Daily Food Demand Forecast with Artificial Neural Networks: Kirikkale University Case

Abstract

In food service organizations, demand estimation is very important in planning of production. When an accurate demand forecast is made, the resources are used more efficiently, and the production wants to be lost in some places. In the institutions where the number of people to make a request is not known clearly, the demand forecasts of the quantitative and qualitative targets are made. It has been proposed a model of artificial neural networks to estimate the daily meal demand. Artificial neural networks are a qualitative method that targets a predetermined target by using the previous example data using a predefined demand estimate. Kirikkale University cafeteria, where a selection should be made, the demand can affect, your criteria, MATLAB program was prepared a suitable model was created and the data were analyzed in the meantime. © 2019 IEEE.