Restaurant Inventory Management

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Restaurant inventory management involves keeping track of raw and processed goods to plan purchasing, understand food costs, use the raw materials based on freshness and donating the excess food to avoid wastage based on machine learning algorithm models.


Technology has made a great impact everywhere and restaurant industry is no different. Machine Learning already helps restaurants in many ways such as:

  • Forecasting sales more accurately
  • Identifying staff theft
  • Matching customers to restaurants based on their pre-selected taste profile
  • Teaching robots how to cook
  • Identifying the contents of food at a molecular level

What is Machine Learning?

Machine Learning describes the ability of algorithms to learn from data processing. The more data they process, the more they learn, and the more accurate they are at making conclusions. 

How can the Machines Learn?

When algorithms try to answer a problem, they estimate a solution by considering several parameters. Naturally, initial predictions of most of the algorithms will be wrong. The algorithm learns by testing what was predicted against by what really happened - this type of Machine Learning is called supervised learning. For each round, the algorithm modifies internal parameters or parts of its structure based on the initial fallacies and tries again. This process continues, which includes discarding the changes that reduce the algorithm’s accuracy and keeping the changes that increase the accuracy. The algorithm is said to have “learned” when new images are presented and are accurately classified. 

Problem Statement

Problem 1: Wastage of produce because of the lack of a method to determine freshness

Usually the fruits and vegetables have relatively short product availability period. If it goes beyond the expiration date, people throw it away. This causes huge waste of money and resources. So, we need certain freshness monitoring system in the restaurant to save money and health.

Problem 2: Detect stock deficit or surplus and order the materials accordingly

A restaurant's inventory management is the process of monitoring and updating the inventory on a regular basis. Keeping an accurate inventory is necessary to maintaining the correct balance of all essential elements to make a restaurant function. The inventory not only allows to see when items are running low and need reordering, but it also helps you to avoid excessive inventory that may lead to waste. 

Problem 3: :  Wastage of prepared food in restaurants

A lot of food is wasted in restaurants because of improper planning, incorrect prediction, and many other reasonsAccording to a recent report, a half a pound of food is wasted per meal in restaurants, whether it is from what is left on a customer's plate, or in the kitchen itself. Approximately 85% of the food that is not used in a typical American restaurant is thrown out.

How Machine Learning can help the restaurant industry? 

Basic Algorithms vs Machine Learning Algorithms

Basic algorithms can forecast future sales based on simple parameters such as sales for last week and year as well as considering holidays, weather, etc.

As an example, to forecast tomorrow’s sales a basic algorithm will:

  1. Calculate the average between last year sales the same day and last week sales the same day
  2. Increase sales by 20% if it is a holiday
  3. Increase sales by 20% if it is a sunny day

However, all those parameters do not have the same impact for your restaurants. For instance, weather can be an important game-changer for an ice cream restaurant next to the beach but a small factor for a pizza restaurant in a mall. A basic algorithm cannot personalize the forecast and understand that every restaurant is different. 

Machine Learning Algorithms can help increase forecast accuracy over time personalizing every parameter for each location, which means learning while processing data which parameter has the greater impact on sales for a specific location. Machine Learning algorithms consider the historical data as follows:

  • All previous data (i.e. seasonal data, weekly data, growth trend)
  • Weather (i.e. temperature, precipitation, sun hours)
  • Holiday (i.e. bank holidays, half-term, Christmas)
  • Events tailored by customers (i.e. football matches, theatre production)


Machine Learning algorithms for solving our problems?

Problem 1: Wastage of produce because of the lack of a method to determine freshness

Solution: We can use sensors connected to IoT devices to record temperature, humidity, oxygen, and carbon dioxide levels. Based on these attributes, we predict the freshness of the produce which helps restaurants to use the resources efficiently. Machine Learning algorithms can be used to classify the produce according to the freshness levels such as fresh, semi-fresh and spoiled.


Problem 2: Detect stock deficit or surplus and order the materials accordingly

Solution : User demand, supplier backorders, warehouse optimization, stock levels are all being guided by machine learning models such as time series prediction and reinforcement learning systems. This can be used for the demand forecast and ordering the resources accordingly.  IoT could monitor customer number, peak periods and average orders, allowing them to view the information on a smartphone app from wherever they are to better schedule staff and market their business in quiet periods.

Problem 3: :  Wastage of prepared food in restaurants

Solution: Machine learning models can be used to predict the excess food in restaurants  and then connecting with NGO’s to donate the excess food.



Restaurant inventory management plays an important role in the success of a restaurant. It can be a time-consuming and challenging task. To overcome the problems associated with the inventory management, IoT devices can be used with sensors connected to them to get real-time data and this historical data can be used to get fresh produce, forecast the demand and avoid wastage of cooked food.