ARTIFICIAL INTELLIGENCE FOR CIRCULAR TRANSFORMATION IN FAST FASHION:SYSTEMATIC REVIEW THROUGH THE 12R FRAMEWORK

Abstract

The rapid development of artificial intelligence (AI) creates new opportunities for the circular transformation of fast fashion, yet evidence on its contribution to individual circular-economy strategies remains fragmented. This study applies a systematic literature review (SLR) structured around the 12R framework to identify the principal mechanisms of AI application, compare their distribution across circular strategies, and assess whether reported operational improvements are accompanied by demonstrated environmental effects. Publications were identified through Google Scholar and screened using thematic relevance criteria; journal quartile was subsequently applied as an additional source-selection criterion. The analytical stage combined qualitative content analysis, deductive 12R coding, frequency analysis, and comparative synthesis. The results show a strong concentration of research in R1 Rethink and R2 Reduce, particularly in demand forecasting, personalization, inventory optimization, digital design, and quality control. Later-loop strategies are represented much less frequently in the retrieved corpus. However, this imbalance is interpreted cautiously because the search query did not explicitly enumerate all 12R-specific terms. The review also demonstrates that operational efficiency should not automatically be equated with environmental improvement: only evidence based on measurable changes in waste, resource use, or material flows can substantiate such effects. The study contributes a strategy-level mapping of AI applications and identifies directions for more targeted empirical research.