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X-bar and sigma

What is it?

An X-bar and s (sigma) chart is a special purpose variation of the X-bar and R chart. Used with processes that have a subgroup size of 11 or more, X-bar and s charts show if the system is stable and predictable. They are also used to monitor the effects of process improvement theories. Instead of using subgroup range to chart variability, these charts use subgroup standard deviation. Because standard deviation uses each individual reading to calculate variability, it provides a more effective measure of the process spread. X-bar and sigma charts To create an X-bar and sigma chart using software, download a copy of SQCpack.

What does it look like?

The X-bar chart, on top, shows the mean or average of each subgroup. It is used to analyze central location. The sigma chart, on the bottom, shows how the data is spread and used to study system variability.

g-chart

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X-bar and range chart

What is it?

An X-bar and R (range) chart is a pair of control charts used with processes that have a subgroup size of two or more. The standard chart for variables data, X-bar and R charts help determine if a process is stable and predictable. The X-bar chart shows how the mean or average changes over time and the R chart shows how the range of the subgroups changes over time. It is also used to monitor the effects of process improvement theories. As the standard, the X-bar and R chart will work in place of the X-bar and s or median and R chart. To create an X-bar and R chart using software, download a copy of SQCpack.

What does it look like?

The X-bar chart, on top, shows the mean or average of each subgroup. It is used to analyze central location. The range chart, on the bottom, shows how the data is spread. It is used to study system variability.

g-chart

When is it used?

You can use X-bar and R charts for any process with a subgroup size greater than one. Typically, it is used when the subgroup size falls between two and ten, and X-bar and s charts are used with subgroups of eleven or more.

Use X-bar and R charts when you can answer yes to these questions:

  1. Do you need to assess system stability?
  2. Is the data in variables form?
  3. Is the data collected in subgroups larger than one but less than eleven?
  4. Is the time order of subgroups preserved?

 

Getting the most

Collect as many subgroups as possible before calculating control limits. With smaller amounts of data, the X-bar and R chart may not represent variability of the entire system. The more subgroups you use in control limit calculations, the more reliable the analysis. Typically, twenty to twenty-five subgroups will be used in control limit calculations.

X-bar and R charts have several applications. When you begin improving a system, use them to assess the system’s stability.

After the stability has been assessed, determine if you need to stratify the data. You may find entirely different results between shifts, among workers, among different machines, among lots of materials, etc. To see if variability on the X-bar and R chart is caused by these factors, collect and enter data in a way that lets you stratify by time, location, symptom, operator, and lots.

You can also use X-bar and R charts to analyze the results of process improvements. Here you would consider how the process is running and compare it to how it ran in the past. Do process changes produce the desired improvement?

Finally, use X-bar and R charts for standardization. This means you should continue collecting and analyzing data throughout the process operation. If you made changes to the system and stopped collecting data, you would have only perception and opinion to tell you whether the changes actually improved the system. Without a control chart, there is no way to know if the process has changed or to identify sources of process variability.

How is it used?

Variables data is normally analyzed in pairs of charts which present data in terms of location or central location and spread. Location, usually the top chart, shows data in relation to the process average. It is presented in X-bar, individuals, or median charts. Spread, usually the bottom chart, looks at piece-by-piece variation. Range, sigma, and moving range charts are used to illustrate process spread. Another aspect of these variables control charts is that the sample size is generally constant.

Use the following types of charts and analysis to study variables data:

These charts, and more, can be created easily using software packages such as SQCpack.

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Variables data (measurements)

What is it?

Variables data is data that is acquired through measurements, such as length, time, diameter, strength, weight, temperature, density, thickness, pressure, and height. With variables data, you can decide the measurement’s degree of accuracy. For example, you can measure an item to the nearest centimeter, millimeter, or micron.

How is it used?

Variables data is normally analyzed in pairs of charts which present data in terms of location or central location and spread. Location, usually the top chart, shows data in relation to the process average. It is presented in X-bar, individuals, or median charts. Spread, usually the bottom chart, looks at piece-by-piece variation. Range, sigma, and moving range charts are used to illustrate process spread. Another aspect of these variables control charts is that the sample size is generally constant.

Use the following types of charts and analysis to study variables data:

These charts, and more, can be created easily using software packages such as SQCpack.

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Measurement Systems Analysis – Improving measurement accuracy with gage R&R

What is it?

Variation is inherent to any system, and the data collection process is no exception. However, excessive variation in the data collection process will appear as variation on the control chart and can have a negative effect on process analysis. In addition to using operational definitions to ensure measurement consistency, you should periodically perform repeatability and reproducibility tests and recalibrate gages.

