Looking Ahead: Using AI to Build Dynamic Cost Models
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To make better decisions, you need accurate and scalable emissions data. Carbon Origin automatically determines Product Carbon Footprints, prepares data for reporting and identifies the best opportunities to reduce emissions.
Optimize reductions across your supply chain.
Provide the carbon risk and rating of suppliers and industries based on their country of operation
Provide the carbon risk and rating of suppliers and industries based on their country of operation
For every Carbon Risk score generated, customers also have access to global benchmarks, allowing them to evaluate if their chosen supplier or region is the best fit for their risk profile.
Multi-dimensional Carbon Risk score evaluate the level of risk associated with a process at the country level and compares that process across all countries, factoring in different elements such as the regulatory environment and compliance.
Compare global Carbon Risk ratings for the identified process and determine if suppliers in other countries are lower risk.
Evaluate forward-looking projections for Carbon Risk and determine the best suppliers and materials for your products, creating long-term solutions for your company.
Incorporate sustainability and carbon risk into forward-looking procurement decisions.
Carbon Origin streamlines the reporting and reduction of your product's carbon footprint. Use Carbon Origin to report the emissions of goods sold, meet compliance requirements like CBAM and identify easy opportunities to reduce your emissions while protecting your bottom line.
Muir AI's ISO 14067 certified methodology, autonomously provides the embedded emissions for a given product, even complex systems. Muir's novel technology enables a rapid transition from spend-based emissions accounting to actionable footprinting at scale, without onerous data collection.
Get a complete picture of your upstream emissions by product, supplier and material measured against global benchmarks.
Automatically identify opportunities to decarbonize for your unique supply chain considering ease implementation, and overall business.
Rapidly measure the embedded emissions of goods for regulatory reporting like EU's Carbon Border Adjustment Mechanism (CBAM).
Project forward and compare scenarios to set a plan to meet your corporate initiatives.
Meet regulatory requirements, minimize carbon risk, and stay ahead of the competition.
Carbon accounting tools focus on reporting for regulatory compliance. Muir goes above and beyond this, providing actionable and high quality product carbon footprint data for companies to move from reporting to reducing emissions. Our intelligence models allow you to do so without requiring suppliers’ data and can scale across your supply chain.
We use a blend of different proprietary and open source data sets to fuel our models. We leverage trade, manufacturing, emissions and material data to generate our estimates.
Addressing Scope 3 emissions is key to reversing climate change. With more than 70% of emissions found upstream of corporations, supply chain reductions are necessary to take on climate change.
Our methodologies follow industry standards, such as GHG Protocol, have been third party certified to ISO 14067:2018 and have been demonstrated to have significantly lower uncertainty compared to typical emission estimate methodologies. More importantly, our data also adds unprecedented granularity, enabling insightful management of supply chain emissions.
Our models can be easily accessed via our application or integrated into existing systems via API. Our services can operate with minimal procurement data and evolve as more complex Bill of Materials, traceability and primary emissions data is provided.
Our AI engines are designed to be industry agnostic, servicing a corporation’s complete supply chain. This includes the ability to service industries with complex supply chains, like apparel, consumer goods, and industrial.
Don't worry, we have all been there! For those struggling to say Muir, it rhymes with pure.
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