Lean Sigma: A Practitioners Guide

2017-07-07 02:10:07

27. MSAContinuous

Overview

Process variation affects how resulting products and services appear to Customers. However, what you (and ultimately the Customer) see as the appearance does not usually include only the variability in the entity itself, but also some variation from the way the entity is measured. A simple example of this is to pick up a familiar object, such as a pair of glasses. If you were to measure the thickness of the middle of the left lens and then you handed the same pair of glasses to three other people, it is highly likely that there would be a difference in answers between everyone. It is also highly likely that if someone handed you the same pair of glasses later (without you knowing it was the same pair) and asked you to measure again, you would come to a different answer or conclusion. The pair of glasses itself has not changed; the difference in answers is purely due to the Measurement System and specifically errors within it. The higher the Measurement Error, the harder it is to understand the true process capability and behavior.

Thus it is crucial to analyze Measurement Systems before embarking on any Process Improvement activities.

The sole purpose of a Measurement System in Lean Sigma is to collect the right data to answer the questions being asked. To do this the Team must be confident in the integrity of the data being collected. To confirm Data Integrity the Team must know

The type of data

If the available data is usable

If the data is suitable for the project

If it is not suitable, whether it can be made usable

How the data can be audited

If the data is trustworthy

To answer these questions, Data Integrity is broken down into two elements:

Validity. Is the "right" aspect of the process being measured? The data might be from a reliable method or source, but still not match the operational definitions established for the project.

And after Validity is confirmed (some mending of the Measurement System might be required first):

Reliability. Is the valid measurement system producing good data? This considers the accuracy and consistency of the data.

Validity is covered in "MSAValidity" in this chapter. Reliability is dependent on the data type. Continuous Measurement Systems are covered here with a tool called Gage Repeatability and Reproducibility^[44] (Gage R&R); Attribute Measurement Systems are covered in the section "MSAAttribute" in this chapter.

^[44] For more detail see "Measurement Systems AnalysisMSA" an ASQC and AIAG Publication.

Gage R&R is an audit conducted on a Continuous Measurement System which is done using 23 people and multiple entities to measure. Each person measures every entity at least twice and from the ensuing data the tool determines

Percentage overall agreement (% Repeatability & Reproducibility)

Percentage agreement within individuals (% Repeatability, agreement with themselves)

Percentage agreement between individuals (% Reproducibility, agreement with others)

Belts often think that the Gage R&R Study is just on the Gage itself, but the Study examines the whole Measurement System including the samples, people, techniques, and methods. This becomes important in "Interpreting the Output" in this section.

Many Belts confuse a Gage R&R Study with calibration of a piece of equipment; the two are different. Calibration considers only the average reading of a gage. The calibrator measures a known entity using the Measurement System 1015 times and then takes the average of the readings. The Measurement System is then adjusted so that it is zeroed correctly (i.e., the mean is changed and any bias removed). This is generally quite straightforward for many Measurement Systems, similar to zeroing a set of bathroom scales.

Gage R&R is significantly different and often far more difficult, in that it analyzes the variation in the measurement system. The reason for doing this is that the variability detected in the process (entities measured) is actually comprised of the true process variation, but also the variability in the Measurement System (see Figure 7.27.1):

Total Variation = Process Variation + Measurement Variation

Or in statistical terms:

Figure 7.27.1. The effect of Measurement System Variation on the Total Variation in a process.

Thus, to effectively see the variation in the process data, the variability due to the Measurement System should be small. The purpose of a Gage R&R Study is to determine

The size of the measurement error

The sources of measurement error

Whether the Measurement System is stable over time

Whether the Measurement System is capable for the study

Where in the Measurement System to focus improvements

As previously mentioned, the Study breaks the total observed variation in the process down into the two components of Actual variation and Measurement System variation. It also takes the Measurement System Variation and breaks it into the variation due to Repeatability plus the variation due to Reproducibility:

Repeatability is the inherent variability of the Measurement System and is the variation that occurs when repeated measurements are made of the same variable under absolutely identical conditions. It is the variation between successive measurements of the same sample, of the same characteristic, by the same person using the same instrument. Figure 7.27.2 shows a graphical representation of Repeatability. Poor Repeatability causes an increase in decision error. When the same person looks at the same attribute and estimates different values then that person likely makes different decisions based on those estimates. Some of these decisions are the wrong decision!

Figure 7.27.2. A graphical representation of Repeatability.^[45]

^[45] Source: SBTI's Lean Sigma Methodology training material.

