Demand Forecasting New Fashion Products: A Review Paper

Abstract

New product demand forecasting is an important but challenging process that extends to multiple sectors. The paper reviews various forecasting models across different domains, emphasizing the unique challenges of forecasting new fashion products. The challenges are multifaceted and subject to constant change, including consumer preferences, seasonality, and the influence of social media. Understanding such difficulties enables us to provide an approach for improved and flexible prediction techniques. Machine learning techniques have the potential to address these issues and improve the accuracy of fashion product demand forecasting. Various advanced algorithms, including deep learning approaches and ensemble methods, employ large datasets and real-time data to predict demand patterns accurately. The paper suggests valuable information to experts, researchers, and decision-makers in the fashion industry, as it addresses the unique challenges and examines innovative solutions in new product forecasting.