Identification of technological risks in the production of dry building mixtures
https://doi.org/10.31660/2782-232X-2026-1-92-103
EDN: jeuzjv
Abstract
Technological risks during the production of dry building mixtures can lead to product defects. The purpose of this article is to assess and manage these risks to reduce the defect rate of finished products. We examine the influence of raw material quality and the state of the technological production process on the probability of defects. Dry mixtures CemPLAST, Bundes Koroed B2, and Bundes Koroed C3 from the New MIX company (Penza) were analyzed, using various raw materials: Sengileyevskiy CEM I 42.5B and Haldenberg CEM I 42.5N cement. It was established, that using cement with lower variability in its activity index in the formulation contributes to reducing the risk of defective dry building mixtures. The quality assurance of dry mixtures with varying ratios of the mean square deviation within the tolerance field was demonstrated. The values for producer and consumer risks were calculated based on the technological process state during the production of dry building mixtures. For unstable and non-reproducible processes, these risks accounted for 0.195847 and 0.139404, respectively. A risk assessment map for the production of dry building mixtures was developed. Each stage of the production process was analyzed in terms of risk probability and severity of consequences (assessed on a five-point scale). Risk levels were calculated, and risk management measures were proposed. It was determined that the highest severity of consequences is associated with raw material acceptance (high – 4 points), dosing (critical – 5 points), and component mixing (high – 4 points). The quantitative assessment of risk levels for these stages ranged from 8 to 10, indicating an average level.
About the Authors
V. I. LoganinaRussian Federation
Valentina I. Loganina - Dr. Sci. (Engineering), Professor, Head of the Department of Quality Management, Penza State University of Architecture and Construction.
Penza, 28 German Titov St., 440028
T. V. Uchaeva
Russian Federation
Tatiana V. Uchaeva - Cand. Sci. (Economics), Associate Professor in the Department of Economics, Organization and Management of Enterprises, Penza State University of Architecture and Construction.
Penza, 28 German Titov St., 440028
M. V. Zaytseva
Russian Federation
Maria V. Zaytseva - Cand. Sci. (Engineering), Associate Professor in the Department of International Business, Plekhanov Russian University of Economics.
Moscow, 36 Stremyanny lane, 115054
References
1. Fadeeva E. A., Rodina Ch. A. The classification and problems of risk assessment of industrial enterprises on the example of the production companies. Business. Education. Law. 2018;(1):136–140. (In Russ.) URL: https://vestnik.volbi.ru/webarchive/142/yekonomicheskie-nauki/klassifikacija-i-problemy-ocenki-riskov-.html
2. Ponyatova N. V., Kabanenko M. N. Formation of system of a risk management at the enterprise. Economy and Society. 2017;(1–2):374–379. (In Russ.) URL: https://cyberleninka.ru/article/n/formirovanie-sistemy-risk-menedzhmenta-na-predpriyatii-1/viewer
3. Tanaka K., Akimoto H., Inoue M. Production risk management system with demand probability distribution. Advanced Engineering Informatics. 2012;26(1):46–54. https://doi.org/10.1016/j.aei.2011.07.002
4. Zaytseva M. V. Quality management of the processes of cement concrete finishing coatings production. Regional Architecture and Engineering. 2021;(3):78–81. (In Russ.) URL: https://elibrary.ru/bizdtq
5. Loganina V. I., Uchaeva T. V. To the problem of quality control system on the industrial enterprises. Regional Architecture and Engineering. 2010;(1):31–33. (In Russ.) URL: https://elibrary.ru/mqpjjx
6. Mukhtarova K. S., Kozhakhmetova A. K. Statistical methods as a tool of high-tech products quality management. Bulletin of the National Academy of Sciences of the Republic of Kazakhstan. 2017;(3):243–250.
