The SPC module provides control charts for measurable and non-measurable sample value as well as charts for analysis:

For more information on selecting the right control chart for an application, www.isixsigma.com provides a handy  Guide to Control Charts  that may help.

X Bar Range Chart

The X Bar Range chart (XBar and R) is used when there are multiple measurements taken in one sample and plots the process mean (Xbar chart) and process range (R chart) over time for variables data in subgroups, to help plot the stability of processes.

For example, if the pH is taken for five different pieces of product, the five different measurements will show up in the X Bar Range table. If all of these values are added together and then divided by the number of measurements taken, it will equal the average value, or x bar. This is what is graphed on the X Bar chart. When the lowest value is subtracted from the highest value, this equals the range, which is graphed on the Range chart. The range shows the overall consistency of the attribute being measured. The larger the range is, the less consistent the measurements are. If a point is not consistent with the rest of the data and is affecting other calculated values, this data point can be deleted. This will allow other calculated values, such as the x-double-bar and control limits, to reflect the data more accurately. X-double-bar is the average of all the averages, or the average of all the data points shown on the graph. Control limits are calculated to show where most data points on the graph will fall, provided the process is not out-of-control.

The X Bar Range chart should be used when data is generated frequently and is variable. This chart is useful for detecting small changes in the process and when multiple measurements are taken to represent a larger group of product.

See component reference documentation:

• X Bar Range Chart: Vision is a java-based client
• XBar Chart - Perspective: Perspective is a web client

Individual Chart

An individual control chart ( XmR  chart,  I -chart) can be used for time series tracking of a process to determine if the process is in statistical control and can be considered stable. When a process is considered stable, it experiences only common cause variability. When a process is not in control, special cause conditions can be causing nonstability.

The Individual Chart is similar to the X Bar Range Chart, however, only one measurement is taken per sample instead of multiple. This means that the X Bar will always be the same value as the measurement, and a moving range will be calculated instead of the basic range. This means that instead of subtracting the lowest value from the highest value in one sample, moving range will calculate the difference between one sample and the next, showing the change from sample to sample. If a single measurement is used on the X Bar Range Chart, the range will always be zero, which fails to show the consistency between measurements.

Individual charts are useful in situations when testing of a product results in the destruction of the product or if the testing is time consuming. It can also be used when a sample will yield the same result for a long period of time no matter how many measurements are made, such as batch operations. When using the Individuals Chart, the variable data should fall into a normal distribution, meaning the data points are equally likely to fall on either side of the average. This would appear as a bell curve on a histogram.

See component reference documentation:

• Individual and Range Chart - Vision: Vision is a java-based client
• Individual Chart - Perspective: Perspective is a web client
• Range Chart - Perspective: Perspective is a web client

Median Chart

A median chart is a special purpose variation of the X-bar chart. This chart uses the median instead of the subgroup average to show the system’s central location. The median is the middle point when data points are arranged from high to low. The chart shows all the individual readings and can be used to determine if the system is stable and predictable or to monitor the effects of process improvement theories. Although median charts show both central location and spread, they are often paired with range charts.

The Median Chart is also known as the MA-MR Chart or Moving Average-Moving Range Chart. Because data is generated slowly, the data on this chart is displayed differently. The first sample will contain three new data points. The second sample will contain the two most recent data points from sample one, in addition to one new data point. Sample three will contain the two most recent from sample two, as well as one new data point, and so on. Even though there are three samples with three data points each, there is only a total of five data points. On this chart, the median and the moving range are graphed. The median is the middle value based on the measurements in the sample (this is not the same as the average), while the range is the highest value minus the lowest value for each sample.

Like an individual chart, this chart should be used when the data is variable. In addition, data may also be costly or time-consuming to gather, or remain constant for a long periods of time. This chart should also be used when the data will not be normally distributed or when detecting small process changes.

See component reference documentation:

• Median and Range Chart - Vision: Vision is a java-based client
• SPC Median Chart - Perspective: Perspective is a web client
• Range Chart - Perspective: Perspective is a web client

X Bar Standard Deviation Chart

An Xbar-S chart plots the process mean (Xbar chart) and process standard deviation (S chart) over time for variables data in subgroups. This combination control chart is widely used to examine the stability of processes in many industries.

This chart is very similar to the X Bar Range Chart. The major difference between the two is that the X Bar and S chart uses standard deviation to find the amount of variation within a sample instead of the range. Data must be in variable form to use this chart. It should also be used when data is plentiful enough that samples can have ten measurements or more, or when there is a need to rapidly detect small changes.

See component reference documentation:

• X Bar and S Chart - Vision: Vision is a java-based client
• XBar Chart - Perspective: Perspective is a web client

Process Capability Chart

Capability or Process Capability refers to the statistical position of the normal distribution compared to the product or process specification. A process is capable when a bell curve is created by +/- 3 Standard Deviation and fits easily inside the desired specification. Indicators of capability are calculated based on the number of Sigma or Standard Deviations fitting between the process Mean and the closest specification.

