A Study on Six Sigma Techniques And Its application in reduction of seat rejection At BOSCH LTD

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)
1

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.
2

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
3

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.
Literature Survey
Case study of manufacturing Industry
Identification of problem
Industry
Data Collection
Identify Specific problem
Define customer Requirements
Define
Set Goals
SIPOC diagram
Measurement System Analysis
Data Collection Plan
Measure
Identify variation due to measurement system
SIPOC diagram
Draw conclusion from data verification
Process Capability Analysis
Analyze
Determine root causes
Map cause & effect diagram
Create improvement Ideas
Create solution statement
Improve
Implement improvement solutions
Monitor Improvement progress
Make needed adjustments
Control
Establish standard measures to maintain
performance
Improvement Results
Conclusions
Scope of future work
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
4

5. Product Definition:
Fig 3: DSLA Nozzle Assembly
Fig 4: Injector Assembly
Step Turning
Shoulder Seat
Dowel hole
Turning Profile
drilling Grinding
Guide Bore
Drilling
Inlet hole Drilling
Pressure Chamber
machining
Sack Hole
Fig 5: Body of DSLA type nozzle
Seat
Surface
Seat- seen under Microscope
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
5

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.
SUPPLIER INPUT PROCESS OUTPUT CUSTOMER
Man
Machine
Inspection
Electricity UVA Seat Grinding over
Company Repair process
Drawings process parts
Electricity Stores
H.T. over parts High Worn out tooling
Maintenance Scrap yard
Gauges, Tooling Precision Grinding muck
TEF1 Etamic check,
Compressed air Internal PMI chart
Purchase Honing
JML ,Cutting oil Grinding Re-release chart
Profile Grinding
Check list Process
Instruction charts
Program
Soft Stage Hardening UVA process Seat Visual Profile
Operations (High Precision Inspection Grinding
Internal Grinding)
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.
6

7. Taper repair
Guide bore repair
To reduce Repair
UVA
Seat repair
process
Repair
Scrap Guide bore scrap
Seat scrap
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
7

8.  Less the better
As per Kano model of Quality A CTQ specification table is generated for giving the specifications of
rejections.
CTQ MEASURE SPECIFICATION DEFECT DEFINITION KANO STATUS
G.B. size out of
G.B. Repair Monthly PPM -- Must Be
specification
Seat Damage/
Seat Repair Monthly PPM Seat visually not O.K. Less the Better
Finish Bad
Taper bad Taper out of
Monthly PPM -- Less the Better
Repair specification
G.B. size out of
G.B. Scrap Monthly PPM -- Less the Better
specification
Seat Scrap Monthly PPM Seat Damage Seat visually not O.K. Less the Better
Fig: CTQ table
MEASURE PHASE:
Collect baseline Develop a Validate your Analyze Determine
data on defects & sampling measurement patterns process
possible causes strategy system using in data capability
Gauge R & R.
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.
8

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
Related How/where
Measure type/ How Sampling
What conditions to recorded
data type measured notes
record (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:
BOSCH Name_________________________ Token No:
Nashik plant Date Shift
Seat Defects
Qty. Qty. Qty Item
Bad Rubbing at No sack Unground Scrap Lot No. Type
Inspected OK Rejected No. Rings Patches
Finish sack hole hole seat
Segregation of defects observed at seat visual section:
Rubbing at sack
Total no. of Bad finish Unground No sack
Day count Date Rings Patches hole end
parts checked (rough surface) seat hole
due to burr
Day-1 6/8/2008 372 277 93 0 1 1 0
Day-2 7/8/2008 367 182 172 6 5 1 1
Day-3 8/8/2008 174 100 63 9 2 0 0
Day-4 10/8/2008 1114 646 440 12 4 12 0
Day-5 12/8/2008 607 416 165 25 0 1 0
Day-6 13/08/08 47 20 24 2 0 0 1
Day-7 14/08/08 163 80 78 4 0 0 1
Day-8 17/08/08 450 90 293 57 0 8 2
Day-9 18/08/08 46 2 43 0 0 0 1
Day-10 19/08/08 85 20 56 0 0 2 7
Day-11 20/08/08 170 74 95 0 0 1 0
Day-12 22/08/08 214 115 90 8 0 0 1
Day-13 23/08/08 308 204 90 12 0 0 2
Day-14 25/08/08 189 113 70 6 0 0 0
Day-15 26/08/08 192 82 94 16 0 0 0
Day-16 270808 119 32 86 1 0 0 0
Day-17 28/08/08 101 38 63 0 0 0 0
Day-18 30/08/08 163 32 55 9 66 1 0
Day-19 1/9/2008 99 12 37 48 1 0 0
9
Day-20 2/9/2008 78 31 43 4 0 0 0
5058 2566 2150 219 79 27 16

