SynKit: A Event-Driven and Scalable Microservices-Based Kitting System

Authors: Bruno Nascimento, Cristina Wanzeller, Jorge Silva, João A. Dias, André Barbosa, José Ribeiro

Abstract:

The increasing complexity of logistics operations stems from evolving business needs, such as the shift from mass production to mass customisation, which demands greater efficiency and flexibility. In response, Industry 4.0 and 5.0 technologies provide improved solutions to enhance operational agility and better meet market demands. The management of kitting zones, combined with the use of Autonomous Mobile Robots, faces challenges related to coordination, resource optimisation, and rapid response to customer demand fluctuations. Additionally, implementing Lean Manufacturing practices in this context must be carefully orchestrated by intelligent systems and human operators to maximise efficiency without sacrificing the agility required in an advanced production environment. This paper proposes and implements a microservices-based architecture integrating principles from Industry 4.0 and 5.0 with Lean Manufacturing practices. The architecture enhances communication and coordination between autonomous vehicles and kitting management systems, allowing more efficient resource utilization and increased scalability. The proposed architecture focuses on the modularity and flexibility of operations, enabling seamless flexibility to change demands and efficiently allocate resources in real-time. Conducting this approach is expected to significantly improve logistics operations’ efficiency and scalability by reducing waste and optimising resource use while improving responsiveness to demand changes. The implementation of this architecture provides a robust foundation for the continuous evolution of kitting management and process optimisation. Designed to adapt to dynamic environments marked by rapid shifts in production demands and real-time decision-making. It also ensures seamless integration with automated systems, aligning with Industry 4.0 and 5.0 needs while reinforcing Lean Manufacturing principles.

Keywords: Microservices, event-driven, kitting, lean manufacturing, industry 4.0, industry 5.0.

References:

[1] C. Stockinger, T. Steinebach, D. Petrat, R. Bruns, and I. Z¨oller, “The effect of pick-by-light-systems on situation awareness in order picking activities,” Procedia Manufacturing, vol. 45, pp. 96–101, 2020.
[2] A. Astigarraga, A. Lopez-Gasso, D. Golpe, A. Beriain, H. Solar, D. Del Rio, and R. Berenguer, “A 21 m operation range rfid tag for “pick to light” applications with a photovoltaic harvester,” Micromachines, vol. 11, no. 11, p. 1013, 2020.
[3] M. Mkansi, S. de Leeuw, and O. Amosun, “Mobile application supported urban-township e-grocery distribution,” International Journal of Physical Distribution & Logistics Management, vol. 50, no. 1, pp. 26–53, 2020.
[4] G. Plakas, S. Ponis, K. Agalianos, E. Aretoulaki, and S. Gayialis, “Augmented reality in manufacturing and logistics: Lessons learnt from a real-life industrial application,” Procedia Manufacturing, vol. 51, pp. 1629–1635, 2020.
[5] M. Helmold, “Principles of a lean production system,” in Lean Management and Kaizen: Fundamentals from Cases and Examples in Operations and Supply Chain Management. Springer, 2020, pp. 79–89.
[6] A. Shojaeinasab, T. Charter, M. Jalayer, M. Khadivi, O. Ogunfowora, N. Raiyani, M. Yaghoubi, and H. Najjaran, “Intelligent manufacturing execution systems: A systematic review,” Journal of Manufacturing Systems, vol. 62, pp. 503–522, 2022.
[7] M. Attaran and B. G. Celik, “Digital twin: Benefits, use cases, challenges, and opportunities,” Decision Analytics Journal, vol. 6, p. 100165, 2023.
[8] A. Akundi, D. Euresti, S. Luna, W. Ankobiah, A. Lopes, and I. Edinbarough, “State of industry 5.0—analysis and identification of current research trends,” Applied System Innovation, vol. 5, no. 1, p. 27, 2022.
[9] E. Commission, D.-G. for Research, Innovation, M. Breque, L. De Nul, and A. Petridis, Industry 5.0 – Towards a sustainable, human-centric and resilient European industry. Publications Office of the European Union, 2021.
[10] C. Aron, F. Sgarbossa, E. Ballot, and D. Ivanov, “Cloud material handling systems: a cyber-physical system to enable dynamic resource allocation and digital interoperability,” Journal of Intelligent Manufacturing, pp. 1–22, 2023.
[11] N. Ghodsian, K. Benfriha, A. Olabi, V. Gopinath, E. Talhi, L. A. Hof, and A. Arnou, “A framework to integrate mobile manipulators as cyber–physical systems into existing production systems in the context of industry 4.0,” Robotics and Autonomous Systems, vol. 169, p. 104526, 2023.
[12] N. Ghodsian, K. Benfriha, A. Olabi, V. Gopinath, E. Talhi, L. Hof, and A. Arnou, “Msoa: A modular service-oriented architecture to integrate mobile manipulators as cyber-physical systems,” Journal of Intelligent Manufacturing, pp. 1–20, 2024.
[13] Y. Pan, R. Y. Zhong, T. Qu, L. Ding, and J. Zhang, “Multi-level digital twin-driven kitting-synchronized optimization for production logistics system,” International Journal of Production Economics, vol. 271, p. 109176, 2024.
[14] M. Zafarzadeh, Y. Jeong, and M. Wiktorsson, “Data flow structure for multimodal human-robot collaboration in material handling,” in 2023 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC). IEEE, 2023, pp. 1–8.
[15] G. Sch¨afer, H. Waclawek, S. Riedmann, C. Binder, C. Neureiter, and S. Huber, “It/ot integration by design,” arXiv preprint arXiv:2305.19735, 2023.
[16] A. Rahmatulloh, F. Nugraha, R. Gunawan, and I. Darmawan, “Event-driven architecture to improve performance and scalability in microservices-based systems,” in 2022 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS). IEEE, 2022, pp. 01–06.
[17] H. Aljawawdeh, M. Sabri, and L. Maghrabi, “Toward serverless and microservices architecture: Literature, methods, and best practices,” in Artificial Intelligence, Internet of Things, and Society 5.0. Springer, 2023, pp. 573–584.
[18] S. Baˇskarada, V. Nguyen, and A. Koronios, “Architecting microservices: Practical opportunities and challenges,” Journal of Computer Information Systems, 2020.
[19] Y. Cao, B. Wang, W. Zhao, X. Zhang, and H. Wang, “Research on searching algorithms for unstructured grid remapping based on kd tree,” in 2020 IEEE 3rd International Conference on Computer and Communication Engineering Technology (CCET). IEEE, 2020, pp. 29–33.
[20] G. Fragapane, R. De Koster, F. Sgarbossa, and J. O. Strandhagen, “Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda,” European Journal of Operational Research, vol. 294, no. 2, pp. 405–426, 2021.
[21] M. De Ryck, M. Versteyhe, and F. Debrouwere, “Automated guided vehicle systems, state-of-the-art control algorithms and techniques,” Journal of Manufacturing Systems, vol. 54, pp. 152–173, 2020.
[22] B. Mishra, B. Mishra, and A. Kertesz, “Stress-testing mqtt brokers: A comparative analysis of performance measurements,” Energies, vol. 14, no. 18, p. 5817, 2021.
[23] B. Mishra and A. Kertesz, “The use of mqtt in m2m and iot systems: A survey,” IEEE Access, vol. 8, pp. 201 071–201 086, 2020.
[24] H.-Y. Chien and N.-Z. Wang, “A novel mqtt 5.0-based over-the-air updating architecture facilitating stronger security,” Electronics, vol. 11, no. 23, p. 3899, 2022.
[25] R. Banno and T. Yoshizawa, “A scalable iot data collection method by shared-subscription with distributed mqtt brokers,” in International Conference on Mobile Networks and Management. Springer, 2021, pp. 218–226.
[26] M. Matic, M. Antic, P. Istvan, and S. Ivanovic, “Optimization of mqtt communication between microservices in the iot cloud,” in 2021 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2021, pp. 1–3.
[27] N. Markkanen, “Industry 4.0 automation system and network architecture to enhance data transmission and availability of infor-mation in industry automation,” 2023.
[28] R. N. G. Xavier, C. Cuarelli, P. J. I. Kaneshiro, O. L. Asato, J. R. Moro, and F. Y. Nakamoto, “Architecture proposal for smt production line in the context of industry 4.0,” in 2023 15th IEEE International Conference on Industry Applications (INDUSCON). IEEE, 2023, pp. 518–524.
[29] A. A. Abouzied, H. K. Kouta, M. A. Elhadek, and S. Aly, “Unlocking the potential of industry 4.0 technologies in the egyptian industry,” Port-Said Engineering Research Journal, 2024.
[30] Y. Mao, Y. Fu, S. Gu, S. Vhaduri, L. Cheng, and Q. Liu, “Resource management schemes for cloud-native platforms with computing containers of docker and kubernetes,” arXiv preprint arXiv:2010.10350, 2020.
[31] Y. Gong, F. Gu, K. Chen, and F. Wang, “The architecture of micro-services and the separation of frond-end and back-end applied in a campus information system,” in 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA). IEEE, 2020, pp. 321–324.
[32] J. Arundel and J. Domingus, Cloud Native DevOps with Kubernetes: building, deploying, and scaling modern applications in the Cloud. O’Reilly Media, 2019.
[33] T. Gkamas, V. Karaiskos, and S. Kontogiannis, “Performance evaluation of distributed database strategies using docker as a service for industrial iot data: Application to industry 4.0,” Information, vol. 13, no. 4, p. 190, 2022.
[34] H. F. O. Rocha, Practical event-driven microservices architecture: building sustainable and highly scalable event-driven microservices. Springer, 2021.