Digital Twin Driven Real Time Performance Optimization of Smart Factory Production Systems Using Edge Computing and Industrial Internet of Things Architecture

Suyahman Suyahman; Dwi Utari Iswavigra; Helmi Wibowo; Ahmad Budi Trisnawan; Ardy Wicaksono; Dwi Atmodjo WP

doi:10.61132/ijiime.v1i2.396

Authors

Suyahman Suyahman Universitas Sugeng Hartono
Dwi Utari Iswavigra Universitas Sugeng Hartono
Helmi Wibowo Politeknik Keselamatan Transportasi Jalan
Ahmad Budi Trisnawan Universitas Mahakarya Asia
Ardy Wicaksono Universitas Sugeng Hartono
Dwi Atmodjo WP Perbanas Institute

DOI:

https://doi.org/10.61132/ijiime.v1i2.396

Keywords:

Digital Twin, Edge Computing, Industry 4.0, Reinforcement Learning, Smart Manufacturing

Abstract

Background: The rapid advancement of Industry 4.0 has accelerated the integration of digital technologies such as the Industrial Internet of Things (IIoT), edge computing, and Digital Twin systems in smart manufacturing environments. However, many existing implementations remain fragmented and heavily dependent on centralized cloud infrastructures, resulting in latency constraints, limited scalability, and suboptimal real-time decision making. Objective: This study aims to develop and validate an integrated edge based Digital Twin optimization framework that combines IIoT sensing, hybrid edge cloud architecture, and reinforcement learning based adaptive control. Methods: The research adopts a multi phase design consisting of framework development, simulation based validation, and industrial pilot implementation. The proposed system integrates real time data acquisition, localized edge processing, Digital Twin synchronization, and intelligent optimization mechanisms to enhance operational efficiency. Results: The findings demonstrate significant performance improvements compared to conventional cloud based systems, including substantial latency reduction, increased production throughput, reduced downtime, and improved energy efficiency. Scalability and robustness testing further confirm that distributed edge intelligence enhances system resilience under increased workloads and network disruptions. These results indicate that integrating edge computing with Digital Twin modeling and reinforcement learning provides a scalable, responsive, and energy efficient solution for next-generation smart factories.

References

Azevedo, A., & Almeida, A. H. (2024). Smart factories-Design and results of a new course in a MSc curriculum of engineering. IEEE Global Engineering Education Conference (EDUCON). https://doi.org/10.1109/EDUCON60312.2024.10578669

Baldoni, G., Cominardi, L., Groshev, M., De La Oliva, A., & Corsaro, A. (2023). Managing the far-edge: Are today's centralized solutions a good fit? IEEE Consumer Electronics Magazine, 12(3), 51-61. https://doi.org/10.1109/MCE.2021.3082503

Barakat, M., Saeed, R. A., & Edam, S. (2023). A comparative study on cloud and edge computing: A survey on current research activities and applications. Proceedings of MI-STA 2023, 679-684. https://doi.org/10.1109/MI-STA57575.2023.10169821

Boomiga, S. S., & Prasanna Venkatesan, V. (2020). A generic model for scheduling IoT jobs at the edge. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 31, pp. 370-377). https://doi.org/10.1007/978-3-030-24643-3_44

Chen, G., Wang, P., Feng, B., Li, Y., & Liu, D. (2020). The framework design of smart factory in discrete manufacturing industry based on cyber-physical system. International Journal of Computer Integrated Manufacturing, 33(1), 79-101. https://doi.org/10.1080/0951192X.2019.1699254

Condori, P. P. C. (2022). Digital twin in development of products. In Digital Twin Technology: Fundamentals and Applications (pp. 205-218). https://doi.org/10.1002/9781119842316.ch13

de Moura, R. L., Brasil, T. M., Werner, L. B., Dal' Col, C. J. B., Gonzalez, A., & Quadri, S. (2021). Technologies for industrial Internet of Things (IIoT): Guidelines for edge computing adoption in the industry. In LNICST (Vol. 372, pp. 259-273). https://doi.org/10.1007/978-3-030-76063-2_19

Fancy, C., Anand, M., & Sheeba, T. M. (2024). Blockchain for edge association in digital twin empowered 6G networks. In Artificial Intelligence-Enabled Blockchain Technology and Digital Twin for Smart Hospitals (pp. 123-151). https://doi.org/10.1002/9781394287420.ch7

