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Small Enterprise Association of Australia and New Zealand 26th Annual SEAANZ Conference Proceedings 11-12 Sydney 2013

Leveraging Six Sigma Disciplines to Reduce Scrap in

Indian Foundry SMEs

Bikram Jit Singhᵃ and Dinesh Khandujaᵇ

ᵃMechanical Engineering Department, Maharishi Markandeshwar University, Sadopur, Ambala

(Haryana) India, contact: Email: [email protected]; ᵇMechanical Engineering Department, National

Institute of Technology Kurukshetra, India.

Abstract:

The study focuses on scrap reduction in foundries and tries to find out the reasons of low productivity

index among Indian subcontinent. It briefly discusses some facts and figures about foundry scenario in

world and in the India. The Indian foundry industry is the fourth largest in the world. There are more

than 70,000 foundries in India and most foundries (nearly 95%) fall under small and medium scale

category. For global competitiveness, Indian industries need overall operational and service excellence

and are extensively engaged in Quality Circles, TQM and ISO Certifications. However, these methods

have failed to deliver required performance over the last decade or so. The average growth rate of

productivity for Indian SMEs has been 4.95% in comparison to 7.31% for China, 9.45% for Singapore and

8.65% for Pakistan. It seems a comprehensive quality approach like ‘Six Sigma’ is not fully explored

among Indian industries. This paper tends to shatter the various phobias of SMEs in context of Six Sigma

concepts and its implementation by validating the compatibility of it by performing a case study in an

Indian environment itself. In present case, overall sigma level has been raised by 0.24 by reducing the

scrap of a non-ferrous piston foundry from 22% to 10% after successfully implementing the DMAIC

(Define-Measure-Analyse-Improve-Control) methodology of Six Sigma.

Keywords: Productivity, Non-Ferrous Foundry, DMAIC, Susceptible Sources of Variations

(SSVs), Analysis of Variance (ANOVA), Design of Experiments (DOE)

©copyright Singh and Khanduja (2013) all rights reserved. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden.

mailto:[email protected]

INTRODUCTION

India has around 5500 foundries, producing about 3.24 MT of castings worth ten millions (Government

of India, 2002). These units are mostly located in clusters with numbers varying from less than 100 to

around 400 per cluster. Some of the notable clusters in this regard are Agra, Howrah, Batala,

Coimbatore, Kolhapur, Rajkot and Belgaum (Chhabilendra and Roul, 2001). The foundry produces a wide

variety of castings such as manhole covers, pipe and pipe fittings, sanitary items, tube well body, metric

weights, automobile components, railway parts, electric motor, fan body etc. 90% of the castings

produced are from the Small Scale Industry sector (Government of India, 2006). India has exported

castings worth USD 131.35 Million and sanitary castings worth USD 55.39 in 1999-2000, mainly to USA

and Europe (Alistair Nolan, 2003).

LITERATURE REVIEW

In India, productivity levels of SMEs are alarmingly low due to host of problems (Director of Industries,

2003). For higher productivity in SMEs, ‘Defects reduction’ will be one of the most promising and viable

strategy and it will also be capable to cope up the emerging future challenges (Antony et al., 2005). Six

Sigma concept has been widely used in manufacturing sector from last 25 years as company like

Motorola has been improving its processes since 1986 by using its defect reduction approach (Eckes,

2001). Similarly manufacturing giants like General Electric and Honey Well have been using it as cycle

time reduction tool, since 1996 (Zu et al., 2011). Other well-known companies like Ford, Caterpillar, Our

lady of Lourdes medical centre, LG and Samsung etc. are also practicing Six Sigma as a quality

improvement technique in their respective manufacturing processes from 1999. Table 1 cites major

works of the researchers related to application of Six Sigma in manufacturing sector during the past

decade.

After analyzing significant contribution of Six Sigma approach among SMEs, an effort has been made to

implement DMAIC methodology in non-ferrous (medium scale) foundry, without ignoring its existing

Indian constraints. It further demystifies various myths regarding Six Sigma and SMEs, specifically for the

foundry units.

Table-1: Application of Six Sigma in Manufacturing Sector

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

6. Hollenstein (2005)

A medium scale IC engine manufacturing unit

Improve the Cpk Process capability improved from 1.1 to 2.9

7. Andersson 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. Lin et al. (2008)

Cranberry Drinks Ltd. Improvement in packing process.

DPMO level improved from 3011 to 178 only. 17% reduction in packing time.

9. Antony and Desai (2009)

Wilson Tools Shorten the heat treatment time

Roughly $10000 per year savings. 2% reduction in overall Lead time

10. Singh and Khanduja (2010)

A copper wire manufacturing plant

Quality improvement in rolling operation

Defect are decreased by 19% within nine months of DMAIC project

[[[[[

PROBLEM FORMULATION

In India, manufacturing industries like foundries do not enjoy monopoly but they have to face

competition (Chaganti and Greene, 2002). To overcome this problem and to retain the share of the

market, it is necessary to constantly improve the quality of the cast product without increasing price of

the products. The price is influenced by the cost of production, which in turn is influenced by rework or

rejection. Attention to quality assurance can reduce the wasteful rework. Aiming for quality in the first

instance can reduce the cost of casting production. This quality production results in the company’s

growth and profitability. Among various reasons responsible for this malady, low utilization of

productive capacity is the major reason, which should cause concern for production planning and for

this capacity waste, rejection and scrap accounts for a major share (Hollenstein, 2005). The technology

gap is alarming and a company in India spends less than 0.6% on average, of its turnover on R&D as

against the world average of 2.5% (Coronado and Antony, 2002). In a small unit, where investment in

plant and machinery is less than Rs.10 million, productivity and profitability are indispensable to assess

the performance of such an organization. Arita and McCann (2002) in their study have observed that

reduction in cost and product rejection rate are among the main pressures on small units. The main

barriers for these units to be competitive are inadequate technologies causing lot of defects, poor

human expertise and scarcity of resources impairs their ability to become internationalized (Lucas,

2002).

METHODOLOGY ADOPTED

For global competitiveness, Indian industries are working hard to achieve overall operational excellence

in their business (Zu et al., 2011). Six Sigma has evolved into a powerful business improvement

methodology in many Indian industries and its importance is growing (Voelkel, 2002). Within Indian

SMEs, this paper has validated the concept of Six Sigma successfully by unveiling a tested DIMAC

methodology for foundry SMEs. In the present market, competitors are looking for flexibility and shorter

production lead times because only such a configuration of production system can fulfil the ever-

changing demands of customers (Antony, 2004). But for this it is necessary to have less scrap as it is well

known that this will result into economic production (Eckes, 2001). To handle these challenges,

competitors are forced to move towards such strategies/techniques which can make production less

costly and of optimum quality. Wright and Basu (2008) highlight that by Scrap reduction, one can have

maximum utilization of machine or equipment, which will obviously enhance the production rate and

make the overall production more feasible. This can be remarkably tackled by inculcating above cited

DMAIC approach uniformly in the given foundry conditions, particularly in foundry SMEs of India. Six

Sigma is a highly structured program developed by Motorola and used to improve quality world widely

(Singh and Khanduja, 2010). This contains a number of management and statistical tools and techniques

in its respective phases (Lin et al., 2008). There is always a risk of choosing wrong tools due to

negligence or production constraints for performing improvements, that ultimately leads to failure of

this approach and it only bounds to produce paper work projects that are far away from real world

savings. The proposed work tries to simplify the phases of Six Sigma and categories the given

tools/techniques with respect to their utility and further successfully validates its effectiveness by

conducting a successful case study in a non-ferrous foundry.

