TVS Motor company An Introduction - ASQ · 2019-09-23 · 3 5 TVS Motor Company Ltd. Sundaram-Clayton Limited (SCL) started Moped Division at Hosur to manufacture TVS 50 mopeds, indigenously

1

1 TVS Motor Company Ltd.

TVS Motor Company Limited, Hosur, Tamil Nadu, India

Material Cost Reduction Through Painting Process Optimization


TVS Motor company

An Introduction

2


TVS Motor Company

TVS Motor is the flagship Company

of Sundaram Clayton group

Brakes & Die

casting division

TVS Motor

Company

Sundaram

Clayton

TVS Group

Sundaram Auto

components

Sundaram Clayton

Group companies

Shri Venu Srinivasan

Chairman


Offer a two wheeler to carry a family

of lower middle class

at an affordable price (ie @ Rs 5000) ……….

Dream...

The Dream

3


Sundaram-Clayton Limited (SCL) started Moped Division at

Hosur to manufacture TVS 50 mopeds, indigenously designed

and developed.

1979

The Reality

TVS Motor…

“Championing progress through Mobility”

34 Years and Beyond…..


Sa

les v

olu

me

in

La

kh

s

1997 - Mysore Plant

2006 – Himachal Plant

2007 – Indonesia Plant

Sales 3.4 million vehicles with

Turnover of USD 2.3 billion

Leading to growth…

1979 – Hosur Plant

TVS Motor, The Journey…

2006 - 3 Wheeler Plant

4


Product Portfolio

Mopeds Scooters

Motor cycles - Premium Motor cycles - Economy

Three Wheelers Bebek (Indonesia)


Our organization achievement

ISO, TS & TPM Certificates

Deming Award (2002)

2016

TPM Certification

2017

OSHAS 18001 ISO/IEC 27001

2016

ISO/TS 16949

2015

J.D.Power award (2018)

TVS Motor Company won Deming award,

1st two wheeler company to get this award

5


TEI

TEI (Total Employee Involvement ) forms the core of all initiative

In TEI QCC & suggestion are the foundation

TEI Pyramid

SENIOR

MANAGERS

EXECUTIVES &

MANAGERS

WORKMEN & STAFF

TASK

FORCE

CFT & SIT

QCC, JUNIOR QCC &

TEIAN

SENIOR

MANAGERS

MANAGERS &

EXECUTIVES

WORKMEN & STAFF

Cross functional teams (CFT)

&Supervisory

Improvement teams (SIT)

Suggestion & QCC

Task Force(TF)

Quality concepts at TVS Motor company


Global presence

Collaborate to Produce Premium Motorcycles in the Global ...

6


Section 1: Project Background & Purpose


Customer Expectations

Competition Actions

Economical factors

Previous Year non-

achievements

Gaps of Long Range plans

Mentor’s feedback

External environmental

analysis

Internal Factors

Environmental Scan

Annual Goals

Company Annual Policy

Departmental Policy

Implementation & review

Mission

Vision

Long Range plans

Policy 1: Improve Customer

Satisfaction

Policy 2: Improve Turnover

Policy 3: Improve EBIDTA

278 Critical projects

identified towards

achieving the Company

Annual Policy

1.01 Organizational approach to project planning

Major Measure Execution

through:

• Taskforces

• Cross functional Teams (CFTs)

• Supervisory Improvements

Team (SITs)

7


1.01 Organizational approach to project planning

Project Approval Committee (BPL & TQC Dept.)

Sponsor (Head – QAD)

Project Steering Committee (HODs of relevant functions)

Annual goals (KPI) through • Environment scan • Internal factors

Policy guidelines from • Diagnosis of Last year

gaps • System level root causes

• Taskforces • Cross functional Teams (CFTs) • Supervisory Improvements Team

(SITs)

• Target (What) Image (How) Plan ( Major measure) • Interfacing chart (Catch ball)

Top down targets

Bottom-up targets

Project Leader

Policy Targets (What) Means (How)

Deployment of Goals through:

Project Bank execution through: Business Planning & Total Quality Control, HOD, Planners

Section Heads Planners

Responsibility : PDCA on KPIs during the

year

Long Range Plan (LRP) – Rolling plan on 3 years

Quarterly PDCA


1.02 Project identification process

Project identification – Company wide approach

4

6

8

10

12

Year 1 Year 2

EBIDTA Core CFT Leader (Leader of Major

Measure)

Member 1 (Responsible for

Means 1 – Facilitator for Sub CFT)

Sub CFT 1

Sub CFT 2

Sub CFT 3



Sub CFT 1

Sub CFT 2

Sub CFT 3



Sub CFT 1

Sub CFT 2

Sub CFT 3

Business

KMI

4

6

8

10

12

Year 1 Year 2

Material Cost

Department

KMI (Major

Measure)

4

6

8

10

12

Year 1 Year 2

Paint cost

Department

KPI (Sub

Measure)

KMI – Key Management Indices; KPI – Key Performance Indices

UOM - %

UOM – Rs/Vehicle

8


Target setting

Management reviews

Quarterly diagnosis

Process

Yearly

Monthly

Quarterly

Frequency

Project bank (Taskforces, CFTs, SITs)

Root cause analysis, corrective actions preventive actions & standardization

Updation of project bank

(Revisit Taskforces, CFTs

& SITs)

Stage outcome Policy deployment stage

Establishment and

deployment

Implementation &

review and control

1.03 Project selection process



4

6

8

10

12

Year 1 Year 2

EBIDTA

Business KMI

4

6

8

10

12

Year 1 Year 2

Material Cost

Department KMI (Major Measure)

Company X - Matrix 2W Business HOD - Operations

4.9

4.8

4.7

4.6

4.5

4.4

4.3

4.2

4.1

3.5

3.4

3.3

3.2

3.1

10.0

800.

0

16.5

31.7 2.7

60 /

198

36 /

173

60 58 875

872

873

62*

To To

0 40

7.9

375.

0

14.2

25.0

67 /

42 /

100

70 854

861

862

56

From

From

0 0 22

%

Rs.

Cr %

Lac

Lac

Scor

e

UO

M

UO

M

Ove

rall

EBIT

DA

FCF

Ret

ail M

arke

t sha

re –

2W

Sale

s Vo

lum

e –

2W

NPI

Sal

es: N

251,

U24

2, U

286,

N28

9

(Incl

uded

in s

ales

pla

n)

Cus

tom

er r

eten

tion:

LT T

GW

Apa

che1

60 (3

M /

12M

)

Jupi

ter

(3M

/ 12

M)

Apa

che

200

(3M

)

XL10

0 (3

M)

TGR

Jupi

ter

Apa

che

160

Apa

che

180/

200

Serv

ice

NPS

PA

RE

TO

AC

TIO

N P

LAN

(3W

1H)

No

of p

robl

ems

stag

e 80

clo

sed

with

M3

actio

ns w

ithin

10

days Apa

che

Jupi

ter

XL10

0M

ater

ial h

andl

ing

audi

t sco

re

(from

sto

res

to P

OC

)

Qua

lity

syst

em ra

ting

of M

IW c

ells

Early involvement in NPD & adhere to MPT / stage gate process

l

l

l

l

l

l

l

Implement TVS Production system

Use taskforce approach & GB/BB for critical projects

Leverage IT to improve DWM & Productivity levels

Ensure timely completion of waste elimination projects through product cost loss

approach

Improve skill & will in identified areas

Implement and adhere to levelled production; Establish pull system in entire supply

chain

Rai

sed

vs

Clo

sed

No

of

stor

es

scor

e>=7

.5

No

of c

ells

scor

e >=

7.5

43

Def

/100

APE

AL

Scor

e

Implement technology projects for recurrence prevention complaints in IB

Establish strong sustenance mechanism towards surprise in Q issue/ shortage

Functional Guidelines

l Leverage TVS PS - pull system for optimizing end to end inventory

l

l Leverage suppliers capability and support for material cost reduction

Adopt cost loss tree across all business processes to eliminate waste

l Rationalize roles to contain overall head count

Cost driver based budgeting in new product design and developmentl

GUIDELINES

OB

JEC

TIV

ES

PRESIDENT & CEO

2W Business

HOD operations

Department X - Matrix 2W Business HOD - Operations

4.9

4.8

4.7

4.6

4.5

4.4

4.3

4.2

4.1

3.5

3.4

3.3

3.2

3.1

10.0

800.

0

16.5

31.7 2.7

60 /

198

36 /

173

60 58 875

872

873

62*

To To

0 40

7.9

375.

0

14.2

25.0

67 /

42 /

100

70 854

861

862

56

From

From

0 0 22

%

Rs.

