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__What is Capability Six Pack (Normal)__

__What is Capability Six Pack (Normal)__

The term "Capability Six pack" typically refers to a graphical representation of process capability analysis in Six Sigma methodology. Six Sigma is a quality management approach that aims to identify and reduce process variation to improve efficiency and minimize defects.

A Capability Sixpack, also known as a Capability Analysis Sixpack or a Capability Analysis Report, consists of a set of six plots or charts that provide a comprehensive view of process capability. These charts are used to assess whether a process meets customer specifications and to identify areas for improvement.

__When to use Capability Six Pack (Normal)__

__When to use Capability Six Pack (Normal)__

Here are some situations where a Capability Sixpack can be useful:

**Process Improvement:**When you are implementing process improvement initiatives or trying to reduce process variation, a Capability Sixpack can help you understand the current state of the process and identify areas that need improvement.**New Process Validation:**If you are introducing a new process or making significant changes to an existing process, a Capability Sixpack can be used to evaluate the process's initial capability and assess its ability to meet customer requirements.**Continuous Monitoring:**A Capability Sixpack can be employed as part of ongoing process monitoring efforts to ensure that the process remains within acceptable limits and meets customer specifications over time. Regular monitoring using the Capability Sixpack can help detect any process shifts or variations that may impact quality.**Supplier Evaluation:**When evaluating potential suppliers or assessing the capability of existing suppliers, a Capability Sixpack can be used to determine if the supplier's process is capable of consistently producing products that meet your specifications.

__Guidelines for correct usage of ____Capability Six Pack (Normal)__

__Guidelines for correct usage of__

__Capability Six Pack (Normal)__

- Ensure that the data is continuous and suitable for analysis.
- For attribute data (counts of defectives or defects), use Binomial or Poisson Capability Analysis.
- Collect a sufficient amount of data (at least 100 total data points) to obtain reliable estimates of process capability.
- Collect data in rational subgroups that are representative of the process and collected under similar conditions.
- Verify that the process is stable and in control using control charts in the capability sixpack output.
- The data should follow a normal distribution for accurate process capability estimates.
- If the data are non-normal, consider transforming them using Box-Cox or Johnson transformations.
- Use the normal probability plot and histogram in the capability sixpack output to assess data distribution.

__Alternatives: When not to use ____Capability Six Pack (Normal)__

__Alternatives: When not to use__

__Capability Six Pack (Normal)__

- If you desire a comprehensive evaluation of capability measures, including the main indices, consider employing Normal Capability Analysis.
- To assess the assumptions necessary for nonnormal capability analysis, utilize Nonnormal Capability Sixpack.
- In the case of substantial variation between subgroups, such as in a batch process, use Between/Within Capability Sixpack to evaluate the assumptions for a between/within capability analysis.

__Example of Capability Six Pack (Normal)__

__Example of Capability Six Pack (Normal)__

The quality engineers at an engine manufacturer are evaluating the process capability of their forging process used to produce piston rings. They gather data by measuring the diameters of 25 subgroups, each consisting of five piston rings. The specified limits for the piston ring diameter are 74.0 mm ± 0.05 mm.

To assess the assumptions necessary for normal capability analysis and to determine the extent to which the diameters of the piston rings meet the specified requirements, the engineers conduct a normal capability sixpack analysis. She has performed this in following steps:

- She worked all day and gathered the necessary data.
- width="500" height="500"
- Now, she analyzes the data with the help of https://qtools.zometric.com/
- Inside the tool, she feeds the data. Also, she puts lsl as 73.95, usl as 74.05, target as 74 and K as 6.
- After using the above mentioned tool, she fetches the output as follows:

__How to do ____Capability Six Pack (Normal)__

__How to do__

__Capability Six Pack (Normal)__

The guide is as follows:

- Login in to QTools account with the help of https://qtools.zometric.com/
- On the home page, you can see Capability Six Pack (Normal) under Process Capability.
- Click on Capability Six Pack (Normal) and reach the dashboard.
- Next, update the data manually or can completely copy (Ctrl+C) the data from excel sheet and paste (Ctrl+V) it here.
- Next, you need to fill the desired details such as lsl, usl, target, K, etc.
- Finally, click on calculate at the bottom of the page and you will get desired results.

