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A Study on Six Sigma Techniques And Its application in reduction of seat rejection At BOSCH LTD
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1
OPERATIONS MANAGEMENT PROJECT
A Study on Six Sigma Techniques
And Its application in reduction of seat rejection
At BOSCH LTD.
Submitted by Ankur Bhaskar Ghosh(11FN-013)
Saurabh Bakshi(11IB-052)
Chandra Shekhar L(11DM-031)
Pankhuri Agrawal(11FN-071)
Hitesh Kothari(11IB-025)
Pranjal Singh(11DM-107)
2
Introduction to Six Sigma:
Sigma (σ) is a letter in the Greek alphabet that has become the statistical symbol and metric of
process variation. The sigma scale of measure is perfectly correlated to such characteristics as
defects-per-unit, parts-per-million defectives, and the probability of a failure. Six is the number of
sigma measured in a process, when the variation around the target is such that only 3.4 outputs out of
one million are defects under the assumption that the process average may drift over the long term by
as much as 1.5 standard deviations. Six sigma may be defined in several ways. Tomkins defines Six
Sigma to be “a program aimed at the near-elimination of defects from every product, process and
transaction.” Harry (1998) defines Six Sigma to be “a strategic initiative to boost profitability, increase
market share and improve customer satisfaction through statistical tools that can lead to breakthrough
quantum gains in quality.”
Six sigma was launched by Motorola in 1987. It was the result of a series of changes in the quality
area starting in the late 1970s, with ambitious ten-fold improvement drives. The top-level management
along with CEO Robert Galvin developed a concept called Six Sigma. After some internal pilot
implementations, Galvin, in 1987, formulated the goal of “achieving Six-Sigma capability by 1992” in a
memo to all Motorola employees. The results in terms of reduction in process variation were on-track
and cost savings totaled US$13 billion and improvement in labor productivity achieved 204% increase
over the period 1987–1997.In the wake of successes at Motorola, some leading electronic companies
such as IBM, DEC, and Texas Instruments launched Six Sigma initiatives in early 1990s. However, it
was not until 1995 when GE and Allied Signal launched Six Sigma as strategic initiatives that a rapid
dissemination took place in non-electronic industries all over the world. In early 1997, the Samsung
and LG Groups in Korea began to introduce Six Sigma within their companies. The results were
amazingly good in those companies. For instance, Samsung SDI, which is a company under the
Samsung Group, reported that the cost savings by Six Sigma projects totaled US$150 million. At the
present time, the number of large companies applying Six Sigma in Korea is growing exponentially,
with a strong vertical deployment into many small- and medium-size enterprises as well. Six sigma
tells us how good our products, services and processes really are through statistical measurement of
quality level. It is a new management strategy under leadership of top-level management to create
quality innovation and total customer satisfaction. It is also a quality culture. It provides a means of
doing things right the first time and to work smarter by using data information. It also provides an
atmosphere for solving many CTQ (critical-to-quality) problems through team efforts. CTQ could be a
critical process/product result characteristic to quality, or a critical reason to quality characteristic.
Defect rate, PPM and DPMO:
The defect rate, denoted by p, is the ratio of the number of defective items which are out of
specification to the total number of items processed (or inspected). Defect rate or fraction of defective
items has been used in industry for a long time. The number of defective items out of one million
inspected items is called the ppm (parts-per-million) defect rate. Sometimes a ppm defect rate cannot
be properly used, in particular, in the cases of service work. In this case, a DPMO (defects per million
opportunities) is often used. DPMO is the number of defective opportunities which do not meet the
required specification out of one million possible opportunities.
3
Sigma quality level
Specification limits are the tolerances or performance ranges that customer's demand of the products
or processes they are purchasing. Figure 1 illustrates specification limits as the two major vertical
lines in the figure. In the figure, LSL means the lower specification limit, USL means the upper
specification limit and T means the target value. The sigma quality level (in short, sigma level) is the
distance from the process mean (μ) to the closer specification limit. In practice, we desire that the
process mean to be kept at the target value. However, the process mean during one time period is
usually different from that of another time period for various reasons. This means that the process
mean constantly shifts around the target value. To address typical maximum shifts of the process
mean, Motorola added the shift value ±1.5 s to the process mean. This shift of the mean is used when
computing a process sigma level. From this figure, we note that a 6 sigma quality level corresponds to
a 3.4ppm rate.
Fig 1: Sigma quality levels of 6σ and 3σ
4
DMAIC Process in Six Sigma methodology: The most important methodology in Six Sigma management is perhaps the formalized improvement methodology characterized by DMAIC (define-measure-analyze-improve control) process. This DMAIC process works well as a breakthrough strategy. Six Sigma companies everywhere apply this methodology as it enables real improvements and real results.
Fig 2: Flow diagram of DMAIC methodology adopted
Sigma level for discrete data:
Suppose two products out of 100 products have a quality characteristic which is outside of
specification limits. Then in one million parts 20,000 parts will be defects so, sigma level will be
between 3 & 4.Preciously it will come as 3.51σ. The broad classification of sigma level is shown
below-
PPM Defectives Sigma level
6,91,000 1
3,09,000 2
67,000 3
6,200 4
230 5
3.4 6
Literature Survey
Case study of manufacturing Industry
Identification of problem Industry
Create solution statement
Create improvement Ideas
Implement improvement solutions Improve
Data Collection
Define Define customer Requirements
Identify Specific problem
Set Goals
SIPOC diagram
Data Collection Plan
Measurement System Analysis
Identify variation due to measurement system
SIPOC diagram
Measure
Analyze Process Capability Analysis
Draw conclusion from data verification
Determine root causes
Map cause & effect diagram
Make needed adjustments
Monitor Improvement progress
Establish standard measures to maintain performance
Control
Scope of future work
Improvement Results
Conclusions
5
Product Definition:
Fig 3: DSLA Nozzle Assembly
Fig 4: Injector Assembly
Fig 5: Body of DSLA type nozzle
DEFINE PHASE:
1. Why the project? (The Business case) DSLA nozzle parts are hardened at UDA (Hardening
process) and after subsequent chamfer grinding they come at UVA (High precision internal grinding)
machines for Guide bore and Seat grinding. The seat and guide bore surface grinding is done on UVA
and then they are sent to inspection for seat visual checking. At seat visual checking section the no. of
parts getting rejected are quite high. From Jan08 to July08 average 22600 ppm (parts per million)
were rejected due to Bad seat problem (Rejection due to other reasons are not included in the scope
of the project).
Due to these rejections the first pass yield and type wise fulfillment of parts decreases. Also Due to
added seat repair operation at UVA the m/c utilization decreases and at the same time it increases
Step Turning
Guide Bore Drilling
Seat Profile Grinding
Inlet hole Drilling
Dowel hole drilling
Shoulder Turning
Pressure Chamber machining
Sack Hole
Seat Surface
Seat- seen under Microscope
6
the defect cost associated with it. By successfully implementing the project we can save up to 1, 50
TINR.per month.
2. SIPOC (Supplier-Input-Process-Output-Customer):
SIPOC is a six sigma tool. The acronym SIPOC stands for Suppliers, inputs, process, outputs, and
customers. A SIPOC is completed most easily by starting from the right ("Customers") and working
towards the left.
