Development of an Integrated Framework for Quality & Reliability Across the Supply Chain

This paper proposes an integrated framework for enhancing supply chain quality and reliability using data analytics. It covers key components like aligning quality objectives, standardizing practices, and fostering collaboration. Implementation strategies include Six-Sigma metrics and multi-criteria decision-making. The study also explores blockchain and IoT for field recall traceability, and examines data analytics, big data, and machine learning for predicting disruptions and improving reliability. Additionally, it addresses the impact of asset residual value and predictive reliability methods. This framework aims to advance effective and sustainable supply chain management.