This article proposes an innovative methodology for data-driven modeling and simulation of transportation management through cross-sectoral collaboration in small businesses. The present research is multidisciplinary and interdisciplinary in nature. We investigate the improvements in logistics management that can be achieved through cross-sector collaboration in agriculture and forestry. A data-driven method, such as symbolic regression, is used to identify the relationships between factors in a modeled system using mathematical expressions. These expressions are directly integrated into the simulation models. Simulation spreads the modeling of transportation processes over a period of time. The system dynamics model is designed to analyze and assess the performance of a system based on its past behavior and is, therefore, deterministic. The discrete-event model enables the simulation of future scenarios and outcomes over time, given random input variables. As new data become available, relationships within the symbolic regression method are discovered more accurately, and simulations are updated accordingly. The tools offered for implementation are supplemented by a multi-user web simulation. The proposed case study is based on a real-life example. The obtained results allow small agricultural companies to use transportation and labor resources more efficiently when organizing the transportation of their agricultural and forestry products. Integrating data-driven models into simulations enables a better interpretation of data across the entire data value chain.