3.1. Most Relevant sources
Top Contributing Journals
Chart articles' main sources illuminate this field's discourse. "Quality and Reliability Engineering International," with the highest number of articles, dominates chart research. The "Journal of Quality Technology" and "Quality Engineering" follow closely with articles, respectively, indicating their importance in quality control and engineering scholarship, see Fig. 4. The "International Journal of Production Research" and "Computers and Industrial Engineering" show how charts are interdisciplinary in production and technology. Chart statistics are covered papers in "Communications in Statistics: Simulation and Computation" and "Communications in Statistics - Theory and Methods" publications. The variety of sources, including the "International Journal of Advanced Manufacturing Technology," "IIE Transactions," and "Journal of Statistical Computation and Simulation," shows the complexity of chart research in industrial engineering, advanced manufacturing, and statistical computation. Researchers can use this information to discover significant platforms for staying updated and contributing to the chart.
Author by Most Local Cited Sources
The most frequently referenced sources in the charts show how much specific writers have shaped this field's scholarly discourse, see Fig. 5. With 3,410 citations, D. C. Montgomery is the most cited author. He shaped chart literature with his copious contributions. W. H. Woodall and D. M. Hawkins follow with 1,758 and 1,162 citations, respectively, demonstrating their significant impact on chart research. J. M. Lucas, Z. Wu, and W. A. Shewhart also have over 1,000 citations. The list includes famous authors like A. F. B. Costa, G. E. P. Box, E. S. Page, and C. Zou, who have transformed chart scholarship. This compilation of locally cited sources highlights the importance of certain scholars in driving and shaping domain conversations, giving significant insights to those interested in chart discourse.
Core Sources by Bradford's Law
Bradford's Law methodically lists the foremost chart journals, see Fig. 6. Bradford's Law states that a few journals will publish most publications in a particular field. The top three journals, "Quality and Reliability Engineering International" (569 articles), "Journal of Quality Technology" (280 articles), and "Quality Engineering" (192 articles), are the most frequently cited sources for chart research. Quality control and industrial engineering scholars likely use these journals as their main platforms for academic discussions and advances. According to Bradford's Law, succeeding publications like "The International Journal of Production Research," "Computers and Industrial Engineering," and others expand the area but publish less often. This compilation helps scholars and practitioners find chart core journals, making it easy to access the most influential material in the area.
Sources' Local Impact
The sources by local impact metrics show journals' chart influence and importance. The "Journal of Quality Technology" has a strong impact and citation history with an h-index of 76, g-index of 127, and m-index of 1.407. This publication contributes to the field in both quality and quantity with 18,591 citations and 280 articles. Similarly, "Quality and Reliability Engineering International" has a high h-index of 53 and TC of 11,924, bolstering its importance. The high g-index of 79 for "Technometrics" suggests a wide and influential range of citations for its 79 articles.
"Journal of Quality Technology" and "Quality and Reliability Engineering International" are top sources due to their high citation counts and strong h-indices, showing sustained and considerable scholarly influence. These journals and others on the list balance research quantity and quality to increase chart literature's local influence. Despite lower h-indices, "Computers and Industrial Engineering," "IIE Transactions," and "Quality Engineering" have significant influence and citation counts, demonstrating their persistent contributions to the subject. This technique helps chart researchers evaluate journals and choose venues.
Sources' Production over Time
Chart papers from prominent journals over the past 25 years, revealing the changing research landscape. The data shows several trends:
Overall Growth
The number of chart articles published in all journals has increased over time, demonstrating greater interest and participation. Production has increased steadily due to the growing understanding and continued significance of research.
Prominent Journals
"Quality and Reliability Engineering International" consistently leads article production and is gradually expanding. "Journal of Quality Technology" is also steadily increasing, confirming its industry leadership. "Quality Engineering," "International Journal of Production Research," and "Computers and Industrial Engineering" all contribute significantly and steadily.
Stability and Fluctuations
While some years show a continuous increase in articles, others show oscillations. Emerging research trends, technological advances, and changes in academic and industrial chart applications may affect output.
