The Influence of Social Network on Forecasting in Fashion Industry

The fashion industry is a significant contributor to environmental degradation, with its rapid turnover of trends leading to substantial waste. This underscores the necessity for precise forecasting methods to optimize production, procurement, lead time management, and inventory control. Traditionally, fashion forecasting relied on statistical analyses and retail sales data. However, the rise of social media has significantly influenced fashion preferences, trends, and consumer behavior, yet few forecasting models effectively integrate social media data. This paper introduces a forecasting model that leverages social media data from TikTok, Facebook, and Instagram, considering the distinct engagement metrics and demographic segments of each platform. By analyzing data from these social networks and incorporating operational parameters, the model aims to provide more accurate sales forecasts. The integration of diverse data sources is expected to enhance the predictive accuracy, offering a robust approach to demand forecasting in the fashion industry. Further support for the precision and reliability of predictions derived from social network data comes from the anticipated growth in social media usage