Cr %

Lac

Lac

Scor

e

UO

M

UO

M

Ove

rall

EBIT

DA

FCF

Ret

ail M

arke

t sha

re –

2W

Sale

s Vo

lum

e –

2W

NPI

Sal

es: N

251,

U24

2, U

286,

N28

9

(Incl

uded

in s

ales

pla

n)

Cus

tom

er r

eten

tion:

LT T

GW

Apa

che1

60 (3

M /

12M

)

Jupi

ter

(3M

/ 12

M)

Apa

che

200

(3M

)

XL10

0 (3

M)

TGR

Jupi

ter

Apa

che

160

Apa

che

180/

200

Serv

ice

NPS

PA

RE

TO

AC

TIO

N P

LAN

(3W

1H)

No

of p

robl

ems

stag

e 80

clo

sed

with

M3

actio

ns w

ithin

10

days Apa

che

Jupi

ter

XL10

0M

ater

ial h

andl

ing

audi

t sco

re

(from

sto

res

to P

OC

)

Qua

lity

syst

em ra

ting

of M

IW c

ells

Early involvement in NPD & adhere to MPT / stage gate process

l

l

l

l

l

l

l

Implement TVS Production system

Use taskforce approach & GB/BB for critical projects

Leverage IT to improve DWM & Productivity levels

Ensure timely completion of waste elimination projects through product cost loss

approach

Improve skill & will in identified areas

Implement and adhere to levelled production; Establish pull system in entire supply

chain

Rai

sed

vs

Clo

sed

No

of

stor

es

scor

e>=7

.5

No

of c

ells

scor

e >=

7.5

43

Def

/100

APE

AL

Scor

e

Implement technology projects for recurrence prevention complaints in IB

Establish strong sustenance mechanism towards surprise in Q issue/ shortage

Functional Guidelines

l Leverage TVS PS - pull system for optimizing end to end inventory

l

l Leverage suppliers capability and support for material cost reduction

Adopt cost loss tree across all business processes to eliminate waste

l Rationalize roles to contain overall head count

Cost driver based budgeting in new product design and developmentl

GUIDELINES

OB

JEC

TIV

ES

PRESIDENT & CEO

2W Business

HOD operations

Guidelines for Target Achievement

Objective of TPM Cost loss approach

To identify the loss cost in the 6M & 4P.

6M : Man, Machine, Method, Material,

Measurement, Material handling,

4P : People, Plant/ Policy ,Product,

Process

Company Annual Policy

Departmental Policy

Implementation & review

UOM - % UOM – Rs/ vehicle

9


Inference: Cost loss identification for the product done using cost loss

tree. Then, product cost loss is grouped under 3 categories. Our project

scope of paint cost loss reduction is falling under the category of bought

out material cost reduction.

TPM Cost loss Tree - Jupiter



Cost loss stratification Yield loss stratification

MANAGING POINTS & CHECK POINTS : 2017-18

Department: Operations NAME

Ref MANAGING POINT UOM VCS REF Ref CHECK POINT UOM VCS REF RESP. FOR CP

B11 Conversion cost Stores & Tools cost (XL 100 / Scooter) Rs/Veh71.8 /

177.4

61.1/

151NUK

XL 100 Rs/Veh 651 570 B11 Power & Fuel cost (XL 100 / Scooter) Rs/veh158.5/

195

134.7 /

166NVP

Scooter 1059 921 B11 Scrap cost Rs. In Cr 1.60 0.82 NUK

Overall % to TO 2.9 2.52 B11 Overtime cost Rs. In Cr 4.65 1.9 All UMs

Paint cost saving Rs in Cr 0 2.5 GVR

B12 Employee cost B12.1 Conveyor utilisation

XL 100 - Vehicle 77 90 B12.1 AR

Direct & Indirect % to TO 1.35 1.14 B12 Scooter - Vehicle 70 90 B12.1 AR

XL 100 - Engine 78 90 B12.1 SB

B12 Scooter - Engine 72 90 B12.1 SB

Nos 93 86 B12 B12.2 Head count Nos 1780 1950 B12.2 All UMs

Employee Productivity B12 B12.3 Nos 0 / 0 47 / 115 B12.3 All UMs

Eq.V/MY 521 614 B12

C11 Weekly domestic colour model service level C11.1 Line stopper due to Bought out parts by

Stock accuracy % 1.3 0.7 C11.1

Truck TAT due to unloading delay % of veh 3 1 C11.1

XL 100 % 91 95 C11

C11.2 Line stopper due to MIW Parts (Q&D)

Jupiter % 87 95 C11 XL 100 % 6.5 1 C11.2

Jupiter % 5.2 1 C11.2

C11.3 OEE of paint shop - Top coat Plant

C11 Production as per policy plan 9.24 10.59 C11 OEE % 78 85 C11.3Availability %

69 60

C11.6 OEE of frame welding cell % 82 90 C11.6 KCS

Accountability K.Dhanapal DESIGNATION : GM - Operations

Target

From To

Target

From To

VSR

SB/GVR/

RNK/KCS/NU

K/NVP

GVR

C11.5 NVP

Nos in

Lakhs

Freq

C11.5 Breakdown Hours Hrs 26 21

No. of Re-engineering projects Vs Head count saved

C11.4 Breakdown frequencyC11.4

NVP

BProfit and

Profitability

%

Employee Productivity E&M (For base

volume Mar-'17)

Business

Growth C

B11.1 B11.1

Departmental KMI- KPI

1.04 Project selection process (Specific)

TVS Motor Company Limited

Accountability S No Managing Point Measure Check Point Measure

B11.1 Reduce paint cost

Scooter Rs/Veh 421 358 B11.11 Implement identified projects to reduce paint costNo of

projects0

B11.2Reduce conversion

cost

Scooter Rs/Veh 215 195 B11.21 Paint shop Extra operating hours % 170 130

B11.22 Reduce specific power consumption Units/Veh

B11.23 Reduce specific fuel consumption Kg/Veh

B11.24 Scrap cost Rs in lakhs 0 18

B11.25 Tools & Consumable Cost Rs per veh

B11.26 Reduce Over time Rs in lakhs

B11.27 Paint cost Saving Rs in crores 0 3

B12.1Improve Employee

productivityV/M/D 8.5 12 B12.11

No of projects implemented to improve employee

productivity

No of

projects 0 5

Name : GVR DESIGNATION :Unit Manager

6

DEPT/SECTION : OPNS/painting1

MANAGING POINTS & CHECK POINTS : 2017-18

Target

From To

Target

From To

Profit &

Profitability

B

Section KPI - KAI

UOM : Rs/Veh

UOM : Rs/Veh

UOM : %

44%

10


Base coat FOZ

Base coat manual painting

Base coat robot

painting

Primer FOZ

Conductive primer painting

Tag rag wiping

Sanding Loading Receive

parts

Top coat robot

painting

Top coat manual painting

Top coat FOZ

Baking Inspection

Unloading


Brief overview of Spray painting process

Total conveyor length – 420 m Speed – 5 m/min Pitch 1.2 m Operating patter – 3 shifts Models – Jupiter and Wego Application method – Robotic Electrostatic spray painting No. of parts – 10 varieties



Material

Paint Cost Per liter

(Rs/Litre)

Process

Transfer efficiency

(%)

Rework (%)

Flushing loss(%)

Dry film thickness

(micron)

Volume Solids (%)

Surface area

(Sq.m)

Drivers for Paint

yield improvement

EFFORT

BE

NE

FIT

Low High

Lo

w

Hig

h

Transfer efficiency

Dry film thickness

Rework reduction

Volume solids

Paint cost/litre

Flushing loss

EFFORT vs BENEFIT

1

2

1

2

Inference: 7 Cost drivers identified for paint yield

improvement .By using Effort Vs benefit analysis

Transfer efficiency improvement & Dry film

thickness optimization taken for improvement

502

153

196

99 38 16

0

150

300

450

600

Consumption Paint over sprayto Jig/Water

(Transfer efficiency loss)

Excess DFT Rework Flushing loss Paint Depositedon Component

(Recommended)

Transfer efficiency – Ratio of paint deposited on the part to the paint discharged from the gun DFT – Dry film thickness is the thickness of the paint film on the part

11


1.05 Project goals and benefits

To achieve paint cost saving of 2.4 Cr/ Annum by reducing cost / Vehicle from

421 to 358 Rs/Veh

Paint

consumption

reduction

Sludge

generation /

VOC

reduction

Transfer

efficiency

improvement

DFT

optimization

Rework

reduction Inventory

reduction

Goal

Statement

Primary Benefits Tertiary Benefits

Business

Benefits Paint cost saving of 2.4 Cr/annum

Department KPI

Material cost reduction by 0.7%

Department KMI

EBITDA improvement by 0.7%

Business KMI


1.06 Success measure / Criteria identified

KPI 1.1 - Improve Transfer efficiency

KPI 1.2 - Optimise DFT

KPI 1.3 - Reduce rework

KAI - Implement projects for transfer efficiency

improvement

KAI - Implement projects for DFT optimisation

KAI - Implement projects for rework reduction

Success measures

Target

Image

Do Check

Act

Plan 1. Setting objectives and goals (T) 2. Preparing plan of activities (I) 3. Assigning responsibilities (I) 4. Completing milestones (D) 5. Checking effectiveness (C) 6. C-A-P-Do analysis on the gap (A) 7. Consolidation of activities (A) 8. Presentation (A)

Business KMI - Improve EBITDA

Departmental KPI 1 - Reduce Paint cost

Department KMI - Reduce Material cost

12


Section 2: Project Framework


2.01 Concise project statement

PROJECT STATEMENT

PROJECT TYPE

ASSUMPTIONS/EXPECTATIONS PROJECT SCOPE

J Baskaran Project Champion

BUDGET (Financial and/or Resource Constraints)

Out of scope

-Paint price increase

-Surface area increase due to new variant

-Paint wastage while internal logistics

Project deliverables

-Paint cost reduction

-Transfer efficiency improvement

-Rework reduction

Financial:

Budget requirement for capturing robot spray pattern using high definition

slow speed camera – Rs 1 lakhs

Resource:

Exclusive team member is required from each stakeholderProject Acceptance criteria

The project will be accepted when it meets

the deliverables without affecting the quality

parameters

Constraints Time & Resource availability

Material Cost Reduction through Painting Process Optimization

The project uses a Six Sigma DMAIC approach (D-Define M-Measure A-Analyse I-Improve C-Control)

Assumptions:

1. No rapid increase in demand from market

2. No increase in paint cost

3. Financial support will be obtained if ROI is justified

Expectations:

1. No compromise on Quality

2. On time completion of project

3. CFT members contribution without affecting daily routines

4. Knowledge management portal & FMEA is used for capture past

problems of similar projects

5. Risk assessment & mitigation plans are arrived

Project Name Paint cost reduction

Project Sponser K Dhanapal

Scope description

In scope

-Rework

-Dry Film Thickness

-Flushing loss

-Transfer efficiency

13


2.01 Concise project statement

Improve the painting process to reduce the paint cost through yield loss reduction

421 390 358

Recoverable

loss

Opportunity

loss

114

Theoretical

loss

257

Ideal loss

ROTI

Approach

Methodology for

improvement Waste elimination

Process optimization

Efficiency

improvement

Technological

improvement Process innovation

Paint Consumption UOM: Rs/Veh

Current state Desired

future state

Gap – Rs.63/Veh

Gap between standard

and actual


and best in class

conditions


and theoretical

conditions


and zero loss

conditions

Inference: Yield loss is the difference between current condition and ideal condition.


