The Role of Operator Training Simulator to Sustain XYZ Plant Performance in Digital Era
Main Article Content
Abstract
Background: In digital and highly dynamic business contexts, sustainable performances are essential to meet customer’s expectation and to keep plant running smoothly and safely. However, many companies still adopted traditional training methods which are not sufficient enough to develop talent to reach expected competency level. XYZ plant faces multiple challenges to maintain its performance, yet continues to strain to speed-up talent development by adopting Operator Training Simulator (OTS) – high-fidelity digital imitation of real process plant.
Aims: This study aims to evaluate the role of OTS in sustaining XYZ plant performance and how the learnings can be documented and accessed readily by employees.
Methods: A case study uses a quantitative approach with a descriptive method was adopted in this research. Data were collected through quantitative direct observation to gain data on actual training sessions and document analysis related to OTS and plant performance.
Result: The findings show that despite from high dynamic business environments particularly manpower disturbances, the OTS support the company to develop its talent by enabling experienced-based learning to operate the plant in several scenarios and handling emergency situation in a free risk context, test and certify operator, and support the plant knowledge management system improvement. OTS application enhanced the employee knowledge and skills including problem-solving skills, emergency handling skills and communication skills.
Conclusion: The study concludes that OTS plays a critical role in sustaining plant performance by developing talent to reach its designed competency, improving knowledge preservation and information management, contributing to digital knowledge repositories, and learning archives.
Downloads
Article Details
Copyright (c) 2025 Made Mariana

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
- Authors retain copyright and acknowledge that the Journal of Multidisciplinary Applied Natural Science is the first publisher, licensed under a Creative Commons Attribution License.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges and earlier and greater citation of published work.
References
Abbas, A. N., Amazu, C. W., Mietkiewicz, J., Briwa, H., Perez, A. A., Baldissone, G., Demichela, M., Chasparis, G. G., Kelleher, J. D., & Leva, M. C. (2024). Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2402.13219
Abdullaeva, K. R. (2025). PERFORMANCE ASSESSMENT OF OPERATORS UNDER DIGITAL SIMULATOR-BASED TRAINING IN AUTOMATED INDUSTRIAL SYSTEMS. Himičeskaâ Tehnologiâ. Kontrolʹ i Upravlenie/Chemical Technology. Control and Management, 2025(6), 117–123. https://doi.org/10.59048/2181-1105.1742
Alaa, A. A. A. (2024). Narrowing the Competency Rift: Analyzing Skill Levels with Digital Doubles. https://doi.org/10.2118/219074-ms
Aljaberi, S.A.A. & Abd Rahman, N.I. (2023). Development of operator training simulator (OTS) in refining process for atmospheric distillation column. Journal of Engineering Science and Technology, 18 (4). 2221–2237. https://jestec.taylors.edu.my
Balaji, B., Shahab, M. A., Srinivasan, B., & Srinivasan, R. (2023). ACT-R based human digital twin to enhance operators' performance in process industries. Frontiers in Human Neuroscience, 17, 1038060. https://doi.org/10.3389/fnhum.2023.1038060
Bělohlav, V., Jirout, T., Malecký, M. & Herink, T. (2024). Effective application of operator training simulator in experiential education. Computer Applications in Engineering Education, vol. 32 (22743), 1–13. https://doi.org/10.1002/cae.22743
Čech, M. & Vosáhlo, M. (2022). Digital twins and HIL simulators in control education—industrial perspective. IFAC‐PapersOnLine, 55, 67–72. https://doi.org/10.1016/j.ifacol.2022.09.226
Cuypers, F., Boelen, T., & Logist, F. (2025). Exploiting Operator Training Systems in chemical plants: learnings from industrial practice at BASF. Systems and Control Transactions, 4, 339–345. https://doi.org/10.69997/sct.152404
