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1

OPERATIONS MANAGEMENT PROJECT

A Study onSix Sigma Techniques

AndIts application in reduction of seat rejection

At BOSCH LTD.

Submitted byAnkur 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 pilotimplementations, 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 therequired 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 isusually 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 improvementmethodology characterized by DMAIC (define-measure-analyze-improve control) process. ThisDMAIC process works well as a breakthrough strategy. Six Sigma companies everywhere apply thismethodology 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 53.4 6

Literature Survey

Case study of manufacturing Industry

Identification of problem

Create solution statement

Create improvement Ideas

Implement improvement solutionsImprove

Data Collection

DefineDefine customer Requirements

Identify Specific problem

Set Goals

SIPOC diagram

Data Collection Plan

Measurement System Analysis

Identify variation due to measurement system

SIPOC diagram

Measure

AnalyzeProcess Capability Analysis

Draw conclusion from data verification

Determine root causes

Map cause & effect diagram

Make needed adjustments

Monitor Improvement progress

Establish standard measures to maintainperformance

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 BoreDrilling

SeatProfileGrinding

Inlet hole Drilling

Dowel holedrilling

ShoulderTurning

Pressure Chambermachining

Sack Hole

SeatSurface

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

CompanyElectricity

Maintenance

TEF1

Purchase

Man

Machine

Electricity

Drawings

H.T. over parts

Gauges, Tooling

Compressed air

JML ,Cutting oilCheck list

Instruction charts

Program

UVA

process

High

Precision

Internal

GrindingProcess

Seat Grinding over

parts

Worn out tooling

Grinding muck

PMI chart

Re-release chart

Inspection

Repair processStores

Scrap yard

Etamic check,

Honing

Profile Grinding

Soft Stage

Operations Hardening UVA process

(High PrecisionInternal Grinding)

Profile

Grinding Seat VisualInspection

http://en.wikipedia.org/wiki/Six_sigmahttp://en.wikipedia.org/wiki/Six_Sigmahttp://en.wikipedia.org/wiki/Six_Sigmahttp://en.wikipedia.org/wiki/Six_sigma



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 reduceUVA

process

Repair

http://en.wikipedia.org/wiki/Customer_satisfactionhttp://en.wikipedia.org/wiki/Noriaki_Kanohttp://en.wikipedia.org/wiki/Noriaki_Kanohttp://en.wikipedia.org/wiki/Customer_satisfaction



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

specificationMust Be

Seat Repair Monthly PPMSeat Damage/

Finish BadSeat visually not O.K. Less the Better

Taper bad

RepairMonthly PPM --

Taper out of

specificationLess the Better

G.B. Scrap Monthly PPM --G.B. size out of

specificationLess the Better

Seat Scrap Monthly PPM Seat Damage Seat visually not O.K. Less the Better

Collect baselinedata on defects &possible causes

Develop asamplingstrategy

Validate yourmeasurementsystem usingGauge R & R.

Analyzepatternsin data

Determineprocesscapability



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

WhatMeasure 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-1500067011318925/08/08Day-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-1500067011318925/08/08Day-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

