results, they promote research collaboration between institutions and achieve significant research results in the field of automotive supply chain risk management. This was followed by Univ Tehran Med Sci, Islamic Azad Univ and Cranfield Univ, Also available are Urmia Univ Technol, Allameh Tabatabai Univ, Univ New Haven, Birla Inst Technol & Sci, Michigan State Univ. Further analysis from Figure 5 shows that the cooperation network of research institutions is relatively simple, only some universities and research structures are designed, and there are few studies in which enterprises participate. At present, there are more theoretical research works than practical ones in the research of automotive supply chain risk management. There is not a stable core research group in the study of automotive supply chain disruption risk management, which needs more researchers to participate in and carry out more in-depth research in this field. According to the research, the reason why theoretical research is more than practical research in the current research is that the communication between various research institutions is mostly online academic discussion, so the importance of offline practice is ignored. Therefore, under this background, the research will be based on the current theoretical research situation of the risk management of automobile supply chain interruption, and will be carried out with practice as the core of the follow-up research.

research.

**Figure 5.** Institutions working together in the field of automotive supply chain disruption risk management.

some universities and research structures are designed, and there are few studies in which enterprises participate. At present, there are more theoretical research works than practical ones in the research of automotive supply chain risk management. There is not a stable core research group in the study of automotive supply chain disruption risk management, which needs more researchers to participate in and carry out more in-depth research in this field. According to the research, the reason why theoretical research is more than practical research in the current research is that the communication between various research institutions is mostly online academic discussion, so the importance of offline practice is ignored. Therefore, under this background, the research will be based on the current theoretical research situation of the risk management of automobile supply chain interruption, and will be carried out with practice as the core of the follow-up

**Figure 5.** Institutions working together in the field of automotive supply chain disruption risk management. Based on the sample data collected in this paper, Cite Space software was used to detect the strongest citation bursts on research institutions to identify active institutions in automotive supply chain disruption risk management research in a specific period. According to Figure 6, it can be seen that the research field of automotive supply chain disruption risk management has been the focus of the following top 15 institutions since 2000, among which the first institutional mutation in 2001 was related to Inst Work & Hlth, Univ Waterloo and Univ Toronto. In 2002, scholars at Univ New Haven paid great attention to the field of automotive supply chain disruption risk research. In 2003, Michigan State University began to focus on the research perspective on the disruption risk of the automotive supply chain. Among the top 15 institutions are research institutes Based on the sample data collected in this paper, Cite Space software was used to detect the strongest citation bursts on research institutions to identify active institutions in automotive supply chain disruption risk management research in a specific period. According to Figure 6, it can be seen that the research field of automotive supply chain disruption risk management has been the focus of the following top 15 institutions since 2000, among which the first institutional mutation in 2001 was related to Inst Work & Hlth, Univ Waterloo and Univ Toronto. In 2002, scholars at Univ New Haven paid great attention to the field of automotive supply chain disruption risk research. In 2003, Michigan State University began to focus on the research perspective on the disruption risk of the automotive supply chain. Among the top 15 institutions are research institutes in the United States and China. The reason may be that the development level of the automobile manufacturing industry in China and the United States is at the forefront of the world, and it is easier for scholars to start with the industry with certain development advantages in their own countries. By collecting research data and materials and using research methods, the development of the automobile industry is becoming better and better.

#### in the United States and China. The reason may be that the development level of the *4.3. Keyword Analysis*

automobile manufacturing industry in China and the United States is at the forefront of To fully understand the history and development of automotive supply chain disruption risk management research, we used Cite Space software to visually analyze the keywords of the papers in the sample data and identify the research hotspots in this field. Figure 7 shows the network visualization of the keyword co-occurrence map from 2000 to 2022. With the continuous progress and development of the automotive industry, automotive supply chain risk management has gradually become an important strategic means to promote the core competitiveness of automotive enterprises. The node size in Figure 7 is proportional to the frequency of the keyword.

better.

the world, and it is easier for scholars to start with the industry with certain development advantages in their own countries. By collecting research data and materials and using research methods, the development of the automobile industry is becoming better and

**Figure 6.** Top 15 institutions with the strongest citation bursts.

**Figure 6.** Top 15 institutions with the strongest citation bursts.

**Figure 7.** Network visualization of keyword co-occurrence analysis.

**Figure 7.** Network visualization of keyword co-occurrence analysis. **5. Citation Analysis and Reference Co-Citation Analysis**  In the research of automotive supply chain risk management, the most important keyword is automotive industry (102), risk (81), performance (73), model (64), design (50), strategy (47), safety (39), reliability (39), simulation (38) and optimization (35). In general, the research in this field mainly involves supply chain performance improvement, supply

Ivanov et al. have made the most prominent contribution in the field of supply chain disruption risk research, He and his research team applied mathematical programming,

Munir et al. explored the relationship between supply chain integration and supply chain risk management to improve supply chain operation performance, and studied the mediating role of supply chain risk management between supply chain integration and enterprise operation performance. The study shows that SCM plays a partial mediating role between internal integration and operational performance, and a fully mediating role between supplier and customer integration and operational performance [51]. Govindan et al. were cited five times [52]. Tomlin was cited four times. Tomlin believed that the ratio of supplier uptime to disruption frequency is the key factor of the optimal strategy. By comparing different types of suppliers, the author proposes a hybrid procurement strategy to alleviate the supply disruption risk of automobile enterprises [53]. Fan and Stevenson were cited four times. Fan and Stevenson conducted a systematic literature review on supply chain risk management, mainly including risk identification, risk assessment, and risk detection [31]. The network of the co-cited reference in the field of automotive supply chain disruption risk management is shown in Figure 8. It can be seen from the reference network diagram in Figure 8 that in the current research, the discussion

networks in production, logistics, and supply chains, exploring supply chain structure dynamics and control, with an emphasis on global supply chain design and disruption considerations, distribution planning and dynamic rescheduling, among others [46–48]. Kamalahmadi et al. were cited seven times [49]. Zimmer et al. were cited five times. Zimmer et al. developed a model combined with the fuzzy AHP method to effectively evaluate the supplier selection of the German automobile industry in the global supply

chain [50]. Munir et al. were cited five times.

chain operation, supply chain optimization and simulation, and supply chain sustainable development, indicating that with the increasing market competition between enterprises, researchers are trying to improve the level of automotive supply chain disruption risk management through the above methods. It makes the automobile enterprises maximize their profit while maximizing the cooperation and synergy effect among enterprises, realizes the optimal development path of the automobile supply chain system, and helps and promotes the sustainable development of the automobile enterprises in a benign competitive environment.

#### **5. Citation Analysis and Reference Co-Citation Analysis**

Ivanov et al. have made the most prominent contribution in the field of supply chain disruption risk research, He and his research team applied mathematical programming, simulation, control, and fuzzy theory methods to study the dynamics of complex networks in production, logistics, and supply chains, exploring supply chain structure dynamics and control, with an emphasis on global supply chain design and disruption considerations, distribution planning and dynamic rescheduling, among others [46–48]. Kamalahmadi et al. were cited seven times [49]. Zimmer et al. were cited five times. Zimmer et al. developed a model combined with the fuzzy AHP method to effectively evaluate the supplier selection of the German automobile industry in the global supply chain [50]. Munir et al. were cited five times.

Munir et al. explored the relationship between supply chain integration and supply chain risk management to improve supply chain operation performance, and studied the mediating role of supply chain risk management between supply chain integration and enterprise operation performance. The study shows that SCM plays a partial mediating role between internal integration and operational performance, and a fully mediating role between supplier and customer integration and operational performance [51]. Govindan et al. were cited five times [52]. Tomlin was cited four times. Tomlin believed that the ratio of supplier uptime to disruption frequency is the key factor of the optimal strategy. By comparing different types of suppliers, the author proposes a hybrid procurement strategy to alleviate the supply disruption risk of automobile enterprises [53]. Fan and Stevenson were cited four times. Fan and Stevenson conducted a systematic literature review on supply chain risk management, mainly including risk identification, risk assessment, and risk detection [31]. The network of the co-cited reference in the field of automotive supply chain disruption risk management is shown in Figure 8. It can be seen from the reference network diagram in Figure 8 that in the current research, the discussion is mainly based on the previous research, that is, the discussion on the risk of interruption of the automobile supply chain is still more traditional. For this reason, the research believes that the main research directions of the risk of automobile supply chain interruption can be screened out by analyzing the number of references cited in the past, providing theoretical support for the follow-up research.

Analyzing the keywords in the title and abstract and performing cluster analysis can help to determine the research trend of this topic. In order to identify the hot topics in the research field of automotive supply chain disruption risk, Cite Space was used to visually analyze the dynamic evolution path of research topics in this research field. The horizontal line in Figure 9 represents the change in research hotspots over time. The number one theme was supply chain resilience (#0) which exploded around 2001. This shows that there may have been many disruption risk events in the automobile industry around 2001, and the research on the improvement of supply chain resilience in this field began to attract the attention of scholars. Then, the clustering themes are respectively: network design (#1), game theory (#2), stochastic demand (#3), supply chain coordination (#4), green supply chain (#5), supply chain performance (#6), disruption management (#7), mixed integer programming (#8), risk management (#9). Green supply chain (#5), supply chain performance (#6), and disruption management (#7) may be the research direction that scholars focus on recently.

providing theoretical support for the follow-up research.

**Figure 8.** Network of the co-cited reference in the field of automotive supply chain disruption risk management. proposed that supply chain resilience can be measured by five key dimensions, including: supplier performance, internal business, innovation and learning, customer service, and finance [58].

connotation interpretation in different fields and dimensions [57]. Quang and Hara

is mainly based on the previous research, that is, the discussion on the risk of interruption of the automobile supply chain is still more traditional. For this reason, the research believes that the main research directions of the risk of automobile supply chain interruption can be screened out by analyzing the number of references cited in the past,

external forces, namely "engineering resilience". In 1973, Holling, a Canadian ecologist, applied the idea of resilience to ecology for the first time and put forward the concept of "ecological resilience", which considered the ecosystem as a dynamic system with **Figure 9.** Timeline view of reference clusters in the field of automotive supply chain disruption risk management.

