A bibliometric analysis of statistical process control (SPC) applied on manufacturing process

Authors

  • Antonio Karlos Araújo Valença Universidade Federal da Paraíba (UFPB), João Pessoa, PB, Brasil. https://orcid.org/0000-0001-6994-4577
  • Rodrigo César Reis de Oliveira Universidade Federal de Alagoas (UFAL), Maceió, AL, Brasil.

DOI:

https://doi.org/10.14488/1676-1901.v23i4.5096

Keywords:

Statistical Process Control, Manufacturing Process, Bibliometrics, SPC

Abstract

In this work, an extensive bibliometric review was carried out on the scientific production of statistical process control, applied to the manufacturing industry, mapping the main research in the literature, as well as the main journals that are publishing this research. Statistical process control is one of the main tools that allow production managers to determine whether processes meet the requirements predetermined by customers, providing better product and process quality. The analysis illustrates the evolution of research over the last decades, the main journals for publication, the level of concentration or fragmentation of the scientific community, and the geographical density of research collaborations. Finally, the main themes that have been addressed by the scientific community that debate the SPC in manufacturing applications and future research for the direction of this theme are also presented.

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Author Biographies

Antonio Karlos Araújo Valença, Universidade Federal da Paraíba (UFPB), João Pessoa, PB, Brasil.

Doutorando em Engenharia Mecânica na Universidade Federal da Paraíba. Mestrado em Engenharia Mecânica com ênfase na área de Processos de Fabricação pela Universidade Federal da Paraíba (PPGEM/UFPB). Graduado em Engenharia de Produção pela Faculdade de Administração e Negócios de Sergipe (FANESE). Ex-Professor Substituto (2022-2023) no Departamento de Engenharia de Produção da Universidade Federal do Rio Grande do Norte (DEP/UFRN).

Rodrigo César Reis de Oliveira, Universidade Federal de Alagoas (UFAL), Maceió, AL, Brasil.

Doutor em Administração pelo Núcleo de Pós-Graduação em Administração da UFBA (NPGA-UFBA). Mestre em Administração pelo Programa de Pós-graduação em Administração da UFPE (PROPAD-UFPE). Graduado em Administração pela Universidade Federal da Paraíba. Professor Adjunto da Faculdade de Economia, Administração e Contabilidade (FEAC), da Universidade Federal de Alagoas. Professor de graduação e do Mestrado Profissional em Administração Pública (PROFIAP-UFAL).

References

ABBAS, Nasir. Homogeneously weighted moving average control chart with an application in substrate manufacturing process. Computers & Industrial Engineering, v. 120, p. 460-470, 2018.

ABBAS, Zameer et al. Enhanced nonparametric control charts under simple and ranked set sampling schemes. Transactions of the Institute of Measurement and Control, v. 42, n. 14, p. 2744-2759, 2020.

ACOSTA, Simone Massulini; SANT'ANNA, Angelo Marcio Oliveira. Machine learning-based control charts for monitoring fraction nonconforming product in smart manufacturing. International Journal of Quality & Reliability Management, n. ahead-of-print, 2022.

ALSHRAIDEH, Hussam; DEL CASTILLO, Enrique; DEL VAL, Alain Gil. Process control via random forest classification of profile signals: An application to a tapping process. Journal of Manufacturing Processes, v. 58, p. 736-748, 2020.

ANDRES-JIMENEZ, Jose et al. An Intelligent Framework for the Evaluation of Compliance with the Requirements of ISO 9001: 2015. Sustainability, v. 12, n. 13, p. 5471, 2020.

ARGOUD, M. et al. 300mm pilot line DSA contact hole process stability. In: Alternative Lithographic Technologies VI. SPIE, 2014. p. 474-484.

AZIZI, Amir. Evaluation improvement of production productivity performance using statistical process control, overall equipment efficiency, and autonomous maintenance. Procedia manufacturing, v. 2, p. 186-190, 2015.

BAHRIA, Nadia et al. Maintenance and quality control integrated strategy for manufacturing systems. European Journal of Industrial Engineering, v. 12, n. 3, p. 307-331, 2018.

BEDFORDJONES, P. E. The application of statistical process-control (spc) in the manufacturing of steel tubing. In: CIM BULLETIN. 101 6TH AVE SW, STE 320, CALGARY AB TZP 3P4, CANADA: CANADIAN INST MINING METALLURGY PETROLEUM, 1984. p. 57-57.

BOTTANI, Eleonora et al. Statistical process control of assembly lines in manufacturing. Journal of Industrial Information Integration, v. 32, p. 100435, 2023.

BRADFORD, Samuel C. Sources of information on specific subjects. Engineering, v. 137, p. 85-86, 1934.

CASSADY, C. Richard et al. Combining preventive maintenance and statistical process control: a preliminary investigation. IIE Transactions, v. 32, p. 471-478, 2000.