R o u g h f i

n i s h

R i n g s

P a t c h

e s

U n g r o

u n d s e a t

O t h e r s

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

Count

PercentCum %

P e r c e n t

C o u n t

Seat Defect Segregation

http://en.wikipedia.org/wiki/Experimenthttp://en.wikipedia.org/wiki/Test_methodhttp://en.wikipedia.org/wiki/Test_methodhttp://en.wikipedia.org/wiki/Measuring_instrumenthttp://en.wikipedia.org/wiki/Six_Sigmahttp://en.wikipedia.org/wiki/Quality_management_systemhttp://en.wikipedia.org/wiki/Repeatabilityhttp://en.wikipedia.org/wiki/Reproducibilityhttp://en.wikipedia.org/wiki/Measurement_Systems_Analysishttp://en.wikipedia.org/wiki/Measurement_Systems_Analysishttp://en.wikipedia.org/wiki/ANOVAhttp://en.wikipedia.org/wiki/Gauge_(engineering)http://en.wikipedia.org/wiki/Gagehttp://en.wikipedia.org/wiki/Measuring_instrumenthttp://en.wikipedia.org/wiki/Test_methodhttp://en.wikipedia.org/wiki/Test_methodhttp://en.wikipedia.org/wiki/Measuring_instrumenthttp://en.wikipedia.org/wiki/Gagehttp://en.wikipedia.org/wiki/Gauge_(engineering)http://en.wikipedia.org/wiki/ANOVAhttp://en.wikipedia.org/wiki/Measurement_Systems_Analysishttp://en.wikipedia.org/wiki/Measurement_Systems_Analysishttp://en.wikipedia.org/wiki/Reproducibilityhttp://en.wikipedia.org/wiki/Repeatabilityhttp://en.wikipedia.org/wiki/Quality_management_systemhttp://en.wikipedia.org/wiki/Six_Sigmahttp://en.wikipedia.org/wiki/Measuring_instrumenthttp://en.wikipedia.org/wiki/Test_methodhttp://en.wikipedia.org/wiki/Test_methodhttp://en.wikipedia.org/wiki/Experiment



11

If GRR



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 onseat in soft stage

Roundness, Straightness,

Guide bore to seat TR

No specification in

drawing

A 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% onBenzinger, 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

boreOn 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 hardstage

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 c lamping pressure Chuck c lamp gri nding Once in a mont h

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.

http://en.wikipedia.org/wiki/Frequency_probabilityhttp://en.wikipedia.org/wiki/Frequency_probabilityhttp://en.wikipedia.org/wiki/Null-hypothesishttp://en.wikipedia.org/wiki/Null-hypothesishttp://en.wikipedia.org/wiki/Frequency_probability



15

(Input part parameters continued..)

C o n c l u s i o n s

T h e i m p a c t o f N o

s a c k h o l e p a r t s o n

s e a t r e j e c t i o n s i s

S i g n i f i c a n t

T h e i m p a c t o f

t y p e m i x u p o n

S e a t r e j e c t i o n s i s

S i g n i f i c a n t .


S e a t a n g l e m o r e

o n s e a t r e j e c t i o n s

i s I n s i g n i f i c a n t


c h a m f e r m a n d r e l

a n g l e o n s e a t

r e j e c t i o n s i s

I n s i g n i f i c a n t

R e s u l t s o b t a i n e d

N o s a c k h o l e p a

r t

b r e a k s t h e g r i n d i n g

w h e e l t i p & m / c g

e t s

i m m e d i a t e l y

s t o p p e d ,

d u r i n g

r e d r e s s i n g 5 0 p a

r t s

c a m e b a d .

p - t y p e i n D S L A l o t

b r e a k s t h e

a d a p t o r & g r i n d i n g

w h e e l , w h i c h r e s u l t s

i n 5 0 b a d i n 5 0 , w

i t h

n o r m a l p a r t s 0 b a d

i n 5 0 .

