1. Control Phase Statistical Process Control
We will now continue in the Control Phase with “Statistical Process Control or SPC”.

2. Statistical Process Control
Methodology
Elements and Purpose
Special Cause Tests
Examples
Six Sigma Control Plans
Lean Controls
Welcome to Control
Statistical Process Control (SPC)
Wrap Up & Action Items
Statistical techniques can be used to monitor and manage process performance. Process performance, as we have learned, is determined by the behavior of the inputs acting upon it in the form of Y = f(X). As a result it must be well understood we can monitor only the performance of a process output. Many people have applied Statistical Process Control (SPC) to only the process outputs. Because they were using SPC their expectations were high regarding a new potential level of performance and control over their processes. However, because they only applied SPC to the outputs they were soon disappointed. When you apply SPC techniques to outputs it is appropriately called Statistical Process Monitoring or SPM.
You of course know you can only control an output by controlling the inputs exerting an influence on the output. This is not to say applying SPC techniques to an output is bad, there are valid reasons for doing this. Six Sigma has helped us all to better understand where to apply such control techniques.
In addition to controlling inputs and monitoring outputs control charts are used to determine the baseline performance of a process, evaluate measurement systems, compare multiple processes, compare processes before and after a change, etc. Control Charts can be used in many situations that relate to process characterization, analysis and performance.
To better understand the role of SPC techniques in Six Sigma we will first investigate some of the factors that influence processes then review how simple probability makes SPC work and finally look at various approaches to monitoring and controlling a process.

3. SPC Overview: Collecting Data
Population:
An entire group of objects that have been made or will be made containing a characteristic of interest
Sample:
A sample is a subset of the population of interest
The group of objects actually measured in a statistical study
Samples are used to estimate the true population parameters
Population
Sample
Control Charts are usually derived from samples taken from the population. Sampling must be collected in such a way it does not bias or distort the interpretation of the population. The process must be allowed to operate normally when taking a sample. If there is any special treatment or bias given to the process over the period the data is collected the Control Chart interpretation could be invalid. The frequency of sampling depends on the volume of activity and the ability to detect trends and patterns in the data. At the onset you should error on the side of taking extra samples then if the process demonstrates its ability to stay in control you can reduce the sampling rate.
Using rational subgroups is a common way to assure you collect representative data. A rational subgroup is a sample of a process characteristic in which all the items in the sample were produced under very similar conditions over in a relatively short time period. Rational subgroups are usually small in size, typically consisting of 3 to 5 units to make up the sample. It is important that rational subgroups consist of units produced as closely as possible to each other especially if you want to detect patterns, shifts and drifts. If a machine is drilling 30 holes a minute and you wanted to collect a sample of hole sizes a good rational subgroup would consist of 4 consecutively drilled holes. The selection of rational subgroups enables you to accurately distinguish Special Cause variation from Common Cause variation.
Make sure your samples are not biased in any way; meaning they are randomly selected. For example, do not plot only the first shift’s data if you are running multiple shifts. Do not look at only one vendor’s material if you want to know how the overall process is really running. Finally do not concentrate on a specific time to collect your samples; like just before the lunch break.
If your process consists of multiple machines, operators or other process activities producing streams of the same output characteristic you want to control it would be best to use separate Control Charts for each of the output streams.
If the process is stable and in control the sample observations will be randomly distributed around the average. Observations will not show any trends or shifts and will not have any significant outliers from the random distribution around the average. This type of behavior is to be expected from a normally operating process and that is why it is called Common Cause variation. Unless you are intentionally trying to optimize the performance of a process to reduce variation or change the average, as in a typical Six Sigma project, you should not make any adjustments or alterations to the process if is it demonstrating only Common Cause variation. That can be a big time saver since it prevents “wild goose chases.”
If Special Cause variation occurs you must investigate what created it and find a way to prevent it from happening again. Some form of action is always required to make a correction and to prevent future occurrences.
You may have noticed there has been no mention of the specification limits for the characteristic being controlled. Specification limits are not evaluated when using a Control Chart. A process in control does not necessarily mean it is capable of meeting the requirements. It only states it is stable, consistent and predictable. The ability to meet requirements is called Process Capability, as previously discussed.

4. SPC Overview: I-MR Chart
An I-MR Chart combines a Control Chart of the average moving range with the Individuals Chart.
You can use Individuals Charts to track the process level and to detect the presence of Special Causes when the sample size is one batch.
Seeing these charts together allows you to track both the process level and process variation at the same time providing greater sensitivity to help detect the presence of Special Causes.
Individual Values (I) and Moving Range (MR) Charts are used when each measurement represents one batch. The subgroup size is equal to one when I-MR charts are used. These charts are very simple to prepare and use. The graphic shows the Individuals Chart where the individual measurement values are plotted with the Center Line being the average of the individual measurements. The Moving Range Chart shows the range between two subsequent measurements.
There are certain situations when opportunities to collect data are limited or when grouping the data into subgroups simply does not make practical sense. Perhaps the most obvious of these cases is when each individual measurement is already a rational subgroup. This might happen when each measurement represents one batch, when the measurements are widely spaced in time or when only one measurement is available in evaluating the process. Such situations include destructive testing, inventory turns, monthly revenue figures and chemical tests of a characteristic in a large container of material.
All these situations indicate a subgroup size of one. Because this chart is dealing with individual measurements it, is not as sensitive as the X-Bar Chart in detecting process changes.

5. SPC Overview: Xbar-R Chart
If each of your observations consists of a subgroup of data rather than just individual measurements an Xbar-R chart provides greater sensitivity. Failure to form rational subgroups correctly will make your Xbar-R Charts incorrect.
An Xbar-R is used primarily to monitor the stability of the average value. The Xbar Chart plots the average values of each of a number of small sampled subgroups. The averages of the process subgroups are collected in sequential, or chronological, order from the process. The Xbar Chart, together with the Rbar Chart shown, is a sensitive method to identify assignable causes of product and process variation and gives great insight into short-term variations.
These charts are most effective when they are used as a matched pair. Each chart individually shows only a portion of the information concerning the process characteristic. The upper chart shows how the process average (central tendency) changes. The lower chart shows how the variation of the process has changed.
It is important to track both the process average and the variation separately because different corrective or improvement actions are usually required to effect a change in each of these two parameters.
The Rbar Chart must be in control in order to interpret the averages chart because the Control Limits are calculated considering both process variation and Center. When the Rbar Chart shows not in control, the Control Limits on the averages chart will be inaccurate and may falsely indicate an out of control condition. In this case, the lack of control will be due to unstable variation rather than actual changes in the averages.
Xbar and Rbar Charts are often more sensitive than I-MR but are frequently done incorrectly. The most common error is failure to perform rational sub-grouping correctly.
A rational subgroup is simply a group of items made under conditions that are as nearly identical as possible. Five consecutive items made on the same machine with the same setup, the same raw materials and the same operator are a rational subgroup. Five items made at the same time on different machines are not a rational subgroup. Failure to form rational subgroups correctly will make your Xbar-Rbar Charts dangerously wrong.

