See only control phase of presentation... of this six sigma's DMAIC Case Study performed by Dr. Bikram Jit Singh

2. SOME SIX SIGMA IMPERATIVES FOR
INDIAN FOUNDRIES:
A CASE STUDY
By
Bikram Jit Singh
Professor
MMDU Mullana

3. Main Focus
1. Introduction
2. Research Plan
3. Literature Survey
4. Research Gap
5. Problem Formulation
6. Objectives of Study
7. Research Work Findings
8. Case Study Based on DMAIC
9. Result Appraisal
10. Economic Analysis
11. Conclusions
12. Scope in Future
13. References
14. Publications From Work

4. FOUNDRIES OVER THE WORLD
• 35000 foundries in the world with annual
production of 90 MMT
• Provide employment to about 20 million
people.
• Indian foundry industry is the fourth largest in
the world.
• There are more than 7,000 foundries in India
INTRODUCTION

6. FOUNDRIES IN INDIA
• 80% of foundries are in SME sector.
• Sector employs 0.5 million people directly and
1.5 million indirectly.
• Rs 7000 per ton produced to National
exchequer by way of Excise and other levies.

8. TYPES OF FOUNDRIES IN INDIA

9. FOUNDRY CUSTOMERS

10. Performance
Norms
Indian
Foundries
Overseas
Foundries
Production per annum (%) 8 to 9 92 to 93
Rejection (%) 10 to 25 2 to 12
Capacity Utilization (%) 45 to55 60 to 75
Productivity (T/Man/Year) 12 to 20
100 to 120 (Japan)
50 to 55 (Germany)
10 to 30 (China)
Energy Requirement
(KWH/T)
700 to 900 400 to 600
Comparison of Indian and
Overseas Foundries

11. • For global competitiveness, Indian industries need
overall operational and service excellence and are
extensively engaged in Quality Circles, TQM and ISO
Certification (Panchal, 2010).
However, these methods have failed to deliver required
performance over the last decade or so (Sadagopan et al.,
2005).
• It has been seen that Six Sigma is not fully explored by
Indian industries (Antony and Banuelas, 2002). With the
reduction of geographical barriers and the pressure of
competing in the global market, overall operational and
service excellence has become a necessity for these
industries to remain globally competitive (Rao and Rao,
2007).
Quality Programmes Undertaken

12. • Satisfaction with other quality and productivity improvement
initiatives turned out to be the strongest reason for not
embarking on Six Sigma program, followed by lack of
awareness and unsuitability of the initiative to their type of
business (Montgomery, 2009).
• Approximately 30 per cent of them have applied Six
Sigma in their business operations and the remaining 70
per cent are yet to experience the Six Sigma initiative for
a number of reasons (Sehwail and Yong, 2003).

13. SO…
To check low productivity levels and for global
competitiveness, this industry must strive hard to
achieve overall operational excellence in business by
looking for flexibility, shorter lead time and defects
reduction; because only such a configuration of
production system can fulfill the customer
expectations.
In this context Six Sigma can be a powerful business
improvement strategy, that enables industry to use
simple but powerful statistical methods for achieving
operational excellence.

14. SIX SIGMA ??
A break through strategy to significantly
improve customer satisfaction and shareholder
value by reducing variability in every aspect
of our business
Improve Performance & Service
• reduce defects
• stabilize processes
• customer satisfaction
Reduce Costs
• improve efficiency
• eliminate waste (3.4 ppm)
• reduce cost of poor quality

15. SIX SIGMAS PARADIGMS
SN Years/Author
Company /
Unit
Parameters
Achievements;
Financial Savings
LARGE SCALE UNITS
1. 1986-2001 Motorola In-process defect level 200-times reduction;
$15billions
2. 1996-1999 General Electric Turnaround time at
repair shops
62% reduction; $2billions
3. 1998-2000 Honeywell Concept to shipment
cycle time
Reduced from 18 months
to 8 months; $1.2 billions
4. 2000-2002 Ford recalls and production
delays
Reduced defects by 70%;
$1billions
5. 2000-2002 Caterpiller Quality improvement,
Reduced cost structure
Reduced defects by 75%;
Not Reported
6. 1999-2002 Our lady of
Lourdes Medical
Centre
Hospital bed availability
delays
Bed availability reduced
from 267 to 235 minutes
(12% reduction); $66000

16. MEDIUM SCALE UNITS
1. Ingle and Roe
(2001)
Medium sized
welding unit
Optimization of welding
process parameters
Joint strength is increased
by 26% and scrap work is
reduced by 3%
2. Zu et al. (2008) A bulb
manufacturing
SME
Improve the process
and reduced the shell
cracking of bulbs
Sigma level increased from
3.1 to 4.5
3. Sharma (2003) Baxer Battery
Limited
Life of battery
increased by 15% by
proper utilizing of
resources.
Customer satisfaction and
12% annual increased in
sale.
4. Holtz and
Campbell
(2004)
Ford Motors Maintenance
management by
improving MTBF.
Savings of $ 60000
achieved in six months.
Sigma level improved from
1.4 to 2.1
5. Wang et al., et
al. (2006)
A medium scale
IC engine
manufacturing
unit
Improve the Cpk Process capability
improved from 1.1 to 2.9

17. SMALL SCALE UNITS
1. Johnson
(2002)
Orange box Limited Office seat and
furniture design
£60000 saving
2. Basu (2004) A washing machine
manufacturing unit
Failure of drum
bearings
reduction in customer
complaints and customer
satisfaction achieved
3. Marti (2005) A Steel valves and
fittings manufacturing
unit
Production
planning
improvement
50% improvement in delivery
failures and a level of 2 sigma
achieved
4. Desai (2006) Two wheeler
automobile company
Gear pinion noise Noise reduced by 13%, Sigma
level increased from 3.134 to
3.944
5. Gadallah
(2009)
“P S Soot Girani Ltd”
Maharashtra -a cotton
mill
Manufacturing
system
performance
Sigma level improved result in
production of premium quality
yarn

18. MICRO SCALE UNITS
1. Byrne (2003) Carriage and
Wagon Works
Axle rejection 59% reduction in axle
rejections
2. Wang et al.
(2004)
Tata Honeywell
Limited
Travel costs per
kilometer per month
Decreased travel cost
3. Flott (2000) General Electric
company
Keys for successful
implementation of
quality tools
Finding Keys factors for
successful implementation
4. Antony et al.
(2007)
A Bulb
manufacturing
unit
Shell cracking of bulb Sigma level increased from
3.2 to 4.5
5. Garg (2010) Indian
automobile
sector
Effective
implementation of
quality tools in Indian
automobile sector
Finding Success factors

19. RESEARCH PLAN
OBJECTIVES OF THE STUDY
LITERATURE SURVEY
RESEARCH GAPS
PROBLEM FORMULATIONS
RESEARCH PLAN
IMPLEMENTATION OF SIX SIGMA’S
DMAIC METHODOLOGY
EXECUTION OF EACH PHASE AS PER
PROPOSED FRAMEWORK
RESULT APPRAISAL
CONCLUSIONS AND SCOPE FOR FUTURE WORK
RESEARCH FINDINGS: A CASE STUDY

20. LITERATURE SURVEY
The following issues were predominantly addressed
during an extensive literature review on various aspects of
Six Sigma and its application to foundry industry. These
issues primarily covered in various journals, reference
manuals, handbooks, text books, e-resources etc are:
a) Six Sigma: Concept and Evolution
b) Six Sigma in Manufacturing Sector

21. (a) SIX SIGMA-CONCEPT AND EVOLUTION
A chronological review of the concept of Six Sigma is given in
table
Six Sigma- The Concept
SN Author(s) Six Sigma as a Concept
1. Behara et al. (1995) Six Sigma is a rating that signifies “best in class”, with only 3.4 defects per million units or
operations. A defect can be anything froma faulty part to an incorrect customer bill.
2. Katayama and
Bennett (1996)
Six Sigma is a rigorous data gathering and statistical analysis technique which can be used
to pinpoint sources of errors and suggests ways of eliminating them.
3. Mikel (1997) Six Sigma is a systematic, highly disciplined, customer-centric and profit-driven
organization-wide strategic business improvement initiative.
4. Hendricks and
Kelbaugh (1998)
A quality improvement program with a goal of reducing the number of defects to as low as
3.4 parts per million opportunities or 0.0003 per cent.
5. Hahn et al. (1999) Six Sigma is a quality improvement initiative and quality management framework which is
disciplined for quality improvement through defect reduction.
6. Sanders and Hild
(2000)
Six Sigma methodologies link the tools and techniques in a sequential manner. Finally, Six
Sigma creates a powerful infrastructure for training of Champions, Master Blac k Belts etc.
7. Ingle and Roe
(2001)
A philosophy that employs a well-structured continuous improvement methodology to
reduce process variability and drive out waste frombusiness using various techniques.

22. 8. Antony (2002) A business performance improvement strategy that aims to reduce the number of defects –
to as low as 3.4 occasions per million opportunities
9. Hong and Goh
(2003)
Six Sigma is a customer focus, data-driven, and robust methodology, which is well rooted
in mathematics and statistics. It is based upon the phenomenon of defect reduction
10. Markarian (2004) Six Sigma is a business improvement strategy that seeks to find and eliminate causes of
defects in business process by focusing on outputs that are critical for customers.
11. Marti (2005) Sigma is basically Greek term for variation. Six Sigma is extremely structured program
whose prime objective is to improve business process by minimizing the variation.
12. Kwak and Anbari
(2006)
A business strategy used to improve business profitability, to improve the effectiveness
and efficiency of all operations to meet or exceed customer needs and expectations
13. McCarty and Fisher
(2007)
Six Sigma becomes ingrained in the cultural context of the organization and represents a
management and cultural philosophy of process orientation and continuous improvement.
14. Hamza (2008) Six Sigma is a process improvement methodology that aims to increase business
performance through accurate focus. It is a symmetric approach to achieve improvement.
15. Snee (2009) A strategic, company-wide, approach focusing on variation reduction, projects have the
potential of simultaneously reducing cost and increasing customer satisfaction
16. Pepper and
Spedding (2010)
Six Sigma is an approach to the improvement of the quality of products or services which
strive for near perfection. I.e. not more then 3.4 DPMO.

