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Data Measurement61.9 KB Data Measurement - Solution
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Quick reference
Data Measurement
Businesses rely on data and data relies on accurate measurements to be meaningful. The data measurement is made of two components, the value of the true item and the variation due to the measurement system.
When to use
Data measurement is used to measure an item and draw conclusions about that item. It is used to measure a process and draw conclusions about the process. And it is used to measure a portion of an item or process, or to draw inferences about the rest of the item or process.
Instructions
Data measurement is everywhere in business today. Virtually everything is measured, and a data item is created to convey the value of the item or process that is measured. Businesses use data to make both strategic and tactical decisions. More and more business people are questioning the accuracy and reliability of the data they receive.
Lean Six Sigma projects rely on data throughout the process. The data is used to both understand the problem and discover a solution. Again, reliable and accurate data is critical to making wise decisions in a Lean Six Sigma project.
Data measurement is a process. As a process it is always susceptible to variation. There is common cause variation from the measurement system. There may also be special cause variation from incorrectly using a measurement system.
The variation in the measurement system is graphically depicted below. The measured value is represented by the hypotenuse of a right triangle. One side of the right triangle represents the variation in the item being measured. The other side represents variation in the measurement system. This variation in the measurement system introduces an error that causes the measured value of the item to be different than the actual value. The magnitude of this measurement error must be known in order to determine if the measurement systems will provide accurate reliable data.
Hints & tips
There are many possible causes of measurement error which will be discussed in more detail in other lessons.
- A measurement system may be perfectly adequate for providing measurements in one application but inadequate in another.
- A thermometer for checking body temperature when you are sick will explode if you try to use in in the oven for checking on items being baked.
- Likewise, a baking thermometer does not have the discrimination to be used effectively to check your body temperature when you are sick.
- 00:04 Hi, I'm Ray Sheen.
- 00:06 Measurement system analysis is all about trusting the data.
- 00:10 So let's start with a quick review of the need for data.
- 00:15 Data is often something that is measured.
- 00:17 The data may be used to make specific judgments
- 00:20 about the thing that is being measured.
- 00:22 That's what we do with the inspection data on a production line.
- 00:26 We decide if the item was built correctly because of some aspect that is measured.
- 00:31 Or we may be measuring something around us
- 00:34 to be able to make judgments about how it could have affected a problem.
- 00:38 For instance, we may measure the temperature or
- 00:40 the lighting to see if they contributed to an accident.
- 00:43 We'll also make measurements and create data in order to draw an inference about
- 00:48 something that the item that's being measured represents.
- 00:51 In manufacturing, we do this by taking samples from a batch of chemicals in
- 00:55 a pharmaceutical line to determine whether the entire batch is acceptable.
- 01:00 Or we may collect data from a consumer about their satisfaction
- 01:04 as part of an opinion poll, and use that to extrapolate marketing data for
- 01:07 the entire population.
- 01:10 Sometimes the data we measure is not about the item of interest,
- 01:13 but rather it is about the process.
- 01:16 This could include measuring and
- 01:17 collecting cycle time data for a manufacturing line or a call center.
- 01:22 It could be used to measure the uptime on an IT network or
- 01:25 to measure the ability of a sensor to detect color change.
- 01:29 Regardless of the type of measurement, data is created.
- 01:32 And that data is used to make business decisions.
- 01:36 It's not a great leap of logic to come to the conclusion that it's better to have
- 01:40 accurate data when making a decision than data that is unreliable.
- 01:44 Which brings us to the next point about data measurement system, an analysis of
- 01:49 these systems to make sure that the data is accurate and can be relied upon.
- 01:54 Let's focus the discussion a bit more.
- 01:57 When a company decides to improve the business, products or
- 01:59 processes, they want to ensure that the effort is effective.
- 02:03 That means they need to measure the results of what they have done.
- 02:06 The Lean Six Sigma methodology for problem solving and
- 02:09 process improvement relies heavily on the use of data.
- 02:12 That means it also relies heavily on a data measurement system.
- 02:17 When a data measurement system is detecting changes in the item that
- 02:20 is being measured,
- 02:21 there are always two causes potentially contributing to that change.
- 02:25 There is the actual change or variation of the item being measured.
- 02:29 There is also the variation or uncertainty in the measurement system.
- 02:33 Obviously, if the measurement system variation is too large
- 02:36 compared to the changes of the item being measured,
- 02:39 a wrong conclusion could be drawn about the item.
- 02:42 The uncertainty of the measurement system would overwhelm the changes it is being
- 02:46 asked to detect.
- 02:47 Which is why it is necessary to conduct a measurement system analysis.
- 02:51 This analysis determines whether the measurement system is appropriate for
- 02:55 the types of changes and the attributes that are being measured.
- 02:58 If the magnitude of variation of the measurement system is too large,
- 03:01 the resulting data measurement cannot be relied upon, and
- 03:05 the measurement system should be improved or replaced.
- 03:08 If the measurement system variation is small and
- 03:11 predictable, the measurement coming from the system can be trusted.
- 03:15 Let's explore the idea of measurement error a little bit more.
- 03:19 I'll start with one of the basic tenets of Lean Six Sigma.
- 03:22 All processes have variation in them, and the measuring of an item is a process.
- 03:28 And like with any type of variation,
- 03:30 if it is large it can impact the overall system performance.
- 03:34 That means that it could significantly change the measured value of an item
- 03:37 as compared to its true value.
- 03:39 And, again, it's no surprise that since this is a process with variation,
- 03:44 the process must be designed and managed well to minimize and
- 03:47 control the variation that comes from using the measurement system.
- 03:51 This diagram illustrates the point.
- 03:53 The triangle represents the measurement system.
- 03:56 The horizontal side is the standard deviation or
- 03:58 typical variation in the item being measured.
- 04:01 The vertical side of the triangle is the standard deviation of the measurement
- 04:05 system error.
- 04:06 The measurement is the value that is actually measured and recorded.
- 04:10 Using the Pythagorean Theorem, we see that the square of the measurement value
- 04:15 is equal to the sum of the squares of the true value of the item variation and
- 04:19 the measurement system variation and/or error.
- 04:22 If the measurement system error is small,
- 04:25 the measured value will be almost identical to the true value.
- 04:29 If the measurement system error grows,
- 04:31 it soon becomes a major contributor to the length of the hypotenuse.
- 04:35 And if it's too large as compared to the variation of the item that the system is
- 04:38 trying to detect, it will totally overwhelm the measurement, and
- 04:42 the data cannot be trusted.
- 04:44 I had this condition with a manufacturing company not long ago.
- 04:48 Their product required a precise alignment of some optics to work correctly.
- 04:52 They were having a lot of problems aligning those optics.
- 04:55 At times it would take many tries until they had acceptable performance.
- 04:59 Part of the alignment process included measuring the product at
- 05:03 a partially assembled state and then making final adjustments in the assembly.
- 05:07 We did a measurement system analysis and found that the measurement system
- 05:10 variation for this partially assembled alignment check
- 05:14 was ten times larger than the variation they were attempting to detect.
- 05:19 This alignment system was worthless for this application.
- 05:22 The Lean Six Sigma project that was charged with improving the product design
- 05:26 quickly changed to improve the measurement system.
- 05:29 The product was good, the measurement system was the problem.
- 05:33 Business relies on data.
- 05:36 Measuring business products and processes creates that data.
- 05:40 We need to have a measurement system that ensures that we have
- 05:44 accurate data available to us.
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