KMAN Publication Inc. AI and Tech in Behavioral and Social Sciences 4 Serial Number 15 2026 07 01 Explaining and Structuring a Smart Supply Chain Resilience Model in the Face of Global Market Turbulence: A Case Study of the Carpet Industry Explaining and Structuring a Smart Supply Chain Resilience Model in the Face of Global Market Turbulence: A Case Study of the Carpet Industry 1 7 EN Journal Article 2026 02 10 <p>Global market turbulence, technological change, and competitive pressure have exposed the Iranian carpet industry's supply chain to significant structural and operational vulnerabilities. In this context, a smart resilience model is needed to preserve competitiveness, improve adaptability, and support data-driven decision-making. This study aimed to explain and structure a localized model of smart supply chain resilience in the carpet industry. The study used an applied, exploratory mixed-methods design combining grounded theory and interpretive structural modeling (ISM). In the qualitative phase, data were collected through 15 semi-structured interviews with experts in the carpet industry, supply chain management, production management, and digital technologies. The interviews were analyzed through open, axial, and selective coding using MAXQDA. The qualitative analysis identified six main dimensions: causal conditions, core phenomenon, contextual conditions, intervening conditions, strategies, and consequences. In the structural phase, ISM and MICMAC analysis were used to determine hierarchical relationships and driving-dependence patterns among the extracted dimensions. The findings show that AI-enabled smart supply chain resilience in the carpet industry is shaped by global market turbulence, weak traditional structures, data limitations, digital maturity, technological acceptance, and coordinated data-driven strategies. The ISM results indicate a three-level structure in which causal conditions, the core phenomenon, and intervening conditions form the foundational level; strategies and consequences form the intermediate level; and contextual conditions emerge as the dependent outcome layer. MICMAC analysis shows that most dimensions operate as linkage variables with high driving power and medium dependence, while contextual conditions are highly dependent. The findings suggest that digitalization, predictive analytics, data governance, digital networking, supplier intelligence, and human-skill upgrading can strengthen resilience, agility, transparency, and sustainable competitive advantage in the carpet industry.</p> https://www.journals.kmanpub.com/index.php/aitechbesosci/article/download/5592/10625