Journal of Primeasia
Integrative Disciplinary Research | Online ISSN 3064-9870 | Print ISSN 3069-4353
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Volume 3 Number 1 2022
REVIEWS (Open Access)
Climate-Adaptive Sustainable Apparel Supply Chains: Integrating Machine Learning, Digitalization, and Circular Economy
Nadira Kulsum Papri 1*, Mahmud Kamal Anamul Haque 2, Zannatul Mouwa 2
Journal of Primeasia 3 (1) 1-8 https://doi.org/10.25163/primeasia.3110730
Submitted: 26 April 2022 Revised: 04 July 2022 Accepted: 10 July 2022 Published: 12 July 2022
Abstract
The apparel industry is increasingly confronted by a difficult convergence of environmental degradation, resource inefficiency, supply chain opacity, and growing climate vulnerability. While sustainable supply chain management and circular economy strategies have gained substantial attention, much of the existing literature remains fragmented across disciplinary boundaries and often stops short of addressing how supply chains can become genuinely adaptive under conditions of environmental uncertainty. This narrative review examines the evolving intersections among sustainability, circular economy practices, digitalization, and machine learning within apparel supply chains. A structured literature search was conducted across major academic databases, and the retrieved studies were synthesized thematically to identify dominant research patterns, conceptual blind spots, and emerging opportunities. The review reveals that sustainability and circularity are relatively well established in apparel supply chain research, whereas digital technologies such as blockchain and the Internet of Things are discussed primarily in relation to traceability and transparency. In contrast, machine learning remains only marginally represented, and climate adaptation is almost entirely absent as an explicit analytical focus. In response to these gaps, this study proposes a conceptual framework for machine learning-enabled climate-adaptive sustainable apparel supply chains. The framework integrates sustainability practices, circular economy principles, digital infrastructure, and machine learning intelligence into a unified system capable of supporting predictive, adaptive, and resilience-oriented decision-making. By bringing these traditionally disconnected domains into closer dialogue, this review offers a foundation for future empirical research and provides practical direction for developing more intelligent and environmentally responsive apparel supply chains.
Keywords: Sustainable apparel supply chains, Machine learning, Climate adaptation, Circular economy, Digitalization
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