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 for computing the process capability.
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4 | Param: Specify the required parameter value for some situations. If the data type is discrete (defects), then you would need to enter the Opportunities for Error (OFE) value. This field would then be enabled to enter the OFE value. 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 variation of data 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, then 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, then 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). If no lower 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 the values entered for LSL.
Click on the checkbox next to the LSL to mark that limit as a boundary. For example, if you mark the LSL as a boundary, this means that 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 the values entered for LSL. Click on the checkbox next to the USL to mark that limit as a boundary. For example, if you mark the USL as a boundary, this means that the physical measurements for this product or service cannot be greater than 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 on 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 that 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 if you want 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, you wil need to 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 defects data follows Poisson distribution and defectives data follows Binomial distribution. |
2 | OFE Value: If your discrete data type is defects, you will need to specify the Opportunities for Error (OFE) value. For defectives data, the OFE value is set at 1. This value is used to translate the DPMO values to Sigma Level. |
3 | Subgroup Constant: For the discrete data type, you need to specify the number of samples in your subgroup. For example, if you inspect 100 units for defects and found 4 defects, then your subgroup size is 100. |
4 | Target: For the discrete data type, you can optionally specify a target value. For the defects data, this is the target value of defects per unit (DPU). For the defectives 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 to speed up finding the right data to use 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 does not have any 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 important that you not only specify the column name but also the table name. | ||||||||||
3 | Add or View Data: Click on this button either to add more data into 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 up the Data Editor dialog box where you can import more data into your workbook, or you can 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, then you will need to select only numeric columns. If the code starts with a T, then you can select both 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, then you need to select a minimum of 2 columns of data and a maximum of 4 columns of data 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 select 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 select the data is to double click on the columns in the list of Available Data. Finally, you can also 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:
If the right amount of data columns has been specified, the list box header will be displayed in the black color. If sufficient data has not been specified, then the list box header will be displayed in the 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 you have specified in the Setup tab.
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7 | View Selection: Click on this button to view the data you have specified for this analysis. The data can be viewed either in a tablular format or in 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 for continuous data, the PPM values are reported. For discrete data, the DPMO values are reported. |
3 | Potential Capability: Click this checkbox if you want to compute the potential capability values (Cp and Pp). This analysis is only available for continuous data and requires that you specify both the LSL and USL values. |
4 | Actual Capability: Click this checkbox if you want to compute the actual capability values (Cpk and Pp). This analysis is only available for continuous data. |
5 | Target Capability: Click this checkbox if you want to calculate the target capability (Cpm). Note that you will need to specify the target value to calculate this metric. This metric is only available for continuous data. |
6 | Sigma Level: Click this checkbox if you want to calculate and report the Sigma Level for your process. |
7 | 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. |
8 | 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 will be reported by the software. This is provided for historical compatibility in calculating the Sigma Levels. The default option is to not use the Sigma Shift in the calculations. |
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 would like to override that with your own 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 would like to override that with your own subtitle you can 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 own 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 own 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 then 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 will not be able to 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 then the minimum y-axis scale is set at 10 and the maximum y-axis scale 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: If you want to add a few extra horizontal reference lines on top of your chart you can specify the values here. The format for this input is numeric values separated by semi-colon. For example, if you specify 12;15 then two horizontal lines are plotted at Y = 12 and Y = 15 respectively. Note that this input is optional. |
8 | Vertical Lines: If you want to add a few extra vertical reference lines on top of your chart you can specify the values here. The format for this input is numeric values separated by semi-colon. For example, if you specify 2;5 then two vertical lines are plotted at X = 2 and X = 5 respectively. Note that this input is optional. |
1 | Input Summary: The Input Summary section shows a summary of 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 for summarized data, the process is assumed to be normally distributed. |
3 | Capability Metrics: This section shares a summary of the capability metrics that are computed for your data. Note that for summarized data, only the overall capability numbers are displayed. |
4 | Conclusion: This section shares a brief conclusion of 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 with respect to Green, Yellow, Red zones. |
6 | Probability Plot: This plot shows a histogram, probability plot, or any other suitable graphical representation of data. |
1 | Input Summary: The Input Summary section shows a summary of 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, then the software will use the capability analysis flowchart to determine the appropriate test. For this, it will check the normality of the data 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 pre-requisite for capability analysis. |
4 | Within Capability: If data contains subgroups, then the software will calculate the within process capability analysis using standard deviation of the data within subgroups. These are reported in this section. |
5 | Overall Capability: This section reports the overall capability of the process which is calculated by taking all the data points together. It reports this for the LSL side, USL side, and considering both LSL and USL. In our example, the LSL is not specified, 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 with respect to Green, Yellow, 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 specc limits are shaded. |
1 | Input Summary: The Input Summary section shows a summary of 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 since the Sigma Level is low, it is 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 shows a summary of 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 since the Sigma Level is low, it is 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 which is an indication that we can relatively trust our analysis results. |