THE METHOD OF MULTIVARIATE STATISTICAL ANALYSIS OF TIME MULTIVARIATE CRITICAL ATTRIBUTES OF MANUFACTURING QUALITY WITH DATA FACTORIZATION

Authors

  • Євген Володимирович Гаврилко State University of Telecommunications, Ukraine
  • Олег Анастасійович Курченко State University of Telecommunications, Ukraine https://orcid.org/0000-0002-3507-2392
  • Ігор Володимирович Терещенко Kharkiv National University of Radio Electronics, Ukraine https://orcid.org/0000-0002-6197-1914
  • Антон Ігорович Терещенко State University of Telecommunications, Ukraine

DOI:

https://doi.org/10.30837/2522-9818.2018.5.005

Keywords:

quality-by-design, critical quality attributes, critical process parameters, the design of experiments, multivariate statistical analysis

Abstract

The object of the study is the process of product quality assurance at the stage of the initial design of the manufacturing process. The subject matter is informational technologies for assessing the factor influence of critical parameters of the process of manufacturing (CPPs) on the critical quality attributes of a product (CQAs). The goal of the study is to use the method of multivariate statistical analysis for assessing the character and features of the influence of time multivariate critical process parameters on time multivariate critical quality attributes at the stage of designing the manufacturing process. The task of the study is to determine the structure and hierarchy of time multivariate data of CPPs and CQAs and to determine qualitatively and quantitatively the relationship among the formed objects of the specified parameters. The following methods were consistently used – statistical procedures of the exploratory analysis of multivariate data; transforming the homogeneous observed values matrices of CPPs and product CQAs into the data table with factorized data; deriving the regression trees of multivariate CPPs with multivariate responses (CQAs). The methods implement the software packages of the R language. The following results were obtained – the method to solve the problem of product quality assurance at the stage of designing the initial manufacturing process in accordance with the process-analytical technology for designing modern certified manufacturing standards such as QbD (Quality-by-Design) is suggested. The method uses the information technologies of multivariate statistical analysis (MSA) to evaluate the influence of time multivariate critical process parameters (CPPs) on the time product critical quality attributes (CQAs). Preparatory transformation of clusters of critical process (manufacture process) parameters into factors of product critical quality attributes was carried out. Factorized time multivariate CPPs enable using the methods of multivariate statistical analysis for assessing the impact of CPP factors on the time multivariate CQAs. Conclusions. This method of statistical analysis along with statistical multivariate canonical analysis present the up-to-date information technology for the detailed assessment of the influence of time multivariate CPP objects and some CPP components on CQAs. The method is oriented to practical application to assure the quality of products at the stage of designing (improving) manufacturing processes.

Author Biographies

Євген Володимирович Гаврилко, State University of Telecommunications

Doctor of Sciences (Engineering), Associate Professor, State University of Telecommunications, Professor at the Department of Computer Sciences

Олег Анастасійович Курченко, State University of Telecommunications

PhD (Engineering Sciences), Associate Professor, State University of Telecommunications, Head at the Department of Management of Information and Cyber Security

Ігор Володимирович Терещенко, Kharkiv National University of Radio Electronics

PhD (Engineering Sciences), Associate Professor, Kharkiv National University of Radio Electronics, Assistant Professor at the Department of Infocommunication Engineering

Антон Ігорович Терещенко, State University of Telecommunications

State University of Telecommunications, Post-graduate Student at the Department of Management of Information and Cyber Security

References

Deming, W. E. (1986), Out of the crisis, Cambridge University Press, Cambridge, 507 p.

Lionberger, R. A., Lee, S. L., Lee, L. M., Raw, A., Lawrence, X. Yu. (2008), "Quality by Design: Concepts for ANDAs", American Association of Pharmaceutical Scientists Journal, No. 10 (2), P. 268–276. DOI: https://doi.org/10.1208/s12248-008-9026-7.

ICH Q8, "Pharmaceutical Development", available at : http://www.ich.org/products/guidelines/quality/quality-single/article/pharmaceutical-development.html (Last accessed: 02.08.2018).

Eshan, S. J. (2012), "A Case Study on Quality Function Deployment (QFD)", IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), Vol. 3, Issue 6, P. 27–35. DOI: https://doi.org/10.9790/1684-0362735.

Zhang, L., Mao, S. (2017), "Application of quality by design in the current drug development", Asian journal of pharmaceutical sciences, No. 103, P. 1–8. DOI: https://doi.org/10.1016/j.ajps.2016.07.006.

"Guidance for Industry PAT – A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance", available at : http://www.fda.gov/cder/OPS/PAT.htm (Last accessed: 02.08.2018).

ISO 9001: 2015, "Quality management systems", available at : https://www.iso.org/standard/62085.html (Last accessed: 02.08.2018).

ISO 22000: 2005, "Food safety management systems", available at : https://www.iso.org/standard/35466.html (Last accessed: 02.08.2018).

Legendre, P. (2006), "Short course on Advanced spatial ecology", Research Centre of Plant Ecology and Biodiversity Conservation. Institute of Botany, Chinese Academy of Sciences, P. 575–600, available at : http://biol09.biol.umontreal.ca/PLcourses/Legendre_Canonical.pdf (Last accessed: 12.08.2018).

Rao, S., Samant, P., Kadampatta, A., Shenoy, R. (2013), "An Overview of Taguchi Method: Evolution, Concept and Interdisciplinary Applications", International Journal of Scientific & Engineering Research, Vol. 4, Issue 10, P. 621–626, available at : https://www.ijser.org/onlineResearchPaperViewer.aspx?An-Overview-of-Taguchi-Method-Evolution-Concept-and-Interdisciplinary-Applications.pdf (Last accessed: 12.08.2018).

