2. Objectives
At the end of this topic,
DEFINE and EXPLAIN
1.QFD,
2.Benchmarking,
3.Kanban,
4.JIT.
you should be able to
DESCRIBE, EXPLAIN
S.Quality Tools
and DEMONSTRATE
3. Why need Quality tools?
• Most decision point and root causes remain
unclear until valid data are studied and
analyzed.
• Collecting and analyzing data using total
quality tools make the task easy for everyone.
4. Why need Quality tools?
• No matter where you fit into organization in
future, you may use all or some of these tool
and employers will serve you well for better
prospects.
This chapter will explain the most widely
used of quality tools.
•
5. What is the Quality tools?
The Seven Basic Tools of Quality is a
designat ion given to a fixed set of graphical
techniques ident if ied as being most helpf ul
troubleshoot ing issues relat ed to qualit y.
•
in
6. What is the Quality tools?
•
•
•
The tools are:-
1) the Pareto charts
2) the Cause-and-Effect I Ishikawa diagram
/Fishbone diagram
the Check Sheet
the Flow Chart
the Histogram
the Scatter Diagram
the Control Chart
•
•
•
•
•
3)
4)
5)
6}
7)
7. 1) Pareto Chart
• Pareto charts are useful for separating
important from the trivial.
the
• Named after Italian economist and sociologist
Vilfredo Pareto {1848-1923). Was promoted
by Dr.Josep Juran.
• Pareto charts are important
help an organization decide
because they can
where to focus
limited resources.
• On a Pareto chart, data are arrayed along an
X-axis and a Y-axis.
8. Example
• In a factory, Only20% of problems will produce
80%
80%
20%
of
of
of
defects.
defect's cost will be assigned to only
the total number of defect types
•
occurri•
ng.
So, 80% of defect costs will spring 20% of total
cost element .
•
9. Purpose of Pareto
• Pareto can show you where to apply your
resources by "revealing few from the trivial
many"..
• ➔(Highlight few most important issues out of
many)
11. Pareto Chart
Figure 15-1 represents
and All others.
75% sales are from 2 customers; A,B
All ot hers include many more customers
brings insignificant sales (>5%)
Which customers should be kept happy?
D, E
• customers A,B, C,
•
• but
•
13. Pareto Chart
Figure 15-2 shows sales of particular model
automobile by age group of the buyers.
• of
• The manufacturer has limited
advertising.
budget in
• The chart reveals the most logical choice to
target to advertise.
Concent rat ing on advertising on 26-45 age
will result in the best return of investment.
(75%)
•
The significant few ➔26-45 age
•
• The insignificant many are those under 26 &
above 45
14. Pareto Chart
• Figure 15-3
80
70
90
80
-
~ 60
6
-
- 50
40
30
20
10
0
U)
o
o
- ¢
1i-
o0
3
q
cr
Part
Failed
Miswire Incorrect
Part
PC Bd
Short
PC Bd
Open
AII Others
15. Pareto Chart
• Figure
defect
All the
15-3 shows 80% of the cost was related to 5
causes.
other (about 30 more) were insignificant.
•
• The longest bar ($70k) accounted for 40%, if solved,
immediate reduction in rework cost will happen.
After eliminate the longest bar, the team sorted data
again to develop level 2 Pareto Chart Read page
484-489 for further understanding
•
16. Pareto Chart
• Figure 15-4
•
_ -------
45
40
' !
90
80
70
60
50
40
30
? (
IO
(
- I
l
t
«4
t
m
I I
t )
«
· )
' 0
f
0
t
E
D
(
I
+
' -
)
I '
I
tt
( t
IO
't) I l
I
f
[ l o (p
/ w p
Mt
II
tolny A
I
L
( )1
/of
Helo
yt YI
J II
17. Steps in Constructing Pareto Chart
1.
2.
3.
4.
Select the subject of the chart
Determine what data to be gathered
Gather the data related to the quality problem
Make a check sheet of the gat hered data, record
total numbers in each category.
