Integrated Manufacturing Management Platform

Authors

Keywords:

integrated management, monitoring, control, dynamic and real time based platform

Abstract

INTRODUCTION: This paper presents a brief overview of the Integrated Manufacturing Management Platform (IMMP), designed to enable dynamic, real-time integration of supply chain and manufacturing management across multiple factories, workstations, and external business partners. The platform fosters interoperability and shared decision-making across strategic, tactical, and operational levels, leveraging integrated middleware comprising both hardware and software components, as well as associated technologies and tools.

OBJECTIVES: The primary objective of this work is to describe the development and capabilities of the IMMP, highlighting how it supports collaborative and integrated manufacturing planning, scheduling, monitoring, and control. The platform aims to enhance joint manufacturing management by enabling information sharing and process coordination among a distributed set of stakeholders.

METHODS: The IMMP was developed using Microsoft Visual Studio to facilitate dynamic updates to its information, tools, and functionalities. The underlying databases were implemented in SQL Server Management Studio and iteratively refined to accommodate evolving platform requirements. The system architecture is based on modular components deployed across participating factories and work centers, supported by smart objects and appropriate interfaces for seamless integration and communication.

RESULTS: The resulting platform offers a fully integrated environment comprising multiple modules and functions provided by each collaborating entity. These modules support various tasks such as integrated manufacturing planning and scheduling, leveraging smart technologies and networked organizational structures. Data collected from the participating network of factories and facilities enabled the validation of the platform's core functionalities and guided successive improvements.

CONCLUSION: The IMMP demonstrates strong potential to support real-time, collaborative manufacturing management across diverse stakeholders. Through its modular, scalable, and dynamic architecture, the platform enhances interoperability and decision-making across all management levels, laying the foundation for more responsive and integrated industrial operations.

References

[1] Lopes, N., Costa, B., Alves, C.F., Putnik, G. D., Varela, M.L.R., Cruz-Cunha, M. M., Ferreira, L. (2022).The Impact of Technological Implementation Decisions on Job-Shop Scheduling Simulator Performance using Secondary Storage and Parallel Processing. 1st International Symposium on Industrial Engineering and Automation (ISIEA 2022), Managing and Implementing the Digital Transformation, 21st-22nd June 2022, Bozen-Bolzano, Italy. Lecture Notes in Networks and Systems (pp. 227-236), Springer.

[2] Pinedo, M. L. (2022). Scheduling: Theory, Algorithms, and Systems (6th ed.). Springer.

[3] Pinedo, M., & Chao, X. (1999). Operations scheduling with applications in manufacturing and services.

[4] Baker, K.R.; Trietsch, D. Safe scheduling: Setting due dates in single-machine problems. Eur. J. Oper. Res. 2009, 196, 69–77.

[5] Zijm, W. H., & Kals, H. J. J. (1995). The integration of process planning and shop floor scheduling in small batch part manufacturing. CIRP annals, 44(1), 429-432.

[6] Shen, W., Wang, L., & Hao, Q. (2006). Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 36(4), 563-577.

[7] Madureira, A.; Ramos, R.; Carmo Silva, S. Using Genetic Algorithms for Dynamic Scheduling. In Proceedings of the 14th Annual Production and Operations Management Society Conference (POMS’2003), Savannah, GA, USA, 4–7 April 2003.

[8] Goren, S.; Sabuncuoglu, I. Robustness and Stability Measures for Scheduling: Single Machine Environments. IIE Trans. 2008, 40, 66–83.

[9] Ouelhadj, D., & Petrovic, S. (2009). A survey of dynamic scheduling in manufacturing systems. Journal of scheduling, 12, 417-431.

[10] Aytug, H.; Lawley, M.A.; McKay, K.; Mohan, S.; Uzsoy, R. Executing production schedules in the face of uncertainties: A review and some future directions. Eur. J. Oper. Res. 2005, 161, 86–110.