Suppliers to UVA process are Company, TEF1, TEF2, PLP, and MSEB.
Inputs to UVA process are Man, Machine, Electricity, Drawings, and H.T. over parts, Gauges, Tooling
Compressed air, JML, Cutting oil, Check list , Instruction charts, Program etc.
Process taking place at UVA process is Internal grinding of seat surface.
Output of the UVA process are Seat Grinding over parts, Worn out tooling, Grinding muck, PMI chart,
Re-release chart.
Customers of the UVA process are Inspection, Repair process, Stores, Scrap yard, Etamic check,
Honing, Profile Grinding.
Using this data a SIPOC diagram is created.
Fig 6: SIPOC for UVA (Internal grinding) process.
3. CTQ (Critical to Quality) Identification:
A CTQ tree (Critical-to-quality tree) is used to decompose broad customer requirements into more
easily quantified requirements. CTQ Tree is often used in the Six Sigma methodology.
CTQs are derived from customer needs. Customer delight may be an add-on while deriving Critical to
Quality parameters. For cost considerations one may remain focused to customer needs at the initial
stage. CTQs (Critical to Quality) are the key measurable characteristics of a product or process
whose performance standards or specification limits must be met in order to satisfy the customer.
CTQ tree is generated when there are Unspecific customer/business requirements or complex, broad
needs from the customer.
SUPPLIER INPUT PROCESS OUTPUT CUSTOMER
Company
Electricity
Maintenance
TEF1
Purchase
Man
Machine
Electricity
Drawings
H.T. over parts
Gauges, Tooling
Compressed air
JML ,Cutting oil
Check list
Instruction charts
Program
UVA
process
High
Precision
Internal
Grinding
Process
Seat Grinding over
parts
Worn out tooling
Grinding muck
PMI chart
Re-release chart
Inspection
Repair process
Stores
Scrap yard
Etamic check,
Honing
Profile Grinding
Soft Stage
Operations Hardening UVA process
(High Precision Internal Grinding)
Profile
Grinding Seat Visual Inspection
7
Fig 7: CTQ tree for UVA process.
By the reference of CTQ tree there are 5 elements in UVA process seat repair. To select the right
CTQ for the project Pareto Analysis was performed on the data gathered from Jan’08 to July’08.
Pareto Analysis:
The Pareto chart was introduced in the 1940s by Joseph M. Juran, who named it after the Italian
economist and statistician Vilfredo Pareto, 1848–1923. It is applied to distinguish the “vital few from
the trivial many” as Juran formulated the purpose of the Pareto chart.
From this Analysis we clearly see that Seat repair is the most critical of all rejections.
Kano model of Quality:
The Kano model is a theory of product development and customer satisfaction developed in the 80's
by Professor Noriaki Kano which classifies customer preferences into five categories:
Attractive
One-Dimensional
Must-Be
Indifferent
Seat repair
Guide bore repair
Taper repair
Repair
Scrap
Seat scrap
Guide bore scrap
To reduce UVA
process Repair
8
Less the better
As per Kano model of Quality A CTQ specification table is generated for giving the specifications of
rejections.
Fig: CTQ table
MEASURE PHASE:
Fig 10: Approach to measure phase.
Creating a data collection plan: As per the approach specified a plan for collecting the base line
data is created. It is given below.
CTQ MEASURE SPECIFICATION DEFECT DEFINITION KANO STATUS
G.B. Repair Monthly PPM -- G.B. size out of
specification Must Be
Seat Repair Monthly PPM Seat Damage/
Finish Bad Seat visually not O.K. Less the Better
Taper bad
Repair Monthly PPM --
Taper out of
specification Less the Better
G.B. Scrap Monthly PPM -- G.B. size out of
specification Less the Better
Seat Scrap Monthly PPM Seat Damage Seat visually not O.K. Less the Better
Collect baseline data on defects & possible causes
Develop a sampling strategy
Validate your measurement system using Gauge R & R.
Analyze patterns in data
Determine process capability
9
Data Collection Plan Action: Data collection from Seat Rejection
What question do you want to answer? Body seat visually OK?
Data Operational definition and procedures
What Measure type/
data type
How
measured
Related
conditions to
record
Sampling
notes
How/where
recorded
(Attached form)
Seat defects Discrete data visually lot wise 100% --
Fig 11: Data collection plan
It was decided to change the format for recording of parts checked at seat visual section as it was
outdated. So with the help of line foremen new format was developed by. It is as follows:
New format developed for Seat visual section:
Segregation of defects observed at seat visual section:
Unground
seat
No sack
hole
Rubbing at
sack holePatchesRings
Bad
Finish
TypeLot No.Scrap
Seat DefectsItem
No.
Qty
Rejected
Qty.
OK
Qty.
Inspected
Token No: Name_________________________ShiftDate
BOSCH
Nashik plant
Unground
seat
No sack
hole
Rubbing at
sack holePatchesRings
Bad
Finish
TypeLot No.Scrap
Seat DefectsItem
No.
Qty
Rejected
Qty.
OK
Qty.
Inspected
Token No: Name_________________________ShiftDate
BOSCH
Nashik plant
162779219215025665058
00044331782/9/2008Day-20
001483712991/9/2008Day-19
01669553216330/08/08Day-18
0000633810128/08/08Day-17
00018632119270808Day-16
00016948219226/08/08Day-15
00067011318925/08/08Day-14
200129020430823/08/08Day-13
10089011521422/08/08Day-12
0100957417020/08/08Day-11
720056208519/08/08Day-10
10004324618/08/08Day-9
280572939045017/08/08Day-8
1004788016314/08/08Day-7
100224204713/08/08Day-6
0102516541660712/8/2008Day-5
012412440646111410/8/2008Day-4
0029631001748/8/2008Day-3
11561721823677/8/2008Day-2
0110932773726/8/2008Day-1
Rubbing at sack
hole end
due to burr
No sack
hole
Unground
seatPatchesRings
Bad finish
(rough surface)
Total no. of
parts checkedDateDay count
162779219215025665058
00044331782/9/2008Day-20
001483712991/9/2008Day-19
01669553216330/08/08Day-18
0000633810128/08/08Day-17
00018632119270808Day-16
00016948219226/08/08Day-15
00067011318925/08/08Day-14
200129020430823/08/08Day-13
10089011521422/08/08Day-12
0100957417020/08/08Day-11
720056208519/08/08Day-10
10004324618/08/08Day-9
280572939045017/08/08Day-8
1004788016314/08/08Day-7
100224204713/08/08Day-6
0102516541660712/8/2008Day-5
012412440646111410/8/2008Day-4
0029631001748/8/2008Day-3
11561721823677/8/2008Day-2
0110932773726/8/2008Day-1
Rubbing at sack
hole end
due to burr
No sack
hole
Unground
seatPatchesRings
Bad finish
(rough surface)
Total no. of
parts checkedDateDay count
10
Pareto Analysis of Seat rejections:
Measurement System Analysis:
A Measurement System Analysis, abbreviated MSA, is a specially designed experiment that seeks
to identify the components of variation in the measurement.
Just as processes that produce a product may vary, the process of obtaining measurements and data
may have variation and produce defects. A Measurement Systems Analysis evaluates the test
method, measuring instruments, and the entire process of obtaining measurements to ensure the
integrity of data used for analysis (usually quality analysis) and to understand the implications of
measurement error for decisions made about a product or process. MSA is an important element
of Six Sigma methodology and of other quality management systems.
