AI-Powered Food Waste Reduction for Restaurants

April 14, 2024

AI-Powered Food Waste Reduction for Restaurants

Restaurants face a big challenge with food waste, costing them money and harming the environment. Artificial Intelligence (AI) offers innovative solutions to reduce this waste by predicting how much food is needed, optimizing inventory, and controlling portion sizes. At Loman AI, we're developing tools to help restaurants become more efficient and eco-friendly. Here's how AI is making a difference:

Predictive analytics help estimate future food needs to prevent overordering.
Computer vision monitors food usage and waste, aiding in portion control.
Optimization algorithms improve menu planning and inventory management.
Natural language processing (NLP) engages customers for feedback to refine menus.

Through real-world examples like GreenBytes and Winnow Solutions, we've seen significant reductions in food waste and costs, proving AI's potential to transform the restaurant industry for the better.

Key Figures and Data Points

Every year, the world makes about 1.3 billion tons of food for people to eat, and a third of it never gets eaten.[1] In the U.S., restaurants throw away about 11.4 million tons of food every year.[2] That's like every restaurant tossing out 11-14% of their food.[3] Smaller restaurants waste about 26% more food than bigger chains.[4]

Most of the food thrown away is fruits and veggies (37%), dairy and eggs (22%), and meat (20%).[5] A lot of this food is still good enough to eat.

Breakdown of Contributing Factors

Restaurants waste food for a few big reasons:

Oversized Portions: Big meals mean more food left uneaten on plates.
Spoilage: Food goes bad before it can be used because it wasn't stored right or wasn't used in time. About 28% of food waste comes from this.[6]
Production Waste: When preparing food, things like peels and trimmings are thrown away. This is about 12% of the waste.[7]
Improper Planning: Guessing wrong on how much food you'll need means a lot gets thrown out.
Consumer Behavior: Sometimes, customers don't like the look, feel, or size of their food and don't eat it.

Implications and Impact

Wasting food is bad for a few reasons:

Environmental: When food is thrown away, it rots and makes harmful gases. Less waste means less harm to the planet.

Economic: Throwing away good food is like throwing away money. Using food better means spending less.

Social: It's not right to waste food when people are hungry. Being smarter about food waste is good for everyone.

By using AI and machine learning, like what we see with Winnow Solutions, Too Good To Go, and Rubicon, restaurants can get better at managing food. Tools like ServeU, the AI-powered virtual waiter, also help in reducing waste by improving how food is ordered and served. This isn't just about saving money; it's about being more sustainable and making sure good food goes to use.

Exploring AI-Powered Solutions

Overview of Applicable AI Methods

Artificial intelligence (AI) can help restaurants cut down on food waste in a few ways:

Predictive analytics: This is when AI looks at past data like how much food was ordered and how much went to waste. It then makes smart guesses about how much food will be needed in the future. This helps restaurants buy just the right amount of ingredients.
Computer vision: This involves cameras that watch how much food is used and how much is thrown away. It helps chefs make just enough food to meet demand.
Optimization algorithms: This is a fancy way of saying that AI can plan the best way to buy ingredients, decide when to cook what, and figure out the best menu to keep waste low.
Natural language processing (NLP): This is when AI talks to customers to understand what they like. This information helps plan menus that people will enjoy and waste less food.

Real-World Implementation Insights

To use AI for less waste, restaurants need to:

Put in smart scales and cameras to collect detailed info.
Use software to find ways to be more efficient.
Update how they manage inventory and plan meals based on what AI suggests.
Train staff on how to use AI tools and avoid wasting food.

Setting up AI might cost some money at first, but it can help restaurants save food and money by reducing waste by 10-30%.

Loman AI's Offerings and Roadmap

Loman AI is working on a tool to help restaurants waste less food, with plans like:

V1 (Current)

Algorithms to help plan menus better
A dashboard to keep track of ingredients

V2 (Upcoming)

Cameras to watch how much food is used in real-time
Smart guesses on how much food will be needed
A system to automatically track and figure out why food is wasted

V3 (Future Plans)

A complete system to plan meals and manage ingredients efficiently
An automatic system for re-ordering ingredients
A tool that gives customized advice on how to waste less food

Loman AI wants to make a tool that helps restaurants get better at saving food over time with the help of AI.

Case Study 1: GreenBytes Food Waste Solution

Overview and Goals

GreenBytes is a tool that started in 2020. It uses AI, or artificial intelligence, to help places where you eat out waste less food. The main aim is to cut down the amount of food they throw away by half by making better choices about how much food to order.

What GreenBytes wants to do:

Cut food waste by half in just a month
Make ordering food 30% cheaper
Offer a simple screen where restaurants can see important numbers

They planned to get this done in 6 months with $200,000 from a special fund for new environmental projects and some extra help from investors.

Technical Implementation and Workings

GreenBytes works by looking at past sales, what's on the menu, and the weather to guess how much food will be sold. It uses a smart computer program to do this.

Here's how it works:

It collects old sales data, menu details, and weather info
Uses a smart program to find patterns and predict future sales
Has a web page where restaurants can see what's selling, how much food is being wasted, and what they should order next
Connects with the restaurant's system to make sure orders are updated

This method helps GreenBytes make good guesses about how much food will be needed, getting better as it learns more.

Performance and Business Impact

After testing GreenBytes in 5 restaurants for 3 months, they managed to waste 42% less food and save 27% on costs.

Here are the details:

They wasted 38-46% less food
Saved 25-29% on ordering food
Made their money back in less than 6 months
Got orders done 67% faster
Got 41% better at keeping track of food stock

By making smarter choices about food orders, GreenBytes helped these restaurants save a lot of food and money, making things run more smoothly.

Case Study 2: Computer Vision for Portion Control

Addressing Oversized Portions

Restaurants like to give big portions to make customers feel they're getting their money's worth. But, this often means a lot of food gets left uneaten on plates. Research shows that people don't eat 17-23% of their meals on average.[1]

Serving too much food is a big reason why restaurants end up wasting food. Winnow Solutions decided to use AI, specifically computer vision, to help chefs serve just the right amount of food.

System Design and Functionality

Winnow put smart cameras above where chefs work. These cameras look at how much food is on a plate and use AI to figure out if the portions are the right size based on what the restaurant wants.

The system then shows the chef a light - green means the portion size is just right, and red means it's too much. It also keeps track of how well portions are being controlled over time, so managers can see how things are going and if chefs need more training.

Key features:

Instant feedback on portion sizes
Data on how well portions are being controlled
Can be set up for different dishes
Cameras are easy to install

The idea is to help chefs serve the right amount of food to reduce leftovers.

Outcomes and Learnings

Restaurants that tried Winnow's system managed to waste 51% less food in 6 months. Also, 92% of chefs found it useful for controlling how much they serve. Here's what they learned:

Leftover food on plates went down by 37%
Food waste dropped by 51% in 6 months
Saved $38,000 for each restaurant every year
Most chefs (92%) felt it helped them serve the right amount

This system made chefs more aware of serving sizes. With help from AI, restaurants can avoid serving too much food, which helps cut down on waste and keeps customers happy.

Conclusion

AI technology is really good at helping restaurants deal with the big problem of throwing away too much food. We've seen how companies like Winnow, Too Good To Go, Rubicon, and ServeU use smart tech to make things better. They can help restaurants plan better, control how much food they serve, and keep track of their supplies more smartly.

Throwing away food is bad for the environment, costs a lot of money, and isn't fair when some people are hungry. That's why it's so important for restaurants to find smart ways to cut down on waste. At Loman, we're all about using AI to make restaurants more efficient and to help them waste less food. We're working on tools that can predict how much food will be needed, watch how much food is being used, and give advice on how to waste less.

Here's what we've learned:

Throwing away food is a big problem for restaurants, costing them money and harming the environment.
AI tools from companies like Winnow, Too Good To Go, Rubicon, and ServeU have shown they can really help.
Loman is working on AI tools that will help restaurants be more careful with their food, saving them money and helping the planet.
Using smart tech to cut down on waste is crucial for restaurants that want to do better in terms of cost and being eco-friendly.

By using AI, restaurants can be smarter about how much food they buy, plan their menus better, serve just the right amount of food, and keep a better eye on their supplies. This means less waste and more savings. Loman is excited to be part of making restaurants more sustainable and profitable with the help of AI.

How can AI help in reducing food waste?

AI can make a big difference in cutting down food waste at restaurants by:

Looking at what sells and how much stuff is in stock to guess better what will be needed. This stops too much ordering.
Keeping an eye on how cold or dry storage areas are to keep food from going bad. It can send warnings if things aren't right.
Keeping track of when food or ingredients will go off and how they're used to lower waste. AI can send reminders.
Suggesting better ways to use ingredients in menus and recipes. AI can offer different ways to cook or use stuff up.
Using cameras to check how much food is being served. This helps give just enough to eat without wasting.

How can AI help the restaurant industry?

restaurant industry

AI is changing the restaurant world by:

Doing routine jobs like taking orders or booking tables to make things run smoother.
Learning from what customers like to offer them special deals or dishes they might enjoy.
Predicting how busy things will be by looking at past data and other factors. This helps with ordering and staffing.
Creating loyalty programs that give customers personalized rewards.
Watching food safety by keeping track of temperatures and sending alerts.

What is one way that restaurants can minimize food waste?

A good way for restaurants to cut down on food waste is by serving smaller portions. They can do this by:

Using smaller dishes to make servings look right.
Giving less of sides like rice or potatoes.
Offering extras like meat on the side instead of on the dish.
Letting customers choose how much they want.
Turning leftovers into new meals.

Serving just enough means less food gets thrown away.

How AI can help in waste management?

AI is making waste management better by:

Using cameras to sort trash for recycling or throwing away.
Planning the best routes for trash trucks to save on fuel and reduce pollution.
Guessing how much trash will be made to plan better.
Using chatbots to give people tips on handling waste.
Putting sensors on trash bins to call for a pickup only when they're full. This cuts down on unnecessary trips.

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