[11] Vieira, G., Varela, M. L. R., Putnik, G. D. (2012). Technologies Integration for Distributed Manufacturing Scheduling in a Virtual Enterprise. Putnik, GD; Cruz-Cunha, MM (Ed.). 1st International Conference on Virtual and Networked Organizations Emergent Technologies and Tools (ViNOrg 2011). Communications in Computer and Information Science. Vol. 248, 337-347.

[12] Wang, D., Shen, R., & Shen, L. (2002). Collaborative learning based on multi-agent model. In Web-Based Learning: Men And Machines (pp. 107-114).

[13] Ramakurthi, V. B., Manupati, V. K., Machado, J., & Varela, M. L. R. (2021). A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems. Applied Sciences, 11(14), 6314. MDPI, Switzerland. https://doi.org/10.3390/app11146314.

[14] Varela, M.L.R. (2019). An Industry 4.0 oriented tool for supporting dynamic selection of dispatching rules based on Kano model satisfaction scheduling. FME Transactions. 47, 757–764.

[15] Azevedo, B.F.; Varela, M.L.R.; Pereira, A.I. Production Scheduling Using Multi-objective Optimization and Cluster Approaches. In Proceedings of the International Conference on Innovations in Bio-Inspired Computing and Applications, Online, 16–18December 2021; pp. 120–129.

[16] Varela, M. L. R., Putnik, G.D., Romero, F. (2022). The Concept of Collaborative Engineering: a Systematic Literature Review. Production & Manufacturing Research, 10(1), 784-839, Taylor & Francis, https://doi.org/10.1080/21693277.2022.2133856

[17] Varela, M. L. R., Trojanowska, J., Cruz-Cunha, M. M., Pereira, M. A., Putnik. G. D., Machado, J. (2023). Global Resources Management: A Systematic Review and Framework Proposal for Collaborative Management of CPPS, Applied Sciences, 13, 750, 1-19, MDPI, Swizerland. Doi: 10.3390/app13020750.

[18] Li, L. China’s manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”.Technol. Forecast. Soc.Chang.2018, 135, 66–74.

[19] Ladj, A., Varnier, C., & Tayeb, F. B. S. (2016). IPro-GA: an integrated prognostic based GA for scheduling jobs and predictive maintenance in a single multifunctional machine. IFAC-PapersOnLine, 49(12), 1821-1826.

[20] Biondi, M., Sand, G., & Harjunkoski, I. (2017). Optimization of multipurpose process plant operations: A multi-time-scale maintenance and production scheduling approach. Computers & Chemical Engineering, 99, 325-339.

[21] Modekurthy, V. P., Saifullah, A., & Madria, S. (2021). A distributed real-time scheduling system for industrial wireless networks. ACM Transactions on Embedded Computing Systems (TECS), 20(5), 1-28.

[22] Zhai, S., Gehring, B., & Reinhart, G. (2021). Enabling predictive maintenance integrated production scheduling by operation-specific health prognostics with generative deep learning. Journal of Manufacturing Systems, 61, 830-855.

[23] Varela, M. L. R., Ávila, P., Castro, H., Putnik, G. D., Fonseca, L.M.C., & Ferreira, L. (Editors) (2022a). Manufacturing and Management Paradigms, Methods and Tools for Sustainable Industry 4.0-Oriented Manufacturing Systems (Editorial). Sustainability. 14, 1574 (2022), MDPI. https://doi.org/10.3390/su14031574.

[24] Yang, W. H., & Takakuwa, S. (2017). Modeling and analysis of the customer checkout process with flexible servers for a retail store. In Proceedings of the 23rd International Conference on Industrial Engineering and Engineering Management 2016: Theory and Application of Industrial Engineering (pp. 301-304). Atlantis Press.

[25] Ferreirinha, L., Santos, A. S., Madureira, A. M., Varela, M. L. R., & Bastos, J. A. (2020). Decision support tool for dynamic scheduling. In Hybrid Intelligent Systems: 18th International Conference on Hybrid Intelligent Systems (HIS 2018) Held in Porto, Portugal, December 13-15, 2018 18 (pp. 418-427). Springer International Publishing.

