Develop a Canteen Circuit in a Real-World System

Abstract

In the current corporate and educational environment canteen plays a crucial role in feeding the nutritional needs of the employees and the students but looking forward to the traditional canteen management system lacks in various ways like long waiting queues, inefficiency, resource wastage, scalability issues and many more problems. To address this issue this research proposes Canteen-Circuit, a solution to the canteen management system that uses MERN stack and AI to provide a seamless connection for both canteen staff and customers. The Research will involve designing, developing, and evaluating the Canteen Circuit in a real-world system. Overall research not only streamlines daily operations but also provides a foundation for enhanced canteen experience for customers and administrators.

Introduction

I. INTRODUCTION

In the fast-paced environments of educational institutions, corporate offices and various other establishments, the management of the canteen plays a pivotal role because these places are the vital hubs where students, faculty and employees assemble to fulfil their culinary needs so it must ensure efficient operations and a satisfying dining experience for the users. On the other hand, the traditional canteen system lacks efficient operations due to its manual working style which results in long queues, limited visibility, waste, accessibility, scalability and many more issues. To overcome these issues and compete in the present era of streamlined and properly managed canteens, this research embarks on a journey to plan, develop and evaluate Canteen Connect, a smart canteen management system embedded with AI.

 The primary purpose of Smart-Bite is to enhance overall efficiency and user experience within canteen facilities. The backbone of the system lies within cutting-edge technologies i.e. MERN (MongoDB, Express.js, React.js, Node.js) and Artificial Intelligence (AI). MERN stack offers a robust and scalable for developing dynamic web applications. MongoDB is used for data storage as it is flexible and scalable for storing both structured and unstructured data, Express.js is used for the server-side logic, Node.js for the dynamic request handling and React for the dynamic user interface. Using these features of the MERN stack Canteen Connect will deliver a responsive and feature-rich platform for canteen management. AI will act as a catalyst as it will provide insights into customer behaviour, enhance waste management and many more things using various AI algorithms.

This Research mainly focuses on these objectives:

1. Efficient Order Processing through a user-friendly Interface for customers and AI utilization to predict peak hours and optimize performance.
2. Inventory Management using MongoDB for data storage and AI integration for optimizing stock levels and reducing wastage of food.
3. Real-time analysis to monitor popular items and AI utilisation for analyzing customer feedback and identify areas of improvement.
4. Integrate a secure payment gateway for secure transactions and implement fraud detection mechanisms using AI.
5. Design scalable system architecture using microservices and optimise API calls for high performance.

II. LITERATURE SURVEY

Canteen Management Systems are essential for the productive operation of the canteen of the organizations including various operations like order processing, inventory management and many more. Integration of modern technologies like MERN stack and AI will be a great enhancement in canteen management systems making them smart and scalable by providing real-time data processing and improved user interface. This literature survey explores existing research and applications of these technologies.

“Digital Transformation in CMS” is research by Zhao et al. [1] which thoroughly demonstrates the effects of digital transformation in canteens that will enhance efficiency and customer satisfaction. The study focuses on the importance of the integration of digital solutions for streamlined operations.

“MERN Stack: A Comprehensive Guide for Modern Web Development” by Johnson et al. [2] tells us how MERN stack is a comprehensive solution for building dynamic and scalable web applications. This research provides several case studies where the MERN stack was very

suitable for building CMS as MongoDB is a NoSQL database that is more flexible to handle dynamic data, Express.js provides a robust set of features for web and mobile applications, React.js a JavaScript library for building user-friendly interface and Node.js allowing the server-side scripting.

“AI in Inventory Management: Techniques and Applications” by Lee et al. [3] This study tells the effectiveness that AI can bring in predicting inventory needs which helps in minimizing waste and availability of enough stock of every item in the canteen. This research provides some ideas about how AI can be leveraged for demand forecasting using various techniques like regression analysis, clustering etc. hence improving the management of inventory and making it easy.

“Personalization in Digital Interfaces Using AI” by Smith et al. [4] In their work they discussed the role of AI in personalizing with the use of various machine learning algorithms through which the behaviours of the user can be adapted and they should be recommended the product according to their interest.