Faria, R. D. R., Capron, B. D. O., de Souza Jr, M. B., & Secchi, A. R. (2023). One-layer real-time optimization using reinforcement learning: A review with guidelines. Processes, 11(1), 123. https://doi.org/10.3390/pr11010123

Gautami, A., & Gowthaman, N. (2021). A holistic approach: Issues and challenges in autonomic computation toward Industry 4.0. In EAI/Springer Innovations in Communication and Computing (pp. 111-121). https://doi.org/10.1007/978-3-030-71756-8_6

Ghosh, S., Sarkar, S. K., Roy, B., & Paul, S. (2024). Challenges and innovations in digital twin creation. In Exploring the Advancements and Future Directions of Digital Twins in Healthcare 6.0 (pp. 86-104). https://doi.org/10.4018/978-8-3693-5893-1.ch005

Huang, H., Zhang, J., & Liu, Y. (2023). IOWA-based dynamic trusted assessment model for system operational state behavior. https://doi.org/10.1007/978-981-99-1260-5_28

Konur, S., Lan, Y., Thakker, D., Morkyani, G., Polovina, N., & Sharp, J. (2023). Towards design and implementation of Industry 4.0 for food manufacturing. Neural Computing and Applications, 35(33), 23753-23765. https://doi.org/10.1007/s00521-021-05726-z

Kuchtíková, N., & Maryska, M. (2023). A comparison of cloud computing vs fog vs edge for IIoT sensors. IDIMT 2023 Proceedings, 151-159. https://doi.org/10.35011/IDIMT-2023-151

Langlotz, P., Klar, M., Yi, L., Hussong, M., Sousa, F. J. P., & Aurich, J. C. (2022). Concept of hybrid modeled digital twins and its application for an energy management of manufacturing systems. Procedia CIRP, 112, 549-554. https://doi.org/10.1016/j.procir.2022.09.098

Mashaly, M. (2021). Connecting the twins: A review on digital twin technology & its networking requirements. Procedia Computer Science, 184, 299-305. https://doi.org/10.1016/j.procs.2021.03.039

Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2024). Industry 4.0 and smart manufacturing. In Comprehensive Materials Processing (2nd ed., Vol. 3, p. V3:14--V3:38). https://doi.org/10.1016/B978-0-323-96020-5.00010-8

Muzammal, S. M., Tayyab, M., Zahra, F., Jhanjhi, N. Z., & Ashraf, H. (2024). Smart factories greener future: A synergy of Industry 4.0 and sustainability. In Digital Transformation for Improved Industry and Supply Chain Performance (pp. 70-92). https://doi.org/10.4018/979-8-3693-5375-2.ch004

Padmini, M. S., & Kuzhalvaimozhi, S. (2023). Critical analysis of life span improvement techniques in energy constraints edge IoT devices. SN Computer Science, 4(3), 221. https://doi.org/10.1007/s42979-022-01601-3

Peng, P., & Soljanin, E. (2024). Redundancy management for fast service (rates) in edge computing systems. IEEE/ACM Transactions on Networking. https://doi.org/10.1109/TNET.2024.3507372

Polsonetti, C. (2020). Automation at the Industrial IoT edge. Control Engineering, 67(9), P1--P3.

Powell, B. K. M., Machalek, D., & Quah, T. (2020). Real-time optimization using reinforcement learning. Computers and Chemical Engineering, 143, 107077. https://doi.org/10.1016/j.compchemeng.2020.107077

Raghav, Y. Y., & Kait, R. (2024). Edge computing empowering distributed computing at the edge. In Emerging Trends in Cloud Computing Analytics, Scalability, and Service Models (pp. 67-83). https://doi.org/10.4018/979-8-3693-0900-1.ch003

Ramarumo, A., Selvaraj, R., Segwele, T., Kuthadi, V., & Thapelo, T. S. (2024). Machine learning driven energy efficiency framework using digital twin: A review. 2024 IEEE 4th International Conference on ICT in Business Industry and Government (ICTBIG 2024). https://doi.org/10.1109/ICTBIG64922.2024.10911267

Renteria-Marquez, I. A., Basaldua, J. C. G. M., Aguirre, O., Lara-Medrano, B. E., & Tseng, T.-L. (2024). Cyber-physical production system framework for production scheduling in smart factories. Proceedings of the Winter Simulation Conference, 1740-1748. https://doi.org/10.1109/WSC63780.2024.10838901

Resman, M., Protner, J., Simic, M., & Herakovic, N. (2021). A five-step approach to planning data-driven digital twins for discrete manufacturing systems. Applied Sciences, 11(8), 3639. https://doi.org/10.3390/app11083639

Rochford, L. (2024). Edge or cloud? Taking the best computing direction for industrial applications. Electronics World, 129(2032), 40-41.