Coghlan, A. (2017), A Little Book of R for Multivariate Analysis, Release 0.1, available at : http://a-little-book-of-r-for-time-series.readthedocs.org/ (Last accessed: 12.08.2018).

"Food and Drug Administration. Final Report on Pharmaceutical cGMPs for the 21st Century – A Risk Based Approach", available at : https://www.fda.gov/downloads/drugs/developmentapprovalprocess/manufacturing/questionsandanswersoncurrentgoodmanufacturingpracticescgmpfordrugs/ucm176374.pdf (Last accessed: 12.08.2018).

Kannissery, P., Tahir, M. A., Charoo, N. A., Ansari, S. H., Ali, J. (2016), "Pharmaceutical product development: A quality by design approach", International Journal of Pharmaceutical Investigation, No. 6 (3), P. 129–138. DOI: https://doi.org/10.4103/2230-973X.187350.

ISO 31000, "Risk management", available at : https://www.iso.org/iso-31000-risk-management.html (Last accessed: 12.08.2018).

"Guidance for Industry. PAT – A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance", available at : https://www.fda.gov/downloads/drugs/guidances/ucm070305.pdf (Last accessed: 12.08.2018).

Mohamed, I. (2013), "Progressive Modeling: The Process, the Principles, and the Applications", Procedia Computer Science, Vol. 16, P. 39–48. DOI: https://doi.org/10.1016/j.procs.2013.01.005.

Araujo, F. (2018), "Variable selection methods in multivariate statistical process control: A systematic literature review", Department of Industrial Engineering: Federal University of Rio Grande do Sul, 90035-190 Porto Alegre, RS, Brazil, Computers & Industrial Engineering, Vol. 115, P. 603–619. DOI: https://doi.org/10.1016/j.cie.2017.12.006.

Göhler, S. M., Husung, S., Howard, T. J. (2016), "The Translation between Functional Requirements and Design Parameters for Robust Design", Procedia College International pour la Recherche en Productique (CIRP), Vol. 43, P. 106–111. DOI: https://doi.org/10.1016/j.procir.2016.02.028.

Gausemeier, J., Gaukstern, T., Tschirner, C. (2013), "Systems Engineering Management Based on a Discipline-Spanning System Model", Procedia Computer Science, Vol. 16, P. 303–312, DOI: https://doi.org/10.1016/j.procs.2013.01.032.

MacCalman, A., Kwak, H., McDonald, M., Upton, S. (2015), "Capturing Experimental Design Insights in Support of the Model-based System Engineering Approach", Procedia Computer Science, Vol. 44, P. 315–324. DOI: https://doi.org/10.1016/j.procs.2015.03.030.

Kaiser, L., Bremer, C., Dumitrescu, R. (2016), "Exhaustiveness of Systems Structures in Model-Based Systems Engineering for Mechatronic Systems", Procedia Technology, Vol. 26, P. 428–435. DOI: https://doi.org/10.1016/j.protcy.2016.08.055.

MacCalman, A., Lesinski, G., Goerger, S. (2016), "Integrating External Simulations Within the Model-Based Systems Engineering Approach Using Statistical Metamodels", Procedia Computer Science, Vol. 95, P. 436–441. DOI: https://doi.org/10.1016/j.procs.2016.09.309.

Lemazurier, L., Chapurlat, V., Grossetête, A. (2017), "An MBSE Approach to Pass from Requirements to Functional Architecture", International Federation of Accountants (IFAC) : Papers OnLine, Vol. 50, Issue 1, P. 7260–7265. DOI: https://doi.org/10.1016/j.ifacol.2017.08.1376.

Clement, S., Register, A., Wise, R. (2015), "Key Enablers for Leveraging Non-Development Items in a System", Procedia Computer Science, Vol. 44, P. 164–173. DOI: https://doi.org/10.1016/j.procs.2015.03.009.

Cohen, J., Cohen, P., West, S. G., Aiken, L. S. (2003), Applied multiple regression/correlation analysis for the behavioral sciences, Mahwah, New Jersey, US: Lawrence Erlbaum Associates Publishers, Third Edition, 703 p.

Azevedo, A., Santos, M. F. (2008), "KDD, SEMMA and CRISP-DM: a parallel overview", IADIS European Conference on Data Mining : Amsterdam, the Netherlands, 22-27 July 2008, Proceedings of the IADIS European Conference on Data Mining, P. 182–185, available at : https://pdfs.semanticscholar.org/7dfe/3bc6035da527deaa72007a27cef94047a7f9.pdf (Last accessed: 12.08.2018).

Downloads

Published

2018-09-26

How to Cite

Гаврилко, Є. В., Курченко, О. А., Терещенко, І. В., & Терещенко, А. І. (2018). THE METHOD OF MULTIVARIATE STATISTICAL ANALYSIS OF TIME MULTIVARIATE CRITICAL ATTRIBUTES OF MANUFACTURING QUALITY WITH DATA FACTORIZATION. INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, (3 (5), 5–16. https://doi.org/10.30837/2522-9818.2018.5.005

Issue

No. 3 (5) (2018)

Section

Technical Sciences

License

Copyright (c) 2018 Євген Володимирович Гаврилко, Олег Анастасійович Курченко, Ігор Володимирович Терещенко, Антон Ігорович Терещенко

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Our journal abides by the Creative Commons copyright rights and permissions for open access journals.

Authors who publish with this journal agree to the following terms:

  • Authors hold the copyright without restrictions and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.

  • Authors are able to enter into separate, additional contractual arrangements for the non-commercial and non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.

  • Authors are permitted and encouraged to post their published work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.