Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network

IEEE Access

卷:9

Monika Hajdas¹  Krzysztof Lutoslawski²  Agata Kozina²  Ewa Walaszczyk²  Marcin Hernes²  Joanna Radomska³ 

[1] Department of Marketing Management, Wroclaw University of Economics and Business, Wroclaw, Poland;

[2] Department of Process Management, Wroclaw University of Economics and Business, Wroclaw, Poland;

[3] Department of Strategic Management, Wroclaw University of Economics and Business, Wroclaw, Poland;

关键词: Food industry;    sustainable development;    neural networks;    machine learning;    demand forecasting;   

DOI  :  10.1109/ACCESS.2021.3123255

来源: DOAJ

【 摘 要 】

Food demand prediction is a significant issue for both business process improvement and sustainable development. Data science methods, including artificial intelligence methods, are often used for this purpose. The aim of this research is to develop models for food demand prediction based on a nonlinear autoregressive exogenous neural network. The research focuses on processed foods, such as bread or butter. The architectures of the developed models differed in the number of hidden layers and the number of neurons in the hidden layers, as well as with different sizes of the delay line, were tested for a given product. The results of the research show that depending on the type of product, the prediction performance slightly differed. The results of the R² measure ranged from 96,2399 to 99,6477, depending on particular products. The proposed models can be used in a company’s intelligent management system for the rational control of inventories and food production. This can also lead to a reduction in food waste.

【 授权许可】

Unknown