Determine total numbers of nonconformities,
calculate percentage each.
Select scales of the chart
Draw PARETO Chart from largest category to
smallest.
Analyze the chart
the
5.
6.
7.
8.
18. ) Cause and Effect Diagrams/ Ishikawa
Diagrams
19. Cause and Effect Diagrams
• Use to identify and isolate causes of a
problem. Developed by Dr. Kaoru Ishikawa.
(1915-1989)
• Also called
Diagram.
Ishikawa Diagram/ Fishbone
20. Cause and Effect Diagrams
• Benefits;
-Creating the diagram - enlightened,
instructive process.
- Focus a group, reducing irrelevant
discussion.
-Separate causes from symptoms
-Can be used with any problems
21. Cause and Effect Diagrams
¢
C
A
U
S
E
C
A
U
S
E
C
A
U
S
E
i
w
E
F
F
E
C
T
I
Figure15-8
BasicCause-and-Effect
orFishboneDiagram
I
C
A
U
S
E
I
C
A
U
S
E C
A
U
S
E
22. 6 Common major factors
Diagrams
in Ishikawa
1.
2.
3.
4.
5.
6.
Man (Operator)
Method
Measurement
Material
Machine
Environment
24. Cause and Effect Diagrams
15-8, the spine points to the
• From figure
effect.
The effect is
•
•
the problem we are interested in.
The lower level factors affecting major factor
branch.
Check Figure 15-7 to see whether the major
•
causes can be identified.
25. Cause and Effect Diagrams
• E,g: Machine soldering defects
- Six major groupings of causes are;
•
•
•
•
•
•
Solder machine itself
Operators Materials
Methods/procedures
Measurement of accuracy
Environment
26. Cause and Effect Diagrams
MACHINE OPERA
TOR
attitude
attention
training
skill
- MA
TERIALS
handling
solderability
maintenance
vendors - ◄
ollor
-
storage
age
s
.,,
;.
) , e o o t a i a « o n
',parts flux
u
wt /it I
[ l I t t I
l"
humidity waveheight
temperature
waittime
partprep
preheat
conveyorangle
con
" .s
S
p
peed
lighting
roomtemp
cleanliness instruments
h f l
,
calibration skill
)
fluxtype lighting
'
ing
pollution
ENVIRONMENT
train
MEA'UHL M
ENI
specific gravity
Figure15--10
ComplotodCauso-and-EttoctDia@rm
27. Cause and Effect Diagrams
• Normally created by teams to brainstorm the
cause/effect.
Completed diagram reveals factors & relat ionship
which not been obvious.
Some problems previously were isolated now can
identified.
Therefore, further action shall be taken.
Cause and Effect Diagrams serve as a reminder.
•
• be
•
•
28. FIVE WHYs
The '5 Whys' is a question-asking method
used to explore the cause/effect relationships
underlying a particular problem.
Objective: To determine a root cause of a
defect or problem.
The technique was originally developed by
Sakichi Toyoda (1867-1930)and was later used
•
•
•
within Toyota Motor Corporation during the
evolution of their manufacturing
methodologies.
• Part of Toyota Production System activities.
29. of 5 whys
Example
•
•
•
My car cannot start. (the problem statement)
Why?
Why?
why)
Why?
Why?
-
-
The
The
battery is dead. (first why)
alternator is not functioning. (second
•
•
-
-
The
The
alternator
alternator
belt has broken. (third why)
belt was well beyond its useful
service
why)
Why? -
life and has never been replaced. (fourth
• I have not been maintaining my car according
to the recommended service schedule. (fifth why,
root cause)
This example could be taken further to a sixth, sevent h,
or even greater level.
30. How To Complete The 5 Whys
1. Write down the specific problem. Describe it
completely. It also helps a team focus on the same
problem.
2. Ask Why the
answer down
problem happens and write the
below the problem.
3. If the answer
problem that
doesn't identify the root cause of the
you wrote down in step 1, ask Why
again and write that answer down.
