For most processes, we have a choice of measurement options that vary with cost. Ideally, we seek the most accurate measurement at the lowest cost with the expectation that the result will be satisfactory. When measurements are critical to operations, we should validate these assumptions.
The obvious reasons for automating quality in manufacturing are to reduce scrap, rework, overtime and costs while simultaneously increasing productivity and customer satisfaction. The non-obvious reasons include employee satisfaction, customer referrals and market growth. Automation also give managers and line workers insights into ongoing production.
Discovering the underlying factors that influence compliance, product quality, production efficiency and your performance as a supplier requires greater accuracy and precision than many manufacturing metrics provide.
The first step to process improvement is machine performance measurement and diagnosis. However, it’s a step that many OEMs and service providers—even quality professionals—fail to approach with as much rigor as other steps, like process setting and in-process control.
The process of analyzing gage variability is often highly structured, involving an examination of the gages themselves for sensitivity to temperature changes, magnetic fields, and other factors. These are the easy ones. The second area of variability has its source in gage operators themselves, who may have different levels of training, experience, fatigue, and even attitude.
Real-life quality problems are conundrums. Dorian Shainin realized that recognizing the distinctive characteristics of a problem was critical. He also knew that applying the right tactics was the key to the solution; however, many of the analysis tools of his time were not effective.
Generation Z, a demographic cohort comprised of people born from the mid 1990s to the early 2000s, is the first generation to have no memory of life before cell phones, laptops and widespread use of the Internet.
Check out the December 2020 edition of Quality: Not all that is green is good; methods that hide bad product behind green numbers, additive manufacturing, calibration documentation, managing unanticipated risk and much more!