Analysis of Cybersecurity-Induced Quality Risks in Connected Manufacturing Systems Using the FMEA Method

With the emergence of Industry 4.0, manufacturing systems have become increasingly connected and data-driven through sensors, PLC systems, SCADA infrastructures, and databases. While this transformation enables real-time monitoring and more effective management of quality control processes, it also makes production systems more vulnerable to cybersecurity threats. In particular, the manipulation of sensor data, unauthorized changes to production parameters, and corruption of quality records can directly affect the accuracy of quality control decisions.

In this study, cybersecurity-induced quality risks in connected manufacturing systems were analyzed using real production data, scenario-based simulation, and an improved FMEA approach. Open-access real manufacturing sensor data were used, and several cyberattack scenarios were applied to the dataset, including data manipulation, data loss, PLC parameter changes, network delay, and noise injection. The effects of these scenarios on quality control decisions were then evaluated, and the risks were prioritized using the FMEA method.

The findings showed that the most critical risk was the manipulation of sensor data. In this scenario, the deviation in quality decisions reached the highest level, and the initial RPN value was higher than those of the other risks. In addition, a significant reduction was observed in the residual RPN values after the proposed preventive actions were applied. This result indicates that data integrity, access control, and anomaly detection mechanisms are critical for quality assurance in connected manufacturing environments.

This study contributes to the literature by addressing cybersecurity risks from a quality control perspective and by combining scenario-based analysis with an improved FMEA approach using real manufacturing data.