2.02 Type of project

Improve

bottom

line

Improve

top line

Cash flow ( Speed of money )

Margin

( Quality of money )

Revenue ( Quantum of money )

More output

Less resource

More output

Same resource

More output

More resource

Need

of

pro

ject

Natu

re o

f p

roje

ct

Process

intensive

Data

intensive

Idea

intensive DFSS

( DMADV )

Lean six sigma

DMAIC

Type of project

What is the problem

High paint yield loss of 72%

(with respect to ideal condition - ZERO LOSS approach)

Where is the problem

Plant 1 paint shop

When is the problem

March to May

Who Identifies the problem

Engineering Executive

How much

14


2.03 Scope statement

Project Name

Project Sponser J Baskaran Project Manager K Dhanapal

Project deliverables

Project Acceptance

criteria

Constraints

Project Scope Statement

-Paint cost reduction

-Transfer efficiency improvement

-Rework reduction

The project will be accepted when it meets the deliverables

without affecting the quality parameters

Time & Resource availability

In scope

-Rework

-Dry Film Thickness

-Flushing loss

-Transfer efficiency

Out of scope

-Paint price increase

-Surface area increase due to new variant

-Paint wastage during internal logistics

-Paint wastage during testing

Scope description

Paint cost reduction


2.04 Assumptions / Expectations

FMEA Kaizen Management System

Assumptions and expectations are arrived using FMEA, Kaizen Management system, Failure mode directory of solved problems, expert opinions and domain knowledge

15


Expectations:

• The Quality status is maintained

• On time completion of project

• CFT members contribution without affecting daily routines

• Knowledge management portal & FMEA is used for capture past problems of similar

projects

• Risk assessment & mitigation plans are arrived

Assumptions:

• Demand expected to be stable during the course of the projects

• Paint cost expected to be stable

• Financial support will be obtained if ROI is justified

2.04 Assumptions / Expectations


2.05 Project schedule

Sl no Type of project Category Stage TGW / TGR Failure / Success Learning Updated by date

1 Lean Dealay Analysis TGW More time took for anlysis Member from reliability competency not available 3381 24-Oct-18

2 DMAIC Project delete Observation TGR

3 DFSS Scope not met Analysis TGW

4 Lean Resource Action

5 DMAIC Budget Check

6 DFSS Standardisation

7

8

Learning from Projects

Learning from database

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

Plan

Actual

Plan

Actual

Plan

Actual

Plan

Actual

Plan

Actual

Measure

Analyse

Improve

Control

1

2

3

4

5

Week No.S.No Activity P/A

Define

16


TVS MOTOR COMPANY LIMITED

REVEX : 2018-19

Department: OPERATIONS

Cost

centreGL Code Unit 2017-18 Activity description

Budget required

2018-19

2018-19

Requirement

Type of requirement

(Regular/New)

Priority

(Vital/Essential

/ Desirable)

Sodexo technical service for

paint shop maintenance40,25,208 Regular Vital

Ion exchange limited for DM

plant maintenance9,26,640 Regular Vital

Bhuvan enterprises for

sludge pit maintenance11,86,289 Regular Vital

Electro static manual gun

spares 22,45,715 Regular Vital

Robot bell 25,60,000 Regular Vital

Process lab chemicals 90,960 Regular Essential

Equipment calibration

charges2,61,000 Regular Vital

Process lab apparatus and

equipments2,69,600 Regular Essential

PMR filters, ED filters, and ED

spares39,79,480

New requirement - ED

spray nozzles, moped

mask for ACED&top coat

Essential

Primer guns 1,60,000 Regular Vital

Sanding guns 4,21,600 Regular Vital

1220 545000R & M TOOLS, JIGS &

GAUGES - GENERAL3,68,650.00 Jigs materials 3,68,650 3,68,650 Regular Essential

1,64,95,142 1,64,95,142

1220 542009

R & M PLANT AND

MACHINERY - SERVICE

CONT

50,00,000 61,38,137

1220 542010

R & M PLANT AND

MACHINERY -

MACHINERY SP

29,00,000 48,05,715

1220 542014

R & M PLANT AND

MACHINERY -

MEASURING IN

1,99,998.01 6,21,560

1220 542013

R & M PLANT AND

MACHINERY -

METHODS IMPR

10,00,000.01 45,61,080

Total

2.06 Budget

1. Financial Budget requirement for capturing robot spray pattern

using high definition slow speed camera – Rs 1 lakhs

2. Resource (People) Exclusive team members from various functions on

full time basis for the entire duration of the project

Budget Planning Budget Provision

Capex/Revex System Budget/Resource requirement - Project


2.07 Risk management

External Internal External Internal

Increase in paint film

thickess

1. It may lead to customer complaint

related to paint defect

2. Line stopper due to defective parts

delivery

1. Paint peel off / Chip off

2.Inter coat adhesion failure

P M 6

1. Inspection gate at Paintshop

unloading and inspection area

2. Traceability of RT parts and

special focus while inspection

1. Feedback to supplier for improving part

quality at incoming stage

2. Training to loading operators for all the

occurred phenomenon through OPL

3. Identify and implement projects for

supplier and in-house defects elimination

4. Implement projects in paintshop to reduce

dry film thickess (N = 6 projects)

1.Improve DPR , reduce line rework

2.Improve Shift output

3.Reduction in scrap cost

4.Reduction in paint consumption

GVR/GEP

Increase in rework



2. Line stopper due to defective parts

delivery

1. Accumulation of RT Parts

2. Increase in scrap value P M 6

1. Inspection Gate at Paintshop

Unloading area.

1. Training to loading operators for all the


2. Implement projects in paintshop to reduce

rework (N = 10 projects)




4. Reduction in paint consumption

GVR/GEP

Increase in paint

consumption

1. It may lead to customer dissatisfaction

due to short supply of vehicles (colour)

due to shortage of raw materials at

supplier end

1. Intercoat adhesion failure due

to high DFT

2. Mottling & colour mismatch

issue

P M 61. Inspection gate at Paintshop

unloading and inspection area

1. Training to unloading operators for all the


2. Promix weekly calibration

3. Weekly stock correction





GVR/GEP

Increase in high

voltage



2. Variation in Finish value lead to Line

stopper

1. Increase in minor line

stoppage

2. Non uniform DFT build up on

components

3. Variation in paint finish

R H 51. Inspection Gate at Paintshop

Unloading area.

1. Training to unloading operators for all the


2. Training of plant operators





GVR/GEP

Risk Assessments for paint cost reduction

Impact of the risk Mitigation Plan

Risk item Likelihood SeverityTotal

RiskOpportunity Responsibility

Using risk assessment 1 high risk and 3 medium risks were identified. Mitigation plans were arrived considering internal and external impact. Eg. Increase in high voltage is considered as High risk leading to product and process output deviations. The risk was mitigated through additional checks for product and process parameters were planned during trials

17


Section 3: Project Stakeholders and the

project team


SIPOC

Project charter

Stake

holders

Stake

holders Unaware Resistant Neutral Supportive Leading

A C D

B C D

C C,D

C : Current, D: Destination

Keep satisfied Manage closely

Monitor Keep informed

Low High

Lo

w

Hig

h

Interest

Po

we

r

A C

B

3.01 Stakeholders and How Identified

18



Base coat FOZ Base coat manual

painting

Base coat robot painting

Primer FOZ Conductive primer

painting Tag rag wiping Sanding Loading Receive parts

Top coat robot painting

Top coat manual painting

Top coat FOZ Baking Inspection

Unloading



19


3.02 Project champion

Core CFT Role

Leader/

Champion

Deliver the major measure targets

Standardize the learning from sub CFT and recurrence prevention at

system level

Member Deliver the means which he is responsible (TIP of the major measure)

against the major measure target

Identify & facilitate sub CFTs required for achieving the means

Facilitator PDCA on the major measure progress

Identify further CFTs/Activities to achieve Major measure targets

Identify a project’s strategic objectives.

Prioritize the project in phased manner

Communicate the project vision to the team &

translate into the requirements and solution

design.

Critically analyze and motivate the team to

benchmark best practices

Train the team to identify &eliminate the

obstacles

Conduct frequent reviews on team progress

Resource allocation

Roles of Project Champion


Organization – Project

Team Selection Criteria

3.03 Project Team Selection

Team introduction

Operation – Production

Production Engineering

Operation – Quality Assurance &

Online Engineering

Project stakeholders

20


Project Team &

Stake Holders

Functional Competency

Training

Required?