Reproducibility is the variation that results when different people are used to make the measurements using the same instrument when measuring the identical characteristic with different conditions (time, environment, and so on). When two or more individuals return the same value for a given attribute, that measure is said to be Reproducible. A graphical representation is shown in Figure 7.27.3. When Reproducibility is not present, the value of a metric depends on who collects the measurements.

Figure 7.27.3. A graphical representation of Reproducibility.^[46]

^[46] Source: SBTI's Lean Methodology training material.

To effectively use a Gage R&R Study, it is important to understand the purpose of the Measurement System in question. Is it a "production gage" used to determine if product is in or out of specification, or is it a tool to measure a process characteristic in a project to improve process performance by reducing process variation? In the former it is important to understand the size of the Measurement System error with respect to the size of the specifications. The associated metric is known as the "Precision to Tolerance Ratio" and is defined as

The P/T Ratio represents the percent of the tolerance taken up by measurement error.^[47] The metric includes both Repeatability and Reproducibility. An excellent Measurement System has a P/T Ratio less than 10%. A value of 30% is barely acceptable. It is important to note that having the correct value for the Tolerance is crucial. In many cases, the specifications are too tight or too loose, which can be misleading.

^[47] 5.15 standard deviations account for 99% of Measurement System variation. The use of 5.15 is an industry standard, but more recently some texts recommend the use of 6 standard deviations, representing 99.73% of Measurement System variation. Either value is appropriate provided that it is used consistently across the business.

If the Measurement System is used for process improvement, then a more appropriate metric is the %R&R, which represents the percentage of the Total Variation taken up by measurement error:

The metric includes both Repeatability and Reproducibility. An excellent Measurement System has a %R&R less than 10%. A value of 30% is barely acceptable.

The final metric of interest to a Lean Sigma Belt is Discrimination, which represents the number of decimal places that can be measured by the system. Increments of measure should be about one-tenth of the width of the product specification or process variation (depending on the use of the Measurement System).

Logistics

Conducting a Gage R&R Study is about careful planning and data collection. This is certainly a Team sport because at least two appraisers are required, and it is unlikely that Belts apply the Measurement System in their regular job (i.e., the Belt almost certainly won't be one of the appraisers used in the MSA).

Planning the MSA takes about two hours, which usually includes a brief introduction to the tool made by the Belt to the rest of the Team and sometimes to the other appraisers. Data collection (conducting the appraisals themselves) can take anywhere between an hour and a week, depending on the complexity of the measurement.

Roadmap

The roadmap to planning, data collection, and analysis is as follows:

Step 1.	Identify the metric and agree within the Team on its Operational Definition (see "KPOVs and Data" in this chapter).

Step 2.	Select samples to be used in the Study. From 6 to 12 Samples are necessary and selection of each should be independent from the others. Samples should span the normal variation of the process; for example, for a material with a mean thickness of 0.020 inches and a standard deviation of 0.001 inches samples should have thickness from 0.017 to 0.023 inches (99% of the range). Do not randomly draw samples from the process as they tend to be grouped close to the mean and not represent the full width of the process. If the same process generates three different products with three (significantly) different thicknesses, then the Team should perform three separate studiesone for each thickness. If data for the samples were lumped together, the %R&R value would be artificially low.
Step 3.	Select 24 appraisers to conduct the MSA. These should be people who normally conduct the assessment. If process uses only one operator or no operators at all, then perform the study without operator effects (Reproducibility effects are thus ignored).
Step 4.	Select the number of trials. This needs to be at least two and the total number of data points (samples x appraisers x trials) should be greater than 30. For example, for five samples and two operators, it would be best to use three or four trials to generate 30 or 40 data points.
Step 5.	Calibrate the gage, or assure that it has been calibrated.
Step 6.	Perform the appraisal. Randomly provide the samples to one appraiser (without them knowing which sample it is) and have them measure the item. After the first appraiser has measured all the entities, repeat with the remaining appraisers. Appraisers must measure independently and out of sight of other appraisers to minimize potential bias. After all appraisers have measured each item, repeat the whole process for the required number of trials.
Step 7.	Enter the data into a statistical software package and analyze it. Data is usually entered in columns (Appraiser, Sample, and Response). The analysis output typically includes Repeatability Reproducibility %R&R P/T Ratio

Interpreting the Output

Statistical software packages generally produce both analytical and graphical analysis information. Each graph shows a different piece of the puzzle. Belts often try to read too much into each of the graphs; it is the combined story from all of the graphs that describes the Measurement System. Figure 7.27.4 shows an example Xbar-R Chart from a Gage R&R Study (for more details see "Control Charts" in this chapter).

For the Xbar-R Chart, if the averages for each operator are different, then the reproducibility is suspect. The majority of the points on the chart should fall outside the control limits consistently for all operators. If there are no points outside the control limits, it is generally because samples were not selected to cover the full range of the process (i.e., there was not enough Part-To-Part variation).

