Smart Manufacturing Monitoring System: A Web-Based Platform for Centralized Operational Visibility

Abstract

Manufacturing operations require coordinated visibility into equipment condition, production output, maintenance activities, and inventory availability. When these details are maintained in separate records, operators and supervisors may need to spend additional time collecting information and identifying operational changes. This paper presents the design and current development of a Smart Manufacturing Monitoring System, a web-based application that brings these functions together through a centralized dashboard. The frontend is built with React.js and Vite, the backend uses Node.js and Express.js to expose REST APIs, and MongoDB with Mongoose is used for data persistence. The application includes machine monitoring, production tracking, maintenance work-order management, inventory monitoring, and dashboard-level summaries. A software-based telemetry simulator generates changing machine indicators such as operating status, temperature, vibration, and Overall Equipment Effectiveness (OEE) for demonstration. The project is still under development; telemetry is not sourced from physical factory sensors, and machine-learning-based failure prediction has not yet been implemented or validated. The paper describes the architecture, modules, workflow, and proposed functional testing, and identifies real sensor integration, stronger validation, and predictive maintenance as future work.

Introduction

The paper presents a Smart Manufacturing Monitoring System, a web-based academic project designed to centralize important manufacturing information, including machine status, production progress, maintenance activities, and inventory records in a single dashboard.

The system uses a three-layer architecture consisting of a React.js frontend, Node.js and Express.js backend, and MongoDB database. Mongoose is used to manage database models, while a software-based simulator generates machine telemetry values to demonstrate real-time-style monitoring without requiring physical industrial equipment.

The application consists of five main modules:

1. Dashboard and Analytics: Displays an overview of machine conditions, production performance, maintenance activities, and inventory status.

2. Machine Monitoring: Shows machine details, operating status, temperature, vibration, and OEE values using simulated data.

3. Production Monitoring: Tracks production targets, actual output, and production-line performance.

4. Maintenance Management: Organizes maintenance work orders, priorities, descriptions, and scheduled dates.

5. Inventory Monitoring: Manages stock quantities, SKU details, stock thresholds, and unit costs.

The project aims to reduce the difficulty of managing manufacturing information across disconnected systems by providing a centralized platform. It also uses REST APIs for communication between the frontend and backend and MongoDB for structured data storage.

The research is supported by existing studies on Industry 4.0, cyber-physical manufacturing systems, and manufacturing performance indicators. However, the current implementation is an academic demonstration rather than a fully deployed industrial solution. Machine readings are simulated, and validated predictive-maintenance or machine-failure prediction capabilities have not yet been implemented.

Future improvements include integrating real industrial sensors, implementing automated alerts, developing predictive-maintenance models, and conducting comprehensive functional and performance testing.

Conclusion

This paper presented the design and current development of a Smart Manufacturing Monitoring System that combines machine monitoring, production tracking, maintenance work orders, inventory information, and dashboard summaries within a web-based application. React.js and Vite provide the frontend, Node.js and Express.js expose REST APIs, and MongoDB with Mongoose provides data persistence. A software telemetry simulator supports demonstration of changing machine indicators without physical industrial equipment. The system offers a modular foundation for centralized operational visibility, but its current status and limitations must be considered: it is still under development, uses simulated telemetry, and does not yet provide validated machine-learning-based failure prediction. Further implementation and testing are required before drawing conclusions about industrial impact.

References

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Copyright

Copyright © 2026 R. Dhanalakshmi, Balaji G., Ganesh C. B.. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.