4. Loop back to step 3 until the team is in agreement
that the problem's root cause is identified. Again,
this may take fewer or more times than five Whys.
31. 5 Whys Examples
Problem Statement: Customers are unhappy because they are
being shipped products that don't meet their specificat ions.
1. Why are customers BEING SHIPPED BAD PRODUCTS?
Because manufacturing built the products to a specification that is
different from what the customer and the sales person agreed to.
2. Why did manufacturing build the products to a different specification
than that of sales?
- Because the sales person expedites work on the shop floor by calling the
head of manufacturing directly to begin work. An error happened when
the specifications were being communicated or written down.
3. Why does the sales person call the head of manufacturing directly to
company?
- Because the "start work" form requires the sales director's approval
before work can begin and slows the manuf acturing process (or stops it
when the director is out of the office).
start work instead of following the procedure established in the
4. Why does the form contain an approval for the sales director?
Because the sales director needs to be continually updated on salesfor
discussions with the CEO.
33. Check sheets
• Many organizations:
They are :"DATA RICH, INFORMATION POOR"
• Check sheet can
applications.
be a valuable tool in wide
Purpose: To make it easy to collect data for
specific purposes or to convert into valuable
inf ormation.
•
35. Check sheets
Figure 15-11 reports how the works
produced relates to the shaft length
specificat ions.
• being
limits 1.120
waste!
is the check
- 1.130
• Machine setup
Outside range
So Figure 15-12
inches.
• sheet set up to
display useful information.
It produces histogram.
•
36. Chec k S heet
S h a ft le n g t h : W eek o f 7 / 11 (Spec: 1.120--1.130)
1.118°·
1. 119°
1.120
13
11
11 13
.... Out of Limits
1.121
1.122
1.123
1.124
12 1315 15
11 11 13 14 14 15 15 15
11 11 11 13 1313 14 15 15 15
11
11
12 12 12 1313 14 14 141515 15
1212 12121313 14 14
1.125
11 12 12 13 14 14
1.126
1.127
1.128
1.129
1.130
1.131°°
1.132°°
12 12 14 15
11 11 •
14 Enter day of month for
data point.
Figure 15--12
Check Sheet of Shaft Dimensional
Tolerance Results
38. Flow Chart
• A flowchart is a type of diagram that
represents an algorit hm or process, showing
the steps as boxes of various kinds, and their
order by connecting these wit h arrows
40. Example of the flow chart symbol
Flcwchart
•
Name
(Alterrates)
P r e s s
Description
5ymbol
An nnratirn nr artinn step.
A start or sop pIn: In a process.
Iermmator
)
0
D
t
_
t _
LI
Aques:ion or banch in the proces,
Decision
A waiting pcriod.
I
Aterrte Process
A formally defired sub-process.
An alternate to the normal process step,
Indicates data Inputs ard outputs to anc from a process.
Dacumert Adocument or report.
42. Histograms
• Used to chart frequency of
does something happen?)
occurrence. (How often
processes: attributes
• Commonly associated
variables
with and
DATATYPES EXAMPLES
Has/ has not
Attributes
Good/ bad
Pass/ fail
Accept/ Reject
Conform / non-conform
Measured values (Dimension, weight,
voltage, surface, etc.)
Variables
43. Histograms
•
•
•
Attribute data: Go/no go information.
Variable data: measurement informat ion.
Looking at Figure 15-14, we are using attributes data;
either
But, it
to the
they passed or they failed the screening.
does not reveal about the process cont ribut ing
adjustment .
•
• Also, does not tell the robust process. This is why
variables data is needed.
45. Histograms and Statistics
• Example: textbook - page 500
BEAD EXPERIMENT
There are 900 white beads, 100 red beads =1000beads
1.
2.
The beads mixed thoroughly.
50 beads are drawn at random. - Count how many
Check mark is entered in histogram.
red beads. -
3.
4.
All the beads are put back into container and mixed again.
Repeat Step 1 ➔Step 3
The process does not change, but the output changed!
