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Prediction of Turnover in Gastronomy

by Means of Machine Learning Algorithms

The structural relationship between turnover and explanatory variables is determined from historical data in order to predict future turnover.

At a glance


How many guests will visit a restaurant and at what time of the day? Which menus will be ordered? Planning is absolutely crucial in gastronomy but not at all easy. It must be ensured that the correct amount of food is purchased and enough staff is present to run the shop. The planning which has been done intuitively for the time being can now be replaced by our machine learning algorithms.
Data from operation and cash register systems are read into a database and used to train machine learning algorithms and to predict turnover as well as staff needed. Relations between the influencing factors are complex and specific to each restaurant and yet the prognosis should be feasible with as little manual intervention as possible. Variables to consider include time of day, day of week, holidays, school holidays, weather conditions, special offerings or local events etc.

Further information