Introduction: Reverse logistics is a series of processes through which products, materials and other resources are recovered from customers and returned to the company. Returnable transport items are widely used to support reverse logistics operations conforming closed-loop supply chains in companies and organizations, and projecting and planning of the behavior of these inventories improves their control and performance. Data envelopment analysis is applied to measure the relative efficiency of planning scenarios. Objective: To apply a planning methodology for returnable transport items, using a simulation approach with systems dynamics, for decision making based on projections of the behavior of the productive system, allowing the continuity of the operation in companies producing and distributing beverages. Method: From a literature review and the context of a company that produces and distributes beverages, variables are identified and used for the design, structure and application of a methodology for inventory planning for returnable transport items in two levels (bottles and crates). Data is collected for the input variables of the system dynamics model based on the software Vensim, the system behavior is projected and 8 performance indicators are calculated through 24 scenarios. The effectiveness of the methodology is evaluated through the comparison of the performance indicators through the technique of data envelopment analysis. Results: The inventory planning variables are defined for returnable transport items, the data are collected and integrated into the system dynamics model and indicators are calculated for each scenario. The data envelopment analysis model is executed and the relative efficiencies are determined for each scenario. A frontier analysis is carried out and the variables are analyzed through a sensitivity analysis. Five key variables are identified that increase the performance of the scenarios up to 10% of relative efficiency. Conclusions: The planning of returnable transport items and reverse logistics offers numerous advantages in companies from different sectors. The focus on systems dynamics facilitates the design of the planning model, integrating 84 variables. The model allows to simulate the behavior of the system over time, obtaining performance indicators that evaluate the system in a time horizon. The data envelopment analysis model identifies which scenarios have a higher relative performance, and makes it easier for logistics managers to make informed decisions about the system’s variables.