DIGITAL TWIN INTEGRATION WITH SAP QM FOR PREDICTIVE DEFECT ANALYSIS

Nirmala Parixit Patel; Elayaraja Subbaiah

DIGITAL TWIN INTEGRATION WITH SAP QM FOR PREDICTIVE DEFECT ANALYSIS

Authors

Nirmala Parixit Patel

Solution Architect, .

Elayaraja Subbaiah

Cloud Solution Architect, .

Keywords:

Digital Twin Technology, SAP Quality Management (SAP QM), Predictive Defect Analysis, Real-Time Data Integration, Quality Management Systems

Synopsis

This dissertation examines how Digital Twin technology joins with SAP Quality Management (QM) systems. It aims to improve how factories predict defects in manufacturing processes. The work focuses on how current methods fail to use real-time data for good defect detection. The study shows how to build a strong predictive framework for these processes. It combines factory data, defect logs, and performance numbers from SAP QM with simulation data from Digital Twins. The results show that defect rates dropped by up to 30 percent. Teams responded much faster to potential quality problems. These results prove that combining these systems works well. The meaning of these results goes beyond just manufacturing. They suggest big changes for the healthcare industry. Hospitals need quality management to keep patients safe and keep operations running well. Healthcare groups find and lower risks in medical devices and medical steps. They use advanced predictions made possible by Digital Twin technology to do this. This work changes how people handle quality management practices. It shows that digital tools help healthcare run better and improve patient health. This study shows why healthcare must use new data methods for quality checks. These tools help make healthcare more reliable and safe for everyone who needs care.

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