1 | Data Type:
Specify the type of data you are dealing with.
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2 | Input Type:
Specify the data source for this analysis.
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3 | Methodology:
Specify the methodology to use to compute the process capability.
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4 | Param: Specify the required parameter value for some situations. If the data type is discrete (defects), you must enter the Opportunities for Error (OFE) value. This field would then enable the OFE value to be entered. If this parameter is not required, then this field is disabled. | ||||||||||
5 | Subgroup Type:
Specify if your data has subgroups. Subgroups are data collected within a small time interval such that the data variation within the subgroup is governed by common cause
variation.
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6 | Subgroup Constant: You can specify a constant subgroup size by entering a subgroup size here. Note that if you have a variable subgroup size, you will need to specify the Subgroup Source to a worksheet and select your data from the data file. If you have specified the subgroup source as a worksheet, the subgroup constant field is not enabled, and the software will expect to find the subgroup information through the data file. | ||||||||||
7 | LSL:
Specify your customer-defined lower specification limit (LSL). You can leave this value blank if no lower specification limit exists for your process. Note that only numeric values are allowed for the specification limits. Both LSL and USL cannot be blank at the same time. The values entered for USL should be greater than those entered for LSL.
Click the checkbox next to the LSL to mark that limit as a boundary. For example, if you mark the LSL as a boundary, the physical measurements for this product or service cannot be lower than the LSL. An example could be surface finish, where the values cannot be lower than 0. Hence, 0 would be marked as a lower boundary for this data. This limit will not be used to calculate the process capability numbers. | ||||||||||
8 | USL:
Specify the customer-defined upper specification limit (USL). If no upper specification limit exists for your process, you can leave this value blank. Note that only numeric values are allowed for the specification limits. Both LSL and USL cannot be blank at the same time. The values entered for USL should be greater than those entered for LSL. Click the checkbox next to the USL to mark that limit as a boundary. For example, if you mark the USL as a boundary, the physical measurements for this product or service cannot exceed the USL. An example could be if your metric is the efficiency numbers, then 100 could be a theoretical upper boundary for the data, and no numbers are expected to be greater than 100, so 100 would be marked as the upper boundary in that case. | ||||||||||
9 | Flow Chart: Click this button to open and view the flowchart for the various process capability analysis methods. Note that you can also click on the buttons on this flowchart to select the methodology for your analysis. | ||||||||||
10 | Help Button: Click on this button to open the help file for this tool. | ||||||||||
11 | Cancel Button: Click on the Cancel Button to exit this dialog box without performing any computations. | ||||||||||
12 | OK Button: Click on the OK button to compute the process capability calculations. If not all the required data has been specified, you will need to specify the data before you can perform the computations. If you change or update the data, click on Compute Outputs to recompute the process capability calculations. |
1 | Data Type: Specify the data type. For discrete data, this could be either defects or defectives. Note that defect data follows the Poisson distribution and defective data follows the Binomial distribution. |
2 | OFE Value: If your discrete data type is defects, you must specify the Opportunities for Error (OFE) value. For defective data, the OFE value is set at 1. This value is used to translate the DPMO values to Sigma Level. |
3 | Subgroup Constant: Specify the number of samples in your subgroup for the discrete data type. For example, if you inspect 100 units for defects and find four defects, your subgroup size is 100. |
4 | Target: For the discrete data type, you can optionally specify a target value. This is the target value of defects per unit (DPU) for the defects data. For the defective data, this is the target value of % defective. The data you specify here will be used to update the histogram. |
1 | Search Data: The available data displays all the columns of data that are available for analysis. You can use the search bar to filter this list and speed up finding the right data for analysis. Enter a few characters in the search field, and the software will filter and display the filtered data in the Available Data box. | ||||||||||
2 | Available Data: The available data box contains the list of data available for analysis. If your workbook has no data in tabular format, this box will display "No Data Found." The information displayed in this box includes the row number, whether the data is Numeric (N) or Text (T), and the name of the column variable. Note that the software displays data from all the tables in the current workbook. Even though data within the same table have unique column names, columns across different tables can have similar names. Hence, it is crucial that you not only specify the column name but also the table name. | ||||||||||
3 | Add or View Data: Click on this button to add more data to your workbook for analysis or to view more details about the data listed in the available data box. When you click on this button, it opens the Data Editor dialog box, where you can import more data into your workbook. You can also switch from the list view to a table view to see the individual data values for each column. | ||||||||||
4 | Required Data: The code for the required data specifies what data can be specified for that box. An example code is N: 2-4. If the code starts with an N, you must select only numeric columns. If the code begins with a T, you can select numeric and text columns. The numbers to the right of the colon specify the min-max values. For example, if the min-max values are 2-4, you must select a minimum of 2 columns of data and a maximum of 4 columns in this box. If the minimum value is 0, then no data is required to be specified for this box. | ||||||||||
5 | Select Button: Click on this button to select the data for analysis. Any data you choose for the analysis is moved to the right. To select a column, click on the columns in the Available Databox to highlight them and then click on the Select Button. A second method to choose the data is to double-click on the columns in the list of Available Data. Finally, you can drag and drop the columns you are interested in by holding down the select columns using your left mouse key and dragging and dropping them in one of the boxes on the right. | ||||||||||
6 | Selected Data:
The list box header will be displayed in black if the right number of data columns is specified. If sufficient data has not been specified, then the list box header will be displayed in red color. Note that you can double-click on any of the columns in this box to remove them from the box. The data you specify for this analysis depends on the options in the Setup tab.
