AI in the Chain

Building Sustainable and Circular Supply Chains with AI

Context

Sustainability has moved from a corporate social responsibility initiative to a strategic imperative for supply‑chain leaders. Governments are rolling out stricter emissions and waste regulations, investors are scrutinising environmental, social and governance (ESG) metrics, and consumers demand transparency on sourcing and lifecycle impacts. However, most supply chains still operate in a linear model—extract, manufacture, use and discard—leading to resource depletion, pollution and massive waste. A circular supply chain seeks to close the loop by extending product life, recovering materials and creating regenerative flows. To build circularity at scale, companies need accurate data and intelligent optimisation: this is where AI can play a transformative role.

Recent reports show that sustainability and circularity are rising priorities. A Supply Chain Management Review article notes that leading retailers are reevaluating their business models to embed sustainability and circularity at the core, similar to pioneers like Patagonia. The article explains that circularity moves away from selling increasing volumes of new products and instead aims to slow and close material loops. It also highlights the booming global secondhand clothing market, forecast to reach $350 billion by 2027, up from $43 billion in 2023. Technologies such as FibreTrace provide digital transparency into the lifecycle of apparel, tracking materials from origin to end of life. These trends underline the momentum toward circular practices and the need for data‑driven tools to enable them.

Why it matters

Building sustainable and circular supply chains offers benefits beyond environmental stewardship:

• Regulatory compliance. Emission reporting, waste reduction and extended producer responsibility laws are expanding worldwide. AI‑based systems can automate data collection and reporting, reducing compliance costs and avoiding penalties.
• Cost savings and new revenue. Recovering materials reduces raw‑material costs and opens new revenue streams through resale and recycling. Circular models can improve profitability by selling products multiple times or remanufacturing them.
• Risk mitigation. By diversifying sources through recycling and remanufacturing, companies reduce exposure to commodity price volatility and supply disruptions. Circularity also lowers exposure to environmental incidents and reputational damage.
• Brand differentiation. Consumers favour companies with credible sustainability practices. Transparent, circular supply chains build trust and loyalty.

Immediate impacts and case examples

Fashion and apparel. The resale of second‑hand clothing is accelerating rapidly, with the market projected to $350 billion by 2027. Brands such as REI, H&M and Carhartt run take‑back programs and second‑hand shops. AI helps authenticate, sort and price used garments. Machine‑vision algorithms can classify clothing by style, colour and wear, while recommender systems match second‑hand products to customer preferences. Predictive analytics optimise refurbishment and resale decisions, maximising recovery value.

Material tracking and traceability. FibreTrace technology embeds scannable markers in textile fibres, providing digital transparency from material origin to product end‑of‑life. AI‑powered blockchain platforms ingest this data and verify authenticity. Brands can use machine‑learning models to trace the carbon footprint and ethical compliance of each product, enabling responsible sourcing and recycling.

Circular product design. Generative AI can design products and packaging for durability, repairability and recyclability. By simulating material properties and lifecycle impacts, algorithms suggest design modifications to reduce waste and improve ease of disassembly. AI tools also optimise packaging shapes to reduce empty space, lowering shipping emissions and costs.

Waste and inventory reduction. Demand sensing (see previous article) and inventory optimisation reduce overproduction and unsold inventory. AI‑powered quality inspection detects defects early and reduces scrap. For end‑of‑life products, machine‑learning models identify which components can be recovered and where they can be reused.

Limitations of traditional models

Linear supply chains do not account for product end‑of‑life or closed loops. Traditional systems face several limitations:

• Visibility gaps. Without granular data on material flows, companies cannot measure or manage sustainability metrics. Tracking provenance and post‑use journeys is manual and error‑prone.
• No optimisation for circularity. Planning tools optimise cost and service but rarely consider material reuse, return logistics or recycling capacities. As a result, recyclable or reusable components are discarded.
• Regulatory complexity. ESG reporting and compliance often require manually gathering data from multiple systems, leading to delays and inaccuracies.
• Behavioural inertia. Organisations focus on quarterly results; changing mindsets to prioritise long‑term environmental value requires cultural shifts.