7. Cowden D. J. Statistical methods in quality control. Prentice-Hall; 1957.
8. Adler Y., Shper V., Maksimova O. Assignable causes of variation and statistical models: another approach to an old topic. Quality and Reliability Engineering International. 2011;27(5):623–628. https://doi.org/10.1002/qre.1207
9. Loganina V. I., Uchaeva T. V. Technological risks of building materials production, products and structures. PGUAS Bulletin: construction, science and education. 2021;(1):68–72. (In Russ.) URL: https://elibrary.ru/shbxse
10. Shper V. L., Sheremetyeva S. A., Smelov V. Yu., Khunuzidi E. I. Shewhart control charts – A simple but not easy tool for data analysis. Izvestiya. Ferrous Metallurgy. 2024;67(1):121–131. https://doi.org/10.17073/0368-0797-2024-1-121-131
11. Shper V., Adler Y. The importance of time order with Shewhart control charts. Quality and Reliability Engineering International. 2017;33(6):1169–1177. https://doi.org/10.1002/qre.2185
12. Shper V., Gracheva A. Simple Shewhart control charts: Are they really so simple? International Journal of Industrial and Operations Research. 2021;4(1):010. http://doi.org/10.35840/2633-8947/6510
13. Schindowski E., Schürz O. Statistische Qualitätskontrolle. Berlin: Veb Verlag Technik; 1974. 636 p. (In Germ.).
14. Loganina V. I. To the question on regulation of technological. Processes of manufacture of concrete. News of higher educational institutions. Construction. 2009;(3–4):42–46. (In Russ.) URL: https://www.izvuzstr.sibstrin.ru/fulltext/
15. Ilei L. Taguchi methods are a thought put in a system. Automotive Industry in the United States. 1988;(2):20–22.
16. Uchaeva T. V. Economic evaluation the quality of dyeing building products and designs. Regional Architecture and Engineering. 2015;4:132–136. (In Russ.) URL: https://elibrary.ru/vhuisp
17. Boldyrev I. V., Selivanova T. Ya., Sheveleva V. I. Risk and opportunity management in the testing laboratory. Production Quality Control. 2018;(12):4–12. (In Russ.) URL: https://elibrary.ru/vnjxhq
18. Trofimov D. P.The use of Shewhart control charts for quality control of pile work. Molodoyuchenyy. 2022;(31):41–44. (In Russ.) URL: https://moluch.ru/archive/426/94340
19. Asbjørnslett B. E. Assess the vulnerability of your production system. Production Planning & Control. 1999;10(3):219–229. URL: https://www.researchgate.net/publication/245310099_Assess_the_vulnerability_of_your_production_system
20. Tanaka K., Akimoto H., Inoue M. Production risk management system with demand probability distribution. Advanced Engineering Informatics. 2012;26(1):46–54. https://doi.org/10.1016/j.aei.2011.07.002
21. Bakashin P. E. Comparison of formal and rational approaches in risk management at industrial enterprises. Eurasian Union of Scientists. 2015;(5–1):23–25. (In Russ.) URL: https://www.elibrary.ru/wzuxhh
22. Timofeeva E. M., Timofeeva A. S. Improvement of the management system of productive costs at an iron and steel plant. International Journal of Applied and Fundamental Research. 2015;(3–2):250–252. (In Russ.) URL: https://applied-research.ru/ru/article/view?id=6524
23. Prikhodko R. V., Kochegarova T. S. Methods of risk management in metallurgical industry. Scientific Journal NRU ITMO. Series "Economics and Environmental Management". 2014;(3):463–475. URL: https://economics.ihbt.ifmo.ru/ru/article/10549/metody_upravleniya_riskami_v_metallurgicheskoy_promyshlennosti.htm
24. Henschel Th., Durst S. Risk management in Scottish, Chinese and German small and medium-sized enterprises: A country comparison. International Journal of Entrepreneurship and Small Business. 2016;29(1):112–132. https://doi.org/10.1504/IJESB.2016.078048
Review
For citations:
Loganina V.I., Uchaeva T.V., Zaytseva M.V. Identification of technological risks in the production of dry building mixtures. Architecture, Construction, Transport. 2026;6(1):92-103. (In Russ.) https://doi.org/10.31660/2782-232X-2026-1-92-103. EDN: jeuzjv
JATS XML








