An Estimation of Personnel Food Demand Quantity for Businesses by Using Artificial Neural Networks


JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, vol.22, no.3, pp.675-686, 2019 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 3
  • Publication Date: 2019
  • Doi Number: 10.2339/politeknik.444380
  • Journal Indexes: Emerging Sources Citation Index (ESCI), TR DİZİN (ULAKBİM)
  • Page Numbers: pp.675-686
  • Keywords: Business, artificial neural networks, food, demand, estimation, ENERGY-CONSUMPTION, PREDICTION, OXYGEN
  • Karadeniz Technical University Affiliated: Yes


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.