Figure 7.27.4. An example of a Gage R&R Xbar-R Chart (output from Minitab v14).

The Range Chart should show a process that is in control. The Ranges are the differences between trials and should not show any special causes of variation (i.e., remain in control). If a point is above the UCL, the operator is having a problem making consistent measurements. The Range Chart can also help identify inadequate discrimination; there should be least five distinct levels within the Control Limits. Also, if there are five or more levels for the range but more than 1/4 of the values are zero, then Discrimination is suspect. Repeatability is questionable if the Range Chart shows out-of-control conditions. If the Range Chart for an operator is out-of-control and the other Charts are not, then the method is probably suspect. If all operators have ranges out-of-control, the system is sensitive to operator technique.

Figure 7.27.5 shows an example of an Operator-Part Interaction Plot. For a reliable Measurement System, the lines should follow the same pattern and be reasonably parallel to each other. Crossing lines between operators indicates significant interactions. Also the part averages should vary enough that the differences between parts are clear.

Figure 7.27.5. An example of Gage R&R Operator-Part Interaction Plot (output from Minitab v14).

Figure 7.27.6 shows an example of a By Operator graph, which shows the average value (Circle) and the spread of the data for each operator. The spread should be similar across all operators and there should be a flat line across the means of the operators.

Figure 7.27.6. An example of a Gage R&R By Operator Plot (output from Minitab v14).

Figure 7.27.7 shows an example of a By Part graph. The graph shows the average (circles) and spread of the values for each sample. There should be minimal spread for each part (all the circles on top of each other), but variability between samples(different means).

Figure 7.27.7. An example of a Gage R&R By Part Plot (output from Minitab v14).

Figure 7.27.8 shows an example of a Components of Variation graph. The Gage R&R bars should be as small as possible, driving the Part-to-Part bars to be larger. This is better understood by looking at the analytical representation of the same data, which is shown in Figure 7.27.9.

Figure 7.27.8. An example of a Gage R&R Components of Variation Plot (output from Minitab v14).

Figure 7.27.9. An example of Gage R&R analytical results (output from Minitab v14).
Source	Std Dev (SD)	Study Var (5.15*SD)	%Study Var (%SV)	%Tolerance (SV/Toler)
Total Gage R&R	0.066615	0.34306	32.66	68.61
Repeatability	0.035940	0.18509	17.62	37.02
Reproducibility	0.056088	0.28885	27.50	57.77
Operator	0.030200	0.15553	14.81	31.11
Operator* Sample	0.047263	0.24340	23.17	48.68
Part-to-Part	0.192781	0.99282	94.52	198.56
Total Variation	0.203965	1.05042	100.00	210.08
Number of Distinct Categories = 4

The key metrics to look at in Figure 7.27.9 are

The P/T Ratio (listed as the %Tolerance) at 68.61%this gage is clearly not suitable as a production gage (30% is acceptable).

The %R&R (listed as %Study Variation) at 32.66%this gage is less than acceptable to help make improvements to the process in question (30% is acceptable).

The majority of the variation comes from Reproducibility, which can be seen from its standard deviation (0.056088) versus Repeatability (0.035940). Appraisers aren't agreeing with one another.

The largest portion of Reproducibility comes from an Operator-Sample interaction. In some way the Operators measure different samples in a different way. This sometimes occurs when one or more Appraisers aren't good with small parts, but can adequately measure larger parts, whereas others can measure all samples equally well.

The Number of Distinct Categories is an indication of the Discrimination of the measurement system. If the number of categories is less than two, the measurement system is of minimal value because it is difficult to distinguish one entity from another. If the number of categories is two, the measurement system can only divide the data into two groupslow and high. If the number of categories is three, the measurement system can divide the data into three groupslow, medium, and high. A measurement system that is acceptable and useful for process improvement activities must have five or more distinct categories; ten or more is ideal.

Remember, the measurement system must be mended before collecting the data! The graphical and analytical results help guide the Team in understanding where to focus the improvement. Improvement could be as simple as (re)training appraisers or it could be a project in itself. For more details see the Problem Category for Measurement System Improvement in Chapter 3, "Global Process Problems."

Other Options

MSA is a broad, relatively well-documented subject area.^[48] The approach shown in this section is for a straightforward non-destructive Measurement System. When considering other variations, such as destructive testing or on-line measures (where there are no operators), things become trickier. Analysis of this kind is beyond the scope of this book.

^[48] A useful reference here is "Measurement Systems AnalysisMSA" an ASQC and AIAG Publication.