Transcript Control Charts

Control Charts
Statistical Process Control
The objective of a process control system is to provide a
statistical signal when assignable causes of variation are
present
Control Charts
Constructed from historical data, the purpose of control charts is to
help distinguish between natural variations and variations due to
assignable causes
Steps In Creating Control Charts
1. Take samples from the population and compute the appropriate
sample statistic
2. Use the sample statistic to calculate control limits and draw the
control chart
3. Plot sample results on the control chart and determine the state
of the process (in or out of control)
4. Investigate possible assignable causes and take any indicated
actions
5. Continue sampling from the process and reset the control limits
when necessary
Control Charts for Variables
 For variables that have continuous dimensions
 Weight, speed, length,
strength, etc.
 x-charts are to control the central tendency of the process
 R-charts are to control the dispersion of the process
 These two charts must be used together
x-charts
For x-Charts when we know s
Upper control limit (UCL) = x + zsx
Lower control limit (LCL) = x - zsx
where
x = mean of the sample means or a target value set
for the process
z = number of normal standard deviations
sx = standard deviation of the sample means
= s/ n
s = population standard deviation
n = sample size
x-charts - Example
The weights of boxes of Oat Flakes within a large production lot are
sampled each hour. 12 different samples where selected and weighted
and the average of each sample is presented in the following table.
Each sample contains 9 boxes and the standard deviation of the
population is 1. Managers want to set control limits that include
99.73% of the sample mean.
Hour Sample average
of 9 boxes
Hour
Sample average
of 9 boxes
1
16.1
7
15.2
2
16.8
8
16.4
3
15.5
9
16.3
4
16.5
10
14.8
5
16.5
11
14.2
6
16.4
12
17.3
x-charts - Example
For 99.73% control limits, z = 3
UCLx = x + zsx = 16 + 3(1/3) = 17 ounces
LCLx = x - zsx = 16 - 3(1/3) = 15 ounces
x-charts - Example
Control Chart for sample of 9 boxes
Variation due
to assignable
causes
Out of
control
17 = UCL
Variation due to
natural causes
16 = Mean
15 = LCL
| | | | | | | | | | | |
1 2 3 4 5 6 7 8 9 10 11 12
Sample number
Out of
control
Variation due
to assignable
causes
Patterns in Control Charts
Upper control limit
Target
Lower control limit
Normal behavior. Process is “in control.”
Patterns in Control Charts
Upper control limit
Target
Lower control limit
One plot out above (or below). Investigate for
cause. Process is “out of control.”
Patterns in Control Charts
Upper control limit
Target
Lower control limit
Trends in either direction, 5 plots. Investigate for
cause of progressive change.
Patterns in Control Charts
Upper control limit
Target
Lower control limit
Two plots very near lower (or upper) control.
Investigate for cause.
Patterns in Control Charts
Upper control limit
Target
Lower control limit
Run of 5 above (or below) central line.
Investigate for cause.
Patterns in Control Charts
Upper control limit
Target
Lower control limit
Erratic behavior. Investigate.
x-charts
For x-Charts when we don’t know s
Upper control limit (UCL) = x + A2R
Lower control limit (LCL) = x - A2R
where
R = average range of the samples
A2 = control chart factor found in the Table in the
next slide
x = mean of the sample means
Control Chart Factors (3 sigma)
Sample Size
n
Mean Factor
A2
Upper Range
D4
2
3
4
5
6
7
8
9
10
12
1.880
1.023
.729
.577
.483
.419
.373
.337
.308
.266
3.268
2.574
2.282
2.115
2.004
1.924
1.864
1.816
1.777
1.716
Lower Range
D3
0
0
0
0
0
0.076
0.136
0.184
0.223
0.284
x-charts - Example
Super Cola bottles soft drinks labeled “net weight 12 ounces”. Indeed,
an overall process average of 12 ounces has been found by taking
many samples, in which each sample contained 5 bottles. The average
range of the process is 0.25 ounces. We want to determine the upper
and lower control limits for averages in this process.
x-charts - Example
Process average x = 12 ounces
Average range R = .25
Sample size n = 5
UCLx
LCLx
= x + A2R
= 12 + (.577)(.25)
= 12 + .144
= 12.144 ounces
= x - A2R
= 12 - .144
= 11.857 ounces
UCL = 12.144
Mean = 12
LCL = 11.857
R–Chart

Type of variables control chart

Shows sample ranges over time

Difference between smallest and largest values in sample

Monitors process variability

Independent from process mean
R–Chart
For R-Charts
Upper control limit (UCLR) = D4R
Lower control limit (LCLR) = D3R
where
R = average range of the samples
D3 and D4 = control chart factors from the previous Table - Control
Chart Factors (3 sigma)
R–Chart - Example
The average range of a product at the National Manufacturing Co. is
5.3 pounds. With a sample size of 5, the owners want to determine the
upper and lower control chart limits for the range
UCLR
LCLR
= D4R
= (2.115)(5.3)
= 11.2 pounds
= D3R
= (0)(5.3)
= 0 pounds
UCL = 11.2
Mean = 5.3
LCL = 0
Mean and Range Charts
(a)
(Sampling mean is
shifting upward but
range is consistent)
These
sampling
distributions
result in the
charts below
UCL
(x-chart detects
shift in central
tendency)
x-chart
LCL
UCL
R-chart
LCL
(R-chart does not
detect change in
mean)
Mean and Range Charts
(b)
These
sampling
distributions
result in the
charts below
(Sampling mean is
constant but
dispersion is
increasing)
UCL
(x-chart does not
detect the increase in
dispersion)
x-chart
LCL
UCL
R-chart
LCL
(R-chart detects
increase in
dispersion)