1. 1 Improving Quality Give a small boy a hammer, and he will
find that everything he encounters needs pounding." Abraham Kaplan
(1964) Mark Twain, somewhat earlier More Tools = Greater success
Copyright 2010 Monty Webb. All rights reserved.
2. 2 Main tools for quality improvement Classical Taguchi
Shainin Six Sigma Lean Manufacturing Poka-Yoke TRIZ
3. 3 Classical SPC Full Factorial Designs ( 7 variables = 128
tests) Anova f-Test Probability curve applied to different process
distributions
4. 4 Taguchi Robust Design Consistent output even with some
uncontrolled noise Fractional Factorial Designs
5. 5 Shainin Dorian Shainin developed a series of problem
solving tools only taught by his consulting groups Multi-Vari
charts Full Factorials B vs. C (using Tukey End Count) Scatter
Plots Pre-contol
6. 6 Six Sigma Attempt to control each individual process so
tight that a drift of 1.5 sigma will not create any rejects to the
agreed specification (Motorola started, GE jumped on it). ...In
fact, of 58 large companies that have announced Six Sigma programs,
91 percent have trailed the S&P 500 since, according to an
analysis by Charles Holland of consulting firm Qualpro (which
espouses a competing quality- improvement process).
7. 7 Six Sigma
8. 8 Lean Manufacturing The four goals of Lean manufacturing
systems are to: * Improve quality * Eliminate waste * Reduce time *
Reduce total costs
9. 9 Poka-Yoke (Mistake proofing) Examples of 'attention-free'
Poke Yoke solutions: 1) a jig that prevents a part from being
misoriented during loading 2) non-symmetrical screw hole locations
that would prevent a plate from being screwed down incorrectly 3)
electrical plugs that can only be inserted into the correct outlets
4) notches on boards that only allow correct insertion into edge
connectors 5) a flip-type cover over a button that will prevent the
button from being accidentally pressed
10. 10 TRIZ, a theory of Invention Altshuller screened over
1,500,000 patents looking for inventive problems and how they were
solved. Only 40,000 had somewhat inventive solutions; the rest were
just improvements. Altshuller more clearly defined an inventive
problem as one in which the solution causes another problem to
appear, such as increasing the strength of a metal plate causing
its weight to get heavier. Usually, inventors must resort to a
trade-off and compromise between the features and thus do not
achieve an ideal solution. In his study of patents, he found that
many described a solution that eliminated or resolved the
contradiction and required no trade-off.
11. 11 TRIZ Altshuller categorized these patents in a novel
way. Instead of classifying them by industry, such as automotive,
aerospace, etc., he removed the subject matter to uncover the
problem solving process. He found that often the same problems had
been solved over and over again using one of only forty fundamental
inventive principles. If only later inventors had knowledge of the
work of earlier ones, solutions could have been discovered more
quickly and efficiently.
12. 12 TRIZ
13. 13 TRIZ My Problem Previously well- solved Problems
Analogous solutions from Patents in different fields 1 2 3 4 5 1 2
3 4 5 n40 . . . . . . My Solution Triz Prizm
14. 14 TRIZ Example, a problem in using artificial diamonds for
tool making is the existence of invisible fractures. Traditional
diamond cutting methods often resulted in new fractures which did
not show up until the diamond was in use. What was needed was a way
to split the diamond crystals along their natural fractures without
causing additional damage.
15. 15 TRIZ A method used in food canning to split green
peppers and remove the seeds was used. In this process, peppers are
placed in a hermetic chamber to which air pressure is increased to
8 atmospheres. The peppers shrink and fracture at the stem. Then
the pressure is rapidly dropped causing the peppers to burst at the
weakest point and the seed pod to be ejected. A similar technique
applied to diamond cutting resulted in the crystals splitting along
their natural fracture lines with no additional damage.