6. C Charts and U Charts are for tracking defects.
SPC Overview: U Chart
C Charts and U Charts are for tracking defects.
A U Chart can do everything a C Chart can so we will just learn how to do a U Chart. This chart counts flaws or errors (defects). One “search area” can have more than one flaw or error.
Search area (unit) can be practically anything we wish to define. We can look for typographical errors per page, the number of paint blemishes on a truck door or the number of bricks a mason drops in a workday.
You supply the number of defects on each unit inspected.
The U Chart plots defects per unit data collected from subgroups of equal or unequal sizes. The “U” in U Charts stands for defects per Unit. U Charts plot the proportion of defects that are occurring.
The U Chart and the C Chart are very similar. They both are looking at defects but the U Chart does not need a constant sample size as does the C Chart. The Control Limits on the U Chart vary with the sample size and therefore they are not uniform; similar to the P Chart which we will describe next.
Counting defects on forms is a common use for the U Chart. For example, defects on insurance claim forms are a problem for hospitals. Every claim form has to be checked and corrected before going to the insurance company. When completing a claim form a particular hospital must fill in 13 fields to indicate the patient’s name, social security number, DRG codes and other pertinent data. A blank or incorrect field is a defect.
A hospital measured their invoicing performance by calculating the number of defects per unit for each day’s processing of claims forms. The graph demonstrates their performance on a U Chart.
The general procedure for U Charts is as follows:
1. Determine purpose of the chart
2. Select data collection point
3. Establish basis for sub-grouping
4. Establish sampling interval and determine sample size
5. Set up forms for recording and charting data and write specific instructions on use of the chart
6. Collect and record data.
7. Count the number of nonconformities for each of the subgroups
8. Input into Excel or other statistical software.
9. Interpret chart together with other pertinent sources of information on the process and take corrective action if necessary

7. NP Charts and P Charts are for tracking defectives.
SPC Overview: P Chart
NP Charts and P Charts are for tracking defectives.
A P Chart can do everything an NP Chart can so we will just learn how to do a P Chart!
Used for tracking defectives – the item is either good or bad, pass or fail, accept or reject.
Center Line is the proportion of “rejects” and is also your Process Capability.
Input to the P Chart is a series of integers — number bad, number rejected. In addition you must supply the sample size.
The P Chart plots the proportion of nonconforming units collected from subgroups of equal or unequal size (percent defective). The proportion of defective units observed is obtained by dividing the number of defective units observed in the sample by the number of units sampled. P Charts name comes from plotting the Proportion of defectives. When using samples of different sizes the upper and lower control limits will not remain the same - they will look uneven as exhibited in the graphic. These varying Control Chart limits are effectively managed by Control Charting software.
A common application of a P Chart is when the data is in the form of a percentage and the sample size for the percentage has the chance to be different from one sample to the next. An example would be the number of patients arriving late each day for their dental appointments. Another example is the number of forms processed daily requiring rework due to defects. In both of these examples the quantity would vary from day to day.
The general procedure for P Charts is as follows:
1. Determine purpose of the chart
2. Select data collection point
3. Establish basis for sub-grouping
4. Establish sampling interval and determine sample size
5. Set up forms for recording and charting data and write specific instructions on use of the chart
6. Collect and record data. It is recommended that at least 20 samples be used to calculate the Control Limits
7. Compute P, the proportion nonconforming for each of the subgroups
8. Load data into Excel or other statistical software.
9. Interpret chart together with other pertinent sources of information on the process and take corrective action if necessary

8. SPC Overview: Control Methods/Effectiveness
Type 1 Corrective Action = Countermeasure: improvement made to the process which will eliminate the error condition from occurring. The defect will never be created. This is also referred to as a long-term corrective action in the form of Mistake Proofing or design changes.
Type 2 Corrective Action = Flag: improvement made to the process which will detect when the error condition has occurred. This flag will shut down the equipment so the defect will not move forward.
SPC on X’s or Y’s with fully trained operators and staff who respect the rules. Once a chart signals a problem everyone understands the rules of SPC and agrees to shut down for Special Cause identification. (Cpk > certain level).
Type 3 Corrective Action = Inspection: implementation of a short-term containment which is likely to detect the defect caused by the error condition. Containments are typically audits or 100% inspection.
SPC on X’s or Y’s with fully trained operators. The operators have been trained and understand the rules of SPC, but management will not empower them to stop for investigation.
S.O.P. is implemented to attempt to detect the defects. This action is not sustainable short-term or long-term.
SPC on X’s or Y’s without proper usage = WALL PAPER.
Worst
Best
The most effective form of control is called a type 1 corrective action. This is a control applied to the process which will eliminate the error condition from occurring. The defect can never happen. This is the “prevention” application of the Poka-Yoke method.
The second most effective control is called a type 2 corrective action. This a control applied to the process which will detect when an error condition has occurred and will stop the process or shut down the equipment so that the defect will not move forward. This is the “detection” application of the Poka-Yoke method.
The third most effective form of control is to use SPC on the X’s with appropriate monitoring on the Y’s. To be effective employees must be fully trained, they must respect the rules and management must empower the employees to take action. Once a chart signals a problem everyone understands the rules of SPC and agrees to take emergency action for special cause identification and elimination.
The fourth most effective correction action is the implementation of a short-term containment which is likely to detect the defect caused by the error condition. Containments are typically audits or 100% inspection.
Finally you can prepare and implement an S.O.P. (standard operating procedure) to attempt to manage the process activities and to detect process defects. This action is not sustainable, either short-term or long-term.
Do not do SPC for the sake of just saying that you do SPC. It will quickly deteriorate to a waste of time and a very valuable process tool will be rejected from future use by anyone who was associated with the improper use of SPC.
Using the correct level of control for an improvement to a process will increase the acceptance of changes/solutions you may wish to make and it will sustain your improvement for the long-term.

