Intelligent Agent for Automated Data Analysis, Dashboard Generation, and Training/Evaluation in Hospitality KPIs

Résumé fourni par la source

Modern hotel management increasingly depends on data-driven decision-making based on Key Performance Indicators (KPIs), yet many hotels face significant challenges: operational data arrives in heterogeneous CSV formats with inconsistent structures, manual KPI calculation is time-consuming and error-prone, and staff training in KPI interpretation requires substantial resources. This paper presents an intelligent agent that integrates generative AI with automated data processing to address these challenges in a unified conversational system. The system leverages Google Gemini 2.0/2.5 Flash for natural language understanding and code generation, integrated with a Python-based architecture (FastAPI backend, Chainlit conversational interface). Core capabilities include: (1) automated CSV preprocessing with format detection, missing value imputation using K-Nearest Neighbors, and temporal variable normalization; (2) AI-driven KPI suggestion and automated calculation through secure code generation with multi-tier validation and sandboxed execution; (3) interactive HTML dashboard creation using Plotly with temporal organization and year-over-year comparisons; (4) contextual training delivery with adaptive question generation across multiple formats (true/false, multiple-choice, open-ended, situational); and (5) automated assessment with personalized feedback. Experimental validation at Technology Readiness Level 4 (TRL 4) demonstrates system feasibility in controlled laboratory conditions. Testing with 30 datasets (12 real hotel data, 18 synthetic) achieved 91.5% success rate across 200 functional test cases, including 100% success in CSV preprocessing, 87.5% first-attempt code generation success, 95% dashboard generation accuracy, and strong correlation (r=0.82) between automated and expert scoring for open-ended questions. Performance benchmarking shows acceptable response times (median 11.5s for KPI calculation) and resource utilization suitable for moderate-scale deployment. These results establish technical feasibility for advancing to TRL 5 validation in operational hotel environments, demonstrating that generative AI can effectively automate hospitality analytics while maintaining security and reliability standards.

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