Quality management systems rely on accurate and reliable data to support effective decision-making, continuous improvement, and organizational transparency. In organizations certified according to ISO 9001, large volumes of numerical data are generated through quality performance indicators, internal audit findings, customer complaints, corrective actions, and process monitoring activities. However, the reliability of such data is rarely assessed using statistical methods. This paper investigates the application of Benford’s Law as a statistical tool for detecting irregularities in datasets used within quality management auditing processes. The analysis focuses on numerical distributions associated with selected quality management indicators in ISO 9001 certified firms. By comparing the observed distribution of leading digits with the theoretical distribution predicted by Benford’s Law, potential anomalies in reported data can be identified. The results suggest that Benford’s Law can serve as a useful preliminary screening method in quality management audits, enabling auditors and managers to detect unusual numerical patterns that may indicate reporting inconsistencies or data manipulation. The proposed approach contributes to strengthening internal control mechanisms and improving the reliability of data used in quality management systems.

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