Decoding PPAP to Identify Early-Stage Production Risks and Quality Gaps in Automotive Manufacturing

Abstract

Production Part Approval Process (PPAP) is an essential quality gateway in automotive manufacturing, yet it is often implemented as a checklist rather than leveraged as a quantitative source of risk intelligence. This study unravels PPAP documentation to detect early-stage production risks and supplier quality gaps during new product launches. A simulation of 30 PPAP Level 3 submissions—modeled after AIAG plastics guidelines, battery enclosures, and powertrain component standards—was evaluated across 18 mandatory elements. Element scores were generated by integrating a five-point completeness matrix with an Analytic Hierarchy Process (AHP) weighting model to produce overall PPAP completeness scores and a composite PPAP Risk Index. Pearson correlation results identify weak PFMEA alignment (r = 0.82) and insufficient or low-quality capability studies (r = 0.78) as highly correlated with increased modelled launch deviation rates. The findings demonstrate that PPAP completeness, along with robust cross-linkages between FMEA, Control Plan, and process capability, serves as a statistically significant leading indicator of launch stability. The paper proposes an analytics framework enabling OEM and supplier quality teams to segment suppliers by risk, prioritize mitigation actions, and embed PPAP-derived predictive insights into APQP-driven, data-informed quality management. These insights ultimately strengthen launch readiness and enhance quality performance across OEM and supplier networks.