Problem solving competency

Product Process

Required

Available Required Available Required Available

K Dhanapal Expert Expert Expert Expert No Level - 3 Level - 3

P Vivekaandan Expert Expert Expert Expert No Level - 2 Level - 2

K Sanakarapamanaban Expert Expert Expert Expert No Level - 2 Level - 2

R Kamalrajan Expert Expert Expert Expert No Level - 2 Level - 3

GVR Naidu Expert Expert Expert Expert No Level - 2 Level - 2

G Eswaraperumal Practitioner Practitioner Practitioner Practitioner No Level - 1 Level - 1

R Manikandan Practitioner Practitioner Practitioner Practitioner No Level - 1 Level - 1

3.04 Team preparation

Project Scope

Interpersonal • Team building workshops

• Sensitizing the team through

monthly address

• Teaser mails to Dealer personnel

Problem Solving • DMAIC process for new members

• Refresher for existing Brown belts (QC

Tools)

• Multi voting, Segmentation, & Solution

Selection Matrix – Workshop Session

Project-specific engagement • Project Kick off sessions – Stakeholder

meeting

• CSI Process – Interaction by Subject

Matter Expert

• On-job training - Gemba visits


Gemba Walk 4 hours Used by the project team to see the process on the floor

Team Building, Alignment & Result Technique - 3 hours

Used by the project team to discuss the desired result, existing strengths of members, & alignment of work accordingly with team building exercise

`

.

.

.

Forming

Project kick off meeting with team building activities was initiated by Project Lead

Storming

Resistances, Risk management, Conflict Management, Coaching and Clarity of purpose were brought in

Norming

Agreement and consensus, Clear roles and responsibilities And Facilitation

Performing

Clear vision and purpose, Focus on goal achievement, Delegation


21


Knowledge and skills were brought in to make the project successful :-

Project

Scope

Problem Solving • DMAIC process for new

members

• Refresher for 7 QC Tools

• Green belt training

• Black belt training

• Statistical Quality Control

(SQC)

Project-specific engagement • Project Kick off sessions –

Stakeholder meeting

• Integrated FMEA with subject

Matter Experts & Suppliers

• On the job training - Gemba visits

Interpersonal • Team building workshops

• Sensitizing the team through

monthly address



3.05 Team Routines

Core

CFT Role

Review

frequency Reviewer

Leader Deliver the major measure targets

Standardize the learning from sub CFT and recurrence

prevention at system level

Fortnightly

Monthly

Quarterly

Facilitator

Cluster HODs

P&CEO

Member Deliver the means which he is responsible (TIP of the

major measure) against the major measure target

Identify & facilitate sub CFTs required for achieving the

means

Facilitato

r

PDCA on the major measure progress

Identify further CFTs/Activities to achieve Major

measure targets

Sub CFT Role Review

frequency Reviewer

Leader Deliver the CFT deliverable On time completion of the project

Weekly Monthly

Facilitator Core CFT

Member Support the project leader to Deliver the CFT deliverable And on time completion of the project

Facilitator PDCA on sub CFT progress Identify further Sub CFTs/Activities to achieve Major measure targets

Co

re C

FT

S

ub

CF

T

Online system to monitor project status

Feedback by reviewer

Submit

Team routines, communication to be team :-

Communication

PlanFrequency Attendees/Target Chairperson

Daily sunrise

meetingDaily

Project team

membersProject Leader

Project status update WeeklyProject team

membersFacilitator

Progress Review

MeetingFortnightly

Project team

membersProject Champion

Tollgate ReviewBased on Charter

Milestone

Project team

members

Sponsor / Steering

committee

Routine activities and review schedules were prepared and adhered as per the project milestone

22


Section 4: Project overview


4.01 Project approach

TYPE OF PROJECT: Material Cost Reduction through Painting Process Optimization

SIPOC to capture process,

interactions, & stakeholders

Outlier test to identify the variation in

the measurement FMEA to update the learnings

Stakeholder Analysis to prioritize

stakeholders

QCPC to update and

standardise the revised

parameters

Stakeholder Prioritization

In Scope/Out of Scope in order to

fully understand the Scope.

Gage R&R to identify the variation

between the operators

Taguchi DOE to identify the best

operating conditions

Validation of quality

requirements as per

company standard TSES 355

DESCRIPTION OF PROJECT APPROACH: DMAIC

ROTI approach to identify the gap

between current condition & ideal

condition

Stratification to identify high yield

loss paint

Stability to identify whether the

process is stable without any

clusters, mixtures,

trends,&oscillations

Normality test to check whether the

process follows normal distribution

The project used a typical DMAIC process: Define, Measure/Analyze, Improve, Control

DEFINE MEASURE/ANALYZE IMPROVE CONTROL

Brainstorming & Cause&effect

diagram to identify the causes

contributed to this problem and

validate the causes

Hypothesis test to identify the

relationshiop between the factors

Response surface method to explore

the relationship between the response

and the factors affecting response

Regression analysis to identify the

relationship between the independent

and dependent variables

Research papers to idenfity the

contributing factos

23


4.01 Project approach

Define the Customer, their Critical to Quality (CTQ) issues, and the Core

Business Process involved.

Measure the performance of the Core Business Process involved.

Improve the target process by designing creative solutions to fix and

prevent problems.

Analyze the data collected and process map to determine root causes

of defects and opportunities for improvement.

Control the improvements to keep the process on the new course.


Cost loss Identification methodology

To identify the cost connected to each loss in

the plant and functions

Prioritize the cost with respect to financial

indicators

Identify and implement projects to eliminate

the cost loss

Tool – Product cost loss tree

4.02 Tools used throughout project

Objective

Legend – Only for reference

https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=2ahUKEwjsuefisaPeAhWKSH0KHVzvDDYQjRx6BAgBEAU&url=https://traccsolution.com/blog/dmaic/&psig=AOvVaw2aYrnxAdf9Vakx1vMiJ4r-&ust=1540618582754806

24


Tool – Product cost loss tree



Multiple Regression

• Method for studying the relationship between a dependent variable and

two or more independent variables.

• Purposes:

– Prediction

– Explanation

– Theory building

Design of Experiment

• To investigate how different parameters affect the mean

and variance of a process performance characteristic.

• The Taguchi method is best used when there are an

intermediate number of variables (3 to 50), few interactions

between variables, and when only a few variables

contribute significantly.


https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=2ahUKEwjolurmlZ_eAhUQWysKHaSTDs4QjRx6BAgBEAU&url=https://blog.biolab.si/tag/regression/&psig=AOvVaw3I7nmNgO9qGCrWzYlyxPx3&ust=1540473657831362

25


Response Surface Method

Response surface method is used to identify and optimize the operating range of control factors to meet

the desired results




Stages of project Tools

Deliverable of tools in project stages

D M A I C Define Measure Analyse Improve Control

Cost loss tree To identify various losses in terms of monetary values

To Quantify and prioritise the losses

ANOVA To compare the differences between causes

Test of Hypothesis

To compare the differences between causes and standards

To statistically validate the effectiveness of improvement

Probability plot

To validate the distribution type of data

Regression Analysis

To understand relationships between Xs' and Ys'


To understand the main effects

To identify target value for critical X towards improvement in Y

Response surface method

To optimise identified Xs' towards achievement of Ys'

Control Plan To standardise the revised process conditions

FMEA

To understand the effect of revised process conditions in terms of product and process deliverables

https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=2ahUKEwiEmYuClp_eAhUDXn0KHS9wAXkQjRx6BAgBEAU&url=https://onlinecourses.science.psu.edu/stat503/node/57/&psig=AOvVaw2fNpN5SV_WD2AzWCljRqhc&ust=1540473716488534

26


Ra

w

ma

teria

l

cost

Fu

el

cost

Po

we

r

cost

We

ar

&

Te

ar

cost

Dire

ct

lab

ou

r

cost

Ind

irect

lab

ou

r

cost

Die

co

st

R &

M

Co

st

Ad

min

cost

De

pric

ia

tion

cost

Inte

rest

cost

To

tal

loss

Rs/Vehicle

Planned shutdown 47964 47964 0.48 0.08 0.4

Equipment Failure 99632 136189 225205 247000 1809015 2517040 25.17 4.26 22.6

Set up & adjustment (or) colour change 170398 191249 146381 508028 5.08 0.86 4.6

Tool change loss 0 35018 37978 72995 0.73 0.12 0.7

Start up 2144 4398 8762 15304 0.15 0.03 0.1

0.0

Minor stoppage 179586 212263 232820 624668 6.25 1.06 5.6

Speed loss 0 0.00 0.00 0.0

Idle running 0 0.00 0.00 0.0

0.0

Rejection 965280 965280 9.65 1.63 8.7

Rework 54611 143098 740904 938612 9.39 1.59 8.4

0.0

Management loss 113949 255671 2899398 3269018 32.69 5.53 29.4

Operating motion loss 32847 32847 0.33 0.06 0.3

Line organisation loss 1332500 1332500 13.33 2.26 12.0

Logistics loss 84735 1602976 1687711 16.88 2.86 15.2

Measurement & adj loss 2006 559000 561006 5.61 0.95 5.0

0.0

Inventory 14435665 14435665 144.36 24.43 129.8

Energy loss 0 4000000 4000000 40.00 6.77 36.0

Die tool & jig loss 4820362.0 4820362 48.20 8.16 43.3

Yield loss 16254742 16254742 162.55 27.51 146.1

Admin loss 0 0 0.00 0.0

0.0

Durability loss 7000000 7000000 70.00 62.9

Others -1.1 0 0.00 0.0

Others cost 0 0 0 0.00 0.00

59083742 590.84

Rs in lakhs242.20 6.20 49.78 48.20 57.91 24.09 0.00 18.09 0.00 0.00 144.36 590.84 531