Literature review also shows that Six Sigma research has been mostly empirical in nature which

reinforces the use of real-world data. Case study was the dominant approach in Six Sigma research and

this is perhaps due to the fact that quality problems in manufacturing and service contexts are usually

treated as a case in terms of documentation and analysis. Figure 1.9 defines the growing gap over the

years between case study method and other research methods, particularly survey research. Case study

method is used to document and analyze Six Sigma implementation in particular contexts; industry,

service, process or phase of a specific project. In addition, the lack of implementing Six Sigma tools and

methodologies across a wide range of processes or organizations makes the use of survey approach

impractical. The graph in figure 1 shows that case study based approach has been well acceptable and

successful since 1992, as far as Six Sigma concept and its implementation are concerned. From 2004 to

2008, researchers seem to be using this approach exponentially as compared to survey based and

review based frameworks by analyzing the benefits and authenticity of case study based works in the

field of Six Sigma.

Figure-1: Historical Trend of Six Sigma Implementations

(Source; Aboelmaged, 2009)

The present work has taken this case study based approach to achieve the pre-defined goals and

objectives relating to Six Sigma implementation in Indian foundries.

A CASE STUDY

A case study has been carried out in a non-ferrous foundry at Federal Mogul India Limited Bhadurgarh,

Patiala (Punjab) which casts around 9.5 million pistons annually. Foundry has a covered area of about

50144 m2 and was established in 1954. It is a medium scale unit used to cast pistons for export to US

and uses mostly semi-automatic die casting machines. Dies of different types of pistons have been

installed on machines as per the monthly planning/scheduling and pouring of metal is performed

manually by operators. Foundry under consideration is used to cast piston of diameter ranging from

30mm to 300mm and capable to manufacture 13 million pistons per annum. In July 2010, a six months

Six Sigma project was initiated to reduce the scrap of export-pistons (form 22% to 10% approximately).

The main five phases of project (Define, Measure, Analyse, Improve and Control) have been executed in

the given foundry environment to make the Six Sigma implementation more compatible with the

present dynamic environments of foundry industry.

Define phase

The first step was to precisely define the problem, keeping in mind business objectives, customer needs

and feedback (Singh and Khanduja, 2012a). 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, Historical analysis of

problem, Process by Value Stream Mapping and Project Goals.

Findings: The house of quality was generated to hear the voice of customers effectively and ‘reduction

in casting scrap’ has been adopted as the most critical issue (refer figure 2). To define the problem of

large scrap quantitatively, previous six month’s data was collected regarding scrap and good pieces from

production reports. It was found that H-749 pistons had around 20% to 24% of scrap, which was causing

substantial financial and non-financial losses. The impact of high rejection became clearly obvious as net

rejection cost came out to be around Rs.30,70,000/-per annum and is substantial for a medium scale

foundry. For intense focusing on the foundry. The whole process was mapped (refer figure 3). Key

process input variables at each operation were found out and further classified into three categories;

Noise Variables, Critic Variables and Controlled Variables respectively.

Measure phase

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 (Singh and

Khanduja, 2012c).

Major Tools Used: Sigma Calculator, Pareto Charts, Cause and Effect Matrix, Gauge R&R study, Bias

Checking and Stability Test.

Findings: The existing sigma level of casting process has been calculated by a sigma calculator that works

on the principle of DPMO and this was calculated as 3.43 for the process. The existing foundry process

has been mapped minutely and drawn as in figure 3 ahead. Pareto chart has defined all the 20% factors

that are causing 80% of the problem (see figure 4). In the present case shrinkage at ring zone and skirt,

bottom thickness (BT) variation, blow holes in ring zone and bottom, cold lap and porosity are emerging

as the prime reasons of high casting scrap in H-749 pistons.

Figure-2: House of Quality

Θ Θ Θ Θ

Imp

orta

nce

to

Cu

stom

er

Eva

ult

ion

of

Ven

dor

s

Goo

d P

roce

ss C

ontr

ols

Red

uct

ion

in

Scr

ap d

ue

to

Cas

tin

g D

efec

ts

Les

s D

ie/M

ach

ine

Set

up

tim

e

Imp

lem

enta

tion

of

Mai

nte

nan

ce S

ched

ule

s

En

sure

Ad

her

ence

of

Qu

alit

y

Sys

tem

Goo

d I

nsp

ecti

on P

lan

s

Tec

hn

olog

y

Up

grad

atio

n/I

nn

ovat

ion

Tra

inin

g P

lan

s fo

r W

ork

For

ce

Eff

icie

nt

Sch

edu

lin

g to

com

ple

te P

rod

uct

ion

Ord

ers

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 Θ

N No Relation (N) 0

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

26th

Annual SEAANZ Conference 2013 Page | 13

Figure-3: Process Mapping with Key Performance Input Variables (KPIVs)

