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.
I am always an advocate of finding the right tool for your specific project, so I propose that you get to know MSA. It’s a great foundational tool and a great way to start building in the practice of good measurement within your organization. There are a few things you need to know when looking at your measurement system, let’s start with these.
What is a measurement system?
However your organization measures data, in Six Sigma we define your measurement system as ‘your complete process used to measure data’. The thing to know about measurement systems is the more moving parts you have, the more potential sources of errors you have.
What effects measurements?
Measurements are effected by a variety of factors, but some of the usual suspects are:
Accuracy-The numerical difference between what you think and what actually is.
Linearity-The change in the operating system of your measuring system. Think about when you have a different operating system on your laptop. Screens are viewed and you may have some errors. Same principal.
Stability-Something about your measurement system is inconsistent. It may be the way you intake data or the way your process it, but something is not consistent.
Precision-This is all about how much variation occurs in whatever it is you are measuring.
What are the red flags?
If your measurement system give you a reason to pause before you do anything, take a look at the repeatability and reproducibility of your measurement system. When you are looking for repeatability, you are looking for the variation that occurs when you measure the same piece of data using the same measurement method. For repeatability you are looking for the variation that occurs when different people measure the same thing using the same methods. To be fair there will always be some variation when multiple people are involved, but you want to get your measurement system as close to no variation as possible.
In creating your ideal situation, you may have to critical eye on your measurement system. It’s hard, but it is worth it. We will pick up on this subject next week and continue to fine tune your measurement systems!
In Six Sigma we are always collecting data, generally we are collecting data to address a current problem in our operations or services. The wonderful thing about Six Sigma is that we are also able to collect passive data. The usefulness of passive data is that it provides us with the ability to identify patterns, the catch to visualizing these patterns is in selecting the right graph to view the data.
Why use a graph?
The first benefit that comes to mind is the ability to see the error trends from a visual perspective. The other reasons graphs are a great tool are:
- Alongside identifying trends, they also help you see potential variable relationships. When you have a situation that could have multiple culprits, a graph can help you see which ones are a real potential.
- They can help you identify the risks that your customers will determine critical. This move allows your customer to be proactive instead of reactive, a much more desirable trait.
- It allows you to systematically dismiss variables and determine which one’s control other ones.
- It shows you the results of the passive data you’ve collected.
Where do I get the information for a graph?
Data is everywhere right? Yes and No. Your graph is only as good as your data, so we don’t want questionable data. The integrity of your data will be defined by your individual organization, but if you stick to these three questions you should be fine:
- What do you need the data to tell you?
- How often do you need to collect it?
- How do you need to collect it?
Next week we will get into the types of graphs and what times of data are appropriate for them. Until then happy hunting!
As we go over Six Sigma statistics, we have to talk about normal distribution. Before we get to that though we have to talk about why distribution is important to the way you interpret your data. In interpreting your data there is something you should know before you tackle how the information observed, confidence intervals. Confidence intervals is more complicated than this blog, but basically what you need to know is the greater the confidence level the less likely the variation is to occur and the more you can guarantee the accuracy of data analysis. In confidence levels there are 3 common ones that we use in data analysis, 99%, 95% and 90%. The standard of measurement is 95%, the higher the better but as a baseline 95% is a solid analytic benchmark.
Okay so back to normal distribution. Here’s what you need to know.
What is it?
You find normal distribution when you take all of your data and create a visual representation of the information. You will illustrate when recurring variations show up in your process. It is actually more helpful when you have a distribution that isn’t normal because then you can say ‘Aha it was the 3 hour traffic jam that affected the process’. When you hear people talk about the curve, this is what they are referring to.
When do you use it?
This is a tool that is best when used as a continuous probability model with measurements that you don’t have to create. Think about the weight of a cargo shipment or the number of a specific product you receive.
Raw scores and Z scores
Each normal distribution will have a raw score which is made up of two parameters: the mean and the standard deviation. The Z score measures how far you varied from a particular point on your data line. In real terms it means, if you want to see how many errors occurred on the 5th then standard deviation shows you that.
Why is it important?
The area under the curve shows the proportion of the curve and which tells you how important this data is to your business. Is the curve is small then you now that the distribution occurs within a relatively small set of circumstances which is easier to control within process. A wider distribution shows you that your process can be interrupted by a variety of factors and may need you to keep a close eye on it.