Total Observed Variation

Definition of Total Observed Variation:

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When looking at your processes and measurement systems, it is often helpful to be able to group them together in order to view the big picture.

Overview: What is the total observed variation?

It can be defined as the combined variation that is derived from all sources. This essentially refers to the process and the measurement and is written as “total observed = process + measurement system”.

3 drawbacks of total observed variation

1. Quality

Studies using simple total variation can have poorer methodological quality than other methods.

2. Potential bias

The possibility of noncontrolled variables as part of the total observed variations viewed by the observer could feasibly cause bias in obtained estimates.

3. Elimination of outliers

In the likelihood of the elimination of even moderate outliers in the combined variation, the trustworthiness and transferability of the findings are limited.

Why is total observed variation important to understand?

It is important to have an understanding of this concept for the following reasons:

1. Monitoring

The monitoring and measuring of variance is a fundamental aspect of quality management.

2. Accurate data

Being able to spot inconsistencies in measuring systems is important in order to provide accurate data.

3. Knowing what is considered an acceptable amount of variance

By understanding total variation, you are more likely to be able to keep process and measurement variation levels under control.

An industry example of total observed variation

A candy company is doing an audit of its different plants. It is found that the various plants have some variance as to how the candy is produced. There is also some variance as to how the candy is measured, depending on the manufacturing plant. The two types of variance are combined in order to get an idea of how much overall variance there is amongst all the plants.

3 best practices when thinking about total observed variation

Here are some practices to keep in mind when combining process and measurement variations:

1. Consistent measuring

When doing measurements, it is extremely important that all parties involved are measuring in the same manner. This will keep variation to a minimum and help ensure that the data collected is as consistent as possible.

2. Process improvement

Be sure to work towards continuous process improvement in order to reduce the number of process variations.

3. Keep an eye on measurement variation impacted by process changes

With process improvements, the total observed variations will decrease, but without improvement of measurement systems, the percentage of measurement variation in relation to the total will increase. This may push the percentage of measurement variation to an unacceptable level, so careful monitoring of measurement accuracy is of paramount importance.

Frequently Asked Questions (FAQ) about total observed variation

1. How much measurement error variation is considered acceptable?

The general rule is that under 10 percent is considered satisfactory. 10-30 percent can be considered acceptable, but this is dependent upon a variety of factors. Some of these include the cost of repair, the cost of the device being used to measure, and how important accuracy is.

2. What is process variation?

Process variation occurs when processes do not follow a precise pattern. Changes in the pattern can occur from inconsistent raw materials, equipment issues, variances in human action, environmental change, and changes in methodology.

3. What amount of measurement variation is unacceptable?

If measurement variation exceeds 30%, the measurement system requires improvement and is considered unacceptable.

Reducing total observed variation in your business

In reduction efforts, you can look towards a focus on continual process improvement. Improvement of processes will bring down the process variations and thereby reduce the number of total observed variations. Having accurate and consistent measurement techniques will also minimize the amount of variation.

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