Skip to main content
Log in

Comparative case study of finite mixture and T2-Hotelling control charts for multiple stream monitoring

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The multiple stream process involves equipment with several modules producing the same items, but with respective random variables’ subpopulations. This particularity demands specific control charts for the statistical processes monitoring. Among the initiatives proposed are the finite mixture control chart and T2-Hotelling control chart, but there are no comparative studies between them. This article aims to study and compare the proposed use of a finite mixture control chart with the T2-Hotelling control chart for monitoring the multiple stream process (MSP). The modeling and simulation were performed using actual data from the dietary sector to develop an illustrative case. The results show that the T2-Hotelling charts’ performance was superior in detecting special causes and generating false alarms about the process and supplementary to having less mathematical difficulty in implementing. However, the finite mixture chart is a more recently used approach, considering the literature’s occurrence, compared with the T2-Hotelling chart. Furthermore, the finite mixture chart considering all streams and the variability between them in just one control chart is more straightforward in terms of sampling, but more difficult in mathematical terms for its application. In conclusion, the finite mixture and T2-Hotelling charts are suitable for solving MSP, monitoring with no losing information on each stream variability, and not masking effects from special causes in the process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Abbas N, Riaz M, Ahmad S, Abid M, Zaman B (2020) On the efficient monitoring of multivariate processes with unknown parameters. Mathematics 8(5):823–834. https://doi.org/10.3390/math8050823

    Article  Google Scholar 

  2. Ahangar NE, Chimka JR (2016) Attribute control charts with optimal limits. Qual Reliab Eng Int 32(4):1381–1391. https://doi.org/10.1002/qre.1839

    Article  Google Scholar 

  3. Ahsan M, Mashuri M, Kuswanto H, Prastyo DD (2018) Intrusion detection system using multivariate control chart Hotelling’s T2 based on PCA. Int J Adv Sci Eng Inf Technol 8(5):1905–1911. https://doi.org/10.18517/ijaseit.8.5.3421

    Article  Google Scholar 

  4. Bersimis S, Psarakis S, Panaretos J (2007) Multivariate statistical process control charts: an overview. Qual Reliab Eng Int 23(5):517–543. https://doi.org/10.1002/qre.829

    Article  Google Scholar 

  5. Boyd DF (1950) Applying the group chart for X and R. Ind Qual Control 7(3):22–25

    Google Scholar 

  6. Brown AR, Schaffer JR (2020) A nonparametric CUSUM control chart for multiple stream processes based on a modified extended median test. Commun Stat - Theory Methods 1–14. https://doi.org/10.1080/03610926.2020.1738492

  7. Chen YK, Hsieh KL (2007) Hotelling’s T2 charts with variable sample size and control limit. Eur J Oper Res 182(3):1251–1262. https://doi.org/10.1016/j.ejor.2006.09.046

    Article  MATH  Google Scholar 

  8. Doǧu E (2015) Identifying the time of a step change with multivariate single control charts. J Stat Comput Simul 85(8):1529–1543. https://doi.org/10.1080/00949655.2014.880704

    Article  MathSciNet  Google Scholar 

  9. Doǧu E, Kim MJ (2020) Self-starting single control charts for multivariate processes: a comparison of methods. Production 30:1–12. https://doi.org/10.1590/0103-6513.20190136

    Article  Google Scholar 

  10. Epprecht EK (2015) Statistical control of multiple-stream processes: a literature review. Front Qual Stat Control 11:49–64. https://doi.org/10.1007/978-3-319-12355-4_4

    Article  MATH  Google Scholar 

  11. Epprecht EK, Barbosa LFM, Simões BFT (2011) SPC of multiple stream processes—a chart for enhanced detection of shifts in one stream. Production 21(2):242–253. https://doi.org/10.1590/S0103-65132011005000022

    Article  Google Scholar 

  12. Grasso M, Colosimo BM, Semeraro Q, Pacella M (2014) A comparison study of distribution-free multivariate SPC methods for multimode data. Qual Reliab Eng Int 31(1):75–96. https://doi.org/10.1002/qre.1708

