ItemConference Paper

AI-Driven Predictive Analytics for Sustainable Restaurant Operations and Waste Minimization

Title
AI-Driven Predictive Analytics for Sustainable Restaurant Operations and Waste Minimization
Creator
Padmavathy, M.; Dharmendra, H.; Thilagavathi, K.; Naresh, Burri; Devi, P. Praba; Tiwari, Mohit
Description
There is mounting pressure in the restaurant business to minimize the waste of their operations and use of resources, and still be able to make a profit. Unreasonable forecasting, over-procurement, and poor management of resources are the key causes of environmental and financial waste. As a potential solution to the issue, it presents an AI-based Predictive Analytics Framework (AID-PAF), which combines both a Temporal-Fusion Neural Architecture (TFNA), which is an asset demand prediction framework, and a waste-conscious linear programming model used to solve an inventory and resources allocation problem. Real restaurant operational datasets were used to test the system in a hybrid AnyLogic-MATLAB simulator. Experimental findings show that the proposed framework achieved 40%, 18%, 15%, and 21% reductions in the food waste, energy use, water use, and costs, respectively, and in addition enhanced the accuracy of the forecast, MAPE of 6.5%, the customer fill-rate 96.2%, and the Sustainability Score 78.7. The results prove that predictive analytics based on AI can greatly contribute to the sustainability, efficiency, and profitability of restaurant operations by making intelligent decisions with the assistance of data. 2025 IEEE.
Source
Proceedings of 2025 10th International Conference on Science Technology, Engineering and Mathematics, ICONSTEM 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
AI-Driven Predictive Analytics Framework; Sustainability-Driven Resource Utilization Model; Temporal-Fusion Neural Architecture; Waste Reduction Rate
Coverage
Padmavathy M., SRM Institute of Science & Technology, Department of Commerce, Tamil Nadu, Chennai, India; Dharmendra H., CHRIST (Deemed to Be University), Department of Commerce, Bangalore, India; Thilagavathi K., Saveetha School of Law, SIMATS, Department of Commerce, Saveetha University, Tamil Nadu, Chennai, India; Naresh B., CVR College of Engineering, Department of CSE(CS), Telangana, Hyderabad, India; Devi P.P., Sona College of Technology, Department of Management Studies, Tamil Nadu, Salem, India; Tiwari M., Bharati Vidyapeeth's College of Engineering, Department of Computer Science and Engineering, Delhi, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833156187-1;
Format
online
Language
English
Type
Conference paper

Citation

Padmavathy, M.; Dharmendra, H.; Thilagavathi, K.; Naresh, Burri; Devi, P. Praba; Tiwari, Mohit, “AI-Driven Predictive Analytics for Sustainable Restaurant Operations and Waste Minimization,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26077.

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