Methodology of development and implementation of an intelligent sales forecasting information system for effective inventory management

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.