A digital twin-based framework for predictive quality assurance and supply chain resilience in the automotive industry

The automotive industry faces growing challenges in ensuring supply chain resilience (SCR) and predictive quality assurance (PQA), particularly amid global disruptions. Traditional quality systems often lack the traceability and adaptability needed in this dynamic environment. Addressing this gap, this study proposes a novel digital twin-enabled framework based on a structured seven-phase, five-stage methodology, termed the 7D model. Aligned with international automotive task force (IATF) standards, the framework leverages real-time IoT data and historical metrics to simulate disruptions, monitor key performance indicators (KPIs), and enable data-driven, proactive quality interventions. A case study from a tractor manufacturer illustrates the framework’s applicability in an emerging market context. Despite operating with limited digital infrastructure, the company’s engagement with lean practices demonstrates the feasibility and scalability of the 7D-PQA model. Comparative analysis against conventional problem-solving methods validates the framework’s enhanced capacity for resilience, traceability, and predictive quality. This work advances the field by offering the first IATF-aligned DT framework for PQA in the automotive sector, with broader implications for digital transformation across manufacturing industries.