**Figure 9.** Timeline view of reference clusters in the field of automotive supply chain disruption risk

meaning of resilience is the ability of the system to resolve external shocks and maintain

Although the focus of research in different fields is different, the relevant research on

Network design (#1): In view of the discussion of supply chain structure in the existing literature, Cox et al. studied the concept of supply chain management by focusing on the linear relationship between buyers and suppliers. While this approach is useful for understanding the relationship between buyers and suppliers, it does not capture the complexity of a firm's strategy, which relies on a larger network of firms that are embedded together to form a complex network [59]. Croxton [60] and Carter [61] have shown that the supply chain is a complex network that exhibits a similar organizational

its main functions when crises occur.

management.

Supply chain resilience (#0): The word Resilience first comes from the concept of resilience in engineering. Its meaning is "returning to the original state", which refers to the ability of an object to recover to its original state after deformation under the action of external forces, namely "engineering resilience". In 1973, Holling, a Canadian ecologist, applied the idea of resilience to ecology for the first time and put forward the concept of "ecological resilience", which considered the ecosystem as a dynamic system with multiple stable states, overturning the traditional ecology view that the ecosystem has a single equilibrium state [54]. Since then, scholars' research on resilience has gradually spread from natural ecology to ecology, engineering, economy, and society. Alberti believed that resilience is the ability of a system to absorb and resolve changes before restructuring after a series of structural and process changes [55]. Rose believed that resilience is the inherent adaptive ability of individuals or enterprises to avoid potential losses in the face of disasters. It is usually expressed in two types, one emphasizing its inherent characteristics, and the other emphasizing its adaptive characteristics [56]. With the continuous infiltration of the concept of resilience, resilience has been given connotation interpretation in different fields and dimensions [57]. Quang and Hara proposed that supply chain resilience can be measured by five key dimensions, including: supplier performance, internal business, innovation and learning, customer service, and finance [58].

Although the focus of research in different fields is different, the relevant research on the connotation of comprehensive resilience generally believes that the most basic meaning of resilience is the ability of the system to resolve external shocks and maintain its main functions when crises occur.

Network design (#1): In view of the discussion of supply chain structure in the existing literature, Cox et al. studied the concept of supply chain management by focusing on the linear relationship between buyers and suppliers. While this approach is useful for understanding the relationship between buyers and suppliers, it does not capture the complexity of a firm's strategy, which relies on a larger network of firms that are embedded together to form a complex network [59]. Croxton [60] and Carter [61] have shown that the supply chain is a complex network that exhibits a similar organizational pattern to other network types. The company's "supply network" includes the connection with direct suppliers and customers and the connection between subsequent suppliers and customers, which is essentially a recursive relationship.

In addition, the researchers also explored the connections between the layers of the supply chain network. Kito et al. used the data in the Mark Line database to construct the supply chain network of Toyota Motor, and the study determined that the layered structure of Toyota was "barrel shaped", which was contrary to the pyramid structure assumed before. However, the authors also did not find the topology to be scale-free [62]. Brintrup et al. studied the automotive industry to explore robustness properties. While these studies discussed part of the topological structure features, the study did not evaluate the dynamic nature of these relationships [63].

Game theory (#2): Existing research mainly uses game theory to solve the following two problems:

tives and compensation measures to stimulate the sales of distributors and retailers and eliminate their worries, not only can distributors and retailers obtain more profits [66], but manufacturers' profits will also increase; on the other hand, incentives and compensation measures taken by manufacturers to achieve benefit sharing and risk sharing can maintain a good cooperative relationship among members of the supply chain, increase the elasticity of the supply chain, and reduce the occurrence of disruption risk. The commonly used incentive and compensation strategies are: wholesale price discounts, revenue-sharing strategies, buyback strategies, quantity flexibility contracts, reward and penalty strategies, lead time adjustments, order options, etc. [67].

Stochastic demand (#3): In the supply chain system, uncertainty is common due to the complexity of the supply chain network structure and the interaction between enterprises in the supply chain. These uncertainties come not only from the uncoordinated operation between internal suppliers and manufacturers, but also from the external environment such as natural disasters, policies, and regulations. Uncertainty will be transmitted step by step with information flow, logistics, and capital flow, and ultimately affect the overall operation and profitability of the supply chain.

There are many and mixed factors affecting demand, and the reasons for random demand are as follows: the operation mode and organizational structure of each member enterprise in the supply chain are different, and the way to deal with problems in the cooperation process is different. If not handled properly, the interests of some member enterprises can not only be guaranteed, but also cause vicious competition and other bad situations, and the needs of customers can not be met at all [68]; The opacity of market information. There are many factors that affect the quantity demanded, which also makes it difficult to predict the quantity demanded, thus leading to the deviation of the demand [69]; Supply chain members amplify demand information step by step based on maximizing their own interests [70]; The occurrence of an emergency. Emergencies are the product of the complexity and uncertainty of the supply chain system. The particularity of the automobile supply chain structure determines that it is more fragile and has a weak ability to resist the risk of emergencies [71].

Supply chain coordination (#4): Adhikari et al. considered that different enterprises in the supply chain have different risk resistance capabilities, and set up different risk-sharing mechanisms to improve the profitability of enterprises. At the same time, they designed joint pricing and order quantity decisions, and finally designed a two-way repurchase sales rebate punishment contract to coordinate the supply chain [72].

Some research works put forward that members of the supply chain share risks to promote good cooperation among the member enterprises in the supply chain. Obviously, insufficient supply can not fully reflect the characteristics of the automobile supply chain network only from the perspective of supply chain equilibrium. Therefore, it is particularly critical to improve the elasticity and stability of the automobile supply chain, reduce the damage of risks to the automobile supply chain network, and effectively prevent the occurrence of risks [73]. The Ivanov team presented the results of a simulation study that revealed some new research on the impact of COVID-19 on global supply chain risk. The specific features that frame epidemic outbreaks as a distinct type of supply disruption risk are first clarified. Secondly, the coronavirus and supply chain optimization software are used as examples to demonstrate how simulation-based methods can be used to examine and predict the impact of the outbreak on supply chain performance. The main observation of the simulation experiment is that important factors for risk propagation include lead time, the speed of epidemic spread, and the duration of upstream and downstream disruptions in the supply chain [74–76].

Green supply chain (#5): For the research on the green supply chain in the field of automotive supply chain research, the design of a reverse recycling network is the most discussed research issue. Many countries have introduced policies to minimize waste after vehicle scrap, which has led to a special focus on reverse supply chain research in the automotive industry in product design, electrical and electronic equipment, and battery industries. Mansour and Zarei proposed a network design and mathematical formula for recycling ELVs from the perspective of manufacturers to obtain maximum economic benefits and fulfill relevant legal provisions. The model focuses on establishing optimal locations for collection centers and demolition personnel, as well as material flows between facilities [77].

Zach proposed the EV recycling network design and its mathematical model [ ˙ 78]. Minimization of the total cost of the system including the costs of setting up the network and transportation was aimed in the model. Jonrinaldi and Zhang proposed an integrated production and inventory control model for supply chains containing multiple products. They considered 3PL providers collecting used products from end customers and dismantling used products [79].

Supply chain performance (#6): The occurrence of supply chain risk will have a significant impact on supply chain performance [80,81]. Different risks have different attributes and different impacts on supply chain performance. For example, Rice and Caniato proposed that core risks related to product processes may directly affect the dayto-day operations of the supply chain, and that these risks have a high probability of occurring but a lower impact on supply chain performance than external supply chain risks [82]. External supply chain risks, on the other hand, are rare but can disrupt supply chain activities directly or indirectly (e.g., the shutdown of Hyundai Motor in South Korea due to COVID-19). The interruption of information, finance, time, and other factors may have a serious impact on the supply chain process (especially the supply, manufacturing, and downstream links) [1]. Quang and Hara constructed a hypothetical model of the relationship between risk and supply chain performance. The core of the model is three core risks related to product process: supply risk, operation risk, and demand risk, which have a direct impact on supply chain performance [58]. Thun and Hoenig pointed out that the impact of internal and external supply chain risks on supply chain performance is significantly different [83]. Altay and Ramirez studied the impact of natural disasters on enterprises, analyzing the impact of more than 3500 natural disaster events (including fastoccurring natural disasters such as typhoons, floods, and earthquakes) on the performance of more than 100,000 enterprises, including financial leverage, total asset turnover, and operating cash flow [84]. Bergmann et al. believed that the excessive consumption of these natural resources leads to the escalation of climate change, and this exogenous risk can further significantly affect the operation and financial performance of organizations [85].

For the research on the relationship between operational risk and supply chain performance, most of the existing studies believe that the successful implementation of the organizational strategy has a positive role in promoting supply chain performance [86], however, the research on interruption risk management has not received enough attention. Cuthbertson identifies the broader consequences of failing to manage risk effectively. These include not only financial losses, but also reduced product quality, damage to property and equipment, loss of reputation in the eyes of customers, suppliers, and the wider public, and delivery delays [87]. Based on the background of promoting the safe and efficient flow of goods, Bueno et al. proposed a system dynamics model to analyze the impact of the spread of disruption risk caused by terrorist acts on the performance of global supply chains [88].

Disruption management (#7): Wagner et al. studied the data of several automobile enterprises, discussed the differences between just-in-time sequential delivery and other delivery concepts of automobile manufacturing enterprises, and then proposed a method to improve supply chain resilience based on JIS [89].

