Model-based system engineering approach for existing industrial enterprise digital transformation

Vladimir Badenko, Vladimir Yadykin, Elena Tishchenko, Galina Badenko, Luka Akimov, Victor Barskov

Article ID: 7983
Vol 8, Issue 14, 2024

VIEWS - 1 (Abstract) 5 (PDF)

Abstract


The article presents an answer to the current challenge about needs to form methodological approaches to the digital transformation of existing industrial enterprises (EIE). The paper develops a hypothesis that it is advisable to carry out the digital transformation of EIE based on considering it as a complex technical system using model-based system engineering (MBSE). The practical methodology based on MBSE for EIE digital representation creation are presented. It is demonstrated how different system models of EIE is created from a set of entities of the MBSE approach: requirements—unctions—components and corresponding matrices of interconnections. Also the principles and composition of tasks for system architectures creation of EIE digital representation are developed. The practical application of proposed methodology is illustrated by the example of an existing gas distribution station.


Keywords


model-based systems engineering; system architectural model; digital representation; digital transformation; complex technical system; existing industrial enterprises; gas distribution station

Full Text:

PDF


References


Badenko, V. L., Bolshakov, N. S., Tishchenko, E. B., et al. (2021). Integration of digital twin and BIM technologies within factories of the future. Magazine of Civil Engineering, (1 (101)), 10114. https://doi.org/10.34910/MCE.101.14

Bai, C., Dallasega, P., Orzes, G., et al. (2020). Industry 4.0 technologies assessment: A sustainability perspective. International journal of production economics, 229, 107776. https://doi.org/10.1016/j.ijpe.2020.107776

Bemmami, K. E., & David, P. (2021). State-of-practice survey in industry on the deployment of simulation in systems engineering. IFAC-PapersOnLine, 54(1), 1132-1137. https://doi.org/10.1016/j.ifacol.2021.08.133

Bolshakov, N., Rakova, X., Celani, A., et al. (2023a). Operation Principles of the Industrial Facility Infrastructures Using Building Information Modeling (BIM) Technology in Conjunction with Model-Based System Engineering (MBSE). Applied Sciences, 13(21), 11804. https://doi.org/10.3390/app132111804

Bolshakov, N., Badenko, V., Yadykin, V., et al. (2023b). Cross-Industry Principles for Digital Representations of Complex Technical Systems in the Context of the MBSE Approach: A Review. Applied Sciences, 13(10), 6225. https://doi.org/10.3390/app13106225

Boehm, B., Koolmanojwong, S., Lane, J. A., et al. (2012). Principles for successful systems engineering. Procedia Computer Science, 8, 297-302. https://doi.org/10.1016/j.procs.2012.01.063

Bone, M. A., Blackburn, M. R., Rhodes, D. H., et al. (2019). Transforming systems engineering through digital engineering. The Journal of Defense Modeling and Simulation, 16(4), 339-355. https://doi.org/10.1177/1548512917751873

Bretz, L., Kaiser, L., & Dumitrescu, R. (2019). An analysis of barriers for the introduction of Systems Engineering. Procedia CIRP, 84, 783-789. https://doi.org/10.1016/j.procir.2019.04.178

Browning, T. R. (2015). Design structure matrix extensions and innovations: a survey and new opportunities. IEEE Transactions on engineering management, 63(1), 27-52. https://doi.org/10.1109/TEM.2015.2491283

Buschhaus, C., Gerasimov, A., Kirchhof, J. C., et al. (2024). Lessons learned from applying model-driven engineering in 5 domains: The success story of the MontiGem generator framework. Science of Computer Programming, 232, 103033. https://doi.org/10.1016/j.scico.2023.103033

Cameron, B., & Adsit, D. M. (2018). Model-based systems engineering uptake in engineering practice. IEEE Transactions on Engineering Management, 67(1), 152-162. https://doi.org/10.1109/TEM.2018.2863041

Campo, K. X., Teper, T., Eaton, C. E., et al. (2022). Model‐based systems engineering: evaluating perceived value, metrics, and evidence through literature. Systems Engineering, 26(1), 104-129. https://doi.org/10.1002/sys.21644