If these steps are taken over and over, Histogram as in Figure 15-15
occur
will
46. Histograms
.
£
and Statistics
'10
25
20
15
I'll
0
a
E
cU
0
6
....
Ill
10
. 0 I
E
•
I
[
z 5
0
I
i
I I
r
'
:
1 I
I
Figure 15--15
roquency Distribution of Red
Hoads in Samples
1 5 6 7
2 3 4 9 10
8
Number of Red Beads in Sample
Sampleswith O red beads
Samples with 1 red bead
Samples wit h 2 red beads
Sampleswit h 3 red beads
Samples with 4 red beads
Sampleswit h 5 red beads
Sampleswith 6 red beads
Samples with 7 red beads
Samples with 8 red beads
Samples with 9 red beads
Samples with 10 rod boads
Total samplos takot
0
1
3
9
19
31
21
11
3
2
0
100
1
5 1
Figure
Data on /tot Itontdn In {amplo
47. Histograms and Statistics
• The flatter and wider the frequency
distribution curve, the greater
variability.
Taller and narrower the curve,
variability.
- 2 things in process variability;
• Standard deviation, a
•Mean,µ
the process
• the less process
48. Histograms and Statistics
• Mean is the sum of the observations divided by the number
of observations
Also describes the central location of the data in the chart,
•
- Standard Deviation describes the spread or dispersion of data.
Calculating the Mean, µ
= X : n
X=product of the number of beads in a sample
of samples containing the number of beads.
times the number
*See Figure 15-17b, page 503 for further understanding.
49. 1 4
2 3 5 6
Measured data from
Figure 15--16
# of Red
Beads
0
1
2
3
4
5
6
7
8
9
10
# of
Samples
•
0
1
3
9
19
31
21
11
3
2
0
n = 100
Figure 15-17a
Raw Data from the Colored Bead Experiment (see Figure 15--16)
50. Histograms and Statistics
1 2 3
Mu ltiply Co l
by Col 2
4 5 6
Measured data fror
Figure 15--16
1
# of Hed
Beads
0
1
2
3
4
5
G
7
8
9
1 0
# of
S a m p le s X Value
0
1
3
9
19
31
21
11
3
2
0
100
0
1
6
2 7
76
155
126
7 7
24
18
0
X = 5 1 0
•
n --
= X : n
=510 :
=5.1
100
Figure 15--17b
Calculating Values of X and 2X
51. Histograms and Statistics
2 6
Sum of
Distance?
1 4
Deviation
from
5
Deviation
squared
(Col 4?
3
Multiply Col
by Col 2.
Measured data from
Figure 15--16
1
- )
(Col 1 (Col2 Col5 )
# of Red
Beads
# of
Samples d?
X Value d
-5.1
- 4 . 1
-3.1
-2.1
1 . 1
-0.1
0.9
1.9
2.9
3.9
4.9
0
1
6
27
76
155
126
77
24
18
0
2 X = 5 1 0
0
16.81
28.83
39.69
22.99
0.31
17.01
39.71
25.23
30.42
0
1
2
3
4
5
6
7
8
9
10
0
1
3
9
19
31
21
11
3
2
0
100
26.01
16.81
9.61
4.41
1.21
0.01
0.81
3.61
8.41
15.21
24.01
•
0
221
p =
= 2d?
Figure 15--17c
Completed Deviation Data Table
52. Histograms and Statistics
Deviation, a
• Calculating Standard
o=[d/(n-1)
d = The deviation of any unit from
n = the number of units sampled.
the mean
■ From Figure 15-17c,
■ n(100)
a=/2MIR1
o=V221799
= 1 . 4 o = 1.49, 20 = 2.99, 30 = 4.47
53. I
I
30
25 I
I
I
0 11
a
m
I
E 20 I
I
I
cU
0
- 15
I
1;
I
o I •
I,,.
m I
I
n 1
0
E
I
z
2 I
5 I
I
I
I
II I
I
3
I .I'
i
I y
d4
•
I
I
8
l
I
0
II
I
2
I
1
1
5
I
6
•
I
I
7
I
I
9
I
0 I
r
1
I
10
t I
I
~
. -
+
a
.
