Impact AI Eliminating Food Waste with Nathan Fenner from Afresh
The climate crisis is one of the most important and complex challenges of our age, and solving it will require collaboration, innovation, and commitment. According to Project Drawdown (a non-profit organization that functions as a top resource for climate solutions), one of the key drivers of climate change that we can meaningfully address as a society, is food waste.
In today’s episode, we learn about Afresh, a company that is leading the way in providing food waste solutions to grocers across America by creating optimized food orders through pioneering AI and machine learning solutions. You’ll hear from Afresh Co-Founder, Nathan Fenner, as we discuss the founding mission behind the company and how they are leveraging AI in a way that is fundamentally different from other established legacy companies in their field. We discuss the challenges of working with perishable products, how it results in noisy data, and why it’s so important for Afresh technology to not only provide predictions but also make decisions in the face of uncertainty.
Today’s conversation unpacks a particularly exciting area of AI and demonstrates how advancements in the field are paving the way for impactful climate solutions. Be sure to tune in to learn about the real-world impact of AI innovation in an area where we need it most urgently!
Key Points:
- Get to know today’s guest, Nathan Fenner, and how he co-founded Afresh.
- Why reducing food waste is a key part of mitigating climate change.
- How Afresh is helping the grocery industry optimize supply chains for perishable products.
- The role that machine learning plays in Afresh’s technology.
- An overview of the three main sources of data that they feed into their system.
- The biggest challenges they experience with their data sources.
- Understanding how past retail system solutions were built with non-perishable items in mind.
- Why perishable items result in extremely noisy data.
- The challenges that noisy data poses to machine learning models.
- How Afresh is addressing the challenges inherent to noisy data.
- What differentiates Afresh from other established legacy companies in their field.
- How Afresh is leveraging AI to make decisions, rather than simply providing a forecast.
- How Afresh measures the impact of their technology on profits, food waste, and the planet.
- Unpacking the difficulty in finding, hiring, and attracting machine learning specialists.
- The confluence of factors that are helping Afresh attract top talent.
- What Nathan is most excited about for the future of Afresh.
Quotes:
“We're hyper-focused on building supply chain software to optimize all the perishable supply chains in retail. The big outcome of optimizing that supply chain is that we dramatically reduce food waste. Food waste is one of the biggest macroscopic contributors to climate change.” — Nathan Fenner
“Good machine learning is key to writing an optimal order that maximizes profit, but also minimizes waste.” — Nathan Fenner
“All the technology that had been built for the grocery industry, and that was being used in supply chain and inventory management, had all been built for the non-fresh side of the business. It had all been built for things that come in boxes that have barcodes.” — Nathan Fenner
“We leverage AI in a fundamentally different way. We definitely do forecasting, but the critical thing we're doing is really decision-making under uncertainty. The output from our models is actually a decision as opposed to simply a forecast.” — Nathan Fenner
“Leveraging this more frontier area of machine learning has allowed us to make really good decisions in a really uncertain environment.” — Nathan Fenner
“If we can build a technology that reduces food waste by 50%, it will become uneconomic for grocers to not use our technology (or a similar technology) that produces that much in cost savings.” — Nathan Fenner
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