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# Introduction¶

Six Sigma is a quality management plan that improves a company's operational performance by identifying and correcting defects that exist in that company's processes. In business, it is used to produce a satisfactory product while minimizing losses suffered in production. This approach allows a company to achieve a cost effective equilibrium between production and costs. In addition to being several mathematical ideas, Six Sigma is also a business conduct methodology. It has both its skeptics and zealots, but regardless of opinion, its impact is undeniable.

This add-on focuses on the control charts used by Six Sigma to analyze production, and allows for creation of these extensive charts. The charts are easy to use and understand, thus both Six Sigma black-belts and beginners can benefit from the charts created. These charts also assist with the business methodology, although that methodology is not discussed in this article.

# Control Charts¶

A control chart is a chart that is used to detect and verify process variations. In most cases, the user looks in the data for an unusual variation that can be attributed to a special cause. A special cause is something out of the ordinary for that process, like for instance, a machine missing a part on an assembly line. Special cause variations are something that needs to be rectified quickly, and therefore are important to identify.

Control charts also show common cause variations. Common cause variations are variations that occur often and with no specific reason. Reducing these variations saves money and makes the whole process more efficient.

In Six Sigma, a control chart is a chart that has 3 lines: a center line, an upper control limit (UCL) line, and a lower control limit (LCL) line.

Figure 1 displays a sample C-Chart with the three lines marked; the center line is called the C-Bar line because this chart is a C-Chart. These three lines are the primary means of deciding whether a process is said to be "in control" or not.

The center line is always a straight line, unlike the upper control (UCL), and lower control (LCL) limit lines which are not always straight.

The U-Chart shown in Figure 2 contains a UCL and LCL that are not straight lines. This means that data can fluctuate in varying degrees within various subgroups, and still be considered in control. The difference between U-Charts and C-Charts is that unlike C-Charts, U-Charts have a different number of items in each subgroup.

Interpretation of control charts is complicated, and involves analyzing a number of different things. That said, the simplest way to determine if a chart is in control, or not, is to verify whether any points in the plot fall outside of the chart's control limits. Using this simple test, you can see that both of the above charts are in fact, out of control.

Figure 2 illustrates several out of control points. These data points are clearly out of the control limits and therefore easy to identify. Interpretation of the chart is left to the user, since the chart is only a tool to graph the data, however your analysis should be vigilant not to overreact to any anomaly until it can be verified that there is indeed a cause behind it which warrants further investigation.

# Chart Types¶

Choosing which type of chart best represents your data is based on the data itself. Charts are split into two categories: those for measurement data, and those for count data. While the measurements of a part coming off of an assembly line would be considered measurable data suitable for a measurement type chart, on the other hand, the number of non-conformities counted each day would be considered as countable data which is more suited to a counts data type chart. Once the decision has been made on which type of chart to use, then each chart group has several charts available within it for use.

## Measurements Group¶

The following charts are available within the measurements group:

Range and Sigma charts are generally used to validate that a process variation is in statistical control. Once the chart is plotted, then the process is validated using an R or S-Chart. An X-Bar Chart is then constructed to further analyze the resulting data. Generally, only one or the other is selected to calculate the theoretical control limits, and that choice is left to the user. Run charts are simply plots of the data with a line to show the median, no manipulation of the data occurs before the plot.

## Counts Group¶

The following charts are available within the counts group:

The simplest of these is the C-Chart, which is the plot of the number of non-conformities per unit. A unit is usually referred to as an inspection unit, and is a predefined rate. If the inspection unit is not a fixed size, then a U-Chart is used instead. A U-Chart allows for a plot where the number of inspected units varies because the size of the subgroups is not fixed. NP and P Charts are based on the count of units, which differs from both C and U Charts which are based on the number of occurrences. The NP-Chart is used in the limited case when subgroups are equal size, whereas the P-Chart is used in cases where the subgroups are not of equal size.

# Using Six-Sigma in Dundas Dashboard¶

Leveraging the power of six-sigma in Dundas Dashboard is a very easy process. The first step to using it is to install the plug-in into Dundas Dashboard.

Once installed, the new charts will appear in the "Charts" category of the dashboard designer toolbox.

To add a chart to your dashboard, simply drag it from the toolbox displayed in Figure 3 onto your dashboard. You will end up with an "empty" Six-Sigma Chart, indicating it has no data.

KPIs can be added to the Six-Sigma chart in the exact same manner as adding them to other charts: simply drag and drop a KPI from the toolbox onto the chart. Once data is added, the dashboard designer will show you a sample of how the chart will end up looking. As well, the designer will try to setup your data as accurately as possible to the Six-Sigma chart. However, you can check that it has correctly associated the values by opening the data configuration wizard. To do that, first click the context menu at the top right of the chart and click on "Data Information".

Then, click the KPI/DataSet which is associated with this chart.

This will bring you to the data configuration wizard where you can change which measures are used to supply the required information to the Six-Sigma Chart. Differen't charts require different information, so it's important to ensure the data has been setup correctly.

## Extra Configuration¶

Some of the Six-Sigma charts have special properties for their chart type. You can always find these properties in the "Six Sigma" group in the property toolbox in the dashboard designer. The following are Six-Sigma charts with special properties.