CAVIGGIOLI, Federico; UGHETTO, Elisa. A bibliometric analysis of the research dealing with the impact of additive manufacturing on industry, business and society. International journal of production economics, v. 208, p. 254-268, 2019.

CHAN, L. Y. et al. Cumulative probability control charts for geometric and exponential process characteristics. International Journal of Production Research, v. 40, n. 1, p. 133-150, 2002.

CHI, Hoi-Ming et al. Machine learning and genetic algorithms in pharmaceutical development and manufacturing processes. Decision Support Systems, v. 48, n. 1, p. 69-80, 2009.

CHOU, Shih-Hsiung et al. Implementation of statistical process control framework with machine learning on waveform profiles with no gold standard reference. Computers & Industrial Engineering, v. 142, p. 106325, 2020.

COATES, P. D.; SPEIGHT, R. G. Towards intelligent process control of injection moulding of polymers. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, v. 209, n. 5, p. 357-367, 1995.

COLLEDANI, Marcello et al. Design and management of manufacturing systems for production quality. Cirp Annals, v. 63, n. 2, p. 773-796, 2014.

DURISIN, Boris; CALABRETTA, Giulia; PARMEGGIANI, Vanni. The intellectual structure of product innovation research: a bibliometric study of the journal of product innovation management, 1984–2004. Journal of Product Innovation Management, v. 27, n. 3, p. 437-451, 2010.

ECHCHAKOUI, Saïd; BARKA, Noureddine. Industry 4.0 and its impact in plastics industry: A literature review. Journal of Industrial Information Integration, v. 20, p. 100172, 2020.

EGOROV, Sergey; KAPITANOV, Alexey; LOKTEV, Dmitriy. Implementation of statistical process control methods as a way to reduce production costs and improve product quality. In: MATEC Web of Conferences. EDP Sciences, 2017.

ENSSLIN, Leonardo et al. Avaliação de desempenho na aplicação do controle estatístico de processos: seleção de referencial teórico internacional e análise bibliométrica. Revista Alcance, v. 24, n. 3, p. 396-412, 2017.

ESMONDE-WHITE, Karen A. et al. Raman spectroscopy as a process analytical technology for pharmaceutical manufacturing and bioprocessing. Analytical and bioanalytical chemistry, v. 409, n. 3, p. 637-649, 2017.

GAO, Robert X. et al. Big data analytics for smart factories of the future. CIRP annals, v. 69, n. 2, p. 668-692, 2020.

GODINHO FILHO, Moacir; GANGA, Gilberto Miller Devós; GUNASEKARAN, Angappa. Lean manufacturing in Brazilian small and medium enterprises: implementation and effect on performance. International Journal of Production Research, v. 54, n. 24, p. 7523-7545, 2016.

GUPTA, Munish; PROVOST, Lloyd P.; KAPLAN, Heather C. Challenging Cases in Statistical Process Control for Quality Improvement in Neonatal Intensive Care. Clinics in Perinatology, 2023.

HASSAN, Adnan et al. Improved SPC chart pattern recognition using statistical features. International Journal of Production Research, v. 41, n. 7, p. 1587-1603, 2003.

HE, Q. Peter; WANG, Jin. Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes. IEEE transactions on semiconductor manufacturing, v. 20, n. 4, p. 345-354, 2007.

HE, Shu-Guang; HE, Zhen; WANG, Gang A. Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques. Journal of Intelligent Manufacturing, v. 24, p. 25-34, 2013.

HSU, Jyh-Yih et al. Wind turbine fault diagnosis and predictive maintenance through statistical process control and machine learning. Ieee Access, v. 8, p. 23427-23439, 2020.

HUNT, S. J. The IBM statistical process control implementation program: an interactive videodisc approach. In: 1991 Proceedings IEEE/SEMI International Semiconductor Manufacturing Science Symposium. IEEE, 1991. p. 1-3.

HWARNG, H. Brian; HUBELE, Norma Faris. Back-propagation pattern recognizers for X control charts: methodology and performance. Computers & Industrial Engineering, v. 24, n. 2, p. 219-235, 1993.

KE, Kun-Cheng; HUANG, Ming-Shyan. Quality classification of injection-molded components by using quality indices, grading, and machine learning. Polymers, v. 13, n. 3, p. 353, 2021.

KHANZADEH, Mojtaba et al. Dual process monitoring of metal-based additive manufacturing using tensor decomposition of thermal image streams. Additive Manufacturing, v. 23, p. 443-456, 2018.

KHOZA, Sibusiso C.; GROBLER, Jacomine. Comparing machine learning and statistical process control for predicting manufacturing performance. In: EPIA conference on artificial intelligence. Cham: Springer International Publishing, 2019. p. 108-119.

