Download - Razor Technical Report - FMEA
Razor Technical Report
Components:
Albert Lopes
Andre Rufino
David Ferreira
Eric Araujo
Mohammed Woyeso
Thomas Caiado
Vytor Almeida
December 2010
Summary
1) Introduction: ......................................................................................................................... 3
2) House of Quality: ................................................................................................................... 4
2.1) Customer requirements ......................................................................................................... 4
2.2) Technical characteristics ........................................................................................................ 5
2.3) Relationship matrix ................................................................................................................ 5
2.4) Expected quality deployment ................................................................................................ 6
2.5) Technical comparison ............................................................................................................ 7
2.6) Correlation among technical characteristics ......................................................................... 7
3) Lyman Normalization: ........................................................................................................... 8
4) AHP method: ......................................................................................................................... 9
5) The Q-bench Algorithm: ........................................................................................................ 9
6) Fmeca: ................................................................................................................................. 10
7) Conclusion: .......................................................................................................................... 12
8) References: .......................................................................................................................... 12
Attachement 1............................................................................................................................. 14
Attachement 2............................................................................................................................. 16
Attachement 3............................................................................................................................. 17
1) Introduction:
In this technical report will be analyzed the Razor for a targeted market
segment. There are different types of Razor available on the market such as
the straight blade, 2 blade razor, 3 blade razor, electric razor, etc.
In this project report, it was focused on the Electric Razor. It was identified
some of the criteria that the customers require from a Electric Razor, and then
due to those needs it was described the technical characteristics to satisfy
those requirements. After that the house of quality can be started.
The report is divided in six steps:
1. House of Quality
2. Lyman method
3. AHP Method
4. Q-bench Algorithm
5. Fmeca
6. Attachments
2) House of Quality:
2.1) Customer requirements
In the construction of the house of quality, firstly it should be identified the
customer requirements by making a questionnaire.
The followings are the results obtained from the administering questionnaire:
It should have long cycle life
It should have good appearance
It should be safe
It should be easy to handle
It should be have a design project for faces
It should shave fast
It should not irritate the skin
It should shave close to the skin
It should have a good accuracy when is shaving
The battery should have a good durability
It should not have a loudly noise
Secondly these requirements were ordered as is written below:
Life time of the Blade
Aesthetics
Risk of nicks and cuts
Ergonomics
Facial Adaptability
Shaving Time
Skin Irritation
Close Shaving
Precision
Battery Efficiency
Noise
2.2) Technical characteristics
Thirdly it was developed the following technical characteristics to meet these
requirements; here is the list of the technical characteristics:
Edge straightness tolerance
Blade Edge thickness
Blade tensile strength
Blade Vickers hardness
Weight
Battery Duration
Recharge time
No. Of colors
Length
Body coefficient of friction
Sound intensity level
Rotational Speed
No. Of Blades
Blade range of move
2.3) Relationship matrix
Then the degree of importance was obtained from the questionnaires, and the
relative importance can be easily calculated: just by dividing each degree of
importance by the sum of all the degrees.
In the next level the relationship matrix should be done, which shows how the
technical characteristics are related to the customer requirements (Figure-1)
Figure-1: house of quality with customer requirements and technical characteristics and the relation ship
matrix in which = 9 (strong correlation),O = 3 (medium relationship),Δ = 3 (weak relationship)
Ed
ge
str
aig
htn
es
s
tole
ran
ce
Bla
de
Ed
ge
th
ick
ne
ss
Bla
de
te
ns
ile s
tre
ng
th
Bla
de
Vic
ke
rs h
ard
ne
ss
We
igh
t
Ba
tte
ry D
ura
tio
n
Re
ch
arg
e t
ime
No
. Of
co
lors
Le
ng
th
Bo
dy
co
eff
icie
nt
of
fric
tio
n
So
un
d in
ten
sit
y le
ve
l
Ro
tati
on
al S
pe
ed
No
. Of
Bla
de
s
Bla
de
ra
ng
e o
f m
ov
e
A B C D E F G H I J K L M N
1 Life time of the Blade 2 5,7%
2 Aesthetics 1 2,9%
3 Risk of nicks and cuts 5 14,3%
4 Ergonomics 3 8,6%
5 Facial Adaptability 4 11,4%
6 Shaving Time 3 8,6% r
7 Skin Irritation 4 11,4% r
8 Close Shaving 5 14,3% r
9 Precision 3 8,6% r
10 Battery Efficiency 2 5,7% r
11 Noise 3 8,6%
Re
lati
ve
imp
ort
an
ce
Customer Requirements /
Technical Characteristics
Cu
sto
me
r im
po
rta
nc
e
2.4) Expected quality deployment
A benchmarking was done to understand the customer quality perceived. It was
asked about how the quality of current model and the competitors’ model is,
according to the result, the target was defined.