Chen, X., Zhang, X. E., Cai, Z., et al. (2024). The Non-Linear Impact of Digitalization on the Performance of SMEs: A Hypothesis Test Based on the Digitalization Paradox. Systems, 12(4), 139. https://doi.org/10.3390/systems12040139

Cimino, A., Gnoni, M. G., Longo, F., et al. (2023). Integrating multiple industry 4.0 approaches and tools in an interoperable platform for manufacturing SMEs. Computers & Industrial Engineering, 186, 109732. https://doi.org/10.1016/j.cie.2023.109732

Elhabbash, A., Elkhatib, Y., Nundloll, V., et al. (2024). Principled and automated system of systems composition using an ontological architecture. Future Generation Computer Systems, 157, 499-515. https://doi.org/10.1016/j.future.2024.03.034

Fang, M., Nie, H., & Shen, X. (2023). Can enterprise digitization improve ESG performance?. Economic Modelling, 118, 106101. https://doi.org/10.1016/j.econmod.2022.106101

Fett, M., Wilking, F., Goetz, S., et al. (2023). A Literature Review on the Development and Creation of Digital Twins, Cyber-Physical Systems, and Product-Service Systems. Sensors, 23(24), 9786. https://doi.org/10.3390/s23249786

Gajo, A. H., & Akyuz, G. A. (2023). Digital Transformation Implementation Challenges in Turkish Industrial Enterprises. International Journal of Innovation and Technology Management, 20(06), 2350037. https://doi.org/10.1142/S0219877023500372

Ghobakhloo, M., & Fathi, M. (2019). Corporate survival in Industry 4.0 era: the enabling role of lean-digitized manufacturing. Journal of Manufacturing Technology Management, 31(1), 1-30. https://doi.org/10.1108/JMTM-11-2018-0417

Guide to the Systems Engineering Body of Knowledge (SEBoK), version 2.10 (2024). Available online: https://sebokwiki.org/w/images/sebokwiki-farm!w/d/db/Guide_to_the_Systems_Engineering_Body_of_Knowledge_v2.10.pdf (accessed on 14.08.2024)

Henderson, K., & Salado, A. (2021). Value and benefits of model‐based systems engineering (MBSE): Evidence from the literature. Systems Engineering, 24(1), 51-66. https://doi.org/10.1002/sys.21566

Henderson, K., McDermott, T., Van Aken, E., et al. (2023). Towards developing metrics to evaluate digital engineering. Systems Engineering, 26(1), 3-31. https://doi.org/10.1002/sys.21640

Henderson, K., & Salado, A. (2024). The effects of organizational structure on MBSE adoption in industry: Insights from practitioners. Engineering Management Journal, 36(1), 117-143. https://doi.org/10.1080/10429247.2023.2210494

Henderson, K., McDermott, T., & Salado, A. (2024). MBSE adoption experiences in organizations: Lessons learned. Systems Engineering, 27(1), 214-239. https://doi.org/10.1002/sys.21717

Hennig, A., & Szajnfarber, Z. (2023). The impact of system representation choices on architecting insights. Systems Engineering, 26(5), 531-547. https://doi.org/10.1002/sys.21673

Huang, J., Gheorghe, A., Handley, H., et al. (2020). Towards digital engineering: the advent of digital systems engineering. International Journal of System of Systems Engineering, 10(3), 234-261. https://doi.org/10.1504/IJSSE.2020.10031364

Huldt, T., & Stenius, I. (2019). State‐of‐practice survey of model‐based systems engineering. Systems engineering, 22(2), 134-145. https://doi.org/10.1002/sys.21466

INCOSE (2023). Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities, version 5.0. Hoboken, NJ, USA: John Wiley and Sons, Inc, ISBN: 978-1-119-81429-0.

