Non-alcoholic beverage companies store a significant amount of data in their systems. However, by failing to convert this data into useful information for decision-making. Process mining aims to discover, monitor, and improve real processes by extracting knowledge from event logs available in current systems. This study provides a method for applying process mining to the distribution of non-alcoholic beverages, aiming to increase the quality of service delivered to customers by making the process more transparent. The method provides an orderly and practical guide of much of the knowledge that has been generated in the field of process mining. It allows for a comprehensive analysis by perspectives of process mining - control-flow, organizational, cases, and performance following the L* life-cycle model stages. Additionally, it proposes recommendations and best practices in order to reduce the difficulty and costs, and improve the understanding of non-experts. The method is applied to the distribution process of a company in the non-alcoholic beverage bottling industry.