Usages of AI in Waste Minimization and Recycling Strategies in Textile Manufacturing

The purpose of this research was to assess the potential impact of Artificial Intelligence (AI) technology on the textile industry’s waste-reduction and recycling measures. Primary data were collected through focused survey on package performance, interviews of production managers, quality control engineers, and sustainability officers working in different textile industries. The qualitative interviews were analyzed by way of thematic content analysis. The results show that weaving mills are the sector with the greatest total waste (93±7 kg per 1000 m² of fabric) and cutting of fabric is the main source of it in all the sectors. However, mechanical recycling process, then the chemical recycling and finally the waste to energy process is the major processes in marketing. AI adoption is driven by data analytics for process optimization (65.4±4.8% for garment manufacturing) and machine learning for predictive maintenance (55.2±3.1%), whereas AI for recycling and sorting automation exhibits lower adoption (28.6±2.8%). After AI implementation, garment manufacturing produced the highest total waste percentage reduction (17.1±3.1%) and total cutting waste reduction (20.8±3.4%). The rewards are also seeing are also identified being less wastage of materials by 72%, better efficiency within the production process by 68.4% and less machine rework by 62.5%. The highest barriers to AI deployment are costs of implementation (65.3±4.7% in garment manufacturing) and technical expertise (58.6±3.5%) highly cited, with staff training (68.1±5.1%) and financial incentives (55.7±4.8%) recognized as important fore-runners. In summary, combined with targeted waste reduction and recycling, the practice of AI is a viable route toward sustainable, efficient, and environmentally benign textile manufacturing.