16. 16
17. 17 Classical Detailed Review SPC Full Factorial Designs ( 7
variables = 128 tests) Anova f-Test Probability curve applied to
different process distributions
18. 18 Classical Normal curve and Ogive curve 0 5 0 1 0 0 1 5 0
2 0 0 2 5 0 3 0 0 0 5 1 0 1 5 2 0 2 5 3 0 3 0 5 0 7 0 # o f H e a d
s in t r ia l 1 0 0 c o in t o s s e s , r e p e a t 2 5 0 t im e s
, # o f H e a d s b e ll C U M
19. 19 Classical Normal Cumulative Distribution 1 5 1 0 2 0 3 0
4 0 5 0 6 0 7 0 8 0 9 0 9 5 9 9 - 3 - 1 .5 0 1 .5 3 3 0 5 0 7 0
Cum% N u m b e r o f H e a d s R e s u lt s o f c o in f lip s C u
m % Log expanding From 50% in both directions
20. 20 1 5 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 9 5 9 9 - 3 - 1
.5 0 1 .5 3 3 0 5 0 7 0 Cum% Converting the S curve to a straight
line opens up many new insights C u m %
21. 21 1 5 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 9 5 9 9 - 3 - 1
.5 0 1 .5 3 3 0 5 0 7 0 Cum% Truncation of Data in Green Fibbing
going on C u m %
22. 22 Truncation of Data in Green Fibbing going on This shows
screening to a specifcation tighter than production capability.
(cherry picking) If the process drifts just a little, you will get
no parts. This could be found at incoming QC on parts from a
supplier. It also could occur in your oun process where there is a
rework for parts above or below some limits, and operators speed up
by never finding out of spec parts. They never shut the process
down as they should do in a controlled process.
23. 23 1 5 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 9 5 9 9 - 3 - 1
.5 0 1 .5 3 3 0 5 0 7 0 Cum% Variation due to two distributions
with different Std. Dev. , but the same means mixed together C u m
%
24. 24 1 5 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 9 5 9 9 - 3 - 1
.5 0 1 .5 3 3 0 5 0 7 0 Cum% Output of two different distributions
with the same std. Dev.(slope), but different means C u m %
25. 25 Shainin Detailed Review Dorian Shainin developed a
series of problem solving tools only taught by his consulting
groups Multi-Vari charts Full Factorials B vs. C (using Tukey End
Count) Scatter Plots Pre-contol
26. 26 Summary
27. 27 Pre-Control
28. 28 Pre-control Pre-control: use of chart 1. Start process:
five consecutive units in green needed as validation of set-up. 2.
If not possible: improve process. 3. In production: 2 consecutive
units 4. Frequency: time interval between two stoppages / 6.
29. 29 Evaporator #2 after crystal position change L C F a b L
i m i t e d Q u a lity P r e s e n t a tio n E V A P O R A T O R N
o 2 : N ic k e l a ft e r C r y s t a l P o s it io n c h a n g e 0
. 2 5 0 0 . 2 7 5 0 . 3 0 0 0 . 3 2 5 0 . 3 5 0 0 . 3 7 5 0 . 4 0 0
0 . 4 2 5 0 . 4 5 0 0 . 4 7 5 0 . 5 0 0 0 . 5 2 5 0 . 5 5 0 R U N N
o THICKNESSmicrons
30. 30 Shainin Clue Generation Tools Clue-Generation Tools
Start with 20 to 1000 variables And they are reduced down to 20 or
fewer Multi-Vari Chart Paired Comparisons Product/ Process Search
Components Search Concentration Chart
31. 31 Multi-vari Chart The Multi-Vari Chart graphically shows
variation of a quality characteristic for multiple factors. The
purpose of the chart is to permit identification of the factors
having the greatest effect on variability. An injection molding
process produced plastic cylindrical connectors. Two parts
collected hourly from four mold cavities for three hours consisting
of measurements at three locations on the parts. The figure shows
that cavities 2,3 and 4 had larger diameters at the ends (top and
bottom) while cavity 1 had a taper. Thus, cavity and location have
an interacting effect.
32. 32 Mult-vari
33. 33 Paired Comparisons BOB vs. WOW Best of the Best compared
to Worst of the Worst
34. 34 BOB,WOW sample
35. 35 Tukey test procedure Rank individual units by parameter
and indicate Good / Bad. Count number of all good or all bad from
one side and vice versa from other side. Make sum of both counts.