ANOVA Gauge Repeatability & Reproducibility: (GRR study)
ANOVA Gauge R&R (or ANOVA Gauge Repeatability & Reproducibility) is a Measurement Systems
Analysis technique which uses Analysis of Variance (ANOVA) model to assess a measurement
system. The evaluation of a measurement system is not limited to gauges (or gages) but to all types
of measuring instruments, test methods, and other measurement systems.
In this project GRR study, a quality over checker took 30 parts and checked its angle twice. The
recorded measurements were fed to standard Minitab software and the results obtained are as
follows:
Measuring Table-20249 Measuring Table-19389
Gage R & R 18.82 13.23
No. Of Distinct Categories 8 10
Rough finish
Rings
Patches
Unground seat
Others
2566 2150 219 79 43
50.7 42.5 4.3 1.6 0.9
50.7 93.3 97.6 99.1 100.0
0
1000
2000
3000
4000
5000
0
20
40
60
80
100
Defect
CountPercentCum %
Perc
en
t
Cou
nt
Seat Defect Segregation
11
If GRR <10 Gauge is acceptable If 10<GRR<30 Gauge is conditionally acceptable If 30<GRR Gauge is unacceptable & must be replaced/modified.
Process Capability Analysis
Process capability analysis was performed to find out the actual state of the process.
Minitab was used to draw a process capability analysis curve for Seat Rejections measured over a
month. As the data is discrete the Sigma level what we get is in terms of PPM (Defective Parts per
Million Opportunities)The Minitab output obtained for the Analysis is shown below.
Sample
Pro
po
rti
on
28252219161310741
0.026
0.024
0.022
0.020
_P=0.022624
UC L=0.026045
LC L=0.019202
Sample
%D
efe
cti
ve
30252015105
2.30
2.28
2.26
2.24
2.22
Summary Stats
0.00
PPM Def: 22624
Lower C I: 22217
Upper C I: 23035
Process Z: 2.0024
Lower C I:
(using 95.0% confidence)
1.9947
Upper C I: 2.0100
%Defectiv e: 2.26
Lower C I: 2.22
Upper C I: 2.30
Target:
Observed Defectives
Ex
pe
cte
d D
efe
cti
ve
s
420390360
425
400
375
350
2.45
2.10
1.75
1.40
1.05
0.70
0.35
0.00
8
6
4
2
0
Tar
Capability Analysis of Seat Visual Process
P Chart
Cumulative %Defective
Binomial Plot
Dist of %Defective
Fig 14: Process Capability analysis of Seat visual process before
Implementing DMAIC methodology
From Results the PPM Def level is 22,624 (i.e.22, 624 Defectives in 1 Million parts.)
The below table shows different Sigma levels for PPM rejections.
PPM Defectives Sigma level
6,91,000 1
3,09,000 2
67,000 3
6,200 4
230 5
3.4 6
Fig 15: PPM defectives & Sigma level Comparison
By doing interpolation between 3 & 3σ levels the Sigma level of the Seat visual process comes out to
be 3.5 Sigma.
12
Fig 16: Tree diagram created from brainstorming session for Input part parameters
Chamfer height
variation. Acqueous Cleaning not ok
Jet broken,Pump
pressure less
Uneven chamfer
bandGuide to shaft TR not ok
Guide to shaft TR
not checked after
TBT as per freq.
TR more
than 100
microns
Measure
by gauge
Vibrations &
chatter marks on
seat in soft stage
Roundness, Straightness,
Guide bore to seat TR
No specification in
drawing
UVA PROCESS
REPAIR &
SCRAP
Seat
repair
Rough finish,
Rings, Patches,
No sack hole,
Rubbing at sack hole,
Unground seat
I/P parts
100% sack hole
checking
poka yoke on all 5
spinner
Possibility of poka
yoke failure
Parts without sack
hole from soft
stage
Sack hole Drill breakage on
Retco
Poka yoke not working
properly
Type Mix-up ( P
type in DSLA &
vise versa
Possibility on all operations
during lot change, 80% on
Benzinger, ECM(10%),
Remaining 10%
Manual element
Guide bore to shaft
T.R bad
Guide to shaft TR not
checked after TBT as per
freq.
TR more than 100
microns
Seat TR wrt guide
bore On spinner & retco m/c
more than 70
microns
Seat angle in soft
stageOn spinner & retco m/c
specification 58.8°
(+/- 0.2°)
More/less
than spec.
Chamfer mandrel
angle in hard
stage
More/less than spec.
13
Vibration Today not Known Consult Mr.Kumavat
RPM value-2250
Workhead
Spindle height Repeatability Below 20μ
Female center Grinding Decide freq.
Job clamping pressure Chuck clamp grinding Once in a month
Loading spring wornoutChanging freq. once
in 2 months
Loading cylinder Air leakage
Cylinder swing In / Out positions
Changing freq. To be decided As per freq.
Angle master
Seat profile To be studied
Alignment of both
eyes
Scope condition
to be studied
Prepare
schedule
RPM value-60,000
spindles
Initial setting wheel form wear
New wheel diameter 4,600 mm After dressing 4,300 mm
Adaptor TR < 10μ
Grinding wheel
Dressing depth of cut 3μ
Dressing freq. 6 parts
Grinding
Feed rate Details to be taken
Tip breakage sensing
poka yoke
confirmation of poka
yoke once in a shift
periodic replcment & TRDressing ring
Setting
parameters
changing freq. every
3 months
3.5 to 4 bar grinding
/ dressing coolant
coolant
systems
Provision to fix pressure
gauge atleast to one m/c
Ensure positive cutting
after dressing
Height gauge to
check height diff.New seat wheel
To be asked
to maintenance
Ref.setting piece to be made
Visual inspection
microscopes Frequent checking
by associates
Checking
bench
spindle cooling
Once in
2 months
UVA
process
repair
Seat
Rejections
M/C
parameters
Loading/
Unloading
Loading alignment
of component
Visual check
OK/ Not OK
Fig 17: Tree diagram due to machine related parameters
From two tree diagrams created above it is clear that there are 7 parameters related to input part
parameters & 23 machine related parameters. To know the impact of each parameter on seat
rejections it was necessary to validate each parameter using statistical methods. In Six Sigma method
used for root cause validation is Hypothesis testing.
Statistical hypothesis testing:
A statistical hypothesis test is a method of making statistical decisions using experimental data. It is
sometimes called confirmatory data analysis. In frequency probability, these decisions are almost
always made using null-hypothesis tests.
14
Validation of all SSVs using Statistical testing: (Input part parameters)
Conclu
sio
ns
Th
e im
pact of aqueous
cle
anin
g o
n c
ham
fer
heig
ht
variatio
n is
Ins
ign
ific
an
t.
Th
e im
pact of cham
fer
heig
ht
variatio
n o
n
seat
reje
ctio
ns is
Ins
ign
ific
an
t
Th
e im
pact of
Uneven c
ham
fer
band
on S
eat re
jectio
ns is
Sig
nif
ican
t
Th
e im
pact of drill
life
on
seat re
jectio
ns is
Ins
ign
ific
an
t
Th
e im
pact of drill
dam
age in
soft s
tage
on S
eat re
jectio
ns is
Sig
nif
ican
t.
