WinnowVision is a patented smart bin technology (Mekhsian, Duffy, and Zornes 2023). It uses a camera, set of smart scales, and machine learning technology to learn to recognise different foods being thrown away, and calculate the financial and environmental cost of this discarded food to commercial kitchens (Winnow, n.d.-a). In early 2015, IKEA installed Winnow into two pilot sites in the UK to determine if food waste could be reduced by giving workers digital measurement tools and analysis. In 2019, WinnowVision’s additional AI element was launched at IKEA’s flagship store in Greenwich, with cameras determining what food had been thrown away rather than manual input being needed (Zornes 2022). IKEA was prompted to use WinnowVision by their internal strategic commitment to cut food waste by 50% across operations before the end of August 2020 (Klupacs 2019), and achieved this across all stores in 2022 (Zornes 2022). They use the technology to identify: the top errors contributing to food needing to be thrown away; which foods were not being ordered and so had to be disposed of; and patterns in customer preferences in order to avoid overproduction (for example, if it is warm, people are more likely to buy food they can eat outside, like wraps or hot dogs, which led IKEA kitchens to adjust production levels according to weather forecasts) (Winnow, n.d.-b). The primary benefit to the technology is a large reduction in food waste: according to Winnow’s data, a typical kitchen wastes between 5-15% of food they purchase, largely due to overproduction, and WinnowVision’s ability to make forecasting decisions has saved approximately 45,000,000 meals and 78,000 tonnes of CO2 (Winnow, n.d.-a). WinnowVision is now being used at scale by both IKEA and other companies. As of August 2020, WinnowVision has been rolled out in IKEA stores in more than 30 countries, and installed in 23 IKEA UK and Ireland stores (Zornes 2022). Winnow is also used by many other large corporations in businesses like hotels, contract caterers, casinos and cruise ships (“Food Waste Reduction | Winnow,” n.d.). Noted facilitators to implementation include strong internal leadership by adopting companies, clear internal communication around a goal that can be definitively measured, and the opportunity for shared learning between adopters (Zornes 2022). However, differing market priorities and attitudes towards sustainability can be barriers to roll out, as can differing team cultures or structures (Zornes 2022). The other potential issue to seeing widespread adoption of WinnowVision or a similar technology is cost; currently WinnowVision is used by very large corporations with multiple kitchens. Small or medium businesses are not likely, in the immediate future, to invest in this technology, and as it is patented, competitors for smaller-scale operations may also struggle to emerge. Otherwise, there appears to be potential for Winnow to adapt its technology for wider use, either outside of commercial kitchens, or with a focus on collecting different or more specific data. For example, currently Winnow is rolling out a plate waste solution which uses a motion sensor camera connected to a scale to automatically recording food waste from plates, rather than after it has been put in the bin (“Food Waste Management Software | Winnow,” n.d.). This allows greater insight into customers’ eating habits. Smart bins however also have potential to be scaled beyond commercial kitchens. Wang et al. (2021) have developed a proof of concept for waste classification using deep learning at the start of rubbish collection. This technology utilises gas and ultrasonic wave sensors, rather than cameras, to identify the amount of food waste in bins. But, as Workie et al. (2023) note, further research is needed on strengthening the transport of food waste from source to processing or anaerobic digestion site before any such technology could be implemented, so we are unlikely to see this deployed in the immediate future.