Dividing the target by degree of importance, it was found the improvement
ratio. The strength column is to identify the product’s potential strength for an
improvement, 1.5 very important strength, 1.2 possible strength, 1.0 not
considered as strength.
Then the absolute weight is calculated by multiplying the degree of importance
and improvement ratio and strength (Figure-2).
Figure-2: Expected quality deployment
3 5 3 4 1,33 1,00 3 5%
3 4 3 4 1,33 1,00 1 3%
4 3 4 4 1,00 1,20 6 11%
3 4 2 3 1,00 1,00 3 6%
2 4 3 3 1,50 1,20 7 14%
3 3 4 4 1,33 1,00 4 8%
4 3 3 4 1,00 1,50 6 11%
4 5 4 5 1,25 1,50 9 18%
4 3 4 5 1,25 1,20 5 9%
3 4 3 4 1,33 1,50 4 8%
2 2 3 3 1,50 1,00 5 9%
53 100%
Ta
rge
ts
Imp
rove
me
nt ra
tio
Str
en
gth
Ab
so
lute
we
igh
t
Re
lative
we
igh
t
Cu
rre
nt M
od
el
Ph
ilip
s 8
26
0 X
L
Re
min
gto
n R
-81
50
2.5) Technical comparison
In this part, for each technical characteristic it is multiply the relationship value
by the relative importance of the correspond customer requirement then it is
sum up the results for each column this number will show the technical
importance which says how important is the technical characteristic due to
customer satisfaction.
The Absolute weight can be found by doing the same calculation but instead of
relative importance, it is used the relative weight column. This shows the
absolute weight of each technical characteristic.
At the end the technical characteristics of the current mode should be simply
compared with other competitors’ model to find its level of competitiveness.
(Figure-3)
Figure-3: Technical comparison
2.6) Correlation among technical characteristics
It was build the roof of the house of quality. At first the vector for each
technical characteristic was found. Analyzing the relationship matrix if exist any
relation the number 1 is sign, otherwise number 0, this process should be
repeat for all of the technical characteristics. Then formulas are applied with
the vectors value found:
A: (a1, a2, a3... a14)
B: (b1, b2, b3... b14)
Cosα = A.B [0: 1]
|A| |B|
Then if Cosα > 0.66, they are correlated, then it is check logically if the result found is right if no, the word NO is sign, otherwise it is check between those two technical characteristics that are correlated if one increase the other one
will increase too (positive correlation) it is sign with “+”, if they have negative correlation it is sign with “-“. (Attachment_1) As showed on the attachment_1 the result shows that:
- Blade Edge thickness & Blade Vickers hardness are positively correlated;
- Weight & Length are positively correlated;
- Battery Duration & Recharge time are positively correlated;
- Edge straightness tolerance & Blade tensile strength are negatively
correlated;
- The other correlations are irrelevant.
3) Lyman Normalization: In Lyman normalization method, the Absolute weight of technical
characteristics can be founded by normalized relationship values.
It means that first should normalize the relationship values due to each
customer requirements then this number is used instead of exact relationship
value for calculating the absolute weight.
As noticed in Figure 4 and Figure 5, the results are a bit different but not so
much.
Figure-4: Comparison of technical characteristics importance with and without Lyman
Normalization
Figure-5: Graphic comparison of technical characteristics importance with and without Lyman
Normalization
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1 2 3 4 5 6 7 8 9 10 11 12 13 14Techn. Char.
RANKING Comparison no LYMAN
with LYMAN
In Lyman method, it is also taken in to account the proportion in the
contribution of the requisites; it will give a better ranking for technical
characteristics.