“Enhancing Security through Biometric Systems” by Sundararajan et al. [5] In their study they highlighted the effectiveness of biometrics can preventing unauthorized access and ensuring that transactions are secure and seamless without any delay.

“Efficiency Gains from Biometric Authentication in Canteen Systems” by Nguyen et al. [5] In their study discusses the efficiency that can be gained as biometric systems significantly reduce the time required for authentication compared to traditional methods such as ID cards or passwords.

“User Experience and Acceptance of Biometric Systems in Canteens” by Kumar et al. [6] and   Sharma et al. [7] This study explores the acceptance of biometric systems in CMS. This study suggests that users were impressed with the convenience and security of the biometric systems as it was fast and provided cashless transactions reducing the time consumption and making payment faster.

There have been many case studies that depicted how various organizations have adapted to modern CMS which resulted in a 20% reduction in food wastage, a 15% increase in customer satisfaction a 30% reduction in stockouts and improved order processing times. AI has also provided personalized meal suggestions to the users with machine learning algorithms therefore overall user experience was boosted with this idea.

This literature survey provides an overview of some existing research on canteen management systems using MERN stack and AI.

III. METHODOLOGY

The approach to developing the Canteen Circuit is grounded in a systematic and iterative way that covers all the stages involved in developing the Canteen Circuit from conceptualization to the deployment and stages that are further in the development phase which might be added later. This methodology outlines the various phases of the development.

A. Requirement Analysis:

Identify the problems that were faced in traditional canteen management systems through interaction with various users and our daily experience and documenting what features are needed to add such as order processing, inventory management, authentication etc.

 B. System Design:

Design the overall structure of the system divide the system into different modules and form a well-mannered structure for the system including various databases schema, server APIs and user interfaces also defines the interface between components and the modules.

C. Frontend Development:

Various technologies are used in developing the frontend i.e. the user interface but the backbone of the frontend is React.js which makes the frontend responsive through the react hooks, use-states, and properties to help the components interact with one another and also connects the frontend with the backend and make the frontend fully responsive and to make it more interactive and look the website good Tailwind CSS is used which provides the user interface finishing touch and make it more attractive and easy to use for the users.

D. Backend Development:

This phase deals with the development of the backend logic using Node.js and Express.js. Node.js is used to write the server-side logic including various JSON files which are used in the project and Express.js is used to handle API requests. Implement authentication and authorization using JWT (JSON Web Tokens) and finally connect the backend with the database (MongoDB) for storing and fetching data.

E. Database Design and Implementation:

This phase deals with the design of the MongoDB database schema to store the information of the users, admins, orders, and other information and make the user information secure and easy to fetch overall it deals with the database structure and ensures the integrity of the data stored.

F. Development and Integration of AI models:

This phase is for embedding the AI into the canteen management system this can be done by identifying the use cases of the AI like recommendation and demand forecasting and integrating them with the backend part. Then establishing fluent communication between the server side and AI modules to enable real-time decision-making power to the CMS.

G. Payment and Security Implementation:

A secure payment gateway is facilitated to ensure secure and fast online transactions and implement an AI-driven fraud detection mechanism to enhance payment security Razor Pay API is used for the payment. Also, ensure that the communication between the client and the server is encrypted and protects data and payment transactions.

H. Testing and Deployment:

Conduct extensive testing of all the functions, performance and security and gather feedback to identify the areas where improvement can be done and after all the testing has been performed then deploy it to the production environment.

I. Maintenance:

The last phase is where we have to set up an automated testing environment and plan regular updates and maintenance schedules to address bugs and add new features for a better experience for every user.

This methodology ensures the creation of a robust and user-friendly canteen management system that uses the latest technologies to enhance operational efficiency and user experience.

IV. OUTPUT

This output section of the research paper provides details about the result and outcome of the project with a glimpse of some coding parts.

Coding glimpse:

VI. FUTURE SCOPE

The implementation of CMS using the MERN stack and AI has shown some promising results but several areas for future research can further improve the overall system’s capability and extend its applications. Key areas of potential exploration include:

A. Integration with Emerging Technologies:

1. Artificial Intelligence and Machine Learning: Leveraging AI and ML for predictive analytics can enhance inventory management by forecasting demand based on historical data and current trends.
2. Internet of Things (IoT): Incorporating IoT devices can improve real-time monitoring of stock levels, equipment status, and environmental conditions, leading to more efficient operations.