Kaiser, J., McFarlane, D., Hawkridge, G., et al. (2023). A review of reference architectures for digital manufacturing: Classification, applicability and open issues. Computers in Industry, 149, 103923. https://doi.org/10.1016/j.compind.2023.103923

Khandoker, A., Sint, S., Gessl, G., et al. (2022). Towards a logical framework for ideal MBSE tool selection based on discipline specific requirements. Journal of Systems and Software, 189, 111306. https://doi.org/10.1016/j.jss.2022.111306

Kukushkin, K., Ryabov, Y., & Borovkov, A. (2022). Digital twins: a systematic literature review based on data analysis and topic modeling. Data, 7(12), 173. https://doi.org/10.3390/data7120173

Laing, C., David, P., Blanco, E., et al. (2020). Questioning integration of verification in model-based systems engineering: an industrial perspective. Computers in Industry, 114, 103163. https://doi.org/10.1016/j.compind.2019.103163

Liu, S., Zheng, P., & Bao, J. (2024) Digital Twin-based manufacturing system: A survey based on a novel reference model. Journal of Intelligent Manufacturing, 35, 2517–2546. https://doi.org/10.1007/s10845-023-02172-7

Lu, J., Ma, J., Zheng, X., et al. (2021). Design ontology supporting model-based systems engineering formalisms. IEEE Systems Journal, 16(4), 5465-5476. https://doi.org/10.1109/JSYST.2021.3106195

Madni, A. M., & Sievers, M. (2018). Model‐based systems engineering: Motivation, current status, and research opportunities. Systems Engineering, 21(3), 172-190. https://doi.org/10.1002/sys.21438

Madni, A. M., & Purohit, S. (2019). Economic analysis of model-based systems engineering. Systems, 7(1), 12. https://doi.org/10.3390/systems7010012

Madni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging digital twin technology in model-based systems engineering. Systems, 7(1), 7. https://doi.org/10.3390/systems7010007

Maier, M. W. (1998). Architecting principles for systems‐of‐systems. Systems Engineering: The Journal of the International Council on Systems Engineering, 1(4), 267-284. https://doi.org/10.1002/(SICI)1520-6858(1998)1:4%3C267::AID-SYS3%3E3.0.CO;2-D

Matarazzo, M., Penco, L., Profumo, G., et al. (2021). Digital transformation and customer value creation in Made in Italy SMEs: A dynamic capabilities perspective. Journal of Business Research, 123, 642-656. https://doi.org/10.1016/j.jbusres.2020.10.033

Meißner, M., Jacobs, G., Jagla, P., et al., (2021). Model based systems engineering as enabler for rapid engineering change management. Procedia CIRP, 100, 61-66. https://doi.org/10.1016/j.procir.2021.05.010

Mitola III, J., & Prys, M. (2023). Cyber oriented digital engineering. Systems Engineering, 27(1), 109-119. https://doi.org/10.1002/sys.21710

Papavasiliou, S., Gorod, A., & Reaiche, C. (2024). System of systems engineering governance framework for digital transformation: A case study of an Australian large government agency. Systems Engineering, 27(2), 267-283. https://doi.org/10.1002/sys.21719

Psarommatis, F., & May, G. (2023). A literature review and design methodology for digital twins in the era of zero defect manufacturing. International Journal of Production Research, 61(16), 5723-5743. https://doi.org/10.1080/00207543.2022.2101960

Purohit, S., & Madni, A. M. (2021). A model-based systems architecting and integration approach using interlevel and intralevel dependency matrix. IEEE Systems Journal, 16(1), 747-754. https://doi.org/10.1109/JSYST.2021.3077351

Rehberg, L., & Brem, A. (2024). Industrial prototyping in the German automotive industry: bridging the gap between physical and virtual prototypes. Journal of Engineering and Technology Management, 71, 101798.

San Cristóbal, J. R., Carral, L., Diaz, E., et al. (2018). Complexity and project management: A general overview. Complexity, 2018. 4891286. https://doi.org/10.1155/2018/4891286

Sanfilippo, E., Kitamura, Y., & Young, R. I. (2019). Formal ontologies in manufacturing. Applied Ontology, 14(2), 119-125. https://doi.org/10.3233/AO-190209

Schlemitz, A., & Mezhuyev, V. (2024). Approaches for data collection and process standardization in smart manufacturing: systematic literature review. Journal of Industrial Information Integration, 38, 100578. https://doi.org/10.1016/j.jii.2024.100578

Schummer, F., & Hyba, M. (2022). An approach for system analysis with model-based systems engineering and graph data engineering. Data-Centric Engineering, 3, e33. https://doi.org/10.1017/dce.2022.33

Sharon, A., de Weck, O. L., & Dori, D. (2013). Improving project–product lifecycle management with model–based design structure matrix: a joint project management and systems engineering approach. Systems Engineering, 16(4), 413-426. https://doi.org/10.1002/sys.21240