Technological Impact
The growth of "Computers and Industrial Engineering," indicating the integration of computational approaches in chart study, demonstrates the impact of technology. The journal's production rises with technology's impact on industrial engineering and quality control.
3.2. Most Relevant and Local Cited Authors
The top 10 charts researchers have changed the field with their prolific and influential work. MBC Khoo tops the cohort, demonstrating his extensive and impactful work. Following closely is WH Woodall, whose articles are significant. Castagliola P, Haq A, and Riaz M contribute significantly with outstanding numbers of articles and fractionalized counts, showing sustained and impactful charts research. Z Wu, A Amiri, F Tsung, and Z Wang have advanced the discipline with their major research efforts. The other names include Celano G, Rahali D, Wu S, and Amorso PJ. These authors are diverse and influential, contributing to chart literature's number, quality, and academic impact, see Fig. 7.
Aauthors Production Overtime and h-index
Although the Montgomery, DC timeline is far longer, and he inspired many researchers, we will focus on the writers who wrote after 1987 when we go back in time. The writers Goh N, Woodal WH, Xie M, Wu Z, and ZHANG Y have been contributing to the field of charts for the past thirty years, and they are all continuing to do so. The candidates Khoo MBC, Castagoliola P, Haq A, Riaz M, and Aslam M are on the side referring to young people who became well-known after 2000. The top 20 contributors are displayed in Fig. 8 based on the contribution timeline. Authors standing by using h-index is measured. By local impact, Woodall WH (h = 44), Tsung F (h = 33), Castagolia P (h = 30), Khoo MBC (h = 28), and Wu Z (h = 27) remain at the top.
3.3. Most Relevant Affiliations
The top affiliations in chart research show the global distribution of academic and research centers that contribute to the area. The School of Mathematical Sciences at Universiti Sains Malaysia in Penang, Malaysia, leads by articles, highlighting charts research's global reach. The Quaid-i-Azam University Department of Statistics in Islamabad, Pakistan, is second on the list, demonstrating its influence on chart methods. The School of Mechanical and Aerospace Engineering at Nanyang Technological University in Singapore contributes articles, demonstrating technological and engineering viewpoints. These associations illustrate the different and widespread academic centers that improve chart approaches worldwide, see Fig. 9.
Over time, chart research articles from various affiliations show several trends. Universiti Sains Malaysia constantly leads, increasing from 156 articles in 2020 to 222 in 2023. This significant growth shows the university's dedication to chart methods. In 2023, Quaid-i-Azam University published 154 articles. Nanyang Technological University also contributes consistently. Nanyang Technological University publishes many articles each year, demonstrating its dedication to chart research. With varying annual outputs, King Fahd University of Petroleum and Minerals, Islamic Azad University, Shahed University, Virginia Polytechnic Institute and State University, and Tianjin University also contribute significantly. Certain institutions have consistently supported chart research, but the data shows a diversified and spread landscape. This diversity reflects global research collaboration and the joint endeavor to enhance chart knowledge and methods.
3.4. Corresponding Author's Countries
International chart study shows a diversified and collaborative landscape. The category "Not Reported" dominated the data with 2933 items, indicating undetermined author countries. However, the US contributes 1085 articles, demonstrating significant charts research influence. China follows with 898 articles, demonstrating its expanding influence. Iran has 356 papers, while Brazil and India share sixth with 175 articles, demonstrating their research contributions. Malaysia, Pakistan, and the UK also contribute, demonstrating a global network of chart scholars. Citation counts (SCP and MCP) for each country show disparities in research priority, collaboration, and impact, emphasizing the diverse character of chart research worldwide, see Fig. 10.
3.5. Countries' Scientific Production
The study discovered charts research scientific production in important regions worldwide. Quality control and industrial engineering methods are advanced most in the US, China, Iran, Brazil, Canada, Malaysia, the UK, India, Italy and Pakistan each contribute significantly to chart approaches. These locations' frequency by output differs due to research infrastructure, academic collaborations, and quality control research priority. Charts research is collaborative and worldwide, and the dataset enhances knowledge of these approaches across countries and academic viewpoints.
