All measurements involve uncertainty. Suppliers of measuring equipment usually specify the measurement uncertainty as a symmetrical interval around the measurement result:
Measurement result ± measurement uncertainty
Example: The length of a special measuring rod is 2000 mm ± 1 mm. With a 95 % confidence interval, which is normal to use, there is a 95% probability that the rod is between 1999 mm and 2001 mm.
Measurement System Analysis (MSA), also referred to as Measurement System Evaluation (MSE), can determine the measurements accuracy, precision, and stability. By measuring the same item repeatedly you can decide the precision.
Measurements are done many different ways, using different kind of technologies.
Lean Tech has evaluated subjective measurement systems like appearance, taste, and smell. Surprisingly, the measurement error for a visual test was greatest for the quality manager, who made the decision when there was doubt about the quality. The quality manager did not perform the test frequently, only occasionally when the quality was questioned. Ironically, this person's evaluation was less consistent than for operators who performed the test more frequently.
Maybe you use cameras / automatic optical inspection (AOI) to control product quality? The measurement can be performed based on reference points identified by the camera. Measures of distance can be made between reference points to control product quality. Measurement System Evaluations performed by Lean Tech for camera controls, show that they are both accurate and precise if they find the right reference point.
Unfortunately, they become unstable if they fail to find the correct reference point and thus render incorrect measurement.
To make the right decisions, you need reliable measurement results. Lean Tech can help you perform Measurement System Analyzes or train you how to do it yourself.
Here is a video about weight measurements, which determine measurement uncertainty, within day variation and between day variation: