By Bailey Ramsey, Contributor
Food waste is a critical issue in the restaurant industry. To put this in perspective, restaurants waste anywhere between 4% to 10% of the food they purchase. This leads to huge financial losses, operational inefficiencies, and environmental harm.
However, traditional waste management methods often fail to address the root causes, depriving businesses of all the benefits of reducing food waste. This is where artificial intelligence (AI) comes in, providing game-changing technology that restaurants can use to streamline operations, minimize waste, and boost profits.
In this article, we'll explore five practical ways AI helps restaurants reduce food waste by up to 30%, making sustainability an attainable goal for the industry.
Predicting Customer Demand Accurately
One of the leading causes of food waste in restaurants is overproduction. To battle this issue, AI systems forecast customer demand by analyzing historical sales data and seasonal trends.
This way, restaurants can prepare the right amount of food, minimizing waste while ensuring customer satisfaction.
Example: IHG Hotels & Resorts partnered with Winnow, an AI-powered food waste solution that helps the hotel chain optimize food production across several of their properties. By using smart technology like meters, scales, and cameras to forecast demand, Winnow identified which food items end up in the bin, helping IHG reduce food waste by 30% at participating locations.
Optimizing Inventory Management
Spoiled or unused inventory is another major contributor to food waste.
AI-powered inventory tools can optimize inventory levels by 35% as they monitor stock levels in real time, alerting staff to slow-moving or soon-to-expire ingredients. This enables restaurants to adjust orders, create specials, and repurpose ingredients before they spoil.
Example: The Mandarin Oriental Hotel Group implemented AI technology across several of its locations, leading to real-time visibility into inventory levels and ingredient usage. This allowed chefs to track and manage stock more effectively. Within a year, the hotel group reduced food waste by 36% in four of its pilot hotels.
Real-Time Monitoring of Kitchen Waste
AI waste tracking systems use smart technology and machine learning to analyze food discarded during preparation or service. These tools identify common waste sources, such as over-trimmed produce or uneaten leftovers, enabling chefs to refine processes and reduce waste.
Example: Orbisk allows real-time waste monitoring through its smart camera systems. This allows kitchens to plan meal preparation better as they can tap into powerful waste pattern insights and make more informed decisions. This can potentially help restaurants cut food waste by up to 70% and increase profits by 2%-8%.
Enhancing Menu Engineering
Some menu items contribute disproportionately to food waste due to low popularity or inefficient ingredient use. To solve this problem and optimize menu offerings, AI tools analyze sales trends, customer preferences, and waste data. This can involve adjusting portion sizes, replacing high-waste ingredients, or eliminating underperforming dishes altogether.
Example: Loman AI is developing a tool aimed at enhancing menu engineering for restaurants. By analyzing customer behavior, sales patterns, and food costs, it will provide actionable insights to optimize menus. This can lead to improved operational efficiency, reduced costs and waste, and increased sales and customer satisfaction.
Automating Waste Recycling Efforts
AI can also streamline how restaurants handle food waste. Some systems classify and sort waste into compostable, recyclable, or landfill categories, ensuring proper disposal and maximizing recycling rates.
Example: Yale's dining halls implemented AI-powered food monitoring systems to track discarded food and analyze compostable materials.
For restaurants, adopting similar technology ensures that compostable materials are efficiently processed, reducing landfill contributions. By turning waste into nutrient-rich compost, these AI systems not only lower disposal costs but also contribute to more eco-friendly dining.
Conclusion
Thanks to AI technology, restaurants can finally achieve optimal results in terms of cutting costs and improving efficiency. From demand forecasting and inventory optimization to waste tracking and recycling, these technologies reduce waste and help restaurants become more sustainable than ever before.
By integrating AI-driven tools, restaurants can embrace a smarter, greener future, benefiting both their bottom line and the planet. The time to act is now—because every scrap saved makes a difference.
About the Author
Bailey Ramsey is a dedicated content creator with a deep passion for environmental sustainability, waste management, and the innovative role of technology in these fields. Her most significant contributions in this realm can be found in the articles she has authored for the blog on Shapiroe.com.
