Cybernetic Strategies: Fashion’s Sustainable Supply Chain Evolution with Ship-then-Shop

Purpose – This study explores cybernetics and systems theory in the fashion supply chain, focusing on integrating predictive AI and the Ship-then-Shop model to enhance sustainability. It investigates how these innovations optimize resource allocation, reduce waste, and minimize environmental impact. The study also examines how AI insights align production with consumer demand, enhancing supply chain resilience and adaptability. This holistic approach aims to engage manufacturers, retailers, and consumers effectively, advancing toward a sustainable and efficient supply chain paradigm. Design/methodology/approach – The study employed a comprehensive literature review, covering established and emerging sources from national and international scientific journals. It aimed to gain a thorough understanding of current knowledge and identify areas for future research. Additionally, the study explored the potential impacts of the ship-then-shop model on different supply chain stages, including consumer preferences, sourcing strategies, warehousing operations, production methods, and distribution processes. It also analyzed the handling and reintegration of returned products, emphasizing the model’s influence on reverse logistics and sustainability efforts. Findings/Results – The fashion industry’s increasing focus on sustainability necessitates a holistic view of the supply chain. Employing systems theory and cybernetics, technologies like Blockchain and AI improve transparency and resource management. Predictive AI acts as a cybernetic control mechanism, adjusting to data changes to optimize supply chain decisions and predict consumer demands, enhancing flexibility and reducing waste. The Ship-then-Shop model operates as a feedback loop, allowing retailers to adjust inventory strategies based on consumer behavior insights. Integrating advanced technologies with cybernetic principles promotes a more efficient, sustainable, and consumer-centric fashion supply chain, improving operational efficiency and fostering consumer trust through better supply-demand alignment. Originality/value – The research investigates predictive AI in fashion using cybernetics and systems theory, focusing on advancements in consumer preference analysis and product recommendations. It highlights AI’s role in understanding complex consumer behaviors in B2C interactions and creating personalized profiles, providing competitive advantages to retailers. Despite progress, gaps remain in understanding AI’s transformative impact in retail. The study explores integrating predictive AI into the Ship-then-Shop model to optimize resource allocation, minimize waste, and enhance sustainability. By analyzing feedback loops and information flows in AI-driven supply chain management, this research aims to uncover AI’s potential in fostering a more adaptive, resilient, and sustainable fashion supply chain. Research/ Practical/ Social/ Environment implications - Implementing the ship-then-shop model offers significant cybernetics and systems theory benefits in three key areas. It enhances competitive advantage by leveraging predictive AI to address consumer preference uncertainties, optimize operations, and deliver personalized experiences. This model accelerates consumer searches and tailors offers, improving the shopping experience. Moreover, it supports sustainability by cutting production waste, prolonging product lifecycles, and minimizing environmental impact and resource depletion. Efficient logistics and transportation also reduce carbon emissions, aligning product regulations with consumer preferences and promoting an environmentally conscious fashion supply chain. Research limitations - This study analyzes the ship-then-shop model in the fashion industry’s supply chain using theoretical frameworks and conceptual models. Despite offering conceptual insights, empirical validation is lacking. Future research should adopt an empirical approach with case studies and real-world data to assess the model’s impact on consumer experience, business performance, and sustainability. Integrating systems theory, cybernetics, and information theory could provide deeper insights into its effectiveness. Comparative studies across industries could also offer lessons for different contexts, highlighting implementation variations and impacts. This combined approach would enhance understanding of the ship-then-shop model’s implications for supply chain management and sustainability.