The problem of supply chain disruption management is studied. The supply chain dynamic simulation model is considered as a suitable method to observe and predict the changing behavior of the supply chain over time. On the basis of the original model, Torabi and Sadghiani et al. added additional dynamic characteristics to simulate the impact of risk on supply chain performance [90,91]. Most of the existing studies use discrete event simulation methods [92,93], some researchers choose to use heuristics to study supply chain optimization problems [94], and several studies also use methods based on multi-agent simulation [95,96]. Few studies have incorporated supply chain simulation studies into simulations and transportation disruptions during epidemic crises [97]. In the study of the impact of disruptive events on supply chain performance, Klibi and Ivanov et al. believe that adding some parameter conditions that change according to time dependence in the model will be more conducive to simulation model calculation [98,99]. In addition, Li and Pavlov et al. analyze detailed control policies based on various financial, customer, and operational performance indicators [100,101]. The simulation model considers logical and stochastic constraints such as disruptions, stochastics of inventory, production, procurement, and transportation control policies, and stepwise capacity degradation and restoration [102–105]. Simulation studies play an important role in model simulation studies because they involve time-varying parameters, duration of recovery measures, and capacity degradation and recovery. The advantage of simulation is that it allows for further optimization studies by extending complex problems through time-varying situational behavior in the system.

Mixed integer programming (#8): Spengler et al. focused on the MILP model of product recycling in the steel industry [106]. Munoz et al. argue that in the present work, LCA was used to evaluate an existing automotive component, a plastic facade panel, and compare it with a recyclable prototype panel based on a compatible polyolefin design. From an environmental point of view, LCA has proven to be a very useful tool for validating redesigned automotive components; Moreover, it allows one to identify not only the benefits of increased recyclability, but also the improvement of other stages of the life cycle. From this case study, some recommendations are made for companies in order to design eco-friendly components for automotive interiors [107]. Zarei et al. proposed a mathematical model in which new vehicle distributors are responsible for ELV collection, and they use joint potential facilities for distribution and collection. The effectiveness of the GA-based model and solution method is verified by generating several test problem instances [108]. Ozceylan and Paksoy proposed an integrated, multi-echelon, multi-period, and multi-part MILP model to optimize production and distribution plans for CLSC networks. They presented computational results for many scenarios [109]. Farel et al. proposed a mathematical model for an ELV glazing recycling network including car manufacturers, dismantlers, shredders, collection and transportation facilities, and glass treatment facilities [110].

Risk management (#9): Through the literature review, the existing research mainly uses the fault tree analysis method for supply chain risk assessment research [111], risk matrix method [112], fuzzy comprehensive evaluation method [113], and other methods. Kolotzek et al. used the analytic hierarchy process to rank 11 indicators from the perspectives of political risk, demand increase, and supply decrease to evaluate the supply risk of manufacturing enterprises [114]. Abdel et al. constructed an evaluation index system considering demand, supply, environment, and business risks, and introduced the ideal solution similarity order preference technology TOPSIS method to evaluate supply chain risks [115]. In addition, some studies use probability theory and survey methods to evaluate supply risk. For example, Vilko and Hallikas used probabilities and losses to characterize supply risk and assessed the risk of multi-modal supply chains [116]. Defining demand as a probabilistic parameter, Nooraie and Mellat Parast constructed a multi-objective approach to assess supply risk [117].

In addition to the overall assessment of risk factors, existing studies also discuss the causal relationship between risk factors in the supply chain. Shin used quantitative methods to map the causal relationship between supply chain security risks [118]. Studies have found that quantitative methods have limited interdependence ability to compare and distinguish risks, such as the petri net model [119], and coupled with a lack of quantitative data or inappropriate model parameters, quantitative methods do not always work [120]. Therefore, in the existing literature, such as explanatory structural modeling [121] and Bayesian networks are used to evaluate causality in supply chain risk. Among them, when

using the Bayesian network to analyze the risk of a supply chain system, it can identify the causal relationship between the key factors and the final result, so it is recognized by the majority of researchers. Bayesian networks can effectively combine statistical data and subjective judgment and model in the absence of data. Bayesian network is widely used in supply chain risk management. The existing research mainly focuses on solving specific problems in supplier selection, supplier evaluation, and selection, and most of the research problems only focus on some specific problems, such as: automotive enterprise parts supplier selection and evaluation problem, Boeing Company, and supply evaluation problem [122].

By analyzing the keywords in the references, we can more accurately understand the main research contents and directions in the previous research, and also provide technical support for the follow-up research. In addition, according to the frequency of keywords in the existing relevant literature, it is of great significance to analyze the resilience of automobile supply chain disruption risk, and is also the focus of the follow-up work.

#### **6. Research Gaps and Future Research Opportunities**

The above research results provide a theoretical basis for this paper, and prove the importance of research on risk management and resilience improvement strategy of the supply chain of automotive enterprises. However, there are still some deficiencies in the existing research results, and the detailed analysis is as follows:

#### **7. Conclusions**

In this paper, we systematically review the literature on automotive supply chain disruption risk management research. We used Cite Space software to analyze and visualize the number of journal papers, core authors, research institutions, source journals, research hotspots, and future development directions of automotive supply chain disruption risk management research. The main findings are as follows:

First of all, in the past 20 years, the number of publications related to the topic of automotive supply chain disruption risk management increased steadily, which grew from five related publications in 2000 to a peak number of 105 in 2021. This indicates that research on automotive supply chain disruption risk management had attracted increasingly more attention from society and scholars. The influential works, their authors, and the existing and emerging research clusters/themes are identified.

Second, at present, automotive supply chain disruption risk management research has not formed a stable core author group, but has formed core research institutions and core research journals.

Third, "The effects of an integrative supply chain strategy on customer service and financial performance: an analysis of direct versus indirect relationships" published by "Journal of Operations Management" is the most cited paper among the 866 documents. Furthermore, reference co-citation analysis presented ten clustered groups and the literature of each cluster might provide potential future research opportunities regarding automotive supply chain disruption risk management. These research contents will bring useful inspiration to managers of automotive manufacturing companies, and supply chain management-related scholars and help them in their future work and study in supply chain-related management and research work.

Finally, there are some limitations that need to be addressed in future work. The data source was collected from the core collection of WOS, which may cause deviations in the results of bibliometric analysis. We could extend the data source to include more publications about the field of automotive supply chain disruption risk management such as ProQuest Dissertations and Theses. Moreover, although we could obtain objective results about the field of automotive supply chain disruption risk management based on bibliometric analysis, some underlying reasons for these results are not explained. Some social science research methods, such as expert interviews, could be employed in the future to address this limitation.

**Author Contributions:** Conceptualization, K.H.; Data curation, K.H.; Formal analysis, K.H.; Funding acquisition, J.W.; Methodology, J.Z.; Project administration, J.W.; Software, K.H.; Supervision, J.W.; Validation, K.H.; Visualization, K.H.; Writing—original draft, K.H.; Writing—review & editing, J.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was funded by the National Social Science Fund of China (No. 18BGL018).

**Data Availability Statement:** The datasets used in this study are available from the corresponding author upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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## *Article* **Bilateral Matching Decision Making of Partners of Manufacturing Enterprises Based on BMIHFIBPT Integration Methods: Evaluation Criteria of Organizational Quality-Specific Immunity**

**Qiang Liu 1 , Hongyu Sun 1 and Yao He 2,\***

**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** This study aims to examine how the bilateral matching decision making of manufacturing enterprises that are seeking partners in the manufacturing supply chain can be improved by taking into consideration evaluation criteria for organizational quality-specific immunity. This study constructs an evaluation indicator system to measure organizational quality-specific immunity based on immune theory. The system's evaluation criteria are based on the key components of organizational quality-specific immunity. We also construct bilateral matching evaluation and decision-making models using interval-valued hesitant fuzzy information and bidirectional projection technology (BMIHFIBPT). The interval-valued bilateral fuzzy bidirectional projection technology is applied to solve a combination satisfaction and matching optimization model. Empirical analysis is carried out to assess both the supply and demand sides of representative manufacturing enterprises in the manufacturing supply chain, match the main supply and demand bodies of two subjects, and help manufacturing enterprises select the optimal cooperation partners. The empirical analysis results indicate that the bilateral matching evaluation and decision-making models based on BMIHFIBPT can overcome the lack of information to some extent and help solve interval-valued hesitant fuzzy decision-making problems. In turn, the models can provide a basis for manufacturing enterprises to effectively select the best cooperation partners and conduct bilateral matching decision making in the manufacturing supply chain area that supports organizational quality-specific immunity.

**Keywords:** bilateral matching decision making; interval-valued hesitant fuzzy information; bidirectional projection technology; organizational quality-specific immunity

### **1. Introduction**

Today's increasingly fierce and complex competitive environment poses unprecedented challenges to manufacturing enterprises' development. Quality plays a vital role in staying competitive. Quality management thus not only offers many benefits for enterprises but also involves huge security risks [1]. In recent years, the quality management problems of manufacturing enterprises have become increasingly prominent. Many well-known manufacturing enterprises have experienced quality crises and faced criticism by the public and media. Takata Airbags had an issue with an abnormal gas generator rupture. After the incident came to light, more than 60 million vehicles globally were affected and recalled. The recall incident brought huge losses to Takada, which declared bankruptcy in June 2017. In 2016, Samsung was caught in the "Battery Gate" incident. Within a month of the release of the Galaxy Note7 mobile phone, more than 30 explosions and fires occurred due to battery defects. Samsung permanently stopped production and sales of the Galaxy Note7, and the event became one of the most shameful events in the company's history. Quality

**Citation:** Liu, Q.; Sun, H.; He, Y. Bilateral Matching Decision Making of Partners of Manufacturing Enterprises Based on BMIHFIBPT Integration Methods: Evaluation Criteria of Organizational Quality-Specific Immunity. *Processes* **2023**, *11*, 709. https://doi.org/ 10.3390/pr11030709

Academic Editors: Conghu Liu, Xiaoqian Song, Zhi Liu and Fangfang Wei

Received: 10 January 2023 Revised: 13 February 2023 Accepted: 14 February 2023 Published: 27 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Weifang 262700, China

management problems cause serious economic losses for manufacturing enterprises, affecting in turn the survival and development of other manufacturing enterprises in the supply chain.

