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.
As we cover Six Sigma Statistics, I want to make sure that I go over the illustrative part of the statistics. We know Six Sigma is technical but the key to making it stick, is to make it simple and understood by the non-technical people using it. So let’s talk about the Box Plot or the Whisker Plot. A key thing to remember in Six Sigma is that everyone using different terminology, so ask questions and make sure you are speaking the same language.
What is a Box Plot?
Simply put a box plot helps to put a picture to the data showing you where most of the data falls, how the data is distributed and where the outliers are. So it basically shows you what you’ve got, how it looks and what is unusual about it.
What does it measure?
Say you have a process that has multiple variables affecting it and you want to know what is what. If you have a delivery truck with 4 alternative routes a box plot can show you which ones, according to the data, are the most problematic. Additionally a box plot will tell you how symmetrical your data is. Knowing if your data is skewed or not can affect how you interpret your data. In a box plot, if the data is mostly symmetrical the median will appear in the middle of the box and the whiskers will appear to be mostly the same length. IF the data is skewed to one direction, the median will not be in the middle and the whiskers will be different sizes.
How does it work?
Box plot measurements are based on quartiles and the distributions are shown within the graphic. Think back to your SAT’s or ACT’s. Remember how they told you that you scored in the 25th percentile? Well that’s a box plot. You will have an upper limit and a lower limit and those limits will be determined by your organization’s goals. The outliers will be the extreme values, values that are so far outside of the normal distribution that it is unlikely they will be reproduced.
Interpreting your data is just as important as gathering it, so choose carefully and with purpose. Talk to your belt and use that advice to help you find the best method for your organization.
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!
This is a micro blog this week, because next week we get into measures of variation which is a dry subject and will challenge my creative ability. As we continue our trek into statistics and how to interpret them, there is a very specific area that I want you to pay attention to, variation. The reason variation is so important is that it tells you why something is different and how that matters to the data set as a whole. It also provides you the knowledge of what the data won’t be able to tell you because of the interference the variation causes. This is important because when you interpreting data understanding the limitations is almost more important than understanding what is being told to you.
The first thing to consider is range. Range will tell you the difference between the most obvious observation and the smallest one. This is important because this is where you identify your outliers (variables that are outside the norm, think of road work on a delivery path or a maternity event as an obvious observation). A large range would be the maternity event; it’s so big there is no way to avoid noticing it. A small range would be a traffic event, it may have impact but the impact will not be evenly distributed and it may or may not impact the final result.
There is a measurement range that is good for a sample size of 2, it’s called the inter-quartile range. For a bigger sample stick to standard deviation; Standard deviation tells you the average number of times a variation occurs from the mean.
By all means as usual this is not a step by step approach to understanding variation, but it is enough of a foundation to have a conversation with your belt about the metrics and what they mean to your organization and its strategic goals. If you need help starting this conversation, give us a call and we will be happy to get you started.
This blog is about Six Sigma data analysis. Because statistics are such a big part of the Six Sigma world, it makes sense that we talk about the data that is gathered and what it means. So here we go….
There are different types of data and anytime you measure something you going to need how to interpret it. There are two main types of data: attributive and variable.
Some people call this the most basic form of data, but for business purposes I don’t accept that. Qualitative data is simple in the fact that it is generally data that can be gathered by asking a yes or no question. For example, ‘Did they buy the new product?’ What is limiting about attributive data is that you really can’t analyze the results in a meaningful way, but it can give you a pretty good place to set your focus.
Variable data is also called quantitative and this is the data that you can measure and analyze. In order to decide if data you have is variable ask yourself these questions:
- Can you classify the data and count the results? (Think number of defects for a particular product line)? If you can this is called discrete data and the limitation of discrete data is that it cannot be broken down into smaller measurements to create additional meaning. It’s a one hit wonder.
- Can the data be measured on a time line with meaningful divisions (Think time, production speed, delivery dates etc…) If you can this is called continuous data and it can be divided further to create additional data.
As with all of these blogs, this is to get you started and statistical data clearly has more to it than one paragraph. But information is the first step and one you know what type of data you have, you have a better idea of what you need to know. Give us call and we can help you create where you need to go next.