CONTROL RESEARCH AND AUTOMATION OF STOCHASTIC DEVIATIONS IN ORGANISATIONAL MANAGEMENT PRODUCTION PROCESS SYSTEMS

Objectives. The identification of deviations and incidents in the activities of enterprises is an integral part of the entire process of compliance with regulatory industry standards. In particular, these standards include good manufacturing practices (GMP) and the system of corrective and preventive actions (CAPA). The aim of the research is to automate the processes of identifying deviations and determining the probable causes of their occurrence in complex stochastic systems.

Methods. Questions concerning the modeling of compliance processes with industry standards are studied on the example of the organisational management of pharmaceutical production. A method is proposed for the automated realisation of deviation from identification processes based on the processing of unstructured messages, the automatic generation of characteristic control parameters and a comparison of registered values with normative ones. The problem of detecting the primary cause of deviations is considered on the basis of the algorithm for processing the cause-effect relationships and probabilistic values of the relationship between the different defined groups of deviations.

Results. Based on the proposed method of machine detection and identification of deviations, the information management system of CAPA procedures has been developed and successfully implemented at several enterprises within the pharmaceutical industry. The feature of the proposed method is the principle of system self-development based on the processing of “historical” data, which allows the probabilistic values of the relationship between the different groups of deviations to be dynamically calculated. This in turn allowed decisions on the implementation of changes according to the expert reports generated by the information system to be quickly and accurately taken by quality specialists. The generation of the electronic production dossier has significantly reduced the time of production protocol preparation and eliminated human factor errors.

Conclusion. The full-cycle automation of CAPA procedure management allowed the enterprises to solve the primary task of continuous compliance with industry standards due to timely detection of deviations or deviation trends and promptly carrying out corrective and preventive actions to eliminate inconsistencies.

1. Raj A. A review on corrective action and preventive action (CAPA). African Journal of Pharmacy and Pharmacology. 2016;10(1):1-6.

2. Van Trieste M. CAPA within the Pharmaceutical Quality System. ICH Q10 Conference. P9: Pharmaceutical Quality System Elements: Continual Improvement of the Process (CAPA). Brussels, Belgium. 2011.

3. Rodriguez J. CAPA in the Pharmaceutical and Biotech Industries. 1st Edition. Woodhead Publishing; 2015. 248 p.

4. Pravila nadlezhashchei proizvodstvennoi praktiki (GMP) evraziiskogo ekonomicheskogo soyuza. Versiya 4.0 ot 20.02.2015. [Rules of Good Manufacturing Practice (GMP) of the Eurasian Economic Union. Version 4.0 of 02/20/2015. (in Russ.)]

5. Grazal J.G., Earl D.S. EU and FDA GMP Regulations: Overview and Comparison. Quality Assurance Journal. 1997;2:55-60.

6. Hiob M., Peither T., Reuter U. GMP Focus. Principles of Equipment Qualification. A Guide for Drug and Device Manufacturers. First edition. Maas&Peither AG - GMP Publishing; 2017.

7. Abou-El-Enein M., Römhild A., Kaiser D. and others. Good Manufacturing Practices (GMP) manufacturing of advanced therapy medicinal products: a novel tailored model for optimizing performance and estimating costs. Cytotheraphy. 2013;15(3):362–383.

8. Kolesov Yu.B. Ob"ektno-orientirovannoe modelirovanie slozhnykh sistem. SPb.: Izd-vo SPbGPU; 2004. [Kolesov Yu.B. Object-oriented modeling of complex systems. SPb.: Izd-vo SPbGPU; 2004. (In Russ.)]

9. Vlasov M.P., Shimko P.D. Modelirovanie ekonomicheskikh sistem i protsessov. M.: NITs INFRA-M; 2013. 336 s. [Vlasov M.P., Shimko P.D. Modeling of economic systems and processes. M.: NITs INFRA-M; 2013. 336 p. (In Russ.)]

