What is Common Cause Variation? Common cause variation is the kind of variation that is part of a stable process. These are variations that are natural to a system and are quantifiable and expected. Common cause variations are those that are predictable, ongoing, and consistent. Major changes would typically have to be made in order…
A box plot may seem like a simple tool, but as this example shows it can help reveal common and special cause variation that may not have otherwise been noticed. Be sure you’re looking below the surface to optimize your performance!
Short production runs are a necessity in high-mix, low-volume manufacturing environments. The trend in manufacturing has been toward smaller production runs, with production runs – as well as products – tailored to the individual customer’s needs. Although this minimizes inventory and improves responsiveness to the customer, it complicates the application of statistical process control (SPC). Classical…
The run chart is a powerful, simple and easy-to-use process improvement tool. Often, the run chart is shortchanged as the statistical tests that can be used with run charts are overlooked. This article takes the reader through the benefits of a run chart as well as how to correctly create and analyze one. A process…
Every process falls into one of four states: Ideal: produces 100 percent conformance and is predictable Threshold: predictable but produces the occasional defect Brink of chaos: not predictable and does not produce defects Chaos: not predictable and produces defects at an unacceptable rate Processes tend to migrate toward chaos if not effectively managed. Pre-control Charts…
Statistical process control (SPC) is the application of statistical methods to identify and control the special cause of variation in a process. Control charts, in theory, are used in product and process development to analyze processes. When a process is shown to be in control in both an average and range chart the process can…
Multivariate analysis techniques may be useful in statistical process control (SPC) whenever there is more than one process variable. Multivariate control charting is usually helpful when the effect of multiple parameters is not independent or when some parameters are correlated. This article focuses on parameters that correlate when the Pearson correlation coefficient is greater than…
Article authors, participants in discussion forums and even software documentation claim that any data and especially time-sequenced data are useful only if they are homogeneous – that is, only if they display control chart stability (CCS).1,2,3,4 According to one quality control expert, Donald J. Wheeler, when out-of-control signals occur, “all of our computations, and all of…
The recent spate of events such as rogue trading losses, flash trades and a seeming outbreak of Ponzi schemes has made many in financial services wary about their existing risk management practices. In post-financial crisis times, financial services companies are already under pressure to cut their operational costs while also being expected to comply with…
Control charts have two general uses in an improvement project. The most common application is as a tool to monitor process stability and control. A less common, although some might argue more powerful, use of control charts is as an analysis tool. The descriptions below provide an overview of the different types of control charts…
This article introduces the concept of earned value management (EVM) indexes, a project assessment technique, and control charts, a statistical tool for monitoring variation in a process, and describes how both may be used in tangent to capture more insight from project performance. What to Know About EVM Indexes EVM is a project management technique…
Statistical process control (SPC), despite sounding esoteric, is a subject that every process owner and worker should – and can – understand, at least at a high level. Knowing whether a process is in control and stable is paramount to producing a product or service that meets customer needs. In this hour-long Minitab training course…
A true control strategy should be aligned with the process’s statistically based control limits.
To plot individual data or to group the data and plot the mean on a control chart, that is the question. Several authors (not including Shakespeare) have weighed in on this issue and I want to present their arguments and then my own. First, let us assume that the process conditions are such that using…
From tedious time consuming task to opportunity for improvement. When applying control charts it is common practice to establish the control limits based on the process capability study and then use fix limits on the chart during production. This method is also frequently applied in SPC software programs. The advantage of using fixed limits is…
Control charting is a tool used to monitor processes and to assure that they remain in control or stable. Proposed by Walter Shewart in 1924, control charts help distinguish process variation due to assignable causes from those due to unassignable causes. Table 1: Types of Process Variation Found on a Control Chart Types of Variation Variation…
Everybody is familiar with control charts for quality control. An example of a control chart is shown below. In the example a packaging company who made blisters for the pharmaceutical industry found the process average for a critical characteristic was out of control. There were some ideas about possible causes but, as in most other…
A problem that has often confronted practitioners using control charts is when to consider recomputing the control limits. I’ve asked this question to several experts and researched it in numerous SPC texts. Oddly, either the issue is ignored or there is no clear answer to this important question. So I’ll offer my recommendation in this…
Six Sigma practitioners often state that Six Sigma is not about learning statistics, but is instead about understanding which tool to apply to each situation and how to properly interpret the results. We will attempt to understand the meaning of this statement in four real world examples I have experienced in industry. Control Charts Subgrouped…
Some practitioners mistakenly believe that it is not necessary to transform data before creating an individuals control chart when the underlying process distribution response is not normal. An individuals control chart, however, is not robust to non-normally distributed data. Therefore, it is important to use an alternate control charting approach. Necessary Transformation Consider a hypothetical…
Selecting the right type of control chart is a vital starting point for statistical process control (SPC). Which chart to use depends mainly on the classification of the data, the type of underlying distribution and the intent of the application. Selecting the wrong type can result in many false alarms, leading to expensive and fruitless…
Statistical quality control (SQC) and statistical process control (SPC) are two powerful tools, which have different goals and requirements for successful application. By using a methodology that combines the strengths of both approaches, it is possible to overcome the individual weaknesses of each one. The volume of calculations required by this technique prohibits manual data…
When the principles of statistical process control (SPC) are used to manage a business or manufacturing process, any indication of the process going out of control will prompt some action on the part of the process owner. When it is well used, SPC will detect shifts in the process due to issues such as tool…
Steps in Constructing a p-Chart: p Charts can be used when the subgroups are not of equal size. This guide shows you the steps involved in constructing a p Chart.