• Cp or Cpi is the measurement of the ratio of Six Sigma divided into the allowable specification. Cpi does not indicate how well the process is performing, rather how good it could be.

Cp = Process Capability. A simple and straightforward indicator of process capability.
Cpk = Process Capability Index. Adjustment of Cp for the effect of non-centered distribution.

• Cpk is a typical indicator used to describe actual process capability. Cpk is used to determine the number of defects that are being produced, even if none have been found up to this point.
• Cpk is the capability on K side of the distribution. The K factor, or side, has the most risk and therefore is the worst of two possible measurements in a bilateral specification.
• Cpk of 1.33 indicates 4 Sigma Capability or 4/3rds.
• Cpk of 1.67 indicates 5 Sigma Capability or 5/3rds.

The greater the Cpk the less likely nonconformance will be present.

Capability is often misunderstood or considered a difficult concept. Most people are unaware that they are affected by Capability while driving to work. Here is an example of Capability in effect:

• Specification: width of one lane.
• Process: our car and its variation.
• Driving in our lane over a distance, the car easily stays within our allowable variation. We seldom hit the guardrail or oncoming traffic, indicating a very capable process.
• Cpk is likely to be 1.33 or greater.
• Larger vehicles have a smaller Cpk and smaller cars a larger Cpk.

See component reference documentation:

• Process Capability Chart | Vision: Vision is a java-based client
• Process Capability Chart - Perspective: Perspective is a web client

Process Performance Chart

Process Performance can be displayed on the same chart used for Process Capability. 

Ppk is an index similar to Cpk but considers more sources of variation in the process over a longer period of time.

Pp = Process Performance. A simple and straightforward indicator of process performance.
Ppk = Process Performance Index. Adjustment of Pp for the effect of non-centered distribution.

P Chart

P charts are a type of control chart used to monitor the proportion of nonconforming units in a sample, where the sample proportion nonconforming is defined as the ratio of the number of nonconforming units to the sample size, n. The number of nonconformities per item is irrelevant for this type of chart, which only tracks the total number of items; however, it is possible to have the types of nonconformities displayed on the same chart. P charts are used only when looking at the number of nonconforming items and when the sample size is not consistent.

See component reference documentation:

• P Chart:  Vision is a java-based client
• SPC P-Chart - Perspective: Perspective is a web client

C Chart

C-chart, also known as a count chart, is used to monitor count type data, typically total number of nonconformities per unit. It is also occasionally used to monitor the total number of events occurring in a given unit of time. Often, the types of nonconformities and their individual counts are noted as well. This chart is best used when counting nonconformities when the sample size will not vary. It is also important that each sample has equal opportunity for nonconformities.

See component reference documentation:

• C-Chart | Vision: Vision is a java-based client
• SPC C-Chart - Perspective: Perspective is a web client

Histogram Chart

A histogram shows the distribution of the data provided from the samples. A typical histogram has a normal distribution, meaning that most data points will fall in the middle of the graph and fewer will fall towards the outside, forming a bell curve. A distribution that is normal is just the most common pattern. There are other types of curves, such as skewed distribution or double-peaked distribution, which may be typical for certain processes. If a bell-shaped curve is formed on the histogram, then any variations in the data are most likely due to an assignable cause. Assignable causes influence variations, which can occur in materials, environment, machines, peoples, etc. Ultimately, the histogram shows the consistency of a process.
Histograms should be used when data is numerical and the shape of the distribution is to be observed. Observing the shape of the graph can help to determine whether or not the data is distributed normally, if a change has occurred in the process over time, or if two or more processes are different. This graph can also help to communicate with others about the data distribution or determine if a process will be able to meet the requirements of a customer.

See component reference documentation:

• Vision Histogram Chart | Vision: Vision is a java-based client
• SPC Histogram Chart - Perspective: Perspective is a web client

Pareto Chart

The Pareto chart, named after Vilfredo Pareto, is a type of chart that contains both bars and a line graph, where individual values are represented in descending order by bars, and the cumulative total is represented by the line.

The pareto chart is a bar chart that is used to show which factors are the biggest problems. The bars are arranged so that the most significant factor, that is, the factor that occurs the most frequently or cost the most(whether that be in time or money), is on the left, while the shortest bar, or least significant, is on the right. Because of the organization of the pareto chart, it is best used when looking at how often problems or causes occur and which of those are the most significant, or when looking at a specific component of a larger problem. Like the histogram chart, the pareto is also useful for the communication of data.

See component reference documentation:

• Vision Pareto Chart - Vision: Vision is a java-based client
• SPC Pareto Chart - Perspective: Perspective is a web client

Box and Whisker Chart

The Box and Whisker chart is used to summarize some of the most important statistical characteristics of a frequency distribution. It shows the mean point, a horizontal box that includes a percentage of the data samples (between the 'hinge' points), as well as 'whiskers' for plotting information about outlying sample points.

See component reference documentation:

• Vision Box and Whisker Chart - Vision: Vision is a java-based client
• Box and Whisker Chart - Perspective: Perspective is a web client