Many options have been developed for the implementation of the algorithm for updating the fleet of grain harvesters to date. In accordance with the yield and other indicators, recommendations for the formation and renewal of the harvester fleet are proposed discretely in the form of tables or charts. This form of information does not always meet the requirements of operational correction and does not allow assessing the technological capabilities of the harvesting units, depending on the harvesting conditions. The method to improve the formation of the initial information for operational decision-making on the effective upgrading of technical means of grain harvesting complex taking into account the zonal features of a particular agricultural enterprise is proposed. A graph-analytical method for determining the main parameters of the basic harvesting tools depending on the predicted yield level is developed and the influence of the factors determining the composition of the grain harvesting fleet is assessed. This method makes it possible to identify the most rational basic parameters of alternative basic harvesting tools for a specific agricultural enterprise. The first step is to determine the basic parameters of the basic equipment, then select the appropriate size series of self-propelled threshers for combine harvesters and reapers. Further, alternative versions of various models of grain harvesting units and complexes are formed. For the subsequent selection of rational types of cleaning agents and their criterion assessment, technical and technological, environmental and other indicators are used. The expert-logical analysis of information resources makes it possible to identify and assess the factors that determine the quantitative composition of the technical means of the grain harvesting complex. The final stage in the formation of the initial information for making a decision on updating the technical means of the grain harvesting complex should be their economic assessment, which makes it possible to predict the competitiveness of the threshed grain.