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Message added by Chitra Singh

Sigma Level 

 

Sigma Level (σ) - is a representation of the process capability in terms of opportunities and defects. It refers to number of defects per million opportunities (DPMO). Higher the sigma level, lower the number of defects

1σ = 691,462 DPMO
2σ = 308,538 DPMO
3σ = 66,807 DPMO
4σ = 6,210 DPMO
5σ = 233 DPMO
6σ = 3.4 DPMO"

 

An application-oriented question on the topic along with responses can be seen below. The best answer was provided by Vastupal Vashisth on 14th May 2019.

 

Applause for the respondents -Vastupal Vashisth, Ransingh Satyajit Ray, Sabbyasachi Maukherjee, KiranKumar, Sreyash Sangam

 

 

Also review the answer provided by Mr Venugopal R, Benchmark Six Sigma's in-house expert.

 

 

 

Featured Replies

Q. 158  Even when well established measures of process performance exist, why do Six Sigma professionals insist on a Z score during Measure phase of a Six Sigma project? 

 

Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday.

 

Solved by Vastupal Vashisth

Benchmark Six Sigma Expert view by Venugopal R.

 

Z-score is one of the measures used for assessing the Process Capability of a process. The following are some of the benefits of using the Z score:

  1. It is a versatile measure that can be used for variable and attribute data.
  2. Very often, six sigma projects are pursued without establishing a baseline measure of process performance, which makes it difficult to quantify the post improvement benefits. The Z score helps in assessing and comparing pre and post process performance
  3. Computation of Z score forces the project team to define Specification limits, mean and standard deviation, for variable data. In case of attribute data, it forces team to define Defects or Defectives, Sample size, Opportunity for errors.
  4. When we deal with multiple projects in an organization, be it Operations, Maintenance, Supply Chain, Administration, HR and so on, the Z score serves a universal measure for comparing process performance across different functions.

On the whole, one should remember that the objective of a Six Sigma project is to improve process(es) and it is important to be clear of the process that is being addressed by the project and establish the measurement method. Considering the benefits discussed, the Z score is insisted.

While managing the quality driven processes and parameters, there is always a question that arise of whether variables affecting the product or service meet the requirement. For this several parameters like Cp, Cpk (When data is fairly normal) and Ppk indices when the data is non normal (after suitable transformation for stable data without out of control data points), Z Scores, DPMO, etc are used. 

 

Many of the indices (Cp, Cpk and Ppk) used require a good amount of data (in terms of number of data points per sample) for providing valid result on the other hand with respect to many organisation faced with short production runs for being responsive to customer needs and specialising the product for future demand require a Capability measuring parameter to meet their quality requirements. The Z Score comes handy delivering status of process performance in both the cases.

 

 

Z Score| LSL= (Individual value (Or Mean) -LSL)/ Process Standard Deviation

 

Z Score| USL= (USL-Individual value (Or Mean))/ Process Standard Deviation

 

By comparing the Z Score with the critical value (at a given alpha level) we check the status of performance.

Z is the process sigma score between the sample mean and the specification limit of the process. So, while going into the Measure phase of the DMAIC, it is must for a Six Sigma professional to have a Sigma Level score and then they would compare the same with the Sigma Level Score during the Implementation phase of the DMAIC. The comparison between the scores at Measure phase and the Implementation phase would give them a clear picture of the improvement in the process performance. That's why a Z score is very necessary during the measure phase of DMAIC. 

  • Solution

Six Sigma methodology can be implemented in every industry. The structure followed five important phases - DMAIC, in which output of one phase is the input of next phase. It is a top driven approach and it needs commitment from top management for the successful implementation and desired results.

 

Six Sigma professional insist on Z Score during measure phase even though there is well established process performances. 

Z Score tell us about errors within the system or in other words we can say that it tells us about the number of standard deviation present between the mean and specification limit. 

