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.
Collecting data offers clues to sources of variability. But when this disciplined analysis fails to uncover real reasons for variability, it may be time for the Sherlock Holmes of variability to look at operational definitions—often the most overlooked consideration when evaluating variation among measurement devices.
Elementary, my dear Watson? Perhaps, but nonetheless these definitions can lead to levels of variation in gage output if they are vague or nonexistent. “In the opinion of many people in industry, there is nothing more important for transaction of business than use of operational definitions. It could also be said that no requirement of industry is so much neglected” (Deming, 276).
Because we are used to somewhat loose definitions of tasks, with the expectation that “everyone knows how to do this,” it’s easy to forget the importance of clearly defined instructions for collecting data. It’s one thing to say “Put the groceries away,” and another to specify, “Put cold things in refrigerator, frozen food in freezer, and canned goods in pantry,” if the operator is a child, perhaps. Since everyday tasks such as this do not demand precise measurement, one can afford to be casual about the instructions, defining them in terms of the person carrying them out. In manufacturing and service environments, however, lack of clear and specific operational definitions can create chaos, rendering data that is produced meaningless and outcomes unclear.
If inspectors are asked to identify defective devices, each will have his or her own sense of “defective.” If they have a clear understanding of a specific characteristic of interest (inaccurate measurement, for example), as well as the method for measuring it and the decision criteria that are to be considered, they are more likely to arrive at the same conclusions about what constitutes “defective.”
Sometimes an operational definition may appear to be appropriately focused and clear:
“When measuring the part, hold the gage firmly and tighten the thimble firmly. Measure to 1/16th inch.”
Sounds good, right? But do you know how tight the thimble really should be? Or whether to round up if the measurement is close to 1/16”?
How about this approach to an operational definition:
- Setup: Start the gage lab with all eight lights on. The temperature in the lab must be between 73 and 75 degrees Fahrenheit, with 20 – 30% humidity. All parts to be measured must be in the lab for a minimum of 30 minutes prior to any measurements being taken, to assure uniform temperature.
- When measuring tubes up to 3”, hold the 0 – 3” micrometer at a right angle to the tube or use the gage fixture. The Anvil and Spindle will be perpendicular to the tube.
- Tighten the thimble until the slipping clutch clicks.
- Measure to the nearest 16th of an inch. Round up if the measurement falls between scales.
This detailed operation definition helps to assure that all operators approach the task in the same way, reducing the levels of variation among them.
In another example, the directive, “Gages must be checked at regular intervals” invites chaos. What does the “checking” entail? It might be glancing at the inventory to make sure gages are in the right place. And “regular intervals” could mean anything from every hour to once a year on my birthday. When operators are left to create their own definitions and understandings, the outcomes will not be reliable.
Not only are operational definitions essential to establishing a measurement system, but they also provide a diagnostic tool. When a system appears to be changing, the cause may be a change in the ways in which operational definitions are used. Whenever a system is unstable, operational definitions and their use should be evaluated for their impact (Total Quality Tools, p. 171).
You may find that this evaluation brings with it the “Eureka!” moment that Sherlock himself experienced.
Deming, W. Edwards. Out of the Crisis. Cambridge: Massachusetts Institute of Technology, sixth printing, 1989.
Total Quality Tools. Dayton: PQ Systems, Inc., 1996.