Traditional storage systems often rely on direct access storage locations, but these solutions can be inefficient and environmentally exhausting due to higher energy consumption and limited scalability. In previous studies, we demonstrated that higher efficiency could be achieved with indirectly-accessible storage systems compared to fully directly accessible ones. To compare the different layouts, we developed a simulation model through which warehouse operations were executed. The resulting time values became comparable, allowing us to rank the solutions. However, the conventional method of calculating simple averages, often applied in the evaluation of logistic performance, has limited capacity to capture the complexity of data distributions and the small differences between individual observations. The aim of this study is to interpret warehouse operations from a new perspective using a fuzzy logic-based model, explicitly addressing the research gap that traditional statistical averages cannot adequately capture subtle performance differences. The novelty of our contribution lies in applying fuzzy signatures to warehouse simulation, which enables more precise differentiation between competing system layouts. During the simulation analysis, traditional average values are compared using a classification method based on fuzzy sets, demonstrating that this new approach is more sensitive to performance fluctuations and better reflects real operational conditions. This approach supports the selection of the most suitable storage system for warehouse operations, the initial placement of products, or the operational process that best aligns with emerging demands, enabling greater energy savings and enhancing the overall sustainability of the system.