-26
.-
-30
-
+20
,_
-
.~
-
+30
-
-~
-·
Jl
Red Beads in Sample
15-18
Figure
Application of Standard Deviation Calculations to
Red
BeadHistogram
54. Shape of Histograms
A F K
..,,,.
'1
L
B G
_ /
c
I ~
~
llll
ucL
L c u
--
LCL
ucu
UCL
E J L c u
Figure 15-19
Histograms of Varying
'hapes H
55. Figure 15-19
Process A is much tighter, normal
favorable.
Process B greater variances.
C and Dare not cent ered, skewed
product will be lost .
• distribution,
•
• to left and right,
• F - someone has discarded. Take out the reject, and
only collect data within acceptable range.
G -the vendor has screened out the parts, took out
the best to other customers.
H - a proper normal distribut ion between upper and
lower limit s.
I and Jskewing! Signif icant loss of product ...
K until P shifting ... why???
•
•
•
•
57. Scatter Diagram
• A scatter diagram is a tool for analyzing
relationships between two variables. One
variable is plotted on the horizontal axis and
the other is plotted on the vertical axis
While the diagram shows relationships,
It does not by itself prove that one variable
causes the other.
•
•
58. Scatter Diagram
• Scatter diagrams will generally show one of
six possible correlat ions between the
variables:
59. Scatter Diagram
• 1) Strong Positive Correlation - The value of V
increases as the value of X increases.
clearly
i d
, N e t t
i t . t h
M t
,
o - - - - - - - - - - - - - -
oO. 2 4 6
X
60. Scatter Diagram
2) Strong Negative Correlation - The value of V
•
decreases as the value of X increases
clearly
6
.5
- - - - - - - - - - - - - - - -
- - _ ; . _
• d
A l t l
•a
d
4 l l l l ]
te
- l l
l t d . l h
0 - - - - - - - - - - - - -
2 4
0 6
Xx
61. Scatter Diagram
3) Weak Positive Correlation -The value of Y
increases slightly as the value of X increases.
t l
.. td
. •
1
,
" } l b s 4 A l p
~ « i
- + - - - - - - - - - - - - -
0
4
0 . 2 6
x
62. Scatter Diagram
4) Weak Negative Correlation - The value
X
of
Y decreases
■
Increases. slightly as the value of
6 . · ~ ~ ~ - - - - -
$
' m l b l E l l
] }
• 9
f { l s . A l . l b
l b . A . a l . A A
e 0
t t l
flt•
2 4
0 6
X
63. Scatter Diagram
5) Complex Correlation -The value of Y
but
seems to be related to the value of X, the
relationship is
4
3 .5
3
2 . 5
not easily determined
., •
•
-
Y
•
2
1.5
1
0 . 5
0
•
0 2 4 6
X
64. Scatter Diagram
6) No Correlation -There is no
between
demonstrated connection
the two variables
7
6
5
4
•
•
• •
Y
3
2
1
0
• $
4
0 2 6
X
67. Control Chart
• As long as the plots stay between the limits, and don't congregate on 1
side or the ot her of the average line, the process is in STA
TISTICAL
CONTROL.
Common causes/chance: Small random changes in the process
that cannot be avoided - but still in statistical control
Varying out of the centerline of the process
Result of the sum of numerous small resources of natural variation
that are always part of the process.
Eg; Setting on machines, environment, methods etc.
Special causes/Assignable causes: Variations in the process that can be
identified as having a specific cause.
A plot point breaks through UCL or LCL
OR there a several points in a row above/below the lines.
Result of the factors that are not part of the process and only occur
special case.
Eg: New operator involve, electricity blackout, shipment faulty of
material etc.
•
•
is
68. Control Chart
• Only after the special has been identified, it
should be corrected, and restart the process.