Figure-4: Pareto Chart for Defect Analysis

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

7080 90

100

110120130Si

No

Scrap

130140150Si

No

Scrap

160170

Die temperature

Die coating density

Spray gunFree of humidity

Free of slag

Charge relation

(60 Ingot /40 scrap)

Temperature

Metal Temp.

N2 Flow

RPM

TimeTime

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

7080 90

100

110120130Si

No

Scrap

130140150Si

No

Scrap

160170

10 20 30 40 50 60

ID=1,5 max.

Si

No

7080 90

100

110120130Si

No

Scrap

130140150Si

No

Scrap

160170

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 la

p

Def. B

lank

s In

side

Poro

sity Skir

t

Shrin

kage

Skir

t

Blow

Holes

Bot

tom

Shrinka

ge B

otto

m

Poro

sity Rin

g Zo

ne

Blow

Holes

Ring

Zone

Botto

m th

ickne

ss var

iatio

n

Shrin

kage

Ring

Zone

1200

1000

800

600

400

200

0

100

80

60

40

20

0

NU

MB

ERS

Pe

rce

nt

Pareto Chart of H-749

26th


By conducting Cause & effect Analysis, Process parameters (SSVs) like; In-gate Design, Die

Temperature, Die Coating Thickness, Alloy Temperature, Discharge of Cooling Water, Shift

Dependency and Delay during Casting have been measured as the main reasons of the above defects

in piston castings. The next crucial step in the measurement phase is the measurement of the

accuracy and precision of already ‘in-use’ measuring equipment s or gauges. In the present case, it

was decided to validate the calibration by conducting Gage R&R study for bottom thickness gauge

(BT gauge), bias checking of immersion pyrometer and stability test of Vac-tester (metal density

checker) respectively.

Analyze phase

At the end of Measure Phase, seven critical to quality (CTQ) process parameters were short listed

and these seemed to be the major reasons for high scrap. As per DMAIC methodology, before

targeting these susceptible sources of variations (SSVs) through ‘Improve Phase’, the authenticity

and impact realization of each SSV on scrap, is required to be judged by conducting suitable

investigation under Analyse Phase. This phase helps to focus improvement efforts on those SSVs

which can be highly significant (Singh and Khanduja, 2011a).

Major tools used: Table 2 gives a summary of various quantitative and qualitative techniques/tools

used for analyzing the measured critical to quality (CTQ) process parameters.

Tables-2: Identifying Analytical Tools for Each SSV

Analysis Technique Tool SSV Analyzed

Hypothesis Testing (OFAT)

Chi Square Test Analysis of Shift dependency

One Way ANOVA Die Coating thickness

Two Sample t-Test In gate design

Discharge of cooling water

MFAT Multi Regression

Alloy temperature

Die temperature

Delay time

Findings: After this phase, it is amply clear that out of the seven susceptible sources of variations

(SSVs), only four are actually responsible for high value of scrap and these are: Ingate Design, Die

Temperature, Delay Time and Discharge of Cooling Water. Unwanted SSVs have been omitted which

seems to be vital for being focused on serious issues for reducing the scrap significantly. Outcomes

of this phase have been summarized in table 3.

26th


Table-3: Out Comes of Analyse Phase

Analysis of Shift Dependency: The

overall chi- square value is coming

out to be 6.150 and value of p is

0.908 at 12 degree of freedoms. As it

is higher than 0.005. The null

hypothesis cannot be rejected at 5%

significance level. Therefore there is

no difference between the mean

scrap value of morning, afternoon

and night shift. Hence it makes clear

that this factor does not contribute

to scrap in the shop-floor and can be

dropped here only.