3 b a d i n 2 8 5 a n g

l e

m o r e p a r t s ,

0 b a d i n 3 0 0 a n g

l e

o k p a r t s

A s t h e r e i n n o

v a r i a t i o n i n

o u t p u t s t a t i s t i c a

l

t e s t c a n n o t

b e p e r f o r m e d

T e s t u s e d

2

p r o p o r t i o n s

t e s t

2 -


t e s t

2 -


t e s t

N o

v a r i a t i o n

i n o u t p u t

E n

d

d a

t e

1 3 - J

a n -

0 9

2 0 - N

o v -

0 8

2 8 - N

o v -

0 8

2 5 - D

e c -

0 8

S t a r t d a t e

1 3 - J a n -

0 9

2 0 - N o v -

0 8

2 1 - N o v -

0 8

2 5 - N o v -

0 8

T r i a l t a k e n

O n e n o s a c k h o l e

p a r t w a s p u t

o n U V A 2 0 3 1 5 & i t ' s

e f f e c t o n

r e j e c t i o n s w a s

o b s e r v e d

O n e t y p e m i x u p p a r t

w a s p u t

o n U V A 2 0 3 1 5 & i t ' s

e f f e c t o n


o b s e r v e d

2

8 5 p a r t s w i t h s e a t

a n g l e m o r e

w e

r e p r o c e s s e d u p t o

s e a t v i s u a l

a l o n g w i t h 3 0 0 a n g l e

o k p a r t s

C h a m f e r m a n d r e l

a n g l e s c h e c k e d

b

y S i n e b a r m e t h o d

& M i c r o s c o p e

m e t h o d

A c t i o n s t a k e n

C o l l e c t a t l e a s t 1 5

N o s a c k h o l e p a r t s

p r e f a r a b l y o f D S L A

n o r m a l S h a f t

C o l l e c t a t l e a s t 1 5

m i x u p p a r t s

T r i a l i s t a k e n w h i c h

i n v o l v e s

s e a t a n g l e m o r e

p a r t s a r e p r o c e s s e d

u p t o s e a t v i s u a l f o r

c h e c k i n g .

4 m a n d r e l s g i v e n t o

t o o l r o o m

f o r c h a m f e r a n g l e

v e r i f i c a t i o n

S u s p e c t e d s o u r c e s o f

v a r i a t i o n s

( S S

V ' s )

P o k a Y o

k e p u t o f f

d u e

t o v a r i o u s

r e a s o n s

8 0 %

o n

7 5 % B e n

z i n g e r ,

1 0 % o n E C M .

M a n u a l e

l e m e n t

m a y b e p

r e s e n t ,

E l e v a t o r

c o n d i t i o n

i n s o f t s t a g e i s

o o r A

n g l e n o t

c h e c k e

d a s p e r

f r e q u e n c

y / D r i l l l i f e

o v e r , D r i l l

r e s h a r p e n i n g

i m p r o p e r

C h a m f e r m a n d r e l

a n g

l e t o

b e v e r i f i e d i n t o o l

r o

o m

s u b c a u s e

P o k a y o k e

f a i l u r e o n

s p i n n e r

m a c h i n e

P o k a y o k e

f a i l u r e o n

R e t c o m a c h i n e

P o s s i b i l i t y o n

a l l o p e r a t i o n s

O n s p i n n e r

& R e t c o

m a c h i n e s

M o r e o r

l e s s t h a n

s p e c i f i c a t i o n

R o o t c a u s e

P a r t s

w i t h o u t

s

a c k h o l e

f r o m s o f t

s t a g e

P a r t t y p e

m i x u p

S

e a t a n g l e

i n s o f t

s t a g e

C h a m f e r

m a n d r e l

a n g l e

i n s o f t

s t a g e

S r . N o .

4 5 6 7



16

Actions taken for machine related parameters

c o n c l u s i o n s

T h e i m p a c t o f w o r k h e a d

v i b r a t i o n o n s e a t

r e j e c t i o n s i s I n s i g n i f i c a n t

T h e i m p a c t o f W o r k h e a d

r p m

o n s e a t r e j e c t i o n s i s

I n s i g n i f i c a n t

T h e i m p a c t o f S p i n d l e

h e i g h t

r e p e a t a b i l i t y o n S e a t

r e j e c t i o n s i s I n s i g n i f i c a n t

T h e i m p a c t o f f e m a l e

c e n t e r

g r i n d i n g o n s e a t

r e j e c t i o n s

i s I n s i g n i f i c a n t

T h e i m p a c t o f J o b

c l a m p i n g p r e s s u r e o n


I n s i g n i f i c a n t .


W o r k h e a d v i b r a t i o n v a l u e s

o f a l l m a c h i n e s a r e w i t h i n 3

m m / s e c .

A t b o t h r p m v a l u e s a l l

5 0 p a r t s c a m e v i s u a l l y o k

A t b o t h r e p e a t a b i l i t y l e v e l s

a l l p a r t s c a m e v i s u a l l y o k

A l l p a r t s b e f o r e d o i n g

f e m a l e c e n t e r g r i n d i n g

c a m e o k , a l s o a l l p a r t s a f t e

r

d o i n g f e m a l e c e n t e r

g r i n d i n g c a m e o k

A t 5 b a r p r e s s u r e 0 b a d i n

5 0 ,

a t 4 b a r p r e s s u r e 2 9 b a d i n

5 0 p a r t s .

