Six Sigma | The x-bar R Control Chart | Green Belt 2.0® Lean Six Sigma | fkiQuality HD

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Use the x-bar R control chart to monitor and improve processes where data can be collected at about the same time and/or under similar conditions.
The chart will display the variation between the sets of data, called subgroups, in the x-bar section and between the individual observations in the R section.
This way, the x-bar section shows how the process being measure changes day after day, while the R section shows how much the data is changing each day.
Both parts of the chart filter out the random noise existent in all processes so that signals to action may be identified.

Опубликовано:

11 июн 2019

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Комментарии : 38

@GregBenz-de7nt 17 дней назад

This explanation shows the importance of looking into the special causes of variation.

@Jaguzma2 7 месяцев назад

This is a good visual of how both charts tell the same story, just using different ways.

@BeatriceDosu 16 дней назад

A great and "easy to understand" explanation of the uses of Control Chart. They help one to identify regular causes of variations or special causes of variations, calculate UCL and LCL, thereby investigate changes in data and make the right decision.

@mattmack8103 16 дней назад

excellent explanation of how these control charts can improve a process by identifying signals at a granular level.

@JamesBehn-pl4li 16 дней назад

Excellent explanation of how to create X-Bar and R control charts, and why we must monitor both of these charts to detect special cause variations (signals to action), as opposed to common cause variation (random noise). If special cause variations (signals to action) are found, we must do an investigation to find the root cause and eliminate.

@user-yz5cw5st5b 7 месяцев назад

I appreciate this video because it shows how important it is to thoroughly look at the data collected. Without these two charts I'm sure we would take the data at face value and not use it to identify signals as we should.

@Student-ws8wg 7 месяцев назад

Signals of unusual patterns are identified using the x-bar and R charts. Appreciate the clarity in this demonstration on defining when the probability model is used (30+ samples).

@nicholashumphrey8654 3 года назад

I really enjoy seeing all the numerical data points come together visually. It gives a story and a way to decide what to do based off of patterns and trends. Which are normally more difficult to visualize the level importance in your mind. It's easier to notice variation itself than to be able to notice if it is out of the ordinary. This helps make it more objective.

@JacobReuter28 Год назад

A great video showing how to build the x-bar R control chart and the ways that we can use it find more information about a process.

@margaritorios 3 года назад

It's amazing how this kind of chart can tell us so much! It can save to the business time and money just by applying some simple formulas and capturing the information on the right chart.

@parthibio Год назад

Nice Explanation and easy to understand the concept and Purpose. Thanks a Lot

@1999Ashay 2 года назад

This is a great video teaching how to graph xbar-r control carts. Very clear explanation!

@johnstanley491 4 года назад

The charts described in this video are great for determining irregularities in a process.

@musicotriste69 3 года назад

Homero Cardoso: Very useful video, it explains in a very short time the x-bar and how these tool can identified abnormalities within a set of data.

@cjtrumpeter Год назад

Nice demonstration of a x-bar & R control chart.

@jennifersacksteder9846 4 года назад

Its important to these charts to better understand our systems and how to find unusual behavior

@mmaphefoseseni6625 3 года назад

This gives clarification on the very small difference between the X and R chart.

@Ace_theMaster 2 года назад

What is interesting to see is that not only do these control charts request your attention to focus on points that wind up outside of the control limits, but also make you consider looking at patterns, such as the "three points close to the limit". This could give you a heads up on your process going awry sometime soon, and possibly "catch it" in time before it actually does.

@patriciazuno1694 Год назад

It is essential to know that there is a "time" when enough data has been collected to establish the upper and lower limits. Using data in this way allows us to pinpoint where things are out of the ordinary and look into what happened during the period of time to prevent it from happening again.

@nikiansell9949 4 года назад

I never knew much about the R-bar so this video provided a great foundation and good clarity on the difference when using subgroups vs. non-subgroups for charting.

@lesliebdelgado9666 4 года назад

This is a very clear video to understand the x-bar R control chart thanks! It helps also to understand how this is different than other control charts.

@douglasomachel2284 4 года назад

Liked how easy it can be to use averages and double averages to find an out of control time period in a process.

@jenniferb5322 4 года назад

This video in combination with the "Overview of the Control Chart" video helped me understand the differences between the x bar and xmR charts better (subgrouping versus no subgroups).

@mitchellhoem2003 2 года назад

It is important to understand that processes typically have natural variation and that's ok. The x-bar R chart helps identify issues that cannot be accounted for by that natural variation. It will help businesses to identify process changes that can increase quality, and it can also help identify certain aspects or processes that lend themselves to low quality.

@mitchzavesky4734 2 года назад

Very similar to the xmR and range charts, the xbar R control chart shows the variability between samples taken in the same conditions over a period of time. By graphing all of the data, you can easily see signals of unusual process behavior, which can alert the manager/lead to investigate quickly to eliminate this behavior.

@dwartneywortham2353 5 лет назад

Variables are very new for me in the wording I have been using.

@ChristopherStefan Год назад

This video explains the basics of the x-bar and R control chart and shows how they can be used to identify out of trend behavior.

@slee3249 4 года назад

using the control chart is new to me and it's hard for me to identify the signals. this video helps identify signals so investigation and future action can be taken

@domingomoccia2038 4 года назад

Monitoring the process using control charts helps to identify tendencies and potential deviations on time

@victorgomez1437 3 года назад

These charts help to identify alerts and noise from irregularities happening.

@ryanperlic4567 4 года назад

I think it would have been helpful to explain more on how you know the data points are sufficiently close to the control limit such that you can invoke the two out of three rule and begin looking for a signal.

@fkiQuality 4 года назад

Ryan, in this video I just say "sufficiently close" but do not go in more detail. The Western Electric rules in the textbook tell you what "close" means.

@christopherrogers7303 2 года назад

When utilizing these charts, I should only gather 8-10 sample sizes of 5 data points. If I gather more that would be too much and/or would lead to a different formula? If I stay with this amount of observations, I will always utilize [ A2 = .577 / D3 = .000 / D4 = 2.114 ] ?? If within a day I am able to gather more than 5 data points do I hold firm to the 5 data points, and if so, how do I determine which data points to use? Is that when I target different times, employees and aisles within a picking zone to capture the entire population?

@denisek743 4 года назад

The analysis on the x bar chart is more intuitive to me. The R chart shows change and I find it more difficult to analyze.

@rlcardcollection Год назад

I'm still not fully understanding how calculate the control limits for the averages and ranges.

@kslead27 5 лет назад

What can you do if you have a variable number of observations each day? Does this still work?

@fkiQuality 5 лет назад

Kyle Slead, a process may output a different number of observations every day, that is, its output may vary, but you should collect the same number of observations each time so that your basis of comparison stays consistent.

@fkiQuality 5 лет назад

Kyle Slead, a question for you. Why would you collect a different number of observations each day? If the process is the same, there's no reason. However, if the process seems to change in important ways from one day to the next, then maybe you really have two processes on alternating days. In this case, you should sample them and analyze them separately.