In metrics the most honest finding will be that your metrics will have degrees of variation. Understanding where and how those metrics occur, is the key to using your data in a forward thinking strategy. Let’s start with something simple, like toy production. We are going to track some standard variation sources.
Within Unit Encoding
This variation source occurs when you are measuring output from a single production cycle. Some places that variation is likely to occur are the width of parts, color shading, length of toy etc. Now you can choose to analyze different production cycles on the same day or alternating days, but you will always be comparing samples from the same cycle. A new production sample means a new data point.
Between Unit Encoding
These names are dead giveaways, but I digress! This implies that you are looking at samples from two different production cycles. This is different in that you would want to identify two different samples from different production cycles. The variations you are looking for will give you some clue as to whether the variations are operation influenced or process influenced.
This is the trickiest variation source. This specifically calls for you to compare your variation averages from all of your data points in a single day. So you can theoretically have both within unit variation data and between unit variations data, depending on how specific you need to get.
The key to getting the most out of your data is to understand what it’s telling you. Understanding where the variations are coming from is the first step to getting the most out of your data.
In our conversations about process capability, I want to focus your attention on baseline performance. Baseline Performance is an alternative way to view long-term and short-term data. When you hear baseline performance it most likely will be a description of baseline performance and it most likely will be used to describe long-term data.
What it means
Baseline in a nutshell gives you the average long-term performance of a specific process without
controlling any variables. The easiest way to think of this is a visualization of FTY (First Time Yield). Remember FTY shows you the challenges in your process when they are normally run without any interference from you.
What to use it on
When measuring baseline, you are identifying a typical challenge within a process. For example if you are observing the process for returns, your long-term data will include morning, afternoon and evening shift; multiple employees and submission points (email, in-person and via telephone).
Your short term data will appear on the visualization as well, so you will be able to see in a visual representation short-term and long-term average behavior for your processes. If there is always a dip in quality at around lunchtime, you will be able to see that visually represented in your data.
Why use it?
Baseline performance is going to quickly tell you where your burning platform issues are. If you are heading into a meeting with management, this is a report to take with you. It shows the long-term vs. short-term and gives you solid business evidence to support improvement projects.
Next week, we will tackle measures of capability and what they tell you. Remember that this is can be the starting point to discuss improvement with your belt. If you need to get started, give us a call and we can get you started.
One of the key things learnt from 6Sigma is the ability to accurately measure and analyze the information your organization collects. This can be as technical or as general as your organization needs, the key is to understand the level of specificity your organization needs and analyze from there. A Black belt will be able to give you in depth analysis, but a good one will give you exactly what your organization needs. We’ll start the discussion with Multi- Vari Analysis.
What is Multi-Vari Analysis?
Simply put this puts a face to the data. Once you have collected all of your information Multi-Vari studies take the data and illustrate the patterns of variation within the data. It helps you identify group or correlations between subgroups and over time. When you can identify the groups, you can make assumption or draw conclusions based on the data. For example if your data shows the your staff made more errors on product X you can draw the conclusion that your improvement efforts need to be focused on that particular product.
What is it used to assess?
Multi-Vari studies are useful in many ways but the most standard uses are
- to illustrate data in graphics.
- to show how work is influence by defined variables.
- to show the impact of specific material, departments or methods.
- the effects of external factors such as noise, delivery delays etc.
When you need to show stakeholders, influencers or project staff what you have found multi- vari studies are a great way to produce a visual. Since most people learn by doing, a visual representation allows them to see what they have done and to show leadership the gains or losses accordingly.
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.
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!