How AI enables sustainable and circular supply chains

1. Lifecycle data capture and transparency. AI and IoT sensors capture data at every point in a product’s lifecycle—from raw materials and manufacturing to distribution, use and disposal. This continuous data feed enables calculation of carbon footprints and resource use. Computer vision and advanced identification (e.g., digital watermarks) help track items through resale and recycling streams.
2. Demand and returns forecasting. Machine‑learning models predict not only sales but also returns and take‑back volumes. Accurate forecasts allow companies to plan reverse logistics, refurbishment and recycling capacities.
3. Closed‑loop optimisation. AI algorithms match returned products and materials with the highest‑value recovery options (refurbishment, component reuse, recycling, energy recovery). Optimisation models decide whether to repair, dismantle or recycle based on condition, market demand and cost.
4. Smart sorting and remanufacturing. Computer vision systems identify and sort used parts and materials. Robots disassemble products, guided by AI, to preserve valuable components. Generative AI designs modular components that are easy to disassemble.
5. Dynamic carbon accounting. AI tools calculate real‑time emissions and waste metrics. Natural language processing automates ESG reporting by extracting data across systems and generating compliance documentation.
6. Consumer engagement. Chatbots and digital assistants educate consumers about repair, reuse and recycling options. Personalised recommendations encourage participation in circular programs.

Hands‑on adoption roadmap

1. Define sustainability and circularity goals. Set measurable targets for emissions reduction, waste diversion and material recovery. Align these goals with business strategy and stakeholder expectations.
2. Map the value chain and identify hotspots. Use lifecycle assessment tools to understand environmental impacts across sourcing, production, distribution, use and end of life. Identify areas where data is missing and where AI can provide insights.
3. Invest in traceability technology. Implement IoT sensors, RFID tags or digital watermarks to track materials. Integrate data into a blockchain or central repository. Choose technologies like FibreTrace that provide verifiable provenance.
4. Develop AI analytics. Build or buy models for demand sensing, returns forecasting, and carbon accounting. Use generative design tools to explore eco‑friendly product and packaging designs. Apply optimisation algorithms to identify the best recovery options for returned goods.
5. Pilot circular programs. Start with a product line or region. Establish collection and take‑back points, refurbish or remanufacture returned items, and measure economic and environmental impacts. Use AI to monitor performance and refine processes.
6. Engage partners and customers. Circularity requires collaboration across the value chain. Work with suppliers on sustainable materials and with recyclers on material recovery. Use consumer apps and incentives to drive participation in take‑back schemes.
7. Scale and report. Expand successful pilots. Use AI‑generated data to report progress on ESG goals and to inform investors and regulators. Continuously improve models as more data becomes available.

Conclusion

AI is a catalyst for building sustainable and circular supply chains. By capturing detailed lifecycle data, optimising reverse flows and enabling new product designs, AI helps companies shift from linear models to regenerative, closed‑loop systems. The growth of second‑hand markets and technologies like FibreTrace show that the infrastructure for circularity is maturing. Companies that embrace AI‑enabled sustainability will not only comply with regulations but also unlock cost savings, mitigate risks and differentiate their brands. The journey starts with setting clear goals, investing in traceability and analytics, piloting circular programs and scaling successful models. With the right blend of , partnerships and cultural change, supply‑chain leaders can turn sustainability from a buzzword into a competitive advantage.

References

1. Supply Chain Management Review. Supplier diversification, AI readiness, and circularity top supply chain priorities for 2025 – discusses how leading retailers are embedding sustainability and circularity into their core business models and projects the second‑hand clothing market to reach $350 billion by 2027.
2. Supply Chain Management Review. Supplier diversification, AI readiness, and circularity top supply chain priorities for 2025 – describes FibreTrace technology providing digital transparency into product lifecycle and its role in enabling circular supply chains.