Unlock the Power of ImR (XmR) Control Charts - SPC with Excel

Learn how to create an Individuals and Moving Range (ImR) control chart in Excel using this simple tutorial. This control chart is a commonly used tool for measuring variation of a process.
In this video, we'll show you how to create an ImR control chart in Excel using simple steps. This control chart is a commonly used tool for measuring process variation, and is a great way to evaluate process control. After watching this video, you'll be able to create an ImR control chart in Excel quickly and easily!
Control Charts are a great tool for engineering, business, and quality Engineers and other quality professionals. The most basic form of control charts are the Individuals and Moving Range chart (ImR chart).
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[@nickw22689]

Maybe the best Excel tutorial video I've ever seen. Outstanding, thank you.

[@TheEngineeringToolboxChannel]

****Please read**** There was an error with my MR-bar calculations (as Env Cas31 pointed out below). I included the 0 value in the first row when really that value should be null (blank). That way, the MR-Bar average does not take it into account because the MR of the first data point does actually not exist since it is the first data point and there is nothing to compare to. Also, as I pointed out in the description, this video is just an example of how to create an I-MR chart in Excel. It is pretty clear that the process in this example is not stable or in control. While the chart in this case is useful for determining the instability of the process, it may not be useful to monitor the process further until the process is in control.

[@VitaliyKolesov]

Awesome work and thank you for the explanations about how to set up these stylish charts!

[@envcas31]

LOVE THIS video! We're starting up an Industrial Metrology Lab here in Belize and this is totally helpful!

[@TheEngineeringToolboxChannel]

Awesome! I'm really glad it was helpful. Any other quality tools you are looking to learn or need help with? I'm happy to make videos on request! Also, be sure to check out my channel page. Good chance you'll find something else you'll like in there. :)

[@envcas31]

@@TheEngineeringToolboxChannel i did! Your channel has been helpful and this video feels PERFECT as im building an SOP for control charts in use for check standards and environmental controls. Im still fairly new to this and im learning as I go. Keep up the good work and im looking forward to the other video with conditional formating for the violations. By the way. Those 8 rules, some of them felt like they were overlapping, so I modified them and shortened them to about 5 rules for Out Of Control. What's your thought on that?

[@TheEngineeringToolboxChannel]

Env Cas31 ...all 8 of the rules do have their place so I wouldn’t necessarily say some are overlapping or redundant. However, I would say some of the rules are much less common than others or only really occur in certain types of processes. It really comes down to the application and the specific process you are analyzing. If you are trying to standardize your procedures and think the 5 rules you have chosen will get the job done, then I say go for it. But just realize your leaving 3 rules for identifying special cause variation out of your analysis. So I’d be very confident that those types of special cause variation will not be present in your processes before excluding them.

[@TheEngineeringToolboxChannel]

@@envcas31 found this as well. Gives some more explanation for when to use what rules. blog.minitab.com/blog/statistics-in-the-field/using-the-nelson-rules-for-control-charts-in-minitab

[@envcas31]

@@TheEngineeringToolboxChannel I am drafting these around our environmental conditions and so far the variations we've seen are more common than special variation. I've seen tutorials where they took out the special cause variations after determining a result from a root-cause analysis. Our preliminary Charts show little to know Special Cause, and when it does we can easily deduce why; mind you we only have about 6 months worth of data. I'll take your word and I rather be safe than be sorry later on. I noticed for the MR bar you used the average fuction, shouldn't it be 1 less since technically you don't consider the first point (which is empty) a point? Another thing: I was using the actual Std. Dev in my calculations will it have a drastic effect on the results compared to the approximations? I should probably read some more on the topic, but any input of yours is greatly appreciated.

[@1kevinerickson]

Fantastic demo. Thank you! I noticed the was not MR LCL. Why is this and if we wanted one, would it be -3.267*MR Bar?

[@TheEngineeringToolboxChannel]

There would be no LCL for an mR chart. You’ll notice that the D3 value used for calculating LCL of range charts is 0 until a sample size of n=7. See below. www.bessegato.com.br/UFJF/resources/table_of_control_chart_constants_old.pdf ImR charts can be though of as a n=2 x-barR chart. Hope this helps!!

