Level: Intermediate

I have had a couple of requests for help during my live training classes and online training classes on how to build a Six Sigma Control Chart. Now I am not professing to be a Six Sigma black belt, but I do know how to write DAX and I do know how to use Power BI. So here goes. This is what I am going to build.

## The Method

As usual, I used the Adventure Works database in this article. I then used DAX formulas and Power BI visuals to arrive at this six sigma chart. Here are the steps I followed to produce the result.

1. I created a DAX formula for total sales.

Total Sales = SUM(Sales[ExtendedAmount])

2. Then I created a DAX formula to obtain the mean (average) of the sales by day.

Average Sales = CALCULATE( AVERAGEX('Calendar', [Total Sales]), ALLSELECTED('Calendar') )

Note that I used AVERAGEX function iterating over the Calendar table in order to get the average sales over the selected days instead of the average per transaction.

3. Next I calculated the LCL and UCL by adding/subtracting one standard deviation, as follows.

Std Dev = CALCULATE( STDEVX.P('Calendar', [Total Sales]), ALLSELECTED('Calendar') )

= [Average Sales] - [Std Dev]

= [Average Sales] + [Std Dev]

4. I then created a combo chart – the lines show the average and control limits (LCL and UCL) and the columns show the total sales values. I used a slicer to filter the time period.

The idea is to create an interactive dynamic chart that displays sales distribution over the selected time period. The part of the columns below LCL, above UCL, and between LCL and UCL are to be displayed with different colours. To get this, I had segmented the columns with the following DAX formulas.

Part Lower Bar = IF([Total Sales] < [LCL], [Total Sales])

Full Lower = IF([Total Sales] >= [LCL], [LCL])

Middle = IF( [Total Sales] > [LCL] && [Total Sales] <= [UCL], [Total Sales] - [LCL], IF([Total Sales] > [LCL], [UCL] - [LCL]) )

Upper = IF([Total Sales] > [UCL], [Total Sales] - [UCL])

To understand how I arrived at each of these DAX formulas that resulted in the interactive six sigma control chart as given at the beginning of this article, view the video below.

And, here is the sample file that is used.

Sean LarkinHi Matt, Great demo. I am having anissue rolling my control chart into weekly and monthly views as opposed to a daily view. What changes do I need to make to get the mean and U/L CL s to show based on weekly and monthly data?

Matt AllingtonI guess you would just change the calculation to work across the unique months. Something like this.

Std Dev =

CALCULATE(

STDEVX.P(VALUES(‘Calendar'[Month ID]), [Total Sales]),

ALLSELECTED(‘Calendar’)

)

Matt AllingtonPower pivot for Excel can do exactly the same thing. Otherwise you can just use Excel formulas in cells

R.A.I need this exact functionality but instead with Excel… is this possible?

Don GrantMatt, Really nice video and very helpful I am still unable to gather the concept per. my requirements. Will you be able to share the Power BI file so I can understand in more details how the control charts were created?

Peter DonohueWhilst this is a really nice looking demo, it sort of undermines the whole principle of control charts. The concept is that we ‘set’ control ‘limits’ and leave them, until signals in the data (trends) indicate that investigation should take place. Because the limits are set based on historical data, they indicate a level of process predictability.

If these limits can move based on date they fundamentally contradict the concept of ‘control’ limits – you want them set, not moving and only change them based on triggers from your data. I believe this is one of the fundamental reasons people have issues with control charts

NorbertusThank you!!

Marvin Dayrit VillarealAmazing!

Marvin Dayrit VillarealAmazing!

Paul TalbotNifty, thanks.

Paul TalbotNifty, thanks.

Chris WeisOr you could use this XmR custom visual: https://github.com/tcd1nc/PowerBI-Control-Chart

Chris WeisOr you could use this XmR custom visual: https://github.com/tcd1nc/PowerBI-Control-Chart

NickThis would not be considered an acceptable method of creating control limits for an x bar chart. Nor would the use of a bar chart be a good choice for identifying trends and assignables causes that’s aren’t single movements beyond the control limits (something that’s going to happen quite regularly with a 1s limit).

NickThis would not be considered an acceptable method of creating control limits for an x bar chart. Nor would the use of a bar chart be a good choice for identifying trends and assignables causes that’s aren’t single movements beyond the control limits (something that’s going to happen quite regularly with a 1s limit).

Mehdi HAMMADIVery interesting. thanks Matt

Mehdi HAMMADIVery interesting. thanks Matt

Paul CooperThis will be useful in testing boundaries of inventory levels.

I’ll give it a try. Thanks Matt.

Paul CooperThis will be useful in testing boundaries of inventory levels.

I’ll give it a try. Thanks Matt.

Steve DohertyVery clever. The average sales over selected time calculation is an often used formula for many business scenarios. I’ve seen it more usually applied with percentage growth/decline calculations to identify entities that are above the average and those that pull the average down (and require further attention).

Steve DohertyVery clever. The average sales over selected time calculation is an often used formula for many business scenarios. I’ve seen it more usually applied with percentage growth/decline calculations to identify entities that are above the average and those that pull the average down (and require further attention).