We’ve talked about accuracy, repeatability and reproducibility in your MSA’s but now we need to talk about data integrity.
Numbers shouldn’t lie, but when they do it is usually because somewhere along the line the integrity of the data didn’t hold up.
Before you begin your analysis there are two questions you should ask yourself:
- Does my data have known reference points?
- Does the data match control documents? If you’re looking at product returns, does the data match the information on your financial documents?
Accuracy and Precision
The next thing to think about is accuracy and precision. When you are evaluating the accuracy of your data, what you are looking for is how close the average is to the anticipated value. Your precision will tell you how much variation occurs in you data. Think about it in terms of playing pool. Your accuracy tells you how close you were to making the shot and your precision shows you how far apart the balls were from the pocket.
The third thing to look at is any bias your data might have. Formally the definition of bias is the deviation of what was measured from the actual value. What that means is how far off your measurement is from the actual number. The goal is to reduce bias as much as possible, I say reduce because you will never be able to eliminate it. You will need to decide what acceptable bias limits are. If you have a worker who is consistently late and you’re measuring organizational tardiness, you know your bias is going be about 10 minutes.
Next you can move on to stability. Stability is defined as your error rate. The less errors, the more stable the process. All stability does is tell you when accuracy or bias changes in your process. What you should be looking for it to do is serve as an alarm, letting you know that something has changed. This alerts you to areas in your process that are no longer stable.
Last but not least, you have linearity. What this tells you is if your bias is consistent. If something happens once, it’s an outlier. It’s not consistent which means you don’t want to hinge a change or a new process on something that may or may not happen again.
MSA is a big subject and we are far from done with it. Next week we will continue to talk about MSA Windows in Minitab and how to interpret them. In the meantime if you have any questions give us a call and let us help!
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.
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!