Measure step - 3Professor : In step 2, we established a definition for the process we are measuring and a standard against which we'll compare the performance. Now in step 3, we are ready to record the current performance of the process by creating a data collection plan, evaluating the measurement system we use, and properly recording the results. We come away from this step with valid data in a format that is ready for analysis in step 4.
Professor : When you've completed this step, you'll know the purpose of a data collection plan and will have established one for the Rockledge case.
Professor : You'll know the meaning of several terms related to measurement system analysis.
Professor : And, you'll recognize possible sources of variation in a measurement system.
Professor : You will be able to use the Measurement System Analysis checklist to guide your validation of a data source.
Professor : And, in preparation for the analyze phase, you'll become familiar with the software used to record and analyze data.
Professor : A data collection plan is the first step towards gathering accurate data.
Professor : It's intent is to provide a clear, documented strategy for collecting reliable data.
Professor : It gives all team members involved in the measurement process a common reference.
Professor : And, it also helps to ensure that resources are used effectively to collect only data that is critical to the project.
Professor : In some cases, new data collection might not be the only option. Look for any historical data that is available and consider the benefits of new data versus the costs of the collection process.
Professor : Well, talk about timing. Master just told me a very important message.
Master: Hey there, professor Do you remember when Mr. Alberti, the Rockledge General Manager, referred to some research that GE did on nut removal?
Master: Well, it seems that they collected several data samples on turbine casing nut removal and installation over a period of one year.Thought this information might be useful to you and the greenbelt. I'll send the data over to you.
Professor : If the data turns out to be valid, it would save us considerable time and money to use it instead of conducting another study. In order to determine the validity of the data, you need to first evaluate how the measurements were taken. We call this process measurement system analysis. We'll come back to the case in a few minutes.
Professor : Before we look at the Rockledge data, let's revisit the concept of variation.
Professor : Remember that in a perfect world, a process is done exactly the same way every time and every product that comes off an assembly line is identical.
Professor : But in the real world we have Variation or differences from the ideal. In a Six Sigma project, the variation we find in the process is called Actual Process Variation. Our ultimate project goal is to reduce that variation, thus satisfying our customers' needs.
Professor : So at this step in the Measure phase of D M A I C, we measure the output of the actual process. It is the output of the Measurement process that becomes our data.
Professor : But, collecting the measurement is a process itself, with the same potential for variation. Just like a scale on a production line might be slightly off, the timer that is being used to measure nut removal time might be malfunctioning.
Professor : Actually, this variation in your measurement process can come from a few sources.
Professor : The gage, or device, used to measure
Professor : The operator of the device
Professor : And other less common sources, such as the environment in which the measurement takes place. There's master again.
Professor : Instead of telling you how it relates, I'd like you to relate that diagram you just saw to our case. Here are the processes and some examples of potential variation in our Rockledge case. Drag these items to the appropriate place on the variation sources diagram.
Professor : There are three types of error that can result from the gage: accuracy, precision, and resolution. In this context, those words may have a somewhat different meaning than expected, so I'll walk through an explanation.
Professor : Let's address accuracy and precision first, by looking at the analogy of a target.
Professor : The circle here represents the ideal performance of a measurement instrument.
Professor : These dots represent the outcomes from a precise instrument, but not an accurate one. Because they are very close together in the same area, you can assume that the behavior of the instrument is predictable, but unfortunately it's predictably not inside the circle where you want it!
Professor : These dots represent the outcome from an accurate system, but not a precise one. They are all arranged around the center of your circle, so the results are all close to the target, but they are quite distant from one another so no precision here!
Professor : Finally, these dots represent outcomes from a precise and accurate instrument. They are close together, so the instrument is precise, and inside the circle, so they are accurate. With a precise and accurate instrument you get consistent results within the appropriate range. Problems with accuracy can be addressed by calibration of the instrument. Problems with Precision can be addressed using a method called Gage R and R which we will talk about shortly.
Professor : Now, let's take the third variation type for a gage -- Resolution. Here you see a ruler. Imagine you are supposed to measure the length of paperclips with it, and your target length is 1.234 inches. Would this be a good instrument for collecting the measurement?
Professor : The answer is decidedly no. Because the ruler does not have fine enough increments, the only items you could accurately measure with this ruler would be those falling at the exact inch marks. In other words, the resolution of the instrument is too low to collect accurate data.
Professor : Time to apply these terms to some real-world problems. For each gage problem shown, indicate whether it is an Accuracy, Precision, or Resolution error.
Professor : Now you know the ways that a measurement system can be responsible for variation, let's get back to the Rockledge assignment. We are going to conduct an evaluation of the measurement system that was used to collect that nut removal data. The Measurement System Analysis Checklist will guide us through our assessment.
Professor : The first couple of items look at the measurement procedure. We need to use the same procedure for our measurement system test that was used in the collection of the original data. Let's double-check the operational definition and collection procedure with the GE Facility Manager.
