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Looking Ahead: Using AI to Build Dynamic Cost Models
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Looking Ahead: Using AI to Build Dynamic Cost Models

Provide your email address to download Muir AI's white paper on how the changing supplier and product landscape requires innovative approaches for should-cost analysis.

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February 2023

It’s time to tackle scope 3 emissions

Muir addresses corporate Scope 3 emissions by using data fusion, machine learning, and remote sensing to identify reduction opportunities and provide granular supply chain insights.

The window to address climate change is rapidly closing. We must transition from future commitments and high-level discussions to real emissions reductions.

The most impactful opportunity lies within corporate scope 3 emissions. Scope 3 emissions are indirect emissions that occur in a company's value chain, encompassing activities such as the extraction of raw materials, manufacturing, transportation, and disposal, which are not directly owned or controlled by the company. For most corporations, greater than 80% of their emissions lie within scope 3, and more specifically within their supply chains.

Despite the magnitude of scope 3 emissions, corporations have backlogged action for years due to the complexity of identifying and evaluating scope 3 reduction opportunities.The core issue is that today’s tools are either too high-level, relying on broad industry averages, or onerous, requiring manual data collection from all suppliers, to be effective in measuring emissions and identifying reduction opportunities.

The failure can be illustrated by the fact that 90% of corporations cannot comprehensively determine their carbon footprint.1

It is estimated that 5-7% of annual global emissions could be reduced with more precise and scalable scope 3 emissions tools – that’s equivalent to all of the world’s passenger cars!

This is where Muir comes in.

We’ve built the capability to track the emissions of any building, anywhere, without reported data. Our approach, based on data fusion, machine learning and remote sensing, considers the real world features that drive emissions, like building size, climate, local power grid and facility activity, and automatically generates and evaluates high-value reduction opportunities.

Corporations utilize our platform to rapidly generate a granular view of their supply chain and emissions metrics across it. They can then evaluate reduction opportunities such as shifting to a lower emitting supplier or investing in a solar array project at a high electricity usage facility.

Our recommendations not only enable corporations to maximize the impact of their climate reduction plan, but also identify opportunities for cost reduction from efficiency gains, increasing profitability.

We are actively working with customers across the retail and consumer goods industries to combat climate change. Our founders, Harris Chalat and Peter Williams,  are aerospace engineers who have turned their focus to climate change.

Harris spent the majority of his career at SpaceX where he focused on satellite technology and applications; Peter worked at Amazon’s Prime Air and Robotics teams designing, building and integrating cutting edge AI and robotics.

We’re seeking exceptional software, data, and machine learning engineers who have a passion for climate and are ready to make a real difference for our world. If you’re ready to take action, reach out to info@muir.ai!

To stay up to date, follow us on LinkedIn.

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