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August 2024
Why scaling product carbon measurement is needed for forward thinking sustainability and supply chain teams.
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In an era where sustainable decision-making in the supply chain is becoming more of a priority, understanding the carbon footprint of products has become an essential component for businesses striving to minimize their environmental impact. While spend based approaches and manual gathering of data has been the practice many companies have approached setting baselines for Scope 3 emissions, the time, resources and budget for such initiatives at scale have proved prohibitive even for the most sustainably minded corporations. Muir AI blends machine learning with activity-based emissions calculations to shift this paradigm and make simply reporting emissions a thing of the past, unlocking sustainable decision-making at scale in the supply chain.
With Muir AI, businesses gain instant insights into their product carbon footprints across their entire portfolio. This autonomous carbon footprint data allows companies to make informed decisions quickly, avoiding the pitfalls of outdated information, time wasted on gathering data, and costly third parties that can cost an arm and a leg. Traditional methods often require manual data collection and analysis, which can be time-consuming and prone to errors. In contrast, Muir AI leverages data fusion to provide accurate and up-to-date information. This capability empowers sustainability teams to identify key sourcing opportunities, optimize supplier negotiations, and uncover hidden chances to reduce emissions. Having instant access to this data means companies can respond to market changes and sustainability challenges more effectively - instead of providing basic reporting on a yearly basis.
Traditional methods of gathering product carbon footprints have provided companies with a less-that-ideal view into Scope 3 emissions - and companies may not even know there are glaring blindspots in their accounting structure. Lacking accurate data, as well as allocating increasing times, budget and resources to simply account baseline Scope 3 emissions leave event the most sustainably-minded companies unable to actually focus on Scope 3 reduction goals. These approaches can leave companies with outdated and incomplete information, impairing their ability to make smart decisions for the future. This lack of real-time insights means that sustainability teams and supply chain managers can't promptly address emerging sustainability issues or take advantage of new opportunities to reduce emissions. Consequently, traditional methods often result in higher costs and missed sustainability targets. Compared to the dynamic and AI/ML approach of Muir AI, traditional methods fall short in providing the agility and precision needed for effective sustainable decision-making.
Scaled carbon footprint data from Muir AI significantly boosts efficiency and accuracy in decision-making. By providing granular material, activity and manufacturing data, Muir AI allows companies to quickly assess their environmental impact and identify areas for improvement. This immediacy eliminates the delays associated with traditional data collection methods. Scaled PCFs unlock newfound freedom for teams across organizations, for instance enabling informed decision making in the product design and sourcing phase, rather than a retrospective accounting of carbon once a product has hit the shelves. At Muir we’re not only focused on speed to delivery, but accuracy. Our methodology is ISO 14067 certified and has been independently audited for accuracy. This accuracy is crucial for making well-informed decisions that align with sustainability goals. With Muir AI, companies can systematically engage with their supply chain for the first time, evaluate suppliers, and select materials, suppliers, and countries of origin with confidence. The combination of efficiency and precision helps businesses to streamline their processes, reduce emissions, and achieve cost savings. Ultimately, Muir AI's real-time insights facilitate a more proactive and effective approach to sustainable decision-making.
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