    Article  Google Scholar 

  13. Hair JF Jr, Black WC, Babin BJ, Anderson RE (2019) Multivariate data analysis, 8th edn. Cengage, Boston

    Google Scholar 

  14. Huang M, Wang Y, Shirinkam S, Alaeddini A, Yang K (2020) Application of multivariate control chart techniques to identifying nonconforming pallets in automotive assembly plants. SAE Technical Pap. https://doi.org/10.4271/2020-01-0477

    Article  Google Scholar 

  15. Jardim FS, Chakraborti S, Epprecht EK (2019) Chart with estimated parameters: the conditional ARL distribution and new insights. Prod Oper Manag 28(6):1545–1557. https://doi.org/10.1111/poms.12985

    Article  Google Scholar 

  16. Jensen WA, Jones-Farmer LA, Cham PCW, Woodall WH (2006) Effects of parameter estimation on control chart properties: a literature review. J Qual Technol 38(4):349–364. https://doi.org/10.1080/00224065.2006.11918623

    Article  Google Scholar 

  17. Jirasettapong P, Rojanarowan N (2011) A guideline to select control charts for multiple stream processes control. Eng J 15(3):1–14. https://doi.org/10.4186/ej.2011.15.3.1

    Article  Google Scholar 

  18. Johnson RA, Wichern DW (2007) Applied multivariate statistical analysis. 6 ed. Prentice Hall.

  19. Lanning JW, Montgomery DC, Runger GC (2002) Monitoring a multiple stream filling operation using fractional samples. Qual Eng 15(2):183–195. https://doi.org/10.1081/QEN-120015851

    Article  Google Scholar 

  20. Liu X, Mackay RJ, Steiner SH (2008) Monitoring multiple stream processes. Qual Eng 20(3):296–308. https://doi.org/10.1080/08982110802035404

    Article  Google Scholar 

  21. Lowry CA, Montgomery DC (1995) A review of multivariate control charts. IIE Trans 27(6):800–810. https://doi.org/10.1080/07408179508936797

    Article  Google Scholar 

  22. Macgregor JF, Jaeckle C, Kiparissides C, Koutoudi M (1994) Process monitoring and diagnosis by multi-block PLS methods. AIChE J 40(5):826–838. https://doi.org/10.1002/aic.690400509

    Article  Google Scholar 

  23. Marquez SR, Vivas JJ (2020) Multivariate SPC methods for controlling manufacturing processes using predictive models – a case study in the automotive sector. Comput Ind 123:103307. https://doi.org/10.1016/j.compind.2020.103307

    Article  Google Scholar 

  24. Mason RL, Young JC (2001) Multivariate statistical process control with industrial application. Phila Soc Ind Appl Math 2001. https://doi.org/10.1137/1.9780898718461

  25. Meneces NS, Olivera SA, Saccone CD, Tessore J (2008) Statistical control of multiple-stream processes: a Shewhart control chart for each stream. Qual Eng 20(2):185–194. https://doi.org/10.1080/08982110701241608

    Article  Google Scholar 

  26. Meredith JR, Raturi A, Gyampah KA, Kaplan B (1989) Alternative research paradigms in operations. J Oper Manag 8(4):297–326. https://doi.org/10.1016/0272-6963(89)90033-8

    Article  Google Scholar 

  27. Montgomery DC (2019) Introduction to statistical quality control, 8th ed. New York: NJ: Wiley.

  28. Mortell RR, Runger GC (1995) Statistical process control for multiple stream processes. J Qual Technol 27(1):1–12. https://doi.org/10.1080/00224065.1995.11979554

    Article  Google Scholar 

  29. Phaladiganon P, Kim SB, Chen VC, Jiang W (2013) Principal component analysis-based control charts for multivariate nonnormal distributions. Expert Syst Appl 40(8):3044–3054. https://doi.org/10.1016/j.eswa.2012.12.020

    Article  Google Scholar 

  30. Ram M, Davim PJ (2018) Modeling and simulation in industrial engineering. Springer, Cham, pp 85–100

    Book  MATH  Google Scholar 

  31. Salah B, Zoheir M, Slimane Z, Jurgen B (2015) Inferential sensor-based adaptive principal components analysis of mould bath level for breakout defect detection and evaluation in continuous casting. Appl Soft Comput 34:120–128. https://doi.org/10.1016/j.asoc.2015.04.042

    Article  Google Scholar 

  32. Shams MB, Elkamel A, Moorthy K, Rafinejad G, Saxena A (2015) Optimal design of T2 monitoring chart for chemical processes. Int J Process Syst Eng 3(4):232–247. https://doi.org/10.1504/IJPSE.2015.075104

    Article  Google Scholar 

  33. Shao YE, Chang PY, Lu CJ (2017) Applying two-stage neural network based classifiers to the identification of mixture control chart patterns for an SPC-EPC process. Complexity, 1–10. https://doi.org/10.1155/2017/2323082.

