Data clustering of manufacture process multidimensional critical parameters for evaluating the factor's influence on product critical quality attributes

DOI №______

Abstract

Nowadays, competitiveness and efficiency of companies must be continuously improved to face worldwide competitors therefore the product design process require new methodologies of computer-aided development. This article presents an approach for a model-based planning process for the early phases of manufacturing system planning (MSP). The main goals are a better integration of MSP with product development in the early design phases. The presented approach is based on object-oriented modeling and is supported by a modeling scheme which uses the object-oriented modeling language (R). The article suggests a method of multivariate statistical analysis of the affect of critical process parameters (CPPs) and its factors, on product critical quality attributes (CQAs) with data clustering. It is proposed to transform clusters of critical process parameters into influence factors, which increases the number of degrees of freedom for multivariate statistical research. Method propose the clustering of contingency data array based on multivariate statistical analysis (MSA) for evaluating the influence of critical process parameters factors on the time multivariate product critical quality attributes. Factorized multivariate CPPs increase the possibility to use methods of multivariate statistical analysis for evaluating the influence of CPPs on the multivariate CQAs. The proposed method presents an actual information technology for assessing the affect of time multidimensional data objects and separate components of critical process parameters on product critical quality attributes.

Keywords: quality function, quality management system, data clustering, multidimensional data, QbD, Quality-by-Design strategy, process approach, quality function deployment (QFD), statistical analysis.

References
1. Introduction and support package: Guidance on the concept and use of the process approach for management systems // ISO 9000.
2. Yukish M. A. A preliminary model of design as a sequential decision process / M. A. Yukish, S. W Millerb, T. W. Simpson // Procedia computer science. – 2015. – No.44 – P. 174-183.
3. Marijn P. Z. Physics in Design: Real-time numerical simulation integrated into the CAD environment / P. Z. Marijn, W. W. Wessel // Procedia CIRP 60. –2017. – P. 98-103.
4. Lionberger R. A. Quality by design: concepts for ANDAs / R. A. Lionberger, S. L. Lee, L. M. Lee, A. Raw, X. Yu. Lawrence // The AAPS Journal. – 2008. – Jun, 10(2). – P. 268-276.
5. Zhang L. Application of quality by design in the current drug development / L. Zhang, S. Mao // Shenyang Pharmaceutical University, Wenhua Road, Shenyang, China: Asian journal of pharmaceutical sciences. – 2017. – No.103. – P. 1-8.
6. Rao S. An Overview of Taguchi method: evolution, concept and interdisciplinary applications / S. Rao, P. Samant, A. Kadampatta, R. Shenoy // International journal of scientific & engineering research. – October 2013. – Vol. 4, Issue 10. – P. 621-626.
7. Guidance for industry. Quality systems approach to pharmaceutical CGMP regulations. – https://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm070337.pdf (2018.06.20).
8. Jaiswal E. S. A Case study on quality function deployment (QFD) / E. S. Jaiswal // IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) – Nov-Dec. – 2012. – Volume 3, Issue 6 – P. 27-35.
9. Guidance for industry PAT. A framework for innovative pharmaceutical development, manufacturing, and quality assurance. – http://www.fda.gov/cder/OPS/PAT.htm (2018.06.20).
10. Pramod K. Pharmaceutical product development: A quality by design approach / K. Pramod, M. A. Tahir, N. A. Charoo, S. H. Ansari, J. Ali // Int J Pharm Investig. – Jul-Sep 2016. – 6(3). – P. 129-138.
11. Steimera C. Model-based design process for the early phases of manufacturing system planning using SysML / C. Steimera, J. Fischerb, J. C. Auricha // 27th CIRP Design 2017. Procedia CIRP. – 2017. – P. 163-168.
12. Mohamed I. Progressive modeling: The process, the principles, and the applications / I. Mohamed // Procedia Computer Science. – 2013. – Volume 16. – P. 39-48.
13. Blondet G. Simulation data management for adaptive design of experiment. A literature review / G. Blondet, N. Boudaoud, J. Duigou // QUALITA’ 2015. – Mar 2015, Nancy, France.
14. Araujo F. Variable selection methods in multivariate statistical process control: A systematic literature review / F. Araujo, P. Pimentel, F. S. Fogliatto // Department of industrial engineering, Federal University of Rio Grande do Sul, 90035-190 Porto Alegre, RS, Brazil, Computers & Industrial Engineering. – January 2018. – Volume 115. – P. 603-619.
15. Food safety management systems // ISO 22000: 2005.
16. Skold M. Computer intensive statistical methods / M. Skold // Mathematical statistics centre for mathematical sciences Lund University. – October 2005, 2nd printing August 2006. – 133 p.
17. Brereton R. G. Applied chemometrics for scientists / R. G. Brereton. – University of Bristol. ‒ UK, John Wiley & Sons Ltd, 2007. – 379 p.
18. Göhler S. M. The Translation between Functional Requirements and Design Parameters for Robust Design / S. M. Göhler, S. Husung, T. J. Howard // 14th CIRP conference on computer aided tolerancing (CAT). Procedia College International pour la Recherche en Productique (CIRP) 43. – 2016. – P. 106-111.
19. Food and drug administration. Final report on pharmaceutical cGMPs for the 21st century – A Risk based approach. – http://www.fda.gov/cder/gmp/gmp2004/GMP_-finalreport2004.htm (2018.06.20).
20. Kogan J. Grouping multidimensional data / J. Kogan, C. Nicholas, M. Teboulle. – Springer-Verlag Berlin Heidelberg 2006. – 268 p.