10. Pareto Analysis of Seat rejections:
Seat Defect Segregation
5000 100
4000 80
Percent
Count
3000 60
2000 40
1000 20
0 0
t
i sh ea
fi n s es ds rs
ng t ch un he
Defect Ro
ug
h Ri Pa gr o Ot
Un
Count 2566 2150 219 79 43
Percent 50.7 42.5 4.3 1.6 0.9
Cum % 50.7 93.3 97.6 99.1 100.0
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
10

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.
Capability Analysis of Seat Visual Process
P C har t Binomial P lot
0.026 U C L=0.026045 425
Expected Defectives
P r opor tion
0.024 400
_
P =0.022624
0.022 375
0.020 350
LC L=0.019202
1 4 7 10 13 16 19 22 25 28 360 390 420
Sample O bser ved Defectives
C umulative % Defective Dist of % Defective
S ummary S tats Tar
8
2.30 (using 95.0% confidence)
% Defectiv e: 2.26 6
% Defective
2.28 Low er C I: 2.22
U pper C I: 2.30
4
2.26 Target: 0.00
P P M Def: 22624
2.24 Low er C I: 22217 2
U pper C I: 23035
2.22 P rocess Z: 2.0024 0
5 10 15 20 25 30 Low er C I: 1.9947 00 35 70 05 40 75 10 45
Sample 0. 0. 0. 1. 1. 1. 2. 2.
U pper C I: 2.0100
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.
11

12. Chamfer height Jet broken,Pump
Acqueous Cleaning not ok
variation. pressure less
Guide to shaft TR TR more
Uneven chamfer Measure
Guide to shaft TR not ok not checked after than 100
band by gauge
TBT as per freq. microns
Vibrations &
Roundness, Straightness, No specification in
chatter marks on
Guide bore to seat TR drawing
seat in soft stage
Rough finish,
Rings, Patches, 100% sack hole
UVA PROCESS
Seat No sack hole, checking Possibility of poka
REPAIR & I/P parts
SCRAP repair Rubbing at sack hole, poka yoke on all 5 yoke failure
Unground seat spinner
Parts without sack
hole from soft
stage
Sack hole Drill breakage on Poka yoke not working
Retco properly
Type Mix-up ( P Possibility on all operations
during lot change, 80% on
type in DSLA & Benzinger, ECM(10%),
Manual element
vise versa Remaining 10%
Guide to shaft TR not
Guide bore to shaft TR more than 100
checked after TBT as per
T.R bad microns
freq.
Seat TR wrt guide more than 70
On spinner & retco m/c
bore microns
Seat angle in soft specification 58.8° More/less
On spinner & retco m/c
stage (+/- 0.2°) than spec.
Chamfer mandrel
angle in hard More/less than spec.
stage
Fig 16: Tree diagram created from brainstorming session for Input part parameters
12

