4-05 SPC Interpretation

8/6/2019 4-05 SPC Interpretation

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Company ConfidentialCopyright NN, Inc. 2004

SPC: Interpretation of ControlSPC: Interpretation of Control

ChartsCharts


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Interpretation of Control ChartsInterpretation of Control Charts

INTRODUCTION

Proper interpretation of control charts is essential in

order forSPC to be effective.

Control charts provide us with a graphical record of

process behavior over a given period of time.

After establishing proper control limits that take into

account inherent variation, the control chart should

reveal any process that is out of control.

You must determine if the patterns you see are related to

Common orSpecial Causes.


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RANDOM CAUSES OF VARIATION

& If the data on the control chart forms a natural pattern, the points will fluctuate

at random and tend to cluster around the centerline. A few points may spread out

toward the control limits, however, none of the points should exceed the control

limits.

Examples:

- The X-barchart is a measure of the average. A random X-barchart

pattern indicates that the process average did not change during the time

plotted and that most of the parts were near the average.

-A stable pattern on an R chart is an indication of process uniformity. It

could represent the operators technique on the machine. It can comparedifferent operators or different machines. The R chart is a measure of

the spread of the distribution.

-A stable pattern on a p chart is an indication that there was a constant

percentage of non-conforming parts and that the sampling was random.


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& Control charts with common patterns indicate there is a steady,

stable process running that is not being disturbed by special

causes.

Common Pattern

RANDOM CAUSES OF VARIATION


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SPECIAL CAUSES OF VARIATION

& Data that fluctuates too widely or fails to balance itself around the centerline

is characteristic of special case patterns. Several types of special case patterns

have been classified.

Identified Patterns ofSpecial Causes of Variation:

& Trends

& Cycles

&Grouping

& Strays

& Sudden Shift in Level

& Mixture


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PATTERNS OF SPECIAL CAUSES OF VARIATIONTRENDS

& Trends: Defined as a long series of points that lack a change of

direction. There is a continuous movement of points up or down the

control chart. Points will move toward one side of the centerline.

Trends


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Typical causes of trend patterns:

Tool wear / thread wear

Aging

Seasonal effects

Operator fatigue

Change in standards

Inadequate maintenance Gradual introduction of

new material

Something loose

Gradual wear

Dulling of tool

Gradual change in

operator technique

or skill level Effect of process

controls in other areas

Introduction of different

material / tools

Tightening / loosening of

requirements

Better / poorer work

Increased / decreasedskill of operators

X-Bar Chart R Chart p Chart

PATTERNS OF SPECIAL CAUSES OF VARIATIONTRENDS (cont.)


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& Cycles: Defined as short trends in the data that occur in repeated

patterns, so that the pattern becomes predictable or systematic. There is

an indication of an assignable cause because a characteristic of a random

pattern is that it does not repeat. The cause of cycles are processing

variables that come and go on a regular basis.

Cycles

PATTERNS OF SPECIAL CAUSES OF VARIATIONCYCLES


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Typical causes of cycle patterns:

Seasonal effects

Worn threads on locking

devices

Gage differences

Voltage fluctuations

Maintenance schedule

Rotation on fixtures or

gages

Wear of tool or die

Differences between shifts

Sorting practices

Sampling practices

Differences between

suppliers


PATTERNS OF SPECIAL CAUSES OF VARIATIONCYCLES (cont.)


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& Grouping: Occurs when measurements cluster together in a non-random

pattern. This clustering can be indicated by an introduction of a different system

of causes. For example, if a box of rejected parts gets thrown in accidentally,

bunching may be observed on the control chart.

& Strays: Result from a single measurement that is greatly different from the others.

