• supply chain management
• predictive analytics
• new product introduction
• forecasting
• demand planning
• data-driven decision-making

In the rapidly evolving landscape of global markets, where consumer preferences shift with unprecedented speed, the capability to accurately forecast demand for New Product Introductions (NPI) has become a critical determinant of business competitiveness. This thesis delves into the strategic implementation of advanced predictive analytics to revolutionize demand planning at Luxottica, a leader in the global eyewear industry, by presenting a case study of its wholesale clients. The study meticulously evaluates the efficacy of advanced data science techniques, i.e. time series forecasting, regression and classification models, against traditional forecasting methods, which predominantly rely on qualitative insights and intuitive judgment. The analysis underlines the models' robustness in reducing forecast errors, thereby enhancing inventory management and production planning, key points for maintaining an efficient supply chain and reach customer satisfaction. By running a simulation on a period spanning over the last year and a half, forecast error at lag 1 showed an outstanding decrease when applying more innovative procedures, from around 24\% to less than 7\%. Furthermore, the research articulates the broader business implications of integrating data-driven forecasting models, including organizational optimization and the potential for substantial cost reductions, highlighting the strategic value of adopting advanced analytics in global supply chain operations. By bridging the gap between academic theoretical models and practical applications in a real-world business setting, this work contributes significantly to the literature on supply chain management. It also provides valuable insights for enhancing operational efficiency and market responsiveness, offering a blueprint for operational excellence and strategic advantage in the competitive global marketplace. Looking forward, the enrichment of data resources for a more data-oriented design of Luxottica's supply chain would be fundamental to properly support the expansion of real-time data analytics and the exploration of machine learning algorithms, whose adaptability promise to further elevate the precision of demand forecasting.