Quality is key to manufacturing enterprises' survival. There are many manufacturing enterprises in the manufacturing supply chain. These enterprises mainly select partners based on features of quality, for example, their ability to jointly complete quality tasks and performance in the manufacturing supply chain [2–5], maintain resilience [6–11], strengthen quality management practice and sustainability [12–19], achieve sustainable development [20,21], and promote green quality management and green technology innovation [22–25]. Organizational quality-specific immunity is the comprehensive embodiment of the organizational quality of a manufacturing enterprise in the manufacturing supply chain [26–35]. The stronger the organizational quality-specific immunity of a manufacturing enterprise, the easier it is for that enterprise to be selected as a partner.

Therefore, in view of the importance of evaluating organizational quality-specific immunity, this study uses immune theory as an entry point to construct relevant evaluation criteria. We introduce bidirectional projection technology to the bilateral matching decisionmaking method and consider both the supply and demand sides. We use data from the cooperation partners of representative manufacturing enterprises in the manufacturing supply chain as the interval-value hesitant fuzzy information. This study further carries out bilateral (two-sided) matching decision making according to the proposed evaluation criteria for organizational quality-specific immunity. The process involves applying integration methods to create bilateral matching decision-making models that incorporate interval-valued hesitant fuzzy information and bidirectional projection technology (BMIH-FIBPT). The resulting bilateral matching decision-making models and methods will provide the foundation basis for effective selection of partners in the manufacturing supply chain based on organizational quality-specific immunity, providing practical guidance as well as scientific contribution to the field.

#### **2. Literature Review**

#### *2.1. Bilateral Matching Decision-Making Models and Methods*

Bilateral matching decision making is closely related to management science, and various types of bilateral matching decision-making models and methods are widely used in the field of management science and engineering, mainly in the fields of human resources management, price and material management, e-commerce management, enterprise operation management, and risk investment management [36–43]. However, limited relevant empirical research has applied the various types of bilateral matching decision-making models and methods to the supply chain quality management and supply chain operation management fields. The models and methods available to support bilateral matching decision making mainly stem from models and methods related to emerging technology, operational optimization, statistical and artificial intelligence, simulation, and bilateral matching based on the specific preference information of the bilateral subjects [36–43].

Several representative emerging technology models and methods are as follows [36–43]: two-sided matching decision models based on advantage sequences, two-sided matching game analysis methods, two-sided matching models based on the matching efforts of a bilateral platform, two-sided matching decision-making methods based on triangular intuitionistic fuzzy number information, intuitionistic fuzzy two-sided matching methods considering regret aversion and matching aspiration, two-sided game matching methods with uncertain preference ordinals, decision-making models and methods based on two-sided matching dynamic games, and decision-making methods for stable two-sided matching based on linguistic preference information.

Several representative models and methods for operational optimization, statistical and artificial intelligence, and simulation are as follows [44–46]: intuitionistic fuzzy two-sided matching models, multi-objective stable matching methods with distributional constraints, and dynamic matching methods with unknown preferences.

Several representative models and methods for bilateral matching based on specific preference information from bilateral subjects are as follows [47–50]: network visualization of stable matching methods, dynamic two-sided matching methods based on uncoordinated preference information, multiple decision-making matching models based on the characteristics and circumstances of two-sided (bilateral) subjects, and strict two-sided matching methods based on complete preference ordinal information.

#### *2.2. Organizational Quality-Specific Immunity*

Quality accidents are not uncommon. Such issues reveal not only a regulatory mechanism with loopholes but also an imperfect quality management system. In the process of business development, quality accidents are the "symptoms" of enterprises with a qualityrelated disease, and enterprises infected by that disease will soon see their competitiveness drop, at the very least. The quality management departments and quality management personnel in an enterprise constitute the quality management immune system of an enterprise. Their function is to prevent disease from occurring in the enterprise, perpetually maintaining the enterprise's health, and to provide proper treatment the moment the enterprise starts to fall sick. Seeking out and eliminating "quality accident-inducing factors" is an effective means by which the quality management immune system can prevent an enterprise from getting sick.

Lv and Wang [51–53] were the first to apply immune theory to organizational management. Their work explores organizational adaptability and has led to inspiring results about general organizational immunity from the perspective of immunization [54]. They assert that organizational immunity consists of specific immunities and non-specific immunities. Specific immunities, which emerge as the organization establishes key behaviours, help to prevent "viruses" in the enterprise [55]. At the heart of general organizational immunity, i.e., perhaps the most important element in determining the rise or fall of an enterprise, is organizational quality [56]. Organizational quality-specific immunity, in turn, is fostered by specificity, adaptability, initiative attributes, and the characteristics of acquired cultivation and fostering nurture. These features enhance the efficiency and learning effectiveness of the immune system, saving immune response time if the issue arises again, essentially immunizing the enterprise. The main components of organizational quality-specific immunity are thus quality monitoring and cognition, quality defence, quality memory, and immune homeostasis. These components complement each other, allowing for coordinating development in the formation of an orderly dynamic benign cycle [57].

Existing empirical studies on organizational quality-specific immunity have drawn on relevant theoretical analysis frameworks and conceptual framework models to conduct data surveys, questionnaires, interviews, statistical investigations, and case studies. Luca et al. [58] point out that quality management is becoming an increasingly important factor in determining an enterprise's level of economic and technological development. Improving quality is an urgent need for enterprises seeking to survive and develop in the current competitive environment. Luca et al. also note the key success factors for implementing quality improvement measures and propose a performance measurement system to evaluate those factors. Kaynak and Hartley [59] and Tari et al. [60] confirm that quality management practice has a significant positive impact on the product quality and operational efficiency of the company. In biological and immune theory, each time the organism completes the immune response, its immune cells are enhanced; the organism is thus preserved by immunological memory. Considering organizational quality management from the perspective of immune theory, when an enterprise faces a threat to organizational quality due to internal abnormalities or changes in the external environment, its immune system should identify and respond to the threat and then commit the event to memory; doing so ensures that, next time, the enterprise can mobilize available resources in a timely and reasonable manner to remove similar risks or harm, ensuring healthy and sustainable development [61].

Wang et al. draw on the theory of medical immunity to study the adaptability of enterprises. They divide the concept of organizational immunity into two dimensions: nonspecific immunities and specific immunities. Non-specific immunities are determined by three major components: organizational structure, institutional rules, and company culture. Specific immunities, in comparison, are determined by organizational monitoring and surveillance, organizational defence, and organizational memory [61]. Li et al. [62] propose the product quality management model for supply chains based on the characteristics and mechanisms of the biological immune response. With the biological immune system as a parallel, their supply chain quality management model has four stages: immune recognition, learning, memory, and immune effects. Based on that model, the quality identification mechanism and control methods are effective. Passing through the supply chain thus has a positive effect on ensuring product quality and safety.

Liu et al., Shi et al., and Dai and Ding [63–68] indicate that just as organizational quality-specific immunity is at the core of general organizational quality immunity, the latter is at the core of overall organizational immunity. In summary, they find that organizational quality-specific immunity has three main components: organizational quality monitoring and cognition; organizational quality defence, clearance, and repair; and organizational quality memory and immune self-stability. They propose a path towards quality performance improvement based on empirical analysis of the factors that affect quality defect management from the perspective of immune theory. Wang and Li [54], also on the basis on immune theory, consider enterprises' immune recognition ability and use it as a mediator variable to construct a multi-media model. They use the resulting AMOS model to analyse the influencing factors of and risks to immunity to enhance enterprises' ability to resist breaches of immunity. In summary, research has indicated that an enterprise's intrinsic ability to recognize risks to its own survival is equivalent to an immune system's ability to recognize threats to the body's health. Dai and Ding [68] draw on the basic ideas of immune theory in their analysis of the relationships between organizational immunity and the internal control of an enterprise. Based on their findings, they design an internal control evaluation indicator system and construct an evaluation model, which has broadened the research thoughts in the field.

Scholars have expressed concern about the application of immune theory in the field of quality management. The scope of its application remains very limited, and the existing research has not been in depth. At present, the domestic and international investment in the effort to understand quality management as an artificial immune system is insufficient. Few independent empirical analyses have focused on fusing the concept of organizational immunity with quality management. Theoretical and empirical research and evaluations of organizational quality management and organizational quality-specific immunity are relatively rare. Further, no studies have specifically used organizational quality-specific immunity to determine evaluation criteria in conjunction with integration methods and bilateral matching decision-making models based on BMIHFIBPT to solve the problem, on both the supply and demand sides, of matching manufacturing enterprises for cooperation.

#### **3. Evaluation Indicator System Construction of Organizational Quality-Specific Immunity**

Organizational quality-specific immunity reduces quality fluctuation and quality loss, addressing present and potential quality problems. Metaphorically, it can fight a stubborn disease, eliminate the virus, and generate the antigens to protect quality, in the longer term ensuring the re-activation and effectiveness of quality-protecting antibodies. Considering the function of organizational quality-specific immunity, and based on the relevant theories and previous literature review [63–68], the present study selects the following components for its evaluation indicator system: organizational quality monitoring and cognition; organizational quality defence, clearance, and repair (both hard and soft features); and organizational quality memory and immune self-stability. These components correspond to the respective scales and evaluation indicators shown in Table 1.

**Table 1.** Evaluation indicator system for organizational quality-specific immunity.

#### **4. Bilateral Matching Decision-Making Models Based on Interval-Valued Hesitant Fuzzy Information and Bidirectional Projection Technology (BMIHFIBPT)**

In recent years, multi-criteria decision making has been widely used in the field of economic and management [69–79]. Bilateral matching decision making is an important branch of multi-criteria decision making. In bilateral matching (two-sided matching), two subjects (with different finite sets) are matched by a specific algorithm to each other in order to achieve the satisfaction of both parties [80–84]. At present, bilateral matching decision making is becoming an important component of management decision theory. Many economic and management activities entail matching one or more members of two groups, such as the matching of venture capital parties with start-ups [85–87]. Roth [88] first proposed the concept of bilateral matching in a study of marriage matching, in which Roth analyses actual cases of bilateral matching. Bilateral matching decision-making theory has since received extensive attention from scholars. Fan and Yue [17] consider the highest acceptability order of bilateral subjects and propose a strict bilateral matching decisionmaking method based on the complete preference information. Chen et al. [89] examine the dynamic matching decision-making problem based on uncertainty preference sequence information. On the basis of the theory of constructing the ordinal deviation, they apply the evidence fusion method to solve the problem with the help of uncertainty preference sequence information.

