4-1 Collect & Interpret Data The Quality Improvement Model Use SPC to Maintain Current Process Collect & Interpret Data Select Measures Define Process.

4-1

Collect & Interpret DataCollect & Interpret Data

TheQualityImprovementModel

Use SPC to Maintain Current Process

Collect & Interpret

Data

Collect & Interpret

Data

Select Measures

Define Process

IsProcessCapable

?

Improve Process

Capability

IsProcessStable

?

Investigate & Fix

Special Causes

No

Yes

No

Yes

Collect & Interpret Data: Displaying MeasuresPurpose:

Begin collecting and analyzing data from the process.

4-2


Graphical Tools for Displaying Measures from Processes

Run Charts Histograms Pareto Charts

4-3


Run Charts

A plot of the data in time order.

Time is on the horizontal axis and the data values are plotted on the vertical axis.

Run charts show the process variation over time. -100

-50

0

50

100

150

200

5 10 15 20 25 30

Day

Measure

4-4


Histograms

A bar chart showing frequency of occurrence is shown on the vertical axis.

Histograms show the pattern of variation.

-50 0 50 100 150 200

Frequency

Measure

0

5

10

15

20

25

4-5


Pareto Charts

A bar chart showing the relative importance of some observed characteristic.

The frequency, percent or cost is shown on the vertical axis.

The characteristic (type of defect, cause, etc.) is shown on the horizontal axis.

The characteristic is usually plotted in order of decreasing magnitude.

Frequency

Cause

0

5

10

15

20

25

C A E B D F

4-6


Pump Maintenance

For each week (time period) record the number of pump failures.

One possible run chart would be to plot the number of pump failures for each week (time period). The opportunity for failures should remain constant from week to week.

Collect information about causes for each failure for use in a Pareto Chart. Pareto Charts could also be based on pump location, pump environment, etc.

Week # Failures Failure Type

1 6 Seal, Align...

2 1 Fitting, Seal...

3 2 Align, Gear...

4 4 Seal, Fitting...

. . .

. . .

20 7 Align, Seal...

PumpMaintenance

PumpMaintenance

Pump Failure

Week 1 Week 2 Week 3 Week 4 Week 20

6 failed 1 failed 2 failed 4 failed 7 failed

4-7


Pump Maintenance Data

1 3 5 7 9 11 13 15 17 19 21 23 250

2

4

6

8

10

12

14

16

18

20 Run Chart

Numberof

Failures

Week

Pareto Chart

Seal Alignment Fitting Gear Other0

10

20

30

40

50

60

# Failures

Type Failure

Frequency

0-1 2-3 4-5 6-7 8-9 10-11 12-130

1

2

3

4

5

6

Histogram

# Failures

4-8


Shipping Process

ShippingShipping On-TimeOn-Time

Shipments Made

On-TimeOn-Time LateLate On-TimeOn-Time On-TimeOn-Time

For a specified time period:

n = Shipments Made

x = Late Shipments

p = x/n

A good run chart would be to plot p for each time period. A time period could be a week or month.

It would also be good to collect other information about the late shipments for use in a Pareto Chart.

Week n # Late p Reason

1 75 10 0.13 A,C,F...

2 84 6 0.07 B,F,A...

3 78 12 0.15 F,B,B...

. . . . .

. . . . .

30 70 10 0.14 B,F,I...

4-9


Shipping Data

Run Chart

0.00

0.05

0.10

0.15

0.20

0.25

2 4 6 8 1012141618202224262830

Proportion Late Week

0.050 0.075 0.100 0.125 0.150 0.175 0.200

HistogramFrequency

Proportion Late

Pareto Chart

Reason for Being Late

Frequency

B F A E I D J C G H0

10

20

30

40

50

60

70

80

90

100

4-10


Purchase Order Process

PurchaseOrder

Process

PurchaseOrder

Process

Completed Purchase Orders

5 Purchase Orders are selected each week. The time (in days) it took to process each of the 5 PO’s is recorded, and the average of the 5 calculated. The average is the measure tracked.

A possibility would be to subgroup the data( i.e. combine 5 purchase orders and plot their average.)

It might also be informative to plot a histogram of all the times to see the pattern of variation.

Week A B C D E Average

1 2 7 5 4 5 4.6

2 3 10 2 5 3 4.6

3 5 7 3 12 1 5.6

4 4 7 8 3 5 5.4

. . . . . . .

. . . . . . .

20 3 3 9 2 4 4.2

Week 1 Week 2 Week 3 Week 4 Week 20

2,7,5,4,5 3,10,2,5,3 5,7,3,12,1 4,7,8,3,5 3,3,9,2,4

4-11


Purchase Order Data

1 3 5 7 9 11 13 15 17 1901234567891011121314

Run Chartof 20 Averages (of size 5)Time

(Days)

Histogramof 100 total observations

Time (Days)

Frequency

Week Sample Taken

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15>150

5

10

15

20

25

4-12


Polymer Manufacturing Process

One possibility would be to collect a sample of the product every 4 hours, and measure the characteristic of interest on that sample. A run chart could then be constructed of this data.It would also be informative to plot a histogram of all the times to see the pattern of variation.

ProductionProcess

ProductionProcess

Material Produced (lots)

Samples

A quality characteristic is measured on each sample. Sample b*

1 1.51

2 1.89

3 1.42

. .

. .

134 1.63

b* is a measure of yellowness

4-13


Polymer Manufacturing Data

-1

0

1

2

3

4

5

6

20 40 60 80 100 120 140

b*

Sample

Run Chart

Note: b* is a measure of yellowness

LS is the Lower Specification LimitUS is the Upper Specification Limit

0 1 2 3 4 5

b*

Histogram

LS US

4-1 Collect & Interpret Data The Quality Improvement Model Use SPC to Maintain Current Process Collect & Interpret Data Select Measures Define Process.

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