Results o
bta
ined
0 b
ad p
art
s in
275 o
k p
art
s
0 b
ad p
art
s in
25
without cle
anin
g
part
s
All
part
s c
am
e o
k
on U
VA
, cham
fer
heig
ht
variatio
n
did
not cause a
ny
defe
ct on U
VA
.
12 p
art
s b
ad in
50 T
R b
ad p
art
s
1 b
ad in
50 T
R o
k
part
s
Th
e s
eat
RZ
&
Rm
ax v
alu
es o
f
all
part
s a
re
within
lim
its
49 b
ad in
50 w
ith
chatt
er
ma
rks,
1
bad in
50 w
ithout
chatt
er
ma
rks
Te
st used
2
pro
port
ions
test
2
pro
port
ions
test
2
pro
port
ions
test
2
pro
port
ions
test
End
date
8-N
ov-0
8
15-N
ov-
08
3-M
ar-
09
8-J
an-0
8
8-J
an-0
8
Sta
rt d
ate
8-N
ov-0
8
15-N
ov-
08
3-M
ar-
09
16-D
ec-
08
16-D
ec-
08
Tria
l ta
ken
Ta
ke 2
75 p
art
s w
ith c
leanin
g &
25
part
s w
ithout cle
anin
g &
pro
cess
them
on s
am
e c
ham
fer
grin
din
g
m/c
& s
am
e U
VA
m/c
.
Ta
ke 3
0 p
art
s w
ith c
ham
fer
heig
ht
(-30 t
o -
10µ
), 6
0 p
art
s w
ithin
spec (
-
10µ
to +
10)
& 3
0 p
art
s w
ith (
+10 to
+30µ
) &
pro
cess t
hem
on U
VA
.
Ta
ke 5
0 p
art
s w
ith T
R m
ore
than 8
5µ
& p
ut th
em
on U
VA
als
o
pro
cess 5
0 n
orm
al part
s
One p
art
fro
m e
ach m
achin
e
giv
en to F
MR
lab,
Life n
o. are
note
d
50 p
art
s w
ith c
hatter
ma
rks w
ere
pro
cessed o
n U
VA
alo
ng w
ith 5
0
ok p
art
s
Actio
ns t
aken
Ta
ke a
tria
l w
hic
h in
volv
es
pro
cessin
g p
art
s w
ithout
aqueous c
leanin
g.
To
take a
tria
l th
is involv
es
takin
g p
art
s w
ith c
ham
fer
heig
ht
mo
re, le
ss &
within
specific
atio
n &
pro
cessin
g
them
on U
VA
.
A t
rial T
R c
heckin
g g
auge
is d
evelo
ped
Ta
ke o
ne p
art
s e
ach f
rom
spin
ners
& R
etc
o h
avin
g
diffe
rent to
ol lif
e &
giv
e t
hem
to F
MR
lab for
seat fo
rm
checkin
g
When s
uch p
art
s c
om
e o
n
UV
Asort
out such p
art
s &
put
them
on U
VA
for
tria
l.
Suspecte
d s
ourc
es
of varia
tio
ns
(SS
V's
)
Seat
does n
ot
get
cle
aned p
roperly s
o
locatio
n o
f part
on
cham
fer
grin
din
g
m/c
is o
uts
ide d
ue
to d
irt pre
sent.
Th
is
outs
ide lo
catio
n
results in s
eat
reje
ctio
ns.
Part
locatio
n in
UV
A
becom
es
im
pro
per
due to
cham
fer
variatio
n.
Guid
e t
o s
haft T
R is
not
checked in
soft
sta
ge
Th
e d
rill
form
dete
rio
rate
s w
ith
usage &
the p
art
s a
t
late
r sta
ges
of
tool lif
e h
ave
mo
re r
oughness
Due t
o d
rill
dam
age
on m
achin
es
vib
ratio
ns &
deep
lines a
re p
roduced
on s
eat.
sub c
ause
Aqueous
cle
anin
g n
ot ok
Jet bro
ken,
Pum
p p
ressure
less
Cham
fer
heig
ht
varia
tio
n c
auses
seat
reje
ctio
ns a
t U
VA
Guid
e t
o s
haft
TR
not
ok
Roundness,
Str
aig
htn
ess G
B
to s
eat T
R n
ot
checked in s
oft
sta
ge
Drill
dam
age o
n
Spin
ners
&
Retc
o
Root
cause
cham
fer
heig
ht
varia
tio
ns
Uneven
cham
fer
band
Vib
ratio
n
s &
chatt
er
ma
rks o
n
seat in
soft s
tage
Sr.
No. 1
2
3
15
(Input part parameters continued..)
Conclu
sio
ns
The im
pact
of N
o
sack h
ole
part
s o
n
seat re
jectio
ns is
Sig
nif
ican
t
The im
pact
of
type m
ix u
p o
n
Seat
reje
ctio
ns is
Sig
nif
ican
t.
The im
pact
of
Seat
an
gle
more
on s
eat
reje
ctio
ns
is In
sig
nif
ican
t
The im
pact
of
cham
fer
man
dre
l
ang
le o
n s
eat
reje
ctions is
Insig
nif
ican
t
Results o
bta
ined
No s
ack h
ole
part
bre
aks the g
rin
din
g
whee
l tip &
m/c
gets
imm
ed
iate
ly
sto
ppe
d, d
urin
g
redre
ssin
g 5
0 p
art
s
cam
e b
ad.
p-t
ype in D
SL
A lot
bre
aks the
adap
tor&
grin
din
g
whee
l, w
hic
h r
esu
lts
in 5
0 b
ad in 5
0,w
ith
norm
al p
art
s 0
bad
in 5
0.
3 b
ad in 2
85 a
ngle
more
part
s,
0 b
ad in 3
00 a
ngle
ok p
art
s
As there
in
no
variation in
outp
ut sta
tistica
l
test cann
ot
be p
erf
orm
ed
Te
st used
2
pro
port
ions
test
2-
pro
port
ions
test
2-
pro
port
ions
test
No
variation
in o
utp
ut
End
date
13-J
an-
09
20-N
ov-
08
28-N
ov-
08
25-D
ec-
08
Sta
rt d
ate
13-J
an-
09
20-N
ov-
08
21-N
ov-
08
25-N
ov-
08
Tria
l ta
ken
One n
o s
ack h
ole
part
was p
ut
on U
VA
203
15 &
it's
effect o
n
reje
ctions w
as
observ
ed
One type m
ix u
p p
art
was p
ut
on U
VA
20
315 &
it's
effect o
n
seat re
jectio
ns is
observ
ed
285 p
art
s w
ith s
ea
t
ang
le m
ore
were
pro
cessed u
p t
o
seat vis
ua
l
alo
ng w
ith 3
00
an
gle
ok p
art
s
Cham
fer
ma
ndre
l
ang
les c
hecked
by S
ine b
ar
meth
od
& M
icro
scope
meth
od
Actio
ns t
aken
Colle
ct at
least 15
No s
ack h
ole
part
s
pre
fara
bly
of
DS
LA
norm
al S
haft
Colle
ct at
least 15
mix
up
part
s
Trial is
taken
wh
ich
involv
es
seat an
gle
more
part
s a
re p
rocessed
up to
seat
vis
ual fo
r
checkin
g.
