1 Secret Nobody Knew | The Red Beads Experiment 3 of 3 | fkiQuality Green Belt 2.0®
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- čas přidán 10. 09. 2024
- Watch after "The Red Beads Experiment 2 of 3".
Were the management actions appropriate and effective?
Find explanations through the interpretation of control charts.
Timestamps:
0:51 - Introduction & Control Chart Definition Slide
2:14 - How to read the Control Chart
2:36 - Avg., UCL, LCL, & Target Lines + Slide
3:22 - Range of Natural Variation Definition
4:28 - What to first look for in a Control Chart
5:21 - What happens if you discover a special cause outside of the Control Limits
6:56 - Assignable Cause Variation Definition
7:12 - Predictable Processes are a good thing
7:59 - But is that what we want? Adjust a Predictable Process
8:38 - Capability of the Process
9:19 - ONE SECRET NOBODY KNEW: All variation was due to the Process, not the workers
10:08 - Example of how supervisors can misinterpret numbers on the Chart
11:39 - Example of how employees can misinterpret the numbers too
13:03 - Example of how misinterpreting numbers on the Chart can lead to employees getting fired
14:38 - Example of how rare it is to go below the lower control limit
15:56 - Control Chart Recap Summary
16:52 - Summary Slides
Well explained. Having worked in manufacturing for many years I have seen this often where management blames people instead of reviewing their process. Excellent lesson. -Rob Seger
Not a big stats person but the control chart is simple enough to identify low extremes or high extremes that easily facilitate for a person to see a potential issue of the current process. "Process that is behaving in a predictable way will give similar results in the future. "The variation limits serve as a forecast." Spot on, loved the explanation!
There was a co-worker who got high praised by mgmt because they were judging strictly on numbers. He really did a great job beating the system. He would purposely pick the tickets that were easy and fast to solve, so his numbers were always higher. He got called out for doing a great job when he really wasn't.
Like the summary of control charts and how they can help show what you need to improve on as well as if determine if your data falls within the natural range of variation
The control chart quickly shows a stable process. I wish more managers understood this. There are many stable processes where employees are being pushed to do better when they can only do so much.
This video does an excellent job of summarizing the observations from the red bead experiment. It was particularly interesting to note that the variation that occurred was due to variation built into the system. This variation was out of the hands of the workers. It's a great reminder to evaluate the process/control chart before criticizing the worker.
lessons are to be learned, with data and knowledge we can make improvements. Especially identifying uncontrolled variations
I feel pretty well versed in control charts to this point, but this was very helpful to see and hear everything that can be taken from (and not taken from) the use of a control chart.
Important wrap-up to the experiment. Key learning for me is that just because the process is stable, that does not mean it's what we want. Stability is just the starting point for process improvement.
great video that illustrates if a process is stable or not. It also highlights how moral from employee can suffer.
this video shows looking at the right data in the right way really helps point the management team in the right direction.
Easier to modify a predictable process to meet goals-great video highlighting how outcome has more to do with processes rather than the person performing the process
Wrapping up the experiment and discussion, this brings all the points together - through a short, simple to execute exercise, broad lessons on SPC and management in general can be taught and discussed.
Such valuable information on this great tool. If only more management would use this tool!
Good example of an actual process and it's subsequent data analysis. The parallels to real-life organizations are easy to realize and apply these lessons.
control charts are a great way to show them right in front of you and it helps put it into perspective for workers.
This was another great video. It is important to remember that the voice of the process does not equal the voice of customer. It is very rare to go outside the limits, so you should investigate these cases.
Control charts are such a useful tool to visually see uncontrolled processes and areas of the business that need to improve.
I like how the control chart can show that a process is predictable/stable even though it may not be satisfying. Learning how to use this chart correctly and Gemba can help identify the correct targets/ucl/lcl especially in a job that has too many variables that are not controllable..
The Red Bead experiment is a great visual tool to gain the attention of your audience. It is also a good way to illustrate that you must have a system (company) that is under control before one can do any kind of continuous improvement work.
Control Charts can be a manager's great "friend". It aids a manager to make an informed decision, instead of just looking at numbers and trying to impulsively make a decision based on the numbers.
A great explanation of how the data shows that the process itself is the cause of variation. It is also an exposure to the mentality that comes up for workers during process behaviors with variations like this. The ones that are lucky and get good results will grow their egos and feel like they are doing good work. The ones that are not so lucky will feel like failures or become frustrated or discouraged. By working with machines that have a wide range of variability in how they produce, I have seen both sides. And even someone who was otherwise considered a great and experienced operator will become frustrated and discouraged if they cannot get their machine to produce well. In some cases, they will just try to work harder to overcome the machine instead of getting it fixed. This is because they feel it is their fault or their performance that is at fault, and not the process, materials, and equipment that are holding them back. The ones that succeed the most in this environment understand this and will refuse to operate or pass on a machine or materials that are not producing well. If a machine is set up well, and the materials it uses are of good quality, then it does not matter who operates the machine. It will produce well.
