This paper presents a model for analyzing complex logistics and supply chains, based on Generalized Nets (GN) enriched with Intuitionistic Fuzzy Sets (IFS). The integration of IFS, as an extension of Zadeh’s classical fuzzy sets, enables a more precise representation of uncertainty by incorporating degrees of membership, non-membership, and uncertainty. This methodology is particularly effective for evaluating cargo losses and uncertainties in deliveries, such as delays, losses, or theft. The proposed GN model includes interconnected transitions representing key operations in the supply chain. Tokens within the GN framework carry detailed attributes, such as information about goods, quantities, deadlines, and prices, ensuring a reliable representation of real-world dynamics. Additionally, the use of IFS allows for more accurate assessments of potential shipment delays or losses. The proposed model provides a comprehensive tool for analyzing and optimizing complex logistics systems, offering valuable insights for stakeholders involved in supply chain management and operational planning.