The need to understand what the demand will be has been present in all sectors since always, it is vital to understand what we will sell and the volume of work we will have to be able to manage the company or organization efficiently, whether deciding what to buy, what to produce or the volume of staff we need.
Due to the current pandemic situation, this need has increased due to the uncertainty of the market, which is very volatile and reactive to all the social and political changes that we are experiencing. One of the most affected sectors by these measures has been the restoration sector, undergoing a multitude of restrictive changes. In order to face this situation, the sector needs tools or solutions that help them to be more operationally efficient and reduce costs. We understand that providing these companies with a tool that allows them to make better predictions will allow them to be much more efficient.
Objectives: We want to develop a series of artificial intelligence algorithms that allow us to predict various parameters related to demand at the point of sale. These algorithms will be nourished by internal data (from each restaurant) and will be crossed with external data (external databases).
– Predictive tool to predict occupancy and average ticket in restaurants
– Data updated autonomously
– Easy and intuitive – Autonomous learning
– Control of the deviation between predictions and reality
– Increasing customer efficiency thanks to the resources’ optimization. The initial expected output (it can be expanded if deemed appropriate during its development) will be based on two key parameters for a restaurant:
- Occupancy prediction
- Avarage ticket prediction in restaurants
Regarding the temporality of the data, it has to be studied whether it makes more sense to do it on a daily or weekly basis.
Opportunity: Due to the need mentioned above and due to the large volume of restaurateurs that we have in Catalonia and in Spain, we see that we are facing a challenge which can be a great opportunity for growth and implementation. A key point for this type of implementation to be successful is the quality of the data, which we will probably be able to access to, since we live a digitization process of the sector and many restaurants have a solid database.