T e s t u s e d

N o v a r i a t i o n

o u t p u t

o b s e r v e d

N o v a r i a t i o n i n

o u t p u t o b s e r v e d

2 - p r o p o r t i o n s

t e s t

2 - p r o p o r t i o n s

t e s t

2 p r o p o r t i o n s

t e s t

E n d

d a t e

1 6 - F e b - 0 9

1 6 - F e b - 0 9

1 2 - M a r - 0 9

1 2 - M a r - 0 9

3 0 - J a n - 0 9

S t a r t d a t e

1 3 - F e b - 0 9

1 3 - F e b - 0 9

1 2 - M a r - 0 9

1 2 - M a r - 0 9

3 0 - J a n - 0 9

T r i a l

t a k e n

W o r k h e a d v i b r a t i o n v a l u e s o f

a l l m a c h i n e s a r e c h e c k e d w i t h

h e l p o f v i b r a t o m e t e r

T a k e 5 0 p a r t s

w i t h 2 1 5 0 r p m ,

t a k e 5 0 p a r t s

w i t h 1 7 5 0 r p m

5 0 p a r t s e a c h w e r e p r o c e s s e d

w i t h r e p e a t a b i l i t y o f 1 0 µ &

a t 2

0 µ .

5 0 p a r t s w e r e p r o c e s s e d

b e f o r e d o i n g

f e m a l e c e n t e r

g r i n d i n g & 5

0 p a r t s w e r e

p r o c e s s e d a f t e r d o i n g f e m a l e

c e n t e r g r i n d i n g

T h e j o b c l a m p i n g p r e s s u r e w a s

v a r i e d t i 4 b a r & 5 b a r & i t ' s

i m p a c t o n s e a t r e j e c t i o n s i s

o b s e

r v e d .


C h e c k w o r k h e a d v i b r a t i o n

v a l u e s o f a l l m a c h i n e s

R a t e d R P M v a l u e i s 2 1 5 0

R P M

C h e c k r e p e a t a b i l i t y < 2 0 µ ,

t a k e t r i a l w i t h p r o c e s s i n g

p a r t s w i t h d i f f e r e n t

r e p e a t a b i l i t y v a l u e s .

w

e c h e c k e d p a r t s b e f o r e &

a f t e r d o i n g f e m a l e c e n t e r

g r i n d i n g f o r c h e c k i n g

d i f f e r e n c e

A i r s u p p l y t o j o b c l a m p i n g

i s v a r i e d t o d i f f e r e n t l e v e l s

& i t ' s e f f e c t w a s o b s e r v e d

S u s p e c t e d

s o u r c e s o f

v a r i a t i o n s

( S S V ' s )

E a r l i e r n o t

k n o w n

v a l u e - 1 8 0 0

r p m

R e p e a t a b i l i t y

b e l o w 2 0 µ

G r i n d i n g f r e q .

n o t d e c i d e d

C h u c k c l a m p

g r i n d i n g

s u b c a u s e

V i b r a t i o n

R P M

S p i n d l e

h e i g h t

F e m a l e

c e n t e r

J o b

c l a m p i n g

p r e s s u r e

R o o t c a u s e

W o r k h e a d

S

r .

N

o .