KIM, Dongil et al. Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing. Expert Systems with Applications, v. 39, n. 4, p. 4075-4083, 2012.

KOURTI, Theodora; MACGREGOR, John F. Multivariate SPC methods for process and product monitoring. Journal of quality technology, v. 28, n. 4, p. 409-428, 1996.

LEE, Chang Ki. The RNVP-based process monitoring with transforming non-normal data to multivariate normal data. Engineering Applications of Artificial Intelligence, v. 118, p. 105623, 2023.

LIM, Sarina Abdul Halim; ANTONY, Jiju; ALBLIWI, Saja. Statistical Process Control (SPC) in the food industry–A systematic review and future research agenda. Trends in food science & technology, v. 37, n. 2, p. 137-151, 2014.

LIU, Jia et al. Machine learning–driven in situ process monitoring with vibration frequency spectra for chemical mechanical planarization. The International Journal of Advanced Manufacturing Technology, v. 111, p. 1873-1888, 2020.

MULLER, Thomas et al. Why and how to move from SPC (statistical process control) to APC (automated process control). In: Design and Modeling of Mechanical Systems-IV: Proceedings of the 8th Conference on Design and Modeling of Mechanical Systems, CMSM'2019, March 18–20, Hammamet, Tunisia. Springer International Publishing, 2020. p. 33-40.

NICOLAY, Christopher R. et al. Systematic review of the application of quality improvement methodologies from the manufacturing industry to surgical healthcare. Journal of British Surgery, v. 99, n. 3, p. 324-335, 2012.

PADILLA-OSPINA, Ana Milena; MEDINA-VÁSQUEZ, Javier Enrique; RIVERA-GODOY, Jorge Alberto. Financing innovation: A bibliometric analysis of the field. Journal of Business & Finance Librarianship, v. 23, n. 1, p. 63-102, 2018.

PAPADOPOULOS, Chrissoleon T.; LI, Jingshan; O'KELLY, Michael EJ. A classification and review of timed Markov models of manufacturing systems. Computers & Industrial Engineering, v. 128, p. 219-244, 2019.

PEKSEN, Murat; ACAR, Memis; MALALASEKERA, Weeratunge. Computational optimisation of the thermal fusion bonding process in porous fibrous media for improved product capacity and energy efficiency. In: Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, v. 226, n. 4, p. 316-323, 2012.

PRITCHARD, Alan. Statistical bibliography or bibliometrics. Journal of documentation, v. 25, p. 348, 1969.

QURESHI, Karishma M. et al. Assessing Lean 4.0 for Industry 4.0 Readiness Using PLS-SEM towards Sustainable Manufacturing Supply Chain. Sustainability, v. 15, n. 5, p. 3950, 2023.

RAMOS, Alberto Wunderler. Controle estatístico de processo. Gestão de Operações: A Engenharia de Produção a serviço da modernização da empresa, v. 2, 1997.

RATINHO, Tiago; HARMS, Rainer; WALSH, Steven. Structuring the technology entrepreneurship publication landscape: Making sense out of chaos. Technological forecasting and social change, v. 100, p. 168-175, 2015.

RIAZ, Muhammad et al. On efficient phase II process monitoring charts. The International Journal of Advanced Manufacturing Technology, v. 70, p. 2263-2274, 2014.

RIENZO, Thomas F. Planning Deming management for service organizations. Business Horizons, v. 36, n. 3, p. 19-30, 1993.

SACHS, Emanuel; HU, Albert; INGOLFSSON, Armann. Run by run process control: Combining SPC and feedback control. IEEE Transactions on Semiconductor Manufacturing, v. 8, n. 1, p. 26-43, 1995.

SAGHAEI, Abbas; MEHRJOO, Marzieh; AMIRI, Amirhossein. A CUSUM-based method for monitoring simple linear profiles. The International Journal of Advanced Manufacturing Technology, v. 45, p. 1252-1260, 2009.

SAHAY, Chittaranjan; GHOSH, Suhash; BHEEMARTHI, Pradeep Kumar. Process improvement of brake lever production using DMAIC (+). In: ASME International Mechanical Engineering Congress and Exposition. 2011. p. 801-826.

SARKAR, Debasis. Advanced materials management for Indian construction industry by application of statistical process control tools. Materials Today: Proceedings, v. 62, p. 6934-6939, 2022.

SCHONBERGER, Richard J. Total quality management cuts a broad swath—through manufacturing and beyond 078. Organizational Dynamics, v. 20, n. 4, p. 16-28, 1992.

SILVA, A. F. et al. Multivariate statistical process control of a continuous pharmaceutical twin-screw granulation and fluid bed drying process. International Journal of Pharmaceutics, v. 528, n. 1-2, p. 242-252, 2017.

