International Journal of Nonlinear Analysis and Applications

Forecasting demand and optimizing the ordering of goods in the supply chain using artificial intelligence in Kale company

Document Type : Research Paper

Authors

Department of Business Management, Rasht Branch, Islamic Azad University, Rasht, Iran

Abstract

The globalization of business market competition has created an uncertain environment for manufacturing organizations, so the managers are trying and planning more to survive. One of the world's largest and most complex industry sectors is the food supply chain, which plays an important role in economic growth. Food characteristics, especially shelf life, strongly influence the three sustainability criteria. Much attention is paid to perishable food products with a limited lifespan due to the large amount of waste, harmful environmental effects and special storage and transportation conditions. One of the problems faced by organizations facing customer demand is the change in demand, which causes uncertainty on the producer's side (production quantity), which also causes uncertainty in purchasing and, in a sense, in the entire supply chain. In this research, the 2-layer perceptron neural network and the following neural network parameters were used to predict the demand for the product, because the demand value is a large number, for this reason, in the data entry table, we divide the demand output value by 1000 to get the average The squared error should be examined. The results showed that the prediction using the multi-layer perceptron neural network toolbox in MATLAB software, due to the upward trend of Venezuelan demand, the prediction value of the neural network is close to the real value.

Keywords

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International Journal of Nonlinear Analysis and Applications
Volume 16, Issue 12
December 2025
Pages 29-40
  • Receive Date: 11 October 2023
  • Accept Date: 02 December 2023