Gage R&R refers to testing the repeatability and reproducibility of the measurement system. Repeatability is the variation found in a series of measurements that have been taken by one person using one gage to measure one characteristic of an item. Reproducibility is the variation in a series of measurements that have been taken by different people using the same gage to measure one characteristic of an item.

Gage R&R studies let you address two major categories of variation in measuring systems: gage variability and operator variability. Gage variability refers to factors that affect the gage’s accuracy, such as its sensitivity to temperature, magnetic and electrical fields and, if it is mounted, how tight or loose the mount is. Operator variability refers to variation caused by differences among people. It can be caused by different interpretations of a vague operational definition, as well as differences in training, attitude, and fatigue level.

Performing gage R&R studies can be made easier by using software such as GAGEpack.

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Operational definition

What is it?

An operational definition, when applied to data collection, is a clear, concise detailed definition of a measure. The need for operational definitions is fundamental when collecting all types of data. It is particularly important when a decision is being made about whether something is correct or incorrect, or when a visual check is being made where there is room for confusion.

For example, data collected will be erroneous if those completing the checks have different views of what constitutes a fault at the end of a glass panel production line. Defective glass panels may be passed and good glass panels may be rejected. Similarly, when invoices are being checked for errors, the data collection will be meaningless if the definition of an error has not been specified.

When collecting data, it is essential that everyone in the system has the same understanding and collects data in the same way. Operational definitions should therefore be made before the collection of data begins.

When is it used?

Any time data is being collected, it is necessary to define how to collect the data. Data that is not defined will usually be inconsistent and will give an erroneous result. It is easy to assume that those collecting the data understand what and how to complete the task. However, people have different opinions and views, and these will affect the data collection. The only way to ensure consistent data collection is by means of a detailed operational definition that eliminates ambiguity.

What does it look like?

  1. Example 1 Attributes
    Characteristic of interest: Number of black spots per radiator grill.
    Measuring instrument: The observation will be performed with the naked eye (or with corrective lenses if normally worn), under the light available in the work station (in 100% working order, i.e., no burned-out bulbs).
    Method of test: The number of black spots per radiator grill will be counted by taking samples at the work station. The sample should be studied at a distance of 18 inches (roughly half an arm’s length) from the eye. Only the top surface of the grill is to be examined.
    Decision criteria: Wipe the top surface of the grill with the palm of your hand and look for any black specks embedded in the plastic. Any observed black speck of any size counts as a black spot.
    Example 2 Variables
    Characteristic of interest: Diameter of 48 inch rod
    Measuring instrument: Micrometer
    Method of test: The sample size is n=3. Measure 3 rods every hour. When the grinder releases the rod, take one measurement each at 8″ down, 24″ down, and 40″ down from the notched end. Tighten the micrometer as much as possible. Record to 4 decimal points. If the fifth number to the right of the decimal point is 5 or higher, round the fourth number up one.

How is it made?

  1. Identify the characteristic of interest.
    Identify the characteristic to be measured or the defect type of concern.
  2. Select the measuring instrument.
    The measuring instrument is usually either a physical piece of measuring equipment such as a micrometer, weighing scale, or clock; or alternatively, a visual check. Whenever a visual check is used, it is necessary to state whether normal eyesight is to be used or a visual aid such as a magnifying glass. In the example, normal eyesight is sufficient. On some occasions, it may also be necessary to state the distance the observer should be from the item being checked. In general, the closer the observer, the more detail will be seen. In the example, a clear visual indication is given of acceptable and unacceptable, so the observer needs to be in a position where the decision can be made. When completing a visual check, the type of lighting may also need to be specified. Certain colors and types of light can make defects more apparent.
  3. Describe the test method.
    The test method is the actual procedure used for taking the measurement. When measuring time, the start and finish points of the test need to be specified. When taking any measurement, the degree of accuracy also needs to be stated. For instance, it is important to know whether time will be measured in hours, minutes, or seconds.
  4. State the decision criteria.
    The decision criteria represents the conclusion of the test. Does the problem exist? Is the item correct? Whenever a visual check is used, a clear definition of acceptable versus unacceptable is essential. Physical examples or photographs of acceptable and unacceptable, together with written support, are the best definitions.
  5. Document the operational definition.
    It is important that the operational definition is documented and standardized. Definitions should be included in training materials and job procedure sheets. The results of steps 1 through 4 should be included in one document. The operational definition and the appropriate standards should be kept at the work station.
  6. Test the operational definition.
    It is essential to test the operational definition before implementation. Input from those that are actually going to complete the tests is particularly important. The operational definition should make the task clear and easy to perform. The best way to test an operational definition is to ask different people to complete the test on several items by following the operational definition. Watch how they perform the test. Are they completing the test as expected? Are the results consistent? Are the results correct?