Professor : I have John on the line. Based on the operational definition he provides, we'll write the procedures for the measurement system test.
Professor : The next few items from the checklist prompt us to consider what we already know about the system. Answers to these questions are found in a variety of ways, depending on the tool used.
Professor : In our case, the instrument is a well-calibrated stopwatch with guaranteed precision and accuracy from the manufacturer. It measures to the hundredth of a second, so the resolution is more than sufficient based on our twenty-minute specification. Now that you know the procedure to follow and some details about the instrument, it's time to conduct the study of the measurement system to confirm that the procedures are correctly interpreted and the gage is indeed reliable.
Professor : Before we talk about the tests themselves, we need to answer the question "What are we testing for?" In two words, the answer is Repeatability and Reproducibility.
Professor : Repeatability means looking at variation within one component of the process. This is also known as equipment variation because it is most often evaluating if one operator measured the same items several different times with the same instrument, did the instrument produce the same measurements.
Professor : Reproducibility means testing variation across the process. This is also known as appraiser variation because it is most often testing if different operators measured the same items several different times, did the operators get the same results. A good way to remember these terms is there is an "e" in Repeatabilty for Equipment and an "o" in Reproducibility for Operator.
Professor : So, the last item on the list refers to the two tests that you can conduct to determine amount of variation caused by a measurement system. They are the Test-Retest study and the Gage Repeatability & Reproducibility study.
Professor : A test-retest study can look only at repeatability, meaning it can tell you how much variation in your data is due to an inappropriate device.
Professor : The advantage of Gage R and R is it can separate the individual effects of repeatability from those of reproducibility. Basically it shows not only variation due to the gauge, but also how much variation is due to the operators. This allows you to take action to fix the problem. For example, if you found a large amount of variation due to operator, you might improve operator training or the procedure descriptions.
Professor : For this reason, we are going to conduct a Gage R and R for the Rockledge case. It takes some time to collect the Gage R and R data, so I'm going to get that process started now.
Professor : There are 3 components to a Gage R and R test: Operators, Parts and Trials.
Professor : The operator, as you know, is the person operating the measuring device. It is recommended that you run the test using a minimum of 3 operators. The more operators you have, the more certainty you will have that your procedures are universally understandable.
Professor : The part is whatever product or process is being measured. It is recommended that you provide at least 10 representative "parts". By representative, I mean that the parts being tested should reflect the range of measurements possible. It is also important that the operators are all measuring the same 10 parts.
Professor : The trial is each time the item is measured. A minimum of 3 trials per part, per operator is recommended and the parts should be presented in random order to avoid any influence in the individual measurements.
Professor : Well, the data is in on the Rockledge study. I am going to conduct some statistical calculations on the test to determine repeatability and reproducibility variation, but I want you to know how the data should be recorded to allow that analysis.
Professor : In the Rockledge case, each part is the process of removing a single nut. In order to provide the SAME parts to each operator, we had to be a bit creative. We videotaped one turbine casing disassembly, and that gave us 88 examples of nut removal. We took 10 representatives from those 88 to use in our study and numbered the video clips from one to ten. That part number appears in this first column in random order.
Professor : We used the recommended 3 operators. The operator number appears in this second column.
Professor : We'll also did the 3 recommended trials per part, per operator, and that trial number appears in the third column.
Professor : In the last column, the actual nut removal time that the operators recorded appears in minutes. So now that the data is ready, I'm going to run the test.
Professor : Here are the results of the Gage R&R. The first number to check is the Total Gage R&R. From it, you can see that the total variation due to the measurement system is one point seven three percent. The rule of thumb you should use when evaluating the total is that less than two percent is desirable, and up to eight percent is marginally acceptable. So the bottom line is that our measurement system is adequate because it is not responsible for much of the variation. However, before we leave this subject, I want to explain the other numbers you see.
Professor : Under total gage R and R, you see the separations by repeatability and reproducibility. In this case the repeatability, or equipment variation, is only point two four percent. And the Reproducibility, or variation due to operator, is only one point four eight.
Professor : The remainder of the total variation in the data is the part-to-part. That is the actual process variation; or in this case the actual differences in the time it took to remove a nut. So, this test completes the Measurement System Analysis, and with these results we can feel confident that the measurement system is adequate to capture the capability of the actual process. Before you leave Measure, we have one more task, that is to make sure the actual nut removal data is in the proper form for analysis, much like we just did with the Gage R and R test data.
Professor : The goal of six sigma training is to give you the necessary skills to develop and improve business processes, not to turn you into a statistician. However, statistical analysis of the process is an important part of identifying and validating the improvements we make, so you need to be familiar with some statistical measures.