3.6. Most Cited Countries
The most referenced countries in chart study reveal the importance of regional scholarship. The US is the most cited country, with 26,990 total citations (TC), indicating its leadership in high-impact research. Despite a lower average article citation, the volume of citations shows the influence of U.S. research. China follows with 11,133 TC, demonstrating its rising charts scientific impact, see Table 1. Although lower than in other countries, the average article citation suggests a significant influence per publication. Iran, with 4,611, and the UK, with 3,349. Both contribute to the global influence of chart study. These countries' high average article citations (13 for Iran and 27.2 for the UK) show that their research is well-received in academia. With TCs of 2,971, 2,953, and 2,101, Canada, Singapore, and Korea likewise have a significant impact. Canada (27.5) and Singapore (30.4) have high average article citations, indicating that their research has a lasting impact. Although Pakistan and Malaysia have lower TCs (2,082 and 1,905, respectively), they contribute to scholarship. The average article citation for Pakistan (14.7) and Malaysia (11.8) indicates a significant impact per publication.
Table 1
Top cited countries globally by total citation and average article citations
No
|
Country
|
Total Citation
|
Average Article Citations
|
1
|
USA
|
26990
|
24.9
|
2
|
CHINA
|
11133
|
12.4
|
3
|
IRAN
|
4611
|
13
|
4
|
UNITED KINGDOM
|
3349
|
27.2
|
5
|
CANADA
|
2971
|
27.5
|
6
|
SINGAPORE
|
2953
|
30.4
|
7
|
KOREA
|
2101
|
22.6
|
8
|
PAKISTAN
|
2082
|
14.7
|
9
|
MALAYSIA
|
1905
|
11.8
|
3.7. Most Global Cited Documents
The most referenced global charts research documents, mostly from 1990 to 2005, are seminal contributions that have shaped the subject. The 1994 AICHE J paper by Nomikos (DOI: 10.1002/aic.690400809) is the most cited, with 1374 citations, see Table 2. This paper addresses a key feature of chart methodology, attracting academics worldwide. With 1284 citations, Ku's 1995 publication in Chemometrics and Intelligent Laboratory Systems (DOI: 10.1016/0169-7439(95)00076-3) shows its continued significance in intelligent laboratory systems and charts. Technometrics published another highly cited study by Nomikos in 1995 (DOI: 10.1080/00401706.1995.10485888). The author's major field contribution during this time is reinforced. MacGregor's 1995 publication in Control Engineering Practise (DOI: 10.1016/0967-0661(95)00014-L) ranks fourth with 1048 citations, demonstrating its lasting impact on chart application in engineering practice.
Lowry's 1992 Technometrics paper (DOI: 10.1080/00401706.1992.10485232) and Kresta's 1991 Canadian Journal of Chemical Engineering paper (DOI: 10.1002/cjce.5450690105) also stand out, highlighting their contributions to the literature during this influential decade. Benneyan's 2003 work in Quality and Safety in Health Care (DOI: 10.1136/qhc.12.6.458) shows charts' transdisciplinary significance in healthcare. MacGregor's 1994 AICHE J paper (DOI: 10.1002/aic.690400509) and Nomikos' 1995 Chemometrics and Intelligent Laboratory Systems paper (DOI: 10.1016/0169-7439(95)00043-7) remain highly cited, demonstrating their lasting impact on chart methodologies. Finally, Lee's 2004 Journal of Process Control publication (DOI: 10.1016/j.jprocont.2003.09.004) concludes the list with 684 citations, demonstrating the research's relevance and effect into the mid-2000s. These most cited global texts show how important these publications were in defining chart research during this key period.