9. Purpose of Statistical Process Control
Not this special cause!!
Every process has Causes of Variation known as:
Common Cause: Natural variability
Special Cause: Unnatural variability
Assignable: Reason for detected Variability
Pattern Change: Presence of trend or unusual pattern
SPC is a basic tool to monitor variation in a process.
SPC is used to detect Special Cause variation telling us the process is “out of control”… but does NOT tell us why.
SPC gives a glimpse of ongoing process capability AND is a visual management tool.
SPC has its uses because every process has variation known as both Special Cause and Common Cause variation. Special Cause variation is unnatural variability because of assignable causes or pattern changes. SPC is a powerful tool to monitor the variation of a process. This powerful tool is often an aspect used in Visual Factories. If a supervisor or operator or staff is able to quickly monitor how its process is operating by looking at the key inputs or outputs of the process this would exemplify a Visual Factory.
SPC is used to detect Special Causes in order to have those operating the process find and remove the Special Cause. When a Special Cause has been detected the process is considered to be “out of control”.
SPC gives an ongoing look at the process capability. It is not a capability measurement but it is a visual indication of the continued Process Capability of your process.

10. Elements of Control Charts
Developed by Dr Walter A. Shewhart of Bell Laboratories from 1924.
Graphical and visual plot of changes in the data over time.
This is necessary for visual management of your process.
Control Charts were designed as a methodology for indicating change in performance, either variation or Mean/Median.
Charts have a Central Line and Control Limits to detect Special Cause variation.
Process Center
(usually the Mean)
Special Cause
Variation Detected
Control Limits
Control Charts were first developed by Dr. Shewhart in the early 20th century in the U.S. Control Charts are a graphical and visual plot of a process and charts over time like a Time Series Chart. From a visual management aspect a Time Plot is more powerful than knowledge of the latest measurement. These charts are meant to indicate change in a process. All SPC charts have a Central Line and Control Limits to aid in Special Cause variation.
Notice, again, we never discussed showing or considering specifications. We are advising you to never have specification limits on a Control Chart because of the confusion often generated. Remember we want to control and maintain the process improvements made during the project. These Control Charts and their limits are the Voice of the Process. These charts give us a running view of the output of our process relative to established limits.

11. Understanding the Power of SPC
Control Charts indicate when a process is “out of control” or exhibiting Special Cause variation but NOT why!
SPC Charts incorporate upper and lower Control Limits.
The limits are typically +/- 3  from the Center Line.
These limits represent 99.73% of natural variability for Normal Distributions.
SPC Charts allow workers and supervision to maintain improved process performance from Lean Six Sigma projects.
Use of SPC Charts can be applied to all processes.
Services, manufacturing and retail are just a few industries with SPC applications.
Caution must be taken with use of SPC for Non-normal processes.
Control Limits describe the process variability and are unrelated to customer specifications. (Voice of the Process instead of Voice of the Customer)
An undesirable situation is having Control Limits wider than customer specification limits. This will exist for poorly performing processes with a Cp less than 1.0
Many SPC Charts exist and selection must be appropriate for effectiveness.
Please read the slide.

12. The Control Chart Cookbook
General Steps for Constructing Control Charts
1. Select characteristic (Critical “X” or CTQ) to be charted.
2. Determine the purpose of the chart.
3. Select data-collection points.
4. Establish the basis for sub-grouping (only for Y’s).
5. Select the type of Control Chart.
6. Determine the measurement method/criteria.
7. Establish the sampling interval/frequency.
8. Determine the sample size.
9. Establish the basis of calculating the Control Limits.
10. Set up the forms or software for charting data.
11. Set up the forms or software for collecting data.
12. Prepare written instructions for all phases.
13. Conduct the necessary training.
Stirred or Shaken?
Please read the slide.

13. Focus of Six Sigma and the Use of SPC
To get results should we focus our behavior on the Y or X? Y
Dependent
Output
Effect
Symptom
Monitor
X XN
Independent
Input
Cause
Problem
Control
When we find the “vital few” X’s first consider using SPC on the X’s to achieve a desired Y.
Y = f(x)
This concept should be very familiar to you by now. If we understand the variation caused by the X’s then we should be monitoring with SPC the X’s first.
By this time in the methodology you should clearly understand the concept of Y = f(x). Using SPC we are attempting to control the Critical X’s in order to control the Y.

14. Process Sequence/Time Scale
Control Chart Anatomy
Common Cause Variation Process is “In Control”
Special Cause Variation Process is “Out of Control”
Run Chart of data points
Process Sequence/Time Scale
Lower Control Limit
Mean
+/- 3 sigma
Upper Control Limit
Statistical Process Control (SPC) involves the use of statistical techniques to interpret data observing the variation in processes. SPC is used primarily to provide visual notice of out of control processes. However it is also used to monitor the consistency of processes producing products and services.
A primary SPC tool is the Control Chart - a graphical representation for specific quantitative measurements of a process input or output.  In the Control Chart these quantitative measurements are compared to decision rules calculated based on probabilities from the actual measurement of process performance.
The comparison between the decision rules and the performance data detects any unusual variation in the process; variation could indicate a problem with the process.  Several different descriptive statistics can be used in Control Charts. In addition there are several different types of Control Charts that can test for different causes; such as how quickly major versus minor shifts in process averages are detected.
Control Charts quite simply are Time Series Charts of all the data points with one extra addition. The Standard Deviation for the data is calculated for the data and two additional lines are added to the chart. These lines are placed +/- 3 Standard Deviations away from the Mean and are called the Upper Control Limit (UCL) and the Lower Control Limit (LCL). Now the chart has three zones: (1) The zone between the UCL and the LCL which called the zone of common variation, (2) The zone above the UCL which a zone of Special Cause variation and (3) another zone of Special Cause variation below the LCL.
Control Charts graphically highlight data points not fitting the normal level of expected variation. This is mathematically defined as being more than +/- 3 Standard Deviations from the Mean. It’s all based on probabilities. We will now demonstrate how this is determined.

15. Control and Out of Control
Outlier
68%
95%
99.7% 3 2 1 -1 -2 -3
Control Charts provide you with two basic functions; one is to provide time based information on the performance of the process which makes it possible to track events affecting the process and the second is to alert you when Special Cause variation occurs. Control Charts graphically highlight data points not fitting the normal level of variation expected. Common Cause variation level is typically defined as +/- 3 Standard Deviations from the Mean. This is also know as the UCL and LCL respectively.
Recall the “area under the curve” discussion in the lesson on Basic Statistics remembering +/- one Standard Deviation represented 68% of the distribution, +/- 2 was 95% and +/- 3 was 99.7%. You also learned from a probability perspective your expectation is the output of a process would have a 99.7% chance of being between +/- 3 Standard Deviations. You also learned the sum of all probability must equal 100%. There is only a 0.3% chance (100% %) a data point will be beyond +/- 3 Standard Deviations. In fact since we are talking about two zones, one zone above the +3 Standard Deviations and one below it, we have to split 0.3% in two, meaning there is only a 0.15% chance of being in one of the zones.
There is only a (.15%) probability a data point will either be above or below the UCL or LCL. This is a very small probability as compared to .997 (99.75%) probability the data point will be between the UCL and the LCL. What this means is there must have been something special happen to cause a data point to be that far from the Mean; like a change in vendor, a mistake, etc. This is why the term Special Cause or assignable cause variation applies. The probability a data point was this far from the rest of the population is so low that something special or assignable happened. Outliers are just that, they have a low probability of occurring, meaning we have lost control of our process. This simple, quantitative approach using probability is the essence of all Control Charts.

