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.
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!
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!
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.