23. Table depicts certain themes whose synergy with Six
Sigma has created considerable interest among the
researchers during the last 15 years.
Themes Related to Six Sigma
SN Themes References
1. Quality
Approaches
Hoerl (1998); Hild et al. (2000); Klefsjo et al. (2001); Sadagopan et al. (2005); Jones
and Johnson (2009); Parsad and Reddy (2010)
2. Six Sigma
Education
Echempati and White (2000); Maleyeff and Kaminsky (2002); Sehwail and De Yong
(2003); Rao and Rao (2007); Gijo and Scaria (2010)
3. DAMIC/DFSS
Methodology
Bayle et al. (2001); Kuei and Madu (2003); Antony (2006); Bandyopadhyay and
Jenicke (2007); Montgomery (2009)
4. Tools and
Techniques
Hendricks and Kelbaugh (1998); Graves (2002); Banuelas and Antony (2003);
Banuelas et al. (2005); Antony (2007); Harjac et al. (2008)
5. Belt System Henderson and Evans (2000); Ingle and Roe (2001); Andersson et al. (2006);
Savolainen and Haikonen (2007); Schroeder et al. (2008)

24. 6. Benefits Behara et al. (1995); Johnson (2002); Kueiand Madu (2003); Chan et al. (2005);
Das et al. (2006); Agarwaland Bajaj (2008)
7. Organizational
Change
Carnell and Lambert (2000); Brewer (2004); Craven et al. (2006); Davison and Al-
Shaghana (2007); Fazzariand Levitt (2008)
8. Challenges Feld and Stone (2002); Goh and Xie (2004); Gijo and Rao (2005); Goh et al.
(2006); Kumar (2007);
9. Success Factors Antony and Banuelas (2002); Antony and Fergusson (2004); Buch and Tolentino
(2006); Chung et al. (2008)
10. Organizational
Learning
Carnell and Lambert (2000); De Feo and Bar-El(2002); Box (2006); Savolainen
and Haikonen (2007);

25. SN Research Methodology References
1. Case-Focused Articles Smith (1993); Behara et al. (1995); Hoerl (1998); Does et al.
(2002); Brett and Queen (2005); Horell and Snee (2010)
2. Review-Focused Articles Kuei and Madu (2003); Andersson et al. (2006); ; Rao and Rao
(2007); Aggogeri and Gentili (2008)
3. Survey-Focused Articles Bhatnagar and Pandey (2005); Chakrabarty and Tan (2007);
Chang and Wang (2008); Nabeel et al. (2010)
Nature of Research Methodology Used for Six Sigma
Among the surveyed articles of last 20 years, 63 percent
were classified as empirical using either surveys or case
studies, while about 37 percent of the articles were
theoretical articles that usually employ extensive literature
review to focus on the development of concepts, propositions,
models or theory building.

26. (b) SIX SIGMA IN
MANUFACTURING INDUSTRY
SN Author(s) Company / Unit Parameters Achievements
1. Henderson &
Evans (2000)
General Electric
Company
Implementation as a quality
tool. Reduced the cycle time at
repair shops.
Annual saving of $2 billion
Process improvement done
2. Ingle and Roe
(2001)
Medium sized welding
unit
Optimization of welding
process parameters
Joint strength is increased by
26% and scrap work is reduced
by 3%
3. Does et al.
(2002)
A bulb manufacturing
SME
Improve the process and
reduced the shell cracking of
bulbs
Sigma level increased from 3.1
to 4.5
Table cites major works of the researchers related to
application of Six Sigma in manufacturing sector during
the past decade.

27. 4. Sharma
(2003)
Baxer Battery Limited Life of battery increased by
15% by proper utilizing of
resources.
Customer satisfaction and 12%
annual increased in sale.
5. Holtz and
Campbell
(2004)
Ford Motors Maintenance management by
improving MTBF.
Savings of $ 60000 achieved in
six months. Sigma level
improved from 1.4 to 2.1
6. Hu et al.
(2005)
A mediumscale IC
engine manufacturing
unit
Improve the Cpk Process capability improved
from 1.1 to 2.9
7. Sokovic et al.
(2006)
A gravity die casting unit Casting scrap reduced from
23% to 11%
40% reduction in
manufacturing cost with
annual savings of $72000 p.a.
8. Aggogeri and
Gentili
(2008)
Cranberry Drinks Ltd. Improvement in packing
process.
DPMO level improved from
3011 to 178 only. 17%
reduction in packing time.
9. Kuthe and
Tharakan
(2009)
Wilson Tools Shorten the heat treatment time Roughly $10000 per year
savings. 2% reduction in
overall Lead time
10. Bewoor and
Pawar (2010)
A copper wire
manufacturing plant
Quality improvement in rolling
operation
Defect are decreased by 19%
with in nine months of
DMAIC project

28. • After an extensive literature review, it appears that the
application of Six Sigma in companies other than the big
multinational companies has been rare and inconsistent.
Little evidence exists in literature on Six Sigma
implementation in foundry industry, which is counted as
the most important supplier to other manufacturing
industries. Since SME industries are facing serious
sickness levels, an attempt can be made to implement Six
Sigma over various systems and sub systems to
considerably improve their respective productivity levels.
• Literature has also reported tremendous financial gains
through Six Sigma in large manufacturing sector, so an
attempt can be made to replicate it in foundry SME sector too.
RESEARCH GAPS

29. PROBLEM FORMULATION
• Govt. had introduced “Automotive Mission Plan” (AMP-2006) to rise the
GDP by four times up to 2016.
• Main focus of Govt. is on boosting Auto sector industries (that’s why
Car production is increasing exponentially after 2006)
• Due to which orders of foundries (as a major Supplier of most of parts)
have also been elevated from last 6 to 7 years.
• But Indian foundries showed their in-capabilities to supply ordered
quantities because of poor performance & productivity levels.

31. IMPACT of AMP (2006-2016)
• Auto industries are importing casted parts from other countries
like; China, USA, Australia and Russia.
• It means our domestic foundries are losing business because of
their poor production and quality levels.

32. So Primarily, the major objectives of the present study are:
• To formulate a comprehensive strategy for the successful
implementation of Six Sigma in Indian foundry industry
without ignoring its existing constraints.
• To provide standard step by step methodologies to
execute different phases of DMAIC projects
successfully in Indian foundry environments.
• To devise a strategy for selection of right tool/technique for
right problem.
• To demystify the different myths prevailing about Six
Sigma in Indian SMEs after implementing it in a SME itself.
OBJECTIVES OF THE STUDY

33. ABOUT ORGANIZATION
• A case study has been carried out in a non-ferrous foundry at
Federal Mogul India Limited Bhahadurgarh, Patiala (Punjab)
Foundry has a covered area of about 50144 sq. m and was
established in 1954.
• It is a medium scale unit used to cast pistons for export and
domestic market. It uses mostly semi-automatic die casting
machines. The unit under study casts pistons of diameter
range from 30mm to 300mm and is capable to manufacture
13 million pistons per annum.
RESEARCH WORK FINDINGS

34. • Location: Patiala, Punjab, India
• Product : Pistons, Pins
(by semi-automatic die casting)
• FM Share: 48.4%
• TS 16949,
• OHSAS 18001,
• ISO 14001
FEDERAL MOGUL GOETZE
INDIA LIMITED

35. LEADING CUSTOMERS

36. PISTON FOUNDRY
MELTING
NAAM Casting Cell
Domestic Casting Cell
HEAT TREATMENT

37. DIE - CASTING
Alloy Si Cu Mg Ni Fe
FM132 8.5~10.5 2.0~4.0 0.5~1.5 0.5 Max 1.0
FM425 14.8~18 1.9~2.9 0.5~1.0 0.35~0.8 0.85
FM413/CSA12/
AC8A
11~13.5 0.5~1.3 0.8~1.5 0.7~1.3 0.8
CSA18 16~19 0.8~1.5 0.8~1.5 0.8~1.5 0.75
CSA24 22~26 0.5~1.3 0.8~1.5 0.7~1.3 0.7
VERTICAL GRAVITY DIE
CASTING
(Twin Cavity Mould)
VERTICAL
GRAVITY DIE
CASTING
(Single Cavity
Mould)

38. DEFINE
• Problem
• Voice of customer
• Project charter
• Project planning
• Project scheduling
• Snaps of problem
• History of problem
• COPQ matrix
• Project goal
• Process by SIPOC
diagram
MEASURE
• Measure Cpk of
process
• Calculate present
sigma level
• Pareto charts to
find reasons of
scrap
• Brainstorming by
5Ms
• Access CTQs by
Process mapping
• Cause and effect
matrix of CTQs
• MSA
• (Gauge R&R)
• (Bias checking)
• (Stability test)
ANALYSE
•Analysis of Shift
dependency by Chi-
square test
•Analyse the variation
of B.T. with Die coating
thickness by One way
ANOVA
•Apply 2-Sample t-test
to verify the effect of
In-gate design on scrap
•Analyse the impact of
Operator-skill by 2-
sample t-test
• Multi-Regression
analysis of Casting
process- parameters
• Fish bone -analysis of
Metal sticking on pins
of die
• Why-Why analysis of
Dimensional in -
accuracy of machine
variables
IMPROVE
• Optimization
of process-
parameters
by Full
factorial DOE
• Continuous
Improvement
s by Kaizens
• Poka-Yoke
for machine
& Die
CONTROL
• Evaluation of
sigma level
after
improvement
• X-bar R chart
for
controlling
Bottom
thickness
variation of
Piston.
• P-charts to
vigil overall
scrap
continuously
under its
control limits
PHASES OF DMAIC STUDY

39. The first step was to precisely define the problem, keeping
in mind business objectives, customer needs and feed
back. This involves identification of Critical to Quality (CTQ)
issues and other items that have an impact on quality and
customer satisfaction.
Major Tools Used
Voice of Customer (QFD), Project Charter, Project Scheduler,
Snaps of Problem, Historical analysis of problem, COPQ Matrix,
Process by SIPOC Diagram and Project Goals.
1. DEFINE PHASE

40. PROPOSED FRAMEWORK FOR
D-PHASE

41. Θ Θ Θ Θ
ImportancetoCustomer
EvaultionofVendors
GoodProcessControls
ReductioninScrapdueto
CastingDefects
LessDie/MachineSetuptime
Implementationof
MaintenanceSchedules
EnsureAdherenceofQuality
System
GoodInspectionPlans
Technology
Upgradation/Innovation
TrainingPlansforWorkForce
EfficientSchedulingto
completeProductionOrders
Completeness
Criteria
M M H H M H M M L M
15 15 45 45 15 45 15 15 5 15
L M H M M M M M L L
5 15 45 15 15 15 15 15 5 5
M M M N L H H L M N
12 12 12 0 4 36 36 4 12 0
N M L H L L L H M M
0 15 5 45 5 5 5 45 15 15
L M H N N M M M M N
5 15 45 0 0 15 15 15 15 0
L L N N N L N H M L
3 3 0 0 0 3 0 27 9 3
H M H N L M M M M L
27 9 27 0 3 12 12 12 12 3
M H L N H M M M L N
15 45 5 0 15 15 15 15 5 0
M M H N L M L M N M
9 9 27 0 3 9 3 9 0 9
M M H L M M M M H H
15 15 45 5 15 15 15 15 45 45
IMPORTANCE RATING OF X's 106 153 256 110 75 170 131 172 123 95
10%
Symbol Relationship Between X & Y Rating
H Strong (H) 9
M Medium (M) 3
L Weak (W) 1
N No Relation (N) 0 Θ
5
3
5
3
5
5
4
3
5
5
6
5
4
3
Meet Deadlines/Schedules
Strong Information System
Reduce Rework
TARGET DIRECTION
1
7
Satisfy Quality Initiatives (Overall Quality of the Product)
Reduction in Production Cost
Reduce Waste
Develop Closer Supplier Relations
Less Dimensional Problems
Accountabilty of Supplied Product
2
Cycle Time Reduction
Targer Directions
More is Better
Less is Better
Specific Amount
125
48
117
130
230
150
VOC ( Machine Shop)
TARGET VALUE OF CTC (Critical to Customer) FACTOR
128
155
78
23010
9
8
VOC (HOUSE OF QUALITY)

42. NON MEAUUREABLE DEFECTS
CASTING DEFECTS

43. MEAUREABLE DEFECT
(BT VARIATION)

44. 0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
Jan Feb Mar Apr May Jun
H-273 (Avg. 21.5%)
H-749 (Avg. 20.9%)
H-519 (Avg.22.5%)
HISTORICAL ANALYSIS OF
SCRAP

45. COPQ MATRIX
Financial Parameters H-273 H-519 H-749
Average Scrap/Month 1,866 1,100 885
Scrap Cost/Piston 183 183 183
Total Rejection Cost/ Month 3,41,478 2,01,300 1,61,955
Total Rejection Cost / Year 40,97,736 24,15,600 19,43,460
Total Rejection Cost for H-
family Pistons Annually
84,56,796

46. SIPOC DIAGRAM

47. Phases
Scheduled Dates Actual Dates
Start date End date Start date End date
Define 01/07/10 15/7/10 01/07/10 15/7/10
Measure 16/7/10 15/8/10 16/7/10 15/8/10
Analyse 16/8/10 07/10/10 16/8/10 22/9/10
Improve 08/10/10 22/11/10 23/9/10 30/11/10
Control 23/11/10 31/12/10 01/12/10 31/12/10
Schedule Chart
Scheduled dates are decided after brain storming with concerned
people from production, maintenance and quality departments.
Actual dates are filled as different phases were accomplished
with time.

48. Measure phase is the second step after defining the
problem which involves measurement system analysis,
capability studies and finding performance gaps for the
identified problem.
Major Tools Used
Sigma Calculator, Cpk study, Pareto Charts, FMEA Table,
Cause and Effect Matrix, Gauge R&R study, Bias Checking
and Stability Test.
2. MEASURE PHASE

49. PROPOSED FRAMEWORK FOR
M-PHASE

50. PROCESS MAPPING OF FOUNDRY
Step 5:
F
A B B,C C D E
J,K,L I
H G
Steps
10.- Die preparation Input Classification
20.- Ingots transportation to foundry Critic
30.- Ingots storage (in Cell) Controlled
40.- Furnace charge Noise
50.- Melting
60.- Degassing and flux treatment A.- Die coating
70.- Holding time and impurity flotation B.- Furnace charge relation
80.- Start up casting cell machine C.- Molten metal temperature
90.- Pouring D.- Density Index (ID)
100.- Croppers E.- Cycle time
110.- AQFD F.- Pouring speed
120.- Visual inspection and baskets accommodation G.- AQFD
130.- Storage before heat treatment H.- Without visual defects
140.- Heat Treatment I.- Aging
150.- Q.A. Release J.- Microstructure
160.- Transportation to release material area K.- Hardness
170.- Storage before machining L.- Chemical analysis
Write Down and Classify the Key Process Input
CTQ's
10 20 30 40 50 60
ID=1,5 max.
Si
No
70
80 90
100
110120130
Si
No
Scrap
130140150
Si
No
Scrap
160
170
Die temperature
Die coating density
Spray gun
Free of humidity
Free of slag
Charge relation
(60 Ingot /40 scrap)
Temperature
Metal Temp.
N2 Flow
RPM
Time Time
Water cooling
system
Cycle time
Pouring speed
Ladle cleaning
Metal temperature
Die Coating
Water cooling time
Water coling temp.
Ingate separation
AQFD
Free of visual
defects
Separate in baskets by
cavity
Temperature
Time
Chemical Analysis
Microstructure
Hardness
Q.A. Release card
10 20 30 40 50 60
ID=1,5 max.
Si
No
70
80 90
100
110120130
Si
No
Scrap
130140150
Si
No
Scrap
160
170
10 20 30 40 50 60
ID=1,5 max.
Si
No
70
80 90
100
110120130
Si
No
Scrap
130140150
Si
No
Scrap
160
170

51. SIGMA CALCULATION
(EXISTING)
Production results of June-2010
Total number of Machined parts 8482
Scrap type Nos.
Bottom Thickness Defect 480
Blow Holes 432
Cold Lap 120
Depression 120
Hydrogen Porosity 48
Shrinkage 457
Defective Pin Hole 210
Total Scrap in June 1866
Nos. of Opportunities 7
DPMO 31428
Sigma Level of Process 3.43
Yield (%) 76.96

52. 10.089.929.769.609.449.289.12
LSL USL
LSL 9.5
Target *
USL 9.9
Sample Mean 9.59583
Sample N 60
StDev (Within) 0.150559
StDev (O v erall) 0.210772
Process Data
C p 0.44
C PL 0.21
C PU 0.67
C pk 0.21
Pp 0.32
PPL 0.15
PPU 0.48
Ppk 0.15
C pm *
O v erall C apability
Potential (Within) C apability
PPM < LSL 333333.33
PPM > USL 83333.33
PPM Total 416666.67
O bserv ed Performance
PPM < LSL 262219.76
PPM > USL 21678.76
PPM Total 283898.52
Exp. Within Performance
PPM < LSL 324670.34
PPM > USL 74494.82
PPM Total 399165.15
Exp. O v erall Performance
Within
Overall
Process Capability W.R.T. BT (BEFORE)
Cpk STUDY

53. NUMBERS 26 19 52426 203 133 99 98 52 40 32
Percent 2.2 1.6 4.436.1 17.2 11.3 8.4 8.3 4.4 3.4 2.7
Cum % 94.0 95.6100.036.1 53.3 64.6 73.0 81.3 85.7 89.1 91.8
DEFECTS
Other
Cold
lap
Def. Blanks
Inside
Porosity
Skirt
Shrinkage
Skirt
Blow
Holes
Bottom
Shrinkage
Bottom
Porosity
Ring
Zone
Blow
Holes
Ring
Zone
Bottom
thickness
variation
Shrinkage
Ring
Zone
1200
1000
800
600
400
200
0
100
80
60
40
20
0
NUMBERS
Percent
Pareto Chart of H-749
PARETO CHART FOR DEFECTS

54. CTQ PROCESS FACTORS
Critic
Control
Noise
10 9 9 6 6 10 9
Defective
blanksbottom
BlowHoles
ColdLaps
Depression
Hydrogen
Porosity
Shrinkage
DefectivePin
Hole
S.NO Process Step Process Input
1 Design Bottom Seating Design 5 0 0 3 0 0 0 68
2 Design Gate Feeding Design 3 3 3 0 2 5 0 146
3 Die repair Dimensional accuracy of Die parts 5 0 2 5 0 0 0 98
4 Die repair Dimensional accuracy of casting Machine parts 4 0 1 3 0 0 0 199
5 Set up Die Temperature & Preheating 0 5 4 0 5 5 2 179
6 Set Up Die Cooling Connections 0 5 4 0 4 4 0 145
7 Set Up Die Coating Thickness 4 4 4 0 4 4 0 176
8 Set up Water Cooling Pressure too low 0 3 0 0 5 4 0 97
9 Set up Discharge of Cooling water inside die parts 0 5 3 0 3 5 0 140
10 Set up Vaccum Pressure in air vents 0 3 5 0 4 2 0 116
11 Set up Ratio of water to Dycote 0 3 4 0 3 2 0 101
12 Casting Low Pouring Speed 0 2 5 0 0 3 0 93
13 Casting Die Core grouping 3 0 0 2 0 0 0 42
14 Casting Non-continuity in casting process 0 3 5 2 5 5 0 164
15 Casting Metal Sticking on Pin 0 0 0 5 0 0 5 75
16 Pouring Casting Temperature too High 0 4 0 0 5 5 0 116
17 Pouring Casting Temperature too Low 0 4 3 0 5 5 2 161
18 Pouring Degassing Procedure & waiting time 0 5 2 0 4 5 0 137
19 Pouring Skill of Operator 3 1 3 1 2 2 2 122
270 450 432 126 306 560 99
CAUSE AND EFFECT MATRIX
TotalEFFECTS
Correlation of Input to Output
Characteristic
RATING OF IMPORTANCE TO CUSTOMER
Actions Decided

55. GAUGE R&R STUDY (MSA)
PART NAME :PISTON GAGE NAME : BT CHECKING INSTRUMENT
CHARACTERISTICS:BOTTOM THICKNESS GAGE NO.: MBT-03 L.C 0.01 MM
SPECIFICATIONS :0.40 MM
DATA SHEET
PART AVERAGE
APPRAISER TRIALS 1 2 3 4 5 6 7 8 9 10
AVTAR SINGH 1 8.12 7.88 8.16 8.00 7.81 8.12 7.88 7.87 8.13 8.10
2 8.10 7.86 8.16 8.00 7.80 8.11 7.87 7.88 8.12 8.10
3 8.11 7.86 8.13 8.02 7.81 8.10 7.87 7.88 8.12 8.10
Average 8.11 7.87 8.15 8.01 7.81 8.11 7.87 7.88 8.12 8.10 Xa= 8.00
Range 0.02 0.02 0.03 0.02 0.01 0.02 0.01 0.01 0.01 0.00 Ra= 0.01
NACHATTAR SINGH 1 8.12 7.88 8.16 8.02 7.81 8.12 7.88 7.87 8.13 8.10
2 8.10 7.87 8.14 8.00 7.80 8.11 7.86 7.86 8.12 8.11
3 8.11 7.86 8.13 8.02 7.80 8.10 7.87 7.88 8.11 8.10
Average 8.11 7.87 8.14 8.01 7.80 8.11 7.87 7.87 8.12 8.10 Xb= 8.00
Range 0.02 0.02 0.03 0.02 0.01 0.02 0.02 0.02 0.02 0.01 Rb= 0.02
MOHINDER SINGH 1 8.10 7.88 8.16 8.00 7.81 8.12 7.88 7.87 8.13 8.10
2 8.10 7.90 8.16 8.00 7.82 8.11 7.90 7.88 8.12 8.11
3 8.12 7.86 8.13 8.02 7.81 8.12 7.87 7.88 8.12 8.10
Average 8.11 7.88 8.15 8.01 7.81 8.12 7.88 7.88 8.12 8.10 Xc= 8.01
Range 0.02 0.04 0.03 0.02 0.01 0.01 0.03 0.00 0.01 0.00 Rc= 0.02
16 Part Ave Xp 8.11 7.87 8.15 8.01 7.81 8.11 7.88 7.87 8.12 8.10 Rp= 0.34
17 Ra+Rb+Rc/N ofApraiser R= 0.0170
18 Max X-Min X=Xdiff Xdiff= 0.0047
19 R* D4 =UCLR UCL 0.044
20 R* D3 =LCLR LCL 0