Determine confidence level to evaluate significance.
36. 36 Tukey test confidence levels for Tukey End Count Total
End Count Confidence 6 90% 7 95% 10 99% 13 99.9%
37. 37 Tukey test: example =7 GOOD BAD 0.007 0.011 0.014 0.015
TOP end count. All good 4 0.017 0.018 0.019 0.022 0.016 0.017 0.018
0.019 0.021 }overlap region 0.023 0.023 0.024 Bottom end count. All
bad 3
38. 38 Inverted End Count
39. 39 Results
40. 40 Formal DOE Tools 4 or fewer variables Response surface
Methodology Scatter plots B vs. C Variables search Full Factorials
5 to 20 variables 1 variable Root causes distilled Interactions
presentNo interactions Optimization
41. 41 Full-Factorial A Semiconductor company was developing a
new high voltage process A double base containing both Boron and
Gallium was proposed The control on the gallium was so critical,
that a very expensive Ion-Implant was one of the factors to
consider, along with a novel approach to reduce the gallium
concentration with low cost in-house chemicals
42. 42 A l u m i n u m D i f f u s i o n s , L i g h t B a s e
P r o c e s s 1 0 1 0 0 1 0 0 0 1 0 0 0 0 1 2 3 4 5 6 7 8 9 1 0 1 1
A r g o n 1 2 5 0 C D e p , 9 0 m in N 2 @ 1 2 0 0 s tm -s tr ip
& d r iv e a t 1 2 5 0 N 2 O 2 R e s is tiv ity R a n g e fo r
1 9 0 0 v - 2 2 0 0 v N 2 1 2 5 0 C Aluminum, light base study
43. 43 Full-Factorial The questions to answer were Can we make
the required voltage with ion implant? And Can we find our own low
cost process? The following 4 factor, 2 level DOE was run
44. 44 Anova for 4 Variables, 2 Levels
45. 45 Check to be sure results are not just random 1 5 1 0 2 0
3 0 4 0 5 0 6 0 7 0 8 0 9 0 9 5 9 9 - 3 - 1 .5 0 1 .5 3 - 5 0 0 0 -
2 5 0 0 0 2 5 0 0 5 0 0 0 Cum% D O E m a in s + in te r a c tio n s
s c o r e s H o w t o i n t e r p r e t D O E r e s u l t s A B is
f a rt h e s t f ro m b e s t f it
46. 46 Interaction, ab Gallium process vs. Drive gases Best
voltage was A- and B+, very costly implant and argon BUT- with the
right gases, the combination of A+ and B- produce acceptable
voltage 1 5 0 0 2 0 0 0 2 5 0 0 3 0 0 0 B - ( N 2 + s t e a m ) B +
( A r g o n + H 2 ) V o l t a g e A - ( I m p la n t ) A + ( in - h
o u s e )
47. 47 Transition to SPC Maintenance Pre-control Positrol
Process certification Safeguard the gains
48. 48 L C F a b L i m i t e d Q u a lity P r e s e n ta tio n
All key processes are monitored Problem areas are shaded
49. 49 Processes where a DOE resulted in a process change are
monitored To make sure gains are realized. Chart is marked where
change occurred and what changed.
50. 50 It looked OK at first, just as in the tests. But then
the yield dropped dramatically. Production was stopped until the
unknown issue was resolved. That took 3 days. A quick look at some
best runs vs. worst runs showed Mesa etch depth was the main
difference. All were in specification, but those with the deeper
mesa were better on voltage. The original tests came through during
a time the etch was running to the deep side of the spec. Goal was
to improve 1200 volt yield DOE's were run and a deeper base with a
longer base drive looked very good. Process was changed.
51. 51 Problems are commented on as Unknown, or Identified-
Procedure changed on xx/xx/xxx Chart is marked where change
occurred and what changed. Identified-Mesa etch depth not adjusted
for deeper base as needed for high voltage program Procedure
changed on 02/17/2005 Chart is marked where change occurred and
what changed.