However, because the evaluation information of both subjects is uncertain, and because the decision-making environment and situation are complex, the subjects' preference information is often hesitant, fuzzy, and vague [90]. Therefore, how to overcome the lack of decision-making information is a problem worthy of attention. Interval-valued hesitant fuzzy sets have excellent expressive advantages in dealing with hesitant fuzzy information [90]. The present study therefore utilizes interval-valued hesitant fuzzy sets and bidirectional projection technology to measure fuzzy information and effectively solve the problem of bilateral matching decision making. The bilateral matching decisionmaking method used in this study firstly deals with the interval-valued hesitant fuzzy preference information. It then adopts the bidirectional projection technology to obtain

the vector projection formed by different intervals. The TOPSIS method is also used to obtain the closeness of match, which can express the preference of both subjects. Finally, the optimization model is constructed. We then incorporate the closeness into the optimization model to solve the problem.

In our hesitant and fuzzy environment, sets *Ap*(*p* = 2, 3, . . . , *m*) and *Bq*(*q* = 2, 3, . . . , *n*) represent manufacturing enterprises in the manufacturing supply chain. Manufacturing enterprises in these sets all want partners in the supply chain. Manufacturing enterprises in set *Ap*(*p* = 2, 3, . . . , *m*) want to select a partner with suitable partners in the scope of set *Bq*(*q* = 2, 3, . . . , *n*). Set *X* is a non-empty set, such that *R* = {h*x*, *gR*(*xi*)i|*xi* ∈ *X*, *i* = 1, 2, . . . , *n*} refers to interval-valued hesitant fuzzy sets (IVHFSs) in *X*. *gR*(*xi*) refers to interval-valued hesitant fuzzy elements (IVHFEs). The element *x* is from the scope of *X*, which belongs to the membership degree of *R*. *l* is the number and quantity of elements in interval-valued hesitant fuzzy elements. Manufacturing enterprises in set *Ap*(*p* = 2, 3, . . . , *m*) in the manufacturing supply chain present interval-valued hesitant fuzzy preference evaluation information for the potential partners (manufacturing enterprises in set *Bq*(*q* = 2, 3, . . . , *n*)). That information forms interval-valued hesitant fuzzy preference matrix *G* = *gij m*∗*n* . Correspondingly, the potential partners (manufacturing enterprises in set *Bq*(*q* = 2, 3, . . . , *n*) present intervalvalued hesitant fuzzy preference evaluation information for manufacturing enterprises in set *Ap*(*p* = 2, 3, . . . , *m*), and that information forms interval-valued hesitant fuzzy preference matrix *H* = *hij n*∗*m* [84–100].

(1) The construction of bilateral matching decision-making models of partners of manufacturing enterprises based on BMIHFIBPT integration methods

The main model construction principles, procedures, and steps of the bilateral matching decision-making models of partners of manufacturing enterprises based on BMIHFIBPT integration methods are as follows [84–100]:

The interval-valued hesitant fuzzy decision matrix is normalized by the obtained interval-valued hesitant fuzzy information and assembled by the bidirectional projection method to ensure scientific and effective results.

 Then, the correlation coefficients of g*p* and g*q* are:

$$K\_{IVHE}(\mathcal{g}\_{p\prime}\mathcal{g}\_q) = \frac{\mathbb{C}\_{IVHE}(h\_p, h\_q)}{\sqrt{\mathbb{E}\_{IVHE}(h\_p)} \times \sqrt{\mathbb{E}\_{IVHE}(h\_q)}}\tag{3}$$

where:

$$\mathcal{C}\_{IVHE}(h\_p, h\_q) = \frac{1}{2} \sum\_{i=1}^{l} \left( \gamma\_{pi}^{L} \gamma\_{qi}^{L} + \gamma\_{pi}^{II} \gamma\_{qi}^{II} \right) \tag{4}$$

$$E\_{IVHE}(h\_p) = \frac{1}{2} \sum\_{i=1}^{l} \left[ \left( \gamma\_{pi}^{L} \right)^{2} + \left( \gamma\_{pi}^{U} \right)^{2} \right] \tag{5}$$

$$E\_{IVHE}(\hbar\_q) = \frac{1}{2} \sum\_{i=1}^{l} \left[ \left( \gamma\_{qi}^{L} \right)^2 + \left( \gamma\_{qi}^{U} \right)^2 \right] \tag{6}$$

The vector formed by the interval-valued hesitant fuzzy elements g*p* and g*q* are:

$$h\_p h\_q = \left( < \left[ \min \gamma\_1^L, \max \gamma\_1^{\underline{U}} \right] / \left[ \min \gamma\_2^L, \max \gamma\_2^{\underline{U}} \right] / \dots \ , \left[ \min \gamma\_{l'}^L, \max \gamma\_{l}^{\underline{U}} \right] > \right) \tag{7}$$

where:

$$\min \gamma\_i^L = \min (\left| \gamma\_{qi}^L - \gamma\_{pi}^L \right| \left| \gamma\_{qi}^{\underline{U}} - \gamma\_{pi}^{\underline{U}} \right|), \text{ and} \\ \max \gamma\_i^L = \max (\left| \gamma\_{qi}^L - \gamma\_{pi}^L \right| \left| \gamma\_{qi}^{\underline{U}} - \gamma\_{pi}^{\underline{U}} \right|), i = 1, 2, \dots, n.$$

Let the interval-valued hesitant fuzzy element be *g* 0 , the positive and negative ideal interval-valued hesitant fuzzy elements be *g* +, *g* −, and the positive ideal interval-valued hesitant fuzzy element and negative ideal interval-valued hesitant fuzzy element be *g* +*g* −. The positive and negative ideal interval-valued hesitant fuzzy elements and interval-valued hesitant fuzzy elements form the vector *g* 0*g* +, *g* 0*g* −. Then, the bidirectional projection formed by *g* 0*g* − on *g* +*g* − is:

$$\begin{array}{lcl} \text{Pr}\_{\text{g}^-\text{g}^+}^{\text{g}^-\text{g}^{\prime}} & = |\text{g}^-\text{g}^{\prime}| \text{K}\_{IVHEFE}(\text{g}^-\text{g}^{\prime}, \text{g}^-\text{g}^+) \\ & = \frac{\text{C}\_{IVHEFE}(\text{g}^-\text{g}^{\prime}, \text{g}^-\text{g}^+)}{\sqrt{\text{E}\_{IVHEFE}(\text{g}^-\text{g}^+)}} \end{array} \tag{8}$$

The bidirectional projection formed by *g* +*g* − on *g* 0*g* + is:

$$\begin{array}{lcl} \text{Pr}\_{\text{g}'\text{g}^+}^{\text{g}^-\text{g}^+} & = |\text{g}^-\text{g}^+| \text{K}\_{IVHEE}(\text{g}^-\text{g}^+, \text{g}'\text{g}^+) \\ & = \frac{\text{C}\_{IVHEE}(\text{g}^-\text{g}^+, \text{g}'\text{g}^+)}{\sqrt{\text{E}\_{IVHEE}(\text{g}'\text{g}^+)}} \end{array} \tag{9}$$

Based on the closeness formula of TOPSIS and other methods, the interval-valued hesitant fuzzy information is further assembled effectively, and the following formula is constructed:

$$D = \frac{\Pr\_{h^-h^+}^{h^-h^+}}{\Pr\_{h^-h^+}^{h^-h^+} + \Pr\_{h'h^+}^{h^-h^+}} \tag{10}$$

Thus, the principal matching closeness matrices of two-sided subjects *DA* = [*aij*] *m*×*n* and *DB* = [*bij*] *m*×*n* for matching are constructed.

(2) The optimization solution of bilateral matching decision-making models of partners of manufacturing enterprises based on BMIHFIBPT integration methods

The main principles, procedures, and steps of the optimization solution of bilateral matching decision-making models of partners of manufacturing enterprises based on BMIHFIBPT integration methods are as follows [84–100]:

The bilateral matching decision-making optimization model is constructed based on the principal matching closeness matrices of two-sided subjects *DA* = [*aij*] *m*×*n* and *DB* = [*bij*] *m*×*n* .

$$\begin{aligned} \max Z &= \sum\_{i=1}^{n} \sum\_{j=1}^{m} \left[ \lambda (\frac{a\_{ij} + b\_{ij}}{2}) + (1 - \lambda) \sqrt{a\_{ij} \times b\_{ij}} \right] \mathbf{x}\_{ij} \\ \text{s.t.} & \sum\_{j=1}^{m} \mathbf{x}\_{ij} \le 1, i = 1, 2, \dots, m \\ & \sum\_{i=1}^{n} \mathbf{x}\_{ij} \le 1, j = 1, 2, \dots, m \end{aligned} \tag{11}$$

where *xij* is the 0–1 variable, *xij* = 0 indicates that the two subjects do not match, and *xij* = 1 indicates that the two subjects match each other. *λ* is the adjustment parameter, 0 ≤ *λ* ≤ 1, and the value of *λ* is determined by the specific needs of the actual problem. *λ* = 0 and *λ* = 1 indicate that the actual problem satisfies the preference consistency and

complementarity, respectively. In this study, the combination of satisfaction matching analysis method is used to solve the optimization model.

$$\mathfrak{c}\_{ij} = \lambda (\frac{a\_{ij} + b\_{ij}}{2}) + (1 - \lambda)\sqrt{a\_{ij} \times b\_{ij}}, \lambda \in [0, 1] \tag{12}$$

(3) The processes of the bilateral matching decision-making model of partners of manufacturing enterprises based on BMIHFIBPT integration methods

The processes of the bilateral matching decision-making model of partners of manufacturing enterprises based on BMIHFIBPT integration methods are as follows [84–100]:

Step 1: Based on the matching subjects' preference information, obtain the intervalvalued hesitant fuzzy decision matrices *G* = [*gij*] and *H* = [*hij*] respectively. *Processes* **2023**, *11*, x FOR PEER REVIEW 9 of 19

*m*×*n n*×*m* Step 2: Use the optimistic criterion or the pessimistic criterion to consistently process the interval-valued hesitant fuzzy decision matrices G and H and arrange the order of the elements to obtain the normalized interval-valued hesitant fuzzy element matrices *G* 0 = [*g* 0 *ij*] *m*×*n* and *H*0 = [*h* 0 *ij*] *n*×*m* . elements to obtain the normalized interval‐valued hesitant fuzzy element matrices ' ' [ ] *G g ij m n* and ' ' [ ] *H h ij n m* . Step 3: Apply Equations (1) and (2) to construct the positive and negative ideal fuzzy

Step 3: Apply Equations (1) and (2) to construct the positive and negative ideal fuzzy elements *g* +, *g* − and *h* +, *h* − according to the normalized interval-valued hesitant fuzzy element matrix. elements , *g g* and , *h h* according to the normalized interval‐valued hesitant fuzzy element matrix.