13. Vibration Today not Known Consult Mr.Kumavat
RPM value-2250
Workhead
Spindle height Repeatability Below 20μ Once in
Female center Grinding Decide freq. 2 months
Job clamping pressure Chuck clamp grinding Once in a month
Changing freq. once
Loading spring wornout
in 2 months
Loading/ Loading alignment Visual check
Unloading of component OK/ Not OK
Loading cylinder Air leakage
Cylinder swing In / Out positions
Changing freq. To be decided As per freq.
Angle master
Seat profile To be studied
Checking Alignment of both Scope condition Prepare
bench eyes to be studied schedule
Visual inspection
UVA microscopes Frequent checking
Seat M/C
process by associates
Rejections parameters
repair
RPM value-60,000
spindles
To be asked Provision to fix pressure
spindle cooling
to maintenance gauge atleast to one m/c
Initial setting wheel form wear Ref.setting piece to be made
Setting Ensure positive cutting Height gauge to
New seat wheel
parameters after dressing check height diff.
New wheel diameter 4,600 mm After dressing 4,300 mm
Adaptor TR < 10μ
changing freq. every
Dressing ring periodic replcment & TR
3 months
coolant 3.5 to 4 bar grinding
systems / dressing coolant
Tip breakage sensing confirmation of poka
poka yoke yoke once in a shift
Grinding wheel
Dressing depth of cut 3μ
Dressing freq. 6 parts
Grinding
Feed rate Details to be taken
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.
13

14. Suspected sources
Sr.N Root End
sub cause of variations Actions taken Trial taken Start date Test used Results obtained Conclusions
o. cause date
(SSV's)
Seat does not get
cleaned properly so
Aqueous location of part on 0 bad parts in
Take 275 parts with cleaning & 25 The impact of aqueous
cleaning not ok chamfer grinding Take a trial which involves 2 275 ok parts
parts without cleaning & process cleaning on chamfer
Jet broken, m/c is outside due processing parts without 8-Nov-08 8-Nov-08 proportions 0 bad parts in 25
them on same chamfer grinding height variation is
Pump pressure to dirt present. This aqueous cleaning. test without cleaning
m/c & same UVA m/c. Insignificant.
less outside location parts
chamfer results in seat
1 height rejections.
variations
To take a trial this involves All parts came ok
Chamfer height Part location in UVA Take 30 parts with chamfer height The impact of chamfer
taking parts with chamfer 2 on UVA, chamfer
variation causes becomes (-30 to -10µ), 60 parts within spec (- 15-Nov- 15-Nov- height variation on
height more, less & within proportions height variation
seat rejections at improper due to 10µ to +10) & 30 parts with (+10 to 08 08 seat rejections is
specification & processing test did not cause any
UVA chamfer variation. +30µ) & process them on UVA. Insignificant
them on UVA. defect on UVA.
Guide to shaft TR is 12 parts bad in The impact of
Uneven Take 50 parts with TR more 2
Guide to shaft not A trial TR checking gauge 50 TR bad parts Uneven chamfer band
2 chamfer than 85µ & put them on UVA also 3-Mar-09 3-Mar-09 proportions
TR not ok checked in soft is developed 1 bad in 50 TR ok on Seat rejections is
band process 50 normal parts test
stage parts Significant
The drill form The seat RZ & The impact of drill life
Roundness, Take one parts each from Rmax values of on
deteriorates with
Straightness GB spinners & Retco having One part from each machine all parts are seat rejections is
usage & the parts at 16-Dec-
to seat TR not different tool life & give them given to FMR lab, 8-Jan-08 within limits Insignificant
Vibration checked in soft later stages 08
to FMR lab for seat form Life no. are noted
s& of tool life have
stage checking 2
chatter more roughness
3 proportions
marks on
test 49 bad in 50 with The impact of drill
seat in
Due to drill damage
Validation of all SSVs using Statistical testing: (Input part parameters)
soft stage chatter marks, 1 damage in soft stage
Drill damage on on machines When such parts come on 50 parts with chatter marks were bad in 50 without on Seat rejections is
16-Dec-
Spinners & vibrations & deep UVAsort out such parts & put processed on UVA along with 50 8-Jan-08 chatter marks Significant.
08
Retco lines are produced them on UVA for trial. ok parts
on seat.
14