1 2 3 4 5



17

(Machine related parameters continued…)


T h e i m p a c t o f l o a d i n g

s p r i n g b r o k e n o n s e a t

r e j e c t i o n s i s S i g n i f i c a n t

T h e i m p a c t o f L o a d i n g

a l i g n m e n t o f c o m p o n e n t

o n s e a t r e j e c t i o n s i s


T h e i m p a c t o f A i r

c y l i n d e r o n



T h e i m p a c t o f A n g l e

m a s t e r o n



T h e i m p a c t o f s e a t

v i s u a l m i c r o s c o p e

c o n d i t i o n o n s e a t



T h e i m p a c t o f A i r s u p p l y

f o r p a r t s c l e a n i n g o n

S e a t r e j e c t i o n s i s

S i g n i f i c a n t


w i t h o k s p r i n g a l l 5 0 p a r t s

c a m e o k , w i t h b r o k e n

s p r i n g 3 5 b a d i n 5 0 .

w i t h & w i t h o u t c h e c k i n

g

l o a d i n g a l i g n m e n t a l l 5

0

p a r t s c a m e v i s u a l l y o k

T h e q u i c k h i t a c h i e v e d

G R R f o u n d t o b e o k

W h e n 5 0 p a r t s c h e c k e

w i t h f a u l t y m i c r o s c o p e 3 5

c a m e b a d , w h e n t h e y a

r e

c h e c k e d w i t h o k s c o p e

o n l y 5 0 c a m e b a d .

W i t h a i r c l e a n i n g

1 0 p a r t s b a d i n 5 0 ,

w i t h o u t a i r c l e a n i n g

2 2 p a r t s b a d i n 5 0 .

T e s t u s e d


t e s t


t e s t

N o h y p o t h e s i s

t e s t p e r f o r m e d

N o t e s t

p e r f o r m e d


t e s t


t e s t

E n d

d a t e

3 0 - J a n - 0 9

3 0 - J a n - 0 9

3 0 - J a n - 0 9

3 0 - J a n - 0 9

1 8 - D e c - 0

8

3 0 - J a n - 0 9

S t a r t d a t e

3 0 - J a n - 0 9

3 0 - J a n - 0 9

3 0 - J a n - 0 9

3 0 - J a n - 0 9

1 8 - D e c - 0 8

3 0 - J a n - 0 9

T

r i a l t a k e n

C h a n g i n g

f r e q . o n c e i n t w o

m o n t h s .

T a k e a t r i a

l w i t h o u t c h e c k i n g

l o a d i n

g a l i g n m e n t o f

c o

m p o n e n t .

N o p r o b l e m o f a i r l e a k a g e

t a k e

G R R o f s e a t

a n g l e m a s t e r

S c o p e c o n d i t i o n s t u d y

s c h e d u l e t o b e p r e p a r e d

A w o r k s h o p o n m i c r o s c o p e

h a n d l i n g t o b e a r r a n g e d

5 0 p a r t s t a k

e n w i t h a i r c l e a n i n g

& 5 0 p a r t s

t a k e n w i t h o u t a i r

c l e a n i n g


L o a d i n g s p r i n g w a s

c h a n g e d w i t h a b r o k e n o n e

& i t ' s e f f e c t o n s e a t

r e j e c t i o n s w a s o b s e r v e d

W h i l e s e t t i n g m a c h i n e

c h e c k

a l i g n m e n t f o r o k / N o t o k

E l e c t r i c a l s e r v o m o t o r u s e d

C h e c k i n g f r e q .

t o b e

r e d u c e d

C h e c k r e q u i r e m e n t o f

f r e q u e n t v e r i f i c a t i o n o f

m i c r o s c o p e c o n d i t i o n

A s s o c i a t e s a w a r e n e s s

a b o u t m i c r o s c o p e

a d j u s t m e n t t o b e d o n e .

P a r t s t o b e c h e c k e d w i t h

a i r c l e a n e d & w i t h o u t a i r

c l e a n i n g

S u s p e c t e d

s o u r c e s o f

v a r i a t i o n s

( S S V ' s )

C h a n g i n g

f r e q .

V i s u a l c h e c

k

A i r l e a k a g e

M a s t e r

s h o w i n g

w r o n g

r e a d i n g

A l i g n m e n t

o f b o t h e y e

s

n o t t h e r e

F r e q u e n t

c h e c k i n g b y

a s s o c i a t e s

N o s u p p l y

p r o v i d e d

s u b c a u s e

L o a d i n g

s p r i n g

w o r n o u t

L o a d i n g

a l i g n m e n t o f

c o m p o n e n t

L o a d i n g

c y l i n d e r

A n g l e m a s t e r

V i s u a l

i n s p e c t i o n

m i c r o s c o p e

A i r s u p p l y

f o r p a r t s

c l e a n i n g

R o o t c a u s e

L o a d i n g /

U n l o a d i n g

C h e c k i n g

b e n c h

S r . N o .