56. Two-Way ANOVA Table With Interaction
Source DF SS MS F P
PartID 9 0.948327 0.105370 1433.24 0.000
Operator 2 0.000243 0.000122 1.65 0.219
PartID * Operator 18 0.001323 0.000074 0.92 0.564
Repeatability 30 0.002400 0.000080
Total 59 0.952293
Alpha to remove interaction term = 0.25
Two-Way ANOVA Table Without Interaction
Source DF SS MS F P
PartID 9 0.948327 0.105370 1358.39 0.000
Operator 2 0.000243 0.000122 1.57 0.219
Repeatability 48 0.003723 0.000078
Total 59 0.952293
Gage R&R
%Contribution
Source VarComp (of VarComp)
Total Gage R&R 0.0000798 0.45
Repeatability 0.0000776 0.44
Reproducibility 0.0000022 0.01
Operator 0.0000022 0.01
Part-To-Part 0.0175487 99.55
Total Variation 0.0176285 100.00
Study Var %Study Var
Source StdDev (SD) (6 * SD) (%SV)
Total Gage R&R 0.008932 0.053590 6.73
Repeatability 0.008807 0.052844 6.63
Reproducibility 0.001485 0.008909 1.12
Operator 0.001485 0.008909 1.12
Part-To-Part 0.132471 0.794829 99.77
Total Variation 0.132772 0.796633 100.00
Number of Distinct Categories = 20

57. Part-to-PartReprodRepeatGage R&R
100
50
0
Percent
% Contribution
% Study Var
0.04
0.02
0.00
SampleRange
_
R=0.01067
UCL=0.03485
LCL=0
1 2 3
8.2
8.0
7.8
SampleMean
__
X=8.0047
UCL=8.0247
LCL=7.9846
1 2 3
10987654321
8.2
8.0
7.8
PartID
321
8.2
8.0
7.8
Operator
10987654321
8.2
8.0
7.8
PartID
Average
1
2
3
Operator
Gage name: B.T. Gauge
Date of study : 02/08/10
Reported by : Bikram jit singh
Tolerance: 0.40mm
Misc:
Components of Variation
R Chart by Operator
Xbar Chart by Operator
Measure by PartID
Measure by Operator
Operator * PartID Interaction
Gauge R & R for B.T. Gauge
GAUGE R&R STUDY

58. BIAS STUDY
PART NAME: H-273
Gaug Name & Immersion Pyrometer Parameter Temperature
L.C. 1 degree
Appraiser Name Sukhdev Singh - 50972
Reference Value. 755 Degree C
TRIALS(n)
1 756 1.000
2 754 0.000
3 749 -6.000
4 750 -5.000
5 751 -4.000
6 752 -3.000
7 752 -3.000
8 754 -1.000
9 753 -2.000
10 753 -2.000
11 752 -3.000
12 752 -3.000
13 750 -5.000
14 747 -8.000
15 745 -10.000
751.33333 -3.600000
-3.66667
Plot Histogram related to the reference value
Anayse the histogram for any special cause
Max(Xi)-Min(Xi)
Calculate s Reapatability( sr)= d2* 3.0956877
Where d2* & d2 is taken from the table(appendix C)
d2*= 3.5533
Calculate Where n = no. of trials d2= 3.4179
sb 0.7993031
1.7189648
Lower limit -5.38563
Pyrometer is biased and need Caliberation
Upper limit -1.94770 or repair
BIAS
n
rs
X Bias
BIAS STUDY (MSA)

59. Date Feb 1 3 4 5 6 7 8 10 11 12 13 14 15 18 19 20 21 22 24 25
Time
X1
X2
X3
X4
N/s
M/s
A/s N/s
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
X1 0.4 0.3 0.4 0.6 0.3 0.4 0.4 0.4 0.5 0.4 0.3 0.3 0.4 0.3 0.4 0.1 0.6 0.4 0.4 0.3
X2 1.2 0.2 0.3 0.4 0.4 0 0.5 0.3 0.3 0.3 0.4 1.2 0 0.4 0.3 0.2 0.4 0.5 0.4 0.4
X3 0.5 0.3 0.4 0.4 0.4 0.2 0.3 0.5 0.4 0.3 0.5 0.2 0.4 0.3 0.5 0.2 0.4 0.4 0.3 0.3
X4 0.2 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.6 0.5 2.3 0.5 0.4 0.5 0 0.5 0.4 0.4 0.3
Xbar 0.5750 0.3000 0.3500 0.4500 0.3750 0.2500 0.4000 0.4000 0.4000 0.4000 0.4250 1.0000 0.3250 0.3500 0.4250 0.1250 0.4750 0.4250 0.3750 0.3250
Range 1.000 0.200 0.100 0.200 0.100 0.400 0.200 0.200 0.200 0.300 0.200 2.100 0.500 0.100 0.200 0.200 0.200 0.100 0.100 0.100
Sub Group Size= 4 A2= 0.729 D3= 0 D4= 2.28 D2=
Standard deviation (s) R =
Gauge stability= s X100 %
BIAS CONTROL CHART METHOD BIAS
ULX = d2=
Xdbar = = =
LCLx = d2*=
UCLr = tv,1-a/2 = Lower Limit =
R bar =
LCL = 0 alpha = 0.05 Upper Limit =
Result Gage is not well stable
0.036220294
0.163
0.4075
2.05875
0.3350
0.76
2.06813
2.000
0.479612034
0.40750
0.16198208
28.26765674
0.6517
2.059
0.197873597
0.335388
R
e
a
d
I
n
g
s
Tolerance
*2d
R
r =s
g
r
b
s
s =
STABILITY TESTING (MSA)

60. STABILITY TEST
PART NAME : GAUGE NAME &L.C: Vac Tester
TOLERANCE : MM
MASTER VALUE: mm
X=Averrage X UCL= X+A2R= LCL=X - A2R=
R = average R UCL=D4xR LCL=D3xR 0
0.4075
0.3350 0.7645
CSA-18 alloy
0.70
0.16330.6517
0.00
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
1.0000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0.0000
0.5000
1.0000
1.5000
2.0000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Oneunit=Oneunit=
ucl
Lcl
ucl
Lcl
MEAN & RANGE CHARTS

61. 3. ANALYSE PHASE
The authenticity and impact realization of each SSV on
scrap, is required to be judged by conducting suitable
investigation under Analyze Phase. This phase helps to
focus improvement efforts on those SSVs which can be
highly significant for positive scrap reduction.
Major Tools Used
Table ahead gives a summary of various quantitative and
qualitative techniques/tools used for analyzing the
measured critical to quality (CTQ) process parameters.

62. ANALYSIS of SSVs
Type of Analysis Technique
used
Name of Tool used SSV’s to be analysed
Hypothesis testing (OFAT)
Chi Square test Analysis of Shift dependency
One way ANOVA Die coating thickness
2 Sample t-test
In gate design (or Vol. of R&R)
Operator skill
Discharge of Cooling water
MFAT Multi-regression analysis
Alloy temperature
Die temperature
Delay time
Graphical Analysis Interaction Plot Vaccume Pressure
Qualitative technique
Fish-bone analysis Metal sticking on a pin
Why-why analysis
Machine‘s dimensional In-
accuracy
ANALYTICAL PLAN

63. Null Hypothesis Ho: µ1 = µ2 = µ3
Actual Hypothesis Ha: µ1 ≠ µ2 ≠ µ3
Shifts Production Good Scrap
Bottom
thickness
variation
Blow hole Porosity Shrinkage Depression Cold lap Pin hole defect
Night Shift (1) 128 102 26 13 4 1 3 2 1 2
Morning Shift (2) 124 100 24 8 6 2 3 1 2 2
Afternoon Shift (3) 127 101 27 10 2 3 5 2 3 2
( Scrap depends upon the shifts)
(Scrap is independent of Shifts)
Chi-square Test
TEST FOR SHIFT DEPENDENCY

64. Session Confirmation from Minitab
Expected
value =
(Row obs. total) * (Column obs. total)
Grand observation Total
Expected Value of “B.T.” in Defect
type of the N/S
E = (26 X 31)/77 = 10.47
Chi-Square = (O-E) / E
Chi-Square = (13-10.47) /10.47
= 0.613
2
2
Since P Value > 0.05 ;
Reject Ha , Accept Ho
Hence Scrap in piston
foundry is independent of
shifts
B.T.vary B. hole Prsty Shrkg Dep. C. lap P.H. defect Total
1 13 4 1 3 2 1 2 26
10.47 4.05 2.03 3.71 1.69 2.03 2.03
0.613 0.001 0.520 0.137 0.058 0.520 0.000
2 8 6 2 3 1 2 2 24
9.66 3.74 1.87 3.43 1.56 1.87 1.87
0.286 1.365 0.009 0.054 0.200 0.009 0.009
3 10 2 3 5 2 3 2 27
10.87 4.21 2.10 3.86 1.75 2.10 2.10
0.070 1.158 0.382 0.339 0.035 0.382 0.005
Total 31 12 6 11 5 6 6 77
Chi-Sq = 6.150, DF = 12, P-Value = 0.908
Df= 2(n-1) n= no. Of sub groups
CHI SQUARE CALCULATIONS

65. Null Hypothesis Ho: µ1 = µ2 = µ3 = µ4
Actual Hypothesis Ha: µ1 ≠ µ2 ≠ µ3 ≠µ4
Die coating thickness 50µm Die coating thickness 80µm Die coating thickness 110µm Die coating thickness 140µm
9.81 9.80 9.70 9.70
9.70 9.71 9.87 9.80
9.80 9.80 9.80 9.70
9.90 9.89 9.58 9.67
9.70 9.75 9.68 9.71
9.60 9.62 9.60 9.80
9.80 9.65 9.67 9.89
9.76 9.75 9.85 9.75
9.70 9.66 9.95 9.62
9.87 9.66 9.80 9.65
9.80 9.77 9.76 9.75
9.58 9.58 9.66 9.70
9.68 9.68 9.61 9.87
9.60 9.58 9.70 9.80
9.67 9.67 9.80 9.58
9.85 9.85 9.70 9.86
9.95 9.95 9.67 9.90
9.80 9.88 9.71 9.70
9.53 9.53 9.80 9.87
9.95 9.95 9.89 9.80
(Diecoating thickness do not effect Bottom thickness variation)
(Diecoating thickness effects Bottom thickness variation)
One way ANOVA to analyse B.T. defect with Die coating thickness
ANOVA TEST
FOR COATING THICKNESS

