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## Statistical Process Contol (SPC)

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### The concept of process variability forms the heart of SPC. ... Fishbone Diagram. 38. Quality. Problem. Machines. Measurement. Human. Process. Environment ... – PowerPoint PPT presentation

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Title: Statistical Process Contol (SPC)

1
Statistical Process Contol (SPC)
• Lec-1

2
Quality and SPC
• The concept of quality has been with us since the
beginning of time.
• Typically the quality of products was described
by some attribute such as strength, beauty or
finish.
• However, the mass production of products that the
reproducibility of the size or shape of a product
became a quality issue.

3
Quality and SPC
• Quality was obtained by inspecting each part and
passing only those that met specifications.
• With SPC, the process is monitored through
sampling.
• Considering the results of the sample,
process is able to produce defective parts.

4
Process Variability
• The concept of process variability forms the
heart of SPC.
• For example, if a basketball player shot free
throws in practice, and the player shot 100 free
throws every day, the player would not get
exactly the same number of baskets each day.
• Some days the player would get 84 of 100, some
days 67 of 100, and so on.
• All processes have this kind of variation or
variability.

5
Process Variability
• The variation can be partitioned into 2
components.
• Natural process variation (common cause) or
system variation.
• In the case of the basketball player, this
variation would fluctuate around the player's
long-run percentage of free throws made.
• Special cause variation is typically caused by
some problem or extraordinary occurrence in the
system.
• In the case of the player, a hand injury might
cause the player to miss a larger than usual
number of free throws on a particular day.

6
Statistical Process Control (SPC)
• SPC is a methodology for charting the process and
quickly determining when a process is "out of
control.
• (e.g., a special cause variation is present
because something unusual is occurring in the
process).
• The process is then investigated to determine the
root cause of the "out of control" condition.
• When the root cause of the problem is determined,
a strategy is identified to correct it.

7
Statistical Process Control (SPC)
• The management responsible to reduce common cause
or system variation as well as special cause
variation.
• This is done through process improvement
techniques, investing in new technology, or
reengineering the process to have fewer steps and
therefore less variation.
• Reduced variation makes the process more
predictable with process output closer to the
desired or nominal value.

8
Statistical Process Control (SPC)
• The process above is in apparent statistical
control.
• Notice that all points lie within the upper
control limits (UCL) and the lower control limits
(LCL). CL-centerline
• This process exhibits only common cause variation.

9
• The process above is out of statistical control.
• Notice that a single point can be found outside
the control limits (above them).
• This means that a source of special cause
variation is present.
• Having a point outside the control limits is the
most easily detectable out-of-control condition.

10
• The graphic above illustrates the typical cycle
in SPC.
• First, the process is highly variable and out of
statistical control.
• Second, as special causes of variation are found,
the process comes into statistical control.
• Finally, through process improvement, variation
is reduced.
• This is seen from the narrowing of the control
limits.
• Eliminating special cause variation keeps the
process in control process improvement reduces
the process variation and moves the control
limits in toward the centerline of the process.

11
Out-of-Control Conditions
• Several types of conditions exist that indicate
that a process is out of control
• Extreme Point Condition
• This process is out of control because a point is
either above the UCL or below the LCL.

12
Out-of-Control Conditions
• Control Chart Zones
• Control charts can be broken into 3 zones, a, b,
c on each side of the process center line.
• A series of rules exist that are used to detect
conditions in which the process is behaving
abnormally to the extent that an out of control
condition is declared.

13
Out-of-Control Conditions
• The probability of having 2 out of 3 consecutive
points either in or beyond zone A is an extremely
unlikely occurrence when the process mean follows
the normal distribution.
• This criteria applies only to X-bar charts for
examining the process mean.

X, Y, and Z are all examples of this phenomena.
14
Out-of-Control Conditions
• The probability of 4 out of 5 consecutive points
either in or beyond zone B is also an extremely
unlikely occurrence when the process mean follows
the normal distribution.
• Applied to X-bar chart when analyzing a process
mean.

X, Y, and Z are all examples of this phenomena.
15
Out-of-Control Conditions
• Runs Above or Below the Centerline
• The probability of having long runs (8 or more
consecutive points) either above or below the
centerline is also an extremely unlikely
occurrence when the process follows the normal
distribution.
• Applied to both X-bar and r charts.

16
Out-of-Control Conditions
• Linear Trends
• The probability of 6 or more consecutive points
showing a continuous increase or decrease is also
an extremely unlikely occurrence when the process
follows the normal distribution.
• Applied to both X-bar and r charts.

17
Out-of-Control Conditions
• Oscillatory Trend
• The probability of having 14 or more consecutive
points oscillating back and forth is also an
extremely unlikely occurrence when the process
follows the normal distribution.
• Applied to both X-bar and r charts.

18
Out-of-Control Conditions
• Avoidance of Zone C
• The probability of having 8 or more consecutive
points occurring on either side of the center
line and do not enter Zone C.
• This phenomena occurs when more than one process
is being charted on the same chart, the use of
improper sampling techniques, or perhaps the
process is over controlled.

19
Out-of-Control Conditions
• Run in Zone C
• The probability of having 15 or more consecutive
points occurring the Zone C.
• This condition can arise from improper sampling,
falsification of data, or a decrease in process
variability that has not been accounted for when
calculating control chart limits, UCL and LCL.

20
The basics
• Dont inspect the product, inspect the process.
• You cant inspect it in, youve got to build it
in.
• If you cant measure it, you cant manage it.

