View
213
Download
1
Tags:
Embed Size (px)
Citation preview
Variation thinking
2WS02 Industrial Statistics
A. Di Bucchianico
SPC: Philosophy
Let the process do the talking:
Goal: realize constant quality by controlling the
process with quantitative information
Constant quality means: quality with controlled and
known variation around a fixed target
Operator should be able to do the routine
controlling
Variation I
Variation II
Variation III
109876543210
-1-2-3-4-5-6-7-8-9
-10
Example: Dartec
disqualificationwhen outside range
Examples of variation patterns10
9876543210
-1-2-3-4-5-6-7-8-9
-10
Metric for sample variation: range
easy to compute (pre-computer era!)
rather accurate for sample size < 10
minimum maximumrange
Metric for sample variation: standard deviation
11
1
22
1
2
n
XnX
n
XXS
n
ii
n
ii
• 2nd formula easier to compute by hand • 2nd formula less rounding errors• correct dimension of units• n-1 to ensure that average value equals
population variance (“unbiased estimator”)
Visualisation of sample variation
Box-and Whisker plot
Histogram for dartecok
-9 -6 -3 0 3 6 9
dartecok
0
5
10
15
20
25
30fr
equen
cy
histogram
Box-and-Whisker Plot
-8 -4 0 4 8
dartecok
all observations first 60 observationsHistogram for dartecnotok
dartecnotok
frequency
-6 -3 0 3 6 90369
121518
dart
ecn
oto
k
0 20 40 60 80 100-6-30369
Histogram for dartecnotok
dartecnotok
frequency
-7 -4 -1 2 5 8 1105
1015202530
Variation and stability
Can variation be stable?
yes, if we mean that observations
– follow fixed probability distribution
– do not influence each other (independence)
stability -> predictability
How to handle a stable production process?
Why stable processes?
• behaviour is predictable
• processes can be left on itself: intervention may
be expensive
Deming’s funnel experiment
Lessons from funnel experiment
• tampering a stable process may lead to increase
of variation
• adjustments should be based on understanding of
process (engineering knowledge)
•we need a tool to check for stability
Attributive versus variable
two main types of measurements:
– attributive (yes/no, categories)
– variable (continuous data)
hybrid type:
– classes or bins
use variable data whenever possible!
Statistically in control
•Constant mean and spread
•Process-inherent variation only
•Do not intervene
Measurement
Tijd
XX
XX
X
X
X
X
XX
X
XX
X
X
XX
Intervene?
Statistically versus technically in control
“Statistically in
control”
– stable over
time /predictable
“Technically in control”
– within
specifications
Statistically in control vs technically in control
statistically controlled process:
– inhibits only natural random fluctuations (common causes)
– is stable
– is predictable
– may yield products out of specification
technically controlled process:
– presently yields products within specification
– need not be stable nor predictable
Priorities
what is preferable:
– statistical control or
– technically in control ??
process must first be in statistical control
Variation and production processes
Shewhart distinguishes two forms of variation in production
processes:
• common causes
– inherent to process
– cannot be removed, but are harmless
• special causes
– external causes
– must be detected and eliminated
Chance or noise
How do we detect special causes ?
use statistics to distinguish between chance and
real cause
Shewhart control chart
graphical display of product characteristic which is important for
product quality
X-bar Chart for yield
Subgroup
X-b
ar
0 4 8 12 16 2013,6
13,8
14
14,2
14,4
UpperControl Limit
Centre Line
Lower Control
Limit
Control charts
Why control charts?
•control charts are effective preventive device
•control charts avoid tampering of processes
•control charts yield diagnostic information
Basic principles
• take samples and compute statistic
• if statistic falls above UCL or below LCL, then out-of-control signal: e.g.,
X-bar Chart for yield
Subgroup
X-b
ar
0 4 8 12 16 2013,6
13,8
14
14,2
14,4
how to choose control limits?
Normal distribution
•often used in SPC
•“justification” by Central Limit Theorem:
– accumulation of many small errors
Meaning of control limits
•limits at 3 x standard deviation of plotted statistic
•basic example:
9973.0)33(
)33(
)33(
)(
ZP
XP
XP
UCLXLCLP
XX
XX
UCL
LCL
Example
• diameters of piston rings
• process mean: 74 mm
• process standard deviation: 0.01 mm
• measurements via repeated samples of 5 rings yields:
mmLCL
mmUCL
mmn
x
9865.73)0045.0(374
0135.74)0045.0(374
0045.05
01.0
Specifications vs. natural tolerance limits
never put specification limits on a control chart
control chart displays inherent process variance
during trial run charts (also called tolerance chart of tier chart) often
yields useful graphical information