4 m
andre
ls g
ive
n to
tool ro
om
for
cham
fer
an
gle
verification
Suspecte
d s
ourc
es o
f
varia
tio
ns
(SS
V's
)
Poka Y
oke p
ut
off
due
to v
ario
us
reasons
80%
on
75%
Benzin
ger,
10%
on E
CM
.
Manu
al ele
me
nt
may b
e p
resent,
Ele
va
tor
con
ditio
n
in s
oft
sta
ge
is
poor A
ng
le n
ot
checked a
s p
er
freque
ncy/D
rill
life
over,
Drill
resharp
en
ing
impro
per
Cham
fer
ma
ndre
l
ang
le t
o
be v
erifie
d in to
ol
room
sub c
ause
Poka y
oke
failu
re o
n
spin
ner
machin
e
Poka y
oke
failu
re o
n
Retc
o m
achin
e
Possib
ility
on
all
op
era
tions
On s
pin
ner
& R
etc
o
machin
es
More
or
less tha
n
specific
atio
n
Root
cause
Part
s
with
out
sack h
ole
fro
m s
oft
sta
ge
Part
typ
e
mix
up
Seat
an
gle
in s
oft
sta
ge
Cham
fer
mandre
l
ang
le
in s
oft
sta
ge
Sr.
No.
4
5
6
7
16
Actions taken for machine related parameters
conclu
sio
ns
Th
e im
pact of w
ork
head
vib
ratio
n o
n s
eat
reje
ctio
ns is I
nsig
nif
ican
t
Th
e im
pact of W
ork
head
rpm
on s
eat re
jectio
ns is
Ins
ign
ific
an
t
Th
e im
pact of S
pin
dle
heig
ht
repeata
bili
ty o
n S
eat
reje
ctio
ns is In
sig
nif
ican
t
Th
e im
pact of fe
male
cente
r
grin
din
g o
n s
eat
reje
ctio
ns
is In
sig
nif
ican
t
Th
e im
pact of Job
cla
mp
ing p
ressure
on
seat
reje
ctio
ns is
Ins
ign
ific
an
t.
Results o
bta
ined
Work
head v
ibra
tio
n v
alu
es
of
all
machin
es a
re w
ithin
3
mm
/sec.
At
both
rpm
valu
es a
ll
50 p
art
s c
am
e v
isually
ok
At
both
repeata
bili
ty levels
all
part
s c
am
e v
isually
ok
All
part
s b
efo
re d
oin
g
fem
ale
cente
r grin
din
g
cam
e o
k,
als
o a
ll part
s a
fter
doin
g f
em
ale
cente
r
grin
din
g c
am
e o
k
At
5 b
ar
pre
ssure
0 b
ad in
50,
at
4 b
ar
pre
ssure
29 b
ad in
50 p
art
s.
Te
st used
No v
aria
tio
n
outp
ut
observ
ed
No v
aria
tio
n in
outp
ut
observ
ed
2-p
roport
ions
test
2-p
roport
ions
test
2 p
roport
ions
test
End
date
16-F
eb-0
9
16-F
eb-0
9
12-M
ar-
09
12-M
ar-
09
30-J
an-0
9
Sta
rt d
ate
13-F
eb-0
9
13-F
eb-0
9
12-M
ar-
09
12-M
ar-
09
30-J
an-0
9
Tria
l ta
ken
Work
head v
ibra
tio
n v
alu
es o
f
all
machin
es a
re c
hecked w
ith
help
of vib
rato
me
ter
Ta
ke 5
0 p
art
s w
ith 2
150 r
pm
,
take 5
0 p
art
s w
ith 1
750 r
pm
50 p
art
s e
ach w
ere
pro
cessed
with r
epeata
bili
ty o
f 10µ
&
at2
0µ
.
50 p
art
s w
ere
pro
cessed
befo
re d
oin
g f
em
ale
cente
r
grin
din
g &
50 p
art
s w
ere
pro
cessed a
fter
doin
g fem
ale
cente
r grin
din
g
Th
e jo
b c
lam
pin
g p
ressure
was
varie
d ti 4 b
ar
& 5
bar
& it's
impact
on s
eat re
jectio
ns is
observ
ed.
Actio
ns t
aken
Check w
ork
head v
ibra
tio
n
valu
es o
f all
machin
es
Rate
d R
PM
valu
e is 2
150
RP
M
Check r
epeata
bili
ty<
20µ
,
take tria
l w
ith p
rocessin
g
part
s w
ith d
iffe
rent
repeata
bili
ty v
alu
es.
we c
hecked p
art
s b
efo
re &
aft
er
doin
g fem
ale
cente
r
grin
din
g f
or
checkin
g
diffe
rence
Air s
upply
to jo
b c
lam
pin
g
is v
arie
d to d
iffe
rent le
vels
&
it's
effect
was o
bserv
ed
Suspecte
d
sourc
es o
f varia
tio
ns
(SS
V's
)
Earlie
r not
know
n
valu
e-1
800
rpm
Repeata
bili
ty
belo
w 2
0µ
Grin
din
g f
req.
not
decid
ed
Chuck c
lam
p
grin
din
g
sub c
ause
Vib
ratio
n
RP
M
Spin
dle
heig
ht
Fe
male
cente
r
Job
cla
mp
ing
pre
ssure
Root
cause
Work
head
Sr.
No.
1
2
3
4
5
17
(Machine related parameters continued…)
conclu
sio
ns
Th
e im
pact of lo
adin
g
sprin
g b
roken o
n s
eat
reje
ctio
ns is S
ign
ific
an
t
Th
e im
pact of Loadin
g
alig
nm
ent
of com
ponent
on s
eat re
jectio
ns is
Ins
ign
ific
an
t.
Th
e im
pact of A
ir
cylin
der
on
seat re
jectio
ns is
Ins
ign
ific
an
t.
Th
e im
pact of A
ngle
ma
ste
r on
seat re
jectio
ns is
Ins
ign
ific
an
t.
Th
e im
pact of seat
vis
ual m
icro
scope
conditio
n o
n s
eat
reje
ctio
ns is
Sig
nif
ican
t.
Th
e im
pact of A
ir s
upply
for
part
s c
leanin
g o
n
Seat
reje
ctio
ns is
Sig
nif
ican
t
Results o
bta
ined
with o
k s
prin
g a
ll 50 p
art
s
cam
e o
k,
with b
roken
sprin
g 3
5 b
ad in
50.
with &
without checkin
g
loadin
g a
lignm
ent all
50
part
s c
am
e v
isually
ok
Th
e q
uic
k h
it a
chie
ved
GR
R f
ound t
o b
e o
k
When 5
0 p
art
s c
hecke
with f
aulty m
icro
scope 3
5
cam
e b
ad,
when they a
re
checked w
ith o
k s
cope
only
50 c
am
e b
ad.
With a
ir c
leanin
g
10 p
art
s b
ad in
50,
without
air c
leanin
g
22 p
art
s b
ad in
50.
