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Manufacturing processes are susceptible to quality defects, resulting in overall equipment effectiveness reduction. Proactive and reactive methods, such as process failure mode and effects analysis, and root cause analysis, have been developed to eliminate potential causes of failure modes. In this study, data from an assembly case is evaluated using supervised machine learning methods to analyze the challenges of purely data-driven failure mode detection. Assembly step execution times, as indicators, and end-of-the-line quality checklists, as the failure modes, are used to gain insights into failure mode detection. Challenges for data-driven methods are discussed and possible future research streams are proposed.