Occasionally, an apparent stray is the result of a plotting error. Strays are among

the easiest of the patterns to recognize because they are so different from the other

readings in the process.Grouping or Strays

PATTERNS OF SPECIAL CAUSES OF VARIATIONGROUPING AND STRAYS

Grouping Stray


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Typical causes of Grouping patterns:

Measurement difficulty

Different person making

measurements Change in calibration of

a measuring instrument

Mixture of different lots Change in technique of

classification

Changes in sorting ofproduct


Typical causes ofStray patterns:

Wrong setting corrected

immediately

Error in measurement /

plotting

Omitted / incomplete

operation

Damage in handling

Incomplete / omitted

operation

Setup parts included

in data

Mathematical error

Variations in sample size

Sampling from different

distributions

A very good or bad lot


PATTERNS OF SPECIAL CAUSES OF VARIATIONGROUPING AND STRAYS (cont.)


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& Sudden Shift in Level: A sudden shift in level is shown by a change

in one direction. A number of points will appear on one side of the control

chart.

Sudden Shift in Level

PATTERNS OF SPECIAL CAUSES OF VARIATION

SUDDEN SHIFT IN LEVEL


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Typical causes ofSudden Shift in Level patterns:

Change of material

New operator / inspector

New machine setting

Shift change

Different gage

New tools Change in process

method

New operator / equipment

Change of material

Inadequate / improved

maintenance

Decreased / increased

material uniformity Change in process method

New lot of material

Change from one

machine / operator to

another

Change in gage calibration

Change in process method



SUDDEN SHIFT IN LEVEL (cont.)


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& Mixture: Mixture patterns fall near the control limits, with an absence of

points near the centerline. This pattern can be recognized by the unnatural

length of the lines joining the points to create a sawtooth effect. A mixture is

actually a combination of two different patterns on the same chart - one at a high

level and one at a low level.

Mixture


MIXTURE


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Typical causes of Mixture patterns:

Differences in operators

Differences in gages

Over adjustment of

process

Output from two or more

machines mixed together

Chart placed too far

downstream

Different lots of material /

machines / operator

Differences in gages

Too much play in fixture

Tool needs sharpening

Non-random sampling

techniques

Lots from two or more

sources



MIXTURE (cont.)


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WESTERN ELECTRIC RULES TODETECT PROCESS CHANGES

A

B

C

C

B

A

UCL

LCL

Zones Centerline

68%

95%

99.7%

3

2

1

The Western Electric Rules are based on probability theory. Specifically,

the probability of getting certain plot point patterns is so low that the

situation needs investigation and the process may be out of control.

Special Causes of Variation:


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POINTS BEYOND THE CONTROL LIMITS& Points beyond the control limits indicate a special cause that is acting on the

process and causing it to act abnormally.

One Point Falls Beyond Zone A

A

B

C

C

B

A

UCL

LCL

Zones Centerline

& Some Possible Causes:

- error in calculation or plotting

- change in measurement system

- deterioration in process performance ( over time or at that moment)



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TWO OUT OF THREE CONSECUTIVEPOINTS IN ZONE A OR BEYOND

& The probability of the two points happening together in Zone A is

determined by multiplying their individual probabilities. ( 1/40 x 1/40 = 1/1,600).

This is very, very unlikely to happen due to inherent variation with a

controlled process. Most likely a shift has occurred in the process average.

2 out of 3 points in Zone A

A

B

CC

B

A

UCL

LCL

Zones Centerline



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FOUR OUT OF FIVE CONSECUTIVEPOINTS IN ZONE B OR BEYOND

&Through probability analysis statisticians have decided that when 4

out of five points land in the outer 1/3 of the control region, the process

has shifted.

4 out of 5 points in Zone B or Beyond

A

B

C

C

B

A

UCL

LCL

Zones Centerline



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RUN OF SEVEN POINTS ON ONESIDE OF CENTERLINE

&Short runs of a few consecutive points are to be expected and will occur

frequently with a controlled process. However, long runs of many consecutive

points are abnormal in a controlled process. In particular, a run of seven or more

points in a row all on the same side of the centerline is sufficient evidence

that the process has changed and action is required.

Run of seven points on the same side of the centerline

A

B

C

C

B

A

UCL

LCL

Zones Centerline


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4-05 SPC Interpretation