Optimizing Hotel Performance Through AI-Powered Revenue Forecasting and IoT Data Analysis
The Hotel Management System aims to streamline the complexity of overseeing multiple hotel branches by providing a cohesive system that automates numerous procedures, such as guest registration, room reservations, and real-time availability of accommodations. The increasing needs of the hospitality business render the management of operations across several sites burdensome. This solution offers a streamlined and effective platform to address these difficulties proficiently. The suggested system utilizes sophisticated technologies, including Artificial Intelligence (AI) and the Internet of Things (IoT), to optimize hotel operations and improve the guest experience. The system automates guest registration through real-time monitoring, facilitating expedited check-ins and check-outs via self-service kiosks and mobile applications. Room reservations are administered dynamically with real-time updates on availability across several branches, guaranteeing that clients obtain the most precise information. Moreover, IoT-enabled gadgets in guest rooms provide individualized experiences, enabling guests to manage room settings via mobile phones or voice commands. AI algorithms enhance operational efficiency by predicting demand, automating pricing methods, and facilitating predictive maintenance for hotel equipment. The system has a single dashboard that provides real-time statistics on hotel performance, resource utilization, and guest satisfaction, allowing staff to manage operations more efficiently. This Hotel Management System optimizes operational efficiency, elevates client experiences, and facilitates superior resource management throughout all hotel branches.
Authors
- Laxmi Raja (ORCID: https://orcid.org/0000-0001-6040-8794)
- M. S.
- S Parthipan
- R Guruprasath
Institutions
- Karpagam Academy of Higher Education (IN)
Publication Details
- Published
- 2025-06-05
- DOI
- https://doi.org/10.1109/icetea64585.2025.11099801
- Primary Topic
- Consumer Retail Behavior Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00