PART I
ORIGINAL RESEARCH TITLE
Hotel Intelligent System using Machine Learning and Cognitive Tool
Project Description
The aim of this project was to present a novel approach to daily hotel demand forecasting by forming clusters of stay dates from historical booking data. This method is distinct from the traditional forecasting techniques for hotels that assume the booking shapes and trends remain consistent in the trailing period. A machine learning algorithm is used to group past booking curves and the additive pickup model is implemented. The effectiveness of this new forecasting approach is evaluated using the real hotel booking data of three hotels, and the results reveal that hotel demand forecasts are more precise when they are generated on a cluster-level for all forecasting horizons.
PART II
ORIGINAL RESEARCH TITLE
Designing an AI-Driven Strategy Feedback Framework for Hotel Performance Optimization
Project Description
This research project aims to develop a structured roadmap for an AI-enabled strategy feedback system that enables hotels to better understand and respond to the underlying drivers of their performance. The project focuses on designing a conceptual and analytical framework capable of automatically generating actionable feedback from operational and commercial data, thereby supporting more timely, informed, and profit-oriented decision-making within a total revenue management perspective.
The research will concentrate on three key areas. First, it will develop an assessment tool to evaluate data integration and system readiness, identifying the extent to which existing data infrastructures can support AI-driven feedback mechanisms. Second, it will identify and define a set of relevant performance indicators that capture both revenue and broader profit drivers across hotel operations. Third, it will establish the logic and structure through which diverse data inputs can be translated into meaningful and interpretable feedback signals that inform strategic and operational decisions.
