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

<div class="section abstract"><div class="htmlview paragraph">In Automobile manufacturing, maintaining the Quality of parts supplied by vendor is crucial & challenging. This paper introduces a digital tool designed to monitor trends for critical parameters of these parts in real-time. Utilizing Statistical Process Control (SPC) graphs, the tool continuously tracks Quality trend for critical parts and process parameters, predicting potential issues for proactive improvements even before parts are supplied.</div><div class="htmlview paragraph">The tool integrates data from all Supplier partners across value chain into a single ecosystem, providing a comprehensive view of their performance and the parts they supply. Suppliers input data into a digital application, which is then analyzed in the cloud using SPC techniques to generate potential alerts for improvement. These alerts are automatically sent to both Suppliers 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.</div><div class="htmlview paragraph">This tool enables auto selection of control charts based on sample size, frequency & type of parameters (unilateral, bilateral, GD&T) to accommodate variation in Quality standard of parts & manufacturing Process. Additionally Real-time adjustment of control limits based on time period selection make this tool accurate & reliable for monitoring Quality. 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 SPC based Analytic model. This IoT integration also supports predictive maintenance of tooling and equipment and early detection of potential issues, reducing downtime and improving overall productivity.</div><div class="htmlview paragraph">By minimizing the incidence of faulty parts on the shop floor, the tool significantly reduces rejections, 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.</div></div>