Personalized Supply Chain Solutions for Sustainable Fashion: Leveraging Social Media Insights and Machine Learning

The fast fashion industry's rapid growth in clothing consumption since the 1990s has caused a significant global waste problem. Despite producing 80 billion new garments annually, only 1% is recycled, and 73% ends up in landfills. Before the arrival of fast fashion, the fashion industry typically operated on a two-season model. The shift to a 52-season model disrupted traditional cycles causing an imbalance in sustainable product development which is further increased by the nonconverging consumer needs and the clothing manufacturing. To create uniform symmetry between fashion suppliers and retailers, we must enhance information exchange between retailers and consumers to implement sustainable practices and boost business performance effectively. To achieve this, we propose a framework that analyzes consumer interactions on social media platforms like Instagram and TikTok, which are renowned pioneers of trends. Through this analysis, our framework employs real-time sentiment analysis techniques to identify positive emotional responses with user comments and likes and data image processing methodologies to extract garment types from media content. Subsequently, machine learning algorithms can be employed to select the most matching clothing items available on online markets based on user geographic location to offer consumers personalized recommendations based on their social media activity. Through our application, the data can be aggregated and transmitted to manufacturers who can utilize the advanced image, color, and style analysis techniques to dynamically adjust production and inventory with real-time decision-making techniques, enabling a more precise comprehension and prediction of market trends and reducing waste. This systematic analysis may create deep insights into consumer preferences, feedback, and engagement patterns, facilitating highly tailored product offerings and elevating the overall standard of customer satisfaction.