Enhancing Cosmetic Supply Chain Efficiency Through Demand Forecasting Using Machine Learning

The cosmetic industry is characterized by its dynamic nature, influenced by ever-changing consumer preferences and trends. In this context, accurate demand forecasting plays a pivotal role in optimizing the cosmetic supply chain. There is a lack of comprehensive research on the applicability and effectiveness of various demand forecasting techniques within the cosmetic supply chain, considering seasonality as a factor. This paper explores the existing literature on demand forecasting within the cosmetic industry, emphasizing the significance of predictive analytics and advanced forecasting models. Through a case study of real-world data on a cosmetic product, this research assesses the applicability and effectiveness of various forecasting algorithms using machine learning. This study provides a comprehensive understanding of the challenges faced by cosmetic supply chains in demand forecasting, identifies key factors influencing demand and their impact on forecasting accuracy, and evaluates the effectiveness of different forecasting techniques in the context of cosmetic products.