The object of the presented research is the production process of the MK2000 utility vehicle in the conditions of the assembly shop of PJSC MZiK. The MK2000 is a technically complex product that is produced together with ten other types of technically complex products within the same workshop. The uncertainty factor in the production process is the unknown duration of the test cycle that each product is subjected to after the end of assembly. The purpose of the study: development of a method for calculating the duration of the production cycle of a product under conditions of uncertainty. Evaluation of the possibility of production planning and calculation of the annual schedule of shipment of finished products to the consumer by predicting the duration of the cycle. Methods: to solve the problem of predicting the duration of the production cycle, the dependence between the number of products of all types already in the process of testing at the time of the transfer of the product under consideration for testing, and the duration of the test of the transferred product is used. Since the production of various types of products is carried out at the same facilities, the regularity of the mutual influence of the production processes of various types of products is considered as the main one. The duration is calculated using a mathematical model obtained as a result of training the Random Forest algorithm. Results: a new method has been developed for calculating the duration of the production cycle of technically complex products for small-scale production enterprises based on statistical analysis of the results of the previous production activity of the enterprise. The pre-trained Random Forest machine learning algorithm was used for the calculation. Evaluation of the calculation results obtained using a mathematical model of the production process suggests that the prediction of the duration of the production cycle of products with an unknown duration of the test stage is possible using machine learning methods, and the accuracy of the forecast depends on the chosen method and the size of the training sample. Practical significance: the proposed method allows us to predict the duration of production and testing of technically complex products under conditions of uncertainty and can be used as a tool for calculating the annual schedule of product shipment to the consumer.