-- Basic SPC Tools

7/28/2019 -- Basic SPC Tools

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Basic SPC Tools

Presented by

Russell A. Boyles, PhD

Six Sigma Master Black Belt

[email protected]

SPC

Statistical Process Control

Statistical ProcessMonitoring

SPM?


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Mistake proofing

Kanban

Warning systems

Prevent problemsfrom occurring

Visual controls

Periodic audits

Warning systems

Monitor key variables

using statistical controlcharts and documented

response plans

Standardization

Training

Documentation

Visual controls

Periodic audits

Identify and remove causesof problems

Reduce the chance thatproblems will occur

Process Control Strategies

Key Concept in Statistical Monitoring

Common-cause variation:

Assignable-cause variation:


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Systematic

Mistakes, malfunctions, external factors

Occasional large fluctuations

Causes can be determined

Outcomes are not predictable

Random

Inherent in the process

Many small fluctuations

Causes cannot be determined

Outcomes are predictable

Assignable causesCommon causes

Two Kinds of Variation

170

171

172

173

174

175

176

177

Baseline phase

Monitoring phase

Com

moncauses

Assignable cause



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5

10

15

20

Baseline phase

Assignable cause

Monitoring phase

Commoncauses


5

10

15

20

25

30

35

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Month

No assignable causes!

New manager hasspecial meeting

with CEO!

Manager gets bonus!Manager is reassigned!

New manager makes big improvement!

Customer

complaints



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Response Plan Example 1

Assignable cause?

Verify the data

Document

problem

and

solution

Able to fix?

Continue

Verify the gage

Collect and

enter data

Able to diagnose?

Fix the problemEscalate

N

Y

N

N

Y

Y

Assignable

cause?

Take another

sample

Assignable

cause?

Do operator

checklist

Enter into

process log

Call

Technician

Do technician

checklist

Problem

solved?

Start new lot

Call

Engineer

N

Y

N

Y

Y

N

Take sample fromcurrent lot

Problem

solved?YN

Response Plan Example 2


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Most often we use three-sigma limits to distinguish

operationally between assignable causesand common causes

3 + 3

Commoncauses

Baseline distribution of quantity to be monitored

Assignable

causes

Assignable

causes

Calculating Control Limits

If the quantity to be monitored follows a Normal

distribution, there is only a 0.3%

chance of a false alarm

3 + 3

99.7%

Baseline distribution of quantity to be monitored



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0 2 4 6 8 10 12 14 16 18 20 22

99.4%

Dont need a Normal distribution Three-sigma limits are an economic

compromise between false alarms

and missed signals

0 2 4 6 8 10 12 14

99.0%

0 1 2 3 4 5 6

98.1%


Control Chart

Upper Control Limit (UCL)

Average

Lower Control Limit (LCL)

+ 3

3

Baseline

distribution of

quantity to be

monitored

Time

Evidence of assignable causes

Evidence of assignable causes


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Regular sigma calculation

Based on deviations from the data average

Time0

5

10

15

20

25

30

35

40

Control limits based on regular sigma

0

5

10

15

20

25

30

35

40

Average = 24.7Standard deviation = 7.1

45

50

Time

Y


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Often there are assignable causes in the baselinedata (trends, outliers, . . . )

In this case, regular sigma is inflated by assignable

causes, and is not an accurate estimate of common-

cause variation

Control limits based on regular sigma are too wide

to detect assignable causes if and when they occur

in the future

Problem with using regular sigma

0

5

10

15

20

25

30

35

40

Based on deviations from the previous data point

Time

Y

Calculating short-term sigma


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Data Avg.

Regular

sigma

Moving

ranges

Avg.

moving

range

Short-term

sigma

15.10 24.7 7.1 -- 3.73 3.30

14.30 0.80

16.70 2.40

23.10 6.40

25.50 2.40

23.00 2.50

28.70 5.70

29.90 1.20

33.90 4.00

32.10 1.80

28.90 3.20

33.40 4.50

29.70 3.70

28.50 1.20

19.90 8.60

12.40 7.50

Calculatingshort-term

sigma

=STDEV() = Avg. moving range / 1.128

0

5

10

15

20

25

30

35

40

Average = 24.7Short- term sigma 3.3

45

50

Time

Y

Control limits based on short-term sigma


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Often there are assignable causes in the baselinedata (trends, outliers, . . . )

Short-term sigma is not inflated by assignable

causes, so it is still an accurate estimate of

common-cause variation

Control limits based on short-term sigma will

detect assignable causes if and when they occur in

the future

Rationale for using short-term sigma

What about specification limits?

Lower

specification

limit

(LSL)

Customers

expectationis met

Upper

specification

limit

(USL)

Customers

expectationis not met

Customers

expectationis not met


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Out-of-specification event

USL

LSL

What do we do?

Well, that depends on

Process Capability

If our process has good capability, it will virtually never produce a

defective outcome, except by assignable cause

Therefore, any defective outcome should trigger the response plan

Of course, we also need to disposition the affected material (scrap,

rework, . . .)

LSL USL


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LSL USL

If our process has bad capability, there will be defective outcomes

that are notassignable causes

Therefore, not all defect outcomes should trigger the response plan

Of course, we still need to disposition the affected material (scrap,

rework, . . .)

Process Capability

Exercise

LSL USL

Indicate in the table below which of the suggested actions are appropriate

for process outcomes in each of the 4 zones shown above.

1 3

3

Do nothing

1

4

2

Scrap, rework or

other disposition ofaffected material

Initiate responseplanZone

LSL USLLCL UCL LCL UCL

2 2 1 1 34 4 1

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