How to correct? (By eliminating root cause)
Control chart is usually operated under
•
•
Statistical Process Control (SPC) - Chapter 18.
69. Control Chart
Control Chart - Statistical Process Control (SPC)
• What is SPC?
SPC is a statistical method of separating variation
resulting from special causes resulting from natural
causes, to eliminate the special causes, and to
establish and maintain consistency in the process,
enabling process improvement.
70. Control Chart
• Common factors that can affect output are;
5M's
Machines and environment employed
Material used Methods
(work instructions)
Measurements taken
Manpower (People who operate the process)
If these factors are perfect; th is means;
1. Environment facilitates quality work and there are no
misadjustments in the machines
No flaws in materials
Follow work instruction accurate and precisely
Accurate and repeatable measurements
People work with extreme care - follow instructions
extremely well
2.
3.
4.
5.
72. From Figure 18-5,
•
[
G
; Average Range, [R]is
Average of subgroup range, R is
R - 2 « +
R ==(Max value of x - Min value of
[so.=68=12=5.667]
The average,
r-2r=
k
-
x
== number of sub groups
== subgroup average x)
= 1 2 0 0 . 8
s o . = = 1 2
= = 1 0 0 . 0 6 7
A2is the confidence level
data, the larger the value
for the
of A,
the farther the control limits.
UCL=x+A,R
LCL=x-A,R
73. Control Chart
Factors for
x charts
Factors for
R charts
Number of
data points
in subgroup
LCL
0,
UCL
0,
•
(n)
2
3
4
5
1.88
1.02
0.73
0.58
0
0
0
0
3.27
2.57
2.28
2.11
0.48
0.42
0.37
0.34
0.31
0
0.08
0.14
0.18
0.22
6
7
8
9
10
2.00
1.92
1.86
1.82
1.78 I
II
11
12
13
14
15
0.29
0.27
0.25
0.24
0.22
0.26
0.28
0.31
0.33
0.35
1.74
1.72
1.69
1.67
1.65
I
16
17
18
19
20
0.21
0.20
0.19
6.19
0.18
0.36
0.38
0.39
0.40
0.41
1.64
1.62
1.61
1.60
1.59
Figure 18--6
Factors Table for x•
and R-Charts
74. • From Figure 18-6;
n=10, so UCL and LCL in x-bar chart is;
UCL =100.067 +(0.31x5.667) ==101.82377
X
LCL- ==100.067-(0.31x5.667) ==98.31023
X
And UCL and LCL for the values in R chart;
UCL,= D,R =1.785.667 = 10.08726
LCL,=D,R=0.22x5.667 =1.2467
75. (a)
Figure 18-7
Chart
11
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Subgroup Number
76. 18-7 (b)
Figure
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Figure 18--7
x- and FR-Charts
77. Control Chart
• Suppose that we have been setting up a new process (not stable).
in Figure 18-8
• It would look like
5
-g
00
LCl - - - - - - -
Jc
- • - - - - - - - - - - - - - - - - - - - - - - - - -
I I
1 2 3 4 5 6 7 8 9 10 1 1 12 13 14 15
rtuure 18--8
hart tor an Unstable Process Subgroup Number
78. In Figure 18-8;
•
•
•
Subgroup 7 was out of limits.
Can we ignore? NO!
Because-control limit has been calculated with the data inclusive of
special cause event. (E.g: result of untrained operator etc)
We MUST determine and eliminate the cause.
After eliminate it, flush out SUBGROUP 7 and recalculate the process
average (x-bar} and the control limits.
We will find narrower limit, Figure 18-9
•
•
•
79. Figure 18-9
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lSubgroup Number
10 11 1 2 13 14 15
1 2 3 4 5 6 8 9
ruuro 18--9
II Mo
w , Narrower Limits
o I'onotrated Note: Subgroup 7 deleted
• If still penetrates the new out of limits, repeat the same action .. Until the points
are all well between the limits.