Te
st used
2 p
roport
ions
test
2 p
roport
ions
test
No h
ypoth
esis
test
perf
orm
ed
No t
est
perf
orm
ed
2 p
roport
ions
test
2 p
roport
ions
test
End
date
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
18-D
ec-0
8
30-J
an-0
9
Sta
rt d
ate
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
18-D
ec-0
8
30-J
an-0
9
Tria
l ta
ken
Changin
g f
req.
once in t
wo
mo
nth
s.
Ta
ke a
tria
l w
ithout checkin
g
lo
adin
g a
lignm
en
t of
com
ponent.
No p
roble
m o
f air leakage
take G
RR
of seat
angle
ma
ste
r
Scope c
onditio
n s
tudy
schedule
to b
e p
repare
d
A w
ork
shop o
n m
icro
scope
handlin
g t
o b
e a
rranged
50 p
art
s t
aken w
ith a
ir c
leanin
g
& 5
0 p
art
s t
aken w
ithout
air
cle
anin
g
Actio
ns t
aken
Loadin
g s
prin
g w
as
changed w
ith a
bro
ken o
ne
& it's
effect
on s
eat
reje
ctio
ns w
as o
bserv
ed
While
sett
ing m
achin
e
check
alig
nm
ent fo
r ok / N
ot
ok
Ele
ctr
ical serv
o m
oto
r used
Checkin
g f
req. to
be
reduced
Check r
equirem
ent
of
frequent
verificatio
n o
f
mic
roscope c
onditio
n
Associa
tes a
ware
ness
about
mic
roscope
adju
stm
ent to
be d
one.
Part
s t
o b
e c
hecked w
ith
air c
leaned &
without air
cle
anin
g
Suspecte
d
sourc
es o
f
varia
tio
ns
(SS
V's
)
Changin
g
freq.
Vis
ual check
Air leakage
Ma
ste
r
show
ing
wro
ng
readin
g
Alig
nm
ent
of
both
eyes
not
there
Fre
quent
checkin
g b
y
associa
tes
No s
upply
pro
vid
ed
sub c
ause
Loadin
g
sprin
g
worn
out
Loadin
g
alig
nm
ent
of
com
ponent
Loadin
g
cylin
der
Angle
maste
r
Vis
ual
inspectio
n
mic
roscope
Air s
upply
for
part
s
cle
anin
g
Root
cause
Loadin
g /
Unlo
adin
g
Checkin
g
bench
Sr.
No.
6
7
8
9
10
11
18
(Machine related parameters continued…)
conclu
sio
ns
Th
e im
pact of S
pin
dle
coolin
g
on S
eat
reje
ctio
n’s
is
Ins
ign
ific
an
t
Th
e im
pact of S
pin
dle
coolin
g
syste
m o
n S
eat
reje
ctio
ns is
Ins
ign
ific
an
t.
Th
e im
pact of In
itia
l
settin
g
on s
eat
reje
ctio
ns is
Sig
nif
ican
t
Th
e im
pact of new
seat
wheel
sett
ing o
n S
eat
reje
ctio
ns is S
ign
ific
an
t.
Th
e im
pact of A
dapto
r
TR
on
Seat re
jectio
ns is
Ins
ign
ific
an
t.
Th
e im
pact of D
ressin
g
rin
g w
orn
-out
on s
eat
reje
ctio
ns is S
ign
ific
an
t.
Results o
bta
ined
3 p
art
s in
bad 1
00 w
ith 6
0,0
00
rpm
,1 b
ad in 1
00 w
ith 5
0,0
00
rpm
Spin
dle
coolin
g s
yste
ms o
f all
ma
chin
es a
re found t
o b
e
work
ing o
k.
When in
itia
l settin
g o
k 0
bad
in 5
0,
when in
itia
l settin
g
dis
turb
ed 2
5 b
ad in
50.
When n
ew
seat w
heel settin
g
ok 0
bad in
50,
when in
itia
l settin
g n
ot ok 3
0 b
ad in
50.
when T
R<
10µ
0 b
ad in
50,
when T
R>
10µ
0 b
ad in
50
with w
orn
out sprin
g 4
5 b
adin
50,
with o
k r
ing 2
bad in
50.
Te
st used
2 p
roport
ions
test
No t
est
perf
orm
ed
2 p
roport
ions
test
2 p
roport
ions
test
2 p
roport
ions
test
2 p
roport
ions
test
End
date
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
Sta
rt d
ate
15-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
Tria
l ta
ken
100 p
art
s p
rocessed w
ith
60,0
00 r
pm
, 100 p
art
s
pro
cessed
with 5
0,0
00 r
pm
All
syste
ms c
hekced
with M
ain
tenance p
eople
Initia
l settin
g p
ara
me
ters
were
dis
turb
ed &
tria
l is
ta
ken.
Th
e n
ew
seat
wheel
heig
ht
was s
et
at
3.1
5m
m
& it's
effect
on s
eat
reje
ctio
ns w
as o
bserv
ed.
take 5
0 p
art
s w
ith a
dapto
r
TR
<10µ
& a
gain
take 5
0 p
art
s w
ith
adapto
r T
R>
10µ
One w
orn
out
rin
g w
as
pla
ced &
wheel w
as
dre
ssed w
ith that
rin
g.
Part
s a
re t
aken for
tria
l.
Actio
ns t
aken
Ta
ke a
tria
l w
ith
diffe
rent R
PM
valu
es
Check w
heth
er
spin
dle
coolin
g
syste
ms o
f all
ma
chin
es
are
runnin
g o
k
Initia
l settin
g w
as
dis
turb
ed &
it's
im
pact
on s
eat re
jectio
ns w
as
observ
ed
New
seat w
heel heig
ht
to b
e s
et 3.1
mm
, ta
ke
tria
l w
ith m
ore
heig
ht.
Adapto
r T
r checked
every
tim
e m
achin
e is
dis
turb
ed &
it's
im
pact
on s
eat re
jectio
ns
observ
ed
Tria
l ta
ken w
hic
h
involv
es p
lacin
g a
worn
out
rin
g o
n M
achin
e &
takin
g p
art
s
Suspecte
d
sourc
es o
f
varia
tio
ns
(SS
V's
)
valu
e to b
e
60,0
00 R
PM
To
be a
sked t
o
ma
inta
inance
Wheel fo
rm w
ear
Ensure
positiv
e
cuttin
g
aft
er
dre
ssin
g
If T
R o
ut of
specific
atio
n s
eat
bad c
om
es
If d
ressin
g r
ing is
worn
out,
the
grin
din
g w
heel
form
gets
dam
aged.
Due to
whic
h p
art
com
es
seat
bad.
sub c
ause
RP
M
Spin
dle
coolin
g
Initia
l settin
g
New
seat
wheel
settin
g
TR
<10µ
Perio
dic
repla
cem
ent
& T
R
Root
cause
Grin
din
g
spin
dle
s
Settin
g
para
me
ters
Adapto
rs
Dre
ssin
g
rin
g
Sr.
No.
12
13
14
15
16
17
19
(Machine related parameters continued…)
conclu
sio
ns
Th
e im
pact of coola
nt
syste
ms o
n S
eat
reje
ctio
ns is
Ins
ign
ific
an
t.
Th
e im
pact of poka
yoke o
n S
eat
reje
ctio
ns is
Sig
nif
ican
t.