40.99 1.05 8.43 8.16 9.80 4.08 0.00 3.06 0.00 0.00 24.43

Ra

w

ma

teria

l

cost

Fu

el c

ost

Po

we

r

cost

We

ar

&

Te

ar

cost

Dire

ct

lab

ou

r

cost

Ind

irect

lab

ou

r

cost

Die

co

st

R &

M

Co

st

Ad

min

cost

De

pric

iati

on

co

st

Inte

rest

cost

Total

` Rs/Vehicle 217.8 5.6 44.8 43.3 52.1 21.7 0.0 16.3 0.0 0.0 129.8

Loss Cost

Ava

ilab

ilityP

erfo

rma

nceD

efe

ctsH

um

an

efficie

ncy

Efficie

ncy

of S

td u

nits

Wa

rr

en

ty

Material cost

Variable cost Fixed cost

Manufacturing Cost

Variable cost Fixed cost

Manufacturing Cost

Cost Loss - July'15

Material cost

Cost Structure

Loss Structure

Energy cost loss

698

Cost loss Matrix

Inference: Cost loss matrix is used to identify monetary value for each of the loss. Yield loss observed as

Rs.313/vehicle

4.03 Tools output at different stage of project


Multiple Regression

Inference: Regression analysis is used to find out the type of relationship between paint consumption and

operating parameters

Eg. Linear or Curvilinear relationship


27


Inference: Design of experiments is used to identify the key factors causing impact in the response, i.e.

paint consumption




Inference: RSM is used to identify and optimise the operating range of control variables to minimise paint

consumption without affecting Quality and Safety

Response surface method Response optimizer

<0.5

<3


28


4.04 How team was prepared to use the tools

Tools & Techniques

7Quality Tools

Understanding the

basic tools

DMAIC

Understand the DMAIC

framework and Tools

– Check Sheet

– Pareto Diagram

– Cause & Effect

– Graphs

– Histogram

– Scatter Diagram

– Stratification

– 6 Sigma Concept

S No Employee name Communication skills Presentation skills QC story Green belt Black belt

1 K Dhanapal

2 P Vivekanandhan

3 K Sanakarapadmanaban

4 R Kamalrajan

5 GVR Naidu

6 G Eswaraperumal

7 R Manikandan

`


4.05 Dealing with project risk

Category

(What

Process)

What Change / Creation? What Measure/check? Frequency of

Check

Top coat

painting

process

Increase in paint dry film

thickness

Separate batch provided in jig scheduling to monitor

trial results

Monitoring the dry film thickness for each trial

Daily

Increase in painting rework

Conduct trial with minimum batch quantity

Verification of jig scheduling every day

Recording of rework data in TAB and generate as daily

report through IT system

Daily

Increase in high voltage Interlock provided in robot to avoid deviation from the

maximum allowable value Weekly

Increase in paint consumption

Monitoring of paint consumption in robot and Promix

Weekly checking of paint stock

Weekly

Rework

process Increase in rework & scrap

Deployment of additional manpower for rework &

monitoring of deployment

Daily monitoring & clearance of scrap

Daily

29


4.06 Encountering and handling resistance as a risk

Stakeholders What Resistance? How Identified? How Resolved?

Supplier

• Supplier is not agreed to send

technical representative to

optimize paint consumption

• Supplier is not agreed to supply

volume solids at higher end

specification

• No exclusive manpower deployed

for plant 1 paint shop

• Review on inspection test report

• Explained the benefit to supplier &

initiated monitoring of supplier

performance report

Production

engineering &

maintenance

• Maintenance team was resisted to

increase the robot bell tip speed

• Engineering team was resisted to

go beyond specification limits

during trials

• Gemba observation during project

discussion

• Explained the importance of the

project and cross functional team

formed to implement the project

Customer –

Vehicle

assembly

• Customer was resisted this project

may lead to Increase in painting

defect which leads to vehicle loss

in vehicle assembly

• Daily vehicle rework review at

vehicle assembly

Created confidence by ensuring no

defective parts reaching to customer

Quality

Assurance Team • Aesthetic & durability parameters

may get affected

• During validation process

(Experimental Job Order)

All short term and long term

properties were verified before going

for implementation & created

confidence


4.07 Stakeholder Involvement in project

Project

Steering

Committee

Project Lead

Supplier

Production

Engineering

Maintenance

Quality

Assurance

Production

Customer

Stakeholders How were they involved in planning?

Approved the planning timetable & tasks with list of

resources proposed

Team member wise solution implementation

responsibility divided by project lead

Invited for initial discussion and shared the project

details and explained the support required

Formed a team & allocated the activities to each

team member


team member

Prepared the master testing plan & sign off done

along with QAD team


team member. Early planning done along with

production on painting sequence

Created confidence by ensuring no defective parts

reaching to customer

How were they involved in implementation?

Ensured resource availability on timely across the all

departments involved, timely supported with expert

guidance

Monitoring & reviewing the solution implementation

status against the scheduled plan

Monitoring & reviewing the inspection report and

activity plan

Regular meeting at gemba during trials and

monitoring of activities status as per Gantt chart

Regular meeting at gemba during trials and

monitoring of activities status as per Gantt chart

Trial parts are validated as per standard and

updated the testing plan during scheduled meeting

at gemba

Ensured the trial parts meets the requirement and

no line stoppage during trial

Shared the project status with customer at regular

intervals

30


Section 5: Project walkthrough


D M A I C


31


Improve the painting process to reduce the paint cost through

yield loss reduction Actual Norms Bench mark Ideal Theoretical

421 390 358

Recoverable

loss

Opportunity

loss

114

Theoretical

loss

257

Ideal loss

Transfer efficiency

- % 60 60 65 75 100

Dry film thickness -

microns 57 42 35 35 35

Flushing loss – % 10 10 7 5 0

Paint

consumption –

Rs. per vehicle

Methodology

for

improvement

Waste elimination

Process optimization

& Efficiency

improvement

Technological

improvement Process

innovation

Inference: Yield loss is the difference between current condition and ideal condition

D M A I C 5.01 Data-Driven Project Flow


39.9

28.0

20.0 16.3

11.9 11.8 11.7 10.9 9.7 9.3 6.9

3.5 2.7

10.9 9.1

34.7 30.2

0.0

10.0

20.0

30.0

40.0Yield loss stratification – Paint wise UOM: Rs/veh

Inference: Since, Matt clear overall paint consumption is high, it has been taken for optimization.


http://www.ga-institute.com/services-portfolio/stakeholder-identification-monitoring-engagement.html

32


D M A I C


Norms Bench mark Ideal Theoretical

487 282 Opportunity

loss

153

Theoretical

loss

220

Ideal loss

Paint

consumption –

Matt clear

The gap between the current condition

to Ideal is because of

Dry film thickness

Rework

Flushing loss

Transfer efficiency

UOM: ml / Vehicle

502

UOM: ml / Vehicle

Gap analysis

502

196

16 38

99

153

0

150

300

450

600

Consumption Paint over sprayto Jig & water

Paint flushing loss Rework Paint depositedon component

(Excess)

Paint depositedon component

(Recommended)

Paint consumption ml/Veh % Paint dispersion ml/Veh %

Paint supplied from Robot / Gun 502 100Paint deposited on component

(Recommended)153 30

Paint deposited on component (Excess) 99 20

Rework 38 8

Paint flushing loss 16 3

Paint over spray to Jig & water (Transfer

efficiency loss)196 39

Total 502 100 502 100


Recoverable

loss

(Transfer Efficiency loss)


33


Substrate

Primer

Base coat

Top coat

12 - 18 µ (Actual 24 µ)

30 - 40 µ (Actual 52 µ)

20 - 24 µ

Phase 1

The gap between the current condition to Ideal is because of

Dry film thickness

Rework

Flushing loss

Transfer efficiency

5.01 Data-Driven Project Flow D M A I C

Inference : In phase 1 we focused on optimizing dry film thickness

to reduce paint consumption


Training Module – Spraying Techniques


Inference : To minimize the

variation between operators,

all operators have

undergone training which

covers both theoretical and

practical training

34



Norms Bench mark IdealTheoretical

487 282Opportunity

loss

153

Theoretical

loss

220

Ideal loss

Paint

consumption –

Matt clear

UOM: ml / Vehicle

502

Recoverable

loss

498


SDCA # Date Shift Time Batch Paint consumption

1 6 1 3 22 89

2 6 2 10 22 87

3 7 1 5 22 86

4 7 3 19 22 89

5 8 1 1 23 86

6 8 1 5 23 87

7 9 2 9 22 85

8 9 2 13 22 87

9 9 3 20 23 86

10 10 1 1 23 87

11 10 1 6 23 86

12 10 2 10 24 85

13 11 1 1 23 87

14 11 2 11 23 86

15 11 3 19 22 88

16 13 1 2 22 87

17 13 2 11 23 86

18 13 2 14 23 85

19 14 1 3 22 86

20 14 2 10 24 84

21 14 3 17 25 86

22 15 1 2 25 84

23 15 2 9 23 86

24 16 2 13 24 85

25 16 3 18 24 83

26 17 1 3 24 85

27 17 2 10 24 87

28 17 2 14 25 88

29 18 2 11 25 85

30 18 3 19 24 83

Data Collection


Inference : Since P>0.05, i.e. 0.145, Accept H0. There is no

difference in paint consumption between batches

Comparison between batches

H0 : No difference in consumption between batches Ha : Difference in consumption between batches

35


Statistical quantification

Inference : Gauge R&R is at acceptable limit ( 6.7)

General

rules used to

determine

the

capability of

the system:

<10%:

acceptable

10% - 30%:

marginal

>30%:

unacceptabl

e

Gauge R&R Outlier Stability Normality




Inference : P value >0.05, No outlier exists



36


Inference : since P>0.05 for clustering, mixtures, trends & oscillations, Process is stable





Inference : Since, p value > 0.05, Process follows normal distribution




37


D M A I C


Brain storming

Why paint

consumption

more ?

