In manufacturing, machine operators, quality control inspectors, process engineers and automated systems measure various product and process characteristics to ensure quality. These measurements provide valuable data that helps manufacturers monitor their processes in real-time, tracking key performance indicators such as cycle time, throughput, and efficiency.

By comparing these measurements to established benchmarks or targets, manufacturers can identify issues or inefficiencies in their processes.

For example, if the cycle time measurement is consistently higher than the target, it may indicate a bottleneck in the process that needs to be addressed.

Quality control teams also measure the characteristics of a product, such as dimensions, weight, or performance to identify defects or variations in the product to ensure they meet specs, and analyze it to identify trends or opportunities for improvement.

Clearly, measurement is important to quality. But measurement processes aren’t always foolproof. If an operator is inconsistent or equipment needs to be calibrated, quality teams need to know so they can make targeted improvements.

That’s why many teams turn to Measurement System Analysis (MSA) to assess how their measurement systems are performing.

MSA uses statistical tools to analyze repeatability, reproducibility, and accuracy. It can calculate variation within and between operators, compare measurements to known reference values, and pinpoint sources of error.

1. For repeatability, MSA calculates the variation in measurements from the same operator using range, standard deviation, or variance.
2. For reproducibility, it uses ANOVA or Gauge R&R studies to determine the variation between different operators.
3. For accuracy, it compares measurements to known reference values and calculates the difference to assess bias or linearity.

If the variation is too high, MSA helps identify specific problems, giving quality teams the chance to resolve them.