Step 4: Use Equations (8) and (9) to calculate the bidirectional projection matrices *G* ∗ 1 = [(Pr*g* −*g* 0 *g*−*g*+ ) *ij* ] *m*×*n* , *G* ∗ 2 = [(Pr*g* −*g* + *g* 0*g*+ ) *ij* ] *m*×*n* , and *H*∗ 1 = [(Pr*h* −*h* 0 *h*−*h*+ ) *ij*] *n*×*m* , *H*∗ 2 = [(Pr*h* −*h* + *h* 0*h*+ ) *ij*] *n*×*m* respectively. Step 4: Use Equations (8) and (9) to calculate the bidirectional projection matrices ' 1 \* [(Pr ) ] *g g G g g ij m n* , ' \* 2 [(Pr ) ] *g g G g g ij m n* , and ' 1 \* [(Pr ) ] *h h H h h ij n m* , ' \* 2 [(Pr ) ] *h h H h h ij n m* respectively. Step 5: Use Equation (10) to calculate the closeness and obtain the closeness matrices

Step 5: Use Equation (10) to calculate the closeness and obtain the closeness matrices *DA* = [*aij*] *m*×*n* and *DB* = [*bij*] *m*×*n* . [ ] *D a A ij m n* and [ ] *D b B ij m n* . Step 6: Use Equation (11) to obtain the matching degree matrix [ ] *C c ij m n* and

Step 6: Use Equation (11) to obtain the matching degree matrix *C* = [*cij*] *m*×*n* and further construct the optimization model. further construct the optimization model.

Step 7: Use Lingo software to solve the optimization model and analyse the results to obtain bilateral matching decision-making schemes. Step 7: Use Lingo software to solve the optimization model and analyse the results to obtain bilateral matching decision‐making schemes. In summary, the bilateral matching decision‐making model to help manufacturing

In summary, the bilateral matching decision-making model to help manufacturing enterprises choose partners based on BMIHFIBPT integration methods is shown in Figure 1. enterprises choose partners based on BMIHFIBPT integration methods is shown in Figure 1.

**Figure 1.** Process flow chart. **Figure 1.** Process flow chart.

#### **5. Empirical Analysis 5. Empirical Analysis**

Quality management is the key means to ensuring the manufacturing supply chain continues to develop steadily. Therefore, as the main actors in the manufacturing supply chain, manufacturing enterprises place great emphasis on quality management in the production and operation management scope. With good quality management, products will satisfy customers and core competitiveness will improve, and the cooperation and Quality management is the key means to ensuring the manufacturing supply chain continues to develop steadily. Therefore, as the main actors in the manufacturing supply chain, manufacturing enterprises place great emphasis on quality management in the production and operation management scope. With good quality management, products

partnerships among enterprises will become closer and closer. However, to achieve satisfactory matching results and make optimal decisions, managers must be able to

operate an organizational quality‐specific immune system. In turn, managers must also be able to achieve bilateral matching between their own enterprise and these parties. To serve this aim, the present study selects 11 large‐sized manufacturing enterprises in the eastern region of China as the evaluation targets. The selected manufacturing enterprises have a quality management system certification, are good at constructing immunity mechanisms to deal with quality risk events, and display an organizational quality‐specific immunity performance that has placed them in leading positions in the will satisfy customers and core competitiveness will improve, and the cooperation and partnerships among enterprises will become closer and closer. However, to achieve satisfactory matching results and make optimal decisions, managers must be able to identify the most mature enterprises, namely, those enterprises that have established and operate an organizational quality-specific immune system. In turn, managers must also be able to achieve bilateral matching between their own enterprise and these parties. To serve this aim, the present study selects 11 large-sized manufacturing enterprises in the eastern region of China as the evaluation targets. The selected manufacturing enterprises have a quality management system certification, are good at constructing immunity mechanisms to deal with quality risk events, and display an organizational quality-specific immunity performance that has placed them in leading positions in the same industry. The reputation, product market share, assets, profit, competitiveness, finances, production and operation management performance, rank, and scale of all these enterprises are typical and representative. All 11 manufacturing enterprises are state-owned and state-holding enterprises over 20 years old. We selected and invited 80 managers of the 11 manufacturing enterprises in total. All the managers have a master's degree; have been working in a position in manufacturing enterprises for more than 20 years; and are familiar with quality management, production operation, and management of enterprises.

We collected data by extensive on-site questionnaire distribution, field interviews, and survey investigations. There are seven large-sized manufacturing enterprises (*A*1, *A*2, *A*3, *A*4, *A*5, *A*6, *A*7) and four other large-sized manufacturing enterprises (*B*1, *B*2, *B*3, *B*4). All 11 manufacturing enterprises are seeking partners in the manufacturing supply chain with the aim of forming long-term cooperation relationships and close partnerships in the quality management scope. All the enterprises are also core actors in the same supply chain. Based on the organizational quality-specific immunity components (i.e., organizational quality monitoring and cognition; organizational quality defence, clearance, and repair, both soft and hard features; and organizational quality memory and immune self-stability), the evaluation indicator system for organizational quality-specific immunity is constructed. We use organizational quality-specific immunity to determine the evaluation criteria. Based on immune theory, organizational quality-specific immunity is the focus of the benchmarking reference system, decision-making scale, and basis. Organizational quality-specific immunity, as discussed above, is vital to both the supply and demand sides of manufacturing enterprises in the manufacturing supply chain. Therefore, it must be a central consideration in selecting optimal partners and in bilateral matching decision making.

The organizational quality-specific immunity components of the selected manufacturing enterprises are organizational quality monitoring and cognition; organizational quality defence, clearance, and repair (soft and hard features); and organizational quality memory and immune self-stability. Both parties completed a preference evaluation of the above evaluation indicators. Namely, all 80 senior managers of the manufacturing enterprises filled out the evaluation survey items and on-site evaluation questionnaires on their decision making with interval-valued hesitant fuzzy information and decision-making preferences according to the evaluation criteria of organizational quality-specific immunity. In the process of preference evaluation, the subjects' preference information was often hesitant, fuzzy, and vague. By processing the interval-valued hesitant fuzzy information, we determined the best-matched manufacturing enterprises, i.e., the enterprise pairs with optimal cooperative and coordination potential.

Step 1: According to the preference information presented by both subjects, the interval-valued hesitant fuzzy preference matrices *G* = [*gij*] *m*×*n* and *H* = [*hij*] *n*×*m* are obtained, as shown in Tables 2 and 3.

**Table 2.** Interval-valued hesitant fuzzy preference matrix 1.

**Table 3.** Interval-valued hesitant fuzzy preference matrix 2.

Step 2: By consistently processing the lengths of the matrices *G* and *H*, we arranged the order of the elements (this study uses the optimistic criteria to process the length) to obtain the normalized interval-valued hesitant fuzzy element matrices *G* 0 = [*g* 0 *ij*] *m*×*n* and *H*0 = [*h* 0 *ij*] *n*×*m* , as shown in Tables 4 and 5.

**Table 5.** Interval-valued hesitant fuzzy preference matrix 4.

Step 3: We use Equations (1)–(7) to construct positive and negative ideal fuzzy elements *g* +, *g* − and *h* +, *h* −, respectively, according to the normalized interval-valued hesitant fuzzy element matrix:

> *g* + = ({[0.5, 0.6], [0.8, 0.9]}, {[0.7, 0.8], [0.8, 0.9]}, {[0.6, 0.7], [0.7, 0.9], [0.7, 0.9]}, {[0.5, 0.7], [0.8, 0.9]})

> *g* − = ({[0.1, 0.3], [0.2, 0.4]}, {[0.1, 0.2], [0.4, 0.5]}, {[0.1, 0.2], [0.3, 0.4], [0.3, 0.4]}, {[0.2, 0.4], [0.3, 0.5]})

*h* + = ({[0.4, 0.5], [0.7, 0.9]}, {[0.2, 0.6], [0.6, 0.9]}, {[0.6, 0.8]}, {[0.5, 0.6], [0.8, 0.9], [0.8, 0.9]}, {[0.2, 0.4], [0.7, 0.8], [0.8, 0.9]}, {[0.7, 0.9]}, {[0.5, 0.6], [0.8, 0.9], [0.8, 0.9]})

*h* − = ({[0.1, 0.2], [0.4, 0.6]}, {[0.1, 0.2], [0.2, 0.6]}, {[0.2, 0.5]}, {[0.1, 0.2], [0.4, 0.5], [0.5, 0.7]}, {[0.1, 0.3], [0.4, 0.5], [0.5, 0.6]}, {[0.2, 0.5]}, {[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]})

