Demand Forecasting Strategies in Fast Fashion Retail

The fast fashion industry is characterized by short product life cycles, rapid style turnover, and highly volatile demand. Forecast accuracy is therefore a critical determinant of inventory performance, profit margins, and sustainability outcomes. This study systematically reviews the distinctive challenges of demand forecasting in fast fashion, traces the evolution from conventional statistical methods to artificial intelligence-driven approaches, and adopts a comparative case-study design involving Zara and H&M to compare the implementation paths and operational implications of alternative forecasting strategies. The findings suggest an ongoing paradigm shift from forecast-driven production toward demand-driven operations. Rather than pursuing a single high-precision forecast, leading firms are building a dynamic sensing system centered on small-batch testing, real-time data feedback, and agile replenishment. The paper further discusses the implications of this transformation for inventory management, supply chain design, and sustainable development, and proposes differentiated demand forecasting strategies for fast fashion firms.