Table 2
Most cited documents globally and locally
|
Global Top Cited documents
|
No
|
Paper
|
DOI
|
Total Citations
|
1
|
NOMIKOS P, 1994, AICHE J
|
10.1002/aic.690400809
|
1374
|
2
|
KU W, 1995, CHEMOMETR INTELLIGENT LAB SYSTEM
|
10.1016/0169–7439(95)00076 − 3
|
1284
|
3
|
NOMIKOS P, 1995, TECHNOMETRICS
|
10.1080/00401706.1995.10485888
|
1283
|
4
|
MACGREGOR JF, 1995, CONTROL ENG PRACT
|
10.1016/0967 − 0661(95)00014-L
|
1048
|
5
|
LOWRY CA, 1992, TECHNOMETRICS
|
10.1080/00401706.1992.10485232
|
971
|
6
|
KRESTA JV, 1991, CAN J CHEM ENG
|
10.1002/cjce.5450690105
|
818
|
7
|
BENNEYAN JC, 2003, QUAL SAF HEALTH CARE
|
10.1136/qhc.12.6.458
|
772
|
8
|
MACGREGOR JF, 1994, AICHE J
|
10.1002/aic.690400509
|
733
|
9
|
NOMIKOS P, 1995, CHEMOMETR INTELLIGENT LAB SYST
|
10.1016/0169–7439(95)00043 − 7
|
699
|
|
Local Top Cited Documents
|
1
|
FLOTT LESLIE W, 1996, MET FINISH
|
10.1016/S0026-0576(96)80028-6
|
168
|
2
|
NOOROSSANA R, 2011, STAT ANAL OF PROFILE MONIT
|
10.1002/9781118071984
|
61
|
3
|
SCOUSE RA, 1985, PLAST RUBBER INT
|
|
46
|
4
|
LIU DHF, 2005, INSTRUM ENGINEERS HANDB, FOURTH EDITION: PROCESS CONTROL AND OPTIMIZATION
|
17
|
5
|
WINKEL P, 2007, STAT DEV OF QUAL IN MED
|
10.1002/9780470515884
|
13
|
3.8. Most Local Cited Documents
Most locally cited charts research documents cover a variety of themes and applications. Flott Leslie W.'s 1996 Metal Finishing article (DOI: 10.1016/S0026-0576(96)80028-6) may have received local notice due to its relevance to metal finishing and quality control, see Table 2. By focusing on statistical methods for profile monitoring, Noorossana R.'s 2011 paper in Statistical Analysis of Profile Monitoring (DOI: 10.1002/9781118071984) likely contributes to local scholarly discourse by meeting the evolving needs of profile-based quality control industries. Liu DHF's 2005 article in Instrument Engineers Handbook, Fourth Edition: Process Control and Optimisation shows local interest in comprehensive process control and optimization resources. Professionals and academics may find this handbook useful for its practical advice. In 2007, Winkel P. published Statistical Development of Quality in Medicine (DOI: 10.1002/9780470515884), demonstrating charts' interdisciplinary nature and relevance to healthcare quality assurance. Jiang R.'s 2015 Springer Series in Reliability Engineering (DOI: 10.1007/978-3-662-47215-6_14) presumably covers reliability engineering, which is critical for industries that use charts to ensure consistent and reliable processes. Aslam M.'s 2020 book Introduction to Statistical Process Control (DOI: 10.1002/9781119528425) addresses local interest in statistical process control principles. This book may be useful for students, practitioners, and researchers seeking a comprehensive introduction.
3.9. Most Frequent Words
Keywords Plus
The most common Keywords Plus terms in the charts study reveal the field's main themes and emphases. With 3733 occurrences, "Statistical Process Control (SPC)" dominates, incorporating the systematic application of statistical tools to monitor and control processes. "Charts," with 2550 occurrences, are essential to statistical process control, showing process changes over time. "Flowcharting," at 2030 occurrences, shows interest in visualizing processes. The term "Quality Control" appears 1852 times, referring to the objective of product or process quality and its means. "Process Control," with 1413 occurrences, emphasizes the use of control systems to optimize and sustain industrial process results. The term "Article" appears 953 times, indicating a high presence of academic articles in the discourse. Using "Human" and "Humans" 1637 times suggests an emphasis on human aspects and involvement in charts and quality control. "Statistical Methods" (869 occurrences) and "Statistics" (501 occurrences) emphasize rigorous statistical methods. "Process Monitoring" (640 occurrences) and "Graphic Methods" (595 occurrences) emphasize the importance of real-time observation and graphical depiction in process management and trend identification. "Average Run Lengths" (593 occurrences) and "Total Quality Management" (574 occurrences) emphasize efficiency and quality initiatives, respectively, see Figs. 11,12 and 13.
