SPC based Critical Parameters Monitoring of Vendor Supplied Parts by Auto OEM for Quality Control

In Automobile manufacturing, maintaining the Quality of vendor-supplied parts is crucial. This paper introduces a digital tool designed to monitor critical parameters of these parts in real-time. Utilizing Statistical Process Control (SPC) graphs, the tool continuously tracks essential parts and process parameters, predicting potential issues for proactive improvements even before parts are supplied. The tool integrates data from all vendor partners into a single platform, providing a comprehensive view of their performance and the parts they supply. Vendors input data into a digital application, which is then analyzed in the cloud using SPC techniques to predict potential alerts for improvement. These alerts are automatically sent to both vendors and relevant personnel at the OEM, enabling proactive measures to address any quality deviations. 100% data is visualized in an integrated dashboard which acts as a single source of truth for all stakeholders. Real-time monitoring is done with control limits dynamically adjusting based on sample size and frequency. Incorporating Industry 4.0 and smart manufacturing principles, this tool represents a significant advancement in quality control. Integration with the Internet of Things (IoT) enables automatic data collection and monitoring, enhancing the efficiency and accuracy of the quality control process with the AI/ML application, this tool aids autocorrection in machine when parameters goes out of control. This IoT integration also supports predictive maintenance and early detection of potential issues, reducing downtime and improving overall productivity. By minimizing the incidence of faulty parts in logistics and on the shop floor, the tool significantly reduces waste, contributing to more sustainable manufacturing practices. This proactive approach ensures high-quality standards and aligns with smart manufacturing goals by optimizing resource use and enhancing operational efficiency.