Case Introduction

By leveraging AI technology to predict the moisture content of setting machines, fabric feel, dyeing lab color deviation, and abnormal rework loss rates, textile quality stability and production efficiency can be enhanced. Additionally, analyzing key parameters enables the generation of optimal settings. Besides reducing defects and energy consumption, this also helps our clients to make effective decisions, achieving energy conservation, carbon reduction, and high-efficiency production. 

We developed an LLM-based anomaly customer complaint system that structurally processes complaints and anomaly information. For example, it can integrate historical cases and email content, enabling our clients to quickly identify similar cases and obtain the best solutions, thereby improving the accuracy and efficiency of handling anomalies and customer complaints. 

By utilizing an AI digital twin model to monitor, analyze, and predict equipment operations, we optimize KPI performance by inputting machine parameters to enhance efficiency and achieve energy savings. Additionally, integrating a dashboard for data visualization with LLM-powered Q&A functionality enables users to intuitively and efficiently access real-time information, thereby optimizing the decision-making process. 

The textile industry is facing challenges such as labor shortages, talent gaps, and difficulties in knowledge transfer. We leverage LLM technology, combined with 28 years of expertise from the Textile Research Institute. Through system integration and multilingual capabilities, we help enterprises improve technical knowledge transfer and streamline information retrieval processes.