4) AHP method:
Due to comparison evaluation of the customer requirements the matrix which
is symmetrical matrix is can be created (Attachment_2)
Then it will be needed to find λmax, the software Matlab was used, due the
calculation would be quit complicated.
After getting these results it is able to find CR and CI with fallowing formulas
(according to CI it was found RI, as showed in the attachment_2) if CR>0.1 then
it is not acceptable otherwise the result is acceptable. According the result on
attachment_2 CR is smaller than 0.1, so it has a consistency acceptable.
CI= (λmax-n)/(n-1)
CR= CI/RI
As noticed on the comparison graph in attachment_2 it exist differences in
some points but on other points there are not so much differences, because of
the small difference it is possible to accept the result.
5) The Q-bench Algorithm:
First due to the current model and competitor’s model it has been done a
domain contraction, and this it is able to find the characteristic that has the
highest weight (technical relative importance) as is shown by yellow color in
attachment_3.
Then according to the signs inc or dec the target have been chosen. In this way
that the characteristic A which has the highest weight should get to its best
situation in the target and as it has inc sign so it should have the highest
number of the domain which is 13000, but others would be in their worst
cases, except the ones that do not have any sign they will stay in their previous
situation. It means that it has improved the characteristic with highest weight.
Then it was made a comparison between the target and the competitors’
technical characteristics, and fill the matrix with these formulas:
If both situations are satisfied, it is noticed that aOa’ which means a outranks
a’.
Then according to the matrix that is built it is able to draw the graph. Then
possible eliminate all the loops, then the one that outranks all, by doing this
able to find a preference sequence. In this case the target is that last preferred
so the process should be repeated all by improving the next characteristic with
highest weight.
The process has been repeated for 4 times till find a target which is preferred to
all the others. (attachment_3). By this result it has been notice that some
improvement should be in some points.
6) Fmeca:
Failure mode and effect analysis was performed on the prototype to determine
possible causes of failure and severity of the effects. It is important to
anticipate all of the ways a product could fail so that it will operate properly
after repeated use and cause no risk to the operator.
For each component, it was examined all of the possible ways in which the
razor enclosure could fail. The internal components were not considered in this
evaluation outside of their interactions with the enclosure because they will be
purchased. After identifying all of the possible failures, we rated them on
severity of the problem and probability of occurrence. The criteria for rating
can be found in Tables 1, 2 and 3.
W W 2
W 3
)1(
W
W
Table 1. Severity ratings of failure for FMECA
Rating Description
1 Minor
Functional failure of part of machine or process - no potential for
injury
2 Critical
Failure will probably occur without major damage to system or
serious injury
3 Major Major damage to system and/or potential serious injury to personnel
4 Catastrophic Damage causes complete system loss and/or potential for fatal injury
Table 2. Probability ratings of failure for FMECA
Rating Description
1 Frequent Likely to occur frequently
2 Probable Likely to occur several times in the life of the item
3 Occasional Likely to occur sometime in the life of the item
4 Remote Unlikely to occur but possible
5 Improbable So unlikely that occurrence may not be experienced
Table 3. Detection ratings of failure for FMECA
Rating Description
1 No uncertainty
2 Very low uncertainty
3 Low uncertainty
4 High uncertainty
5 Very High uncertainty
The results of the FMECA are presented in the Attachment 4.
7) Conclusion:
The product analyzed in this report was electric razor; the work had been
started with the structure of the house of quality, then the relative importance
of the technical characteristics and relative weight for customer requirements
were found and also the correlation between technical characteristics.
The work has been started by the structure of the house of quality, and then it
was found the relative importance of the technical characteristics and relative
weight of customer requirements, also it was found the correlation between
technical characteristics.
In next steps we found rankings for customer requirements by AHP method and
compare it to the results of the house of quality.
It was applied the same approach for the ranking of technical characteristics by
Lyman method.
Then it was found a target for technical characteristics by applying Q-bench
analysis.
And at end the potential failure mode were analyzes, showing what potential
failure mode the product may have, then also it was found out which failure is
more critical and risky than the other ones.