Author Keywords
The most commonly used terms in chart literature cover the field's main themes and methods. "Statistical process control" and "chart" dominate with 2170 and 1400 instances, respectively, highlighting their crucial roles in industrial process quality monitoring and maintenance. "Average run length," a key chart performance metric, appears 654 times. The emphasis on "quality control" (379 occurrences) and "quality improvement" (185 occurrences) shows a strong commitment to product and process quality. Such as "SPC" and "statistical process control (SPC)," authors describe fundamental concepts differently. Other important keywords, such as "EWMA," "Markov chain," and "multivariate statistical process control," demonstrate how advanced statistical methods improve charts. These keywords show that charts are still relevant and evolving in quality assurance and process optimization.
3.10. Trend Topics and Hotspot Tendencies (2015-23)
The top charts research topics from 2015 to 2023 exhibit changing dynamics and interests. "Charts," mentioned 2550 times since 2015, emphasise the need for statistical tools for process monitoring and stability. The discourse's emphasis on visual representation and scholarship is shown by "Flowcharting" and "Article," which appeared 2030 and 953 times in 2016. The 2018 and 2017 explorations of "Human" and "Humans" reflect a focus on industrial human factors. The constant focus on "Process Monitoring" (640 occurrences in 2017) emphasizes real-time monitoring. "Female" and "Male," with notable 2018 events, are examined. Attention to "Average Run Lengths" (593 in 2016) indicates a continuous focus on efficiency measures. "Total Quality Management" appeared 574 times in 2017, demonstrating a dedication to comprehensive quality strategies. The 2019 theme transition to "Controlled Study" and "Major Clinical Study" reflects a methodological focus and clinical growth. Specialized interest in "Child" in 2020 suggests pediatric research. "Quality Improvement" in 2019 emphasizes product and process quality improvements. In 2022, "Statistical process control" returned, indicating renewed interest in core ideas, and in 2020, "Statistical Process Monitoring" explored real-time observation techniques. The addition of "Coronavirus Disease 2019" in 2023 shows that chart literature adapts to current issues. "Machine Learning" and "Electronic Health Record" show a rising convergence of modern technology and healthcare. Recent research on "Anomaly Detection," "Multidisciplinary Team," and "Nonparametric" shows a more interdisciplinary approach. The 2023 "Robustness" topic suggests an increasing interest in robust statistical methods. The eclectic study themes show that the discipline adapts to new trends and quality control and process optimization difficulties, see Fig. 14.
3.11. Intellectual Structure
Co-citation by Papers
In Fig. 15, the co-citation network analysis shows the prominence and influence of important chart contributors by papers. Important observations: Montgomery's multiple entries (2005, 2007, 2008, 2009, 2012, 2013) show his influence. His 2009 publication has the highest betweenness and PageRank values in the network, suggesting its importance in connecting clusters. Cluster 2 centers on Shewhart's 1931 statistical process control pioneering work, demonstrating his lasting impact. In its cluster, this paper has the highest betweenness and PageRank. The analysis includes 1950s and 1930s studies by Roberts, Page, Lucas, Lorenzen, and Woodall. These early studies affect chart discussions today.
The network has clusters for authors and historical papers. In Cluster 1, Montgomery's recent works dominate, whereas Cluster 2 includes Shewhart and Montgomery's foundational writings. Montgomery's latest advances and Shewhart's foundational works are covered in the co-citation network. Chart research is multidisciplinary. Assigning Qiu, Jensen, and Woodall to separate clusters shows their contributions to chart literature. The 2003 Benneyan J.C. publication has a PageRank of zero, demonstrating no influence on network connectivity. This may imply a less-cited or connected paper. The co-citation network analysis shows the lasting importance of foundational works, the influence of recent academics like Montgomery, and the diversity of chart literature.
