Innovating Mess Services: A Digital Transformation

Abstract

Across industries, digital transformation has reshaped how services are delivered, improving both efficiency and transparency. Yet, most hostel and college mess facilities still rely on manual processes such as paper coupons and handwritten registers. These outdated practices lead to long queues, inaccurate billing, food wastage, and poor accountability. This work presents the design and implementation of a digital mess management system intended to automate daily operations, improve visibility, and minimize wastage. The platform combines mobile and web applications that connect students, vendors, and administrators through modules for meal booking, payment, and feedback. Real- time analytics support better resource planning and service quality. The study shows that applying digital transformation principles even to small campus services can enhance sustainability and operational performance. Traditional hostel mess management systems rely on manual processes, leading to inefficiencies, inaccurate billing, and significant food wastage. This paper presents an AI-driven smart mess management system that integrates digital booking, real-time monitoring, and predictive analytics to optimize resource utilization. The proposed system employs machine learning techniques to forecast meal demand based on historical consumption patterns, thereby minimizing waste and improving operational efficiency. A cloud-based architecture ensures scalability and accessibility across multiple users. Experimental evaluation demonstrates that the proposed system reduces food wastage by up to 25% and improves prediction accuracy compared to traditional methods. The solution contributes to the development of smart campus ecosystems by enhancing transparency, sustainability, satisfaction.

Introduction

The text describes a smart AI-driven mess management system designed to replace traditional manual processes used in college and hostel mess facilities, such as paper records and manual billing. These outdated methods often cause inefficiency, food wastage, and lack of transparency.

To solve this, the proposed system introduces a digital platform with machine learning-based demand prediction. It allows students to book meals digitally while helping vendors and administrators manage food preparation more efficiently. The system uses historical consumption data, weekdays, and special events to predict daily meal demand using a Linear Regression model, which helps reduce food wastage and improve planning.

The system is built using a three-tier architecture:

• Frontend (React/Kotlin) for users,
• Backend (Node.js/Express) for processing,
• Database layer (PostgreSQL and Firebase) for storage and real-time updates.

It includes three main modules:

• Customer module for booking and feedback,
• Vendor module for menu and inventory management,
• Admin module for analytics and reporting.

The literature review shows that while digital systems and cloud-based solutions have improved service efficiency in other sectors, most mess systems still lack intelligent features like demand forecasting and data-driven decision-making.

Conclusion

This paper presents an AI-driven smart mess management system that integrates digital automation with predictive analytics to improve operational efficiency and reduce food wastage. The proposed system successfully addresses the limitations of traditional manual mess management by introducing real-time booking, transparent billing, and data-driven decision-making. The integration of a machine learning-based demand prediction model enables accurate forecasting of meal requirements, achieving an accuracy of approximately 85% and reducing food wastage by up to 25%. These results demonstrate the effectiveness of combining digital transformation with intelligent analytics in institutional service management. In addition to improving efficiency, the system enhances user satisfaction through real-time feedback and improved transparency. The scalable architecture allows the system to be extended to multiple hostels and integrated into broader smart campus ecosystems. Future work may focus on incorporating advanced machine learning models such as time-series forecasting and deep learning techniques, as well as integrating IoT-based monitoring systems for real-time kitchen management. Overall, the proposed solution highlights the potential of AI-driven systems in transforming everyday services into efficient, sustainable, and intelligent platforms.

References

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Copyright

Copyright © 2026 Prof. Abhilasha Narote, Prof. Aishwarya Kadam, Shrinivas Kandhare, Amardip Kuchar, Ashish Kenjale, Aditya Dawane. 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.