[26] Hofer, F., Sehr, M. A., Russo, B., & Sangiovanni-Vincentelli, A. (2020, May). ODRE workshop: Probabilistic dynamic hard real-time scheduling in HPC. In 2020 IEEE 23rd International Symposium on Real-Time Distributed Computing (ISORC) (pp. 207-212). IEEE.

[27] Alves, C., Putnik, G.D., Varela, M.L.R. (2021). How environment dynamics affects production scheduling: Requirements for development of CPPS models. FME Transactions. 49, 827–834.

[28] Ebufegha, A., Li, S. (2021). Multi-agent system model for dynamic scheduling in flexibile job shops. In: 2021 Winter Simulation Conference (WSC). pp. 1–12 (2021).

[29] Varela, M. L. R., Putnik, G. D., Manupati, V. K., Rajyalakshmi, G., Trojanowska, J., & Machado, J. (2021). Integrated process planning and scheduling in networked manufacturing systems for I4.0: a review and framework proposal. Wireless Networks, 27(3), 1587-1599. DOI: 10.1007/s11276-019-02082-8.

[30] de Sousa Oliveira, P., de Oliveira, M. T. B., Oliveira, E., Conceição, L. R., Marcato, A. L. M., Junqueira, G. S., & de Alencar Junior, C. A. V. (2021). Maintenance schedule optimization applied to large hydroelectric plants: Towards a methodology encompassing regulatory aspects. IEEE Access, 9, 29883-29894.

[31] D’Aniello, G., De Falco, M., Mastrandrea, N. (2021). Designing a multi-agent system architecture for managing distributed operations within cloud manufacturing. 14, 2051–2058.

[32] Kalinowski, K., Krenczyk, D., Grabowik, C. (2013). Predictive-reactive strategy for real time scheduling of manufacturing systems. In Applied Mechanics and Materials. 307, 470–473 (2013).

[33] Jimenez, J.F., Bekrar, A., Trentesaux, D., Leitão, P. (2016). A switching mechanism framework for optimal coupling of predictive scheduling and reactive control in manufacturing hybrid control architectures. International Journal of Production Research. 54, 7027–7042 (2016).

[34] Cardin, O., Trentesaux, D., Thomas, A., Castagna, P., Berger, T., Bril El-Haouzi, H. (2021). Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges. Journal of Intelligent Manufacturing. 28, 1503–1517.

[35] Morariu, C., Morariu, O., Răileanu, S., & Borangiu, T. (2020). Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems. Computers in Industry, 120, 103244.

[36] Putnik. G.D., Pabba, S.K., Manupati, V. K., Varela, M. L. R., Ferreira, F. (2021), semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications, CIRP Annals - Manufacturing Technology, 70(1), 365-368. Elsevier, Netherlands. ISSN 0007-8506. https://doi.org/10.1016/j.cirp.2021.04.046

[37] Samala, T., Manupati, V. K., Nikhilesh, B. B. S., Varela, M. L. R., & Putnik, G. (2021). Job adjustment strategy for predictive maintenance in semi-fully flexible systems based on machine health status. Sustainability, 13(9), 5295.

[38] Tighazoui, A., Sauvey, C., & Sauer, N. (2021). Predictive-reactive strategy for identical parallel machine rescheduling. Computers & Operations Research, 134, 105372.

[39] Wenzelburger, P., & Allgöwer, F. (2021). Model predictive control for flexible job shop scheduling in industry 4.0. Applied Sciences, 11(17), 8145.

[40] Azevedo, B. F.; Montaño-Vega, R.; Varela, M. L. R.; Pereira, A. I. (2022). Bio-inspired Multi-objective Algorithms Applied on Production Scheduling Problems (2022). International Journal of Industrial Engineering Computations, 6(2), 145-156. Growing Science, Canada. Doi: 10.5267/j.ijiec.2022.12.001

[41] Santos, A. S., Madureira, A. M., & Varela, M. L. R. (2015). An ordered heuristic for the allocation of resources in unrelated parallel-machines.