21
The SPC steps
• Basic approach
• Awareness that a problem exists.
• Determine the specific problem to be solved.
• Diagnose the causes of the problem.
• Determine and implement remedies.
• Implement controls to hold the gains achieved by
solving the problem.

22
SPC requires the use of statistics
• Quality improvement efforts have their foundation
in statistics.
• SPC involves the
• collection
• tabulation
• analysis
• interpretation
• presentation of numerical data.

23
SPC is comprised of 7 tools
• Pareto diagram
• Histogram
• Cause and Effect Diagram
• Check sheet
• Process flow diagram
• Scatter diagram
• Control chart

24
Pareto Principle
• Alfredo Pareto (1848-1923) Italian Economist
• Conducted studies of the distribution of wealth
in Europe.
• 20 of the population has 80 of the wealth
• Joseph Juran used the term vital few trivial
many or useful many. He noted that 20 of the
quality problems caused 80 of the dollar loss.

25
Pareto diagram
(64)
A pareto diagram is a graph that ranks data
classifications in descending order from left to
right.
Percent from each cause
(13)
(10)
(6)
(3)
(2)
(2)
Poor Design
Defective parts
Operator errors
Machine calibrations
Defective materials
Surface abrasions
Wrong dimensions
Causes of poor quality
26
Pareto diagram
Complaints
27
Pareto diagram
• Sometimes a pareto diagram has a cumulative line.
• This line represents the sum of the data as they
are added together from left to right.

28
Pareto diagram
• Sometimes a pareto diagram has a cumulative line.
• This line represents the sum of the data as they
are added together from left to right.

Above the bars, using the 2nd Y-axis representing
the cumulative data, plot the cumulative
percentage values in the form of a line.
29
Pareto diagram
• The cumulative percentage can be computed (dotted
line).
• On the right, add a vertical percent scale equal
in length to the scale on the left.
• Label this from 0 to 100 .

30
Pareto diagram
Table 1. Example of a Tabulation of Causes of
Ball Bond Lifting for use in a Pareto Chart
31
Pareto diagram
Table 1. Example of a Tabulation of Causes of
Ball Bond Lifting for use in a Pareto Chart
32
Histogram
The histogram, graphically shows the process
capability and, if desired, the relationship to
the specifications and the nominal. It also
suggests the shape of the population and
indicates if there are any gaps in the data.
33
Histogram
34
Histogram
35
Cause-and-Effect Diagrams
• Show the relationships between a problem and its
possible causes.
• Developed by Kaoru Ishikawa (1953)
• Also known as
• Fishbone diagrams
• Ishikawa diagrams

36
Cause and Effect Skeleton
Materials
Procedures
Quality Problem
Equipment
People
37
Fishbone Diagram

38
Fishbone Diagram
39
Cause-and-Effect Diagrams
• making the diagram is educational in itself
• diagram demonstrates knowledge of problem solving
team
• diagram results in active searches for causes
• diagram is a guide for data collection

40
Cause-and-Effect Diagrams
• To construct the skeleton, remember
• For manufacturing - the 4 Ms
• man, method, machine, material
• For service applications
• equipment, policies, procedures, people

41
Check Sheet
Shifts
? ? ?
? ? ? ?
?
? ? ?
? ?
? ? ?
Defect Type
? ? ?
? ? ? ?
? ?
?
42
Check Sheet
43
Flowcharts
• Graphical description of how work is done.
• Used to describe processes that are to be
improved.
• "Draw a flowchart for whatever you do. Until you
do, you do not know what you are doing, you just
have a job.
• Dr. W. Edwards Deming.

44
Flowcharts
Activity
Decision
Yes
No
45
Flowcharts
46
Flow Diagrams
47
Process Chart Symbols
Operations
Inspection
Transportation
Delay
Storage
48
Flow Diagrams
49
(No Transcript)
50
Scatter Diagram
.
(a) Positive correlation
(b) No correlation
(c) Curvilinear relationship
The patterns described in (a) and (b) are easy to
understand however, those described in (c) are
more difficult.
51
Run Charts
• Run Charts (time series plot)
• Examine the behavior of a variable over time.
• Basis for Control Charts

52
Control Chart
27
24
UCL 23.35
21
c 12.67
18
15
Number of defects
12
9
6
LCL 1.99
3
2
4
6
8
10
12
14
16
Sample number
53
Control Chart
7 Quality Tools
54
SUMMARY
• SPC using statistical techniques to
• measure and analyze the variation in processes
• to monitor product quality and
• maintain processes to fixed targets.
• Statistical quality control using statistical
techniques for
• measuring and improving the quality of processes,
• sampling plans,
• experimental design,
• variation reduction,
• process capability analysis,
• process improvement plans.

55
SUMMARY
• A primary tool used for SPC is
• the control chart,
• a graphical representation of certain descriptive
statistics for specific quantitative measurements
of the process.
• These descriptive statistics are displayed in the
control chart in comparison to their "in-control"
sampling distributions.
• The comparison detects any unusual variation in
the process, which could indicate a problem with
the process.

56
SUMMARY - benefits
• Provides surveillance and feedback for keeping
processes in control
• Signals when a problem with the process has
occurred
• Detects assignable causes of variation
• Reduces need for inspection
• Monitors process quality
• Provides mechanism to make process changes and
track effects of those changes
• Once a process is stable, provides process
capability analysis with comparison to the
product tolerance