Evaluating Proportional-Integral-Derivative (PID) controller performance across a wide range of parameter configurations is a complex and time-consuming task, particularly in industrial settings with diverse machine variants and strict control requirements. Engineers must analyse large volumes of simulation data to assess metrics such as rising time, overshoot, and energy consumption—often relying on manual inspection or ad hoc scripts that are error-prone and difficult to scale. In collaboration with an industrial partner, we developed a Python-based analysis and ranking tool to automate this process. STRIPID (Simulation Test Ranking and Interactive Performance Inspection for PID controllers) extracts domain-relevant metrics, applies reference-based normalisation and customisable scoring functions, and enables interactive exploration of simulation results. It supports dynamic adjustment of evaluation thresholds and incorporates expert-informed penalties to refine ranking outcomes. This paper presents STRIPID’s architecture and scoring approach, and shares insights from its development in a real industrial testing context. STRIPID significantly reduces manual effort and enhances the consistency and traceability of PID parameter evaluation, offering a practical solution for tuning support and robustness analysis in control systems engineering.