Professor : To make this easier, GE has purchased a tool called MiniTab that performs the calculations for you. MiniTab is a strategic software package that we will use to understand and analyze our data. It is part of the core load of General Electric. The Gage R&R analysis we just looked at is an example of what Minitab can do. Minitab was used to run a test on the data and generate those results.You can find more information about Minitab in the Resources section of this course.
Professor : Your first job is to put the data in correctly, so Minitab can do its work. This is much like what we just did with the measurement analysis data. Before you leave Measure, we are going to put the nut removal data into Minitab. Then in the Analyze phase you'll look at some of the reports.
Professor : The Worksheet area of Minitab is where you enter the data. It functions much like an Excel spreadsheet. Numbers can be entered in columns or rows; however, the default set up of Minitab is columns, so we will use that format.
Professor : Column titles are entered here and then the data below.
Professor : GE measured nut removal and installation time on eight scheduled outages during the course of a year. During each outage, time measurements were taken for twenty-two randomly selected bolts of the eighty-eight found on one gas turbine.
Professor : Here is the data. In this case, we have four data columns.
Professor : The first column indicates the sequential position of the measurement over the course of all measurement collection. For example, this is the forty-fourth measurement that was recorded in the year.
Professor : The second and third columns contain the measurements themselves. So nut removal time number forty four was twenty-three minutes and installation was fourteen.
Professor : The last column indicates the maintenance cycle. So measurement number forty four was part of the data recorded during the second outage. This format for the data will allow you to conduct an analysis in step four. If you wish to experiment with this data in Minitab, the project file can be found in the course Resources, accessed from the left-hand menu.
Professor : In this step, you learned the purpose of a data collection plan and determined the data to be used for the Rockledge case.
Professor : You've learned several terms related to measurement system analysis.
Professor : And, can recognize possible sources of variation in a measurement system.
Professor : You have learned how the Measurement System Analysis checklist can guide your validation of a data source.
Professor : And finally, you've become familiar with the role of Minitab in a greenbelt project and have prepared the Rockledge data for analysis in step four.
Professor : In preparation for Analyze, let's summarize where we are in the Rockledge project by taking a more visual look at the data.
Professor : A good starting point for data analysis is generating this histogram in Minitab. It shows you the frequency of occurrences for a given measurement, in this case removal times.
Professor : Based on the results of the Gage R&R, we know this data was collected with a valid measurement system. The graph shows that several measurements are over the upper spec limit of thirty minutes,
Professor : and most of the measurements are over the 15 minute target performance, so it appears that our current nut removal process is not doing too well. In Analyze, you'll verify that problem and look for some reasons why it is occurring.
Professor : Well, we did it, we're at the end of Step 3 and that means the end of Measure. Great job. Before you get into Analyze, I'm going to send you to back to Master. He wants to give you his brief review of Measure and see how you're doing.
Master: Hey, glad to see you back. Marks is a real Measure pro, so I'm sure you learned a lot working with him. I want to ask you a few questions to see how you're doing, but first I'll quickly run through the steps you covered and hit the highlights.
Master: In step one you took that CTQ and drilled down further to something more manageable for a greenbelt project. There are five tools to help with this process, and you used three of them on the Rockledge case: the Quality Function Deployment, Fishbone and Process Map. So by the end you had determined the task that you would focus your project on. You also learned how to complete a standard Failure Modes and Effects Analysis.
Master: Hey, glad to see you back. Marks is a real Measure pro, so I'm sure you learned a lot working with him. I want to ask you a few questions to see how you're doing, but first I'll quickly run through the steps you covered and hit the highlights.
Master: In step one you took that CTQ and drilled down further to something more manageable for a greenbelt project. There are five tools to help with this process, and you used three of them on the Rockledge case: the Quality Function Deployment, Fishbone and Process Map. So by the end you had determined the task that you would focus your project on. You also learned how to complete a standard Failure Modes and Effects Analysis.
Master: Finally in step 3 you determined your data collection plan by deciding between collecting new data and using historical data. You then learned the potential for error in a measurement system and some methods for evaluating a system. Finally, you learned how to set up data in the Minitab strategic software package for analysis in Step 4.
Master: That was a quick summary of the key steps in Measure. If you want further review, you can access all of the steps from the Measure Menu at the top of the screen, and the Resources, Glossary and Tools can be found in the left-hand menu. Once you are ready, I'm going to ask you some questions about the Measure phase.
Master : In step one you learned about five tools that can be used to help refine the focus of your project. Each tool has its own specific strengths for that process. Match the tools with their description by placing the letter of the tool in the field next to its description.
Master:Do you know how to use complete a QFD? An empty matrix is shown below. Imagine you are involved in a greenbelt project for Finance group. You are beginning to drill down on the customer CTQs. The items labeled a, b, c and d include a CTQ, the importance rating of the CTQ, one internal process that impacts the CTQ, and the relationship rating between the CTQ and the process. Drag label to the highlighted area on the QFD where that item belongs.
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