How to run a restaurant via neural networks
In order to decide where it’s better to open a restaurant and how much money it could bring, you have to analyze multiple data. Artificial intelligence can easily do it. Neural networks process a huge amount of data and give recommendations to those who are willing to open a cafe.
Nowadays a bunch of companies are trying to implement ideas with the help of artificial intelligence. For example, in 2016 Placer.ai was founded in the US. It was meant to analyze pedestrian traffic in order to advise retailer what and where to place.
The most popular example is Zume Pizza. The company didn’t rent a space for a pizzeria. Instead, cooks made food right in trucks. Zume pizza was seeking to make an intelligent platform that could identify where and when it’s going to be the highest demand for a pizza. Neural networks predicted where the car should go and when to turn on ovens to keep pizzas hot. However, not so many people have used the service, that’s why now the company sells protective masks.
There is also a Russian company - Foodcast.ai. By means of neural networks, it analyzes loads of factors that affect restaurant demand: holidays, the weather, days off, etc. The most unexpected factor is abrupt drop in temperature. If it gets tangibly colder, the demand grows: home delivery gets more relevant.
Besides, the system based on artificial intelligence helps a restaurant allocate resources properly. Neural networks answer essential questions: who cooks for clients in the restaurant, what number of waiters is needed at rush hour, and how many products are necessary to buy.
3 years ago McDonalds bought a startup Dynamic Yield that was engaged in technology for personal data analysis. The system was implemented in Miami restaurants right away. The algorithm analyzed the weather, traffic jams, the events held nearby, and collected data of sales.
What ideas related to neural network do you have? Share