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7 | View Selection: Click on this button to view the data specified for this analysis. The data can be viewed in a tabular format or a graphical summary. |
1 | Param 1: For continuous data, the first parameter is the mean value. You will need to specify the mean value for this analysis. For discrete data, it could either be the number of defects or the number of defectives. |
2 | Param 2: For continuous data, the second parameter is the stdev value. You will need to specify the stdev value for this analysis. For discrete data, it could be the total number of units. |
1 | Percentage(%): Click this checkbox if you want to report the % of defective items in your data set. | ||||||||||
2 | PPM, DPMO: Click this checkbox if you want to report the PPM or DPMO values. Note that the PPM values are reported for continuous data. For discrete data, the DPMO values are reported. | ||||||||||
3 | Sigma Level: Click this checkbox to calculate and report the Sigma Level for your process. | ||||||||||
3a | Use 1.5 Sigma Shift: You can use the 1.5 Sigma Shift to compute your process capability metrics. This will specifically impact the Sigma Level that the software will report. This is provided for historical compatibility when calculating the Sigma Levels. The default option is not to use the Sigma Shift in the calculations. | ||||||||||
4 | Capability Indices: Click this checkbox to compute the potential capability values (Cp and Pp) and process capability values (Cpk and Ppk). This analysis is only available for continuous data and requires that you specify both the LSL and USL values. | ||||||||||
4a | Confidence Intervals:
Use the dropdown box to specify if you want to report the confidence intervals for the capability indices. Note that the confidence intervals shown are based on the normal distribution assumption. If you want to change the confidence levels, you can change the default confidence level settings under Options.
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5 | Target Capability: Click this checkbox to calculate the target capability (Cpm). Note that you must specify the target value to calculate this metric. This metric is only available for continuous data. | ||||||||||
5a | Target Value: This value is used to calculate the Target Capability (Cpm) and is enabled only if you select the reporting of target capability. This analysis is only available for continuous data. Make sure to enter a numeric value for the target. | ||||||||||
6 | Calculate Sample Size: Use this option if you want to determine how many samples are required to achieve a given level of confidence interval. For example, suppose your capability index is 1.5, and you use too few samples to calculate the capability index. In that case, the report capability index may be significantly off - it could report a value like 1.1 or 1.9, for example. You will have to collect more data to ensure that your reported capability indices are close to 1.5. Selecting this checkbox will tell you how many samples of data you should be using. Note that this analysis assumes that your data is normally distributed. | ||||||||||
6a | Width: This value can be entered only if you select the calculate sample size option. Make sure to enter a numeric value for the CI width. For example, if you specify a width of 0.1, then the 95% confidence intervals for this analysis will be 1.45 to 1.55 if the nominal value is 1.5. |
0 | Pick Charts: Select the charts you would like to display for this analysis. |
1 | Title: The system will automatically pick a title for your chart. However, if you want to override that with your title, you can specify a title for your chart here. Note that this input is optional. |
2 | Sub Title: The system will automatically pick a subtitle for your chart. However, if you want to override that with your subtitle, specify a subtitle for your chart here. Note that this input is optional. |
3 | X Label: The system will automatically pick a label for the x-axis. However, if you would like to override that with your label for the x-axis, you can specify a different label here. Note that this input is optional. |
4 | Y Label: The system will automatically pick a label for the y-axis. However, if you would like to override that with your label for the y-axis, you can specify a different label here. Note that this input is optional. |
5 | X Axis: The system will automatically pick a scale for the x-axis. However, if you would like to override that with your values for the x-axis, you can specify them here. The format for this input is to specify the minimum, increment, and maximum values separated by a semi-colon. For example, if you specify 10;20, the minimum x-axis scale is set at 10, and the maximum x-axis scale is set at 20. If you specify 10;2;20, then, in addition to minimum and maximum values, the x-axis increment is set at 2. Note that this input is currently disabled, and you cannot change this setting. |
6 | Y Axis: The system will automatically pick a scale for the y-axis. However, if you would like to override that with your values for the y-axis, you can specify them here. The format for this input is to specify the minimum, increment, and maximum values separated by a semi-colon. For example, if you specify 10;20, the minimum y-axis scale is set at 10, and the maximum y-axis is set at 20. If you specify 10;2;20, then, in addition to minimum and maximum values, the y-axis increment is set at 2. Note that this input is optional. |