Th
e im
pact of dre
ssin
g
depth
of cut
on S
eat
rejc
tio
ns is
Ins
ign
ific
an
t.
Th
e im
pact of dre
ssin
g
freq.
on S
eat re
jectio
ns
is In
sig
nif
ican
t.
Th
e im
pact of fe
ed r
ate
on S
eat
reje
ctio
ns is
Ins
ign
ific
an
t.
Th
e im
pact of O
pera
tor
equaliz
atio
n o
n s
eat
reje
ctio
ns is
Sig
nif
ican
t.
Results o
bta
ined
Th
e c
oola
nt syste
m
para
me
ters
are
within
lim
its
Poka y
oke o
tip
1 b
ad
in 5
0,
when p
oka y
oke n
ot
on
tip
16 b
ad in
50.
0 b
ad in
50 w
ith 3
µ
depth
of cut.
0 b
ad in
50 w
ith 2
µ
depth
of cut.
0 b
ad in
50 w
ith 6
part
s
freq.
0 b
ad in
50 w
ith 8
part
s fre
q.
with 1
00%
feed r
ate
all
50 p
art
s o
kw
ith 5
0 %
feed r
ate
all
50 p
art
s
ok a
gain
.
Due t
o c
ontin
uous
reje
ctio
ns fro
m
assem
bly
sectio
n fear
is s
et in
vis
ual
opera
tors
.
Te
st used
2 p
roport
ions
test
2 p
roport
ions
test
2 p
roport
ions
test
2 p
roport
ions
test
2 p
roport
ions
test
2 p
roport
ions
test
End
date
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
Sta
rt d
ate
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
30-J
an-0
9
Tria
l ta
ken
Only
checkin
g is involv
ed a
s
takin
g a
tria
l is
very
dangero
us.
50 p
art
s t
aken w
hen p
oka
yoke o
n tip
, again
50 p
art
s
taken w
ith p
oka y
oke in
backsw
ord
positio
n.
Ta
ke p
art
s w
ith 3
µ d
epth
of
cut.
Ta
ke p
art
s w
ith 2
µ d
epth
of
cut.
Ta
ke 5
0 p
art
s w
ith 8
part
s
dre
ssin
g fre
q. A
gain
take 5
0
part
s w
ith 6
part
s d
ressin
g
freq.
Ta
ke p
art
s w
ith 5
0%
feed
rate
,
Ta
ke p
art
s w
ith 1
00%
feed
rate
.
50 b
ord
er
case p
art
s w
ere
show
n t
o o
pera
tors
& they
were
show
n t
o a
ssem
bly
opera
tors
.
Actio
ns t
aken
Check p
ressure
,
tem
pera
ture
of coola
nt syste
m
Poka y
oke w
as s
hifte
d to
backw
ard
positio
n &
its
eff
ect
on s
eat
reje
ctio
ns
was o
bserv
ed.
Dre
ssin
g d
epth
of cut is
v
arie
d &
tria
l is
taken
Dre
ssin
g fre
q. changed
& tria
l is
taken
Th
e f
eed r
ate
was
changed m
anually
& it's
eff
ect
on s
eat
reje
ctio
ns is
observ
ed.
Daily
reje
ctio
ns a
t seat
vis
ual is
checked for
verificatio
ns
Suspecte
d s
ourc
es
of varia
tio
ns
(SS
V's
)
Th
e d
ressin
g/
Grin
din
g
pre
ssure
varie
s
Confirm
atio
n o
f
poka yoke o
nce
in a
shift
3 m
icro
ns
6 p
art
s
Ma
nual knob
pre
sent
Incorr
ect
decis
ion
due t
o fear
of
gett
ing r
eje
cte
d
from
assem
bly
.
sub c
ause
3.5
to 4
bar
grin
din
g/
dre
ssin
g
coola
nt
Tip
bre
akage
sensin
g p
oka
yoke
Dre
ssin
g
depth
of cut
Dre
ssin
g fre
q.
Fe
ed r
ate
Lack o
f opera
tor
equaliz
atio
n
Root
cause
Coola
nt
syste
ms
Grin
din
g
wheel
Grin
din
g
pro
gra
m
Opera
tor
Sr.
No.
18
19
20
21
22
23
20
Ishikawa Diagram for Major defects:
Ishikawa diagrams (also called fishbone diagrams or cause-and-effect diagrams) are diagrams that
show the causes of a certain event. Ishikawa diagrams were proposed by Kaoru Ishikawa in the
1960s, who pioneered quality management processes in the Kawasaki shipyards, and in the process
became one of the founding fathers of modern management. It was first used in the 1960s, and is
considered one of the seven basic tools of quality management, along with the histogram, Pareto
chart, check sheet, control chart, flowchart, and scatter diagram. It is known as a fishbone diagram
Causes in the diagram are often based on a certain set of causes, such as the 6 M's, described
below. Cause-and-effect diagrams can reveal key relationships among various variables, and the
possible causes provide additional insight into process behavior. Causes in a typical diagram are
normally grouped into categories, the main ones of which are:
The 6 M's
Machine, Method, Materials, Maintenance, Man and Mother Nature (Environment): Note: a more
modern selection of categories is Equipment, Process, People, Materials, Environment, and
Management.
Causes should be derived from brainstorming sessions. Then causes should be sorted through
affinity-grouping to collect similar ideas together. These groups should then be labeled as categories
of the fishbone. They will typically be one of the traditional categories mentioned above but may be
something unique to our application of this tool. Causes should be specific, measurable, and
controllable.
Rough
Finish &
Rings
formation
on Seat
Environment
Machine
Method
Man
Material
Checking freq. isless
In coming qualitybad
Drill Breakage
Tool Quality
Motivation less
New operator
Negligence
Awareness
Complexprocedures
WorkInstructions are
elevator gettingjammed
Gauges notcalibrated on
Frequent breakdowns
Coolant pressure varies
Detection is poor
No Poka Yoke exist
Dirtaccumulates onpart as it is nearto window
Fish bone Diagram for Vital few Defects
Fig 18: Cause & Effect diagram for majority of defects
The Five elements of Fish bone diagram generated during Brainstorming session are:
Man:
Motivation less in workmen due to incentive less.
New operator working in area
Negligence during night shift
Lack of Awareness among operators
21
Machine:
Frequent Breakdowns, causing increase in vibration level
Detection of Defects is not effective
Coolant pressure varies abruptly
No Poka Yoke present to detect Drill breakage which causes ring formation
Material:
Tool quality not up to the mark, drill life less
Drill breakage due to drill overuse
In coming quality of parts not ok (Part bend which causes drill breakage)
Checking frequency is less
Method:
Gauges are not calibrated on daily basis
Elevator which lifts the part to chuck gets jammed causing part damage
Work instructions are over dated
Program corrections are complex during type change
Environment:
Machine is near to open window which causes dirt accumulation on part which damages
surface during grinding.
Bar chart
The ideas generated during Brainstorming session were verified by Process Experts and the causes
having positive impact on rejections were listed out. Bar chart analysis was performed on these
parameters to know the causes which have significant impact on rejections.
Causes & their contribution in Rejections
21
45
15
811
05
101520253035404550
Drill overuse No Poka
Yoke present
to detect Drill
breakage
Gauges not
calibrated on
time
Coolant
pressure
varies
Others
Causes
% R
eje
cti
on
s
% wise causes
Fig 19: Bar Chart for Significant parameters
Chart clearly indicates that some system for early detection of Drill breakage needs to be
developed.
