### Understanding Process Capability

We’ve spent a fair amount of time learning the ins and outs of MSA’s, so this week I want to focus on process capability and how to understand the information you receive.

**What is Process Capability?**

In a nutshell Process Capability is:

• What it takes for your process to meet your customers’ needs right out of the gate with no modifications. This means for lack of a better term, inherent perfection.

• The information that can be provided on centering, variation and inappropriate measurement limits.

• The baseline metric for improvement

When determining your process capability there are three types of capabilities that we analyze:

• Continuous Capability- If you process is capable and in control, ideally you should get your desired outcome. This analysis measures the life cycle of your process telling you if the process has continued to be capable and in control.

• Concept of Stability-The idea of stability is the ability to answer the question ‘will my process produce the same result at this step every time it is used?’ To be technical, stability measures the ability of your process to meet its requirements at a regular and specific interval.

• Attribute Capability-This analysis makes assumptions about your data and is always long term data.

This week we’ve just scratched the surface on Process Capability. Next week, we’ll start digging a little deeper and show some illustrations of what it looks like.

- Published in lean management tools

### MSA 101

We’ve talked about accuracy, repeatability and reproducibility in your MSA’s but now we need to talk about data integrity.

**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.

**Integrity**

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.

**Bias**

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.

**Stability**

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.

**Linearity**

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!

- Published in business process reengineering

### 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

### 3 Keys to Measurement System Analysis

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.

- Published in lean management tools

### Six Sigma Tools: Scaled Data

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.

- Published in Six Sigma Tools