66. One-way ANOVA: Die coating , Die coating , Die coating , Die coating
Source DF SS MS F P
Factor 3 0.0054 0.0018 0.15 0.932
Error 76 0.9361 0.0123
Total 79 0.9415
S = 0.1110 R-Sq = 0.57% R-Sq(adj) = 0.00%
Level N Mean St Dev
Die coating thickness 50 20 9.7525 0.1217
Die coating thickness 80 20 9.7365 0.1236
Die coating thickness 110 20 9.7400 0.1021
Die coating thickness 14 0 20 9.7560 0.0935
Individual 95% CIs For Mean Based on
Pooled St Dev
Level -+---------+---------+---------+--------
Die coating thickness 50 (----------------*---------------)
Die coating thickness 80 (----------------*---------------)
Die coating thickness 11 (----------------*---------------)
Die coating thickness 14 (---------------*---------------)
-+---------+---------+---------+--------
9.690 9.720 9.750 9.780
Df= n-1 (n= nos. of data population or groups)
Since p value is coming
out to be more than
0.05, so Null hypothesis
is accepted.
Hence in present case,
Die- coating thickness do not
effect the Bottom- thickness
variation, substantially.
ANOVA STATISTICS

67. ANOVA GRAPHS
Die
coating
thickness
140µm
Die
coating
thickness
110µm
Die
coating
thickness
80µm
Die
coating
thickness
50µm
10.0
9.9
9.8
9.7
9.6
9.5
Data
Individual Value Plot of thickness 50µm, 80µm, 110µm, 140µm
Die
coating
thickness
140µm
Die
coating
thickness
110µm
Die
coating
thickness
80µm
Die
coating
thickness
50µm
10.0
9.9
9.8
9.7
9.6
9.5
Data
Boxplot of thickness 50µm, 80µm, 110µm, 140µm
0.40.30.20.10.0-0.1-0.2-0.3-0.4
99.9
99
95
90
80
70
60
50
40
30
20
10
5
1
0.1
Residual
Percent
Normal Probability Plot
(responses arethickness 50µm, 80µm, 110µm and 140µm)

68. Null Hypothesis Ho: µ1 = µ2 Volume of riser & runner has no impact on casting scrap
Actual Hypothesis Ha: µ1 ≠ µ2 Volume of riser & runner has impact on casting scrap
Trails
With Existing volume of
Runner & Riser
(240-250 cubic cm)
With Enlarged volume of Runner
& Riser
(260-270 cubic cm)
1 18% 15%
2 20% 16%
3 19% 14%
4 17% 12%
5 21% 13%
6 16% 11%
Taken 50 pistons per trail to assess the actual scrap in %age
Analysis of Scrap(in %age) with variation in Volume of Riser & Runner
2 Sample t-test
2 SAMPLE t-TEST FOR
IN-GATE DESIGN

69. Conclusion: as P
value is <0.05
So, Discard Ho and
Accept Ha.
It implies, there is a
relation between
Runner & Riser
Volume and casting
scrap.
Two-Sample T-Test and CI: With Existing volume of , With Enlarged
volume of
Two-sample T for With Existing volume of Runner vs With Enlarged
volume of Runner
N Mean StDev SE Mean
With Existing volume of 6 0.1850 0.0187 0.0076
With Enlarged volume of 6 0.1350 0.0187 0.0076
Difference = mu (With Existing volume of Runner) - mu (With Enlarged
volume of Runner)
Estimate for difference: 0.0500
95% CI for difference: (0.0259, 0.0741)
T-Test of difference = 0 (vs not =): T-Value = 4.63
P-Value = 0.001
DF = 10 2(n-1)
n= number of trails or group size
t-TEST CALCULATIONS

70. 2 Sample t - TEST GRAPHS
With Enlarged volume of RunnerWith Existing volume of Runner
22.00%
20.00%
18.00%
16.00%
14.00%
12.00%
10.00%
Data
Individual Value Plot of Existing V/s Enlarged R & R
With Enlarged volume of RunnerWith Existing volume of Runner
22.00%
20.00%
18.00%
16.00%
14.00%
12.00%
10.00%
Data
Boxplot of Existing V/s Enlarged volume of R & R
23.00%
22.00%
21.00%
20.00%
19.00%
18.00%
17.00%
16.00%
15.00%
14.00%
99
95
90
80
70
60
50
40
30
20
10
5
1
With Existing volume of Runner
Percent
Mean 0.185
StDev 0.01871
N 6
AD 0.136
P-Value 0.947
Probability Plot With Existing volume of R & R
Normal
18.00%16.00%14.00%12.00%10.00%
99
95
90
80
70
60
50
40
30
20
10
5
1
With Enlarged volume of Runner
Percent
Mean 0.135
StDev 0.01871
N 6
AD 0.136
P-Value 0.947
Probability Plot With Enlarged volume of R & R
Normal

71. Conclusion: as P value is <0.05
So, Discard Ho and Accept Ha.
It implies, Operator skill has an
impact over scrap
Two-Sample T-Test and CI: Semi-skilled Die operator, Skilled Die operator
Two-sample T for Semi-skilled Die operator vs Skilled Die operator
N Mean StDev SE Mean
Semi-skilled Die operato 6 0.1833 0.0216 0.0088
Skilled Die operator 6 0.1367 0.0216 0.0088
Difference = mu (Semi-skilled Die operator) - mu (Skilled Die perator)
Estimate for difference: 0.0467
95% CI for difference: (0.0189, 0.0745)
T-Test of difference = 0 (vs not =): T-Value = 3.74
P-Value = 0.004
DF = 10
IMPACT OF OPERATOR SKILL

72. 24.00%22.00%20.00%18.00%16.00%14.00%12.00%
99
95
90
80
70
60
50
40
30
20
10
5
1
Semi-skilled Die operator
Percent
Mean 0.1833
StDev 0.02160
N 6
AD 0.143
P-Value 0.934
Probability Plot of Semi-skilled Die operator
Normal
20.00%18.00%16.00%14.00%12.00%10.00%
99
95
90
80
70
60
50
40
30
20
10
5
1
Skilled Die operator
Percent
Mean 0.1367
StDev 0.02160
N 6
AD 0.143
P-Value 0.934
Probability Plot of Skilled Die operator
Normal
Skilled Die operatorSemi-skilled Die operator
22.00%
20.00%
18.00%
16.00%
14.00%
12.00%
10.00%
Data
Individual Value Plot of Semi-skilled Die operator, Skilled Die operator
Skilled Die operatorSemi-skilled Die operator
22.00%
20.00%
18.00%
16.00%
14.00%
12.00%
10.00%
Data
Boxplot of Semi-skilled Die operator, Skilled Die operator
GRAPHICAL RESULTS

73. 24.00%22.00%20.00%18.00%16.00%14.00%12.00%
99
95
90
80
70
60
50
40
30
20
10
5
1
Discharge (3-5 LPM)
Percent
Mean 0.1817
StDev 0.02317
N 6
AD 0.191
P-Value 0.818
Probability Plot of Discharge (3-5 LPM)
Normal
18.00%16.00%14.00%12.00%10.00%
99
95
90
80
70
60
50
40
30
20
10
5
1
Discharge (7-10LPM)
Percent
Mean 0.1383
StDev 0.01941
N 6
AD 0.329
P-Value 0.380
Probability Plot of Discharge (7-10LPM)
Normal
Discharge (7-10LPM)Discharge (3-5 LPM)
22.00%
20.00%
18.00%
16.00%
14.00%
12.00%
10.00%
Data
Individual Value Plot of Discharge (3-5 LPM), Discharge (7-10LPM)
Discharge (7-10LPM)Discharge (3-5 LPM)
22.00%
20.00%
18.00%
16.00%
14.00%
12.00%
10.00%
Data
Boxplot of Discharge (3-5 LPM), Discharge (7-10LPM)
RESULTS FOR DISCHARGE OF WATER
P = 0.007

74. Alloy Temperature Stoppage (IN Sec) Die Temp
Total
pistons
Scrap
%age of
Scrap
755 50 239.7 10 4 40.0
757 85 251.8 5 2 40.0
760 45 240 8 3 37.5
763 5 265 7 0 0.0
764 35 273.6 13 2 15.4
765 120 283 17 6 35.3
752 90 276.7 10 2 20.0
754 10 250.2 10 3 30.0
Mean Multi Regression Data
ANALYSIS OF CASTING PARAMETERS

75. REGRESSION STATISTICS
Regression Analysis: %ageof Scrap versus Alloy Temp.,Stoppage(in secs) & Die Temp.
Theregression equationis
%ageof Scrap = 114 + 0.118 Alloy Temperature+0.306 Stoppage(in Sec) - 0.745 DieTemp
Predictor Coef SE Coef T P
Constant 114.4 476.2 0.24 0.822
Alloy Temperature 0.1184 0.6554 0.18 0.865
Stoppage(INSec) 0.30615 0.08153 3.76 0.020
DieTemp -0.7454 0.2130 -3.50 0.025
S = 7.70734 R-Sq = 83.5% R-Sq(adj)=71.1%
Analysisof Variance
Source DF SS MS F P
Regression 3 1200.75 400.25 6.74 0.048
ResidualError 4 237.61 59.40
Total 7 1438.37
Source DF Seq SS
Alloy Temperature 1 129.92
Stoppage(INSec) 1 343.56
DieTemp 1 727.28
As P>0.05 forAlloy temp.
It implies it is already in control.
As P<0.05 for Stoppage or delay
It implies it effects Scrap positively.
As P<0.05 for Die temperature
It implies it also effects Scrap
substantially.
As P<0.05 for overall regression
It implies, its input variables have
impact on dependent variables.

76. INTERATCTION PLOT DATA
Die No. Vaccume Pressure( in atm) Scrap (in %age)
1 0.2-0.3 14
1 0.4-0.5 16
1 0.6-0.7 17
1 0.8-0.9 21
2 0.2-0.3 15
2 0.4-0.5 15
2 0.6-0.7 18
2 0.8-0.9 20
3 0.2-0.3 12
3 0.4-0.5 14
3 0.6-0.7 16
3 0.8-0.9 19
4 0.2-0.3 14
4 0.4-0.5 19
4 0.6-0.7 21
4 0.8-0.9 22

77. Beside this parameter (Vaccume pressure) affects the scrap,
but as we already working in the range of 0.2-0.3 bar, so scope
of further reduction in Scrap by optimizing Vaccume Pressure,
is not substantial
0.8-0.90.6-0.70.4-0.50.2-0.3
21
18
15
12
4321
21
18
15
12
Die No.
Vaccume Pressure( in atm)
1
2
3
4
No.
Die
0.2-0.3
0.4-0.5
0.6-0.7
0.8-0.9
(in atm)
Pressure
Vaccume
Data Means

