SELECTION OF AN INVENTORY MANAGEMENT MODEL FOR ROAD PAVEMENT PRODUCTION

The article is devoted to substantiating the choice of inventory management models for making effective decisions in the process of road pavement production. Based on a review of scientific papers, the advantages and disadvantages of common models are identified. The main factors, such as production recipe, storage conditions of raw materials, and demand for finished products, are considered. The peculiarities of their influence on inventory management in the field of road pavement production are determined, provided that there is no shortage of raw materials. A comparison of common deterministic inventory management models is carried out: ABC-analysis, Economic Optimal Quantity, Minimum-maximum, Just in Time, and others. The ABC analysis of the main components of the road pavement formulation is carried out. The ABC classification based on financial impact allowed us to identify the component of priority inventory control by concluding long-term contracts with suppliers and managing inventory on the Just in Time principle. The Economic Optimal Quantity model was used to perform calculations under two scenarios: a 12-month constant demand cycle and an 8-month seasonal demand cycle. The results of the study showed that the optimal cycle duration for components of categories B and C remains unchanged at 7 months, while the cycle for component A doubles in the seasonal scenario. This model makes long-term storage of the category A component impractical. Studies have confirmed that the Just in Time approach is more relevant for component A in the production of road pavement to minimise excess storage costs and maintain quality. It is determined that a combined approach maximises the efficiency of inventory management in producing road surfaces. ABC analysis identifies the key cost factor that requires Just in Time management to reduce storage costs. The Economic Optimal Quantity model is applied to components B and C, ensuring optimal ordering while maintaining operational efficiency. The study highlights the need to integrate multiple models to improve inventory decision-making in a dynamic manufacturing environment.