### Individuals Chart¶

### R Chart¶

### S Chart¶

### X Bar Chart¶

# Conclusion¶

Six Sigma is a powerful plan that many businesses now use with great success. Since control charts are one of the more mathematically complicated aspects of Six Sigma, the ability to easily create control charts becomes a great asset to any organization. This add-on allows for the easy creation and customization of these charts to fit many users' needs. In addition to this, the charts produced are aesthetically pleasing, colorful, and do not require that the user learn any complicated mathematical formulas to create and use them.

# Release history¶

# Related topics¶

Click to return to: Documentation | Dundas Dashboard Add-ons

Go to the Dundas Dashboard Add-ons page for instructions on downloading and installing this add-on.

This add-on focuses on the control charts used by Six Sigma to analyze production, and allows for creation of these extensive charts. The charts are easy to use and understand, thus both Six Sigma black-belts and beginners can benefit from the charts created. These charts also assist with the business methodology, although that methodology is not discussed in this article.

Control charts also show common cause variations. Common cause variations are variations that occur often and with no specific reason. Reducing these variations saves money and makes the whole process more efficient.

In Six Sigma, a control chart is a chart that has 3 lines: a center line, an upper control limit (UCL) line, and a lower control limit (LCL) line.

Figure 1: A sample C-Chart showing the three lines. |

Figure 1 displays a sample C-Chart with the three lines marked; the center line is called the C-Bar line because this chart is a C-Chart. These three lines are the primary means of deciding whether a process is said to be "in control" or not.

The center line is always a straight line, unlike the upper control (UCL), and lower control (LCL) limit lines which are not always straight.

Figure 2: A sample U-Chart. |

The U-Chart shown in Figure 2 contains a UCL and LCL that are not straight lines. This means that data can fluctuate in varying degrees within various subgroups, and still be considered in control. The difference between U-Charts and C-Charts is that unlike C-Charts, U-Charts have a different number of items in each subgroup.

Interpretation of control charts is complicated, and involves analyzing a number of different things. That said, the simplest way to determine if a chart is in control, or not, is to verify whether any points in the plot fall outside of the chart's control limits. Using this simple test, you can see that both of the above charts are in fact, out of control.

Figure 2 illustrates several out of control points. These data points are clearly out of the control limits and therefore easy to identify. Interpretation of the chart is left to the user, since the chart is only a tool to graph the data, however your analysis should be vigilant not to overreact to any anomaly until it can be verified that there is indeed a cause behind it which warrants further investigation.

- Range Chart (R-Chart).
- Sigma Chart (S-Chart).
- X-Control Chart (X-Bar Chart).
- Run Chart.

Range and Sigma charts are generally used to validate that a process variation is in statistical control. Once the chart is plotted, then the process is validated using an R or S-Chart. An X-Bar Chart is then constructed to further analyze the resulting data. Generally, only one or the other is selected to calculate the theoretical control limits, and that choice is left to the user. Run charts are simply plots of the data with a line to show the median, no manipulation of the data occurs before the plot.

- C-Chart.
- U-Chart.
- NP-Chart.
- P-Chart.

The simplest of these is the C-Chart, which is the plot of the number of non-conformities per unit. A unit is usually referred to as an inspection unit, and is a predefined rate. If the inspection unit is not a fixed size, then a U-Chart is used instead. A U-Chart allows for a plot where the number of inspected units varies because the size of the subgroups is not fixed. NP and P Charts are based on the count of units, which differs from both C and U Charts which are based on the number of occurrences. The NP-Chart is used in the limited case when subgroups are equal size, whereas the P-Chart is used in cases where the subgroups are not of equal size.

Once installed, the new charts will appear in the "Charts" category of the dashboard designer toolbox.

Figure 3: The Six-Sigma add-ons in the dashboard designer toolbox. |

To add a chart to your dashboard, simply drag it from the toolbox displayed in Figure 3 onto your dashboard. You will end up with an "empty" Six-Sigma Chart, indicating it has no data.

Figure 4: An empty Six-Sigma chart. |

KPIs can be added to the Six-Sigma chart in the exact same manner as adding them to other charts: simply drag and drop a KPI from the toolbox onto the chart. Once data is added, the dashboard designer will show you a sample of how the chart will end up looking. As well, the designer will try to setup your data as accurately as possible to the Six-Sigma chart. However, you can check that it has correctly associated the values by opening the data configuration wizard. To do that, first click the context menu at the top right of the chart and click on "Data Information".

Figure 5: The data information button in the chart's context menu. |

Then, click the KPI/DataSet which is associated with this chart.

Figure 6: The KPIs/DataSets associated with this chart. |

This will bring you to the data configuration wizard where you can change which measures are used to supply the required information to the Six-Sigma Chart. Differen't charts require different information, so it's important to ensure the data has been setup correctly.

Figure 7: The data setup wizard with a Six-Sigma chart requiring two measures. |

- N Value (N) - The number of measurements per subgroup. Must be between 2 and 9.
- X-Bar calculation type (XBarCalculationType) - This tells the Individuals chart whether it should use a S-Chart or R-Chart calculation.

- N Value (N) - The number of measurements per subgroup. Must be between 2 and 9.

- N Value (N) - The number of measurements per subgroup. Must be between 2 and 9.

- N Value (N) - The number of measurements per subgroup. Must be between 2 and 9.
- X-Bar calculation type (XBarCalculationType) - This tells the X-Bar chart whether it should use a S-Chart or R-Chart calculation.

- Dundas Dashboard 2.5.3 - Added advanced property, Ignore Outliers, which indicates whether outlier points should be included in the calculations of UCL and LCL.
- Dundas Dashboard 2.0.0 - First release of this add-on.

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