78. FISH BONE ANALYSIS
(FOR PIN DEFECTS)
Pin hole
Defective
Die
Environment
Measure
Methods
Material
Machines
Man
bed management policies
by HR Dept.
Poor grief management
Lack of trainig audits
worries)
Distraction (mental
casual labours
Faulty procedure to selct
In adequate Training
Zerking of hydraulic rams
time
Given less soldification
parts
Play in between the die
Improper die closing
Non flatness of M/c bed
Moulds
Haphazard movement of
Fix the
No criteria to
control plan
Absence of
the coating
No control over
Selection of
Improper
material coating
Lack of parting
temperature of
Low
unskilled labour
Deputation of
training
Scaracity of Process
procedures
Non-standard set-up
supevision
In adequate
Absence of check lists
procedure
Lack of standard
caliberation
No check on
needed gadgets
Scaracity of
maintenance of
Poor
identify
No system to
assessment
Improper Risk
installing
Lack of MSA for
enviornmental
Absence of
conditions
Bad enviornmental
Less space
Bad state of 5-S
Lack of illumination
Poor working posture
coating
Wear & tear of
coating on Pins
Scarcity of
before set-up
Dirty Pins
on Pins
Metal sticking
Pins
Blunt edges of
diameter
Variable Pin

79. Out of control Causes
With in control Causes
Die Machine Material Method Man Measurement Enviornment
VariablePin diameter Haphazard movementof Moulds Lowtemperatureof metal Lack of standard procedure In adequateTraining Lack of MSAfor installing instruments Poor working posture
Bluntedges of Pins Non flatnessof M/c bed Lack of parting materialcoating Absenceof check lists Faulty procedureto selctcasuallabours Improper Risk assessment Lack of illumination
Metalsticking on Pins Improper dieclosing Improper Selection of parting material In adequatesupevision Distraction (mentalworries) No system to identify deficiencies Bad stateof 5-S
Dirty Pins beforeset-up Play in between thedieparts No controlover thecoating thickness Non-standard set-up procedures Lack of trainig audits Poor maintenanceof instruments Less space
Scarcity of coating on Pins Given less soldification time Absenceof controlplan Scaracity of Processtraining Poor grief managementby HRDept. Scaracity of needed gadgets Bad enviornmentalconditions
Wear &tear of coating Zerking of hydraulic rams No criteria to Fix therecoating period Deputation of unskilled labour bed managementpolicies No check on caliberation periods Absenceof enviornmentalsurveys
ROOT CAUSE RESULTS

80. WHY - WHY ANALYSIS FOR M/C
Improper
seatingof
Die’s
bottom
(B.T.
variation)
Bottom and catcher
assembly mounted
on sameRam
Mis-alignment of
Die’s bottom w.r.t.
mould seat
Play or mis-
alignment of
bottom
carryingRam
Bottom tooland
catcher design of
machineis faulty
Operator’serror
Die is not checked
properlyby Die-
fitter beforeset-up
or installation
Only fourcorner
head padsare
provided for
bottom guidance
Seating
arrangementof
Die’s bottom is not
proper.
Problem in Die
design
Faulty Die-
checking/passing
procedure
Key reasons
Non-criticalreasons

81. RESULT AFTER A-PHASE
Type of Analysis Technique
used
Name of Tool used SSV’s to be analysed
Hypothesis testing (OFAT)
Chi Square test Analysis of Shift dependency
One way ANOVA Die coating thickness
2 Sample t-test
Volume of Runner & riser
Operator skill
Discharge of cooling water
MFAT Multi-regression analysis
Alloy temperature
Die temperature
Delay time
Graphical Analysis Interaction Plot Vaccume Pressure
Qualitative technique
Fish-bone analysis Metal sticking on a pin
Why-why analysis
Machine’s dimensional in-
accuracy
OUTCOMES OF ANALYSE PHASE

82. 4. IMPROVE PHASE
In this phase actions are piloted and real
tolerances are established to deliver desired
performance. Various suggestions and new
activities are added during optimization of the
out-put variable.
Major Tools Used:
Design of Experiments (DOE), Tanguchi’s
Method, RSM, Poka-Yoke, Kaizen etc.

83. STEP-1
STEP-2
Improvement by optimizations
Improvement by incorporating
Error proofing
Define objective of improve phase As prescribed from D, M and A phase
Improve
Phase
Principles of Poka-Yoke, Pilot Studies
DoE, RSM, Tanguchi Methods,
Product/Process Simulation
Technological innovation and
overhauling
Triz, Creativity, Quality Tools etc.
Improvement through Continuous
improvements
Kaizen, Quality Circles, TPM, TQM, 5S
Improvement by virtue of Training
and knowledge
Six Sigma Black Belt, Green Belt Training,
Knowledge of Statistics
PROPOSED FRAMEWORK FOR I-PHASE

84. TOOL USED SSV’s TO BE IMPROVED
OPTIMIZATION
WITH DESIGN OF EXPERIMENTS (DOE)
Volume of Runner & riser
Discharge of cooling water
Die temperature
Delay time
POKA-YOKE
Machine’s dimensional in-accuracy
KAIZEN
Metal sticking on a pin
Operator Skill On-Job Training
IMPROVEMENT PLAN

85. DESIGN OF EXPERIMENTS
StdOrder RunOrder CenterPt Blocks Die Temp Discharge of water Delay Volume of R&R
3 1 1 1 -1 1 -1 -1
27 2 1 1 -1 1 -1 1
6 3 1 1 1 -1 1 -1
4 4 1 1 1 1 -1 -1
1 5 1 1 -1 -1 -1 -1
29 6 1 1 -1 -1 1 1
23 7 1 1 -1 1 1 -1
13 8 1 1 -1 -1 1 1
11 9 1 1 -1 1 -1 1
14 10 1 1 1 -1 1 1
9 11 1 1 -1 -1 -1 1
19 12 1 1 -1 1 -1 -1
24 13 1 1 1 1 1 -1
22 14 1 1 1 -1 1 -1
28 15 1 1 1 1 -1 1
25 16 1 1 -1 -1 -1 1
8 17 1 1 1 1 1 -1
16 18 1 1 1 1 1 1
12 19 1 1 1 1 -1 1
26 20 1 1 1 -1 -1 1
32 21 1 1 1 1 1 1
17 22 1 1 -1 -1 -1 -1
18 23 1 1 1 -1 -1 -1
2 24 1 1 1 -1 -1 -1
15 25 1 1 -1 1 1 1
20 26 1 1 1 1 -1 -1
10 27 1 1 1 -1 -1 1
5 28 1 1 -1 -1 1 -1
31 29 1 1 -1 1 1 1
7 30 1 1 -1 1 1 -1
30 31 1 1 1 -1 1 1
21 32 1 1 -1 -1 1 -1
Factors/
Levels
Die Temperature
oC
Discharge of Water
(Litre/ Minutes)
Delay
(Seconds)
Volume of R&R
(Cm3)
Low 250 7 60 260
High 330 10 180 285

86. StdOrder RunOrder CenterPt Blocks Die Temp Discharge of water Delay Volume of R&R Scrap (%)
3 1 1 1 250 10 60 260 14
27 2 1 1 250 10 60 285 10
6 3 1 1 330 7 180 260 18
4 4 1 1 330 10 60 260 17
1 5 1 1 250 7 60 260 15
29 6 1 1 250 7 180 285 11
23 7 1 1 250 10 180 260 12
13 8 1 1 250 7 180 285 11
11 9 1 1 250 10 60 285 9
14 10 1 1 330 7 180 285 16
9 11 1 1 250 7 60 285 10
19 12 1 1 250 10 60 260 13
24 13 1 1 330 10 180 260 18
22 14 1 1 330 7 180 260 20
28 15 1 1 330 10 60 285 15
25 16 1 1 250 7 60 285 9
8 17 1 1 330 10 180 260 18
16 18 1 1 330 10 180 285 12
12 19 1 1 330 10 60 285 13
26 20 1 1 330 7 60 285 15
32 21 1 1 330 10 180 285 13
17 22 1 1 250 7 60 260 16
18 23 1 1 330 7 60 260 19
2 24 1 1 330 7 60 260 18
15 25 1 1 250 10 180 285 9
20 26 1 1 330 10 60 260 17
10 27 1 1 330 7 60 285 15
5 28 1 1 250 7 180 260 17
31 29 1 1 250 10 180 285 10
7 30 1 1 250 10 180 260 14
30 31 1 1 330 7 180 285 16
21 32 1 1 250 7 180 260 17
EXECUTION OF EXPERIMENTS

87. Factorial Fit: Scrap (%) versus Die Temp, Discharge of water,
Estimated Effects and Coefficients for Scrap (%) (coded units)
Term Effect Coef SE Coef T P
Constant 14.281 0.1362 104.84 0.000
Die Temp 3.937 1.969 0.1362 14.45 0.000
Discharge of water -1.813 -0.906 0.1362 -6.65 0.001
Delay 0.438 0.219 0.1362 1.61 0.128
Volume of R&R -4.313 -2.156 0.1362 -15.83 0.002
Die Temp*Discharge of water 0.062 0.031 0.1362 0.23 0.821
Die Temp*Delay -0.187 -0.094 0.1362 -0.69 0.501
Die Temp*Volume of R&R 0.563 0.281 0.1362 2.06 0.056
Discharge of water*Delay -0.687 -0.344 0.1362 -2.52 0.026
Discharge of water*Volume of R&R 0.312 0.156 0.1362 1.15 0.268
Delay*Volume of R&R -0.187 -0.094 0.1362 -0.69 0.501
Die Temp*Discharge of water*Delay 0.187 0.094 0.1362 0.69 0.501
Die Temp*Discharge of water* -0.813 -0.406 0.1362 -2.98 0.009
Volume of R&R
Die Temp*Delay*Volume of R&R -0.313 -0.156 0.1362 -1.15 0.268
Discharge of water*Delay* -0.313 -0.156 0.1362 -1.15 0.268
Volume of R&R
Die Temp*Discharge of water*Delay* -0.437 -0.219 0.1362 -1.61 0.128
Volume of R&R
S = 0.770552 PRESS = 38
R-Sq = 97.09% R-Sq(pred) = 88.36% R-Sq(adj) = 94.36%
DOE ANALYSIS OF
ORTHOGONAL MATRIX

88. 151050-5-10-15-20
99
95
90
80
70
60
50
40
30
20
10
5
1
Standardized Effect
Percent
A Die Temp
B Discharge of water
C Delay
D Volume of R&R
Factor Name
Not Significant
Significant
Effect Type
ABD
BC
D
B
A
Normal Plot of the Standardized Effects
(response is Scrap (%), Alpha = .05)
C
BC
ABD
B
A
D
1614121086420
Term
Standardized Effect
2.06
A Die Temp
B Discharge of water
C Delay
D Volume of R&R
Factor Name
Pareto Chart of the Standardized Effects
(response is Scrap (%), Alpha = .05)
RELATIVE IMPACT OF
CTQ FACTORS

89. ONE FACTOR AT A TIME ANALYSIS
330250
16
15
14
13
12
107
18060
16
15
14
13
12
285260
Die Temp
Mean
Discharge of water
Delay Volume of R&R
Data Means

90. TWO WAY INTERACTION PLOTS
107 285260
18
15
12
18
15
12
18
15
12
330250
18
15
12
18060
Die Temp
Discharge of water
Delay
Volume of R&R
250
330
Temp
Die
7
10
of water
Discharge
60
180
Delay
260
285
of R&R
Volume
Data Means