6 7 8 9 1 0 1

1



19

(Machine related parameters continued…)


T h e i m p a c t o f c o o l a n t

s y s t e m s o n S e a t



T h e i m p a c t o f p o k a

y o k e o n S e a t



T h e i m p a c t o f d r e s s i n g

d e p t h o f c u t o n S e a t

r e j c t i o n s i s


T h e i m p a c t o f d r e s s i n g

f r e q . o n S e a t r e j e c t i o n s

i s I n s i g n i f i c a n t .

T h e i m p a c t o f f e e d r a t e

o n S e a t



T h e i m p a c t o f O p e r a t o r

e q u a l i z a t i o n o n s e a t




T h e c o o l a n t s y s t e m

p a r a m e t e r s a r e

w i t h i n l i m i t s

P o k a y o k e o t i p 1 b a d

i n 5 0 ,

w h e n p o k a y o k e n o t

o n

t i p 1 6 b a d i n 5 0 .

0 b a d i n 5 0 w i t h 3 µ

d e p t h o f c u t .

0 b a d i n 5 0 w i t h 2 µ

d e p t h o f c u t .

0 b a d i n 5 0 w i t h 6 p a

r t s

f r e q .

0 b a d i n 5 0 w i t h 8

p a r t s f r e q .

w i t h 1 0 0 % f e e d r a t e

a l l

5 0 p a r t s o k w i t h 5 0 %

f e e d r a t e a l l 5 0 p a r t s

o k a g a i n .

D u e t o c o n t i n u o u s

r e j e c t i o n s f r o m

a s s e m b l y s e c t i o n f e

a r

i s s e t i n v i s u a l

o p e r a t o r s .

T e s t u s e d


t e s t


t e s t


t e s t


t e s t


t e s t


t e s t

E n

d

d a

t e

3 0 - J a

n - 0 9

3 0 - J a

n - 0 9

3 0 - J a

n - 0 9

3 0 - J a

n - 0 9

3 0 - J a

n - 0 9

3 0 - J a

n - 0 9

S t a r t d a t e

3 0 - J a n - 0 9

3 0 - J a n - 0 9

3 0 - J a n - 0 9

3 0 - J a n - 0 9

3 0 - J a n - 0 9

3 0 - J a n - 0 9

T r i a l t a k e n

O n l y c h

e c k i n g i s i n v o l v e d a s

t a k i n g a t r i a l i s v e r y

d a n g e r o u s .

5 0 p a r t s t a k e n w h e n p o k a

y o k e o

n t i p , a g a i n 5 0 p a r t s

t a k e n w i t h p o k a y o k e i n

b a c k s w o r d p o s i t i o n .

T a k e p

a r t s w i t h 3 µ d e p t h o f

c u t .

T a k e p

a r t s w i t h 2 µ d e p t h o f

c u t .

T a k e 5 0 p a r t s w i t h 8 p a r t s

d r e s s i n

g f r e q .

A g a i n t a k e 5 0

p a r t s w i t h 6 p a r t s d r e s s i n g

f r e q .

T a k e

p a r t s w i t h 5 0 % f e e d

r a t e ,

T a k e p

a r t s w i t h 1 0 0 % f e e d

r a t e .

5 0 b o r d e r c a s e p a r t s w e r e

s h o w n

t o o p e r a t o r s & t h e y

w e r e

s h o w n t o a s s e m b l y

o p e r a t o r s .


C h e c k p r e s s u r e ,

t e m p e r a t u r e

o f c o o l a n t s y s t e m

P o k a y o k e w a s s h i f t e d t o

b a c k w a r d p o s i t i o n & i t s

e f f e c t o n s e a t r e j e c t i o n s

w a s o b s e r v e d .