10. Emel'yanov S.V. Informatsionnye tekhnologii i vychislitel'nye sistemy: Matematicheskoe modelirovanie. Prikladnye aspekty informatiki. M.: Lenand; 2015. 96 s. [Emel'yanov S.V. Information technologies and computer systems: Mathematical modeling. Applied aspects of computer science. M.: Lenand; 2015. 96 p. (In Russ.)]

11. Shapovalov V.I. Modelirovanie sinergeticheskikh sistem. Metod proportsii i drugie matematicheskie metody. M.: Prospekt; 2016. [Shapovalov V.I. Modeling of synergetic systems. Method of proportions and other mathematical methods. M.: Prospekt; 2016. (In Russ.)]

12. Sirota A.A. Analiz i komp'yuternoe modelirovanie informatsionnykh protsessov i sistem. M.: DIALOG-MIFI; 2009. 416 s. [Sirota A.A. Analysis and computer modeling of information processes and systems. M.: DIALOG-MIFI; 2009. 416 p. (In Russ.)]

13. Ermakov S.M. Stokhasticheskie i kvazistokhasticheskie vychisleniya. Vestnik SpbGU. Ser. 1. Vyp. 3. 2011. [Ermakov S.M. Stochastic and quasi-stochastic calculations. Vestnik SpbGU. Ser. 1. Vyp. 3. 2011. (In Russ.)]

14. Ermakov S. M. Sovremennoe razvitie stokhasticheskikh vychislitel'nykh metodov. Tezisy dokladov Mezhdunarodnogo kongressa ―Nelineinyi dinamicheskii analiz – 2007‖. SanktPeterburg. 2007. 274 s. [Ermakov S. M. Contemporary development of stochastic computational methods. Abstracts of the International Congress ―Nonlinear Dynamic Analysis - 2007‖. Sankt-Peterburg. 2007. 274 p. (In Russ.)]

15. Maksimova O.V., Shper V.L., Adler Yu.P. Kontrol'nye karty Shukharta v Rossii i za rubezhom. Chast' 1. Standarty i kachestvo. 2011;7:82-87. [Maksimova O.V., Shper V.L., Adler Yu.P. Shewhart's control cards in Russia and abroad. Part 1. Standards and quality. 2011;7:82-87. (In Russ.)]

16. Fadeev A.N., Zhuravlev A.I. Lepestkovaya diagramma kak sredstvo otobrazheniya rezul'tatov matematicheskogo modelirovaniya. Obrazovanie i nauka v sovremennykh usloviyakh. Cheboksary. Tsentr nauchnogo sotrudnichestva. 2016;2:72 – 75. [Fadeev A.N., Zhuravlev A.I. The petal diagram as a means of displaying the results of mathematical modeling. Education and science in modern conditions. Cheboksary. Center for Scientific Cooperation. 2016;2:72 – 75. (In Russ.)]

17. Makhonchenko Yu. Postroenie diagrammy Pareto. Sistemy menedzhmenta – konsul'tatsii i obuchenie onlain. 2015. [Makhonchenko Yu. Construction of the Pareto diagram. Management systems - consulting and training online. 2015. (In Russ.)]

18. Agarwal R., Dhar V. Editorial – Big data, data science, and analytics: The opportunity and challenge for IS research. Inform. Systems Res. 2014;25(3):443–448.

19. Chen H., Chiang R.H., Storey V.C. Business intelligence and analytics: From big data to big impact. MIS Quart. 2012;36(4):1165–1188.

20. Mochen Y., Gediminas A., Gordon B., Yuqing R. Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining. Information Systems Research. January 2018.

21. Oganesyan A. Nestrukturirovannye dannye 2.0. Otkrytye sistemy. 2012;04 https://www.osp.ru/os/2012/04/13015772/ [Oganesyan A. Unstructured data 2.0. Open Systems Journal. 2012;04 https://www.osp.ru/os/2012/04/13015772/ (In Russ.)]

22. Ivanov A. Kompleksnyi analiz nestrukturirovannykh dannykh. Otkrytye sistemy. 2013;04. https://www.osp.ru/os/2013/06/13036848/ [Ivanov A. Complex analysis of unstructured data. Open Systems Journal. 2013;04. https://www.osp.ru/os/2013/06/13036848/ (In Russ.)]