Machine learning is currently being used to supplement efforts to reimagine global supply chains, which are riddled with unethical and environmentally unsound practices and behaviours. Materials like cobalt, nickel, tantalum and mica are associated with issues like child labour, slavery, theft of natural resources, environmental damage and human rights abuses, which are commonplace, but often not seen by consumers and manufacturers. A recent article in The Guardian links the global explosion in electric vehicle (EV) sales to extreme air and water pollution causing public health issues around Indonesia’s nickel mines.
Meanwhile, products like coconut, palm oil and intensively-farmed meat products are exacerbating deforestation in some of the world's most fragile ecosystems, and billions of tonnes of food is wasted in global supply chains each year.
For manufacturers, this is a mainstream problem, but one that’s difficult to police. Often, products that are unethical or environmentally unsound aren’t purchased directly, but are instead incorporated into products at different stages of the supply chain. Without knowing the provenance of goods or materials, it's impossible to ensure social and environmental standards are effectively applied as raw materials are transformed into consumer products.
The World Economic Forum has suggested that any technology capable of decarbonising global supply chains would be a “game-changer” for the impact of corporate climate action. Which is where ML comes into the picture. Blockchain tracking and traceability systems appear to offer a solution.
“Our mission is traceability and due diligence of raw materials from source to manufacturer,” says Douglas Johnson-Poensgen, CEO and co-founder of Circulor, a start-up providing blockchain-enabled supply chain solutions. “To achieve this, we’re using blockchain alongside traditional databases and machine learning to establish a new global standard for ethically and sustainably sourced minerals.”
This is achieved by giving an identity to a commodity and tracking and logging real-time supply chain data, including GHG emissions, throughout the supply chain.