  Today, many public or private institutions provide professional food service
for personnels working in their own organizations. Regarding the planning of
the said service, there are some obstacles due to the fact that the number of
the personnel working in the institutions is generally high and the personnel
are out of the institution due to personal or institutional reasons. Because of
this, it is difficult to determine the daily food demand, and this causes cost,
time and labor loss for the institutions. Statistical or heuristic methods are
used to remove or at least minimize these losses. In this study, an artificial
intelligence model was proposed, which estimates the daily food demand quantity
using artificial neural networks for businesses. The data are obtained from a
refectory database of a private institution with a capacity of 110 people
serving daily meals and serving at different levels, covering the last two
years (2016-2018). The model was created using the MATLAB package program. The
performance of the model was determinde by the Regression values, the Mean
Absolute Percentage Error (MAPE) and the Mean Squared Error (MSE). In the
training of the ANN model, feed forward back propagation network architecture
is used. The best model obtained as a result of the experiments is a
multi-layer (8-10-10-1) structure with a training R ratio of 0,9948, a testing
R ratio of 0,9830 and an error rate of 0,003783, respectively. Experimental
results demonstrated that the model has low error rate, high performance and
positive effect of using artificial neural networks for demand estimating.
