Optimising food production in commercial kitchens through machine learning, reducing waste and increasing profits

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The global hospitality industry, particularly buffet style environments, still lacks a tool to accurately help predict how much food to produce, and when. This results in interconnected economic and environmental challenges. Overproduction of food leads to vast financial and economic costs. In the UK Hospitality and Food Service Sector alone, food waste cost was estimated at over £2.5bn in 2011, with 75% of it recorded as avoidable. The environmental impact is monumental; if global food waste were a country, it would be the third largest emitter of greenhouse gases after the USA & China. Overproduction also lowers food quality, harming client satisfaction. Underproduction causes its own issues; hurting customer satisfaction and margins.Providing chefs with a tool which can give them actionable production predictions based on a wide range of factors including weather patterns, historic waste, time of day and demographic of customers would help them optimise their production, and allow for improved control of costs, labour / staffing, and increased understanding of customer preference. This project aims to build on Winnow's experience of building machine learning models and presenting actionable data to chefs in around 1500 kitchens to turn our current prototype production planning tool into a beta market ready tool.This project and associated innovation are extremely well-timed, especially regarding the impact felt by the hospitality industry from Covid-19\. The industry is in great need of this production planning tool to give it more control over operations, margins and environmental footprint. Commercial kitchens have been largely left out of the digital revolution, and stand to benefit greatly from integrated tools that help them to manage their operations better.