Statistical Process Control - Part 1

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From the course by University System of Georgia

Six Sigma Advanced Improve and Control Phases

84 ratings

University System of Georgia

Six Sigma Advanced Improve and Control Phases

84 ratings

Course 4 of 4 in the Specialization Six Sigma Green Belt

This course is for you if you are looking to dive deeper into Six Sigma or strengthen and expand your knowledge of the basic components of green belt level of Six Sigma and Lean. Six Sigma skills are widely sought by employers both nationally and internationally. These skills have been proven to help improve business processes and performance. This course will take you deeper into the principles and tools associated with the "Improve" and "Control" phases of the DMAIC structure of Six Sigma. It is highly recommended that you complete the "Yellow Belt Specialization" and the courses "Six Sigma and the Organization (Advanced)," "Six Sigma Advanced Design and Measure Phase," and "Six Sigma Advanced Analyze Phase" before beginning this course. In this course, your instructors will introduce you to, and have you apply, some of the tools and metrics that are critical components of Six Sigma. This course will provide you with the advanced knowledge of root cause analysis, lean tools, control plan, process control, and statistical process control (SPC) as they are associated with Six Sigma and Lean. Every module will include readings, videos, and quizzes to help make sure you understand the material and concepts that are studied. Registration includes online access to course content, projects, and resources but does not include the companion text The Certified Six Sigma Green Belt Handbook (2nd edition). The companion text is NOT required to complete the assignments. However, the text is a recognized handbook used by professionals in the field. Also, it is a highly-recommended text for those wishing to move forward in Six Sigma and eventually gain certification from professional agencies such as American Society for Quality (ASQ).

From the lesson

Statistical Process Control (SPC) & Control Plan

In this module, Dr. David Cook will cover statistical process control (SPC) and control plan.

Statistical Process Control - Part 15:44

Statistical Process Control - Part 23:23

Statistical Process Control - Part 32:48

Statistical Process Control - Part 45:15

Meet the Instructors

David Cook, PhD
Assistant Professor of Mechanical Engineering Technology
Department of Mechanical Engineering Technology
Bill Bailey, PhD
Assistant Professor of Industrial Engineering
Department of Systems and Industrial Engineering

Statistical process control,

is a graphical tool used to monitor on-going performance.

Control charts can trace their origins back to Shewhart

at Western Electric in the 1920s.

Control charts show process variation while work is underway.

It provides a means for monitoring the state of the process in real-time,

and detecting issues.

Control charts establish performance boundaries.

They also help us uncover assignable causes of variation, and

distinguish these from chance causes.

There are over 30 types of control charts in use today.

So control charting can be applied to many types of data.

They also assist in creating a foundation from which professionals can discuss and

address process performance issues.

Control charts are applicable for any scenario that varies over time.

We particularly are interested in using these types of tools

to assess process performance after an improvement has been implemented.

Remember, control charting is not just for monitoring, it is also for detection.

SPC, or statistical process control, enables real time monitoring.

It allows us to assess the state of a process,

as well as the effectiveness of a change to that process.

SPC can also help us identify process changes.

The type of change and the need for a corrective action used properly.

SPC can ensure our decisions are statistically valid.

Other important benefits include centering of the process, reduction of variation,

and improvement in our understanding of the process and design.

The components of a control chart include a center line and

upper and lower control limits.

Quality characteristic values are plotted along the vertical axis.

There are two main types of control charts.

The first is attribute.

Attribute charts measure percent non-conforming or

the number of non-conformities in a unit.

The other type is variables.

These are based on numerical values.

With control charting, we can know first when to take collective action.

We also know the type of remedial action to take, when to leave and process alone.

We also can learn about the process capability.

Finally, possible avenues for quality improvement.

It also helps us to define a baseline.

Before we can eliminate variation,

we must recognize that there are really two types of variation at play.

Special cause is a signable variation,

something that is not inherent in the process.

Common cause is due to chance.

Variability due to common or chance causes is something inherent in the process.

Deming believed that 15% of all problems are due to special causes.

Action on the part of management and workers can reduce special causes.

Deming also believe that about 85% of all problems are due to common causes.

This can only be solve by management since you must change the system.

Once you have localized an eliminated assignable causes,

we should remove out of control points and revise the center line and control limits.

There is no need to maintain control charts

if the process demonstrates consistent capability.

Instead, focus your attention and resources on other areas.

Be aware that control limits on control charts are influenced by the variability

in the process.

As such, these control limits will change.

Today's technology makes this a very manageable endeavor.

Control charts are easy to set but are very difficult to maintain.

Often, our failure to maintain comes down to our inattention.

In other words,

failing to make adjustments when the control chart detects an issue.

Prioritization or productivity also overshadows control charting.

Lack of training can also derail a control charting program.

In most cases, sampling is done in time order or by process.

We call this type of sampling, the instant of time method.

Observations are selected at approximately the same thing, for

the population under consideration.

If we want to detect a small shift, a larger sample size is needed.

Choosing large samples frequently provides the most information, but

is not always feasible.

We must consider whether we have destructive testing and

the cost of sampling.

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