91. 10
910
15
8
50
20
100 7
150
Scrap (%)
Discharge of water
Delay
Surface Plot of Scrap (%) vs Discharge of water, Delay
Delay
Dischargeofwater
1801601401201008060
10.0
9.5
9.0
8.5
8.0
7.5
7.0
>
–
–
–
–
–
< 10
10 12
12 14
14 16
16 18
18 20
20
(%)
Scrap
Contour Plotof Scrap(%) vs Discharge of water, Delay
SURFACE PLOT FOR BC

92. THREE WAY INTERACTION PLOT
(ABD)
285
260
10
7
330250
Volume of R&R
Discharge of water
Die Temp
13.25
15.5010.25
9.50
17.50
18.7516.25
13.25

93. Scrap (y) = 57.4 + 0.03 (Die Temp) - 0.71 (Discharge of
water) + 0.03 (Delay) - 0.19 (Volume of R&R) -
0.003 (Discharge of water * Delay) + 7.13 (Die
temp. * Discharge * Volume of R&R)
Scrap (y) = 57.4 + 0.03 (A) - 0.71 (B) + 0.03 (C) - 0.19 (D) -
0.003 (B * C) + 7.13 (A * B * D)
OPTIMIZATION EQUATION

94. OPTIMIZATION BY
RESPONSE OPTIMIZER
Cur
High
Low0.99998
D
Optimal
d = 0.99998
Targ: 10.0
Scrap (%
y = 10.0002
0.99998
Desirability
Composite
260.0
285.0
60.0
180.0
7.0
10.0
250.0
330.0
Discharg Delay Volume oDie Temp
[250.0] [9.9602] [136.3636] [281.7172]

95. Response Optimization
Parameters
Goal Lower Target Upper Weight Import
Scrap (%) Target 8 10 20 1 1
Starting Point
Die Temp = 250
Discharge of = 7
Delay = 60
Volume of R& = 260
Solution
Die Temp = 250
Discharge of = 9.96019
Delay = 136.364
Volume of R& = 281.717
Predicted Responses
Scrap (%) = 10.0002 , desirability = 0.999982
Composite Desirability = 0.999982
OPTIMIZATION RESULTS

96. Existing Feeding System with
No Top Riser
After Modification Feeding System
with Top Riser Provided
CHANGE IN RUNNER & RISER DESIGN

97. Existing semi-automatic die casting Machine
POKA-YOKE FOR M/C

98. After improvement
semi-automatic die casting Machine
POKA-YOKE FOR M/C

99. VIDEOS OF MACHINE
IMPROVEMENT

100. Four
head
Pads for
sitting
Full Head
seat
Provided
For Head
Sitting
Existing Design Modified Design
SEATING ARRANGEMENT
(BOTTOM TOOL)

101. Graphite Coating on Die-Pins
Die-Pins with Metal Sticking 1. Pins sorted in the Stand
KAIZEN FOR DIE - PINS

102. 2. Cleaning of Pins by Buffing 3. Dipping of Pins
in Caustic Soda solution for 2 hrs
STANDARD CLEANING PROCEDURE

103. 4. Dipping of cleaned pins in
Graphite solution (1:2) for 50 mins
5. Pins after Cleaning
and Graphite Coating

104. Cell: H-273 Opistons
Master Trainer:
Shift: All Girish Kumar / Daljeet Singh
Supervisor:DPS/SM/MIJ
Status
Date:
30.08.2010
51051 TARLOK SINGH
To Be Trained for Temperature Checking of Holding Furnaces by 30 Sept.
51267 GURBAZ SINGH
To be trained to shot blast and clean the die with shot blasting machine by 4 oct.
51807 PAWAN KUMAR
to be trained to see temp by pyorometer by 30 sept.
51150 SUNIL KUMAR
To be Trained on Full Self Inspection by 30 Sept.
51751 JASSA SINGH
To be trained to do degassing and de slagging the molten metal by oct 5.
56771 GURMEET SINGH
Trained the working on die casting machine 20 by 4 oct.
DCM20
WASHINGMAN
TEMPERATURE
CHECKING
SELFINSPECTION
DCM9
DIEFITTER
LINEHELPER/RING
SHOTBLASTING
COREMAKING
Comments, Training Plans
TRAINING MATRIX
Capture Training Requirements
Empty
Square
No training completed to
date.
and produces the quantity required by production targets.
and is capable of training other team members.
Knows and applies all job instructions
and safety rules . . .
and ensures quality according to work
station job instructions.

105. 23-Dec2-Dec11-Nov27-Oct7-Oct16-Sep26-Aug5-Aug15-Jul
26
24
22
20
18
16
14
12
10
Date
Scrap
Actual
Fits
Variable
Trend Analysis Plot for Scrap
Linear Trend Model
Yt = 25.20 - 0.518610*t
DOE Implementation
(7% reduction in scrap)
Poka-yoke of
M/C (2%) Kaizen (graphite
coating of Pin
2% )
Training of
human resource
(1%)
Target
Scrap
Parameters are
getting Out of
control
SCRAP REDUCTION ACHIEVED

106. 5. CONTROL PHASE
In this phase, process monitoring and corrective or
preventive actions are documented and executed.
Basically this phase tries to check and monitor the
improved process and its parametric values.
Major Tools Used
Control Plan, X bar and R Chart for BT variation and p-
Chart for overall scrap tracking.

107. STEP-1
STEP-2
Final capability is re-defined
Checking the scope of achieved
improvements
Validate the measuring system Do necessary Calibration
Control
Phase
Transfer of technique among
similar areas
Sigma Calculator, Cpk Study
Sustaining the improved process Check Lists, Audit Sheets, 5S,
Poka-Yoke, TPM, Patrolling
teams etc.
Monitoring and revision of control
documents and risk management
Control Charts, SPC, Control
Plan, FMEA, CUMSUM Charts
Financial benefits reviewed Cost and Benefit Analysis
PROPOSED FRAMEWORK FOR C-PHASE

108. IMPROVED SIGMA LEVEL
Production Results of Dec-2010
Total number of Machined parts 14079
Scrap type Nos.
Bottom Thickness Defect 410
Blow Holes 320
Cold Lap 95
Depression 85
Hydrogen Porosity 40
Shrinkage 350
Defective Pin Hole 166
Total Scrap in December 1466
Nos. of Opportunities 7
DPMO 14875
Sigma Level of Process 3.67
Yield (%) 96.96

109. IMPROVED Cpk
9.909.849.789.729.669.609.54
LSL USL
LSL 9.5
Target *
USL 9.9
Sample Mean 9.71617
Sample N 60
StDev (Within) 0.0417718
StDev (O v erall) 0.0421897
Process Data
C p 1.60
C PL 1.72
C PU 1.47
C pk 1.47
Pp 1.58
PPL 1.71
PPU 1.45
Ppk 1.45
C pm *
O v erall C apability
Potential (Within) C apability
PPM < LSL 0.00
PPM > USL 0.00
PPM Total 0.00
O bserv ed Performance
PPM < LSL 0.11
PPM > USL 5.39
PPM Total 5.50
Exp. Within Performance
PPM < LSL 0.15
PPM > USL 6.58
PPM Total 6.73
Exp. O v erall Performance
Within
Overall

110. X BAR & R CHART FOR
BT VARIATION
5:00 AM3:00 AM1:00 AM11:00 PM9:00 PM7:00 PM5:00 PM3:00 PM1:00 PM11:00 AM9:00 AM7:00 AM
1 0 .0
9 .8
9 .6
9 .4
T ime
SampleMean
__
X=9.72
UCL=9.9516
LCL=9.4884
1 21 11 0987654321
0 .8
0 .6
0 .4
0 .2
0 .0
Sample
SampleRange
_
R=0.4014
UCL=0.8488
LC L=0
11
1
Xbar-R Chart of BT

111. p- CHART FOR
OVERALL SCRAP REDUCTION
30th
Dec
27th
Dec
24th
Dec
21st
Dec
18th
Dec
15th
Dec
12th
Dec
9th
Dec
6th
Dec
3rd
Dec
0.20
0.18
0.16
0.14
0.12
0.10
0.08
0.06
Time
individualpvalues
_
X=0.1043
UCL=0.1380
LCL=0.0707
111
1
1
1
1
1
1
1
p-value (np/n) chart

112. RESULT APPRAISAL
Parameters Before After DMAIC
DPMO 31,428 14,875
Cpk Index 0.21 1.47
Sigma Level 3.43 3.67
Scrap (%age) 21.7 10.4
Savings (Rs.) 30.7 Lakhs per Annum
With mere 0.24 improvement in Sigma Level

113. • In Improvement Phase, four casting process parameters (i.e.
Volume of R&R, Die temperature, Discharge of cooling
water and Delay with in process) have been optimized to
bring a significant reduction of 7% in scrap. The machine‟s
dimensional in-accuracy has been enhanced by altering the
design of machine through Poka-yoke principles and this
helped to achieve a net reduction of around 2% in scrap.
The metal sticking on pins has been avoided by introducing a
standard process of pin cleaning by using Kaizen rules of
continues improvement and hence bring a further eduction
of 2% in scrap. Then a proposed training schedule has
reduced the scrap by around 1%. So overall 11% to 12%
(approximately) reduction in scrap has been
achieved in Improvement Phase of the case study.

114. • Diversification of technology definitely increases the
financial/non financial benefits. It leads to stability of system,
which ultimately causes the customer satisfaction. After
successful reduction of scrap for H-273 pistons, the same
techniques and tools have been applied for other H-family
pistons like H-749 and H-519 pistons that are similar as far
as design, process parameters, casting machine and dies are
concerned.
• Total savings of around 30.7 lakhs per annum has been
assessed after improving the sigma level by 0.24 only.

115. 1. H-273 pistons:
Average Scrap Saved per month = 400 (Approx.)
Scrap Cost per Piston = 183 (Approx.)
Total rejection cost saved per month = 400*183 = Rs 73,200/-
Total rejection cost saved per year = 73,200*12
= Rs 8, 78,400/-
2. H-519 pistons:
Average Scrap Saved per month = 600 (Approx.)
Scrap Cost per Piston = 183 (Approx.)
Total rejection cost saved per month = 600*183
= Rs 1, 09,800/-
Total rejection cost saved per year = 73,200*12
= Rs 13, 17,600/-
3. H-749 pistons:
Average Scrap Saved per month = 400 (Approx.)
Scrap Cost per Piston = 183 (Approx.)
Total rejection cost saved per month = 400*183 = Rs 73,200/-
Total rejection cost saved per year = 73,200*12 = Rs 8, 78,400/-
ECONOMIC ANALYSIS

116. • The existing high scrap (21.7% approx.) has been defined as
most serious issue as far as overall productivity of pistons is
concerned in foundry and machine shop.
• Casting scrap has been found primarily due to defects like;
shrinkage, bottom thickness variation, blow holes, porosity, pin
hole defect and cold lap.
• Process parameters (SSVs) like; In-gate design, dimensional
inaccuracy of machine, die temperature, die coating thickness,
discharge of cooling water, metal sticking on pins, alloy
temperature, shift dependency and operator skill have been
measured as main reasons of above defects in piston castings.
SUMMARY

117. • Although during Measurement System Analysis (MSA),
Gauge R&R of BT gauge has been found ok, yet Immersion
Pyrometer (Temp. measuring device) has been found Biased
and Vac-tester (Metal density checker) was coming out to be
un-stable over a span of one month only. So both the
measuring equipments were repaired and calibrated suitably
before starting Analyse phase.
• After analysis of all the eleven SSVs with appropriate statistical
tools, following seven critical process parameters have
emerged as responsible for high scrap. These are In-gate
design (Volume of Runner & Riser), operator skill, discharge
of cooling water, die temperature, delay time, metal sticking
on pins and dimensional in accuracy of machine.

