This study uses cybernetic theory to examine the integration of predictive artificial intelligence (AI) and the ship-then-shop model in fashion supply chains. The study examines how predictive systems can enhance adaptability, reduce waste and promote sustainability by incorporating real-time feedback loops into operational and consumer-facing processes.
A structured literature review was conducted across peer-reviewed sources and supported by a thematic coding matrix. This matrix classifies insights into five key phases of the supply chain: sourcing, production, warehousing, distribution and reverse logistics. The review also incorporates cybernetic and systems theory to conceptualize AI as a control mechanism within adaptive feedback systems.
AI can enhance the sustainability and efficiency of the fashion supply chain by enabling dynamic decision-making. The ship-then-shop model decouples production from immediate purchases, aligns output with demand and reduces overproduction. However, the review also identifies gaps in empirical validation and ethical risks, including algorithmic opacity, labour displacement and consumer surveillance.
This study makes an original contribution by framing AI as a cybernetic enabler of system-level transformation in fashion logistics. Unlike existing literature, it synthesizes predictive intelligence, consumer behaviour and adaptive operations within an integrated conceptual model that highlights opportunities and ethical challenges for sustainable innovation.