16. Typical subgroup sizes are 3-12 for variable data:
Size of Subgroups
Typical subgroup sizes are 3-12 for variable data:
If difficulty of gathering sample or expense of testing exists the size, n, is smaller.
3, 5 and 10 are the most common size of subgroups because of ease of calculations when SPC is done without computers.
Lot 1
Lot 2
Lot 3
Lot 4
Lot 5
Short-term studies
Long-term study
Size of subgroups aid in detection of shifts of Mean indicating Special Cause exists. The larger the subgroup size the greater chance of detecting a Special Cause. Subgroup size for Attribute Data is often 50 – 200.

17. The Impact of Variation
- Natural Process Variation as defined by subgroup selection
- Natural Process Variation - Different Operators
- Natural Process Variation - Different Operators Supplier Source
And, of course, if two additional sources of variation arrive we will detect that too!
First select the spread we will declare as the “Natural Process Variation” so whenever any point lands outside these “Control Limits” an alarm will sound
So when a second source of variation appears we will know!
If you base your limits on all three sources of variation, what will sound the alarm?
-UCL
-LCL
Sources of Variation
Remember the Control Limits are based on your PAST data and depending on what sources of variation you have included in your subgroups the Control Limits which detect the Special Cause variation will be affected. You really want to have subgroups with only Common Cause variation so if other sources of variation are detected the sources will be easily found instead of buried within your definition of subgroups.
Let’s consider if you were tracking delivery times for quotes on new business with an SPC chart. If you decided to not include averaging across product categories you might find product categories are assignable causes but you might not find them as Special Causes since you have included them in the subgroups as part of your rationalization.
You really want to have subgroups with only Common Cause variation so if other sources of variation are detected the sources will be easily found instead of buried within your definition of subgroups.

18. Frequency of Sampling
Sampling Frequency is a balance between the cost of sampling and testing versus the cost of not detecting shifts in Mean or variation.
Process knowledge is an input to frequency of samples after the subgroup size has been decided.
If a process shifts but cannot be detected because of too infrequent sampling the customer suffers
If a choice is given between a large subgroup of samples infrequently or smaller subgroups more frequently most choose to get information more frequently.
In some processes with automated sampling and testing frequent sampling is easy.
If undecided as to sample frequency, sample more frequently to confirm detection of process shifts and reduce frequency if process variation is still detectable.
A rule of thumb also states “sample a process at least 10 times more frequent than the frequency of ‘out of control’ conditions”.
Sometimes it can be a struggle to determine how often to sample your process when monitoring results. Unless the measurement is automated, inexpensive and recorded with computers and able to be charted with SPC software without operator involvement then frequency of sampling is an issue.
Besides what is covered in this slide let’s emphasize some points. First you do NOT want to under sample and not have the ability to find Special Cause variation easily. Second do not be afraid to sample more frequently then reduce the frequency if it is clear Special Causes are found frequently.

19. Frequency of Sampling
Sampling too little will not allow for sufficient detection of shifts in the process because of Special Causes.
All possible samples
Sample every hour
Sample 4x per shift
Sample every half hour
Please read the slide.

20. Choose Appropriate Control Chart
SPC Selection Process
Choose Appropriate Control Chart
type of data
type of attribute data
subgroup size
I – MR Chart
X – R Chart
X – S Chart
CumSum Chart
EWMA
Chart
C Chart
U Chart
NP Chart
P Chart
type of defect
type of subgroups
ATTRIBUTE
CONTINUOUS
DEFECTS
DEFECTIVES
VARIABLE
CONSTANT 1
2-5
10+
Number of Incidences
Incidences per Unit
Number of Defectives
Proportion Defectives
Individuals & Moving Range
Mean & Range
Mean & Std. Dev.
Cumulative Sum
Exponentially Weighted Moving Average
SPECIAL CASES
Sample size
The Control Charts you choose to use will always be based first on the type of data you have then on the objective of the Control Chart. The first selection criteria will be whether you have Attribute or Continuous Data.
Continuous SPC refers to Control Charts displaying process input or output characteristics based on Continuous Data - data where decimal subdivisions have meaning. When these Control Charts are used to control the Critical X input characteristic it is called Statistical Process Control (SPC). These charts can also be used to monitor the CTQ’s; the important process outputs. When this is done it is referred to as Statistical Process Monitoring (SPM).
There are two categories of Control Charts for Continuous Data: charts for controlling the process average and charts for controlling the process variation. Generally the two categories are combined. The principal types of Control Charts used in Lean Six Sigma are: charts for Individual Values and Moving Ranges (I-MR), charts for Averages and Ranges (XBar-R), charts for Averages and Standard Deviations (XBar-S) and Exponentially Weighted Moving Average charts (EWMA).
Although it is preferable to monitor and control products, services and supporting processes with Continuous Data there will be times when Continuous Data is not available or there is a need to measure and control processes with higher level metrics, such as defects per unit. There are many examples where process measurements are in the form of Attribute Data. Fortunately there are control tools available to monitor these characteristics and to control the critical process inputs and outputs as measured with Attribute Data.
Attribute Data, also called discrete data reflects only one of two conditions: conforming or nonconforming, pass or fail, go or no go, present or absent. Four principal types of Control Charts are used to monitor and control characteristics measured in Attribute Data: the P (proportion nonconforming), NP (number nonconforming), C (number of non-conformities) and U (non-conformities per unit) Charts. Four principle types of Control Charts are used to monitor and control characteristics measured in Discrete Data: the P (proportion nonconforming), NP (number nonconforming), C (number of non-conformities) and U (non-conformities per unit) Charts. These charts are an aid for decision making. With Control Limits they can help us filter out the probable noise by adequately reflecting the Voice of the Process.
A defective is defined as an entire unit, whether it be a product or service, failing to meet acceptance criteria regardless of the number of defects in the unit. A defect is defined as the failure to meet any one of the many acceptance criteria. Any unit with at least one defect may be considered to be a defective (Sometimes more than one defect is allowed, up to some maximum number, before the product is considered to be defective).

