AI-Driven Demand Forecasting for Optimal Supply Chain Management

Abstract

During the internship, Tiffanie explored the limitations of traditional demand forecasting methods and their underlying causes. She identified multiple challenges faced by the company, such as intense competition, bias and errors in human judgment, and various demand-influencing factors. To address these limitations, Tiffanie delved into machine learning with the guidance of a data scientist. With support from her supervisor and industry contacts, she developed a customized machine learning-based demand forecasting approach for the FMCG industry. This approach considers product relationships and economic indicators, resulting in highly accurate predictions for fragrance, makeup, and skincare demand. The application of this model has the potential to revolutionize supply chain management, ensuring timely provision of products to meet customer demand. The model's potential to transform the industry is further validated by endorsement letters from renowned companies.