### One Last Thing About MSA…..

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.

- Published in lean management tools

### Measurement System Analysis (MSA)

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!

- Published in lean management tools

### Box Plots-Data in Pictures

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.

- Published in Six Sigma Tools

### Graphing for Six Sigma

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!

- Published in lean management tools

### Six Sigma Tools: Variation and All Its Glory

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.

## Range

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.

- Published in Six Sigma Tools