21. Understanding Variable Control Chart Selection
Type of Chart
When do you need it?
Production is higher volume; allows process Mean and variability to be viewed and assessed together; more sampling than with Individuals Chart (I) and Moving Range Charts (MR) but when subgroups are desired. Outliers can cause issues with Range (R) charts so Standard Deviation charts (S) used instead if concerned.
Production is low volume or cycle time to build product is long or homogeneous sample represents entire product (batch etc.); sampling and testing is costly so subgroups are not desired. Control limits are wider than Xbar Charts. Used for SPC on most inputs.
Set-up is critical, or cost of setup scrap is high. Use for outputs
Small shift needs to be detected often because of autocorrelation of the output results. Used only for individuals or averages of Outputs. Infrequently used because of calculation complexity.
Same reasons as EWMA (Exponentially Weighted Moving Range) except the past data is as important as present data.
Average & Range or S
(Xbar and R or
Xbar and S)
Individual and
Moving Range
Pre-Control
Exponentially
Weighted
Moving Average
Cumulative Sum
Most Common
Less Common
Please read the slide.

22. Understanding Attribute Control Chart Selection
Need to track the fraction of defective units; sample size is variable and usually > 50
When you want to track the number of defective units per subgroup; sample size is usually constant and usually > 50
When you want to track the number of defects per subgroup of units produced; sample size is constant
When you want to track the number of defects per unit; sample size is variable P nP C U
When do you need it?
Type of Chart
The P Chart is the most common type of chart in understanding Attribute Control Charts.

23. Detection of Assignable Causes or Patterns
Control Charts indicate Special Causes being either assignable causes or patterns.
The following rules are applicable for both variable and Attribute Data to detect Special Causes.
These four rules are the only applicable tests for Range (R), Moving Range (MR) or Standard Deviation (S) charts.
One point more than 3 Standard Deviations from the Center Line.
6 points in a row all either increasing or all decreasing.
14 points in a row alternating up and down.
9 points in a row on the same side of the center line.
These remaining four rules are only for variable data to detect Special Causes.
2 out of 3 points greater than 2 Standard Deviations from the Center Line on the same side.
4 out of 5 points greater than 1 Standard Deviation from the Center Line on the same side.
15 points in a row all within one Standard Deviation of either side of the Center Line.
8 points in a row all greater than one Standard Deviation of either side of the Center Line.
Remember Control Charts are used to monitor a process performance detecting Special Causes due to assignable causes or patterns. The standardized rules of your organization may have some of the numbers slightly differing. For example, some organizations have 7 or 8 points in a row on the same side of the Center Line. We will soon show you how to find what your MINITABTM version has for defaults for the Special Cause tests.
There are typically 8 available tests for detecting Special Cause variation. Only 4 of the 8 Special Cause tests can only be used Range, Moving Range or Standard Deviation charts which are used to monitor “within” variation.
If you are unsure of what is meant by these specific rule definitions, do not worry. The next few slides will specifically explain how to interpret these rules.

24. Recommended Special Cause Detection Rules
If implementing SPC manually without software initially the most visually obvious violations are more easily detected. SPC on manually filled charts are common place for initial use of Defect Prevention techniques.
These three rules are visually the most easily detected by personnel.
One point more than 3 Standard Deviations from the Center Line.
6 points in a row all either increasing or all decreasing.
15 points in a row all within one Standard Deviation of either side of the Center Line.
Dr. Shewhart working with the Western Electric Co. was credited with the following four rules referred to as Western Electric Rules.
8 points in a row on the same side of the Center Line.
2 out of 3 points greater than 2 Standard Deviations from the Center Line on the same side.
4 out of 5 points greater than 1 Standard Deviation from the Center Line on the same side.
You might notice the Western Electric rules vary slightly. The importance is to be consistent in your organization deciding what rules you will use to detect Special Causes.
VERY few organizations use all eight rules for detecting Special Causes.
Please read the slide.

25. Special Cause Rule Default in MINITABTM
If a Belt is using MINITABTM she must be aware of the default setting rules. Program defaults may be altered by:
Many experts have commented on the appropriate tests and numbers to be used. Decide, then be consistent when implementing.
Tools>Options>Control Charts and Quality Tools> Tests
When a Belt is using MINITABTM the default tests can be set when running SPC on the variable or Attribute Data. A Belt can always change which tests are selected for any individual SPC chart.

26. Special Cause Test Examples
This is the MOST common Special Cause test used in SPC charts.
As promised we will now closely review the definition of the Special Cause tests. The first test is one point more than 3 sigmas from the Center Line. The 3 sigma lines are added or subtracted from the Center Line. The sigma estimation for the short-term variation will be shown later in this module.
If only one point is above the upper 3 sigma line or below the lower 3 sigma line then a Special Cause is indicated. This does not mean you need to confirm if another point is also outside of the 3 sigma lines before action is to be taken. Do not forget the methodology of using SPC.
If you want to see the MINITABTM output on the left execute the MINITABTM command “Stat, Control Charts, Variable Charts for Individuals, Individuals” then select the “Xbar-R Chart – Options” then “Tests” tab. Remember, your numbers may vary in the slide and those are set in the defaults as you were shown recently in this module. From now on we will assume your rules are the same as shown in this module. If not just adjust the conclusions.

27. Special Cause Test Examples
This test is an indication of a shift in the process Mean.
The second test for detecting Special Causes is nine points in a row on the same side of the Center Line. This literally means if nine consecutive points are above the Center Line then a Special Cause is detected and would account for a potential Mean shift in the process. This rule would also be violated if nine consecutive points are below the Center Line. The amount away from the Center Line does not matter as long as the consecutive points are all on the same side of the Center Line.

28. Special Cause Test Examples
This test is indicating a trend or gradual shift in the Mean.
The third test looking for a Special Cause is six points in a row all increasing or all decreasing. This means if six consecutive times the present point is higher than the previous point then the rule has been violated and the process is out of control. The rule is also violated if for six consecutive times the present point is lower than the previous point on the SPC chart.
This rule obviously needs the time order when plotting on the SPC charts to be valid. Typically these charts plot increasing time from left to right with the most recent point on the right hand side of the chart. Do not make the mistake of seeing six points in a line indicating an out of control condition. Note on the example shown on the right a straight line shows seven points but it takes that many in order to have six consecutive points increasing. This rule would be violated no matter what zone the points occur.