Control chart allows you to see data variables and trends, also works to alarm you if there is a big fall or spike. Great tool!
Looking at this video better explains how control charts adds value and if management adheres to the information they will be able to implement change and improve performance.
Video reinforces what we learned about creating and analyzing control charts
Really interesting to see everything that can be taken from a control chart and how useful control charts can be in managing people.
Although we subconsciously know that just because something is predictable doesn't mean its a good thing ( Chicago winters; addicts relapsing etc) , its eye opening to see it acknowledged in such a way. Thankfully by recognizing the patterns we can prepare/fix the negative.
Good review of control charts and how NOT to misinterpret them. Stability is the key before making adjustments to the process.
Control chat is a very useful tool to see variation and predictability visually. Very good explanation..
Nice way to finish out the simulation with the control chart to really drive home the point of this exercise. We’ve got to look at the process and improve it to see real change in output
Predictability is the key!
Great tutorial on understanding control charts but important how to interpret the data since a predictable process does not always mean its meeting the customers requirements. Crtical to also look at the process in a systemic approach to affect process change versus punish the employee for getting predicable results of a bad process. More leaders could benefit watching the red bead videos.
Great and fun way of explaining stability in a work environment. For our last day you should have us do this experiment.
I think this was a great expansion from part 2 with the greater emphasis on the chart. I thought the part about the worker buying into the lie seemed very realistic because with a streak of good luck, we try to digest that and use data to justify what's happening when it was really just due to the natural variation of the process.
Great point to show that control charts can be a great tool to guide process improvement. But the first step in improvement is interpreting the data correctly. In this case data was misinterpreted and the process was not improved and results did not get any closer to the target of 5.
Good explanation of how data can be used in a negative way -- that being with out some constraints put on initially, the whole process can be futile.
loved the experiment
Control chart is a powerful tool that shows the stability of a process and determines what need to do to improve.
The control chart can indicate whether or not they system is stable and predictable, but does not necessarily mean the system is where you want it to be (i.e., that performance is meeting your target). But having a stable and predictable system is important to then IMPROVE it.
The great statistician Donald Wheeler, lists a few myths about how to construct and use control charts. The following is from his 7 Sep 2011 Quality Digest article "Myths About Process Behavior Charts."
'It has been said that the process must be operating in control before you can place the data on a process behavior chart ... there are correct and incorrect ways to compute the limits ... The most common of the incorrect ways of computing limits consists of using three-standard-deviation limits rather than three-sigma limits. While this approach was identified as incorrect on page 302 of Shewhart’s 1931 book, it is found in virtually every piece of software available today. While three-standard-deviation limits will mimic three-sigma limits whenever the process is operated predictably, they will be severely inflated when the process is being operated unpredictably.
The express purpose of the process behavior chart is to detect when a process is changing, and to do this we have to be able to get good limits from bad data.' Three-sigma limits achieve this.
When this is not understood, then we start to take wrong measures to fix the data without any justification like 'censoring of the data prior to placing them on the chart (removing the outliers), and the use of two-standard-deviation limits.'
Recall the method to compute three-sigma limits for the XmR chart: calculate the x average, calculate the moving ranges and their average, then multiply the moving range average by 2.66 and add/subtract from the x average to obtain the limits, known as upper and lower (control) limits.
Great explanation of control charts and how to interpret the results of the red bead experiment.
Mridu, great that you watched the trilogy of videos, they really build on one another.
Control chart is a good tool to graphically show if a process is in a state of control by representing the data and showing the bounds of natural variation. The video showed how to analyze the data and identify when an anomalous action needs further investigation and improvement.
Control Charts are great for showing improvements.
Control chart is a good tool to graphically represent the bounds of natural variation of a stable predictable system. And, to establish when an anomalous action needs further investigation. It is important to keep in mind that this chart is a representation of system performance not of employee performance.
The control charts are a great visual way to show predictability and variations.
Control charts are super effective!
Each worker will have a different outcome, but working with all of them you will be able to achieve a more universal outcome that would be more benificial to all. Once this is done, you will be able to get a more accurate account of the process
This video explains how to read and interpret data from a control chart. Control chart will show where a process is stable or not. No points outside the limit means no likely point of concern. Points to variation in the process, not the workers.
Kevin Hi: Control charts are a great way to, using your eyeballs, visually see natural variation without only statistics, numbers, or in tl numerical tables.
Having knowledge of the underline process can have a big impact on the workers performance. If a worker does not know all that they are being evaluated on they cannot make the necessary adjustments needed for a more positive outcome.
By understanding if a control chart is stable and predictable, it can help determine the common or special variation and use to predict future performance.
Control charts are a great tool to help with process optimization.
A control chart can immediately point you to your out of spec results so you have a better understanding of where to focus your efforts. If you aren't seeing any results within your spec over an extended study, then it's time to revamp that "process" completely.
The control chart indicates the variables of the process and helps us to predict how it may behave under normal circumstances, however it does not tell us where to focus to improve the process.