STAAL, R.; RATHERT, H.; SCHLOSSER, G. Statistical Process-Control (SPC) and Automatic Process-Control (APC). Chemie Ingenieur Technik, v. 66, n. 1, p. 40-49, 1994.

SUN, Yutao; GRIMES, Seamus. The emerging dynamic structure of national innovation studies: a bibliometric analysis. Scientometrics, v. 106, p. 17-40, 2016.

SUNADI, Sunadi; PURBA, Humiras Hardi; HASIBUAN, Sawarni. Implementation of statistical process control through PDCA cycle to improve potential capability index of drop impact resistance: a case study at aluminum beverage and beer cans manufacturing industry in Indonesia. Quality Innovation Prosperity, v. 24, n. 1, p. 104-127, 2020.

TENCA, Francesca; CROCE, Annalisa; UGHETTO, Elisa. Business angels research in entrepreneurial finance: A literature review and a research agenda. Contemporary Topics in Finance: A Collection of Literature Surveys, p. 183-214, 2019.

TOMBA, Emanuele et al. Latent variable modeling to assist the implementation of Quality-by-Design paradigms in pharmaceutical development and manufacturing: A review. International journal of pharmaceutics, v. 457, n. 1, p. 283-297, 2013.

TRAN, Kim Duc et al. One-sided Shewhart control charts for monitoring the ratio of two normal variables in short production runs. Journal of Manufacturing Processes, v. 69, p. 273-289, 2021.

TRANFIELD, David; DENYER, David; SMART, Palminder. Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British journal of management, v. 14, n. 3, p. 207-222, 2003.

TSUNG, Fu-gee. Statistical monitoring and diagnosis of automatic controlled processes using dynamic PCA. International Journal of Production Research, v. 38, n. 3, p. 625-637, 2000.

VAN ECK, Nees; WALTMAN, Ludo. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, v. 84, n. 2, p. 523-538, 2010.

WANG, Kaibo; TSUNG, Fugee. Using profile monitoring techniques for a data‐rich environment with huge sample size. Quality and reliability engineering international, v. 21, n. 7, p. 677-688, 2005.

WOODALL, William H. Controversies and contradictions in statistical process control. Journal of quality technology, v. 32, n. 4, p. 341-350, 2000.

WU, Zhang et al. A combined synthetic&X chart for monitoring the process mean. International Journal of Production Research, v. 48, n. 24, p. 7423-7436, 2010.

XIE, Min; GOH, Thong Ngee; RANJAN, Priya. Some effective control chart procedures for reliability monitoring. Reliability Engineering & System Safety, v. 77, n. 2, p. 143-150, 2002.

YAO, Yuan; GAO, Furong. A survey on multistage/multiphase statistical modeling methods for batch processes. Annual Reviews in Control, v. 33, n. 2, p. 172-183, 2009.

YE, Zehao et al. In-situ point cloud fusion for layer-wise monitoring of additive manufacturing. Journal of Manufacturing Systems, v. 61, p. 210-222, 2021.

YEGANEH, Ali et al. A network surveillance approach using machine learning based control charts. Expert Systems with Applications, v. 219, p. 119660, 2023.

YU, Jian-Bo. Bearing performance degradation assessment using locality preserving projections. Expert Systems with Applications, v. 38, n. 6, p. 7440-7450, 2011.

YU, Jian-bo; XI, Li-feng. A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes. Expert systems with applications, v. 36, n. 1, p. 909-921, 2009.

ZHAO, Fang et al. 1H NMR‐based process understanding and biochemical marker identification methodology for monitoring CHO cell culture process during commercial‐scale manufacturing. Biotechnology Journal, p. 2200616, 2023.

ZHAO, Xiuxu et al. Research and application of intelligent quality control system based on FMEA repository. In: 2009 International Conference on Information Technology and Computer Science. IEEE, 2009. p. 514-517.

ZHU, Wenjia; GUAN, Jiancheng. A bibliometric study of service innovation research: based on complex network analysis. Scientometrics, v. 94, n. 3, p. 1195-1216, 2013.

ZORRIASSATINE, F.; TANNOCK, J. D. T. A review of neural networks for statistical process control. Journal of intelligent manufacturing, v. 9, p. 209-224, 1998.

ZOU, Changliang; TSUNG, Fugee. A multivariate sign EWMA control chart. Technometrics, v. 53, n. 1, p. 84-97, 2011.

SCHEMES, Moving Average. Monitoring General Linear Profiles Using Multivariate Exponentially Weighted. Technometrics, v. 49, n. 4, 2007.

Published

2024-03-20

How to Cite

Valença, A. K. A., & Oliveira, R. C. R. de. (2024). A bibliometric analysis of statistical process control (SPC) applied on manufacturing process. Revista Produção Online, 23(4), 5096 . https://doi.org/10.14488/1676-1901.v23i4.5096

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Papers