Inference : Totally 22 causes identified for high paint consumption


High solids in paint x3

Low ER of paint x4

Low booth air velocity x13

High paint flow rate x14

Low bell RPM x15

Low shaping air x16r

Low Voltage x18

High paint viscosity x21

Low booth humidity x19 Fast evaporating solvent x5

Robot tip speed low x8

Robot gear pump worn-out x10

Non adherence of flow calibration x20

PMR pump running at high stroke x12

Un skilled robot operator x1

High dist. bell to part x17

Low Hiding power x6

High paint pressure x11

Low conveyor elect. Resistance x7

High booth temperature x22

Distance between gun to comp x2

Shroud blockage x9


38


Setup Hypothesis

Effect : 502 Ml/Veh

Man Machine

Material Method

High solids in paint x3

Low ER of paint x4

Low booth air

velocity x13

High paint flow

rate x14

Low bell RPM x15

Low shaping air x16r

Low voltage x18

High paint

viscosity x21

Low booth

humidity x19

Fast

evaporating

solvent x5

High paint

consumption

Robot tip speed low x8

Robot gear pump

worn-out x10

Non adherence of flow calibration x20

PMR pump running at

high stroke x12

Possible causes (Level 1)

Un skilled robot

operator x1

High dist. bell to part x17

Low

Hiding

power x6

High paint pressure x11

Low conveyor

elect. Resistance x7



Distance between

gun to comp x2

Shroud blockage x9


Gemba verification Cause verification


# Cause Spec InvestigationGemba

verification

x1 Un skilled robot operator CL3 CL3

x2 Distance between gun to comp 200-300 Cm 150

x3 High solids in paint 26-30% 29.2

x4 Low ER of paint 250-500 479

x5 Fast evaporating solvent T311 T311

x6 Low hiding power 16-18m 16m

x7 Jig elect. resistance <1 MΩ 0.9MΩ

x8 Robot tip speed low 1100 – 1400 mm/sec 1300

x10 Robot gear pump worn-out No backlash OK

x11 High paint pressure 5.5 – 8.0 bar 7.1 bar

x17 High dist. bell to part 250 - 300 mm 250 - 270 mm

x20 Non adherence of flow calibration Once in 15 days ok

P

P

P

P

P

P

P

P

P

P

P

P

# Cause Spec InvestigationGemba

verification

x1 Un skilled robot operator CL3 CL3

x2 Distance between gun to comp 200-300 Cm 150

x3 High solids in paint 26-30% 29.2

x4 Low ER of paint 250-500 479

x5 Fast evaporating solvent T311 T311

x6 Low hiding power 16-18m 16m

x7 Jig elect. resistance <1 MΩ 0.9MΩ

x8 Robot tip speed low 1100 – 1400 mm/sec 1300

x10 Robot gear pump worn-out No backlash OK

x11 High paint pressure 5.5 – 8.0 bar 7.1 bar

x17 High dist. bell to part 250 - 300 mm 250 - 270 mm

x20 Non adherence of flow calibration Once in 15 days ok

P

P

P

P

P

P

P

P

P

P

P

PInference : Genba analysis indicates all factors are within specification

39


Man Machine

Material Method

Low booth air

velocity x13

High paint flow

rate x14

Low bell RPM x15

Low shaping air x16r High paint

viscosity x21

Low booth humidity x19

Probable causes (Level 2)

High paint

consumption


Setup Hypothesis

Effect : 502 Ml/Veh


Low voltage x18


Matt Clear Paint consumption

# Air velocity Bell RPMPaint

flow rateVoltage

Shaping

air

Paint

viscosity

Booth

temp

Booth

humidity

Paint

ConsumptionMl/Veh

1 0.4 52000 525 40 300 27.2 23 76 89 524

2 0.4 52000 525 40 300 26.8 25 76 87 512

3 0.4 52000 525 40 300 25.4 22 76 86 506

4 0.4 52000 525 40 300 26.6 23 76 89 524

5 0.4 52000 525 40 300 27 24 73 86 506

6 0.4 53000 525 40 300 25.6 23 73 87 512

7 0.4 52000 495 40 300 26.8 25 73 85 500

8 0.4 52000 495 40 300 27.4 22 73 87 512

9 0.4 52000 495 40 300 26.6 23 73 86 506

10 0.4 52000 495 40 300 26.4 24 73 87 512

11 0.4 52000 495 40 300 27.6 23 73 86 506

12 0.4 52000 495 40 300 26.8 25 73 85 500

13 0.4 53000 495 40 300 26.4 22 73 87 512

14 0.41 52000 525 40 300 27.4 22 75 86 506

15 0.41 52000 525 40 300 26.7 23 75 88 518

16 0.41 54000 525 40 340 27.8 24 75 87 512

17 0.41 52000 525 40 340 26.9 23 75 86 506

18 0.41 52000 525 40 340 25.6 25 75 85 500

19 0.41 52000 525 40 340 26.7 22 75 86 506

20 0.39 52000 475 40 300 26.5 23 71 84 494

21 0.39 52000 475 45 300 26 25 71 86 506

22 0.39 52000 475 45 300 27.2 22 71 84 494

23 0.39 52000 475 45 300 28 23 71 86 506

24 0.39 52000 475 45 300 26.4 24 71 85 500

25 0.39 52000 475 45 320 25.8 23 71 83 488

26 0.39 54000 495 45 320 26.7 25 71 85 500

27 0.39 52000 495 45 320 26.9 22 71 87 512

28 0.39 52000 495 45 320 27.4 23 75 88 518

29 0.39 52000 495 45 320 25.9 24 75 85 500

30 0.39 52000 495 45 300 26.8 23 75 83 488


Inference : 30 experiments conducted to capture the inputs and responses

40


Regression analysis

Inference : As the relation ship in linear regression is low, we have taken the factors with P –

value closer to 0.05 for further analysis through Hypothesis testing



S no Cause Probable causes Data type

Effect (Y)

Data type

cause (x)

How to

validate

1 x14 High paint flow rate Continuous Discrete 2 sample T test

2 x16 Low shaping air Continuous Discrete 2 sample T test

3 x18 Low Voltage Continuous Discrete 2 sample T test

4 x21 High paint viscosity Continuous Discrete 2 sample T test

Plan for cause validation


41


Ho: Paint consumption with high paint flow rate = Paint consumption with low paint flow rate Ha: Paint consumption with high paint flow rate ≠ Paint consumption with low paint flow rate

Cause 1: High

paint flow rate x14

Inference : Since P<0.05, Accept Ha. There is a strong relationship between paint flow rate and Paint

consumption

Hypothesis test



Ho: Paint consumption with high shaping air = Paint consumption with low shaping air Ha: Paint consumption with high shaping air ≠ Paint consumption with low shaping air

Cause 2: Low shaping air

x16

Hypothesis test

Inference : Since P<0.05, Accept Ha. There is a strong relationship between shaping air and Paint

consumption


42


Ho: Paint consumption with high HV = Paint consumption with low HV Ha: Paint consumption with high HV ≠ Paint consumption with low HV

Cause 3: Low Voltage x18

Inference : Since P<0.05, Accept Ha. There is a strong relationship between voltage and Paint

consumption

Hypothesis test



Ho: Paint consumption with high paint viscosity = Paint consumption with low paint viscosity Ha: Paint consumption with high paint viscosity ≠ Paint consumption with low paint viscosity

Cause 4: High paint viscosity x21

Inference : Since P<0.05, Accept Ha. There is a strong relationship between paint viscosity and Paint

consumption

Hypothesis test


43


Sl

no 4M’s/4P’s Cause

Specification

( if any ) P value

Relationshi

p*

1 Method Low booth air velocity x13 0.3 – 0.4m/sec 0.118

2 Method High paint flow rate x14 400 – 600 cc/min 0.000

3 Method Low bell RPM x15 50 K – 60K 0.486

4 Method Low shaping air x16 300 - 450 0.000

5 Method Low Voltage x18 Max 70 KV 0.000

6 Method Low booth humidity x19 50 - 80 % 0.114

7 Method High paint viscosity x21 26-29 Sec 0.000

8 Method Booth temperature x22 20 – 30 deg.C 0..838

Strong relation Weak / No relation

Cause validation



Setup Hypothesis

Man Machine

Material Method

High paint flow

rate x14

High shaping air x16

High paint

viscosity x21

Vital causes (Level 3)