15. Suspected sources of
Sr. End
Root cause sub cause variations Actions taken Trial taken Start date Test used Results obtained Conclusions
No. date
(SSV's)
Poka yoke
failure on No sack hole part
spinner One no sack hole
Parts breaks the grinding
machine Poka Yoke put off Collect at least 15 part was put The impact of No
without 2 wheel tip & m/c gets
due No sack hole parts on UVA 20315 & it's 13-Jan- 13-Jan- sack hole parts on
4 sack hole proportions immediately
to various prefarably of DSLA effect on 09 09 seat rejections is
from soft test stopped, during
reasons normal Shaft rejections was Significant
stage redressing 50 parts
observed
Poka yoke came bad.
failure on
Retco machine
(Input part parameters continued..)
80% on
p-type in DSLA lot
75% Benzinger, One type mix up part
breaks the
10% on ECM. was put The impact of
2- adaptor& grinding
Part type Possibility on Manual element Collect at least 15 on UVA 20315 & it's 20-Nov- 20-Nov- type mix up on
5 proportions wheel, which results
mix up all operations may be present, mix up parts effect on 08 08 Seat rejections is
test in 50 bad in 50,with
Elevator condition seat rejections is Significant.
normal parts 0 bad
in soft stage is observed
in 50.
poor
Angle not Trial is taken which 285 parts with seat
checked as per involves angle more 3 bad in 285 angle The impact of
Seat angle On spinner 2-
frequency/Drill life seat angle more were processed up to 21-Nov- 28-Nov- more parts, Seat angle more
6 in soft & Retco proportions
over, Drill parts are processed seat visual 08 08 0 bad in 300 angle on seat rejections
stage machines test
resharpening up to seat visual for along with 300 angle ok parts is Insignificant
improper checking. ok parts
Chamfer Chamfer mandrel As there in no The impact of
Chamfer mandrel 4 mandrels given to
mandrel More or angles checked No variation in chamfer mandrel
angle to tool room 25-Nov- 25-Dec-
7 angle less than by Sine bar method variation output statistical angle on seat
be verified in tool for chamfer angle 08 08
in soft specification & Microscope in output test cannot rejections is
room verification
stage method be performed Insignificant
15

16. Suspected
Sr. sources of End
Root cause sub cause Actions taken Trial taken Start date Test used Results obtained conclusions
No. variations date
(SSV's)
Workhead vibration values of No variation Workhead vibration values The impact of workhead
Earlier not Check workhead vibration
1 Vibration all machines are checked with 13-Feb-09 16-Feb-09 output of all machines are within 3 vibration on seat
known values of all machines
help of vibratometer observed mm/sec. rejections is Insignificant
The impact of Workhead
value-1800 Rated RPM value is 2150 Take 50 parts with 2150 rpm, No variation in At both rpm values all rpm
2 RPM 13-Feb-09 16-Feb-09
rpm RPM take 50 parts with 1750 rpm output observed 50 parts came visually ok on seat rejections is
Insignificant
Check repeatability<20µ, The impact of Spindle
Actions taken for machine related parameters
Workhead 50 parts each were processed
Spindle Repeatability take trial with processing 2-proportions At both repeatability levels height
3 with repeatability of 10µ & 12-Mar-09 12-Mar-09
height below 20µ parts with different test all parts came visually ok repeatability on Seat
at20µ.
repeatability values. rejections is Insignificant
50 parts were processed All parts before doing The impact of female
we checked parts before &
before doing female center female center grinding center
Female Grinding freq. after doing female center 2-proportions
4 grinding & 50 parts were 12-Mar-09 12-Mar-09 came ok, also all parts after grinding on seat
center not decided grinding for checking test
processed after doing female doing female center rejections
difference
center grinding grinding came ok is Insignificant
The job clamping pressure was At 5 bar pressure 0 bad in The impact of Job
Job Air supply to job clamping
Chuck clamp varied ti 4 bar & 5 bar & it's 2 proportions 50, clamping pressure on
5 clamping is varied to different levels 30-Jan-09 30-Jan-09
grinding impact on seat rejections is test at 4 bar pressure 29 bad in seat rejections is
pressure & it's effect was observed
observed. 50 parts. Insignificant.
16

