Improving business process management in a sugar factory workshop based on digital twin technology

Digital twin technology has been increasingly applied to improve business process management in manufacturing. A first-level digital twin methodology (process simulation model) was tested using the example of a sugar factory acceptance workshop, where the initial processing of sugar beets and their preparation for further processing occur. Digital twins enable enterprises to conduct virtual modeling and testing of new production concepts, optimize processes, predict equipment failures, diagnose production processes using virtual replicas, and improve management methods. The creation of a digital twin for the sugar beet acceptance process allowed for the determination of the process qualitative characteristics. As the process is linear, the throughput of the workshop is determined by the uniform distribution of workload across the entire circuit. A “bottleneck” in the system is defined by an operation that takes more time relative to the preceding and subsequent operations, which creates a queue of transactions. Analysis of the model run revealed “bottlenecks” in the network, specifically, increased queues of trucks at the sugar beet acceptance operation, at the gross weight weighing station, and at the factory entry checkpoint. To address this problem, the system parameters were optimized, with the aim of balancing the load on the nodes of the sugar beet acceptance workshop and reducing queues at the servicing devices (system nodes). The optimization experiment indicated that it is necessary to increase the number of control checks of the beet mass to three and that the loading speed of a truck should be six minutes. As a result, the generalized (averaged) load function on the workshop was reduced by almost a factor of two.

1. Prokhorov A., Lysachev M. Digital twin. Analysis, trends, world experience: a monograph. Moscow, Russia: All’ansPrint, 2020:401. (In Russ.)

2. Petrov S.M., Filatov S.L., Podgornova N.M. Sustainable development of sugar production based on digital twins. Sakhar. 2023;(11):16-21. (In Russ.) https://doi.org/10.24412/2413-5518-2023-11-16-21

3. Bronskaya Yu.K., Vasil’eva A.S., Gusmanov I.U., Gusmanov R.U. et al. The conceptual foundations of the development of the national innovation system of Russia: structural and technological modernization of the domestic economy, socio-economic and technological factors of development: a monograph. Samara, Russia: NITs PNK, 2025:268. (In Russ.)

4. Khafizov A.M., Borsch I.D. General data on digital twins and software platforms for managing digital twins. Innovative Science. 2025;(5-2):78-79. (In Russ.)

5. Sultan N., Petrov V.E. Literature review: application of automated digital production systems based on digital twins. Sovremennaya nauka: aktualnye problemy teorii i praktiki. Seriya: Estestvennye i tekhnicheskie nauki. 2024;(7-2):168-172. (In Russ.) https://doi.org/10.37882/2223-2966.2024.7-2.32

6. Denisov S.G. Analysis of requirements for a digital platform for the development and application of digital twins. Bulletin of innovative technologies. 2025;9(1(33)):30-33. (In Russ.)

7. Both S., Eggersglüß J., Lehnberger A., Schulz T. et al. Optimizing Established Processes like Sugar Extraction from Sugar Beets – Design of Experiments versus Physicochemical Modeling. Chem. Eng. Technol. 2013;36(12):2125-2136. http://doi.org/10.1002/ceat.201300484

8. Sapronov A.R., Sapronova L.A., Ermolaev S.V. Technology of sugar: a textbook. Saint Petersburg, Russia: Professiya, 2013:294. (In Russ.)

9. Leventsov V.A., Radaev A.E., Nikolaevskiy N.N. The aspects of the “Industry 4.0” concept within production process design. St. Petersburg State Polytechnical University Journal. Economics. 2017;10(1):19-31. (In Russ.)

10. Trukhachev V., Bobrishev A., Khokhlova E., Fedisko O. et al. Personnel Training for the Agricultural Sector in Terms of Digital Transformation of the Economy: Trends, Prospects and Limitations. International Journal of Civil Engineering and Technology. 2019;10(1):2145-2155.

11. Shananin V.A., Losev K.Yu. Creating digital doubles in construction using artificial intelligence. Innovation and Investment. 2023;(6):537-360. (In Russ.)

12. Xie J., Jiang H., Qin S., Zhang J. et al. A new description model to provide a more general representation of production systems in a digital double. Journal of Manufacturing Systems. 2024;72:475-491. https://doi.org/10.1016/j.jmsy.2023.12.009

13. Zhao J., Aghezzaf E.-H., Cottyn J. An Extension of the Core Manufacturing Simulation Data Standard to Enhance the Interoperability for Discrete Event Simulation. Procedia CIRP. 2024;130:1632-1637. https://doi.org/10.1016/j.procir.2024.10.293

14. Thürer M., Li S.S., Qu T. Digital Twin Architecture for Production Logistics: The Critical Role of Programmable Logic Controllers (PLCs). Procedia Computer Science. 2022;200:710-717. https://doi.org/10.1016/j.procs.2022.01.269

15. Lehnberger A. Industrial Internet of Things for the Sugar Industry-initial Results. Chemie Ingenieur Technik. 2020;92(7):978-982. https://doi.org/10.1002/cite.202000005

16. Parshina I.S., Frolov E.B. Development of a digital twin of the production system on the basis of modern digital technologies. Russian Journal of Industrial Economics. 2020; 13(1):29-34. (In Russ.) https://doi.org/10.17073/2072-1633-2020-1-29-34