D r e s s i n g d e p t h o f c u t i s

v a r i e d & t r i a l i s t a k e n

D r e s s i n g f r e q . c h a n g e d

& t r i a l i s t a k e n

T h e f e e d r a t e w a s

c h a n g e d m a n u a l l y & i t ' s

e f f e c t o n s e a t r e j e c t i o n s i s

o b s e r v e d .

D a i l y r e j e c t i o n s a t s e a t

v i s u a l i s c h e c k e d f o r

v e r i f i c a t i o n s

S u s p e c t e d s o u r c e s

o f v a r i a t i o

n s

( S S V ' s

)

T h e d r e s s

i n g /

G r i n d i n g

p r e s s u r e v a r i e s

C o n f i r m a t i o n o f

p o k a y o k e

o n c e

i n a s h i f t

3 m i c r o n s

6 p a r t s

M a n u a l k n o b

p r e s e n

t

I n c o r r e c t d e

c i s i o n

d u e t o f e

a r

o f g e t t i n g r e

j e c t e d

f r o m a s s e m

b l y .

s u b c a u s e

3 . 5

t o 4 b a r

g r i n d i n g /

d r e s s i n g

c o o l a n t

T i p b r e a k a g e

s e n s i n g p o k a

y o k e

D r e s s i n g

d e p t h o f c u t

D r e s s i n g f r e q .

F e e d r a t e

L a c k o f

o p e r a t o r

e q u a l i z a t i o n

R o o t c a u s e

C o o l a n t

s y s t e m s

G r i n d i n g

w h e e l

G r i n d i n g

p r o g r a m

O p e r a t o r

S r . N o .

1 8 1 9 2 0 2 1 2 2 2 3



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

Work Instructions are

elevator getting jammed

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

http://en.wikipedia.org/wiki/Diagramhttp://en.wikipedia.org/wiki/Causehttp://en.wikipedia.org/wiki/Eventhttp://en.wikipedia.org/wiki/Kaoru_Ishikawahttp://en.wikipedia.org/wiki/Kawasaki_Heavy_Industrieshttp://en.wikipedia.org/wiki/Quality_managementhttp://en.wikipedia.org/wiki/Histogramhttp://en.wikipedia.org/wiki/Pareto_charthttp://en.wikipedia.org/wiki/Pareto_charthttp://en.wikipedia.org/wiki/Check_sheethttp://en.wikipedia.org/wiki/Control_charthttp://en.wikipedia.org/wiki/Flowcharthttp://en.wikipedia.org/wiki/Scatter_diagramhttp://en.wikipedia.org/wiki/Scatter_diagramhttp://en.wikipedia.org/wiki/Flowcharthttp://en.wikipedia.org/wiki/Control_charthttp://en.wikipedia.org/wiki/Check_sheethttp://en.wikipedia.org/wiki/Pareto_charthttp://en.wikipedia.org/wiki/Pareto_charthttp://en.wikipedia.org/wiki/Histogramhttp://en.wikipedia.org/wiki/Quality_managementhttp://en.wikipedia.org/wiki/Kawasaki_Heavy_Industrieshttp://en.wikipedia.org/wiki/Kaoru_Ishikawahttp://en.wikipedia.org/wiki/Eventhttp://en.wikipedia.org/wiki/Causehttp://en.wikipedia.org/wiki/Diagram



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 PokaYoke present

to detect Drill

breakage

Gauges notcalibrated on

time

Coolantpressure

varies

Others

Causes

% R

e j e c t i o n 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

0510

1520253035404550

Drill overuse No Poka

Yoke present

to detect Drill

breakage

Gauges not

calibrated on

time

Coolant

pressure

varies

Others

Causes

%

R e j e c t i o n 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 thisPoka 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 toensure 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

P r o p o r t i o n

28252219161310741

0.020

0.015

0.010

_ P=0.01104

UC L=0.01344

LCL=0.00864

Sample

% D e f e c t i v e

30252015105

1.2

1.1

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