29. Special Cause Test Examples
This test is indicating a non-random pattern.
The fourth rule for a Special Cause indication is fourteen points in a row alternating up and down. In other words if the first point increased from the last point and the second point decreased from the first point and the third point increased from the second point and so on for fourteen points then the process is considered out of control or a Special Cause is indicated. This rule does not depend on the points being in any particular zone of the chart. Also note the process is not considered to be out of control until after the 14th point has followed the alternating up and down pattern.

30. Special Cause Test Examples
This test is indicating a shift in the Mean or a worsening of variation.
The fifth Special Cause test looks for two out of three consecutive points more than 2 sigma away from the Center Line on the same side. The 2 sigma line is obviously two thirds of the distance from the Center Line as the 3 sigma line. Please note it is not required that the points more than 2 sigma away be in consecutive order they just have to be within a group of three consecutive points. Notice the example shown on the right does NOT have two consecutive points 2 sigma away from the Center Line but two out of the three consecutive are more than 2 sigma away. Notice this rule is not violated if the two points that are more than 2 sigma but NOT on the same side.
Have you noticed MINITABTM will automatically place a number by the point violating the Special Cause rule and that number tells you which of the Special Cause tests has been violated? In this example shown on the right the Special Cause rule was violated twice.

31. Special Cause Test Examples
This test is indicating a shift in the Mean or degradation of variation.
The sixth Special Cause test looks for any four out of five points more than 1 sigma from the Center Line all on the same side. Only the four points that were more than 1 sigma need to be on the same side. If four of the five consecutive points are more than 1 sigma from the Center Line and on the same side do NOT make the wrong assumption that the rule would not be violated if one of the four points was actually more than 2 sigma from the Center Line.

32. Special Cause Test Examples
This test is indicating a dramatic improvement of the variation in the process.
The seventh Special Cause test looks for fifteen points in a row all within 1 sigma from the Center Line. You might think this is a good thing and it certainly is. However you might want to find the Special Cause for this reduced variation so the improvement can be sustained in the future.

33. Special Cause Test Examples
This test is indicating a severe worsening of variation.
The eighth and final test for Special Cause detection is having eight points in a row all more than 1 sigma from the Center Line. The eight consecutive points can be any number of sigma away from the Center Line. Do NOT make the wrong assumption this rule would not be violated if some of the points were more than 2 sigma away from the Center Line. If you reread the rule it just states the points must be more than one sigma from the Center Line.

34. SPC Center Line and Control Limit Calculations
Calculate the parameters of the Individual and MR Control Charts with the following:
Where ~
Xbar: Average of the individuals becomes the Center Line on the Individuals Chart
Xi: Individual data points
k: Number of individual data points
Ri : Moving range between individuals generally calculated using the difference between each successive pair of readings
MRbar: The average moving range, the Center Line on the Range Chart
UCLX: Upper Control Limit on Individuals Chart
LCLX: Lower Control Limit on Individuals Chart
UCLMR: Upper Control Limit on moving range
LCLMR : Lower Control Limit on moving range (does not apply for sample sizes below 7)
E2, D3, D4: Constants that vary according to the sample size used in obtaining the moving range
Center Line
Control Limits
This is a reference in case you really want to get into the nitty-gritty. The formulas shown here are the basis for Control Charts.

35. SPC Center Line and Control Limit Calculations
Calculate the parameters of the Xbar and R Control Charts with the following:
Center Line
Control Limits
Where ~
Xi: Average of the subgroup averages, it becomes the Center Line of the Control Chart
Xi: Average of each subgroup
k: Number of subgroups
Ri : Range of each subgroup (Maximum observation – Minimum observation)
Rbar: The average range of the subgroups, the Center Line on the Range Chart
UCLX: Upper Control Limit on Average Chart
LCLX: Lower Control Limit on Average Chart
UCLR: Upper Control Limit on Range Chart
LCLR : Lower Control Limit Range Chart
A2, D3, D4: Constants that vary according to the subgroup sample size
Another reference.

36. SPC Center Line and Control Limit Calculations
Calculate the parameters of the Xbar and S Control Charts with the following:
Where ~
Xi: Average of the subgroup averages it becomes the Center Line of the Control Chart
Xi: Average of each subgroup
k: Number of subgroups
si : Standard Deviation of each subgroup
Sbar: The average S. D. of the subgroups, the Center Line on the S chart
UCLX: Upper Control Limit on Average Chart
LCLX: Lower Control Limit on Average Chart
UCLS: Upper Control Limit on S Chart
LCLS : Lower Control Limit S Chart
A3, B3, B4: Constants that vary according to the subgroup sample size
Center Line
Control Limits
Yet another reference just in case anyone wants to do this stuff manually…have fun!!!!

37. SPC Center Line and Control Limit Calculations
Calculate the parameters of the P Control Charts with the following:
Where~
p: Average proportion defective (0.0 – 1.0)
ni: Number inspected in each subgroup
LCLp: Lower Control Limit on P Chart
UCLp: Upper Control Limit on P Chart
Center Line
Control Limits
Since the Control Limits are a function of sample size they will vary for each sample.
We are now moving to the formula summaries for the Attribute SPC Charts. These formulas are fairly basic. The upper and lower Control Limits are equidistant from the Mean % defective unless you reach a natural limit of 100 or 0%. Remember the P Chart is for tracking the proportion or % defective.
These formulas are a bit more elementary because they are for Attribute Control Charts. Recall P Charts track the proportion or % defective.

38. SPC Center Line and Control Limit Calculations
Calculate the parameters of the nP Control Charts with the following:
Where ~
np: Average number defective items per subgroup
ni: Number inspected in each subgroup
LCLnp: Lower Control Limit on nP chart
UCLnp: Upper Control Limit on nP chart
Center Line
Control Limits
Since the Control Limits AND Center Line are a function of sample size they will vary for each sample.
The nP Chart’s formulas resemble the P Chart. This chart tracks the number of defective items in a subgroup.

39. SPC Center Line and Control Limit Calculations
Calculate the parameters of the U Control Charts with the following:
Where ~
u: Total number of defects divided by the total number of units inspected.
ni: Number inspected in each subgroup
LCLu: Lower Control Limit on U Chart.
UCLu: Upper Control Limit on U Chart.
Center Line
Control Limits
Since the Control Limits are a function of sample size they will vary for each sample.
The U Chart is also basic in construction and is used to monitor the number of defects per unit.

40. SPC Center Line and Control Limit Calculations
Calculate the parameters of the C Control Charts with the following:
Where ~
c: Total number of defects divided by the total number of subgroups.
LCLc: Lower Control Limit on C Chart.
UCLc: Upper Control Limit on C Chart.
Center Line
Control Limits
The C Control Charts are a nice way of monitoring the number of defects in sampled subgroups.