7 | Horizontal Lines: You can specify the values here if you want to add a few extra horizontal reference lines on top of your chart. The format for this input is numeric values separated by semi-colon. For example, if you specify 12;15, two horizontal lines are plotted at Y = 12 and Y = 15, respectively. Note that this input is optional. |
8 | Vertical Lines: You can specify the values here if you want to add a few extra vertical reference lines on top of your chart. The format for this input is numeric values separated by semi-colon. For example, if you specify 2;5, two vertical lines are plotted at X = 2 and X = 5, respectively. Note that this input is optional. |
1 | Item: The left-hand side shows the major tabs and the items checked within each section |
2 | Status: The right-hand side shows the status of the checks. |
3 | Overall Status: The overall status of all the checks for the given analysis is shown here. The overall status check shows a green thumps-up sign if everything is okay and a red thumps-down sign if any checks have not passed. Note that you cannot proceed with generating analysis results for some analyses if the overall status is not okay. |
1 | Input Summary: The Input Summary section summarizes the inputs used for this analysis. In this example, we have specified the process mean and standard deviation, and the specification limits LSL and USL. |
2 | Assumption Checks: This section shares a summary of any assumption checks that are performed. Note that the process is assumed to be normally distributed for summarized data. |
3 | Capability Metrics: This section shares a summary of the capability metrics that are computed for your data. Note that only the overall capability numbers are displayed for summarized data. |
4 | Conclusion: This section briefly concludes your analysis - whether the capability is poor, acceptable, or good. |
5 | Capability Plot: This plot shows how the process capability compares with other processes - by showing where the process capability falls concerning Green, Yellow, and Red zones. |
6 | Probability Plot: This plot shows a histogram, probability plot, or other suitable graphical data representation. |
1 | Input Summary: The Input Summary section summarizes the inputs used for this analysis. In this example, we have specified the USL and used the diameter data for analysis. The software automatically calculates the required statistics from the entered data. |
2 | Methodology: If the methodology is set to Auto, the software will use the capability analysis flowchart to determine the appropriate test. It will check the data's normality and information about the data type and subgroup size. In this example, it has chosen the Normal analysis. You can override these checks by specifying the type of analysis you want to perform in the Methodology tab of the input dialog box. |
3 | Assumption Checks: This section shares a summary of any assumption checks that are performed. If data is entered, the software will check if the data is normally distributed, and if not, it will report it as such in this section. For this example, the process was found to be stable. Note that stability is a prerequisite for capability analysis. |
4 | Within Capability: If data contains subgroups, then the software will calculate the within-process capability analysis using the standard deviation of the data within subgroups. These are reported in this section. |
5 | Overall Capability: This section reports the process's overall capability, which is calculated by taking all the data points together. It reports this for the LSL and USL sides, considering both LSL and USL. The LSL is not specified in our example, so the metrics are only reported for USL and both. |
5 | Capability Plot: This plot shows how the process capability compares with other processes - by showing where the process capability falls concerning Green, Yellow, and Red zones. In this example, the computed process capability is in the acceptable region. |
6 | Probability Plot: This plot shows a histogram of the data along with the super-imposed capability specifications (LSL & USL). Any data points that fall outside the spec limits are shaded. |
1 | Input Summary: The Input Summary section summarizes the inputs used for this analysis. In this example, we have specified the defects (15) and Units (100). The Opportunities for Error (OFE) is set at 10. This data is used to determine the capability of the process. |
2 | Analysis Results: The software computes the Defects per Unit (DPU), the Defects per Million Opportunities (DPMO), and the Sigma Level (Z) for this process. Note that the Sigma Level is low and marked as poor (falling in the red zone). |
3 | Defects Chart: The graphs section shows the process capability plot and a pie chart of the defects. |
1 | Input Summary: The Input Summary section summarizes the inputs used for this analysis. In this example, we have specified the data for TV Defects as the input variable. |
2 | Analysis Results: The software computes the Defects per Unit (DPU), the Defects per Million Opportunities (DPMO), and the Sigma Level (Z) for this process. Note that the Sigma Level is low and marked as poor (falling in the red zone). |
3 | Defects Chart: The graphs section shows the process capability plot and a convergence plot for the DPU with confidence intervals. Note that as the number of data points increases, the DPU value stabilizes, indicating that we can relatively trust our analysis results. |
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