80. Control Chart
• X-bar chart is used to show the center of
process measurements (accuracy).
R chart is to show the spread of the data
(precision).
the
•
• Without Range, it would not be able to
understand the PROCESS CAPABILITY of the
chart.
81. Control Chart-Advantages of a Stable Process
• Stable process?? It is a process that
common variation.
Advantages;
exhibits only
•
Management knows the process capability, so they can predict
cost well.
Productivity MAX,cost MIN
Management can measure effect faster and more reliable.
Got data if management wants to alter spec limits.
Stable process is basic requirement for process improvement
efforts.
1.
2.
3.
4.
s.
82. 7 NEW QC TOOLS
• A committee for developing QC
affiliated with JUSE was set up
1972.
tools
in April
• Their aim was to develop QC techniques
for use by managerial level and staff.
• In January 1977 the committee announced
the results of its research in
83. 7 NEW QC TOOLS
the form of a new set of methods
New QC Tools'.
The tools are:-
called 'The Seven
•
•
•
•
•
•
•
1) Affinity Diagram
2)Interrelationship
3)Tree diagram
diagram
4)
5)
6)
7)
Prioritization Matrix
Matrix Diagram
Process Decision Process Chart (PDPC)
Activity Network Diagram
85. Quiz
• Define and
Rule 1 and
Show examples (Diagrams picture)
Rule 2 to show the process is not
in statistical control.
86. Rule 1: A process is not in statistical co ntrol if any subgroup statis tic falls
outs
i de of the control limits. This point is marked with an "
X"
directly on the control chart.
- # 4 - - l / p p e f f o p ' f f ( l Lifnyt
Zone A
Zone8
i i . i i i ' i i . « i i
T
Zone C
ZoneC
ZoneB
ZoneA
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. i i . . i i .
- - - - .25 ) L t @ - f [ -[ --f } f - f ? t Lfflf - 0
5 15
1 10 20
F
igur
e 15-8 Rule 1 - - Lack of Statistical Control
87. Rule 2: A process
successive
is not in statistical control
subgroup statistics fall in one
if any two out of three
of the A zones or beyond
second of the two points
on the same side of the centerline. The
in or beyond zone A is marked with an "X."
- lL/pp@ff'ft!fpllffflt
ZoneA
Zone B
Zone C
Zone C
ZoneB
Zone A
. . . . . . . . . . e l l .
L 0 4 Rf f'(flfol[fit
15
1 10
5 20 25
Rule 2 L a ck of Statistical Control
Fgiure15-9
88. control if four out of five successive
Rule 3: A process is not in statistical
subgroup statistics fall in one of the B zones or beyond on thesame
sidteeof1 the center·rlli;ne
.
+
th
.e
..£f,our
~t1
hp o i:.nt marke
ed
el w i;t
h
an
y
• "
0On.l1.y :i.s
Zone B
Zone C
ZoneC
Zone B
Zone A
h i . f l # f l . h f [ t f l e f i f e
- I L _ Q t 4 t f f o f t [ r t ] [ f i t
10
1 5 15 20 25
'
Figure 15-10 Rule 3 -- Lackof Statistical Control
89. Rule 4: A process is not in statistical
in zone C on either side of
is marked with an "X."
control if eight successive points fall
the centerline. Only the eight point
-lJD9per font!fol[Ifft!f
Zone A
Zone B
ZoneC
Z
o ne C
Zone B
Zone A
= l s # t e l # # h f ; f f ' f ] ] f f
= = ' Lower fontfolLirjt
1 10 25
20
15
90. Statistical error: Type I and Type II
• Statisticians speak of two significant sorts of
statistical error.
Type I e
rror: An incorrect decision to REJECT
something when it is true.
- False alarm
Type 1
1e
rror: An incorrect decision to ACCEPT
something when it is true.