[42] Sequeiros J.A., Silva R., Santos A.S., Bastos J., Varela M.L.R., Madureira A.M. (2021). A Novel Discrete Particle Swarm Optimization Algorithm for the Travelling Salesman Problems. In: Machado J., Soares F., Trojanowska J., Ivanov V. (eds). Innovations in Industrial Engineering, (ICIE 2021), Lecture Notes in Mechanical Engineering. Vol.3, pp. 48-55, Springer. https://doi.org/10.1007/978-3-030-78170-5_5.

[43] De Sousa, A. L., & De Oliveira, A. S. (2024). Scheduler with Buffer and Transportation Constraints for Inserting Rush Orders Without Deadlocks During Agent Convergence. IEEE Access.

[44] Santos, F., Costa, L., Varela, M.L.R. (2022). A Systematic Literature Review about Multi-objective Optimization for Distributed Manufacturing Scheduling in the Industry 4.0. In Proceedings of the 22nd International Conference on Computational Science and Its Applications (ICCSA 2022), Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Ana Maria A.C. Rocha, Chiara Garau (Eds.), Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13378 (pp. 157-173), Springer. https://doi.org/10.1007/978-3-031-10562-3_12

[45] Moreira, C., Costa, C., Santos, A.S., Bastos, J.A., Varela, M.L.R., Brito, M.F. (2023). Firefly and Cuckoo Search Algorithm for Scheduling Problems: A Perfomance Analysis. In: Innovations in Mechatronics Engineering II. icieng 2022. Lecture Notes in Mechanical Engineering (pp. 75-88) Springer.

[46] Sousa, B., Guerreiro, R., Santos, A.S., Bastos, J.A., Varela, M.L.R., Brito, M.F. (2023). Bat Algorithm for Discrete Optimization Problems: An Analysis. In: Innovations in Mechatronics Engineering II. icieng 2022. Lecture Notes in Mechanical Engineering (pp. 161-172), Springer. https://doi.org/10.1007/978-3-031-09382-1_14.

[47] Silva, J. C., Lopes, N., Alves, C., Putnik, G., Varela, L., Ferreira, L., & Cruz-Cunha, M. (2023). Evaluation of Solvers’ Performance for Solving the Flexible Job-Shop Scheduling Problem. Procedia Computer Science, 219, 1043-1048.

[48] Varela, M. L. R.; Alves, C.F.V.; Santos, A.S.; Vieira, G.G.; Lopes, N.; Putnik, G.D. Analysis of a Collaborative Scheduling Model Applied in a Job Shop Manufacturing Environment (2022b). Machines, MDPI, 10(12), 1138, 1-16, https://doi.org/10.3390/machines10121138.

[49] Varela, M. L. R., Putnik, G.D., Alves, C.F., Lopes, N., Cruz-Cunha, M.M. (2022c). A Systematic Review of Manufacturing Scheduling for the Industry 4.0. 1st International Symposium on Industrial Engineering and Automation (ISIEA 2022), Managing and Implementing the Digital Transformation, 21st-22nd June 2022, Bozen-Bolzano, Italy. Lecture Notes in Networks and Systems (pp. 237-249), Springer.

[50] Varela, L., Putnik, G., Vieira, G., Manupati, V., & Alves, C. (2024). Group Decision Making Approach for Ranking and Selecting Maintenance Tasks for Joint Scheduling with Production Orders. International Journal for Quality Research, 18(1).

Downloads

Published

2025-05-12

How to Cite

Vieira, G., Pereira, M. Ângelo, Varela, L., Putnik, G., & Cruz-Cunha, M. (2025). Integrated Manufacturing Management Platform. EAI Endorsed Transactions on Digital Transformation of Industrial Processes, 1(1). Retrieved from https://publications.eai.eu/index.php/dtip/article/view/9080