This document provides an overview of statistical process control (SPC). It discusses the history and development of SPC by Walter Shewhart in the 1920s. It describes how SPC uses control charts to monitor process variation and identify when processes are out of control. The document outlines different types of control charts and how they are interpreted. It also discusses key SPC concepts like process capability indices and the seven step process for implementing SPC.

1. Statistical Process Control
Nicola Mezzetti, Ph.D.
Department of Information Engineering and Computer Science
University of Trento
nicola.mezzetti@gmail.com
A.A. 2014/2015
Nicola Mezzetti, Ph.D. Statistical Process Control

2. "How much variation should we leave to
chance?"
W. A. Shewhart
Nicola Mezzetti, Ph.D. Statistical Process Control

3. What is Statistical Process Control?
Statistical Process Control (SPC) is an industry standard
methodology for measuring and controlling quality during the
manufacturing process.
Attribute data (measurements) is collected from products as they
are being produced.
By establishing upper and lower limits, variations in the
processes are monitored before they result in a defective
product,
reducing the amount of material scrap along with direct and
indirect labor waste
eliminating the need for

4. nal inspection
increasing pro

5. tability
Nicola Mezzetti, Ph.D. Statistical Process Control

6. History of Statistical Process Control
In 1924 Walter Shewhart developed a simple graphical method
for plotting collected data with predetermined control limits. This
was the

7. rst of a growing range of SPC charts, commissioned by
Bell Laboratories to improve the quality of telephones
manufactured.
Understanding the causes of variation within an industrial process
proved indispensable to identify actions to improve process and
output. In the 1950's, with the eective use of SPC, Deming
converted post war Japan into the world leader of manufacturing
excellence.
This approach is increasingly being applied in service industry by
thinking of systems as processes. As well as providing a basis for
quality improvement, SPC Charts also oer alternative methods of
displaying data.
Nicola Mezzetti, Ph.D. Statistical Process Control

8. When to use Statistical Process Control?
Are your quality costs really known?
Can current data be used to improve your processes, or is it
just data for the sake of data?
Are the right kinds of data being collected in the right areas?
Are decisions being made based on true data?
Can you easily determine the cause of quality issues?
Do you know when to perform preventative maintenance on
machines?
Can you accurately predict yields and output results?
Nicola Mezzetti, Ph.D. Statistical Process Control

9. About Statistical Process Control
Dr. W. Edwards Deming claimed that the majority of variation in a
process is due to operator over adjustment.
SPC gives operators a tool to determine when a statistically
signi

10. cant change has taken place in the process or when an
seemingly signi

11. cant change is just due to chance causes.
Nicola Mezzetti, Ph.D. Statistical Process Control

12. Why do Companies use SPC?
SPC itself will not make improvements.
SPC will give operating personnel a tool to identify when a
special cause of variation has entered the process so that the
special cause can be
eliminated (if the special cause has a negative impact on the
process), or
built into the process (if the special cause has a positive
impact on the process)
Moreover, SPC allows to
eliminate constant tweaking of the process
identify opportunities for improvement that can lead to
reduced variation and processes that are better aimed at their
target
Nicola Mezzetti, Ph.D. Statistical Process Control

13. Control Charts
Control chart is a tool used to study how a process changes
over time.
Measurements are plotted in time order. A control chart always
has
a central line for the average
an upper line for the upper control limit1
a lower line for the lower control limit
By comparing current data to these lines, you can draw
conclusions about whether the process variation is in control or
aected by special causes of variation.
Control charts for variable data are used in pairs:
The top chart monitors the average (x chart)
The bottom chart monitors the range (R chart)
1Control limits are determined by the capability of the process, whereas
speci

14. cation limits are determined by the customer's needs
Nicola Mezzetti, Ph.D. Statistical Process Control

15. Process Mean Chart
Center Line
x =
Pm
i=1
Pn
j=1 xij
mn
Control Limits
x 3
where 99.73% of all data
points should fall.
Plotted Statistics
xi =
Pn
j=1 xij
n
Nicola Mezzetti, Ph.D. Statistical Process Control

16. Process Variation Chart
Center Line
R
=
Pm
i=1 max(xij ) min(xij )
m
Upper Control Limit
D4R
Lower Control Limit
D3R
Plotted Statistics
Ri = max(xij ) min(xij )
Nicola Mezzetti, Ph.D. Statistical Process Control

17. How to use Control Charts?
Data is collected from the process, typically in subgroups of 3
to 5, and the subgroup mean and range is plotted on the charts.
Once a point is plotted the chart is interpreted to determine if
the process is staying in-control or if the process is out-of-control.
Data that falls within the control limits indicates that everything is
operating as expected.
Any variation within the control limits is likely due to a
common cause, the natural variation that is expected as part
of the process.
If data falls outside of the control limits, this indicates that an
assignable cause is likely the source of the product variation
something within the process should be changed to

18. x the
issue before defects occur.
Nicola Mezzetti, Ph.D. Statistical Process Control

19. Interpreting Control Charts
The most common patterns to watch out for are:
One point outside of the control limits
Eight points in a row on either side of the center line
Eight points in a row trending in the same direction
Cycles or recurring trends
Nicola Mezzetti, Ph.D. Statistical Process Control

20. Combining Variability and Mean Charts
The R chart is examined before the x chart:
if the R chart indicates the sample variability is in statistical
control, then the x chart is examined to determine if the
sample mean is also in statistical control
if the sample variability is not in statistical control, then the
entire process is judged to be not in statistical control
Nicola Mezzetti, Ph.D. Statistical Process Control

21. Control Points
Before initiating any SPC program it is necessary to identify what
to count, that is control points. Control points can be related to
Process
Product
Financials
Nicola Mezzetti, Ph.D. Statistical Process Control

22. When to Use a Control Chart
Putting spec limits on control charts
Using control charts only to satisfy customer needs
Plotting data after the process has already been run
Using the wrong type of control chart for the process
Not reviewing control charts and how they are used on a
regular basis
Not

23. rst conducting a process capability study
Not taking random samples from the process, or not using a
sampling frequency or sample size that captures the variation
in the process
Nicola Mezzetti, Ph.D. Statistical Process Control

24. When to Use a Control Chart
When controlling ongoing processes by

25. nding and correcting
problems as they occur
When predicting the expected range of outcomes from a
process
When determining whether a process is stable (in statistical
control)
When analyzing patterns of process variation from special
causes (non-routine events) or common causes (built into the
process)
When determining whether your quality improvement project
should aim to prevent speci

26. c problems or to make
fundamental changes to the process
Nicola Mezzetti, Ph.D. Statistical Process Control

27. The Seven Step Process
The use of a Seven Step Process improves statistical process
control. Proper application of SPC will improve process, product
and

28. nancial results.
Investigation and benchmarking of current process
Identi

29. cation of appropriate measurable variables
Estimation of available resources and project cost
Estimation of project time line
Application of appropriate statistical techniques
Implementation of corrective action
Statistical monitoring of identi

30. ed variables
Nicola Mezzetti, Ph.D. Statistical Process Control

31. Process Capability Indices
We sometimes talk about Process Capability and de