Key Takeaway
To achieve supply chain traceability, businesses must re-engineer carbon-intensive supply chains, focusing on recovering and reintroducing materials instead of exploiting resources. Strategies should include reducing single-use materials and utilizing data models like Digital Product Passports. A shift in product and market perception is essential, particularly for manufacturers of large assets, who should consider revenue models based on asset usage. Technologies like AI, machine learning, and IoT can facilitate this transition by enhancing collaboration and visibility. Organizations must also track product lifecycles to identify circularity opportunities, leveraging data consolidation for improved sustainability.
Next, they must ensure supply chain traceability. Data that enables traceability in the supply chain is essential for re-engineering carbon-intensive, one-way supply chains, creating more economic opportunities across communities, including those where the products are utilized. Instead of exploiting the environment or labor for raw materials, the focus should be on extracting parts, components, and materials to reintroduce them into the supply chain or repurpose them for new applications.
Businesses must also develop strategies for reintroducing recovered materials. Reducing single-use and intermediate materials in the manufacturing process, such as packaging, is equally important. Data models like Digital Product Passports and Lifecycle Analysis (LCA) are crucial for designing the extended supply chain, contract manufacturing arrangements, and logistics processes.
Finally, circularity necessitates a reevaluation of the product itself, the market in which it’s sold, the ecosystem that supports that market, and the value provided to customers. Manufacturers of large assets, such as turbines and earthmoving equipment, need to think beyond traditional manufacturing to becoming owners of fleets of assets that generate revenue ‘by the hour.’ It is also vital to adopt an integrative perspective on lifecycle costs to maximize value beyond manufacturing. Remote asset management, digital field services, and enhanced aftermarket service models are significant levers for reducing Scope III emissions and improving sustainability.
How can AI and other technologies accelerate circular business models?
AI, machine learning, and IoT are essential enablers of circular business models, assisting companies in overcoming barriers and expediting their transition. By enhancing collaboration, visibility, and operational efficiency, these technologies help businesses redesign systems, optimize resource usage, and unlock new value streams.
Firstly, businesses must engineer a system of change—this will enable companies to capitalize on near-term value opportunities to validate the concept and then pursue more fundamentally transformative opportunities. AI-driven system design also provides a flexible enterprise technology framework to support this evolution.
Organizations must integrate downstream visibility as well. Whether through takeback programs, subscription services, or reverse logistics processes, businesses need to monitor what happens to their products and the materials they contain throughout their entire lifecycle. While much of this information resides within enterprise systems of record, Gen-AI and other technologies can consolidate this data to identify opportunities that lead to circularity.
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