Research on the Ukrainian food market demand forecasting using artificial neural network technologies - Honcharenko-Halitsyn, Serhii; Kopp, Andrii Mykhailovych; Halatova, Olha Serhiivna; Martynyuk, Svetlana (Scientific Publishing Center "InterConf", 2025)
The analysis of demand forecasting processes has shown that manual approaches, especially without AI integration, can be time-consuming, prone to errors, and inefficient in handling complex market variables. Designing software components for demand forecasting using Artificial Intelligence (AI) enables the identification of optimal prediction models and resource allocation strategies across diverse geographical markets. This study aims to improve the accuracy of demand forecasting by automating and optimizing forecasting processes through AI-driven software components. Specifically, the research proposes a mathematical model utilizing neural networks to enhance demand forecasting accuracy. The proposed model is examined using a combination of real-world data from a Ukrainian food production company and various AI algorithms, such as deep learning models, to define optimal forecasting solutions.
Keywords: demand forecasting; process automation; artificial intelligence; neural networks; optimization model; multi-market analysis