Co-citation by Authors
Cluster 1. Montgomery D.C., Woodall W.H., Hawkins D.M., and others are important in the Cluster 1 co-citation network study. Montgomery D.C. has the highest betweenness centrality, indicating a key role in cluster researcher connections. Woodall W.H. and Hawkins D.M. also help build relationships. Zou C., Qiu P., and Chakraborty S. enhance the network. This cluster has a well-connected group of writers, with Montgomery D.C. influencing co-citation dynamics, see.
Cluster 2. A co-citation network in Cluster 2 includes notable authors Mason R.L., Guh R.S., Jackson J.E., Hotelling H., Cheng C.S., and others. Mason R.L.'s strong betweenness centrality indicates significant researcher bridging. Guh R.S. and Jackson J.E. boost cluster connection through co-citation patterns. Hotelling H. and Cheng C.S. deepen the network. Mason R.L. shapes co-citation dynamics in this cluster of authors.
Cluster 3. Lucas J.M., Wu Z., Shewhart W.A., Page E.S., and others are important in Cluster 3 co-citation network analysis. Lucas J.M. has the highest betweenness centrality, indicating a crucial role in connecting cluster researchers. Wu Z. and Shewhart W.A. heavily influence co-citation patterns, boosting partnerships. Other authors like Page E.S. enrich the network. This cluster has a well-connected group of writers, with Lucas J.M. influencing co-citation dynamics.
Cluster 4. Co-citation network analysis shows Costa A.F.B., Duncan A.J., Reynolds Jr. M.R., Prabhu S.S., Faraz A., and Saniga E.M. play a major role in Cluster 4. Costa A.F.B. and Duncan A.J.'s betweenness centrality ratings show they function as key bridges between researchers, driving the cluster's co-citation dynamics. Reynolds Jr. M.R., Prabhu S.S., and Faraz A. increase interconnection, whereas Saniga E.M. deepens co-citation patterns. This network of authors contributes to Cluster 4's knowledge integration.
Cluster 5. In Cluster 5, the co-citation network highlights Haq A., Riaz M., and Aslam M., and another author. Haq A. links researchers in this cluster. Riaz and Aslam considerably increase co-citation patterns and network bonds. An additional author could enhance Cluster 5 knowledge integration. These authors form a network with significant co-citation links, indicating overlapping research themes and partnerships that strengthen the cluster's intellectual coherence, see Fig. 16.
Comparison between Papers, Authors and Sources Co-citation Analysis
Co-citation analysis of papers, authors, and sources reveals field relationships' structure and dynamics. Paper co-citations reveal crucial research subjects and trends, such as statistical process control, quality improvement, and coronavirus illness 2019's recent focus. This analysis shows research themes across time. Author co-citation reveals prominent researcher clusters. Montgomery D.C., Lucas J.M., and Lowry C.A. lead these clusters, demonstrating author network dynamics. Central writers bridge others and influence information flow within their clusters. This analysis illuminates collaboration and major figures in research. Co-citation by sources, see Fig. 17 highlights the importance of specific periodicals or significant works. Lucas J.M., Woodall W.H., and Montgomery D.C. bridge sources, demonstrating their impact on the field. This analysis shows how influential works have shaped discourse.
In conclusion, our co-citation studies provide a comprehensive view of the research's multidisciplinary nature, significant authors promoting collaborations, and key sources changing the intellectual landscape. These viewpoints enhance our understanding of the field's complex network, laying the groundwork for informed conversations, future study, and multidisciplinary collaborations.