Ryalat, M., Franco, E., Elmoaqet, H., Almtireen, N., & Al-Refai, G. (2024). The integration of advanced mechatronic systems into Industry 4.0 for smart manufacturing. Sustainability, 16(19), 8504. https://doi.org/10.3390/su16198504

Sampath, T., Dutt, A., Veeraiah, V., Aishwarya, B. K., Lal, K., & Kapila, D. (2023). Analyzing the effect of edge computing on real-time data processing and latency reduction. 2023 10th IEEE Uttar Pradesh Section International Conference (UPCON 2023), 623-627. https://doi.org/10.1109/UPCON59197.2023.10434652

Shi, Z., Xie, Y., Xue, W., Chen, Y., Fu, L., & Xu, X. (2020). Smart factory in Industry 4.0. Systems Research and Behavioral Science, 37(4), 607-617. https://doi.org/10.1002/sres.2704

Soleymanizadeh, H., Qu, Q., Bamakan, S. M. H., & Zanjirchi, S. M. (2023). Digital twin empowering manufacturing paradigms: Lean, agile, just-in-time (JIT), flexible, resilience, sustainable. Procedia Computer Science, 221, 1258-1267. https://doi.org/10.1016/j.procs.2023.08.114

Sun, L. (2024). Digital transformation in the manufacturing industry: The impact of Industry 4.0 and the case of Tesla. In Exploring the Financial Landscape in the Digital Age - Proceedings of ICFMDE 2023 (pp. 697-703). https://doi.org/10.1201/9781003508816-100

Suvarna, M., Yap, K. S., Yang, W., Li, J., Ng, Y. T., & Wang, X. (2021). Cyber-physical production systems for data-driven, decentralized, and secure manufacturing-A perspective. Engineering, 7(9), 1212-1223. https://doi.org/10.1016/j.eng.2021.04.021

Tang, X., Schneider, T., Kamil, S., Panda, A., Li, J., & Panozzo, D. (2020). EGGS: Sparsity-specific code generation. Computer Graphics Forum, 39(5), 209-219. https://doi.org/10.1111/cgf.14080

Terkaj, W., Annoni, M., Martinez, B. O., Pessot, E., Sortino, M., & Urgo, M. (2024). Digital twin for factories: Challenges and industrial applications. In Lecture Notes in Mechanical Engineering (pp. 255-274). https://doi.org/10.1007/978-3-031-41163-2_13

Thakkar, K., Patel, A., Budhrani, A., Patel, K., Patel, S., & Nayak, A. (2023). Edge computing: Architecture, implications and future with latest trends. Proceedings of ICPCSN 2023, 1065-1070. https://doi.org/10.1109/ICPCSN58827.2023.00181

Thodoroff, P., Li, W., & Lawrence, N. D. (2022). Benchmarking real-time reinforcement learning. Proceedings of Machine Learning Research, 181, 26-41.

Tiwari, S., & Shukla, A. (2024). Digital twins in industry: Real-world applications and innovations. In Transforming Industry Using Digital Twin Technology (pp. 1-18). https://doi.org/10.1007/9783031585234_1

Uehara, T., & Kasamatsu, D. (2024). A method of synchronous control for stream processing in digital twin environment. 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE 2024), 1097-1098. https://doi.org/10.1109/GCCE62371.2024.10760555

Yang, H., Kumara, S., Bukkapatnam, S. T. S., & Tsung, F. (2019). The internet of things for smart manufacturing: A review. IISE Transactions, 51(11), 1190-1216. https://doi.org/10.1080/24725854.2018.1555383

Zhou, J., Liu, J., & Chen, Z. (2023). Task offloading in mobile edge computing based on deep reinforcement learning. Proceedings of SPIE, 12462, 124622T. https://doi.org/10.1117/12.2660982