David, R. M., & Gupta, D. S. (2024). Knowledge management in the oil and gas industry and its transformation in the Industry 4.0 era. Migration Letters, 21(S8), 483–503. https://migrationletters.com/index.php/ml/article/view/9335
Firmansyah, A., Chen, M. H., Junaedi, I. W. R., Arwani, M., & Kistyanto, A. (2022). The role of transformational leadership and knowledge management and learning organization on vocational schools performance during the digital era. Frontiers in Psychology, 13, 895341. https://doi.org/10.3389/fpsyg.2022.895341
Gonyora, M., & Ventura-Medina, E. (2024). Investigating the relationship between human and organisational factors, maintenance, and accidents. The case of chemical process industry in South Africa. Safety Science, 176, 106530.https://doi.org/10.1016/j.ssci.2024.106530
Herink, T., Bělohlav, V., Jirout, T. & Bělohlav, Z. (2022). Opportunities of experiential education in chemical technology and engineering. Educ. Chem. Eng., 41, 32–41. https://doi.org/10.1016/j.ece.2022.08.003
Huang, Y., Zhu, K., Hu, Z., Chen, Y., Li, X., Jiang, Z., ... & Yan, K. (2024). Solvent-free synthesis of foam board-like CoSe2 alloy to selectively generate singlet oxygen via peroxymonosulfate activation for sulfadiazine degradation. Journal of Hazardous Materials, 466, 133611. https://doi.org/10.1016/j.jhazmat.2024.133611
Kallakuri, R. & Bahuguna, P.C. (2021). Role of operator training simulator in hydrocarbon industry – A review. International Journal of Simulation Modelling, 20 (4), 649–660. https://doi.org/10.2507/IJSIMM20-4-575
Lee, J. & Ma, B. (2023). An operator training simulator to enable responses to chemical accidents through mutual cooperation between the participants. Applied Sciences, 13 (1382), 1–16. https://doi.org/10.3390/app13031382
Liu, S., Lei, F., Zhao, D., & Liu, Q. (2023). Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects. Processes, 11(6), 1608–1608. https://doi.org/10.3390/pr11061608
Mariana, M., Sahroni, T. R., & Gustiyana, T. (2018). Fatigue and Human Errors Analysis in Petrochemical and Oil and Gas Plant’s Operation. IEOM Society International. https://fatiguemanagersnetwork.org
Miska, J. W., Mathews, L., Driscoll, J., Hoffenson, S., Crimmins, S. Espera, A., & Pitterson, N. (2022). How do under graduate engineering students conceptualize product design? An analysis of two third‐year design courses. J. Eng. Educ. 111 (2022), 616–641. https://doi.org/10.1002/jee.20468
Nizamova, G. I., & Yashkina, D. A. (2025). FEATURES OF THE USE OF VIRTUAL SIMULATORS IN THE TRAINING OF OPERATORS OF TECHNOLOGICAL INSTALLATIONS. World of Petroleum Products, 5, 51–55. https://doi.org/10.32758/2782-3040-2025-0-5-51-55
Onyekwe, F. O., Odujobi, O., Adikwu, F. E., & Elete, T. Y. (2022). Innovative approaches to enhancing functional safety in Distributed Control Systems (DCS) and Safety Instrumented Systems (SIS) for Oil and Gas Applications. Open Access Research Journal of Multidisciplinary Studies, 3(1), 106–112. https://doi.org/10.53022/oarjms.2022.3.1.0027
Ortiz, J. S., Quishpe, E. K., Sailema, G. X., & Guamán, N. S. (2025). Digital twin-based active learning for industrial process control and supervision in Industry 4.0. Sensors, 25(7), 2076. https://doi.org/10.3390/s25072076
Philip, J. (2022). Operator Training Simulator Handbook: Best Practices for Developing and Investing in OTS. Packt Publishing, Birmingham.
Simone, F., Bortolini, M., Mazzuto, G., Gravio, G. D., & Patriarca, R. (2026). Human-Hardware-in-the-Loop simulations for systemic resilience assessment in cyber-socio-technical systems. Reliability Engineering & System Safety, 272, 112574–112574. https://doi.org/10.1016/j.ress.2026.112574
Yang, G., Shao, Z., Xu, Z., Zhang, D., Lou, H., & Wang, K. (2021). Development of a novel type operator training simulator framework for air separation process. 2021 China Automation Congress (CAC), 15, 4014–4019. https://doi.org/10.1109/cac53003.2021.9727917
Made Mariana