3.12. Departmental Analysis of % Applications
A diversified terrain is reflected in the distribution of chart applications among various departments, in Fig. 18. With 28% of the vote, engineering is in the lead, highlighting its fundamental importance in quality control. At 13%, decision science comes in second, emphasizing the role statistical process control (SPC) plays in data-driven decision-making. SPC is useful in software development and analytical settings, as seen by the percentages of computer science (10%) and mathematics (10%). Accounting and Business Management (8%) highlight the need for uniformity in financial procedures, while Healthcare and Medicine (8%) highlight the critical role that SPC plays in improving healthcare quality. Applications in assuring quality in many scientific and technical processes are revealed by Material Science (3%), Physics and Astronomy (2%), Chemical technical (2%), and Chemistry (2%). The contribution of SPC to environmental parameter monitoring is demonstrated by Environmental Science (1%). Lastly, Multiple Disciplines (13%) highlight how charts can be applied anywhere, filling in any gaps.
3.13. Collaboration Structure
Collaboration by Papers
The nine clusters of the collaboration analysis by papers reveal unique patterns of collaboration within each category. Runger GC is a prominent central node in Cluster 1 with a high betweenness centrality, suggesting a crucial function in mediating connections. Cluster 2, under the direction of Woodall WH, exhibits a varied network of collaboration with noteworthy contributions from writers such as Mahmoud MA and Knoth S. The high betweenness centrality and PageRank scores in Cluster 3, which are indicative of a rich interdisciplinary environment, point to collaborative efforts led by Xie M. Cluster 4, represented by powerful nodes like Zhang X and Zou C, shows a broad, highly impactful collaboration network. Cluster 5, under the leadership of Riaz M, exhibits a focused research focus, but Cluster 6, containing nodes such as Khoo MBC and Castagliola P, depicts an interconnected and productive collaborative setting. Specific and targeted collaboration is shown by Clusters 7 and 9, which feature prominent writers such as Amiri A and Yang S-F, respectively. Finally, Montgomery DC, representing Cluster 8, recommends a focused area of study. A thorough grasp of collaborative dynamics is offered by this sophisticated cluster-wise analysis, which highlights the important nodes and their functions inside each cluster, see Fig. 19.
Collaboration by Institutions
Institutional collaboration is shown in the 8 clusters of the collaborative networks, see Fig. 20. Arizona State University is a major node in Cluster 1, exhibiting strong betweenness centrality and proximity, indicating a crucial role in tying other schools in the cluster together. Universiti Sains Malaysia is a standout in Cluster 2 with a high betweenness centrality, demonstrating its impact in tying together different institutions. Quaid-i-Azam University, Nanyang Technological University, Virginia Polytechnic Institute, and State University are among the notable contributors. Cluster 3, headed by Nankai University, demonstrates a variety of partnerships with highly betweenness centrality organizations, such as Northeastern University and Hong Kong University of Science and Technology. Healthcare organizations in Cluster 4, including the University of Cincinnati and Cincinnati Children's Hospital Medical Centre, emphasize medical cooperation with very small closeness scores, which may indicate specialized relationships. Cluster 5, headed by Shahed University, exhibits a robust network comprising establishments such as Iran University of Science and Technology and Islamic Azad University. There is a lack of a link between Yazd University and the University of Qom in Cluster 6. King Fahd University of Petroleum and Minerals is a key player in Cluster 7, serving as a bridge between universities such as Zhejiang University and Qatar University. Lastly, Cluster 8 shows the cooperation between McMaster University and the University of Piraeus, both of which are important hubs for linking people. This detailed study highlights the roles and connections of the institutions within each cluster, offering insights into the collaborative dynamics among them.
Collaboration by Countries
Cluster 1(South American Collaboration)
The cooperation of Brazil, Spain, Mexico, Colombia, Ecuador, and other countries in Cluster 1 points to a significant regional emphasis. With their high betweenness centralities, Brazil and Spain stick out as the countries that connect other nations in the cluster. This could suggest a focus on collaborative projects and research endeavors in the area, encouraging cooperation and knowledge sharing, see Fig. 21.
Cluster 2 (Global Collaboration Led by the USA)
Global cooperation, spearheaded by the US, is what distinguishes the second cluster. The United States of America's high betweenness centrality indicates the vital role it plays in bridging disparate nations across the globe. The international research network benefits greatly from the contributions of Saudi Arabia, China, Canada, Malaysia, and the United Kingdom. This cluster illustrates how research is becoming more worldwide, with important nations serving as important hubs for cooperation.