On the dashboard of Capability Six Pack (Normal), the window is separated into two parts.

On the left part, Data Pane is present. In the Data Pane, each row makes one subgroup. Data can be fed manually or the one can completely copy (Ctrl+C) the data from excel sheet and paste (Ctrl+V) it here.

On the right part, there are many options present as follows:

**Lsl:**LSL in process capability refers to the Lower Specification Limit. It is the lower boundary or threshold set for a specific process parameter or characteristic.**Usl:**USL in process capability refers to the Upper Specification Limit. It represents the upper boundary or threshold set for a specific process parameter or characteristic.**Target:**The target refers to the desired or ideal value for a specific process parameter or characteristic. It represents the value that the process is intended to achieve or center around. The target is often based on design specifications or customer requirements.**Historical Mean:**The historical mean refers to the average or central tendency of a process parameter or characteristic based on past or historical data. It represents the typical or expected value that the process has demonstrated over a period of time.**Historical Standard Deviation:**The historical standard deviation refers to the measure of the dispersion or variability of a process parameter or characteristic based on past or historical data. It quantifies how much the data points deviate from the historical mean or central tendency.**K:**K refers to the process capability index known as the "Process Capability Index for Non-Centered Distribution" or simply the K-index. It is used to evaluate the capability of a process in relation to the specification limits.**Sd estimation method:****Average Moving Range:**It is calculated as the average of the absolute differences between consecutive observations within each subgroup**.****Median Moving Range:**It is calculated as the median of the absolute differences between consecutive observations within each subgroup.

**Length Moving range:**In process capability analysis, the moving range (MR) refers to the absolute difference between consecutive observations within a subgroup or set of data. The "moving range of length" specifically refers to the calculation of the moving range when the data consists of lengths or measurements.**Check Rule 1: 1 point > K Stdev from center line:**If a data point is K standard deviations from the center line, it means that it is K times the standard deviation away from the mean. This is important in statistical process control because it indicates whether a data point is within acceptable limits or whether there may be a problem with the process that needs to be addressed. Typically, data points that are more than three standard deviations from the center line are considered outliers and may require further investigation.**Check Rule 2: K points in a row on same side of center line:**If there are K points in a row on the same side of the center line in a dataset, it suggests that there may be a bias or trend in the data that is causing the values to cluster together. This could be due to a variety of factors, such as measurement error, sampling bias, or a true underlying pattern in the data.**Check Rule 3: K points in a row, all increasing or all decreasing:**If there are K points in a row, it is certain that at least one of two things must be true:- The points are all increasing (i.e. each point has a greater y-coordinate than the one before it)
- The points are all decreasing (i.e. each point has a smaller y-coordinate than the one before it)

**Check Rule 4: K points in a row, alternating up and down:**If the trend is upwards, it indicates that the process is becoming less consistent and more variable over time. This can be caused by factors such as equipment deterioration, operator error or changes in raw material quality. If the trend is downwards, it indicates that the process is becoming more consistent and less variable over time. This could be due to process improvements or tighter control measures being implemented.**Check Rule 5: K out of K + 1 points > 2 standard deviation from center line (same side):**According to the statement, if K out of K+1 data points fall on the same side of the center line and are more than two standard deviations away from it, it suggests that the process might be out of control, and special causes should be investigated to identify and fix the problem.**Check Rule 6: K out of K + 1 points > 1 standard deviation from center line (same side):**In this statement, K represents the number of consecutive observations that are above the center line (on the same side) and are greater than one standard deviation away from it. This indicates a potential shift in the mean of the process. The K+1 point serves as a reference point to compare the K consecutive observations against.**Check Rule 7: K points in a row within 1 standard deviation of center line (either side):**If K points in a row are within 1 standard deviation of the center line, it suggests that the data points are clustered around the expected value, and there is no significant trend or deviation from the expected pattern.**Check Rule 8: K points in a row > 1 standard deviation from center line (either side):**If K points in a row are more than 1 standard deviation away from the center line, it suggests that there may be a trend or pattern in the data that is moving away from the expected value.