 

The above article is an excerpt from the “Operational definition” chapter of Practical Tools for Continuous Improvement: Volume 1 – Statistical Tools. The full chapter provides more details on creating operational definition.

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Sampling

A resource for data collection tools, including how to collect data, how much to collect, and how frequently to collect it.

What is it?

Sampling is a tool that is used to indicate how much data to collect and how often it should be collected. This tool defines the samples to take in order to quantify a system, process, issue, or problem.

To illustrate sampling, consider a loaf of bread. How good is the bread? To find out, is it necessary to eat the whole loaf? No, of course not. To make a judgment about the entire loaf, it is necessary only to taste a sample of the loaf, such as a slice. In this case the loaf of bread being studied is known as the population of the study. The sample, the slice of bread, is a subset or a part of the population.

Now consider a whole bakery. The population of interest is no longer a loaf, but all the bread that has been made today. A sample size of one slice from one loaf is clearly inadequate for this larger population. The sample collected will now become several loaves of bread taken at set times throughout the day. Since the population is larger, the sample will also be larger. The larger the population, the larger the sample required.

In the bakery example, bread is made in an ongoing process. That is, bread was made yesterday, throughout today, and will be made tomorrow. For an ongoing process, samples need to be taken to identify how the process is changing over time. Studying how the samples are changing with control charts will show where and how to improve the process, and allow prediction of future performance.

For example, the bakery is interested in the weight of the loaves. The bakery does not want to weigh every single loaf, as this would be too expensive, too time consuming, and no more accurate than sampling some of the loaves. Sampling for improvement and monitoring is a matter of taking small samples frequently over time. The questions now become:

  • How many loaves to weigh each time a sample is taken?
  • How often to collect a sample?

These two questions, “how much?” and “how often?” are at the heart of sampling.

When is it used?

  • Sampling is used any time data is to be gathered.
    Data cannot be collected until the sample size (how much) and sample frequency (how often) have been determined.
  • Sampling should be periodically reviewed.
    When data is being collected on a regular basis to monitor a system or process, the frequency and size of the sample should be reviewed periodically to ensure that it is still appropriate

How is it done?

  1. What questions are being asked of the data?
    Before collecting any data, it is essential to define clearly what information is required. It is easy to waste time and resources collecting either the wrong data, or not collecting enough information at the time of data collection. Try to anticipate questions that will be asked when analyzing the data. What additional information would be desirable? When collecting data, it is easy to record additional information; trying to track information down later is far more difficult, and may not be possible.
  2. Determine the frequency of sampling.
    The frequency of sampling refers to how often a sample should be taken. A sample should be taken at least as often as the process is expected to change. Examine all factors that are expected to cause change, and identify the one that changes most frequently. Sampling must occur at least as often as the most frequently changing factor in the process. For example, if a process has exhibited the behavior shown in the diagram below, how often should sampling occur in order to get an accurate picture of the process?
    Factors to consider might be changes of personnel, equipment, or materials. The questions identified in step 1 may give guidance to this step.Common frequencies of sampling are hourly, daily, weekly, or monthly. Although frequency is usually stated in time, it can also be stated in number: every tenth part, every fifth purchase order, every other invoice, for example. If it is not clear how frequently the process changes, collect data frequently, examine the results, and then set the frequency accordingly.
  3. Determine the actual frequency times.
    The purpose of this step is to state the actual time to take the samples. For instance, if the frequency were determined to be daily, what time of day should the sample be taken—in the morning at 8:00 am, around midday, or late in the day around 5:00 pm? This is important because inconsistent timing between data gathering times will lead to data that is unreliable for further analysis. For example, if a sample is to be taken daily, and on one day it is taken at 8:00 am, the next day at 5:00 pm, and the following day at midday, the timing between the samples is inconsistent and the collected data will also be inconsistent. The data will exhibit unusual patterns and will be less meaningful. Stating the time that the sample is to be taken will reduce this type of error. The actual time should be chosen as close to any expected changes in the process as possible, and when taking a sample will be convenient. Avoid difficult times, such as during a shift change or lunch break.”
  4. Select the subgroup (sample) size.
    A subgroup (or sample) is the number of items to be examined at the same time. The terms “subgroup” and “sample” may be used interchangeably. When doing calculations, subgroup size is denoted by the letter n. To choose the most appropriate subgroup size, determine first whether the data being collected is “variables data” or “attributes data.”
    For variables data: When measuring variables data, a subgroup size larger than one is preferable because larger subgroups sizes yield greater possibilities for analysis. However, it may not be possible to get a subgroup size larger than one. Some examples of this are electricity usage per month, profit per month, sales per month, temperature of a room, and the viscosity of a fluid. In situations such as these when a subgroup size larger than one does not make sense, the subgroup (or sample) size is equal to one.If a subgroup size larger than one can be chosen, the size is usually between three and eight. A subgroup size between three and eight has been determined to be statistically efficient. The most commonly-used subgroup size is five. When more data is desired, the frequency of taking samples, not the subgroup size, should be increased.When a sample is taken, it should be selected to assure that conditions within the sample are similar. If gathering a sample size of five, for example, take all five pieces in a row as they are produced in the process. This is known as a rational subgroup.For attributes data: The subgroup size for attributes data depends on the process being sampled. The general rule of thumb is to gather a large enough sample so that all possible characteristics being investigated will appear. That is, the sample is large enough that a “0” occurrence is rare.