Cluster 3 (European Research Collaboration)
European countries make up Cluster 3, with Germany playing a key role. Along with other countries, the Netherlands, Turkey, Belgium, Poland, and Sweden are part of the partnership network. Germany may act as a bridge, encouraging research connections across the European continent, as indicated by its high betweenness centrality. The strength of European collaborations and knowledge exchange is highlighted by this cluster.
Cluster 4 (French-Centric Collaboration)
France leads the fourth cluster, with Greece and Italy making major contributions. France's high betweenness centrality suggests that it plays a role in tying together other nations in this cluster. The close relationships between these European countries are highlighted by this partnership network, which may point to common research interests and cooperative initiatives.
Cluster 5 (Middle Eastern and North African Collaboration)
Cluster 5 highlights partnerships between Middle Eastern and North African nations, with Saudi Arabia and Iran taking the stage. The links point to a regional emphasis, with neighboring nations taking part in cooperative research projects. The inclusion of Algeria and Tunisia in this cluster highlights regional cooperation even further.
The investigation of collaboration networks at the national level sheds light on the global research collaboration landscape. It draws attention to continental cooperation, global hubs, and regional emphases, illuminating how nations are interconnected in promoting international research partnerships and increasing scientific knowledge. The key nodes that have been found, along with their betweenness centrality ratings, provide important insights into the major nations that are influencing collaborative networks within various clusters.
Research Gap and Opportunities
Charts and SPC research efforts can still be improved, according to the results of the current bibliometric analysis and literature review study. The following bolsters the possibility for research capabilities:
Gaps
-
There is a gap in the integration of advanced analytics, machine learning, and artificial intelligence for more prescriptive and predictive insights, even though classic charts are useful for identifying deviations. Quality control could become more proactive by utilizing these technologies.
-
Many charts operate on historical data, and there is a gap in seamlessly integrating real-time data into these charts. A more dynamic and real-time SPC approach could be beneficial, especially in industries requiring rapid adjustments.
-
The significance of human factors in the context of charts is not fully understood. Researching how human choices and actions affect SPC system efficacy may lead to the development of more adaptable and user-friendly solutions.
-
Despite the widespread usage of charts, there could not be enough standardization, particularly among various organizations. SPC methods may be more transferable if more globally applicable standards and best practices emerge.
-
There is a chance to integrate charts with supply chain management systems more easily. This could entail creating chart techniques intended especially for supply chain process monitoring and improvement.
Opportunities
-
The Internet of Things (IoT) offers a chance to gather data in greater detail. A multitude of real-time data can be provided by integrating IoT devices into SPC systems, allowing for more accurate monitoring and control.
-
Blockchain technology presents a chance for data security and integrity in SPC systems. This could improve the data's credibility, which is important for quality control.
-
There is an option to create charts with dynamic control limits that adjust to shifting circumstances. Adaptive algorithms that modify control limits in response to the performance of the process could be used for this.
-
There is a chance to improve SPC tool user interfaces and make them more approachable and intuitive. This can entail applying user-centric design ideas and visualization approaches.
-
Charts present an opportunity to incorporate sustainability data given the growing emphasis on sustainability. This can entail creating SPC procedures that take resource efficiency and environmental impact into account.
-
There is a chance to forecast and stop equipment failures in manufacturing processes by using SPC techniques. By being proactive, you may reduce downtime and increase the overall efficiency of your equipment.
-
There is a chance to develop cooperative SPC platforms that facilitate the instantaneous exchange of high-quality data and insights between stakeholders. This could encourage decision-making and collaboration across functional boundaries.
Charts and Statistical Process Control offer prospects for integrating with cutting-edge technologies, enhancing real-time capabilities, and tackling issues unique to certain industries. Redesigning SPC systems to be more cooperative, user-friendly, and adaptive may help create a quality control paradigm that is more successful and efficient.