High paint

consumption

Effect : 502 Ml/Veh


Low voltage x18

44


Critical factors

1. Paint flow rate

2. Voltage

3. Shaping air

4. Paint viscosity

Critical Input Variables

22 Inputs

4

• Cause & Effect level 1

• Cause verification

Funneling Effect

8

• Cause & Effect level 3

• Hypothesis testing



D M A I C


45


487 502 488

388

0

100

200

300

400

500

600

Norms Before AfterRestoration

After DOE

UOM: ml/veh

Operating Levels for Identified factors

Part Name Paint flow Shaping air voltage Viscosity

Existing L1 L2 Existing L1 L2 Existing L1 L2 Existing L1 L2

Side panel L 475 425 550 325 300 350 40 35 45 26.1 24 27

Side panel R 475 425 550 325 300 350 40 35 45 26.1 24 27

Panel Top 525 425 550 325 300 350 55 50 60 26.1 24 27

Fender 550 500 600 325 300 350 40 35 45 26.1 24 27

Bottom 500 450 550 375 350 400 40 35 45 26.1 24 27



Inference : After DOE, paint consumption reduced to 388 ml/veh


• Design of experiment

2

Inference: High voltage is the most critical factor

followed by paint flow

Response

Surface Method


Gap

46


Experiment and Results

Inference: Rework and HV fault added as

two other responses in addition with paint

consumption

Response Surface Method

Part Name Paint flow High voltage

Existing L1 L2 Existing L1 L2

Side panel L 425 350 425 45 45 60

Side panel R 425 350 425 45 45 60

Analysis

Inference: R-sq = 97.2% indicates very strong

Regression between Paint flow and High voltage with the

identified responses



Response optimizer

<0.5

<3

Finalized parameters after Response surface method

Part Name Paint flow High Voltage

Side panel L 375 50

Side panel R 375 50

Panel Top 375 65

Fender 450 50

Bottom 400 50


UOM: ml/Veh


487 500 488

388 348

294

0

150

300

450

600

Norms Before Afterrestoration

After DOE After RSM-2comp

After RSM-All comp

47


Phase 1

Phase 2 2.1 Transfer efficiency improvement 2.2 Dry film thickness optimization

Dry film thickness optimization


In phase 1 we focused on reducing paint wastage by optimizing the dry film thickness within

the component to reduce paint consumption

In Phase 2 We focused on Paint over spray reduction to improve transfer efficiency, This will increase dry film thickness beyond specification which is reduced by repeating experiment conducted in Phase 1.


The gap between the current condition to Ideal is because of

Dry film thickness (Completed in TPM Level 1)

Rework (QM)

Flushing loss (Best in class)

Transfer efficiency

Phase II

To improve transfer efficiency, paint deposition on the component to be

increased by reducing the paint overspray away from the component


48


Factors contributing to transfer

efficiency

Shaping air

High voltage

Bell RPM

Air velocity

Paint ER

Air pressure

Paint pressure

Jig resistance

Air velocity Bell RPM Shaping air Paint ER Jig earthing resistance HV Paint pressure Air pressure Tansfer efficiency

0.3 30000 250 398 0.8 55 6 6.5 57.6

0.3 30000 250 312 0.7 60 6.5 6.5 59.5

0.3 30000 300 392 0.7 50 6 7 60.3

0.3 30000 350 353 0.8 60 5.5 7 63.9

0.3 30000 300 312 0.5 50 5.5 6.5 64.6

0.4 30000 275 450 0.8 50 7 6.5 58.6

0.4 30000 300 443 0.8 60 7 7 58.6

0.4 30000 275 301 0.6 50 7.5 7 60.3

0.4 30000 350 443 0.6 55 7.5 6.5 60.8

0.4 30000 300 353 0.5 55 6.5 7 62.0

0.3 45000 250 442 0.8 50 6.5 6.5 54.6

0.3 45000 250 398 0.5 60 6 6.5 58.6

0.3 45000 300 342 0.6 55 5.5 6.5 63.9

0.3 45000 300 353 0.7 55 6 7 65.4

0.3 45000 350 297 0.5 60 5.5 7 66.5

0.4 45000 275 412 0.8 55 7 6.5 59.3

0.4 45000 275 301 0.5 55 7.5 7 62.0

0.4 45000 300 342 0.6 60 6.5 7 63.1

0.4 45000 350 353 0.5 60 7.5 6.5 65.4

0.4 45000 300 312 0.5 60 7 7 67.8

0.3 60000 250 446 0.7 50 6 6.5 53.2

0.3 60000 300 460 0.8 55 6 7 55.6

0.3 60000 250 124 0.6 55 6.5 6.5 56.5

0.3 60000 300 352 0.7 60 5.5 6.5 57.2

0.3 60000 350 324 0.7 55 5.5 7 59.5

0.4 60000 275 454 0.8 50 7.5 7 54.3

0.4 60000 275 418 0.7 55 7 6.5 55.6

0.4 60000 300 440 0.6 50 6.5 7 56.1

0.4 60000 300 398 0.6 55 7 7 56.9

0.4 60000 350 301 0.6 50 7.5 6.5 57.8

Data Collection Stage 1 Define the objective of Experiment

Stage 2 Choose measures of performance

Stage 3 Identify factors (control variables)

Stage 4 Choose the experiment design

Stage 5 Conduct experiment & collect data

Stage 6 Analyze and interpret results Stage 1 Define the objective of

Experiment

• To identify the optimum operating

level of the identified factors to

improve transfer efficiency

Stage 2 Choose measures of

performance

• Transfer efficiency





8 Inputs

5

• QM Matrix

• Screening

Funneling Effect

Critical factors

1. Bell RPM

2. Shaping air

3. Paint resistivity

4. Jig resistance

5. High voltage

Inference: In identified factors,

because of safety concerns high

voltage & jig resistance are not

taken forward for conducting

experiment


49


Research papers on rotary bell painting

In order to improve transfer efficiency we referred research papers and

concluded to conduct simulation on identified parameter.

# Factor Standard Level 1 Level 2

1 Shapping air 300 250 350

2 Bell RPM 60000 60000 40000

3 Paint ER 500 500 300

4 Paint Flow 350 350 350

5 High Voltage 60 60 60

Simulation conducted and images are captured through high

speed camera to observe variation in paint spray when modifying

one factor and keeping other factors in nominal operating value.



Simulation to improve transfer efficiency

Shaping air at minimum level

Shaping air at maximum

level

Bell RPM at minimum level

Bell RPM at maximum level

Paint ER at minimum level

Paint ER at maximum level

In minimum shaping air, The spray

cone tends to become more wider

and it leads to over spray.

In maximum Bell RPM, it is possible

to see the effect of larger centrifugal

force and leads to over spray. It also

creates a finer atomization.

In lesser paint resistivity, the over

spray is less when compared to

higher paint resistivity.


50


# Bell RPMPaint

flow rate

Shaping

air

Mixed paint

resistivity with

hardner

Paint

ConsumptionDFT

Tansfer

efficiency

1 60000 350 300 352 296 39 57.2

2 60000 350 350 324 292 40 59.5

3 60000 350 250 398 294 36 53.2

4 60000 350 300 460 289 37 55.6

5 60000 350 250 312 292 38 56.5

6 60000 350 300 440 294 38 56.1

7 60000 350 275 263 289 37 55.6

8 60000 350 300 398 290 38 56.9

9 60000 350 350 301 293 39 57.8

10 60000 350 275 454 288 36 54.3

11 45000 350 300 394 292 43 63.9

12 45000 350 350 297 294 45 66.5

13 45000 350 250 398 289 39 58.6

14 45000 350 300 353 292 44 65.4

15 45000 350 250 297 294 37 54.6

16 45000 350 300 442 289 42 63.1

17 45000 350 275 412 293 40 59.3

18 45000 350 300 312 288 45 67.8

19 45000 350 350 353 292 44 65.4

20 45000 350 275 301 294 42 62.0

21 30000 350 300 312 289 43 64.6

22 30000 350 350 353 292 43 63.9

23 30000 350 250 398 294 39 57.6

24 30000 350 300 392 288 40 60.3

25 30000 350 250 312 292 40 59.5

26 30000 350 300 353 294 42 62.0

27 30000 350 275 450 289 39 58.6

28 30000 350 300 443 289 39 58.6

29 30000 350 350 443 293 41 60.8

30 30000 350 275 301 288 40 60.3

Data Collection

Critical factors

1. Bell RPM

2. Shaping air

3. Paint resistivity


14 Inputs

3

• QM Matrix

• Screening

Funneling Effect




Stage 4 Choose the experiment design

Inference : Since the Bell RPM and shaping air follows the curvilinear effect to transfer

efficiency, We decided to choose mixed taguchi design L18 experiment with 2 level 1 Factor

and 3 level 2 factors.


51


Stage 5 Conduct experiment & collect data Step 6 Analyze and interpret results

Inference : During analysis we found that bell RPM and shaping air has significant

contribution to transfer efficiency.



Stage 5 Conduct experiment & collect data

UOM: % UOM: micron


By completing DOE,

• Transfer efficiency improved from 59 to 71%

• Dry film thickness improved from 39 to 46 microns

which is more than the requirement

52


Paint consumption

Inference : Even though transfer efficiency increased, There is no improvement in paint consumption due

to dry film thickness increase.

# UOM SpecBefore

improvement

After

improvement

Paint consumption ml/Veh 310 294 294

Dry film thickness Micron 35~40 39 47

Rework % 2 5 2

Flushing loss % 10 10 10

Transfer efficiency % 70 59 71

Part Name Shaping air Bell RPM Paint ER

Side panel L 300 45000 350

Side panel R 300 45000 350

Panel Top 350 45000 350

Fender 300 45000 350

Bottom 350 45000 350

Finalized parameters for shaping air , Bell

RPM & paint ER after mixed level DOE


Phase 2 2.1 Transfer efficiency improvement


Phase 2 2.1 Transfer efficiency

improvement 2.1 Dry film thickness optimization

Critical parameter identified to conduct

DOE for reducing the paint consumption

by optimizing the dry film thickness

Critical factors

1. Paint flow rate

2. High voltage

3. Paint viscosity


14 Inputs

3

• QM Matrix

• Screening

Funneling Effect

Screening of parameters for DOE


53


Operating Levels for Identified factors

Part Name

Paint flow High voltage Viscosity

Existing L1 L2 Existing L1 L2 Existing L1 L2

Side panel L 375 350 325 45 40 50 25 24 26

Side panel R 375 350 325 45 40 50 25 24 26

Panel Top 375 350 325 50 45 55 25 24 26

Fender 450 425 400 45 40 50 25 24 26

Bottom 400 375 350 45 40 50 25 24 26


Taguchi L8 DOE experiment conducted with 3 factors and 2 levels to identify the best

operating Levels for Identified factors.