41. SPC Center Line and Control Limit Calculations
Calculate the parameters of the EWMA Control Charts with the following:
Where ~
Zt: EWMA statistic plotted on Control Chart at time t
Zt-1: EWMA statistic plotted on Control Chart at time t-1
: The weighting factor between 0 and 1 – suggest using 0.2
: Standard Deviation of historical data (pooled Standard Deviation for subgroups – MRbar/d2 for individual observations)
Xt: Individual data point or sample averages at time t
UCL: Upper Control Limit on EWMA Chart
LCL: Lower Control Limit on EWMA Chart
n: Subgroup sample size
Center Line
Control Limits
This EWMA can be considered a smoothing monitoring system with Control Limits. This is rarely used without computers or automated calculations. The items plotted are NOT the actual measurements but the weighted measurements. The exponentially weighted moving average is useful for considering past and historical data and is most commonly used for individual measurements although has been used for averages of subgroups.

42. SPC Center Line and Control Limit Calculations
Calculate the parameters of the CUSUM control charts with MINITABTM or other program since the calculations are even more complicated than the EWMA charts.
Because of this complexity of formulas execution of either this or the EWMA are not done without automation and computer assistance.
Ah, anybody got a laptop?
The CUSUM is an even more difficult technique to handle with manual calculations. We are not even showing the math behind this rarely used chart. Following the Control Chart selection route shown earlier we remember the CUSUM is used when historical information is as important as present data.

43. Pre-Control Charts
Pre-Control Charts use limits relative to the specification limits. This is the first and ONLY chart wherein you will see specification limits plotted for Statistical Process Control. This is the most basic type of chart and unsophisticated use of process control.
Red Zones. Zone outside the specification limits. Signals the process is out-of-control and should be stopped
0.5
0.75
0.25
1.0
0.0
Target
USL
LSL
Yellow Zones. Zone between the PC Lines and the specification limits indicating caution and the need to watch the process closely
Green Zone. Zone lies between the PC Lines, signals the process is in control
The Pre-Control Charts are often used for startups with high scrap cost or low production volumes between setups. Pre-Control Charts are like a stoplight are the easiest type of SPC to use by operators or staff. Remember Pre-Control Charts are to be used ONLY for outputs of a process.
Another approach to using Pre-Control Chart is to use Process Capability to set the limits where yellow and red meet.

44. Process Setup and Restart with Pre-Control
Qualifying Process
To qualify a process five consecutive parts must fall within the green zone
The process should be qualified after tool changes, adjustments, new operators, material changes, etc.
Monitoring Ongoing Process
Sample two consecutive parts at predetermined frequency
If either part is in the red, stop production and find reason for variation
When one part falls in the yellow zone inspect the other and:
If the second part falls in the green zone then continue
If the second part falls in the yellow zone on the same side make an adjustment to the process
If second part falls in the yellow zone on the opposite side or in the red zone the process is out of control and should be stopped
If any part falls outside the specification limits or in the red zone the process is out of control and should be stopped
Please read the slide.

45. Responding to Out of Control Indications
The power of SPC is not to find out what the Center Line and Control Limits are.
The power is to react to the Out of Control (OOC) indications with your Out of Control Action Plans (OCAP) for the process involved. These actions are your corrective actions to correct the output or input to achieve proper conditions.
SPC requires immediate response to a Special Cause indication.
SPC also requires no “sub optimizing” by those operating the process.
Variability will increase if operators always adjust on every point if not at the Center Line. ONLY respond when an Out of Control or Special Cause is detected.
Training is required to interpret the charts and response to the charts.
OCAP
If response time is too high get additional person on phone bank
VIOLATION:
Special Cause is indicated
SPC is an exciting tool but we must not get enamored with it. The power of SPC is not to find the Center Line and Control Limits but to react to out of control indications with an out of control action plan. SPC for effectiveness at controlling and reducing long-term variation is to respond immediately to out of control or Special Cause indications.
SPC can be actually harmful if those operating the process respond to process variation with suboptimizing. A basic rule of SPC is if it is not out of control as indicated by the rules then do not make any adjustments. There are studies where an operator responding to off center measurements will actually produce worse variation than a process not altered at all. Remember being off the Center Line is NOT a sign of out of control because Common Cause variation exists.
Training is required to use and interpret the charts not to mention training for you as a Belt to properly create an SPC Chart.

46. Step 3 and 5 of the methodology is the primary focus for this example.
Attribute SPC Example
Practical Problem: A project has been launched to get rework reduced to less than 25% of paychecks. Rework includes contacting a manager about overtime hours to be paid. The project made some progress but decides they need to implement SPC to sustain the gains and track % defective. Please analyze the file “paycheck2.mtw” and determine the Control Limits and Center Line.
Step 3 and 5 of the methodology is the primary focus for this example.
Select the appropriate Control Chart and Special Cause tests to employ
Calculate the Center Line and Control Limits
Looking at the data set we see 20 weeks of data.
The sample size is constant at 250.
The amount of defective in the sample is in column C3.
Now we will do some examples of SPC to understand and learn how to use MINITABTM to aid us in sustaining our project results. Do not forget all of the steps in the methodology.
OK, let’s try it…
Paycheck2.mtw

47. Attribute SPC Example (cont.)
The example includes % paychecks defective. The metric to be charted is % defective. We see the P Chart is the most appropriate Attribute SPC Chart.

48. Attribute SPC Example (cont.)
Notice specifications were never discussed. Let’s calculate the Control Limits and Central Line for this example.
We will confirm what rules for Special Causes are included in our Control Chart analysis.
Please read the slide.

49. Attribute SPC Example (cont.)
Remember to click on the “Options…” and “Tests” tab to clarify the rules for detecting Special Causes.
…. Chart Options>Tests
We will confirm what rules for Special Causes are included in our Control Chart analysis. The top 3 were selected.

50. Attribute SPC Example (cont.)
No Special Causes were detected. The average % defective checks were 20.38%. The UCL was 28.0% and 12.7% for the LCL.
Now we must see if the next few weeks are showing Special Cause from the results. The sample size remained at 250 and the defective checks were 61, 64, 77.
Please read the slide.

51. Attribute SPC Example (cont.)
Let’s continue our example:
Step 6: Plot process X or Y on the newly created Control Chart
Step 7: Check for Out-Of-Control (OOC) conditions after each point
Step 8: Interpret findings, investigate Special Cause variation & make improvements following the Out of Control Action Plan (OCAP)
Notice the new 3 weeks of data
was entered into the spreadsheet.
Remember we have calculated the Control Limits from the first 20 weeks. We must now put in 3 new weeks and NOT have MINITABTM calculate new Control Limits which will be done automatically if we do not follow this technique. Let’s now execute Steps
When done notice that 3 weeks of data was entered into the spreadsheet.