[@bowiecreative]

Great video! Can you anyone provide any guidance on how to build a control chart that captures how long it takes to respond to a new email lead? I’m also not sure which type of control chart to use IMR, etc.

[@amie2151]

Really helpful thank you :)

[@TheEngineeringToolboxChannel]

Glad you enjoyed! 😀 Be sure to check out my other videos on control charting. Also consider subscribing as I will be covering control charting and other similar tools in the future!! Playlist of all Control Charting Videos: kzbin.info/aero/PLAVcy54svuke4wGIv6kbP3uoLqiHT5f0g

[@yuviyuvraj001]

sorry if this is a dumb question but where did the constant value of 1.128 come?

[@TheEngineeringToolboxChannel]

Nope it’s not a dumb question! It is a constant that is used to estimate the standard deviation of a data set given the average moving range.

[@yuviyuvraj001]

@@TheEngineeringToolboxChannel Thanks for the reply! So does the value of constant change with different sets of data? Also is this the same principle for the constant used to determine the MR UCL?

[@TheEngineeringToolboxChannel]

You can easily simulate a proof of where this value comes from in Excel. 1. Generate a standard normal distribution (SD=1, Mean=0). To do this, Type in “=norm.s.inv(rand())” into a cell in drag it down a thousand rows or so. 2. Then calculate the moving range value of that list of a data (so take max-min of each successive pair of data points like I did in this video). 3. Then take the average of that moving range column. This should be roughly 1.128 (you can hit F9 to refresh the simulation to see how it falls around 1.128) So what does all this show you? Well basically when the standard deviation of a perfectly normal data set is =1 then we would expect the moving range to be 1.128 (and more importantly vise versa). This is powerful because true standard deviation is really only good at summarizing the variation of the “whole picture” aka ‘long term’ variation. It isn't really sensitive to the time order of the data. But moving range IS. Because of this we can use it to estimate the “short term” standard deviation of the process. The "short term" variation being the amount of variation we would expect over a short period of time. With control charting the short term variation is what we are interested in because many of the variations that happen over the long term are due to factors external to the process. So for example, if an operator machines 5 parts back to back they will likely be very little variation (short term variation). But if those first 5 parts are compared to 5 parts made by a different operator on a different shift a month later, you would likely see a larger variation between the two sets of parts (long term variation). True standard deviation captures ALL of this variation. Whereas estimating SD using MR-bar/1.128 will only measure the variation between each consecutive part.

[@TheEngineeringToolboxChannel]

@@yuviyuvraj001 The constants change based upon the subgroup sample size. Each type of control chart has their own formulas and constants to use. See this table of control chart constants for example. www.bessegato.com.br/UFJF/resources/table_of_control_chart_constants_old.pdf

[@TheEngineeringToolboxChannel]

This playlist may also help. kzbin.info/www/bejne/i5jGk2qModVjitU

[@thiruchelvamkuppan9765]

Hi, I’m not able to grasp the constants used 1.28, 3.27 etc could you please explain. This is very easy to follow video otherwise

[@TheEngineeringToolboxChannel]

Its on my list of videos to create in the future... This video hints at it but doesn't fully flush out the origin of the values. kzbin.info/www/bejne/i5jGk2qModVjitU

[@marcing223]

There is no chance to learn anything from this video if you are a beginner 🤕

[@TheEngineeringToolboxChannel]

Marcin Głodziński sorry about that...it is a bit more advanced topic. You should definitely try to follow along exactly what I did to complete your own. That will help make sense of things. Also, one key piece that will make things confusing if you miss it is the table. I put my data in a TABLE which is an actually feature that you create by clicking the table button. Do not miss this step. This, in my opinion, is the best way to create a control chart with excel but No one else shows a tutorial doing it this way.

[@marcing223]

@@TheEngineeringToolboxChannel I will try . It is just a bit difficult to pick everything up as you are advanced also in excel and do everything very quick using few shortcuts .

[@TheEngineeringToolboxChannel]

@@marcing223 Sorry about that. I will try to explain better in the future. Feel free to email me with any questions. [e-mail]