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Statistical Process Contol (SPC)

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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.

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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, adjustments are made to the process before the process is able to produce defective parts.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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The basics

Don’t inspect the product, inspect the process.

You can’t inspect it in, you’ve got to build it in.

If you can’t measure it, you can’t manage it.

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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.

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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.

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

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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.

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Pareto diagram

Per

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ause

Causes of poor quality

Mac

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cal

ibra

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Defec

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parts

Wro

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imen

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Poor D

esig

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Ope

rato

r erro

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ater

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Surfa

ce a

bras

ions

0

10

20

30

40

50

60

70(64)

(13)(10)

(6)(3) (2) (2)

A pareto diagram is a graph that ranks data classifications in descending order from left to right.

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Pareto diagram%

Com

pla

ints

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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.

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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.

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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% .

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Pareto diagramTable 1. Example of a Tabulation of Causes of Ball Bond Lifting for use in a Pareto Chart

Ball Lifting Cause Frequency Percent (%)Cum Percent

(%)Bonder Set-up Issues 19 38% 38%Unetched Glass on Bond Pad

11 22% 60%Foreign Contam on Bond Pad

9 18% 78%

Excessive Probe Damage 3 6% 84%

Silicon Dust on Bond Pad 2 4% 88%

Corrosion 1 2% 90%

Bond Pad Peel-off 1 2% 92%

Cratering 1 2% 94%

Resin Bleed-out 1 2% 96%

Others 2 4% 100%

Total 50 100% -

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Pareto diagramTable 1. Example of a Tabulation of Causes of Ball Bond Lifting for use in a Pareto Chart

Ball Lifting Cause Frequency Percent (%)Cum Percent

(%)Bonder Set-up Issues 19 38% 38%Unetched Glass on Bond Pad

11 22% 60%Foreign Contam on Bond Pad

9 18% 78%

Excessive Probe Damage 3 6% 84%

Silicon Dust on Bond Pad 2 4% 88%

Corrosion 1 2% 90%

Bond Pad Peel-off 1 2% 92%

Cratering 1 2% 94%

Resin Bleed-out 1 2% 96%

Others 2 4% 100%

Total 50 100% -

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HistogramHistogram

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.

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HistogramHistogram

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HistogramHistogram

Data Range

Frequency

0-10 1

10-20 3

20-30 6

30-40 4

40-50 2

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Cause-and-Effect Cause-and-Effect DiagramsDiagramsCause-and-Effect Cause-and-Effect DiagramsDiagrams Show the relationships between a

problem and its possible causes. Developed by Kaoru Ishikawa (1953) Also known as …

Fishbone diagrams Ishikawa diagrams

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Cause and Effect Cause and Effect “Skeleton”“Skeleton”Cause and Effect Cause and Effect “Skeleton”“Skeleton”

QualityProblem

Materials

EquipmentPeople

Procedures

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QualityProblem

MachinesMeasurement Human

ProcessEnvironment Materials

Faulty testing equipment

Incorrect specifications

Improper methods

Poor supervision

Lack of concentration

Inadequate training

Out of adjustment

Tooling problems

Old / worn

Defective from vendor

Not to specifications

Material-handling problems

Deficienciesin product design

Ineffective qualitymanagement

Poor process design

Inaccuratetemperature control

Dust and Dirt

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Cause-and-Effect Cause-and-Effect DiagramsDiagramsCause-and-Effect Cause-and-Effect DiagramsDiagrams Advantages

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

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Cause-and-Effect Cause-and-Effect DiagramsDiagramsCause-and-Effect Cause-and-Effect DiagramsDiagramsTo construct the skeleton, remember: For manufacturing - the 4 M’s

man, method, machine, material For service applications

equipment, policies, procedures, people

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Shifts

Def

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COMPONENTS REPLACED BY LABTIME PERIOD: 22 Feb to 27 Feb 1998REPAIR TECHNICIAN: Bob

TV SET MODEL 1013

Integrated Circuits ||||

Capacitors |||| |||| |||| |||| |||| ||

Resistors ||

Transformers ||||

CommandsCRT |

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FlowchartsFlowcharts

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.

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Activity

DecisionYesYes

NoNo

FlowchartsFlowcharts

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FlowchartsFlowcharts

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Flow DiagramsFlow Diagrams

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Process Chart Symbols

Operations

Inspection

Transportation

Delay

Storage

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Flow DiagramsFlow Diagrams

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Scatter Diagram

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(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.

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Run ChartsRun Charts

Run Charts (time series plot) Examine the behavior of a variable

over time. Basis for Control Charts

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12

6

3

9

15

21

24

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2 4 6 8 10 12 14 16

Sample number

Nu

mb

er o

f d

efec

ts

UCL = 23.35

LCL = 1.99

c = 12.67

Control ChartControl Chart

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7 Quality Tools7 Quality Tools

Control ChartControl Chart

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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.

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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. 

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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