Collect & Interpret Data
The
Quality
Improvement
Model
Define
Process
Select
Measures
Collect &
Interpret
Data
Collect & Interpret Data:
Displaying Measures
Purpose: Begin collecting and
analyzing data from
the process.
Is
Process
Stable
?
No
Investigate &
Fix Special
Causes
Yes
Improve
Process
Capability
No
Is
Process
Capable
?
Yes
Use SPC to
Maintain
Current
Process
4-1
Collect & Interpret Data
Graphical Tools for
Displaying Measures from Processes



Run Charts
Histograms
Pareto Charts
4-2
Collect & Interpret Data
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.
Measure
200
150
100
50
0
-50
-100
5
10
15
Day
20
25
30
4-3
Collect & Interpret Data
Histograms
Frequency


A bar chart showing
frequency of occurrence
is shown on the vertical
axis.
Histograms show the
pattern of variation.
25
20
15
10
5
0
-50
0
50
100
150
200
Measure
4-4
Collect & Interpret Data
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
25
20
15
10
5
0
C
A
E
B
D
F
Cause
4-5
Collect & Interpret Data
Pump Maintenance
Pump Failure
Pump
Maintenance
6 failed 1 failed
2 failed
4 failed
7 failed
Week 1
Week 3
Week 4
Week 20
Week 2
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.
For each week (time period) record
the number of pump failures.
Week
# Failures
Failure Type
1
6
Seal, Align...
2
1
Fitting, Seal...
3
2
Align, Gear...
4
4
Seal, Fitting...
.
.
.
.
.
.
20
7
Align, Seal...
4-6
Collect & Interpret Data
Pump Maintenance Data
Run Chart
20
18
Number
of
Failures
16
14


12
10


8
6




60


4



2




1
50

0
3
5
Pareto Chart
# Failures

7

9
11
13
15
17
19
21
Week
23
25
40
30
Histogram
Frequency 6
20
5
10
4
0
3
Seal
2
Alignment
Fitting
Gear
Other
Type Failure
1
0
0-1
2-3
4-5
6-7
8-9
# Failures
10-11
12-13
4-7
Collect & Interpret Data
Shipping Process
Shipments Made
Shipping
On-Time
On-Time
Late
On-Time
On-Time
For a specified time period:
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.
n = Shipments Made
x = Late Shipments
p = x/n
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
0.14
B,F,I...
10
4-8
Collect & Interpret Data
Shipping Data
P
r
o
p
o
r
t
i
o
n
Run Chart
0.25
0.20
Pareto Chart
Frequency
0.15
100
90
0.10
80
70
L 0.05
a
t
e 0.00
60
50
40
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
30
Week
20
10
Histogram
Frequency
0
B
F
A
E
I
D
J
C
G
H
Reason for Being Late
0.050
0.075
0.100
0.125
0.150
0.175
0.200
Proportion Late
4-9
Collect & Interpret Data
Purchase Order Process
Purchase
Order
Process
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.
Completed Purchase Orders
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
Week 1 Week 2 Week 3 Week 4
Week 20
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.
Week
1
2
3
4
.
.
A
2
3
5
4
.
.
B
7
10
7
7
.
.
C
5
2
3
8
.
.
D
4
5
12
3
.
.
E
5
3
1
5
.
.
Average
4.6
4.6
5.6
5.4
.
.
20
3
3
9
2
4
4.2
4-10
Collect & Interpret Data
Purchase Order Data
Frequency
Histogram
of 100 total observations
25
20
Time
(Days)
15
10
5
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15>15
Time (Days)
Run Chart
of 20 Averages (of size 5)
14
13
12
11
10
9

8

7


6


 
5  
 
 

4
 



3
2
1
0
1
3
5
7
9
11 13 15 17 19
Week Sample Taken
4-11
Collect & Interpret Data
Polymer Manufacturing Process
Material Produced (lots)
Production
Process
Samples
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.
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
4-12
Collect & Interpret Data
Polymer Manufacturing Data
6
Run Chart
Histogram
5
LS
4
b*
US
3
2
1
0
-1
0
20
40
60
80
100
Sample
120
140
1
2
3
4
5
b*
LS is the Lower Specification Limit
US is the Upper Specification Limit
Note: b* is a measure of yellowness
4-13