 

For any organisation it is very big challenge to define definition of defects as they have different processes and defects changes from process to process. for example in purchase it may be lead time failure, In fiance it may be vendor payment, in sales it may be not achieving sales target, on shop floor it may of not achieving quality objectives. Now the biggest challenge here to compare performance of all these processes as defects are defined in different units. Here comes Z score in picture which is common platform that is used to compare performance of all processes and find out on which process we should work to improve it further.

 

Higher the Z Score  indicates that  there are more number of standard deviations within specification limits and the mean value is far away from outlying limit. which means that majority of the results lies nearby average value and process is capable and stable both at the same time.

 

Lower Z score indicates that there is less number of standard deviation present or there is presence of variance in output. and presence of variance indicates that product or service quality can change or fluctuate from its defined value which leads to dissatisfaction of customer. 

 

Z score helps a company to ensure the quality of its output and enables the organization to workout for the poor processes in case of any undesired result. Z Score is very useful and helps the six sigma professional to make comparison during measure and control phase to highlight where improvements can be made .

This was a slightly tricky one as the question was about why the Sigma Level or the Z score is the preferred metric for process baselining and not about what it is.

 

The best answer has been provided by Vastupal Vashisht for highlighting the benefits of using the Sigma Level.

 

Do read the response from our in-house expert to get more insights as to why the Sigma Level calculation is so popular.

The purpose for using Z scores because it allows leadership and Six Sigma professionals to compare disparate or very different processes; from office and business applications to manufacturing and service applications.

Z scores allow for comparison between the performance of different processes from different normal curves. The Z score normalizes data to output a single value that can be used to compare processes that are distinct from one another.

Rationale for using Z-score:

A. Most importantly, it is a predictive measure

B. Secondly, it is a reasonable performance measure to normalize on as a standard because it can be computed from other methods (Yield, DPMO, DPU) and it's scale relates to a standard normal distribution

 

Z score is universal..

Z can act as a 'universal translator' between the different measures 

Z score allows comparison between attribute and variable processes;

It is a universal translator

It is a score that can be used to either improve and benchmark a process

Can be used to assess ST and LT performance

A Sigma Level (Z-score) can be determined regardless of data type (continuous / discrete) where other process performance measures do not have this ability.

Z allows for comparisons and evaluations of process performance.  Performance expressed as a Z value provides probabilities that can be summed.  Z values allow normalization for comparisons of unrelated processes, especially useful for benchmarking.

1. A Z score is useful for normalizing how well the process performs relative to expectations for different types of data and measurement systems.  It can compare attribute (Yield, DPU, DPMO) and variable data, so it provides the ability to compare different processes so that appropriate business case and investment decisions can be made

 

2. Secondly, a Z score is a single metric that provides a lot of information about the process, i.e. the process variation, the requirements and the comparison of the two, so it is a useful shorthand that quickly indicates the relative process health

Z score allows Six Sigma professionals to compare two or more populations easily.

Using a Z score allows comparison between two samples.  They assume a normal distribution and can help describe how far above or below the average the data are.

On 5/10/2019 at 4:37 AM, Vishwadeep Khatri said:

Q. 158  Even when well established measures of process performance exist, why do Six Sigma professionals insist on a Z score during Measure phase of a Six Sigma project? 

 

Please remember, your answer will not be visible immediately on responding. It will be made visible at about 5 PM IST on 14th May 2019, Tuesday to all 53000+ members. It is okay to research various online sources to learn and formulate your answer but when you submit your answer, make sure that it does not have content that is copied from elsewhere. Plagiarized answers will not be approved. (and therefore will not be displayed) 

 

All Questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/

 

All rewards are mentioned here - https://www.benchmarksixsigma.com/forum/excellence-rewards/

Z score allows us to calculate the probability of a score occurring within distribution and to compare scores. You can compare the population mean to score.
This allows for critical objective data analysis.

Z scores are useful even when there are other metrics for process performance since it is a uniform metric that can be used for both attribute and variable data.  Z scores are especially useful for variable data since it can be used as a predictive, leading indicator (do not have to wait for defects to be produced).  Note:  Z scores are not always useful for comparisons however, since absolute values are relative to a particular process.

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