Process-Oriented Management of Quality Risk in Global Automotive Supply Chains

Automakers are embedded in globally dispersed supply chains where quality-related risk increasingly arise outside their internal production boundaries due to interdependencies of suppliers, variability of logistics and coordination problems. Digital enablers such as SCM systems, sensor-based monitoring and traceability solutions can enhance visibility, but quality disruptions continue to take place because risk is often managed reactively, through inspection and corrective action, rather than proactively, through structured managerial decision-making embedded in supply chain processes. This paper addresses this gap by taking a process-oriented and decision-centric perspective on quality risk management in global automotive supply chains. The paper starts with a synthesis of managerial insights from the literature on quality risk in supply chain management, sources of risk in supplier-buyer relationships, and organizational impact of quality related disruptions. This forms the basis for proposing a Process-Decision Quality Risk Management framework, which connects quality-critical supply chain processes with explicit managerial control points – decision moments when choices of acceptance, escalation, continuation or intervention have a material impact on risk exposure. The framework views quality risk as an accumulative phenomenon that is shaped by decision patterns over time (risk reduction, transfer or amplification) rather than a standalone technical failure. The framework combines risk-oriented signals reflecting emerging risk accumulation (e.g.: repeated conditional acceptances or delayed escalations) to enable earlier intervention and closes the loop via feedback and organizational learning mechanisms that refine decision rules and governance. This paper contributes a structured but scalable approach for proactive quality risk governance across organizational boundaries, complementing existing quality management systems without adding procedural complexity. Future research needs to empirically validate the framework using industrial case studies and to investigate how data analytics can improve the detection and monitoring of management control points and risk-based signals.