Digital twin
Digital twins are virtual representations of physical products, systems, or processes. They are created using digital models that mirror the characteristics and behavior of their real-world counterparts. These virtual twins are updated in real-time or periodically throughout the life-cycle of the product or system, capturing data and enabling analysis, simulation, and optimization. In the fashion industry, digital twins can be used to enhance various aspects of the product life-cycle, from design and production to retail and post-user stages.
Design and development
Digital twins can be used during the design phase to visualize and simulate garments or fashion collections. Designers can create virtual prototypes that closely resemble the physical products, allowing them to evaluate different design options, materials, and fit. This helps reduce the need for physical samples and iterations, saving time, cost, and resources.
Supply chain optimization
Digital twins enable real-time monitoring and optimization of supply chain processes in the fashion industry. By creating virtual representations of production facilities, logistics networks, and inventory systems, companies can simulate different scenarios, analyze performance, and identify opportunities for improvement. This helps optimize production planning, reduce waste, and enhance overall supply chain efficiency.
Personalized customer experience
Digital twins can be used to create virtual avatars or models that represent individual customers. By capturing data on body measurements, preferences, and style, companies can offer personalized recommendations for sizing, fit, and styling choices. This enhances the online shopping experience, reduces returns, and improves customer satisfaction.
Sustainability and circular economy
Digital twins facilitate the implementation of sustainable practices in the fashion industry. By capturing data on the materials used, energy utilization, and environmental impacts, companies can analyze and optimize the sustainability performance of their products. Digital twins can also aid in tracking and managing the entire life-cycle of garments, from production to recycling or upcycling, promoting circular economy practices.
Smart retail and inventory management
Digital twins can be utilized in retail stores to optimize inventory management and improve customer experience. By creating virtual representations of products and store layouts, companies can simulate and analyze customer flow, optimize shelf space, and forecast demand. This enables more efficient stock replenishment, reduces overstocking or understocking, and enhances the overall shopping experience.
Post-user engagement
Digital twins can be used to engage customers even after purchase. For example, by creating digital twins of fashion items in the hands of customers, companies can offer styling tips, care instructions, and customization options. This fosters a deeper connection with customers, promotes product longevity, and encourages sustainable utilization habits.
Overall, digital twins offer immense potential for the fashion industry by enabling virtual simulations, real-time monitoring, and optimization throughout the product life-cycle. They help enhance design, streamline supply chains, personalize customer experiences, promote sustainability, and improve overall operational efficiency
Case studies
Tommy Hilfiger – Digital showroom as collection twin
Tommy Hilfiger replaced most physical sample collections with a 3D-enabled digital showroom that acts as a living twin of each season’s range. Buyers and designers work on synchronized virtual samples instead of physical ones, reducing lead times, sample waste, and travel while enabling rapid scenario testing across colours, materials, and assortments.
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Adidas – SPEEDFACTORY footwear production twin
Adidas’ SPEEDFACTORY concept used highly sensorised, automated micro-factories whose processes were modelled in parallel digital environments. These factory twins allowed Adidas to simulate throughput, customise designs locally, and test changes to materials and process parameters before implementation, shortening development cycles and reducing overproduction.
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TextileGenesis – Fiber-to-retail digital twins for materials
TextileGenesis builds digital twins of fibre streams (e.g. TENCEL™, wool) by assigning secure digital tokens to each physical batch and tracking them through spinning, weaving, dyeing, and garment-making. Brands use these product-level twins to verify origin, map scope-3 impacts, and demonstrate material integrity to regulators and users, linking physical items to their verified digital records.
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unspun – Body and garment twins for on-demand jeans
unspun creates a high-resolution digital twin of each wearer’s body via 3D scanning and uses it to generate parametric jean patterns that are produced on-demand in local micro-factories. The combination of body-twin and pattern-twin eliminates size grading, cuts unsold inventory, and provides a data backbone for iterating fit and durability over the lifecycle of the garment.
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INDG – Product-level fashion twins for visualization and CX
INDG develops photorealistic digital twins of fashion products—shoes, apparel, accessories—that stay consistent across e-commerce, configurators, and XR experiences. These twins substitute for physical samples in sell-in and sell-out, allow real-time visual A/B testing of colourways and trims, and provide a shared digital reference for design, merchandising, and marketing teams.
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References
Akhtar, W. H., Kusiak, A., Kusiak, M., Kim, H. S., & Yang, H. S. (2022). A new perspective on the textile and apparel industry in the digital transformation era. Textiles, 2(4), 633–656. https://doi.org/10.3390/textiles2040033
Alam, M. D., Kabir, G., & Mirmohammadsadeghi, M. (2023). A digital twin framework development for apparel manufacturing industry. Decision Analytics Journal, 7, 100252. https://doi.org/10.1016/j.dajour.2023.100252
Botín-Sanabria, D. M., Acevedo-Correa, Y. N., González-Castaño, C., Zambrano-Rey, G., Buendía-Sarmiento, J. C., Zamora-Musa, R., & Rodríguez-Muñoz, L. E. (2022). Digital twin technology challenges and applications: A comprehensive review. Remote Sensing, 14(6), 1335. https://doi.org/10.3390/rs14061335
Li, Y., & Liu, Y. (2024). Empowering fashion design and intelligent manufacturing with digital twins in the Metaverse era. Asian Social Science, 20(4), 39–51. https://doi.org/10.5539/ass.v20n4p39
Wagner, R., & Kabalska, A. (2023). Sustainable value in the fashion industry: A case study of value construction/destruction using digital twins. Sustainable Development, 31(5), 3258–3273. https://doi.org/10.1002/sd.2480