What’s very interesting is during the simulation as the worker you know the process is broken but we still try to different things to hit the target even thought there is almost nothing we can do.
I agree that most companies focus on the employee's performance instead of fixing the process. This causes a lot of issues along with high turn over rates.
Control limits=VOP. Spec limits=VOC. Process can be stable/in control but still not meet a customer’s spec. Important concepts to remember!
The control chart is a very valuable tool. What duration do you usually track on one chart before switching to another? Do you show point-in-time from month-to-month or year-to-year or would too much valuable data be lost in larger increments of time?
Essentially, it is the responsibility of management to create stability and promote continuous improvement.
Antrione, that's right, and these tasks must be achieved with the participation of the personnel in order to respond to facts known first-hand, and to make solutions sustainable-- (it's hard to disagree with your own ideas ...
We can use statistics to create a Control Chart, look for problem areas, and predict future performance. In the Red Bead Experiment we can see that the Control Chart shows that all variation is common cause variation and has nothing to do with the workers performance.
without being prejudice to the worker, monitor the process
So once we have reached Stability or repeat-ability we can then address coarse tuning the process. Following that we can then apply more vernier tuning of the process until we have a process that will permit the Target performance to be attainable. Or if the process is so far out of line, we may need to replace this with something that actually works. While this is happening we should not use Performance numbers to punish nor reward the performers. Is this a correct assessment?
H Leonard
If the dots are not outside the control limits the process is stable. The use of the control chart will provide predictable and stable results. However this does not mean the results are good. Need to align the process results with the customer requested outcomes.
It wasn't the employees at fault for the results, it was the process. Using the control chart allowed for getting to the heart of the matter.
I like that the person who got 4 the next time got 14, giving her an average of 9. It's called regression to the mean. I tell my MBA students that the math always wins!
nice info
A control chart helps to determine if a process is stable and predictable. Then you can work towards a satisfying outcome.
Controls charts show whether a process s predictable. We aim for predictable processes because it demonstrates continuous improvements. Control charts show variation.
A control chart helps provide insights into the red bean experiment.
The way I see it is that control charts are a great kickoff point to asking the 5 whys.
Good to know the misconceptions to better understand what the data is saying.
You explained the mystery of why there was a gap between the data sets.
Great video
Basically, the control chart shows that production output is unpredictable. So, management needs to observe their processes to determine a method to close the gap on unpredictable results, and create a stable output.
The control charts give the true picture of the data. The average can be misleading, it doesn't tell the person looking at the data what factors are causing the results. This specific experiment shows that the process is in control because the results are predictable. This means that the problem has one or more specific causes, too many red beads in the mixture, rather than bad techniques of the "workers" This tells the GB they need to improve the process to reduce the amount of red beads available.
You work towards a stable and predictable process; then create a "standard" that can be measured or achieved; and finally you improve the process to achieve the specification limit that you desire. Repeat, if needed.
I think the biggest take away from this video for me was how all this effort went into making a control chart for a stable system that was fairly random by nature.
Before we can identify a root cause the the problem, we have to understand the process itself and ensure unstable variation isn't a factor
The bead experiment is not predictable. As a manager or part of the system, our job would be to get the task at hand controlled and in a more stable state.
The bead experiment actually shows a predictable process, albeit not a satisfying one!
The variation you see in the number of red beads comes entirely from the random effects of common causes, usually unknown, usually not too important.
So, in this case, management must keep the process stable - which is a big achievement already! - and then move the average where it will meet customer needs (fewer than 5 red beads) and reduce variation from what we currently have. Recall the Semmelweiss story with reducing maternal mortality rates at the hospital in the 1840s.
A control chart that has common variation shouldn't freak out management. It's important to understand what these common variations actually mean.
The process has to be stable inorder to improve it. How do we make it stable?
This goes back to mismanaging with numbers. Before we jump to conclusions based on numbers, we need to make sure that we understand what the numbers mean.
If you have a process that is "in control" but producing out of spec results then the problem is something exogenous to the process or maybe the process itself
To truly improve processes they need to be monitored overtime to ensure they are still effective and meets the customers needs. Also, It is not a good practice to measure your employee off of only one factor. This may cause an inaccurate rating and expectations of your employees' skills and knowledge and false process validation.
Joseph Tshulos, you need to know what you are measuring, and what the charts means, to make a good determination.
Control charts help with the process of knowledge. Facts good or bad.
It appears that luck may also determine predictability in this case.
It's not the person, it's the process.
Range of Natural Variation is nowhere close to the Target.
Am I the only one worried their bosses know this information?
Obviously this bodes well for high performers and overall company health but the rest of us better get our acts together.
Y
The red beads experiment was one of futility in my opinion. The beads did not and could not indicate the employees ability to do their jobs well, or evaluate their performance in any way. What it did evaluate was their ability to draw more or less red beads on the paddle than their fellow employees. They may as well have drawn straws or a black ball out of white ones in a blind draw. This is an idiotic technique. The control chart just proves how ineffective it was.