Control Chart Wizard – p-Chart: Control charts dealing with the proportion or fraction of defective product are called p charts (for proportion). There is a difference between a defect and defective, as there is between a nonconformity and nonconforming unit. The p-chart control chart is used with discrete/attribute defective data when the sample size is greater than 50. The sample size may vary.
Steps in Constructing an np-Chart: The np Chart can be used for the special case when the subgroups are of equal size. Then it is not necessary to convert nonconforming counts into the proportions phat(i). Rather, one can directly plot the counts x(i) versus the subgroup number i.
Control Chart Wizard – np-Chart: The np control chart are used to monitor the number of nonconforming units in samples of inspected units. A nonconforming unit is a product which fails to meet at least one specified requirement. There is a difference between a defect and defective, as there is between a nonconformity and nonconforming unit. The np-chart control chart is used with discrete/attribute defective data when the sample size is greater than 50. The sample size must be constant; this control chart is only valid if data is collected in same-size subgroups.
Control Chart Wizard – Discrete Defective Data: How large is your discrete/attribute defective data sample size?
Control Chart Wizard – Discrete Defective Data: How large is your discrete/attribute defective data sample size?
Steps in Constructing a u-Chart: The u Chart is used when it is not possible to have an inspection unit of a fixed size (e.g., 12 defects counted in one square foot), rather the number of nonconformities is per inspection unit where the inspection unit may not be exactly one square foot…it may be an intact panel or other object, different in sizethan exactly one square foot. When it is converted into a ratio per square foot, or some other measure, it may be controlled with a u chart. Notice that the number no longer has to be integer as with the c chart.
Control Chart Wizard – u-Chart: Control charts dealing with the number of defects or nonconformities over time are called u charts. There is a difference between a defect and defective, as there is between a nonconformity and nonconforming unit. The u-chart control chart is used with discrete/attribute defect data when the sample size varies.
Steps in Constructing a c-Chart: The c Chart measures the number of nonconformities per “unit” and is denoted by c. This “unit” is commonly referred to as an inspection unit and may be “per day” or “per square foot” of some other predetermined sensible rate.
Control Chart Wizard – c-Chart: Control charts dealing with the number of defects or nonconformities are called c charts (for count). There is a difference between a defect and defective, as there is between a nonconformity and nonconforming unit. The c-chart control chart is used with discrete/attribute defect data when c-Bar is greater than 5.
Control Chart Wizard – Discrete Defect Data: How large is your discrete/attribute defect data sample size?
Control Chart Wizard – Discrete Data: What type of discrete/attribute data do you have?
Steps in Constructing an X-Bar and s Control Chart: This document contains the step-by-step instructions to construct an X-Bar and s control chart. First the s chart is constructed. If the s chart validates that the process variation is in statistical control, the XBAR chart is constructed.
Control Chart Wizard – Average And Standard Deviation: This document contains the step-by-step instructions to construct an Average And Standard Deviation control chart.
Steps In Constructing An Exponentially Weighted Moving Average (EWMA) Chart: This document contains the step-by-step instructions to construct an Exponentially Weighted Moving Average (EWMA) control chart.
Steps In Constructing An Individuals And Moving Range Control Chart: This document contains the step-by-step instructions to construct an Individuals And Moving Range control chart.
Steps in Constructing an X-Bar and R Control Chart: This document contains the step-by-step instructions to construct an X-bar and R control chart. First the R chart is constructed. If the R chart validates that the process variation is in statistical control, the XBAR chart is constructed.
Steps in Constructing a Median And Range Control Chart: This document contains the step-by-step instructions to construct a Median And Range control chart. The primary reason for using medians is that it is easier to do on the shop floor because no arithmetic must be done. The person doing the charting can simply order the data and pick the center element.
Control Chart Wizard – Median And Range: If the sample size is relatively small (e.g., less than 10-15) and the median is known, we can display how well a process is centered using the median or middle value. In contrast to the X-Bar and R control chart, this chart is useful when you would like to see less influence by data outliers. This can be an advantage or a disadvantage depending on your objective. The range of a sample is simply the difference between the largest and smallest observation. The Median and R control chart is used with continuous/variable data when subgroup or sample size is between 2 and 15.
Average And Range – X-Bar and R: If the sample size is relatively small (e.g., less than 10-15), we can use the range instead of the standard deviation of a sample to construct control charts on X-Bar (arithmetic mean) and the range, R. The range of a sample is simply the difference between the largest and smallest observation. The X-bar and R control chart is used with continuous/variable data when subgroup or sample size is between 2 and 15.
Continuous/Variable Control Chart Table Of Constants
Wizard Home > Continuous/Variable Data Sample Size Selector Control Chart Suggestion Individuals And Moving Range – X and Rm Control Chart Description Control charts for individual measurements (e.g., the sample size = 1) use the moving range of two successive observations to measure the process variability. The combination of the X Chart for Individuals and…
Wizard Home > Control Chart Wizard Home Question How large is your continuous/variable data sample size? Choices Please select your sample size from the choices below: Sample Size Of One Sample Size Is Small (Usually Less Than 10-15) Sample Size Is Small (Usually Less Than 10-15), Median Value Known Sample Size Is Large (Usually Greater…
This Control Chart Wizard will guide you through the process of determining your data type, help you determine the correct control chart, and provide the proper formulas for creating a control chart for your process.
iSixSigma Staff
Felix C. Veroya
Carl Berardinelli
Marc Schaeffers
Kicab Castaneda-Mendez
Abhishek Soni
Michael Cyger
Richard Miller
John J. Flaig
Kerri Simon
Shree Phadnis
Forrest W. Breyfogle III
Vishwajit Joshi
Alastair Muir
iSixSigma-Editorial