Short-term vs. Long-term Variation

Hi all,

I want to know the difference between the short-term and the long-term variation practically (StDev within and StDev between).

Where is the short-term and the long-term in the following 3 scenarios, assuming that the data is normal and the process is stable?

1- Suppose that we are collecting a subgroup of 5 parts once per shift for a month period of time.

2- What if we are collecting only 1 part once per shift for a month period of time.

3- What if we decide to do the analysis weekly, and we need to collect 1 part every hour (the 1 week period is short relatively).

Thanks to all.

are you taking a six sigma class or preparing to sit a CQE or Six Sigma exam?
I will answer these questions btu they really are very simple if you take the tiem to think about them:

Ramizz said:

Where is the short-term and the long-term in the following 3 scenarios, assuming that the data is normal and the process is stable?

1- Suppose that we are collecting a subgroup of 5 parts once per shift for a month period of time.

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short term is the variation of the 5 parts in each subgroup. long term is the variation of all of the parts across all of the subgroups for the entire month

Ramizz said:

2- What if we are collecting only 1 part once per shift for a month period of time.

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long term is the variation of all of the parts over the month. short term is the 'moving range' beteeen the parts.

Ramizz said:

3- What if we decide to do the analysis weekly, and we need to collect 1 part every hour (the 1 week period is short relatively).

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when you do the analysis has no bearing on the short or long term variation. the answer is the same as for 2.


These really are non-sensical questions. and so there are only generic answers. If you aren't sitting an exam or studying, it is much more helpful to your understanding to post real world situations with actual data.

Re: Long-term Variation

Thank you,

I am working in a quality department in a manufacturing company, and currently we are having a green belt training.

I always thought that long-term variation is a result of long-term data collection (monthly or more). I realized now that there will be always long-term variation if the data was taken in a timely manner. Maybe the only case which might not involve long-term variation is when we are collecting data sample from a population (one time), right?

thnx

Re: Long-term Variation

Ramizz said:

Thank you,



I always thought that long-term variation is a result of long-term data collection (monthly or more). I realized now that there will be always long-term variation if the data was taken in a timely manner. Maybe the only case which might not involve long-term variation is when we are collecting data sample from a population (one time), right?

thnx

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how frequently or how often or when you collect the data has nothing to do with the determination of long or short term variation. The phrases long term and short term refer to the type of variation in your process and both the data collection frequency. So even if you collect the data ONCE for a population (assuming that you sampled it randomly and representitively) you can still calculate the long term variation of the process. Additionally if you collect and subgroup your data appropriately you can calculate the short term variation of teh process as well.

there are numerous threads in this forum covering the nature of these calculations and how the data is best collected. you can search fo rthem to do further study.

What does your instructor say about this?

Re: Long-term Variation

He didn't bring up the subject except that we can calculate the Within variation from an individual data set by considering the moving range values.

I also remembered him saying that it's impractical to consider the long term variation and indices if the data collected represent a short time frame.

Attached is what we have in the material about the subject.

Thnx

Re: Long-term Variation

Ramizz said:

Attached is what we have in the material about the subject.

Click to expand...

I find what is even more entertaining is that the distributions - especially for the long term data - presented in your chart are wrong. They would be continuous uniform distributions, not normal. I have attached a more representative illustration. I left the short term distribution as normal, in that most variation detected from short term measurements from a process shown in your illustration are gage and measurement error - not process variation - and tehy tend to be normal. However, the chart does illustrate that you can not reliably predict long term process variation from short term variation. If so, you will develop an incorrect distribution model, as well as induce overcontrol.