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About this lesson
The purpose of the Measure phase of a Lean Six Sigma project is to collect complete, accurate and meaningful data. There is a simple data collection approach that can be used by the team to ensure this is accomplished.
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Quick reference
Data Collection
The purpose of the Measure phase of a Lean Six Sigma project is to collect complete, accurate and meaningful data. There is a simple data collection approach that can be used by the team to ensure this is accomplished.
When to use
The data collection approach should be used in the Measure phase to ensure that data collected is accurate and meaningful. The approach may also need to be used in other succeeding stages if new or additional data is needed.
Instructions
Lean and Six Sigma both rely on data for the analysis and ultimate improvement of the process or product. That is why an entire phase of a Lean Six Sigma project is devoted to Measure. The following simple process provides guidance for managing the data collection process.
- Determine the data collection goals. Decide what data you want to collect. Consider using existing databases and records. Determine the attribute categories to be collected with the data in order to facilitate sorting and segmenting the data for analysis.
- Create the data collection procedure. The procedure is a reference document for anyone involved in the data collection process. It should clarify the definition of what data is to be collected and describe how the data is to be collected and recorded. It identifies when to start and stop collecting data. And, if appropriate, it describes the sampling plan for determining which process points or products to measure.
- Ensure data accuracy and stability. Variation is measured items is due either to true variation from part to part or process to process or it is due to variation within the measurement system. For accurate analysis, the measurement system variation must be minimized. The best technique for ensuring that the measurement system had little effect on the data is to conduct a measurement systems analysis. You should also be certain that everyone involved in collecting data has been trained on your procedure.
- Begin collection data. When the system is in place and validated with a measurement system analysis, start collecting data. Collect data from finished items and historical data when available. Data can be collected during a process operation, but be careful to minimize the impact of the data collection on the process. The data collection can become a special cause to make things better or worse than the normal process.
Hints & tips
- It may feel like a wasted bureaucracy to create a data collection procedure, but the first time you find that all the data collected during the past two weeks is worthless because the person collecting the data didn’t understand what was wanted, you will understand the importance.
- When collecting in-process data, explain to the operators what you are doing and why. Otherwise they may change their behaviour and not perform the process the way they normally do. This is referred to as the Hawthorne Effect based upon research done years ago in Hawthorne, Illinois. During this research, the presence of the researchers collecting data had a far more profound effect on the operator performance than the attribute that was under study. Because of the presence of the researchers, the operators significantly changed their operating practices.
- If you are not familiar with the MSA methodology, take our course on that subject.
- 00:05 Hi, I'm Ray Sheen.
- 00:05 You have been discussing various attributes of data and it's implications,
- 00:10 but haven't actually talked about collecting data.
- 00:13 And since that is the point of the Measure stage,
- 00:16 let's spend just a few minutes on that topic.
- 00:21 I wanna walk you through a four-stage process for
- 00:24 ensuring that your data collection activities are successful.
- 00:27 The first stage is to determine your data goals.
- 00:30 In this first stage,
- 00:31 you need to answer several questions to clarify your data collection goals.
- 00:36 We don't wanna waste time collecting unnecessary data and
- 00:39 we do want to focus on the critical data needs.
- 00:42 The first question is the easy one.
- 00:44 What process attributes are you interested in?
- 00:47 You need to collect data about those attributes in order
- 00:50 to both quantify the magnitude of the problem and
- 00:53 to be able to determine that your solution has made things better.
- 00:57 So based upon these attributes what data is needed, is it time data, quality data,
- 01:02 cost data.
- 01:04 Another important question to ask is what data is already available.
- 01:08 Think about the business systems, quality records, or test systems.
- 01:12 Often there's a wealth of data already available and
- 01:15 there are only a few additional items to collect.
- 01:18 Finally, consider what categories of data you would like to collect.
- 01:22 The category data is often critical for helping to segment and
- 01:26 sort the data to isolate the contributing causes for the problem.
- 01:30 Things like customers or products, location of event,
- 01:34 time of the event, location of the process, the process operators or
- 01:38 the shift, the magnitude of the event or some other type of defect.