17. Suspected
Sr. sources of End
Root cause sub cause Actions taken Trial taken Start date Test used Results obtained conclusions
No. variations date
(SSV's)
Loading spring was
Loading with ok spring all 50 parts The impact of loading
Changing changed with a broken one Changing freq. once in two 2 proportions
6 spring 30-Jan-09 30-Jan-09 came ok, with broken spring broken on seat
freq. & it's effect on seat months. test
wornout spring 35 bad in 50. rejections is Significant
rejections was observed
The impact of Loading
Loading While setting machine Take a trial without checking with & without checking
7 Loading / 2 proportions alignment of component
alignment of Visual check check loading alignment of 30-Jan-09 30-Jan-09 loading alignment all 50
Unloading test on seat rejections is
component alignment for ok / Not ok component. parts came visually ok
Insignificant.
The impact of Air
8 Loading No hypothesis cylinder on
Air leakage Electrical servo motor used No problem of air leakage 30-Jan-09 30-Jan-09 The quick hit achieved
cylinder test performed seat rejections is
Insignificant.
Master The impact of Angle
9 showing Checking freq. to be take GRR of seat No test master on
Angle master 30-Jan-09 30-Jan-09 GRR found to be ok
wrong reduced angle master performed seat rejections is
(Machine related parameters continued…)
reading Insignificant.
Alignment Check requirement of
Scope condition study
of both eyes frequent verification of
schedule to be prepared
not there microscope condition When 50 parts checke The impact of seat
10 Visual with faulty microscope 35 visual microscope
2 proportions
Checking inspection 18-Dec-08 18-Dec-08 came bad, when they are condition on seat
test
bench microscope Frequent Associates awareness checked with ok scope rejections is
A workshop on microscope only 50 came bad. Significant.
checking by about microscope
handling to be arranged
associates adjustment to be done.
With air cleaning The impact of Air supply
Air supply Parts to be checked with 50 parts taken with air cleaning
No supply 2 proportions 10 parts bad in 50, for parts cleaning on
11 for parts air cleaned & without air & 50 parts taken without air 30-Jan-09 30-Jan-09
provided test without air cleaning Seat rejections is
cleaning cleaning cleaning
22 parts bad in 50. Significant
17

18. Suspected
Sr. sources of End
Root cause sub cause Actions taken Trial taken Start date Test used Results obtained conclusions
No. variations date
(SSV's)
100 parts processed with The impact of Spindle
3 parts in bad 100 with 60,000
value to be Take a trial with 60,000 rpm, 100 parts 2 proportions cooling
12 RPM 15-Jan-09 30-Jan-09 rpm,1 bad in 100 with 50,000
60,000 RPM different RPM values processed test on Seat rejection’s is
rpm
with 50,000 rpm Insignificant
Grinding
spindles
The impact of Spindle
Check whether spindle
Spindle cooling systems of all cooling
Spindle To be asked to cooling All systems chekced No test
13 30-Jan-09 30-Jan-09 machines are found to be system on Seat
cooling maintainance systems of all machines with Maintenance people performed
working ok. rejections is
are running ok
Insignificant.
Initial setting was The impact of Initial
Initial setting parameters When initial setting ok 0 bad
disturbed & it's impact 2 proportions setting
(Machine related parameters continued…)
Initial setting Wheel form wear were disturbed & trial is 30-Jan-09 30-Jan-09 in 50, when initial setting
14 on seat rejections was test on seat rejections is
taken. disturbed 25 bad in 50.
observed Significant
Setting
parameters
The new seat wheel The impact of new seat
New seat Ensure positive New seat wheel height When new seat wheel setting
height was set at 3.15mm 2 proportions wheel
wheel cutting to be set 3.1 mm, take 30-Jan-09 30-Jan-09 ok 0 bad in 50, when initial
15 & it's effect on seat test setting on Seat
setting after dressing trial with more height. setting not ok 30 bad in 50.
rejections was observed. rejections is Significant.
Adaptor Tr checked
take 50 parts with adaptor The impact of Adaptor
If TR out of every time machine is
TR<10µ 2 proportions when TR<10µ 0 bad in 50, TR on
16 Adaptors TR<10µ specification seat disturbed & it's impact 30-Jan-09 30-Jan-09
& again take 50 parts with test when TR>10µ 0 bad in 50 Seat rejections is
bad comes on seat rejections
adaptor TR>10µ Insignificant.
observed
If dressing ring is
worn out, the
Trial taken which One worn out ring was
Periodic grinding wheel The impact of Dressing
Dressing involves placing a worn placed & wheel was 2 proportions with wornout spring 45 badin
17 replacement form gets 30-Jan-09 30-Jan-09 ring worn-out on seat
ring out ring on Machine & dressed with that ring. test 50, with ok ring 2 bad in 50.
& TR damaged. Due to rejections is Significant.
taking parts Parts are taken for trial.
18
which part comes
seat bad.

