ARIADNE: a data thread for circular fashion

AI Quick Summary

Circular fashion strategies often fail not because brands lack ambition, but because garments disappear once they leave the store. Ariadne explores the enabling technology that allows brands to reconnect with garments during actual use, closing the data gap between design intent and real-world lifecycle behaviour. At the design stage, brands can already map potential circular end-of-use pathways for each garment – repair, reuse, resale, recycling – together with their costs and environmental impacts. The challenge is activating the right pathway at the right moment. Ariadne investigates how lightweight signals, a user-shared photo, an embedded RFID tag, purchase profile data, or AI recognition algorithms can help infer garment condition and usage patterns and trigger circular recovery actions through brand touchpoints. Complementary to the Digital Product Passport, Ariadne enables brands to move from static, design-time circularity planning to active, data-driven lifecycle management.  Fashion brands are increasingly expected to take responsibility for the full lifecycle of the products they place on the market. Emerging regulatory frameworks and circular economy policies are progressively extending producer responsibility beyond manufacturing and sales, requiring brands to actively manage repair, reuse, resale and end-of-life processes. Similar transitions have already taken place in other sectors under extended producer responsibility schemes, such as electronic waste management. The fashion industry, however, faces a structural data gap that makes this transition particularly difficult. During the design phase, brands can already define the circular end-of-life pathways available for each garment – such as repair, refurbishment, resale, recycling – and model the associated costs, revenues and environmental impacts. These pathways represent potential circular options grounded in known technical product characteristics, such as material composition, repairability, recyclability, expected residual value, and compatibility with different recovery processes. The challenge is therefore not to define circularity from scratch, but to understand which option becomes most appropriate once the garment’s actual condition and use history are taken into account. Yet once a garment leaves the store, this carefully mapped potential goes largely unrealised. Brands lose visibility over how products are actually used, how long they last, whether they are repaired or resold, and when they reach the end of their useful life. Without this information, activating circular flows at the right moment becomes guesswork rather than strategy. ARIADNE addresses this gap not by proposing another transparency layer, but by assessing the feasibility, business value and implementation potential of an enabling technology that could help brands reconnect with garments during use, recover lifecycle data and turn it into actionable circular decisions. The project investigates whether signals already accessible in the brand–customer relationship can be combined into a meaningful garment intelligence layer. These may include purchase and return histories that provide indirect signals of usage intensity and ownership patterns, AI-based wear inference from images shared voluntarily within brand digital touchpoints, interaction patterns from smart laundry appliances or clothing care services, anonymised signals from resale and styling platforms, or embedded identifiers in smart textiles that log interaction events without active user effort. None of these signals alone is sufficient to describe garment use reliably. However, when combined and interpreted through a learned model of garment usage behaviour, they can help reconstruct a rich picture of how a garment is actually living in the world and how its condition may be evolving over time. ARIADNE is conceived as a brand-level intelligence layer: each brand uses signals generated within its own ecosystem and customer relationship to regain visibility over the garments it has placed on the market. Brand-sensitive information remains under the control of the brand, while customer usage-related data is envisioned as voluntary, incentive-based and governed through clear consent and value exchange mechanisms. This technological layer is designed to integrate naturally into existing brand touchpoints: for example, the moment a customer purchases a new item becomes an opportunity to recover and redirect the old one through the most appropriate circular pathway. Brands can recover garments before their residual value is lost, monetise end-of-life management more effectively, and strengthen customer loyalty by offering rewards, discounts or more relevant next-purchase options in exchange for the return of unused or end-of-life garments. The circular pathway decision is not made blindly. Building on the end-of-life maps defined at the design stage, ARIADNE feeds real usage data into a decision layer that evaluates which circular option (repair, resale, refurbishment or recycling) delivers the best combination of economic value and environmental impact at that specific point in the garment's life. This allows brands to activate recovery actions at the most appropriate moment from a value, impact, and operational perspective, rather than waiting for the consumer to spontaneously initiate a return or disposal. Beyond improving reverse logistics and circularity rates, this approach generates a feedback loop back to design. Understanding how garments actually behave in use, which products are worn intensively, which fail early, which are often resold or repaired, allows brands to redesign for durability and repairability, refine their circular pathway assumptions, and continuously improve both product performance and end-of-life recovery rates. In this sense, ARIADNE complements what Digital Product Passport initiatives currently aim to achieve. While DPPs focus on making structured product information available across the value chain, ARIADNE is fundamentally concerned with the dynamic data layer that emerges during use, and with how that data can be captured through enabling technology and turned into operational decisions. The two perspectives are potentially synergistic: ARIADNE can make use of product information associated with the DPP and, where relevant, contribute additional lifecycle intelligence that enriches how product data is used over time. The core hypothesis of ARIADNE is that circular fashion will only scale when brands can connect design-time circularity intent with real-world product intelligence, activating the right circular pathway for the right garment at the right moment, in a way that is both operationally feasible and commercially meaningful.

