Object Relation Technique for Modelling of Digital Production Solutions



A field of Industry 4.0 is one of the most perspective with respect to research and optimization of production and industrial problems. The main problems in this field are connected with necessary analysis and processing of a huge data, which have a different native and sources. Particularly, the data from various automatic sources, as well as getting due to human management, become far away from each other both from the structure point of view and the native meaning. And there often occur, that such a different data in integrated form could contain a significant information for analysis, and moreover for a transformation of the technological process. At the same time, it is rather difficult to provide an analysis in frame of a significant asynchrony, and sometimes it becomes to a practical unsolvable problem. In this regard, the problem on developing of specialized methods for collecting, storing and processing big data of various structures are becoming fundamentally relevant. This is the purpose of the present work, which proposes a new method for organizing data based on the concept of object relations as a universal structure for production modeling. The proposed method is based on the concept of an "object", which is any entity of the production chain (plant, workshop, machine, production operation, unit of production, etc.) The key difference between the method of object relations and agent modeling is the fact that no behavioral scenarios are imposed on objects. Thus, the "object" structure turns out to be authorized to enter into any relationship with other "objects" within the framework of the model under consideration, which in turn allows to accumulate various kinds of data into a single hierarchy and guarantees the reliability of analysis at any level. The proposed method of object relations has been applied at several full-scale production sites, thereby confirming its stability and operability. The paper provides an analysis of key performance indicators of the application of the method of object relations to solve the problem of forming a technological passport of a product at the steel production, and also discusses ways of further development.

General Information

Keywords: industry 4.0, Big Data, object relation technique (ORT), digital transformation, production

Journal rubric: Data Analysis

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2023130101

Acknowledgements. The authors are grateful for assistance in development and formalization of the ORT an administration of the LLC "Data-Center Automation" in faces of Uskov R.Yu. and Volkov A.V.

Received: 17.02.2023


For citation: Berenov D.A., Rasskazova V.A. Object Relation Technique for Modelling of Digital Production Solutions. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2023. Vol. 13, no. 1, pp. 5–18. DOI: 10.17759/mda.2023130101. (In Russ., аbstr. in Engl.)