19. Suspected sources
Sr. End
Root cause sub cause of variations Actions taken Trial taken Start date Test used Results obtained conclusions
No. date
(SSV's)
3.5 to 4 bar The impact of coolant
The dressing/ Check pressure, Only checking is involved as The coolant system
Coolant grinding/ 2 proportions systems on Seat
18 Grinding temperature taking a trial is very 30-Jan-09 30-Jan-09 parameters are
systems dressing test rejections is
pressure varies of coolant system dangerous. within limits
coolant Insignificant.
Poka yoke was shifted to 50 parts taken when poka Poka yoke o tip 1 bad The impact of poka
Tip breakage Confirmation of
backward position & its yoke on tip, again 50 parts 2 proportions in 50, yoke on Seat
19 sensing poka poka yoke once 30-Jan-09 30-Jan-09
effect on seat rejections taken with poka yoke in test when poka yoke not on rejections is
yoke in a shift
was observed. backsword position. tip 16 bad in 50. Significant.
Grinding
wheel
Take parts with 3µ depth of 0 bad in 50 with 3µ The impact of dressing
Dressing Dressing depth of cut is cut. 2 proportions depth of cut. depth of cut on Seat
20 3 microns 30-Jan-09 30-Jan-09
depth of cut varied & trial is taken Take parts with 2µ depth of test 0 bad in 50 with 2µ rejctions is
cut. depth of cut. Insignificant.
(Machine related parameters continued…)
Take 50 parts with 8 parts
0 bad in 50 with 6 parts The impact of dressing
Dressing freq. changed dressing freq. Again take 50 2 proportions
21 Dressing freq. 6 parts 30-Jan-09 30-Jan-09 freq. 0 bad in 50 with 8 freq. on Seat rejections
& trial is taken parts with 6 parts dressing test
parts freq. is Insignificant.
freq.
Grinding
program
The feed rate was Take parts with 50% feed with 100% feed rate all The impact of feed rate
Manual knob changed manually & it's rate, 2 proportions 50 parts okwith 50 % on Seat
22 Feed rate 30-Jan-09 30-Jan-09
present effect on seat rejections is Take parts with 100% feed test feed rate all 50 parts rejections is
observed. rate. ok again. Insignificant.
Due to continuous
Incorrect decision 50 border case parts were The impact of Operator
Lack of Daily rejections at seat rejections from
due to fear shown to operators & they 2 proportions equalization on seat
23 Operator operator visual is checked for 30-Jan-09 30-Jan-09 assembly section fear
of getting rejected were shown to assembly test rejections is
equalization verifications is set in visual
from assembly. operators. Significant.
operators.
19

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.
Fish bone Diagram for Vital few Defects
Env ironment Method Material
Gauges not Tool Quality
calibrated on
elevator getting Drill Breakage
Dirt jammed
accumulates on Work In coming quality
part as it is near Instructions are bad Rough
to window Complex Checking freq. is
procedures less
Finish &
Rings
formation
Frequent breakdowns Motivation less
on Seat
Coolant pressure varies New operator
Detection is poor Negligence
No Poka Yoke exist Awareness
Machine Man
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
20

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
50 45
45
% Rejections
40
35
30
25 21
20 15
15 11
8
10
5 % wise causes
0
Drill overuse No Poka Gauges not Coolant Others
Yoke present calibrated on pressure
to detect Drill time varies
breakage
Causes
Fig 19: Bar Chart for Significant parameters
Chart clearly indicates that some system for early detection of Drill breakage needs to be
developed.
21

22. Causes & their contribution in Rejections
50 45
% Rejections 45
40
35
30
25 21
20 15
15 11
8
10
5 % wise causes
0
Drill overuse No Poka Gauges not Coolant Others
Yoke present calibrated on pressure
to detect Drill time varies
breakage
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.
22