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Six Sigma Metrics And How to Devise Them
Six Sigma is a powerful mathematical and statistical tool deeply rooted in the customer-centric approach. The objective is to reduce production variation and thus meet customer specifications by applying the methodology at the process stage to ensure the achievement of its result objective of 3.4 defects per million parts. The basis of Six Sigma metrics is the measurement of the number of standard deviations between the baseline (which is the average of the processes) and the customer specifications.
The key to achieving the specified target figures lies in the control of process variations which is based on statistical tools. Decreasing process variation involves multitasking various parameters and aspects of a process.
How can we design a Six Sigma metric?
We can start by considering a simplified example where there are only 3 tasks, each one having a performance acceptability parameter. It is entirely possible that all of them have a correlation or none at all. When they are all correlated or if at least 2 of them are, you need to examine how they behave by varying the others. For example, if heating elongates another part, you will also be interested to know if the contraction of the second reduces the temperature of the first. Emphasis should be placed on measuring every aspect of the job.
Considerations before you….
Use composite metrics: You can choose to have a composite metric which in specific areas like a multivariate environment can be exclusively useful. You might consider using the composite metric to calculate the overall deviations of a large amount of data over a considerably long period such as a month. But you have to be careful that one parameter modifies the others.
Divide the project: If time permits, work strategically on using the three tasks independently by dividing the project into several stages and working on the metrics one stage at a time. In this case, you need to have a thorough understanding of the interaction of process flow and statistical viewpoints.
Use a DFSS scorecard: Some experts recommend using a DFSS scorecard. A DFSS dashboard will be extremely useful when each of the parameters is a variable and when the standard deviations of the parameters are known with the DPMOs.
Some guidelines
As a general rule, you should pay attention to every metric/parameter whether you use it or not. Analyze all metrics one by one without neglecting the overall process. A company-wide metric for COPQ may not work very well because many companies have learned from their costly mistakes. However, if you can, get values such as sector-level benchmarks correctly. It takes an analytical approach to have enterprise-wide metrics. Some companies practice percentage point measures for efficiency or using the number of people trained.
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