  1. Ford H., Crowther S. (1922). My Life and Work. Garden City, New York: Garden City Publishing Company, Inc. 231 p. 
  2. Deming W. (1943). Edwards. Statistical Adjustment of Data. Dover. 261 p. 
  3. Taylor F. W. (1911) The Principles of Scientific Management. New York; London: Harper & brothers. 144 p. 
  4. Gantt H. L. (1903). A graphical daily balance in manufacture // Transactions of the American Society of Mechanical Engineers. Vol. 24. P. 1322-1336. 
  5. Lisovskii A. L. (2018). Optimizatsiya biznes-protsessov dlya perekhoda k ustoichivomu razvitiyu v usloviyakh chetvertoi promyshlennoi revolyutsii // Strategicheskie resheniya i risk-menedzhment. № S. 10-19. https://doi.org/https://doi.org10.17747/20788886201841019. (In Russ.)
  6. Tarasov I. V., Popov N. A. (2018). Industriya 4.0: Transformatsiya proizvodstvennykh fabrik // Strategicheskie resheniya i risk-menedzhment. № 3. S. 38-53. doi: 10.17747/ 2078.8886.2018.3.38.53. (In Russ.)
  7. Roblek V., Mesko M., Krapez A. (2016). A Complex View of Industry 4.0 // SAGE Open. doi: 10.1177/2158244016653987. 
  8. Pukha Yu. (2017). Industrial'naya revolyutsiya 4.0 // PricewaterhouseCoopers. URL: https://www.pwc.ru/ru/assets/pdf/industry-4-0/pwc.pdf. (In Russ.)
  9. Rojko A. (2017). Industry 4.0 concept: background and overview // International Journal of Interactive Mobile Technologies (iJIM). Vol. 11, № 5. P. 77-90. https://doi.org/10.3991/ ijim.v11i5.7072. 
  10. Dean P. R., Xue D., Tu Y. L. (2009). Prediction of manufacturing resource requirements from customer demands in mass-customisation production // International Journal of Production Research. Vol. 47. № 5. P. 1245-1268. doi: 10.1080/00207540701557197
  11. Serbul A. (2018). Neironki: kakomu biznesu nuzhen iskusstvennyi intellekt (i laifkhaki, kak ego vnedrit') // Delovoi zhurnal «Inc.». URL: https://incrussia.ru/ understand / nejronki-kakomu-biznesu-nuzhen-iskusstvennyj-intellekt-i-lajfhaki-kak-ego-vnedrit /. (In Russ.)
  12. Ding S. H., Kamaruddin S. (2015). Maintenance policy optimization - literature review and directions // The International Journal of Advanced Manufacturing Technology. Vol. 76, № 5-8. P. 1263-1283. https://doi.org/10.1007/s00170.014.6341.2
  13. Tinga T. (2013) Maintenance concepts // Principles of loads and failure mechanisms. Ed. H. Pham. London: Springer. P. 161-186. 
  14. Vishnu C. R., Regikumar V. (2016) Reliability based maintenance strategy selection in process plants: a case study // Procedia Technology. Vol. 25. P. 1080-1087. doi: 10.1016/j.protcy.2016.08.211
  15. Arshavskii A. (2018). Iskusstvennyi intellekt v metallurgii // NLMK. URL: http://www. cloudmobility.ru/sites/default/files/13.25-13.45_arhavsky_nlmk_new.pdf. (In Russ.)
  16. Harding J. A., Shahbaz M., Srinivas et al. (2006). Data mining in manufacturing: a review // Journal of Manufacturing Science and Engineering. Vol. 128, № 4. P. 969-976. https://doi.org/ 10.1115/1.2194554. 
  17. Chongwatpol J. (2015). Prognostic analysis of defects in manufacturing // Industrial Management & Data Systems. Vol. 115, № 1. P. 64-87. doi: 10.1108/IMDS-05.2014 0158. 
  18. O’Regan P., Prickett P., Setchi R.et al. (2017). Engineering a More Sustainable Manufacturing Process for Metal Additive Layer Manufacturing Using a Productive Process Pyramid // International Conference on Sustainable Design and Manufacturing. Cham: Springer. P. 736-745. https://doi.org/10.1007/978.3.319.57078.5_69
  19. Leachman C., Pegels C., Kyoon Shin S. (2005). Manufacturing performance: evaluation and determinants // International Journal of Operations & Production Management. Vol. 25, № 9. P. 851-874. doi: 10.1108/01443570510613938
  20. Hazen B. T., Boone C. A., Ezell J. D. et al. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications // International Journal of Production Economics. Vol. 154. P. 72-80. https://doi.org/10.1016/j.ijpe.2014.04.018
  21. Gitman, M. B. and Trusov, P. V. and Fedoseev, S. A. On optimization of metal forming with adaptable characteristics. Journal of Applied Mathematics and Computing (2020) vol. 7, no. 2, 387–396.
  22. Gainanov, D. N. and Berenov, D. A. Algorithm for predicting the quality of the product of metallurgical production. In: CEUR Workshop Proceedings (2017) vol. 1987, 194–200.
  23. Gainanov, D.N., Berenov, D.A., Rasskazova, V.A. Algorithm for Predicting the Quality of the Product Based on Technological Pyramids in Graphs. LNCS (2021) 12931, 128--141.
  24. Kampf R., Lorincova S., Hitka M. et al. (2016). The application of ABC analysis to inventories in the automatic industry utilizing the cost saving effect // NASE MORE: znanstveno-strucnicasopisza more ipomorstvo. Vol. 63, № 3. Spec. Issue. P. 120-125. https://doi.org/10.17818/NM/2016/SI8
  25. Kim A., Oh K., Jung J. Y.et al. (2018). Imbalanced classification of manufacturing quality conditions using cost-sensitive decision tree ensembles // International Journal of Computer Integrated Manufacturing. Vol. 31, № 8. P. 701-717. doi: 10.1080/0951192X.2017.1407447
  26. Kvasova N. A., Tselykh V. N. (2012). Metodika otsenki ekonomicheskikh poter' po vidam defektov na osnove sistemy kriteriev KR-benchmarkinga // Sovremennye problem transportnogo kompleksa Rossii. № 2. S. 295-298. (In Russ.)
  27. Rayna T., Striukova L. (2016). From rapid prototyping to home fabrication: How 3D printing is changing business model innovation // Technological Forecasting and Social Change. Vol. 102. Pp. 214-224.

Information About the Authors

Dmitriy A. Berenov, Director on Innovations, Data-Center Automation, Ekaterinburg, Russia, ORCID: https://orcid.org/0009-0002-3732-5899, e-mail: berenov@dc.ru

Varvara A. Rasskazova, PhD in Physics and Matematics, Associate Professor of Department 804 "Probability Theory and Computer Modeling", Moscow Aviation Institute, (NRU MAI), Moscow, Russia, ORCID: https://orcid.org/0000-0003-4943-3133, e-mail: varvara.rasskazova@mail.ru



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