8) References:
www.philips.com.br
www.panasonic.com
www.remingtonproducts.com
www.braun.com
badgerandblade.com
www.consumersearch.com/electric-shavers
shaverguide.com
Attachement 1
Correlation Matrix
A B C D E F G H I J K L M N
A 1.0 0.9 0.7 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.4 0.3
B 0.9 1.0 0.6 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.6 0.5
C 0.7 0.6 1.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.4
D 0.5 0.7 0.7 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.7 0.6
E 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0
F 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0
G 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0
H 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
I 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0
J 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0
K 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.5 0.0 0.0
L 0.3 0.4 0.0 0.3 0.0 0.5 0.5 0.0 0.0 0.0 0.5 1.0 0.4 0.3
M 0.4 0.6 0.5 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 1.0 0.8
N 0.3 0.5 0.4 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.8 1.0
A B C D E F G H I J K L M N
0 0 0 1 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0
1 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 1 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1
0 1 0 1 0 0 0 0 0 0 0 1 1 1
1 1 0 0 0 0 0 0 0 0 0 1 0 0
1 1 1 1 0 0 0 0 0 0 0 0 0 0
1 1 1 1 0 0 0 0 0 0 0 0 1 1
0 0 0 0 0 1 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 1 0 0
A B C D E F G H I J K L M N
0 0 0 0.5 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0
0.5 0.447 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 1 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0.577
0 0.447 0 0.5 0 0 0 0 0 0 0 0.5 0.707 0.577
0.5 0.447 0 0 0 0 0 0 0 0 0 0.5 0 0
0.5 0.447 0.707 0.5 0 0 0 0 0 0 0 0 0 0
0.5 0.447 0.707 0.5 0 0 0 0 0 0 0 0 0.707 0.577
0 0 0 0 0 1 1 0 0 0 0 0.5 0 0
0 0 0 0 0 0 0 0 0 0 1 0.5 0 0
sum 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Attachement 2
w relative w AHP
ranking Original Ranking
A Life time of the Blade -0.031 1.26% 11 9
B Aesthetics -0.051 2.07% 9 11
C Risk of nicks and cuts -0.505 20.72% 2 1
D Ergonomics -0.191 7.86% 5 5
E Facial Adaptability -0.137 5.61% 6 3
F Shaving Time -0.370 15.17% 3 5
G Skin Irritation -0.267 10.95% 4 3
H Close Shaving -0.680 27.94% 1 1
I Precision -0.098 4.01% 7 5
J Battery Efficiency -0.037 1.54% 10 9
K Noise -0.070 2.87% 8 5
Pairwise Comparison Matrix
A B C D E F G H I J K
A 1.00 0.33 0.11 0.14 0.17 0.11 0.13 0.11 0.20 0.50 0.25
B 3.00 1.00 0.13 0.20 0.25 0.14 0.17 0.11 0.33 2.00 0.50
C 9.00 8.00 1.00 4.00 5.00 2.00 3.00 0.50 6.00 9.00 7.00
D 7.00 5.00 0.25 1.00 2.00 0.33 0.50 0.20 3.00 6.00 4.00
E 6.00 4.00 0.20 0.50 1.00 0.25 0.33 0.17 2.00 5.00 3.00
F 9.00 7.00 0.50 3.00 4.00 1.00 2.00 0.33 5.00 8.00 6.00
G 8.00 6.00 0.33 2.00 3.00 0.50 1.00 0.25 4.00 7.00 5.00
H 9.00 9.00 2.00 5.00 6.00 3.00 4.00 1.00 7.00 10.00 8.00
I 5.00 3.00 0.17 0.33 0.50 0.20 0.25 0.14 1.00 4.00 2.00
J 2.00 0.50 0.11 0.17 0.20 0.13 0.14 0.10 0.25 1.00 0.33
K 4.00 2.00 0.14 0.25 0.33 0.17 0.20 0.13 0.50 3.00 1.00
largest eigenvalue = max = 11.74
max = 11.74 CI = 0.074
n = 11 RI (n>9) = 1.49
CR = 0.050 ≤ 0.1
123456789
1011
A B C D E F G H I J K
customer reqs
RANKING Comparisoninterviews/questionnaires AHP
Attachement 3