Abstract:

Forecasting production demand is one of the key elements of effective supply chain management in small and medium-sized enterprises. The paper presents a comparative analysis of different neural network architectures used in production demand forecasting. The study includes the evaluation of neural networks: unidirectional (MLP), recursive (RNN), long-term memory (LSTM), recursive units (GRU), convolutional networks (CNNs) and hybrid models combining multiple architectures. The analysis showed that the choice of the appropriate network architecture depends on the characteristics of the time data, the degree of complexity of demand patterns and the available computing resources. LSTM networks show high performance in modeling long-term time dependencies, while CNN-BiLSTM hybrid models offer the best results in the context of multivariate time series. The article provides recommendations for choosing optimal architectures for specific production scenarios.