Quality Metrics Dashboard
Interpreting quality charts, identifying trends, and using data for continuous improvement.
Dashboard Overview
The quality metrics dashboard provides a consolidated view of your organization's quality performance. Key widgets include: total open NCRs, NCR count by severity, average time from NCR filing to closure, scrap rate trend (percentage over time), and a breakdown of NCR dispositions. The dashboard can be filtered by team, station, and date range to focus on specific areas.
Pareto Analysis Chart
The Pareto chart ranks NCR root causes by frequency, with a cumulative percentage line overlay. This follows the 80/20 principle — typically, a small number of root causes account for the majority of quality issues. Focus your improvement efforts on the top two or three causes shown on the chart. Common categories include: tool wear, incorrect offsets, material variation, setup error, and program revision mismatch.
Scrap Rate Trend
A line chart shows the scrap rate (scrap / total produced × 100%) over time. Look for patterns: is scrap increasing on certain days of the week? After certain shifts? At specific stations? An upward trend in scrap rate is an early warning that something has changed — a dull tool, a new material batch, an operator who needs additional training, or a process that has drifted out of control.
Time-to-Close Metric
This metric measures the average number of hours (or days) between an NCR being filed and being closed. A low time-to-close indicates that your quality review process is responsive. If this number is high or increasing, it may mean NCRs are accumulating in the review queue without timely attention, which can cause work orders to remain on QC Hold for too long.
Using Quality Data for Improvement
The quality dashboard is most valuable when reviewed regularly — weekly or even daily during quality huddles. Ask questions like: What is our yield trend? Are the same root causes recurring? Is one station generating more NCRs than others? Use the data to drive specific actions: retool a worn station, update a program, retrain on a measurement technique, or escalate a supplier quality issue. Data-driven quality improvement is more effective and sustainable than reactive firefighting.