B Enhanced User Experience:

1. Mobile Application Development: Expanding the system’s accessibility through dedicated mobile applications can offer users seamless interaction for placing orders, making payments, and providing feedback.
2. Personalization: Implementing algorithms that suggest menu items based on individual preferences and dietary restrictions can enhance customer satisfaction.

C. Advanced Payment Solutions:

- Blockchain Technology: Utilizing blockchain for secure and transparent transactions can enhance trust and reduce fraud.

   - Contactless Payments: Exploring the integration of various contactless payment methods, including mobile wallets and NFC-enabled devices, can streamline the checkout process.

D. Sustainability Initiatives:

1. Waste Management: Developing features to monitor and reduce food waste can contribute to sustainability goals. Integrating with local food donation programs can be another aspect of this initiative.
2. Eco-friendly Practices: Encouraging the use of biodegradable packaging and promoting digital receipts over printed ones can reduce the environmental footprint

E. Data Analytics and Reporting:

1. Enhanced Reporting Tools: Building advanced analytics and reporting tools can provide deeper insights into customer behaviour, sales trends, and operational efficiency. These insights can drive data-driven decision-making and strategic planning.

F. Security and Privacy:

1. Enhanced Security Protocols: Researching and implementing advanced security measures to protect sensitive customer data and transaction information.
2. Compliance with Regulations: Ensuring that the system complies with evolving data protection laws and standards to maintain user trust and legal integrity.

Conclusion

This Research on the CMS was developed using the MERN stack and AI has demonstrated various improvements in operational efficiency as compared to the traditional CMS. The main findings of the research are mentioned below. A. System Performance: The MERN stack provided a robust and scalable framework for building a comprehensive CMS. The system effectively managed various functionalities such as user authentication, order processing, and inventory management, ensuring smooth and efficient operations including security in the payment. B. AI Integration: AI models significantly enhanced the CMS by providing predictive analytics for inventory management and personalized meal recommendations. The predictive models achieved high accuracy in forecasting inventory needs, reducing waste and stockouts, while personalized recommendations improved user satisfaction and engagement. C. User Experience: The use of React.js for the frontend resulted in a dynamic and responsive user interface. User feedback indicated high levels of satisfaction with the system’s usability, intuitiveness, and real-time capabilities. D. Operational Efficiency: The integration of AI and the streamlined processes of the MERN stack led to notable improvements in order processing times and inventory management. Canteen staff reported enhanced efficiency and a reduction in manual workload. E. Security and Reliability: The system employed secure authentication mechanisms using JSON Web Tokens (JWT) and ensured data integrity and security, with no reported incidents of unauthorized access. In conclusion, the integration of the MERN stack and AI into a canteen management system offers a powerful solution for enhancing efficiency, security, and user experience. This research has laid the groundwork for future innovations in this domain, demonstrating that modern web technologies combined with AI can significantly improve canteen operations.

References

[1] Gupta, S., & Ranjan, P. (2019). Implementing Predictive Analytics in Canteen Management Using Machine Learning. Proceedings of the International Conference on Data Science and Advanced Analytics (DSAA), 219-228. [2] Kumar, A., & Singh, R. (2020). Enhancing User Experience in Canteen Management Systems Using AI and IoT. Proceedings of the IEEE International Conference on Internet of Things (iThings), 113-120. [3] Patel, M., & Shah, D. (2019). A Study on the Implementation of MERN Stack for Scalable Web Applications. Technical Report, Department of Computer Science, University of California. [4] MongoDB. (2021). MongoDB Documentation. Available at: https://docs.mongodb.com [5] Express.js. (2021). Express.js Guide. Available at: https://expressjs.com/en/starter/installing.html [6] React. (2021). React Official Documentation. Available at: https://reactjs.org/docs/getting-started.html [7] Node.js. (2021). Node.js Documentation. Available at: https://nodejs.org/en/docs/ [8] Tailwind CSS. (2021). Official Documentation. Available at: Tailwind CSS Docs

Copyright

Copyright © 2024 Harsh Singh, Keshav Sharma, Anjali Kaushik, Shambhavi Tripathi, Mrs. Ritu Aggarwal. 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.