ByteMatrix: Design and Implementation of an Integrated IoT Platform for Real-Time Manufacturing Monitoring and Workforce Management

We presented ByteMatrix, a web-based IoT platform that handles manufacturing monitoring and workforce management together. It shows that a single open-source full-stack application can meet the main operational and HR needs of small and medium Indian plants without a proprietary MES license. The main technical results are a configurable IoT simulator with bounded random-walk sensor models, an OEE engine based on ISO 22400-2, a payroll engine for EPF, ESI, PT, and TDS under both tax regimes, an automatic status classifier, a deterministic history backfill, and an eight-role RBAC model with plant-scoped isolation. Four directions for future work stand out. Real hardware integration through MQTT or OPC-UA adapters is the first priority for production use. Password hashing with bcrypt or Argon2 has to be added before any real use. The payroll engine should be extended to gratuity and bonus. And the in-process EventEmitter should be replaced with Redis Pub/Sub so that the server can run in a Node.js cluster or under container orchestration. A further extension is machine-learning anomaly detection on the telemetry stream, aimed at predictive maintenance by spotting sensor patterns that come before equipment failure.

[1]     R. Y. Zhong, X. Xu, E. Klotz, and S. T. Newman, “Intelligent manufacturing in the context of Industry 4.0: A review,” Engineering, vol. 3, no. 5, pp. 616–630, 2017, doi: 10.1016/J.ENG.2017.05.015. [2]     S. Jeschke, C. Brecher, H. Song, and D. B. Rawat, Eds., Industrial Internet of Things: Cybermanufacturing Systems. Cham, Switzerland: Springer, 2017. [3]     R. R. Pinheiro, F. Romero, and A. Zuperria, “Comparative study of open-source MES solutions for manufacturing SMEs,” Procedia Manuf., vol. 38, pp. 1502–1509, 2019. [4]     J. Wang, L. Zhang, L. Duan, and R. X. Gao, “A new paradigm of cloud-based predictive maintenance for intelligent manufacturing,” J. Intell. Manuf., vol. 28, pp. 1125–1137, 2017, doi: 10.1007/s10845-015-1066-0. [5]     S. Nakajima, Introduction to TPM: Total Productive Maintenance. Cambridge, MA, USA: Productivity Press, 1988. [6]     P. Muchiri and L. Pintelon, “Performance measurement using overall equipment effectiveness (OEE): Literature review and practical application discussion,” Int. J. Prod. Res., vol. 46, no. 13, pp. 3517–3535, 2008, doi: 10.1080/00207540601146498. [7]     Automation Systems and Integration—Key Performance Indicators (KPIs) for Manufacturing Operations Management—Part 2: Definitions and Descriptions, ISO 22400-2:2014, 2014. [8]     A. Kulkarni, S. Joshi, and R. Patil, “Automated statutory compliance in Indian factory payroll systems,” in Proc. Int. Conf. Adv. Comput., Commun. Informat. (ICACCI), 2018. [9]     B. Tudorica and C. Bucur, “A comparison between several NoSQL databases with comments for MOD db,” in Proc. 20th Int. Conf. Control Syst. Sci. (CSCS), 2011, doi: 10.1109/CSCS.2011.5948461. [10]   H. Kaur and G. Singh, “Real-time industrial dashboard design using React.js and WebSocket integration,” Int. J. Comput. Appl., vol. 175, no. 12, pp. 14–19, 2020.