52. Attribute SPC Example (cont.)
Place the pbar from the first chart we created in the “Estimate” tab. This will prevent MINITABTM from calculating new Control Limits which is step 9.
…… Chart Options>Parameters
The new updated SPC chart is shown with one Special Cause.
Please read the slide.

53. Attribute SPC example (cont.)
Because of the Special Cause the process must refer to the OCAP or Out of Control Action Plan stating what Root Causes need to be investigated and what actions are taken to get the process back in Control.
After the corrective actions were taken wait until the next sample is taken to see if the process has changed to not show Special Cause actions.
If still out of control refer to the OCAP and take further action to improve the process. DO NOT make any more changes if the process shows back in control after the next reading.
Even if the next reading seems higher than the Center Line! Do not cause more variability.
If process changes are documented after this project was closed the Control Limits should be recalculated as in step 9 of the SPC methodology.
Please read the slide.

54. Variable SPC Example
Practical Problem: A job shop drills holes for its largest customer as a final step to deliver a highly engineered fastener. This shop uses five drill presses and gathers data every hour with one sample from each press representing part of the subgroup. You can assume there is insignificant variation within the five drills and the subgroup is across the five drills. The data is gathered in columns C3-C7.
Step 3 and 5 of the methodology is the primary focus for this example.
Select the appropriate Control Chart and Special Cause tests to employ
Calculate the Center Line and Control Limits
Holediameter.mtw
Let’s walk through another example of using SPC within MINITABTM but in this case it will be with Continuous Data. Open the MINITABTM worksheet called “hole diameter.mtw” and select the appropriate type of Control Chart and calculate the Center Line and Control Limits.
Let’s try another one, this time variable…

55. Variable SPC Example (cont.)
VARYING
The example has Continuous Data, subgroups and we have no interest in small changes in this small process output. The Xbar R Chart is selected because we are uninterested in the Xbar S Chart for this example.

56. Variable SPC Example (cont.)
Specifications were never discussed. Let’s calculate the Control Limits and Center Line for this example.
We will confirm what rules for Special Causes are included in our Control Chart analysis.
Please read the slide.

57. Variable SPC Example (cont.)
Remember to click on the “Options…” and “Tests” tab to clarify the rules for detecting Special Causes.
We will confirm what rules for Special Causes are included in our Control Chart analysis. The top 2 of 3 were selected.
……..Xbar-R Chart Options>Tests
Please read the slide.

58. Variable SPC Example (cont.)
Also confirm the Rbar method is used for estimating Standard Deviation.
Stat>Control Charts>Variable Charts for Subgroups>Xbar-R>Xbar-R Chart Options>Estimate
Select the “Rbar” option shown here.

59. Variable SPC Example (cont.)
No Special Causes were detected in the Xbar Chart. The average hole diameter was The UCL was 33.1 and 19.6 for the LCL.
Now we will use the Control Chart to monitor the next 2 hours and see if we are still in control.
Please read the slide.

60. Variable SPC Example (cont.)
Some more steps….
Step 6: Plot process X or Y on the newly created Control Chart
Step 7: Check for Out-Of-Control (OOC) conditions after each point
Step 8: Interpret findings, investigate Special Cause variation, & make improvements following the Out of Control Action Plan (OCAP)
Remember we have calculated the Control Limits from the first 20 weeks. We must now put in two more hours and NOT have MINITABTM calculate new Control Limits which will be done automatically if we do not follow this step. Now we are executing Steps
Notice the new two hours of data was entered into the spreadsheet.

61. Variable SPC Example (cont.)
The updated SPC Chart is shown with no indicated Special Causes in the Xbar Chart. The Mean, UCL and LCL are unchanged because of the completed option .
……..Xbar-R Chart Options>Parameters
Place the Mean from the FIRST chart we created in the estimates tab. The Standard Deviation is Rbar divided by d2. This will prevent MINITABTM from calculating new Control Limits which is Step 9. d2 is found by finding the table of constants shown earlier.

62. Variable SPC Example (cont.)
Because of no Special Causes the process does not refer to the OCAP or Out of Control Action Plan and NO actions are taken.
If process changes are documented after this project was closed the Control Limits should be recalculated as in Step 9 of the SPC methodology.
Please read the slide.

63. Recalculation of SPC Chart Limits
Step 9 of the methodology refers to recalculating SPC limits.
Processes should see improvement in variation after usage of SPC.
Reduction in variation or known process shift should result in Center Line and Control Limits recalculations.
Statistical confidence of the changes can be confirmed with Hypothesis Testing from the Analyze Phase.
Consider a periodic time frame for checking Control Limits and Center Lines.
3, 6, 12 months are typical and dependent on resources and priorities
A set frequency allows for process changes to be captured.
Incentive to recalculate limits include avoiding false Special Cause detection with poorly monitored processes.
These recommendations are true for both Variable and Attribute Data.
Please read the slide.

64. SPC Chart Option in MINITABTM for  Levels
This is possible with ~
Stat>Quality Charts> ….. Options>S Limits “tab”
Remembering many of the tests are based on the 1st and 2nd Standard Deviations from the Center Line some Belts prefer to have some additional lines displayed.
The extra lines can be helpful if users are using MINITABTM for the SPC.

65. At this point you should be able to:
Summary
At this point you should be able to:
Describe the elements of an SPC Chart and the purposes of SPC
Understand how SPC ranks in Defect Prevention
Describe the 13 step route or methodology of implementing a chart
Design subgroups if needed for SPC usage
Determine the frequency of sampling
Understand the Control Chart selection methodology
Be familiar with Control Chart parameter calculations such as UCL, LCL and the Center Line
Please read the slide.

66. Learn about IASSC Certifications and Exam options at…
IASSC Certified Lean Six Sigma Green Belt (ICGB)
The International Association for Six Sigma Certification (IASSC) is a Professional Association dedicated to growing and enhancing the standards within the Lean Six Sigma Community. IASSC is the only independent third-party certification body within the Lean Six Sigma Industry that does not provide training, mentoring and coaching or consulting services. IASSC exclusively facilitates and delivers centralized universal Lean Six Sigma Certification Standards testing and organizational Accreditations.
The IASSC Certified Lean Six Sigma Green Belt (ICGB) is an internationally recognized professional who is well versed in the Lean Six Sigma Methodology who both leads or supports improvement projects. The Certified Green Belt Exam, is a 3 hour 100 question proctored exam. 66