Food waste is a massive problem globally, with nearly one-third of all food produced for human consumption lost or wasted each year, according to the United Nations. This amounts to 1.3 billion tons of food waste annually, which has enormous environmental, economic, and social impacts.
However, technology and data-driven solutions present promising ways to tackle this complex issue and reduce food waste in homes, grocery stores, restaurants, and across the supply chain.
One company using data and technology to combat food waste is Foodiaz, a personalized recipe and meal-planning app.
I spoke with Foodiaz CEO Nicholas Nedelisky to learn more about how they are using data analytics and algorithms to help consumers reduce food waste. A key challenge is the sheer amount of food spoilage that happens in consumers’ own kitchens.
According to Nedelisky, “The majority of food waste happens at home. We didn’t want to bog down users with hours of pantry management and updating expiration dates every time you shop. Instead, we made it as simple as possible to use up ingredients that are about to spoil.”
To accomplish this, Foodiaz focuses on seamlessly integrating into users’ cooking routines and subtly influencing their behaviors regarding food freshness and spoilage.
Nedelisky explained, “Our goal is to keep the app frictionless by staying away from onerous tasks and keeping the experience fun and intuitive.”
Rather than requiring users to input expiry dates or inventory all their groceries, Foodiaz passively tracks what users are cooking and buying. It then gently nudges users towards recipes that feature ingredients they already have on hand that are close to spoiling.
Personalization is Key
This personalization and passive tracking of users’ habits is key to Foodiaz’s approach. As Nedelisky noted, “A lot of the personalization comes directly from users specific pantries. The ingredients they choose tell us a lot about what kind of recipes they are looking for.”
Foodiaz supplements this direct user input with sophisticated AI that monitors user behavior across the app, from recipes viewed, favorited and actually cooked. This allows Foodiaz to learn about each user’s taste preferences and recommend recipes tailored specifically to their purchasing and cooking history.
Importantly, Foodiaz also allows users to specify dietary restrictions like gluten-free, dairy-free or vegan. According to Nedelisky, “If you choose to buy great, healthy, whole ingredients, I can without a doubt find great recipes tailored to those ingredients. This applies to restrictions such as gluten-free, dairy-free, vegan, etc.”
Users can further customize with filters for calories, carbs, sugar and more macros if they desire. This integration of user-provided data, observed usage patterns, and personalized algorithms powers the “Foodiaz learns what you like” feature at the heart of the app.
Grocery Integration
In addition to recipe recommendations, Foodiaz also integrates with grocery shopping by partnering with major grocery chains. This provides another data point – users’ real-time grocery purchases – that improves the app’s ability to suggest recipes using items they already have. However, rolling out this grocery integration presented challenges, as Nedelisky explained: “The biggest hurdle in all of this is incorporating the major grocery systems and APIs into a single interface.”
By syncing with users’ groceries, Foodiaz can incrementally build an inventory of their pantries. This makes recommendations even more tailored while seamlessly helping users eat food before it goes bad. Nedelisky stated they are nearly finished fully implementing this grocery tech nationwide.
Powered by Data Science
Behind the scenes, Foodiaz leverages data science and algorithms to enable this personalization and inventory tracking. While Nedelisky was understandably reticent to reveal proprietary technical details, he noted their tech stack relies on Google’s Firebase platform to ingest usage data and identify trends. He also discussed how their models improve with scale, stating, “Currently, our model does better at scale as we can learn more holistic information, but I am sure we will make adjustments as we monitor the algorithm’s performance.”
Foodiaz is powered by an intelligent data backend that continually optimizes its waste-reducing suggestions based on real-world usage patterns. The algorithms examine both individual user behaviors as well as broader eating habit trends across its user base. This allows for a feedback loop where the product continually improves its waste reduction capabilities even as Foodiaz scales to more users.
Tackling Waste Across the Supply Chain
While Foodiaz focuses on reducing household food waste, data-driven technologies can also make an impact across the broader food system. For example, analytics and IoT sensors can better track perishable inventory at restaurants, grocers and across supply chains. Machine learning algorithms can optimize ordering and human decision-making to minimize over-ordering. Predictive analytics can increase the accuracy of demand forecasting and production planning.
Meanwhile, computer vision systems can automatically inspect food for freshness and quality control both pre and post-harvest. And blockchain solutions can provide transparency into supply chain bottlenecks that lead to spoilage. Even simple barcode scanning apps allow stores, restaurants and consumers to digitally log inventories and expiry dates to minimize waste.
The potential of data-driven food waste solutions also extends into logistics, where route optimization algorithms minimize spoilage during transport. Big data helps retailers identify popular items to stock and price promotions to increase sales of perishable items close to expiring. And digital marketplaces connect consumers with discounted food that would otherwise be landfilled.
Ultimately, waste occurs across the entire food ecosystem. But advanced analytics opens up new possibilities to identify previously hidden waste patterns across this complex system. Artificial intelligence can then optimize systems, tailor recommendations, and modify behaviors throughout the supply chain to cumulatively reduce global food waste.
The Bottom Line
Food waste is an immense challenge globally, but also a major opportunity for technology and data to make a positive impact. As demonstrated by Foodiaz’s use of data personalization, there are compelling waste-fighting solutions available today. And continued innovation in this space can help reduce food waste at scale.
Technology provides insights to raise awareness of the problem, while analytics enables data-driven action across homes, businesses, and supply chains worldwide. With sufficient investment and adoption, data-powered tools provide reason for optimism that we can create a smarter, more sustainable food system with far less waste.
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