Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kumar, Abhinav; Khanna, Smriti; Amajala, Sai Rithwik; Upreti, Shitiz; Radhakrishnan, G.V.; Upreti, Kamal Title A name given to the resource Data-Driven Transformation of Hospitality Supply Chains Using AI-Powered Segmentation Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of International Conference on Digital Innovations for Sustainable Solutions, ICDISS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDISS68238.2025.11320599" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDISS68238.2025.11320599</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033707479?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033707479?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar A., Data Scientist Turing, Palo Alto, CA, United States; Khanna S., School of Management Sciences, Apeejay Stya University, Haryana, Gurugram, India; Amajala S.R., Department of Computer Science, New York University, Tandon, New York, United States; Upreti S., Manav Rachna International Institute of Research &amp; Studies (MRIIRS), Haryana, Faridabad, India; Radhakrishnan G.V., Department of Economics, Finance Kalinga School of Management KIIT, Bhubaneswar, India; Upreti K., Department of Computer Science, Christ University Delhi NCR, Delhi NCR, Ghaziabad, India Description An account of the resource The increasing complexity of supply chain operations in the hospitality sector demands data-driven strategies for efficient resource utilization and service delivery. This study proposes an artificial intelligence (AI)-driven framework leveraging unsupervised machine learning to uncover hidden patterns in patient-related operational data sourced from a publicly available dataset. The research applies clustering algorithmsK-Means, DBSCAN, and Agglomerative Hierarchical Clusteringto segment patient prof iles based on key variables such as length of stay, procedure type, room category, equipment usage, and staffing needs. Principal Component Analysis (PCA) was employed for dimensionality reduction and cluster visualization. The optimal number of clusters was identified using the Elbow Method, with K-Means yielding the highest silhouette score. Comparative analysis of all clustering models revealed varying strengths in noise detection, interpretability, and handling of sparse features. The results demonstrate how intelligent segmentation can support dynamic resource planning, targeted supply allocation, and improved operational responsiveness in hospital-based hospitality systems. This work contributes to the growing domain of AI-enabled supply chain analytics and of fers a practical pathway for enhancing decision-making in smart hospitality environments. 2025 IEEE. Subject The topic of the resource Artificial intelligence (AI); DBSCAN; Elbow Method; Principal Component Analysis (PCA) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155641-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online