Compare actual performance with targeted performance
You can use a line graph to measure your product manufacturing error rate. By doing so, you can tell that, although your error rate has fluctuated over the past six months, it has observed a downward trend from fifteen percent to its current value of about six percent.
You've been gathering data on your performance metrics for some time. What should you do with the data? What do the numbers mean? A good first step is to compare your data with the targets you set for each metric.
For example, suppose you set the target "5% maximum product manufacturing error rate," to be reached at the end of six months.
You've tracked actual error rates over the past six months, and recorded the following rates:
Clearly, the error rate has fluctuated over the period in question, with a general downward trend. And the rate at the end of the six months is not as low as the 5% you set as your target. How should you respond? You might draw several conclusions:
- "We didn't reach our target, so we should overhaul our error-reduction efforts. Perhaps we need a new initiative focused on sharpening shop-floor staff training in using the manufacturing equipment."
- "The trend is generally downward. Even if we didn't reach our target by the end of the six months, we seem to be heading in the right direction. Let's see what happens over the next few months, and decide whether we need to reexamine our error-reduction efforts."
- "The target was too aggressive. We don't have the capacity to reduce errors as much as we had hoped. Let's revise the target to make it less aggressive—say, a 9% error rate."
- "Perhaps the data aren't reliable. How do we know we can trust these rates?"
Given all these possible interpretations, how do you select the best response to what you're seeing in your data? Savvy managers keep a number of guidelines in mind.