Authors: Prof. P. B. Tathe, Atharva Borawake, Shivam Bunge, Suraj Gore, Dhaval Gundecha, Tejas Dawkar, Atharva Jaju
Certificate: View Certificate
This project presents a real-time production monitoring and color indication dashboard for industrial machinery. The system addresses the issue of manually monitoring the quantity and quality of production of each machine in a factory and provides an automated solution that reduces manual errors and improves efficiency. The objective of this project is to implement automation to reduce the manual effort of constantly updating the status of machines and providing accurate and real-time updates to the factory manager. The project utilizes the MERN stack, including MongoDB for data management, AWS for remote data management, and Node.js for the backend. The dashboard incorporates a color indication system to inform the manager about the current production status of each machine. The system uses a color code to indicate the level of production deviation from the planned quantity, with red, yellow, green, and orange colors representing specific levels of deviation. The manager can access the dashboard remotely, and the system sends alerts via SMS and email in case of any deviation from the planned production quantity. The result of this project is the successful implementation of a real-time production monitoring and color indication dashboard. The dashboard provides accurate and timely updates, reducing the manual effort of monitoring production status. The factory manager can monitor the production status from their office or home and make informed decisions based on the dashboard updates. The significance of this project lies in its ability to efficiently manage data from multiple machines in a factory, reducing manual effort, enhancing productivity, and minimizing downtime. The project\'s approach can be implemented in other industries to efficiently manage data and reduce manual efforts. This project contributes significantly to the field of industrial automation and production monitoring, and its outcomes can be applied to other similar projects.
The global industries have recently reached a unanimous decision to transition from a paper-based record system to an online platform. This innovative website allows for seamless form filling using tablets, with data securely processed and stored on the cloud. This shift not only saves considerable time, money, and resources but also eliminates the previously required effort invested in filling out paper forms and reviewing them. In manufacturing industries, efficient production monitoring and control are vital. It is crucial to ensure optimal performance of each machine, thereby maximizing overall production output. However, traditional manual monitoring methods often prove to be tedious and error-prone, leading to significant losses for the industry. To overcome these challenges, our team has developed a real-time production monitoring and color indication dashboard for industrial machinery.
Our project's primary objective is to automate the production monitoring process, enhance efficiency, and reduce manual errors. The dashboard offers a user-friendly interface for monitoring machine performance, tracking production quantities, and receiving alerts when production falls below or exceeds predetermined thresholds. The incorporation of color-coded indicators on the dashboard provides a quick and intuitive visual representation of the production status, empowering managers to make prompt and informed decisions. The implications of our project for the manufacturing industry are substantial. It enables streamlined data management, reduces manual efforts, and ultimately improves overall productivity. This survey paper provides a comprehensive analysis of our project, encompassing its objectives, methodology, results, and significance.
II. LITERATURE REVIEW
III. PROPOSED METHODOLOGY
The proposed system is a Software as a Service (SaaS) product, utilizing a standard Web Application System Architecture. The project development employs the popular and widely adopted MERN stack technology.
To store and maintain data for the full stack website, we have utilized MongoDB Atlas, a fully managed cloud database service.
The cloud platforms employed in this system include AWS, where an EC2 instance is deployed for server hosting, and AWS SNS for SMS notifications. Netlify is used for frontend deployment, with continuous deployment enabled via GitHub branch integration.
In this proposed system, key authorities such as General Managers, Managing Directors, and Supervisors establish initial planning for each machine department and individual machines within the industry. Autonomous systems fitted on the machines monitor their actual production at regular intervals. The variance between actual and planned production triggers color-coded alerts, which are sent to the respective authorities. Operators can also raise alerts in the event of issues such as material shortages, faulty production, or errors in machine handling.
These alerts are then forwarded to the appropriate industry authorities based on their type and severity, enabling immediate action to be taken. This proactive approach significantly contributes to improving overall productivity within the industry.
We begin by expressing our heartfelt gratitude to our project mentor, Prof. P. B. Tathe for their unwavering support, timely assistance, and invaluable contributions in helping us achieve the success of our project. We extend our appreciation to all those who have made significant contributions to this project. We are particularly grateful to Prof. P. B. Thate for providing expert guidance in software designing and connections. We would also like to acknowledge the lab faculty members for providing us with the necessary lab facilities and equipment whenever required.
We would like to thank our fellow batchmates for their unwavering moral support and timely assistance, which played an essential role in our journey. We extend our special appreciation to the team members for their complete coordination, honest efforts, and willingness to complete this project successfully. Finally, we would like to thank all those who have contributed to our project in any way, for their invaluable support and encouragement throughout the project.
A. Conclusion The implementation of the Real-time Production Monitoring and Color Indication Dashboard for Industrial Machinery using the MERN stack has provided an automated solution to a common problem faced by many industries. The dashboard allows for the efficient monitoring of production quantity and provides color-coded alerts in real-time, which reduces the need for manual labor and improves overall efficiency. The system has been successfully implemented and demonstrated to work according to plan, with positive feedback from the manager regarding its usefulness and ease of use. B. Future Scope As, there are several areas for improvement and expansion. Firstly, the system can be further optimized for even more efficient data management and processing. This can be achieved by implementing machine learning algorithms to predict production trends and improve overall accuracy. Secondly, the system can be integrated with other industrial systems such as inventory management, supply chain management, and quality control. This will provide a more comprehensive solution to industrial needs and improve overall productivity. Finally, the system can be scaled to larger industries and organizations to provide a wider reach and benefit to more people.
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Copyright © 2023 Prof. P. B. Tathe, Atharva Borawake, Shivam Bunge, Suraj Gore, Dhaval Gundecha, Tejas Dawkar, Atharva Jaju. 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.