  34. Sivasamy A, Sundan B (2015) A dynamic intrusion detection system based on multivariate Hotelling’s T2 statistics approach for network environments. Sci World J 1–9. https://doi.org/10.1155/2015/850153.

  35. Thissen U, Swierenga H, de Weijer AP, Wehrens R, Melssen WJ, Buydens LMC (2015) Multivariate statistical process control using mixture modelling. J Chemom 19(1):23–31. https://doi.org/10.1002/cem.903

    Article  Google Scholar 

  36. Titterington DM, Smith AFM, Makov UE (1985) Statistical analysis of finite mixture distributions. Wiley, Universidade da Califórnia

    MATH  Google Scholar 

  37. Vanhatalo E, Kulahci M, Bergquist B (2017) On the structure of dynamic principal component analysis used in statistical process monitoring. Chemom Intell Lab Syst 167:1–11. https://doi.org/10.1016/j.chemolab.2017.05.016

    Article  Google Scholar 

  38. Variyath AM, Vattathoo J (2014) Robust control charts for monitoring process variability in phase I multivariate individual observations. Qual Reliab Eng Int 30(6):795–812. https://doi.org/10.1155/2013/542305

    Article  Google Scholar 

  39. Vicentin DS, Silva BB, Piccirillo I, Bueno FB, Oprime PC (2018) Monitoring process control chart with finite mixture probability distribution: an application in manufacture industry. Int J Qual Reliab Manag 35(2):335–353. https://doi.org/10.1108/IJQRM-11-2016-0196

    Article  Google Scholar 

  40. Wise BM, Gallagher NB (1996) The process chemometrics approach to process monitoring and fault detection. J Proc Contr 6(6):329–348. https://doi.org/10.1016/0959-1524(96)00009-1

    Article  Google Scholar 

  41. Woodall WH, Montgomery DC (2014) Some current directions in the theory and application of statistical process monitoring. J Qual Technol 46(1):78–95. https://doi.org/10.1080/00224065.2014.11917955

    Article  Google Scholar 

  42. Woodall WH (1985) The statistical design of quality control charts. J R Stat Soc Ser D (The Statistician) 34(2):155–160. https://doi.org/10.2307/2988154

    Article  Google Scholar 

  43. Zhang H, Albin S (2007) Determining the number of operational modes in baseline multivariate SPC data. IIE Trans 39(12):1103–1110. https://doi.org/10.1080/07408170701291787

    Article  Google Scholar 

  44. Zwetsloot IM, Woodall WH (2019) A review of some sampling and aggregation strategies for basic statistical process monitoring. J Qual Technol 53(1):1–16. https://doi.org/10.1080/00224065.2019.1611354

    Article  Google Scholar 

Download references

Funding

This work was partially supported by the Coordination for the Improvement of Higher Education Personnel (CAPES-Brazil), 0001. The author Damaris Chieregato Vicentin received the doctoral scholarship (CAPES-Brazil) from the Department of Production Engineering at the Federal University Sao Carlos.

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation and analysis were performed by Damaris Chieregato Vicentin, Pedro Carlos Oprime, and Ricardo Coser Mergulhão. The data collection and preparation were performed by Damaris Chieregato Vicentin and Pedro Carlos Oprime. The first draft of the manuscript was written by Damaris Chieregato Vicentin and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Damaris Chieregato Vicentin.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vicentin, D.C., Oprime, P.C. & Mergulhão, R.C. Comparative case study of finite mixture and T2-Hotelling control charts for multiple stream monitoring. Int J Adv Manuf Technol 123, 3233–3242 (2022). https://doi.org/10.1007/s00170-022-10424-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-10424-8

Keywords

Navigation