Section 2: Data Tools
2.0 OverviewPerformance Measuring Introduction Performance measures are an important topic. People are pursuing excellence and are eager for results to show their efforts are working. Having data is fundamental, and the old saying, "without data, you're just another person with an opinion," has its wisdom. Still, the use of performance measures is not as easy as it appears, and poor use can do more harm than good.
The following introduction is taken from "Grassroots Approach to Statistical Methods" and is reprinted with the permission of R. S. (Bud) Leete, Lockheed Martin Energy Systems. Why Do We Measure? Measuring is the act of assigning numbers to properties or characteristics. We measure to quantify a situation, to regulate, or to understand what affects things we see. Sometimes we measure with gauges and instruments; sometimes, we simply count things. Performance measures can help you understand and improve performance. It is exciting to measure, to benchmark, and to stretch to do better. Some Measures are More Direct than Others A first step in deciding what to measure is to decide what you want to improve. Sometimes there is a direct measure. For example, runners or swimmers who want to improve their performance in a 100-yard race can measure their times directly as a performance measure. In golf, measuring performance by the score shot may seem appropriate. However, it is important to note that the golfer's score is not as direct a performance measure as the swimmer's time, because factors like course difficulty and playing conditions vary considerably. In tennis or figure skating, it is even harder to arrive at a performance measure.
Similar difficulties arise at work. Suppose we want to improve morale. Surveys are a possibility. We could ask people, "how is your morale?" and administer a survey periodically. The answer is subjective, and people may tire of being asked this question periodically, especially if morale does not improve. Eventually, we might devise a measure that is indirect, but easier to obtain, like attendance. "If morale is high, people will come to work," will be our logic. A performance indicator is born. Of course, other factors influence attendance, such as sickness, family situations, births, deaths, and the weather.
It is important, therefore, that performance measures be as direct as possible. To improve attendance, measure attendance. To improve cycle times, measure cycle times. If you want to improve morale, you may be better off deciding how to improve morale and measuring your efforts.
Rule 1: The more directly you can measure, the better. Operational Definitions Once you decide what to measure, carefully and thoughtfully determine how to take your measurement. Likewise, if you are counting you need to know exactly what you should and should not count. This is the process of setting operational definitions.
As an example, suppose you want to measure the level of beryllium in a work area as an industrial safety performance measure. You'll be taking smear samples. Should you check places at random? Should you instruct the technicians to look for the "worst" places or at "typical" places? Should you select a set of specific places and check those same places each measuring period? Should you average the values? What measuring method should be used in the laboratory? Should the lab perform duplicate analyses and average them?
Questions like these are important to the long-range success of the indicator. Efforts to standardize and communicate instructions are needed. Operational definitions are instructions and turn general terms, such as "contamination" or "scrap," into specific actions, procedures, and computations. Without them, we leave ourselves vulnerable to variations people will introduce in interpreting incomplete instructions.
Rule 2: Define exactly how to collect the data for the indicator and how to make the computation. Then, make sure everyone understands.
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