articles

A model for forecasting deviations of external and internal environmental factors at an industrial enterprise from planned values is proposed. This model is based on an ensemble of artificial neural networks. Improving the accuracy of such forecasts is a pressing research challenge, as accuracy plays a key role in developing enterprise production programs, enabling the sound development of competitive strategies for relationships with suppliers and customers. Forecast accuracy is significantly affected by instability in internal environmental factors (data directly characterizing production processes in terms of their impact on output volumes and product quality) and external factors (demand volume, delivery times, and the quality of components and raw materials). In the research problem statement, instability was understood not as the variability of factor values per se, but as fluctuations in their generalized characteristics relating to the entire data set. Such characteristics include irregular data receipt and the presence of anomalies in them. The novelty of the research results lies in the proposed structure of the neural network model for forecasting deviations of external and internal environmental factors from planned indicators in the face of irregular data receipt and the presence of anomalies in the data, as well as the algorithm for its application. The model is based on an ensemble of three neural network submodels built on convolutional and recurrent neural network architectures that forecast factors in the internal and external environments (taking into account decomposition into micro and macro environments) of the enterprise. The mutual influence of the instability of these factors is taken into account in the model by using a long short-term memory network at its output to aggregate the results of the submodels to produce the final forecast. The results of the model experiment showed that taking into account the instability of factors allows for increased accuracy in forecasting deviations of external and internal environmental factors from planned values.