    Begin by answering the question, “How many items does this process produce during the frequency interval (per hour, week, etc.)?” When that number is determined, the sample size should be at least the square root of that number. For instance, if a purchasing department processes 100 purchase orders per week, an appropriate sample size would be 10 purchase orders per week (the square root of 100 is 10.)

The above article is an excerpt from the “Sampling” chapter of Practical Tools for Continuous Improvement: Volume 1 – Statistical Tools. The full chapter provides more details on sampling.

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Data Analysis Tools

Tools for analyzing and interpreting data so that areas to improve become apparent.

What type of data do I have?

Variables charts (measurement data)

(Learn more)

Consists of measurements of a characteristic, such as length, weight, density, time, or pressure.

Control charts Is your process stable and in control?
X-bar & range Use this if your data has a subgroup size of 2-10 observations.
X-bar & sigma Use this if your data has a subgroup size of 11 or more observations.
X-MR Use this if your data has a subgroup size of 1 observation.
Median Use this to analyze measurement data when you want to plot all observations.
Run chart Use this to see trends and patterns if there is not enough data for a control chart.
Histogram Use this to determine if your data has a normal distribution.
Capability analysis Use this to determine if your process is capable of producing output within specification limits.

 

Attributes (counts data)

(Learn more)

Consists of defects per item (nonconformities) or the number of defective items (nonconforming). For example, the number of non-working parts in sample or the number of blemishes counted on an individual part.

Control charts Is your process stable and in control?
np-chart Use this if your data is a count of nonconforming units and the subgroups are all the same size.
p-chart Use this if your data is a count of nonconforming units and the subgroup size varies.
c-chart Use this if your data is a count of nonconformities and the subgroups are all the same size.
u-chart Use this if your data is a count of nonconformities and the subgroup size varies.
Capability analysis Use this to determine capability for attributes data.

 

Pareto (counts in categories)

(Learn more)

Consists of a count of items or occurrences, such as the number of defective items, the number of scratches on a door panel, or how often a specific problem occurs.

Pareto diagram Use this to analyze counts that are in categories.

 

Rare event

(Learn more)

Use this when other control charts are not effective to determine if your process is stable.

g-chart Use this if your count data occurs infrequently. It is used by counting the number of events between rarely-occurring error or a nonconforming incident.
t-chart Use this if your error or non-nonconforming incident occurs infrequently. Each point on the chart represents an amount of time that has passed since the prior nonconforming incident occurred.

 

Interpreting quality charts

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Data Collection Tools

A resource for data collection tools, including how to collect data, how much to collect, and how frequently to collect it.

Sampling

A tool used to indicate how much data to collect and how often it should be collected.

Learn More

Operational Definition

A clear, concise, detailed definition of a measure.

Learn More

Improving Measurement Accuracy with Gage R&R

Gage R&R refers to testing the repeatability and reproducibility of the measurement system.

Learn More

Formulas and Tables

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Process performance indices

Process performance indices use sigma of the individuals.

Pp

Pp for one-sided specifications

If you are using one sided specifications, use the following formulas to determine the Cp:

Upper specification

Lower specification

Ppk

Where:

Zmin is the smaller of Zupper and Zlower.

Using sigma of the individuals:

Pr