Proposta de um processo de melhoria contínua na gestão do Production Part Approval Process (PPAP) em uma indústria automotiva

Abstract

Quality management in the automotive industry requires robust and standardized processes to ensure product reliability and compliance with stringent regulatory requirements. In this context, the Production Part Approval Process (PPAP) stands out as a strategic tool to guarantee production part approval and strengthen customer– supplier relationships. This study proposes a continuous improvement process for PPAP management in an automotive company, integrating the PDCA cycle, classical quality tools, and Business Intelligence (BI) resources. The research followed an action-research approach, structured around four specific objectives: mapping the current situation (OE1), designing the process flow (OE2), implementing a pilot (OE3), and assessing results, limitations, and future perspectives (OE4). The initial diagnosis consolidated a master list with 493 pending records for product A, distributed across five projects and thirty suppliers, revealing the predominance of the reason “Without full approval” and a strong concentration in a single critical supplier. Based on tools such as the CSD matrix (certainties, suppositions, and doubts), GUT matrix (severity, urgency, and trend), 5 Whys, and 5W2H, a structured workflow for handling these pending items was developed. In the pilot implementation, lower-complexity issues were prioritized, resulting in the elimination of 51 records and the creation of a unified database providing visibility of all occurrences, along with lead time and approval rate indicators. The evaluation confirmed the model’s feasibility and applicability, indicating gains in transparency, predictability, and governance. Finally, a four-phase roadmap (consolidation, expansion, learning, and sustainment) is presented for continuity and replication of the model in other projects. The study contributes, in practice, by offering an operational design for PPAP management, and academically, by demonstrating the value of integrating classical quality tools and BI resources to address a current and real-world challenge.