Step 4: We use Equations (8) and (9) to calculate the bidirectional projection matrices *G* ∗ 1 = [(Pr*g* −*g* 0 *g*−*g*+ ) *ij* ] *m*×*n* , *G* ∗ 2 = [(Pr*g* −*g* + *g* 0*g*+ ) *ij* ] *m*×*n* , and *H*∗ 1 = [(Pr*h* −*h* 0 *h*−*h*+ ) *ij*] *n*×*m* , *H*∗ 2 = [(Pr*h* −*h* + *h* 0*h*+ ) *ij*] *n*×*m* respectively.

$$\begin{aligned} \mathbf{G}\_{1}^{\*} &= \begin{bmatrix} 0.419 \,0.097 \,0.215 \,0.543 \\ 0.404 \,0.071 \,0.25 \,0.295 \\ 0.313 \,0.223 \,0.324 \,0.304 \\ 0.625 \,0.166 \,0.455 \,0.516 \\ 0.916 \,0.665 \,0.283 \,0.083 \\ 0.593 \,0.388 \,0.812 \,0.295 \\ 0.244 \,0.430 \,0.123 \,0.331 \end{bmatrix} \\ \mathbf{G}\_{2}^{\*} &= \begin{bmatrix} 0.608 \,0.716 \,0.782 \,0.063 \\ 0.550 \,0.721 \,0.713 \,0.511 \\ 0.464 \,0.721 \,0.896 \,0.510 \\ 0.233 \,0.695 \,0.684 \,0.212 \\ 0.589 \,0.400 \,0.749 \,0.535 \\ 0.636 \,0.594 \,0.792 \,0.451 \end{bmatrix} \\ \mathbf{H}\_{1}^{\*} &= \begin{bmatrix} 0.389 \,0.185 \,0.519 \,0.113 \,0.080 \,0.105 \,0.023 \\ 0.283 \,0.230 \,0.354 \,0.161 \,0.356 \,0.409 \,0.255 \\ 0.179 \,0.30 \,0.267 \,0.124 \,0.206 \,0.283 \,0.594 \\ 0.354 \,0.251 \,0.519 \,0.620 \,0.229 \,0.398 \,0.659 \end{bmatrix} \end{aligned}$$

$$H\_2^\* = \begin{bmatrix} 0.212\ 0.354\ 0.283\ 0.585\ 0.422\ 0.453\ 0.707\\ 0.424\ 0.350\ 0.285\ 0.594\ 0.286\ 0.354\ 0.682\\ 0.426\ 0.400\ 0.354\ 0.620\ 0.316\ 0.453\ 0.567\\ 0.300\ 0.447\ 0.705\ 0.319\ 0.402\ 0.353\ 0.392\end{bmatrix}$$

>

Step 5: We use Equation (10) to calculate the closeness and obtain the closeness matrices *DA* = [*aij*] *m*×*n* and *DB* = [*bij*] *m*×*n* .

$$D\_{A} = \begin{bmatrix} 0.408\,0.119\,0.216\,0.896\\ 0.423\,0.090\,0.373\,0.366\\ 0.168\,0.236\,0.225\,0.370\\ 0.688\,0.193\,0.400\,0.709\\ 0.024\,0.624\,0.274\,0.134\\ 0.461\,0.385\,0.910\,0.409\\ 0.277\,0.420\,0.135\,0.424 \end{bmatrix}^{T} \begin{bmatrix} 0.647\,0.403\,0.296\,0.541\\ 0.343\,0.397\,0.433\,0.360\\ 0.657\,0.554\,0.430\,0.424\\ 0.162\,0.213\,0.167\,0.660\\ 0.159\,0.565\,0.395\,0.363\\ 0.188\,0.536\,0.385\,0.536\\ 0.032\,0.272\,0.512\,0.613\end{bmatrix}$$

Step 6: We use Equation (11) to obtain the matching degree matrix *C* = [*cij*] *m*×*n* and further construct the optimization model.

$$\eta = \begin{bmatrix} 0.514 \, 0.219 \, 0.253 \, 0.696\\ 0.381 \, 0.189 \, 0.402 \, 0.363\\ 0.332 \, 0.362 \, 0.311 \, 0.396\\ 0.334 \, 0.203 \, 0.258 \, 0.684\\ 0.062 \, 0.594 \, 0.329 \, 0.221\\ 0.294 \, 0.454 \, 0.592 \, 0.466\\ 0.094 \, 0.338 \, 0.263 \, 0.510 \end{bmatrix}$$

Step 7: We bring the matching degree matrix *η* into the optimization model (11), further solving and obtaining the optimization model with the help of Lingo software:

*X*11 = 1, *X*12 = 0, *X*13 = 0, *X*14 = 0, *X*21 = 0, *X*22 = 0, *X*23 = 0, *X*24 = 0, *X*31 = 0, *X*32 = 0, *X*33 = 0, *X*34 = 0,*X*41 = 0 *X*42 = 0, *X*43 = 0, *X*44 = 1, *X*51 = 0, *X*52 = 1, *X*53 = 0, *X*54 = 0, *X*61 = 0, *X*62 = 0, *X*63 = 1, *X*64 = 0, *X*71 = 0, *X*72 = 0, *X*73 = 0, *X*74 = 0.

> The results are as follows: *A*1 ↔ *B*1 , *A*4 ↔ *B*4 , *A*5 ↔ *B*2 , *A*6 ↔ *B*3 , and for the manufacturing enterprises *A*2, *A*3, and *A*7, there are no suitable manufacturing enterprises to match.

#### **6. Conclusions and Discussion**

#### *6.1. Conclusions*

Decision-making science has undergone continuous development and improvement and is now widely used in many different fields. This study, taking immune theory as a starting point, has applied decision-making science to the organizational quality management field in the context of manufacturing enterprises. The study constructs an evaluation indicator system for organizational quality-specific immunity. The components of organizational quality-specific immunity act as the evaluation criteria. This study further integrates interval-valued hesitant fuzzy information and bidirectional projection technology into bilateral matching decision making, constructing the bilateral matching evaluation and decision-making models based on interval-valued hesitant fuzzy information and bidirectional projection technology (BMIHFIBPT). To use the interval-valued hesitant fuzzy evaluation information to solve the combination satisfaction and matching optimization model, we apply the score formation of left interval value and right interval value, the interval-valued hesitant fuzzy preference, the interval-valued hesitant fuzzy decision-making matrices, the interval-valued hesitant fuzzy elements and closeness, and the interval-valued bilateral fuzzy bidirectional projection technology. We conduct empirical analysis to reflect the supply and demand sides of representative manufacturing enterprises in the manufacturing

supply chain, match the main bodies of two parties and subjects, and help manufacturing enterprises achieve optimal bilateral matching. The empirical analysis results indicate that bilateral matching decision-making models that incorporate interval-valued hesitant fuzzy information and bidirectional projection technology (BMIHFIBPT) via integration methods can offer the following: a bilateral matching evaluation and decision-making process for both supply- and demand-side partners; interval-valued hesitant fuzzy evaluation and decision information; and bidirectional projection technology, which possesses the consistency, feasibility, operability, and rationality to solve the interval-valued hesitant fuzzy decision-making problems. Thus, this study provides a basis for manufacturing enterprises in the manufacturing supply chain area to effectively select the best partners based on organizational quality-specific immunity. Bilateral matching evaluation and decision-making models and methods are embedded to provide a reference for manufacturing enterprises to make optimal decisions. These models and methods offer effectiveness, accuracy, robustness, and convenience in selecting the optimal supply–demand matching relationships, determining coordination and cooperation partners on the basis of organizational qualityspecific immunity, and achieving satisfactory matching evaluation and optimal coupling decisions by operating the organizational quality-specific immune system.

#### *6.2. Discussion*

Bilateral subjects usually give vague, fuzzy, and hesitant preference information [90]. This vague, fuzzy, and hesitant ambiguity emerges in the process of bilateral matching decision making as well [89–94]. The ability to deal with vague, fuzzy, and hesitant ambiguity in bilateral matching decision making has important theoretical value and practical significance. Using the methods and models of the relevant literature [36–43], consistent empirical research results and bilateral matching decision-making results were obtained. Compared with existing studies [44–50], the present study offers the following advantages due to its research methods and models: First, the bilateral matching decision-making models and the methods based on interval-valued hesitant fuzzy information and bidirectional projection technology are different from the bilateral matching decision-making models and methods based on complete preference order information, which effectively help determine the evaluation criteria of organizational quality-specific immunity. The bilateral matching decision-making methods based on interval-valued hesitant fuzzy information and bidirectional projection are determined by the interval-valued hesitant fuzzy information given by the subjects; these methods build on existing methods [44–46]. After processing the length of the interval-valued hesitant fuzzy decision matrix and arranging the order of elements, the normalized interval-valued hesitant fuzzy element matrix is obtained, and the positive and negative ideal fuzzy elements are constructed. The bidirectional projection value matrix is calculated using bidirectional projection technology, and reference is made to TOPSIS. The TOPSIS method is used to calculate the closeness and obtain the closeness matrix. The satisfaction and matching optimization model is thus solved, and the best solution is obtained. Through empirical analysis, this study indicates that the bi-level and bidirectional projection decision-making method with interval-valued hesitant fuzzy information and bidirectional projection technology can solve the problem of matching enterprises on both the demand and supply sides based on organizational quality-specific immunity components. The method, in other words, can select optimal and sustainable partners for representative manufacturing enterprises in the manufacturing supply chain on the basis of organizational quality-specific immunity. The research results show that bilateral matching evaluation models can be achieved using integration methods and bilateral matching decision-making methods that incorporate interval-valued hesitant fuzzy information and bidirectional projection technology based on a biological perspective and an empirical point of view.

