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Research on ChatGPT in the textile industry could focus on how this technology can be used to optimize the production process, generate meaningful and automated customer support, or develop personalized recommendations for shoppers. For instance, the system can be trained on data from a production line to detect any anomalies or process changes that could lead to defect, and alert personnel accordingly. On the customer-facing side, ChatGPT can be used to provide personalized support and advice to shoppers, optimized on the basis of their pre-specified preferences and past behaviour. Furthermore, the system can be used to generate meaningful recommendations according to customer’s preferences, thus making shopping experience more engaging and convenient. Such research could explore the underlying architecture, algorithms, and strategies of ChatGPT employed in the context of the textile industry, as well as their effectiveness and efficiency. It could also evaluate how the technology could be customized or optimized on a case-by-case basis in order to meet the needs and expectations of the relevant stakeholders. But, most of the older business models & technology in textile industry are based on traditional methods and are time-consuming, expensive and often have a 1-way communication from the industry -which becomes both inefficient &gives a poor user experience. Research on ChatGPT on textile industry will help companies to optimize the production process, provide automated customer support and generate personalized recommendations for shoppers, without any additional cost. For example, ChatGPT can be trained on data from production line to detect any anomalies or changes, giving timely alerts to the personnel. It can also be used to provide personalized support and advice to shoppers and generate meaningful recommendations according to customer’s preferences. Hence, with the help of this technology, companies in the textile industry can improve the customer experience and make their services more efficient, cost-effective and prompt. This research aims to explore different ways to mitigate waste generation, improve quality of products and achieve sustainability in textile industry with the use of ChatGPT.
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Stored only when both papers are in the corpus — 29 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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