Project Idea Metadata

• Project Idea Name: ARIADNE: a data thread for circular fashion
• Date: 4/20/2026 9:45:46 AM
• Administrators:
  • Silvia Menato

Project Idea Description

Fashion brands are increasingly expected to take responsibility for the full lifecycle of the products they place on the market. Emerging regulatory frameworks and circular economy policies are progressively extending producer responsibility beyond manufacturing and sales, requiring brands to actively manage repair, reuse, resale and end-of-life processes. Similar transitions have already taken place in other sectors under extended producer responsibility schemes, such as electronic waste management. The fashion industry, however, faces a structural data gap that makes this transition particularly difficult. During the design phase, brands can already define the circular end-of-life pathways available for each garment – such as repair, refurbishment, resale, recycling – and model the associated costs, revenues and environmental impacts. These pathways represent potential circular options grounded in known technical product characteristics, such as material composition, repairability, recyclability, expected residual value, and compatibility with different recovery processes. The challenge is therefore not to define circularity from scratch, but to understand which option becomes most appropriate once the garment’s actual condition and use history are taken into account.

Yet once a garment leaves the store, this carefully mapped potential goes largely unrealised. Brands lose visibility over how products are actually used, how long they last, whether they are repaired or resold, and when they reach the end of their useful life. Without this information, activating circular flows at the right moment becomes guesswork rather than strategy. ARIADNE addresses this gap not by proposing another transparency layer, but by assessing the feasibility, business value and implementation potential of an enabling technology that could help brands reconnect with garments during use, recover lifecycle data and turn it into actionable circular decisions. The project investigates whether signals already accessible in the brand–customer relationship can be combined into a meaningful garment intelligence layer. These may include purchase and return histories that provide indirect signals of usage intensity and ownership patterns, AI-based wear inference from images shared voluntarily within brand digital touchpoints, interaction patterns from smart laundry appliances or clothing care services, anonymised signals from resale and styling platforms, or embedded identifiers in smart textiles that log interaction events without active user effort. None of these signals alone is sufficient to describe garment use reliably. However, when combined and interpreted through a learned model of garment usage behaviour, they can help reconstruct a rich picture of how a garment is actually living in the world and how its condition may be evolving over time.

ARIADNE is conceived as a brand-level intelligence layer: each brand uses signals generated within its own ecosystem and customer relationship to regain visibility over the garments it has placed on the market. Brand-sensitive information remains under the control of the brand, while customer usage-related data is envisioned as voluntary, incentive-based and governed through clear consent and value exchange mechanisms.

This technological layer is designed to integrate naturally into existing brand touchpoints: for example, the moment a customer purchases a new item becomes an opportunity to recover and redirect the old one through the most appropriate circular pathway. Brands can recover garments before their residual value is lost, monetise end-of-life management more effectively, and strengthen customer loyalty by offering rewards, discounts or more relevant next-purchase options in exchange for the return of unused or end-of-life garments.

The circular pathway decision is not made blindly. Building on the end-of-life maps defined at the design stage, ARIADNE feeds real usage data into a decision layer that evaluates which circular option (repair, resale, refurbishment or recycling) delivers the best combination of economic value and environmental impact at that specific point in the garment's life. This allows brands to activate recovery actions at the most appropriate moment from a value, impact, and operational perspective, rather than waiting for the consumer to spontaneously initiate a return or disposal.

Beyond improving reverse logistics and circularity rates, this approach generates a feedback loop back to design. Understanding how garments actually behave in use, which products are worn intensively, which fail early, which are often resold or repaired, allows brands to redesign for durability and repairability, refine their circular pathway assumptions, and continuously improve both product performance and end-of-life recovery rates. In this sense, ARIADNE complements what Digital Product Passport initiatives currently aim to achieve. While DPPs focus on making structured product information available across the value chain, ARIADNE is fundamentally concerned with the dynamic data layer that emerges during use, and with how that data can be captured through enabling technology and turned into operational decisions. The two perspectives are potentially synergistic: ARIADNE can make use of product information associated with the DPP and, where relevant, contribute additional lifecycle intelligence that enriches how product data is used over time.

The core hypothesis of ARIADNE is that circular fashion will only scale when brands can connect design-time circularity intent with real-world product intelligence, activating the right circular pathway for the right garment at the right moment, in a way that is both operationally feasible and commercially meaningful.

Circular fashion strategies often fail not because brands lack ambition, but because garments disappear once they leave the store. Ariadne explores the enabling technology that allows brands to reconnect with garments during actual use, closing the data gap between design intent and real-world lifecycle behaviour. At the design stage, brands can already map potential circular end-of-use pathways for each garment – repair, reuse, resale, recycling – together with their costs and environmental impacts. The challenge is activating the right pathway at the right moment. Ariadne investigates how lightweight signals, a user-shared photo, an embedded RFID tag, purchase profile data, or AI recognition algorithms can help infer garment condition and usage patterns and trigger circular recovery actions through brand touchpoints. Complementary to the Digital Product Passport, Ariadne enables brands to move from static, design-time circularity planning to active, data-driven lifecycle management.