487 282 Opportunity

loss

153

Theoretical

loss

220

Ideal loss

Paint consumption – Matt clear

UOM: ml / Vehicle

502

294 218


54


D M A I C


Validation of Quality Requirements

5.02 Solution Validation D M A I C

Financial Validation:

No Capex or Revex

requirement

LSL USL

1 Zero hour petrol rub resistance (5 double rub) No paint transfer to cloth/ No gloss reduction Visual Passes

2 Adhesion 100/100 should pass As per TSES 355 Passes

3 Hot water resistanceimmerse in hot water ( 70 deg c for 1 hour , recovery 1 hour ) no blister /

peel off , 5 x5 - 2mm checques As per TSES 355 Passes

4 Pencil hardness - F Scoring mark not allowed As per TSES 355 Passes

5 Acid (1N resistance) - 0.2ml of sulphuric acid - 24 hours Hrs 24 No blister /Peel off /Wrinkles /Softening /Cracks /changes of gloss As per TSES 355 Passes

6Alkali (1N resistance) - 0.2ml of sodium hydroxide - 24

hoursHrs 24 No blister /Peel off /Wrinkles /Softening /Cracks /changes of gloss As per TSES 355 Passes

7Impact resistance on panel - 500 gms /20 cm /12.7 mm

dia ball No crack and peel off As per TSES 355 Passes

8 Bend resistance on panel No crack and peel off As per TSES 355 Passes

9 Petrol resistance - 24 hours Hrs 24 No blister, peeling-off, crack, sharp change of luster, softening As per TSES 355 Passes

10 Oil resistance - 24 hours Hrs 24 No blister, peeling-off, crack, wrinkle, softening As per TSES 355 Passes

11 QUV resistance on panel - 500 hours Hrs 500 No Fading , no chalking, no cracking , Gloss reduction Shall not be more

than 10%with out polishing As per TSES 355 Passes

12 Condensation test on panel - 240 hours Hrs 240No blister , Peeling off , crack , Wrinkless , rust ,sharp change of luster ,

softening .As per TSES 355 Passes

13 Corrosion resistance on panel - 600 hours Hrs 600Rust formation, blister, peeling-off and softening shall not exceed 3mm

at each side along scratch mark.As per TSES 355 Passes

14 R Value - Min ( A Area) Unit 5 No dullness and Gloss absorption R Value Meter 5.8

15 DOI - Dark colours Unit 80 No dullness and Gloss absorption R Value Meter 92

16 DOI - Light colours Unit 70 No dullness and Gloss absorption R Value Meter 86

17 R Value - Min ( AB Area) Unit 4 No dullness and Gloss absorption R Value Meter 5

18 DOI - Dark colours Unit 70 No dullness and Gloss absorption R Value Meter 76

19 DOI - Light colours Unit 60 No dullness and Gloss absorption R Value Meter 72

20 Gloss @60° - Glossy shades Unit 90 No dullness and Gloss absorption Gloss meter 98

21 Gloss @60° - Matt shades Unit 3 15 No dullness and Gloss absorption Gloss meter 6

22 Shade Delta E Solid colours < 4, Metallic colours < 5, As per TSES 355 0.5

Status

Short term tests

Long term tests

Part Parameters

SpecAcceptance criteria Check methodS.NO Part parameters UOM


55


5.03 Solution justification D M A I C

No investment is required for this project. Hence, no payback period is applicable

Project declaration & Approval Additional benefits

1. VOC 2. Sludge generation


218

153

33

12 4 16

0

75

150

225

300

Consumption Paint over spray

to Jig/Water (Transfer Loss)

Excess DFT Rework Flushing loss Paint Deposited

on Component(Recommended)

294

153

97

16 12

16

0

75

150

225

300

Consumption Paint over spray

to Jig/Water (Transfer Loss)

Excess DFT Rework Flushing loss Paint Deposited

on Component(Recommended)

Evaluation of results

Before After UOM : ml/Veh

5.04 Results D M A I C

UOM : ml/Veh

Factors UOM Before Norms Ideal Possible (Based

on benchmark ) Actual

Transfer efficiency % 60 60 100 75 71

Rework % 3.4 3 0 2 2.9

Dry film thickness

(avg) microns 35~40 42 35 35 35~40

Flushing loss % 10 10 0 7 10

Consumption ml/veh 294 310 153 210 218

Paint consumption

56



UOM: Ml/veh UOM: Rs/Veh

Matt Clear – Yield loss

29% 27%



487 282 Opportunity

loss

153

Theoretical

loss

220

Ideal loss

Paint consumption – Matt clear

UOM: ml / Vehicle

502

294 218

294

220 218

100

150

200

250

300

Before Target Actual

28

21 19.9

0

10

20

30

40

Before Target Actual




402 234Opportunity

loss

123

Theoretical

loss

182

Ideal loss

Paint consumption – MTP Clear coat

UOM: ml / Vehicle

434

283 202


605 545Opportunity

loss

295

Theoretical

loss

367

Ideal loss

Paint consumption – Top coat White

UOM: ml / Vehicle

640

574 485

Horizontal Deployment to other colors


312 245Opportunity

loss

134

Theoretical

loss

186

Ideal loss

Paint consumption – Base coat Titanium grey

UOM: ml / Vehicle

345

285 212


321 278Opportunity

loss

176

Theoretical

loss

201

Ideal loss

Paint consumption – Base coat Stallion Brown

UOM: ml / Vehicle

367

296 234

Inference : Paint consumption reduced by 38% (Average of 4 colors)

22 projects completed as per plan

57


421

390 386

321

370 366

300

320

340

360

380

400

420

Before Target (Phase 1) Actual (Phase 1) Target (Phase 2) As of nowTarget (Phase 2)

As of nowActual (Phase 2)

Paint cost UOM: Rs/veh

Phase 1 Phase 2

5.04 Results

Cost saving certificate

Cost saving of Rs. 2.26 Cr / Annum

421 390 358

Recoverable

loss

Opportunity

loss

114

Theoretical

loss

257

Ideal loss

Paint

consumption –

Rs. per vehicle

366


421

366

300

350

400

450

500

Before After

Departmental KPI

Bett

er

UOM: Rs/veh

Bett

er

EBITDA %

6.8

7

7.2

7.4

7.6

7.8

8

8.2

Before After

Paint Cost

Bett

er

UOM: %

5.04 Results

1301

1236

1200

1220

1240

1260

1280

1300

1320

Before After

Departmental KMI UOM: Rs/veh

Material Cost

Material cost reduced by

0.7%

EBITDA improved by

0.7%

Business KMI

58


PCR/ ECR No & Date QCPC Reference

Process Sheet No/ Mod .note Dt Operations Standards

TDR No & Date M/c Service Change Ref

Tool Drawing No Date Training to the Doers

Machine Manual Change Ref VCS/ One point Display

SOP No & Date Checklist\JH checklist

IED Standards Cost Standards

OTHERS

PROCESS GENBA

OTHERS

Inference :All applicable standards updated

Updated

Updated

Displayed

Updated

Updated

Given

FMEA Updated

5.05 Maintaining the Gains


PFMEA


59


Quality Control Process Chart



QCPC – Annexure description : Paint flow set value


60


QCPC – Annexure description : High voltage set value




The revised process parameters are being monitored

and maintained as derived specification limit

61


Product Improvements :

Design Changes in the Product

Process Improvements :

Changes in the Process

Implemented in all applicable areas

Proposed in all areas, not implemented

Not applicable

Details of Implementation :-

Implemented for wego models at plant 1

Details of Proposal :-

For motorcycle painting

Horizontally Implemented in their work area



5.06 Project Communication

Online system to monitor project status

Projection completion status is being

monitored in each stage through online

system & completion communicated to all

stake holders.

Online project communication to stake holders

62


Project presentation to Senior Management

Reviewer Inputs

JMD Challenging the current condition statistically is good. Work towards technological

improvement to challenge the ideal conditions.

P& CEO Yield improvement case study in paint plant is good. To be horizontally deployed to all other

colors. Challenging the recoverable loss alone is not sufficient. We need to challenge the

opportunity loss also.

Board of Director Very good case study on paint consumption reduction.

Review by JMD Review by P & CEO Review by Board of director

5.06 Project Communication


QC tools Statistical tools

Pareto 2 sample t test

graph Regression analysis

Brain storming Normality test

Cause and effect diagram Stability test

Why why analysis Capability test

Taguchi design for DOE

Response surface method

Tools used for improvement

Learning's

1. Better understanding of statistical techniques

2. Yield loss is an important loss in paint shop, which will affect bottom line of the company.

Books / Material referred

1. Minitab user guide

2. World class quality by Keki R. Bhote

3. Automotive Rotary Bell spray painting

63


THANK YOU

Documents

TVS Motor company An Introduction - ASQ · 2019-09-23 · 3 5 TVS Motor Company Ltd. Sundaram-Clayton Limited (SCL) started Moped Division at Hosur to manufacture TVS 50 mopeds, indigenously