As we continue our journey in Six Sigma it seems pertinent to discuss the different types of distributions you will see in your analysis. Let’s start with one at a time. The most common distribution is the Normal Distribution and here’s what you should know about it.
First, what is a distribution?
Simply put, a distribution will tell you how often a variable occurs in your process. This is important because the commonness of your variables will inevitable create a foundation for your improvement project.
Types of Distribution
The Normal Distribution
A normal distribution (Gaussian Curve, the average person knows it as the Bell Curve) shows a equal distribution. The mean (the average) divides the data in half, 50% on the data on each side of the mean. The Normal Distribution will have the following hallmarks:
This distribution is considered to be the most important distribution.
The area under the curve should equal 1.
Physical aspects of the curve should resemble a hill and should be symmetrical.
Both directions on either side of the mean extend indefinitely and never touch the horizontal axis.
White noise in your process should produce a normal curve shape
The Z distribution has a mean of 0 and a standard deviation of 1.
The mean (average), median (mid-point) and the mode (most common value) should be the same data value.
Next week, it’s on to non-normal classifications. Get to analyzing and if you need any help, reach out and let us know!
We all know my affinity for MSA but it wouldn’t be fair if we didn’t talk about the measurements for a bit. Six Sigma is built on measurements and the corner stone of effectiveness is to have measurements that are appropriate. So let’s dig in and figure out what defines appropriate measures.
What makes it appropriate?
There are four key areas to consider when you are trying to determine if your metrics are appropriate:
- Is it sufficient?-When you consider this you will need to look at how available the metric is. Ask yourself if you can readily gather the data. If you have to collect it and the collection times require more energy and resources than you can give, it may be time to rethink this metric.
- Is it relevant?-What will this metric tell you? Does it help you understand or identify your problems? If it doesn’t then maybe you need to take a step back and figure out what you need your metric tell you.
- Is it representative?-When you are looking at this metric, you should see a balanced representation of the people and the steps involved in your process. If you can’t see these things, take another look at your goals. Are you measuring the right things?
- Is it contextual?-When this information is put together with all of the other information you collect, do you see the big picture? In other words is the data painting a picture that makes sense to your and the people involved?
So MSA like everything else in Six Sigma is a tool and the thing that we need to remember is that for it to be effective, we have to make sure we are using it appropriately. Check your systems and let me know how they are working. If they aren’t working, give us a call.
Okay for the last two weeks, I’ve been talking about Measurement System Analysis and before I move on to a new topic I have one final post on why you should be thinking about MSA. Here it goes…
Why you use it
- You use MSA to compare you customer’s expectations to your inspection standards. This is a very quick illustration of a value stream map and a good way to ensure that you are providing the best service for your customer.
- It gives you a snapshot of where the training in your organization should be.
- It gives you the opportunity to evaluate your trainers in a truly neutral fashion. The data doesn’t lie and you can assess the training in your organization from a truly objective perspective.
- Creates an opportunity to analyze your existing systems and evaluate new systems.
Why is it important?
- Allows you to measure the amount of variation in your measurement systems.
- Allows you to compare user variation.
- Allows you to compare two or more measurement systems.
- Helps you develop a baseline for measurement systems.
- Helps you develop a system to evaluate the moving pieces in your organization.
- Gives you a true before and after picture.
- Gives you a true measurement of variation and the causes of it.
- Evaluates your training programs.
So I am a big fan of MSA as you can tell, but the bottom line is that it can really affect your organization in the best way. It forces you to be accountable and it forces you to pay attention to the changes. Give it shot and if we can help, let us know.
Everything we measure generates variation, especially when there are multiple hands involved. To be honest even with just one person performing the same function, there will be some variation. Variation is not the enemy, uncontrolled variation is our nemesis!
When creating a Measurement System Analysis (MSA) there are 3 characteristics that you should focus on before you try any of the bells and whistles.
Is it accurate?
You need to know how accurate your measurement system is. If you can’t correctly count the number of variations happening can you really call them variations? Your measurement system is only as good as your accuracy, so it makes sense to spend a fair amount of time ensuring that not only are you counting defects, but you are counting the correct defects. This goes back to knowing why you want to measure something. If you want to find out why your shipments are late, measuring the number of birds around your facility won’t help. So accuracy needs two things: measuring the right data and ensuring the data is being measured in a way that answers your question.
Is it precise?
We’ve talked about precision and for a refresher precision is the reduction in variation. When you have identified your improvement area in the process, you are now ready for precision. So you are going to take one process, completed by the same person, in the same order every single time. Once you have identified this, you can began to reduce the variation and create precision.
Can you reproduce it?
The thought behind automating any process is ultimately making it scaleable, that is can you repeat the success? This is what determines a successful process from a failed one. Any process is good in theory, but where you get a great process is when you find one that can be repeated with the same amount of variation no matter who does it. That’s your end goal folks.
So we’ve covered the 3 basic characteristics of a Measurement System Analysis, so have a conversation with your belt and figure out your current state and your future. Your MSA will heavily influence your future, so take this conversation seriously. If you need more help, give us a call and let us help.
Continuing on my mission to make Six Sigma something that anyone can understand, today I want to keep the statistics conversation going with the scaled data, scales of measurement and what they mean to your company. There are four scales of measurement in Six Sigma to consider: Nominal, Ordinal, Interval and Ratio.
Nominally Scaled Data
This is the most basic scale and basically tells you whether the information is different or not. This applies to your business in the sense that it tells you the baseline in a yes or no format. Think along the lines of ‘does your customer buy product x’? The answer can only be yes or no.
Ordinal Scaled Data
This data applies to data that can be arranged in a specific order but you cannot distinguish what makes the data different. If you are looking for an answer to why a defect is happening, ordinal data is not going to answer that question.
Interval Scaled Data
This is the sweet spot in terms of data analysis, in this scale the data is able to be arranged in a way that tells you why the defect is happening in specific scenarios. Think along the lines of you need to know why you make more sales on Saturdays. You can measure the sales on Saturdays, the specials you offered on Saturday and how many sales corresponded to the specials offered on Saturday.
Ratio Scale Data
This scale is the most advanced analytic method. When you use this method you have data that has an absolute value and when you get a value of 0 is shows that there is no correlation between the variable and the measurement. For example, you have 10 programmers and programmer A completes 20 lines of code, programmer B completes 15 lines of code. If programmer C actually completes) lines of code, then you can say that no code was completed on that specific day.
Knowing how to analyze data is a big tool in your Six Sigma tool bag. Now this is not an exhaustive list, but when you sit down to meet with your belt now you know what you need to ask and what the belts information should be telling you. When you are ready to get started, let us know and we can help you.