Методологія розробки та впровадження інтелектуальної інформаційної системи прогнозування продажів для ефективного управління

• Date: 2025
• Main Authors: Угрин, Д.І., Ушенко, Ю.О., Газдюк, К.П., Довгунь, А.Я., Угрин, А.Д., Козак, Д.В.
• Format: Article
• Language: Ukrainian
• Published: Vinnytsia National Technical University 2025
• Subjects: прогнозування продажів; машинне навчання; управління запасами; AutoML .NET; управління ризиками; оптимізація.
• Online Access: https://oeipt.vntu.edu.ua/index.php/oeipt/article/view/770
• Journal Title: Optoelectronic Information-Power Technologies

The study is devoted to the development and implementation of a flexible sales forecasting methodology for efficient inventory management in stores and warehouses. The proposed model is based on machine learning methods and takes into account changing market conditions, allowing for adaptive forecast updates. The main stages of the research include analysing existing forecasting methods, selecting machine learning algorithms, developing a prototype model, and evaluating its accuracy and economic effect. To implement the model, the AutoML .NET framework was used, which provides automatic selection of the most efficient algorithms and hyperparameters. The results of model training experiments on data sets of different sizes demonstrated high forecasting accuracy using FastTree, FastForest, SDCA, and LightGBM algorithms. The effectiveness of various parameter optimisation strategies was also investigated, allowing the model to adapt to new market changes. The proposed methodology helps to reduce risks in the inventory management process, increase the efficiency of business processes and minimise costs associated with excess or shortage stocks.