Management Optimization Solution for Fashion Retails System

Abstract

The fashion retail industry is a dynamic and expansive sector characterized by its ever-changing nature and continuous growth. Fashion retailing plays a pivotal role in this ecosystem, bridging the gap between manufacturers and consumers. In light of the industry's inherent volatility, the imperative of maximizing sales looms large. This research paper explores management optimization for fashion retail systems, offering innovative solutions to forecast profits, predict stock requirements, anticipate product demand, and identify top-performing manufacturers. Leveraging cutting-edge methodologies such as predictive analytics and machine learning, the study unveils a web-based application tailored to enhancing sales optimization. This application encompasses multifaceted components: profit projection, stock prediction, product demand analysis, and manufacturer performance evaluation. The construct models capable of delivering accurate insights by harnessing the power of algorithms such as Extra Trees Regressor, K-means, and Naïve Bayes. As a result, stakeholders can make informed decisions regarding future stock allocation, product assortment, and manufacturing partnerships. This paper provides an intricate walkthrough of the data preparation, model formulation, and the consequent findings from each research facet. In an industry where adaptability is paramount, the research extends a roadmap to empower fashion retail entities with the tools to navigate uncertainty and achieve sustained growth.