Shoshany‐Tavory, S., Peleg, E., Zonnenshain, A., et al. (2023). Model‐based‐systems‐engineering for conceptual design: An integrative approach. Systems Engineering, 26(6), 783-799. https://doi.org/10.1002/sys.21688

Skare, M., de Obesso, M. D. L. M., & Ribeiro-Navarrete, S. (2023). Digital transformation and European small and medium enterprises (SMEs): A comparative study using digital economy and society index data. International journal of information management, 68, 102594. https://doi.org/10.1016/j.ijinfomgt.2022.102594

Swickline, C., Mazzuchi, T. A., & Sarkani, S. (2024). A methodology for developing SoS architectures using SysML model federation. Systems Engineering, 27(2), 368-385. https://doi.org/10.1002/sys.21727

Tikayat Ray, A., Cole, B. F., Pinon Fischer, O. J., et al. (2023). Agile Methodology for the Standardization of Engineering Requirements Using Large Language Models. Systems, 11(7), 352. https://doi.org/10.3390/systems11070352

Titus, L. M. (2024). Does ChatGPT have semantic understanding? A problem with the statistics-of-occurrence strategy. Cognitive Systems Research, 83, 101174. https://doi.org/10.1016/j.cogsys.2023.101174

Tong, X., Bao, J., & Tao, F. (2024). Co-evolutionary digital twins: A multidimensional dynamic approach to digital engineering. Advanced Engineering Informatics, 61, 102554. https://doi.org/10.1016/j.aei.2024.102554

Varriale, V., Cammarano, A., Michelino, F., et al. (2024). The role of digital technologies in production systems for achieving sustainable development goals. Sustainable Production and Consumption. 47, 87 – 104. https://doi.org/10.1016/j.spc.2024.03.035

Veile, J. W., Kiel, D., Müller, J. M., et al. (2020). Lessons learned from Industry 4.0 implementation in the German manufacturing industry. Journal of Manufacturing Technology Management, 31(5), 977-997. https://doi.org/10.1108/JMTM-08-2018-0270

Verbruggen, C., & Snoeck, M. (2023). Practitioners’ experiences with model-driven engineering: a meta-review. Software and Systems Modeling, 22(1), 111-129. https://doi.org/10.1007/s10270-022-01020-1

Vernadat, F. (2014). Enterprise Modeling in the context of Enterprise Engineering: State of the art and outlook. International Journal of Production Management and Engineering, 2(2), 57-73. https://doi.org/10.4995/ijpme.2014.2326

Voth, J. M., & Sturtevant, G. H. (2022). Digital engineering: expanding the advantage. Journal of Marine Engineering & Technology, 21(6), 355-363. https://doi.org/10.1080/20464177.2021.2024382

Weerakkody, V., Janssen, M., & El-Haddadeh, R. (2021). The resurgence of business process re-engineering in public sector transformation efforts: exploring the systemic challenges and unintended consequences. Information Systems and e-Business Management, 19(3), 993-1014. https://doi.org/10.1007/s10257-021-00527-2

Yadykin, V., Barykin, S., Badenko, V., et al. (2021). Global challenges of digital transformation of markets: Collaboration and digital assets. Sustainability, 13(19), 10619. https://doi.org/10.3390/su131910619

Yang, X., Liu, X., Zhang, H., et al. (2023). Meta-model-based shop-floor digital twin architecture, modeling and application. Robotics and Computer-Integrated Manufacturing, 84, 102595. https://doi.org/10.1016/j.rcim.2023.102595

Younse, P., Cameron, J., & Bradley, T. H. (2022). Comparative analysis of model‐based and traditional systems engineering approaches for simulating a robotic space system architecture through automatic knowledge processing. Systems Engineering, 25(4), 360-386. https://doi.org/10.1002/sys.21619

Zhang, J., & Yang, S. (2024). Recommendations for the Model-Based Systems Engineering Modeling Process Based on the SysML Model and Domain Knowledge. Applied Sciences, 14(10), 4010. https://doi.org/10.3390/app14104010




DOI: https://doi.org/10.24294/jipd7983

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Vladimir Badenko, Vladimir Yadykin, Elena Tishchenko, Galina Badenko, Luka Akimov, Victor Barskov

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License.