With the help of analogy, we can use immune theory to map the operations of organizational quality management and, as a result, generate an understanding of how to achieve organizational quality-specific immunity. There is potential for further application

of interval-valued hesitant fuzzy information in decision-making problems in the field of organizational quality management. The results of the present empirical analysis in the field of organizational quality management drew on immune theory, which offers new ideas and methodological systems. The organizational quality-specific immunity evaluation criteria, based on the results, can also inform theoretical frameworks to help manufacturing enterprises effectively select partners based on organizational quality-specific immunity with a view to forming close and long-term relationships in the manufacturing supply chain.

#### *6.3. Research Limitations and Prospects*

This study is only a preliminary exploration of the application of interval-valued hesitant fuzzy information and bidirectional projection technology to find optimal partners for manufacturing enterprises in the manufacturing supply chain based on organizational quality-specific immunity. The proposed bilateral (two-sided) matching decision-making models that incorporate interval-valued hesitant fuzzy information and bidirectional projection technology via integration methods are only evaluation instruments; they are only part of an approach for the selection of optimal partners of manufacturing enterprises. While the models will ensure the suitability of two partners, the models do not offer an explanation in terms of other partners and subjects in the manufacturing supply chain. The original data on which the models are based were mainly derived from the subjective responses of manufacturing enterprises' managers gathered via on-site questionnaire distribution, field interviews, and survey investigations. In future research, we will carry out longitudinal tracking in order to obtain longitudinal tracking time series data with the aim of combining objective data with subjective data for the models and methods. Incorporating objective data, as well as introducing more partners and subjects of manufacturing enterprises in the manufacturing supply chain, will further improve the proposed bilateral matching decision-making models based on the evaluation criteria of organizational quality-specific immunity.

**Author Contributions:** Q.L. contributed to the motivation, the interpretation of the methods, the formal data analysis, the validation of the results, and provided the draft versions and references. H.S. contributed to the data analysis and results, the software and resources, the literature review and references. Y.H. contributed to the related concepts and major recommendations, investigation, supervision, the extraction of the conclusions and discussion. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research is funded by the National Natural Science Foundation of China (72001055), Research Base of Science and Technology Innovation Think Tank of Liaoning Province (Research Base of High Quality Development of Equipment Manufacturing Industry, NO. 09), and 2022 Scientific Research Project of Department of Education of Liaoning Province (LJKMR20220986).

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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## *Article* **Control-Centric Data Classification Technique for Emission Control in Industrial Manufacturing**

**Zihao Chen 1,\* and Jian Chen 2**

**Abstract:** Artificial intelligence-based hardware devices are deployed in manufacturing units and industries for emission gas monitoring and control. The data obtained from the intelligent hardware are analyzed at different stages for standard emissions and carbon control. This research article proposes a control-centric data classification technique (CDCT) for analyzing as well as controlling pollution-causing emissions from manufacturing units. The gas and emission monitoring AI hardware observe the intensity, emission rate, and composition in different manufacturing intervals. The observed data are used for classifying its adverse impact on the environment, and as a result industry-adhered control regulations are recommended. The classifications are performed using deep neural network analysis over the observed data. The deep learning network classifies the data according to the environmental effect and harmful intensity factor. The learning process is segregated into classifications and analysis, where the analysis is performed using previous emission regulations and manufacturing guidelines. The intensity and hazardous components levels in the emissions are updated after the learning process for recommending severe lookups over the varying manufacturing intervals.

**Keywords:** artificial intelligence hardware; data classification; deep learning; emission control; industrial manufacturing

### **1. Introduction**

Artificial intelligence (AI) hardware is the most used in various fields to enhance the efficiency and reliability of systems. AI hardware is also used in industries that reduce latency and workload in production. Industries require various AI hardware to improve performance [1–3]. AI-based hardware is utilized for emission monitoring and reducing hardware computation costs and maintenance charges. Industries emit certain gases and components during production. Emission monitoring is a complicated task to perform in industry management systems [4]. AI hardware is commonly used in monitoring systems so as to decrease the energy consumption range in the computation process. The emission monitoring system identifies the exact emission ranges from industries to provide feasible information for environmental protection [5–7]. Important factors, components, patterns, and principles of industries are detected based on AI hardware. AI technology-based hardware is commonly used in monitoring systems that detect industries' sources and range of emissions [8]. Artificial neural network (ANN) and support vector machine (SVM) algorithms are generally used in AI hardware that identifies the emission level from both companies and industries [9,10].

Data analysis is crucial in every field that provides necessary information for further processes. Logistics and strategic techniques are implemented in management systems to analyze data [11]. Information and details are stored in a database that provides feasible data for analysis and detection processes. AI hardware is employed in data analysis to discover new patterns and features of data [12–14]. AI techniques and algorithms are

**Citation:** Chen, Z.; Chen, J. Control-Centric Data Classification Technique for Emission Control in Industrial Manufacturing. *Processes* **2023**, *11*, 615. https://doi.org/ 10.3390/pr11020615

Academic Editors: Conghu Liu, Xiaoqian Song, Zhi Liu and Fangfang Wei

Received: 10 January 2023 Revised: 9 February 2023 Accepted: 10 February 2023 Published: 17 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

used in AI hardware to reduce complexity and latency of the analysis process. AI-based hardware detects necessary factors and data principles, decreasing the energy consumption range in the computation process [15]. Big data analytics (BDA) is widely used for analysis processes in various industries and hospitals. AI hardware-based data analysis finds the actual data required to perform a certain task in an application [16]. Important patterns and features contain details about data that enhance the efficiency and performance of analysis systems. Behavioral- and activities-based data are also identified by AI hardware that handles a huge amount of data using the analysis process. Data analytics tools based on AI are also used in data analysis systems. As a result, both significance and reliability are increased in data management systems [17,18].

Machine learning (ML) algorithms and techniques are used for emission data analysis in industries. Real-time emission data analysis is a difficult process to perform in industries. ML techniques reduce computation time and also the range of energy consumption [19]. In addition, ML techniques maximize accuracy in detection and prediction processes, improving the systems' efficiency. The random forest (RF) algorithm is normally used in emission data analysis systems [20]. RF detects the exact intensity level of carbon emissions of industries. The RF algorithm extracts the patterns of emission ranges to gather necessary information for data analysis systems. Both renewable and non-renewable emissions are analyzed by RF, which reduces complexity in the further detection process [21]. The artificial neural network (ANN) algorithm is also used for emission data analysis. ANN scrutinizes the datasets of the database based on certain features and patterns. ANN increases the reliability and mobility of data analysis systems [22]. The deep reinforcement learning (DRL) algorithm is used for emission control measures in the industries. DRL trains the data which are required for control policies. DRL also detects the impact of emission on the environment, which produces relevant data for the environment management process [23].

#### **2. Related Works**

Tao et al. [24] introduced a channel-enhanced spatiotemporal network (CENet) for industry smoke emission recognition. Supervision information and patterns are required to detect the exact smoke emission level of industries. The loss function is used here for detection of essential characteristics and features. It also reduces the latency and energy consumption range in computation. The introduced CENet achieves high emission detection accuracy, enhancing the production efficiency in industries.

Fiscante et al. [25] proposed a new detection method that determines the atmospheric trace gases using hyperspectral satellite data. The main aim of the proposed method is to measure the actual gases that are emitted by industries. Unsupervised sparse mixing gases cause various environmental problems. Temperature and pressure of environmental data are collected, which provide feasible information for the detection process. The proposed method increases detection accuracy, thereby, improving industrial systems' performance.

Guo et al. [26] designed a global meta-analysis for greenhouse gas (GHG) emissions in nitrogen fertilizer (NF) applications. The NF increases crops' cultivation and product range as well as it maximizes overall cultivation. Crop production emits a huge amount of GHG into the environment. NF-based crop production reduces the GHG emission range in the environment. Experimental results show that the proposed meta-analysis identifies the actual GHG emission ratio, which provides necessary data for emission control policies.

Sikdar et al. [27] have developed a deep learning approach for classification and damage-resource detection. A convolutional neural network (CNN) algorithm is employed to classify damages which are based on certain patterns and principles. The feature extraction technique in CNN brings out the important features from scalogram images. Acoustic emission (AE) is also detected by CNN, which reduces false alarm rates in industries. The developed approach maximizes accuracy in damage resource detection, increasing the systems' efficiency and reliability.

Choi et al. [28] presented a machine learning (ML)-based classification model in urban areas. The proposed model mainly aims to recognize odor sources and content in urban areas. Odor-causing substances and materials emit gases and smoke that cause certain environmental problems. A decision tree (DT) is used in the classification model that identifies the source of the odor. The proposed DT model achieves high accuracy in detection and classification, enhancing the application's significance and effectiveness.

Tacchino et al. [29] developed a multi-scale model for steam methane reforming reactors in industries. The finite element method (FEM) is used here to detect the gas and pressure range of gas emissions from industries. FEM divides the resultant gases into their types based on features and patterns collected by reactors. The multi-scale model validates the actual gas range which is emitted by industries. Compared with other models, the proposed model increases accuracy in emission detection.

Tuttle et al. [30] proposed a nonlinear support vector machine (SVM)-based NOx emission prediction model. Both spatial and temporal features are detected from the database, which leads to the production of optimal information for further prediction. Furthermore, an artificial neural network (ANN) is also used to identify features about NOx emission details. As a result, the proposed SVM model achieves high accuracy in NOx emission prediction, enhancing the industries' efficiency and product range.

Sun et al. [31] introduced a VIIRS thermal anomaly data-based detection method for heavy industries. The main aim of the proposed method is to detect the air pollution emission range of the industries. Industrial management systems gather air quality, gas emission, energy charges, and spatiotemporal features. Spatiotemporal patterns provide relevant data which are required for the emission detection process. The introduced method increases detection accuracy, reducing the computation and further processing complexity.

Ju et al. [32] proposed a new atmospheric pollutant emission prediction method for industries. Quantification results of pollutant emission standards (QRPES) are used in the paper to produce the necessary information for the proposed prediction method. In addition, machine learning (ML) techniques such as random forest regression (RFR) and support vector regression (SVR) are used in the emission prediction process. As a result, the proposed method maximizes accuracy, improving industries' performance and efficiency levels.