118. • Earlier existing sigma level was 3.43 and after improvement it
has been raised by 0.24 but even this has resulted in savings
of around 30.7 lakhs per annum and is remarkable for a
medium scale make-to-order foundry unit.
• Overall scrap of foundry has been monitored by drawing
shift wise p-chart and controlled up to 10.43% only, after
Six Sigma case study. BT variation has also been
monitored at casting machine itself by chalking out X bar &
Range chart hourly. Cpk study has also been conducted
by SQC operator after every 4 shifts for time to time
process capability-checking of casting process with
respect to bottom thickness variation.

119. • This study has significantly reaffirmed the efficacy of Six
Sigma strategy in Indian foundry industry to reduce
scrap/waste from the operations, thus greatly improving the
production efficiency. It also demystify the myth that
Six-Sigma is only meant for big industries and require a lot
of effort money and training etc.
• „Project based‟ approach for Six Sigma implementation
(rather then planning or investing in different
phases of Six Sigma approach) is more motivating and
helps a lot to demystify various fears on Six Sigma.
CONCLUSIONS

120. • Researchers have laid more emphasis on defining
various tools and techniques of Six Sigma but „tool
selection criterion‟ for each phase, as per the given
constraints and environment, is not available to
industries.
• Due to lack of this vital information, usually wrong
tools and techniques are chosen and so DMAIC
project fails by moving in a wrong direction,
with course of time. But this fear is eradicated because
of step-wise proposed frameworks for each phase of
DMAIC project, which also categorizes concerned tools.
• The challenge for all organizations is to integrate
Six Sigma into their core business processes and
operations rather than managing it as a separate
initiative.

121. FUTURE SCOPE
• Beside non-ferrous foundries, Six Sigma approach can be
explored for ferrous foundries to bring breakthrough in
rejections and increase yield per annum.
• Six Sigma can also be used in energy intensive foundries,
as it not only enhances productivity by process
improvement but also it is a step to create „zero defect
foundries‟ which indirectly leads to huge energy savings.
• It can be implemented in green sand foundries specifically
in sand re-use plants for not only to reduce the lead time of
process but also to cast cost effective castings.

122. • Apart from foundry industries, other manufacturing
sectors like; forging, forming, welding and machining
industries can also take benefit to lean their business
operations.
• Six Sigma should further be explored service sector
like; hospitals, education institutes, banking, traffic etc
in India.
FUTURE SCOPE

123. •Agarwal, R. and Bajaj, N. (2008), “Managing outsourcing process: applying Six Sigma”, Business
Process Management Journal, Vol. 14, No. 6, pp. 829-37.
•Aggogeri, F. and Gentili, E. (2008), “Six Sigma methodology: an effective tool for quality management”,
International Journal of Manufacturing Technology and Management, Vol. 14, No 3/4, pp. 289-98.
•Andersson, R., Eriksson, H. and Torstensson, H. (2006), “Similarities and differences between TQM, Six
Sigma and lean”, The TQM Magazine, Vol. 18, No. 3, pp. 282-96.
•Antony, J. (2002), “Design for Six Sigma: a breakthrough business improvement strategy for achieving
competitive advantage”, Work Study, Vol. 51, No. 1, pp. 6-8.
•Antony, J. and Banuelas, R. (2002), “Key ingredients for the effective implementation of Six Sigma
program”, Measuring Business Excellence, Vol. 6, No. 4, pp. 20-7.
•Antony, J. and Fergusson, C. (2004), “Six Sigma in the software industry: results from a pilot study”,
Managerial Auditing Journal, Vol. 19, No. 8, pp. 1025-32.
•Antony, J. (2004), “Six Sigma in the UK service organizations: results from a pilot survey”, Managerial
Auditing Journal, Vol. 19, No. 8, pp. 1006-13.
•Antony, J. (2006), “Six Sigma for service processes”, Business Process Management Journal, Vol. 12,
No. 2, pp. 234-48.
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129. PUBLICATIONS OUT OF WORK
(A) Papers ‘Published’ in International Journals
1. Singh, B.J. and Khanduja, D. (2010), DMAICT: A Roadmap to Quick Changeovers, International Journal of Six Sigma
and Competitive Advantage, Vol.6, No.1/2, pp.31-52.
2. Singh, B.J. and Khanduja, D. (2010), SME Sector of Punjab (India): From Renaissance to Recession, International
Journal of Indian Culture and Business Management, Vol. 3, No. 5, pp. 544-559.
3. Singh, B.J. and Khanduja, D. (2010), SMED: For Quick Changeovers in Foundry SMEs, International Journal of
Productivity and Performance Management, Vol. 59, No. 1, pp.98-116.
4. Singh, B.J. and Khanduja, D. (2010), Synergy of Cross Functional Process Mapping and SMED for Quick Changeovers: A
Case Study, International Journal of Science Technology & Management, Vol-2, No. 2, pp.107-116.
5. Singh, B.J. and Khanduja, D. (2011), Enigma of Six Sigma for Foundry SMEs in India: A Case Study, International
Journal of Engineering Management and Economics, Vol. 2, No. 1, pp. 81-105.
6. Singh, B.J., Khanduja, D. and Singh, A. (2011), Demystifying MSA: A Structured Approach for Indian Foundry SMEs,
International Journal of Quality and Innovation, Vol. 1, No. 3, pp. 217-236.
7. Singh, B.J. and Khanduja, D. (2011), Does Analysis Matter in Six Sigma?: A Case Study, International Journal of Data
Analysis Techniques and Strategies, Vol. 3, No. 3, pp. 300-324.
8. Singh, B.J. and Khanduja, D. (2011), Introduce Quality Processes through DOE: A Case Study in Die Casting Foundry,
International Journal of Productivity and Quality Management, Vol. 8, No. 4, pp. 373-397.

130. 9. Singh, B.J. and Khanduja, D. (2011), Design for Set-ups: A Step towards Quick Changeovers in Foundries,
International Journal of Sustainable Designs, Vol. 1, No. 4, pp. 402-422.
10. Singh, B.J. and Khanduja, D. (2012), Essentials of D-phase to Secure the Competitive Advantage through
Six Sigma, International Journal of Business Excellence, Vol. 5, No. 1/2, pp. 31-51.
11. Singh, B.J. and Khanduja, D. (2012), Ambience of Six Sigma in Indian Foundry SMEs-An Empirical
Investigation, International Journal of Six Sigma and Competitive Advantage, Vol. 7, No. 1, pp. 12-40.
12. Singh, B.J. and Khanduja, D. (2012), Risk Management in Complex Changeovers through CFMEA: An
Empirical Investigation, International Journal of Industrial and System Engineering, Vol. 10, No. 4, pp. 470-
494.
13. Singh, B.J. and Khanduja, D. (2012), Scope of Six Sigma in Indian Foundry Operations, International
Journal of Services and Operation Management, Vol. 13, No.1, pp.65-97.
14. Singh, B.J. and Bakshi, Y. (2012), Six Sigma for Sustainable Energy Management: A Case Study,
International Journal of Science Technology & Management, Vol. 2, No. 2, pp. 60-72.
15. Singh, B.J. and Khanduja, D. (2012), Developing Operation Measurement Strategy during Six Sigma
Implementation: A Foundry Case Study, International Journal of Advanced Operation Management, Vol.4,
No. 4, pp. 323-349.
16.Singh, B.J. and Khanduja, D. (2014), “Perspectives of Control Phase to manage Six Sigma implements: A
Foundry Case”, International Journal of Business Excellence (IJBEX), Vol.7, No.1, pp. 88-111.

131. (B) Papers ‘Presented’ in International Conferences
1.Singh, B.J. and Khanduja, D. (2010), DMAIC(S): Incubates Core Competencies in Indian Foundry SMEs: An
Empirical Study in State of Punjab, 2nd International Conference on Production and Industrial Engineering
(CPIE-2010), NIT, Jalandhar, India, pp. 1443-53.
2.Singh, B.J. and Khanduja, D. (2011), Enhancing Competitiveness of Foundry SMEs through Design for
Changeover (DFC): A Case Study, International Conference on Emerging Trends in Mechanical Engineering
(ICETME-2011), Thapar University, Patiala, India, pp. 507-515.
3.Singh, B.J., Khanduja, D. and Jaglan, P. (2012), “Six Sigma for Sustainable Energy Management in
Foundries: A Case Study”, SOM-2012 Conference, IIT, Delhi (Held in Dec 2012).
4.Bakshi, Y., Singh, B.J., Singh, S.S. and Singla, R. (2012), “Performance Optimization of Backup Power
Systems through Six Sigma: A Case Study”, IETET-2012, GITA Institutes, Kurukshetra (Held in Nov 2012)
5.Sodhi, H.S., Singh, B.J. and Khanduja, D. (2012), “Behavior Study of Cutting Parameters on Material
Removal Rate for a Non-Ferrous Material While Turning on a CNC Turning Center”, IETET-2012, Kurukshetra
(Held in Nov 2012).
6.Singh, B.J. and Khanduja, D. (2013), “Leveraging Six Sigma Disciplines to Reduce Scrap in Indian Foundry
SMEs”, 26th SEAANZ Conference, Sydney (NSW), Australia, pp. 6-24. (Held in July-2013)

132. (C) Papers ‘Presented’ in National Conferences
1. Singh, B.J. and Khanduja, D. (2009), Set-Up Time Reduction for Higher Productivity in
Indian Foundries, Indian Institute of Foundry Men (IIF) organized by Chandigarh Chapter at
Ludhiana, pp. 51-57.
2. Singh, B.J. and Khanduja, D. (2010), Exploring Set-up Management in Indian Foundries,
Advances in Mechanical Engineering (AME-2010), Organized by B.B.S.B.C.E. Fatehgarh
Sahib, Punjab, pp. 70-76.
3. Singh, B.J., Bakshi, Y. and Kaushik, P. (2011), Role of Six Sigma in Engineering Institutes: A
Case Study, 41st ISTE Convention, India, pp. 7-20.
4. Sachin, Singh, B.J. and Dhull, V. (2013), “Six Sigma: From Concept to Implementation”,
AMMM-2013 National Conference, Haryana.

133. THANKS