- 01:43 The next step in the process is to create a data collection procedure to be used.
- 01:48 Now you may be thinking this is just useless bureaucracy.
- 01:51 But if you will have multiple people involved in the data collection,
- 01:55 this is very important element.
- 01:57 Otherwise, everyone collects the data a little differently, and
- 02:00 the next thing you know, you're comparing apples to oranges.
- 02:03 So, take a few minutes to create a procedure.
- 02:06 In the procedure, precisely define the data you want collected.
- 02:11 Where on the procedure or process will the observations occur?
- 02:15 What are the units to use and
- 02:16 what are the associated attributes that are to be recorded.
- 02:20 Specify how the data is collected, what measurement system is used.
- 02:24 Any special factors associated with how you collect this data.
- 02:27 Also clarify how the data is to be recorded.
- 02:31 What system or form is used and what fields are completed.
- 02:35 What attributes are measured or inspected?
- 02:38 Think about the type of analysis that might be done and
- 02:40 make sure you're recording the data in a way that will facilitate that analysis.
- 02:45 Now determine where you want data collection to start and
- 02:48 when it should stop.
- 02:49 Also, if multiple shifts are involved, clarify which shifts.
- 02:54 And finally, if sampling plan is used, you should spell out how many samples and
- 02:59 how the samples are to be selected.
- 03:02 Now on to stage 3, data accuracy and stability.
- 03:06 There are two components that are sources of variation
- 03:08 in any data collection system.
- 03:11 There's the variation of the product or
- 03:13 process that is being measured, we wanna find that out.
- 03:16 And there is the variation that occurs within the measurement system itself.
- 03:20 I've already introduced the concept of a Measurement Systems Analysis,
- 03:24 sometimes called the Gage R&R Analysis, in a previous module.
- 03:28 We have a short course on how to conduct a Measurement Systems Analysis.
- 03:31 If you're not experienced in this area, I suggest you take the course.
- 03:35 In addition to the MSA, there are several other things you need to do.
- 03:39 One is to provide training for everyone who will be collecting the data.
- 03:43 This training may only be an informal five-minute discussion
- 03:47 to review the procedure and data collection form.
- 03:50 Or it may need to be a more formal process if complex equipment is used.
- 03:54 After conducting a pilot run of the data collection process,
- 03:57 make sure everything works.
- 03:59 And I like to do that pilot run with known good and known bad parts or
- 04:03 items in the process so that I'm certain that the measurement system is able to
- 04:07 discriminate between the two.
- 04:10 The fourth stage of the data collection process is pretty obvious,
- 04:13 collect the data.
- 04:14 Collect data on items that have finished going through the process.
- 04:18 That way you know you are seeing what the process customers are seeing.
- 04:22 If there's historical data in databases or other systems, retrieve that data.
- 04:26 Be careful that you make sure that the data definitions are consistent
- 04:30 with what you are using.
- 04:32 If applicable, collect data at various points within the process.
- 04:36 Just be aware that your data collection is creating a special cause effect.
- 04:41 Because you are watching the operator may do some things differently.
- 04:45 This is called the Hawthorne Effect, based upon research down in Hawthorne, Illinois,
- 04:49 years ago.
- 04:50 History should show that the presence of the researchers had a significant effect
- 04:55 on process performance.
- 04:57 One way to minimize this is to talk with process operators.
- 05:01 Explain to them what you are doing, discuss how you are measuring, and
- 05:04 why you are measuring the process or product.
- 05:07 Answer their questions and reinforce that you want them to do
- 05:10 everything the normal way, so that you can understand what is happening.
- 05:14 I find that often I have to reassure them that we're not out to find a problem so
- 05:19 we can fire someone, or reprimand someone.
- 05:22 Rather we want to improve the customer experience, so
- 05:26 we need to understand what is happening.
- 05:29 So go collect data.
- 05:31 We're going to need that to find the root cause or
- 05:34 causes in the Analyze phase of our Lean Six Sigma project.
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