- Oversight
•
•
91. OUTSIDE CONTROL LIMITS [INSIDE CONTROL LIMIT
ype error
ecause presen
Chance cause present
]
Actual condition
Innocent Not innocent
FalsePositive(i.e. guilty but not caught)
Type l error
-
Judged "innocent" True Positive
Test result
False Negative (i.e. innocent but condemned)
Type ll error
Judged "not innocent" True Negative
92. Common Use Control Chart for attribute data
(Counted values)
P chart - No. of defects in samples of varying size
percentage of fraction.
(e.g anywhere defects can be counted)
• as a
•
• np chart- no. of defective pieces in samples
size.
C chart - No. of defects in a single product .
blemish, deform, scrat ches in one part)
of fixed
.
• (e.g:
• U chart - No. of defects per-unit area. (Carpet area,
lenght)
93. Exercise
1. Control charts for X and RR are to be established on a certain dimension part,
measured
in mm. Data were collected in subgroup sizes of6 and are give below. Determine the trial
forX-barandRchart
centerline and control limits
SUBGROUP
NUMBERS
1
2
3
4
5
6
7
8
9
10
1
1
12
20.40
20.41
20.45
20.34
20.36
20.42
20.50
20.31
20.39
20.39
20.40
20.41
20.40
0.39
0.36
0.34
0.36
0.37
0.33
0.38
0.35
0.38
0.33
0.32
0.34
0.30
13
14
15
16
1
18
19
20
21
22
23
24
25
-
X
20.35
20.40
20.36
20.65
20.20
20.40
20.43
20.37
20.48
20.42
20.39
20.38
R
0.34
0.36
0.32
0.36
0.36
0.35
0.31
0.34
0.30
0.33
0.30
0.37
96. Quality Function Deployment
• Defined as:
- A systematic method for transferring customer
wants/needs/expectations into product and
process characteristics
97. QUALITY FUNCTION
DEPLOYMENT
Quality Function Deployment
•
➔
➔
Voice of the customer
House of Quality
QFD: An approach that integrates the "voice of the
customer" into the product and service
development process.
98. House of quality
A
technical
correlations
engineering
metrics
A
relative
importance
I _ I ' -! _I _ I 1
relationships
between
customer needs
and
engineering
I I I I I I I
I I I I I I I
- T - - r 7 - - ; •
benchmarking
on needs
I I I I I I
I
customer
needs - T - - r 7 - - ; •
- I-
'-
' ' ' ' '
- - - -
' ' '
-- - -
' '
-
metrics
_ I
I
I I I _ I I I
I
e g s e g g g
I I I I I
' ' ' } A - ' } A ' -'• I I I I I I I
-
- T
- - r 7 - ; •
I I I I I I I I I I I
target and final specs
99. QFD & House of Quality
Identify customer wants
Ident ify how the good/ service will sat isfy customer
wants
Relate the customer's wants to the product's hows
Identify relationships between the firm's hows
Develop importance ratings
Evaluate competing products
•
•
•
•
•
•
100. Example
Facial Foam 100ml :
QFD
•
•
•
A :
B :
C:
Nivea Visage (Biersdorf
L'Oreal (Paris)
Hamburg)
Biore (Kao)
Facial Foam C
Facial Foam A Facial Foam B
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104. Benefit s Of QFD
•
•
•
•
Customer Driven
Reduces Implementat ion
Promotes T
eamwork
Provides Documentat ion
Time
105. Quality Function Deployment (QFD)
• QFD seeks to bring the voice of customers into
process of designing and developing a product
the
or
.
service.
QFD can point out areas of strength as well as
weaknesses in both existing or new products.
When a company uses QFD, they stop developing
products/ services on their own int erpretat ion.
•
•
106. Main benefits of QFD
1. - QFD gives information which is then
customer requirements.
Customer focused
translated into a set of specific
2. Time efficient -- Time is not wasted on
have no value to customers.
Teamwork oriented - All decisions are
developing features that
3. based on consensus and
involve in-depth
Documentation
documentation.
discussion and brainstorming
oriented - QFD forces the issue of
This document changes as new information
4.
gained. Having up-to-date information about customer
requirements, will be very helpful.
