<![CDATA[The Indonesian textile MSMEs industry faces significant challenges in demand forecasting due to high market volatility and limited resources. This study aims to examine the application of Artificial Intelligence (AI) in demand forecasting in the textile MSMEs supply chain through a Systematic Literature Review (SLR) approach. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 347 articles were identified in the initial stage, which were then narrowed down to 286 articles after removing duplicates. Furthermore, 78 articles passed the title and abstract screening stage, and 25 articles met the full-text assessment criteria for analysis. The analysis was conducted through a thematic synthesis of AI methods, implementation success factors, and challenges faced. The study results indicate that AI-based forecasting methods, specifically Long Short-Term Memory (LSTM), Random Forest, and Neural Network, generally demonstrate higher accuracy levels than traditional forecasting methods based on the reviewed studies. Furthermore, 15 key factors for successful AI implementation were identified, encompassing four main dimensions: technology, organization, data, and environment. The study also found that the most frequently reported key obstacles to AI implementation in MSMEs include limited data infrastructure, lack of technical expertise, and limited financial resources. This research provides a conceptual framework and practical application of AI in demand forecasting in Indonesian textile MSMEs and offers recommendations for further research related to organizational readiness, data governance, and AI implementation strategies.]]>
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