The research introduces Cloud-SCIM (Supply Chain Integration and Cloud-Based Operations Management to Resilient Smart Manufacturing) model, which aims to solve the problems of the modern manufacturing system. The model leverages cloud computing, IoT, and AI analytics to harmonize supply chain operations, boosting efficiency, flexibility, and resilience. Cloud-SCIM helps ensure effective production, demand projections, inventory maintenance, and sustainability by enabling real-time monitoring, predictive analytics, and automated decision-making. Performance indicators were compared and tested between the suggested model and traditional systems, including Overall Equipment Efficiency (OEE), Mean Absolute percentage Error (MAPE), and inventory turnover. The outcomes indicate significant changes: OEE has improved by 20 points (65 to 85), MAPE decreased by half (15 to 8), and inventory turnover has increased (5 to 9 times a year). Besides, Cloud-SCIM decreases the number of stockouts, downtime and consumes less energy as well as increases the effectiveness of risk mitigation. This study demonstrates that Cloud-SCIM can successfully modify smart manufacturing systems to provide a scalable, flexible solution that enhances operational productivity, resilience, and durability.
Smina N, Gahi Y, Gharib J. Data Management in Smart Manufacturing Supply Chains: A Systematic Review of Practices and Applications (2020–2025). Information. 2025;17(1):19.
2.
Giannakis M, Spanaki K, Dubey R. A cloud-based supply chain management system: effects on supply chain responsiveness. Journal of Enterprise Information Management. 2019;32(4):585–607.
3.
Challouf K, Alhloul A, Nemeth N. Mapping the role of industry 4.0 technologies in green supply chain management: a bibliometric and structured text analysis. Discover Sustainability. 2025;6(1).
4.
Frank AG, Dalenogare LS, Ayala NF. Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics. 2019;210:15–26.
5.
Zhu Q, Sarkis J, Geng Y. Green supply chain management in China: pressures, practices and performance. International Journal of Operations & Production Management. 2005;25(5):449–68.
6.
Yang D, Li R, Liu S. Exploring the Influence of Cloud Computing on Supply Chain Performance: The Mediating Role of Supply Chain Governance. Journal of Theoretical and Applied Electronic Commerce Research. 2025;20(2):70.
7.
Yenugula M, Sahoo SK, Goswami SS. Cloud computing in supply chain management: Exploring the relationship. Management Science Letters. 2023;13(3):193–210.
8.
Ivanov D, Dolgui A, Sokolov B. Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service.” Transportation Research Part E: Logistics and Transportation Review. 2022;160:102676.
9.
Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE communications surveys & tutorials. 2015;(4):2347–76.
10.
Surucu-Balci E, Iris Ç, Balci G. Digital information in maritime supply chains with blockchain and cloud platforms: Supply chain capabilities, barriers, and research opportunities. Technological Forecasting and Social Change. 2024;198:122978.
11.
Zheng X, Zhang X. Robustness of Cloud Manufacturing System Based on Complex Network and Multi-Agent Simulation. Entropy. 2022;25(1):45.
12.
Aron C, Sgarbossa F, Ballot E, Ivanov D. Cloud material handling systems: a cyber-physical system to enable dynamic resource allocation and digital interoperability. Journal of Intelligent Manufacturing. 2023;35(8):3815–36.
13.
Rasheed A, San O, Kvamsdal T. Digital Twin: Values, Challenges and Enablers From a Modeling Perspective. IEEE Access. 2020;8:21980–2012.
14.
Su N, Huang S, Su C. Elevating Smart Manufacturing with a Unified Predictive Maintenance Platform: The Synergy between Data Warehousing, Apache Spark, and Machine Learning. Sensors. 2024;24(13):4237.
15.
Romero D, Stahre J. Towards The Resilient Operator 5.0: The Future of Work in Smart Resilient Manufacturing Systems. Procedia CIRP. 2021;104:1089–94.
16.
Fowler DS, Epiphaniou G, Higgins MD, Maple C. Aspects of resilience for smart manufacturing systems. Strategic Change. 2023;32(6):183–93.
17.
Parhi S, Joshi K, Wuest T, Akarte M. Modelling resilience dynamics for smart manufacturing systems: quantification and empirical analysis. International Journal of Computer Integrated Manufacturing. 2024;38(11):1538–59.
18.
Farooq MS, Riaz S, Helou MA, Khan FS, Abid A, Alvi A. Internet of Things in Greenhouse Agriculture: A Survey on Enabling Technologies, Applications, and Protocols. IEEE Access. 2022;10:53374–97.
19.
Parhi S, Kumar S, Joshi K, Akarte M, Raut RD, Narkhede BE. Evaluation of operational transformations for smart manufacturing systems. Journal of Global Operations and Strategic Sourcing. 2023;17(3):541–73.
20.
Sofic A, Rakic S, Pezzotta G, Markoski B, Arioli V, Marjanovic U. Smart and Resilient Transformation of Manufacturing Firms. Processes. 2022;10(12):2674.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.