Predict trends and personalize designs: AI algorithms can analyze consumer data and fashion forecasts to develop garments that resonate with specific demographics and preferences.
Optimize production processes: AI can streamline scheduling, resource allocation, and inventory management, reducing waste and downtime while boosting productivity.
Enhance quality control: AI-powered vision systems can detect even the tiniest defects with eagle-eyed precision, ensuring consistent quality throughout production.
Personalize shopping experiences: AI chatbots and virtual try-on rooms can offer personalized recommendations and immersive shopping experiences, leading to increased customer satisfaction and conversion rates.
Despite the vast potential of AI, its adoption in the apparel industry faces several challenges. Limited research, fragmented knowledge, and a lack of expertise are some of the hurdles that hold manufacturers manufacturers manufacturers back from embracing this transformative technology.
To bridge this gap, our paper conducts a comprehensive review of existing research, analyzing 149 articles from esteemed databases like Scopus and Web of Science. We categorize these articles based on the AI methods employed and the stages of the supply chain they target, offering a clear picture of current research trends and future avenues for exploration.
Furthermore, we delve into the specific needs of business-to-business (B2B) and business-to-consumer (B2C) segments within the apparel industry, highlighting the unique challenges and opportunities that AI presents for each.
By shedding light on the current state of AI in apparel manufacturing and identifying key areas for future research, this chapter aims to serve as a valuable resource for both researchers and industry professionals. With a collective effort to unlock the potential of AI, the apparel industry can weave a future of sustainability, efficiency, and personalized style, one intelligent thread at a time.