22
Causes & their contribution in Rejections
21
45
15
811
05
101520253035404550
Drill overuse No Poka
Yoke present
to detect Drill
breakage
Gauges not
calibrated on
time
Coolant
pressure
varies
Others
Causes
% R
eje
cti
on
s
% wise causes
Fig 20: Bar chart for causes & their contribution
IMPROVE PHASE:
A) Detection of drill breakage on machine:
To reduce rejections which were caused by drill breakage, a new Laser sensor was installed on
machine and its feedback was given to PLC logic of machine. When tip of drill is Ok Laser falls on drill
& gets distracted, ensuring the machine to run continuously. This Tip Breakage Sensor (TBS) was
installed such that it overlaps with part loading, so change in cycle time due to Sensor installation is
zero.
Fig 12: Tool breakage sensing Poka Yoke with OK drill mounted on machine
Fig 21: Tool breakage sensing Poka Yoke when tip of drill is broken
After successfully implementing this on one pilot machine, there was horizontal deployment of this
Poka yoke on all 8 machines.
23
B) Drill overuse by operator:
When 5 why Analysis was done for this problem, it was found that the new drills were issues from
stores on monthly basis, so at the end of every month drill overuse was a common problem. It was
decided to top-up drill shortage on every Saturday of week so as to maintain drill float on the line. Line
foremen were given clear instructions about drill records maintenance. Accurate drill
breakage/obsolescence is maintained and this point is added to Surprise audit committee.
C) Gauges & Microscopes are not calibrated on time:
For this cause a team of operators was formed to escalate the matter immediately when gauges are
not calibrated. Also calibration work was equally divided among quality people who calibrate gauges
once in three days.
D) Coolant pressure varies:
For this cause complete hydraulic circuit was checked for leakage. The team found that on Flow
control valve was faulty (worn out). The team insisted to change every valve of the circuit and
complete hydraulic circuit connections were changed with new one. Due to this major action the
leakage completely stopped. The coolant pressure variation problem is completely eliminated.
E) Others:
For all other causes following actions are taken-
Window responsible for dirt accumulation was permanently closed & one exhaust fan was
installed at that place.
For new operator coming in area training sessions & supervision by skilled operators was
made compulsory.
Warning letters were issued for negligence from operators.
New & updated work instructions were put on machine boards.
CONTROL PHASE:
This phase defines control plans specifying process monitoring and corrective actions. It ensures that
the new process conditions are documented and monitored. All possible causes of specific identified
problems from the analysis phase were tackled in the control phase. Control solutions to identified
problems have been prepared in sequence to the improvements as explained above. This will prevent
the problems from recurring. The proposed control solutions to improve the previous solutions are
listed in sequence as follows.
A) Drill breakage Poka Yoke:
A Poka yoke monitoring sheet is maintained by shop. One shop Forman daily checks that all Poka
Yoke are working correctly & records it on a check sheet. A clear escalation model for problem
reporting is prepared for Poka Yoke failure.
B) Drill overuse by operators:
As weekly drill quantity top-up is done, it automatically ensures that every week drill quantity is verified
for shortage. A record sheet is maintained to keep all drills records.
C) Gauges calibration:
This issue was taken seriously by quality department & they have assigned special audit team to
ensure that gauges are calibrated on time.
24
D) Coolant pressure:
For all hydraulic circuits in shop, one preventive maintenance program is prepared. Operators are
given authorities to stop machine if leakage is found on it.
E) Operator related issues:
All operator related issues were taken to Worker Union and after their consent it is decided to take
strict action against the operator negligence is company.
RESULTS:
After completing the DMAIC methodology of Six Sigma, again the process capability Analysis was
done to know the improvement in Sigma level. One month data on Control phase was taken for the
Analysis.
Sample
Pro
po
rti
on
28252219161310741
0.020
0.015
0.010
_P=0.01104
UC L=0.01344
LC L=0.00864
Sample
%D
efe
cti
ve
30252015105
1.2
1.1
1.0
Summary Stats
0.00
PPM Def: 11039
Lower C I: 10754
Upper C I: 11330
Process Z: 2.2890
Lower C I:
(using 95.0% confidence)
2.2791
Upper C I: 2.2989
%Defectiv e: 1.10
Lower C I: 1.08
Upper C I: 1.13
Target:
Observed Defectives
Ex
pe
cte
d D
efe
cti
ve
s
320240160
300
250
200
150
1.81.51.20.90.60.30.0
12
9
6
3
0
Tar
1
1
1
11
1
11111
Capability Analysis of Seat Visual Process
P Chart
Cumulative %Defective
Binomial Plot
Dist of %Defective
Figure 22: Process capability of seat visual process after applying DMAIC methodology
From the Minitab output it is clear that PPM defect level is reduced from 22,624 ppm to 11,031 ppm.
And Sigma level is Improved from 3.5σ to 3.79 σ.
Rejections in PPM
11039
22,624
0
5,000
10,000
15,000
20,000
25,000
PPM rejections before Project PPM rejections after DMAIC
Project
PP
M l
evel
Rejections in PPM
Fig 23: Results showing improvement in Sigma level of the process
A few more agreed recommendations are still to be implemented during plant shut down. The
estimated savings from the project after the implementation of all recommendations are expected to
be 1, 50,000Rs per Annum.
Sigma level-3.5σ
Sigma level
improved to 3.79σ
25
CONCLUSIONS:
The immediate goal of Six Sigma is defect reduction. Reduced defects lead to yield improvement;
higher yields improve customer satisfaction. The ultimate goal is enhanced net income. The money
saved is often the attention getter for senior executives. It has a process focus and aims to highlight
process improvement opportunities through systematic measurement. Six Sigma defect reduction is
intended to lead to cost reduction. Six sigma is a toolset, not a management system and can be used
in conjunction with other comprehensive quality standards present in the industry. The application of
Six Sigma technique for this project shows that company has taken a small step towards Six Sigma
Implementation on Company wide basis. Once Six Sigma finds its rightful place in the minds of higher
management, enormous gains can always be expected from its application. It is clear that the Six
Sigma methodology is highly beneficial to improve the performance of any manufacturing plant.
26
References
1) Kumar, P. (2002) “Six Sigma in manufacturing”, Productivity Journal, Vol. 43, No. 2, pp.196–
202.
2) Harry, M.J. and Schroeder, R. (1999) “Six Sigma: The Breakthrough Management Strategy
Revolutionizing the Worlds Top Corporations”, New York, NY: Double Day.
3) Henderson, K.M. and Evans, J.R. (2000) “Successful implementation of Six Sigma:
benchmarking: General Electric Company”, Benchmarking: An International Journal, Vol. 7,
No. 4, pp.260–281.
4) Mathew.H, Barth.B, and Sears.B, (2005) “Leveraging Six Sigma discipline to drive
improvement”, Int. J. Six Sigma and Competitive Advantage, Vol. 1, No. 2, pp.121–133.
5) Park, S.H. (2002) “Six Sigma for productivity improvement: Korean business corporations”,
Productivity Journal, Vol. 43, No. 2, pp.173–183.
Recommended