Purpose : This research aims to develop a data-driven inspection policy for radio stations utilizing the KS Q ISO 2859-1 sampling method, addressing potential regulatory relaxations and impending management challenges. Methods : Using radio station inspection big data from the past six years, we established a simulation model to evaluate the current policy. A new inspection sampling policy framework was designed based on the KS Q ISO 2859-1 method. The study compares the performance of the current and proposed inspection systems, offering insights for an improved inspection strategy. Results : This study introduced a simulation model for inspection system based on the KS Q ISO 2859-1 sampling method. Through various experimental designs, key performance indicators such as non-detection rate and sample proportion were derived, providing foundational data for the new inspection policy. Conclusion : Using big data from radio station inspections, we evaluated current inspection systems and quantitatively compared a new system across diverse scenarios. Our simulation model effectively verified the feasibility and efficiency of the proposed framework. For practical implementation, essential factors such as lot size, inspection cycle, and AQL standards need precise definition and consideration. Enhancing radio station inspections requires a policy-driven approach that factors in socio-economic impacts and solicits feedback from industry participants. Future study should also explore various perspectives related to legislative, institutional, and operational aspects of inspection organizations.