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.