The manufacturing industry faces an increasing demand for high product customization and high delivery performance in a volatile market environment. Companies rely on high-performing production logistics to meet these market demands, especially minimum TTPs. If extended TTPs are captured in production monitoring and control (PMC), root causes must be identified, and countermeasures must be initiated to avoid jeopardizing their market position. However, complex logistic cause-and-effect relationships often obscure the root causes. Logistic modeling enables a root cause analysis (RCA) based on generally valid cause-and-effect relationships. The increasing data availability unlocks the potential for identifying company-specific root causes using Machine Learning (ML). This paper presents an ML-based methodology for RCA of extended TTPs using clustering and regression algorithms. The methodology orchestrates the application of logistic modeling and ML in PMC to benefit from expert knowledge about generally valid and company-specific cause-and-effect relationships in RCA, thus improving the target-oriented derivation of measures. The methodology is validated with a tool manufacturer's production data.