Effect of Die Coating Thickness:

Under given temperature and time

conditions, die has been made to run

with various coating thicknesses (i.e.

at 50 microns, 80 microns 110

microns and 140 microns)

separately. After giving input of

above data, Minitab has compiled

Analysis of Variance for all the four

groups and generated p value as

0.932 at 95% confidence level. It

implies again that the null

hypothesis is acceptable. The graph

drawn shows less variation among

the mean of all populations. Hence

this factor seems to be less

significant in context of high casting

scrap.

Impact of In-gate Design: This

analysis contains two groups and

each has samples less than 30, hence

2-sample t-test is best for such type

of data populations. For this analysis,

two piston dies of H-749 have been

selected, one has existing runner

riser volume (i.e. 250-260 cm3) and

other is modified to the volume of

runner riser up to 260-270 cm3 (by

altering the gate design). t-value is

coming out to be 4.63 and

probability value (p) is 0.001, which

is less than 0

confidence). It concludes that the

null hypothesis has been rejected in

the favour of alternate hypothesis

for the given two populations.

Session Confirmation from MinitabExpected

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

Die co

ating thick

ness

140

µm

Die co

ating thick

ness

110

µm

Die co

ating thick

ness

80µ

m

Die co

ating thick

ness

50µ

m

10.0

9.9

9.8

9.7

9.6

9.5

Da

ta

With Enlarged volume of RunnerWith Existing volume of Runner

22.00%

20.00%

18.00%

16.00%

14.00%

12.00%

10.00%

Da

ta

Boxplot of With Existing volume of Runner, With Enlarged volume of Runner

26th


It implies that with increase in

runner and riser volume (or altering

In-gate design) casting defects are

reducing.

Discharge of Cooling Water: The p

value for this factor is less than 0.05

while conducting the 2 sample t-test.

So, Discharge of cooling water has also erupted as major factor for

casting defects as null hypothesis of no affect has been rejected.

Analysis of Process Parameters: In

the present study, three process

parameters (namely; Alloy

temperature, Die temperature and

Delay time) have been measured as

more critical as far as question of

high casting scrap of piston castings

is raised. In order to verify their

dependability on dependent out-put

variable (scrap), the Six Sigma team

has decided to perform Multi-

regression analysis.

The regression calculations and

results have been quoted in sideline

figure. From the calculated p values,

it represents Alloy temperature is in

control conditions and only Delay in-

between casting process and Die

temperature are impacting casting

scrap seriously.

Improve phase

In this phase actions are piloted and real tolerances are established to deliver desired

performance (Singh and Khanduja, 2011b). Various suggestions and new activities have

been added during optimization of the out-put variable.

Major Tools Used: Design of Experiments (DOE).

Findings: Scrap reduction is the main problem which depends upon four selected casting

process variables (factors). Each factor is defined in terms of high and low values of levels

(refer to table 4). To realize the effect of each factor or their interactional impact on scrap,

‘full factorial design’ has been selected for optimizing the process in-put factors. No blocking

Regression Analysis: %age of Scrap versus Alloy Temp., Stoppage (in secs) & Die Temp.

The regression equation is

%age of Scrap = 114 + 0.118 Alloy Temperature + 0.306 Stoppage (in Sec) - 0.745 Die Temp

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 (IN Sec) 0.30615 0.08153 3.76 0.020

Die Temp -0.7454 0.2130 -3.50 0.025

S = 7.70734 R-Sq = 83.5% R-Sq(adj) = 71.1%

Analysis of Variance

Source DF SS MS F P

Regression 3 1200.75 400.25 6.74 0.048

Residual Error 4 237.61 59.40

Total 7 1438.37

Source DF Seq SS

Alloy Temperature 1 129.92

Stoppage (IN Sec) 1 343.56

Die Temp 1 727.28

As P>0.05 for Alloy temp.

It implies it is already in control.

As P<0.05 for Stoppage or delay

It effects Scrap positively.

As P<0.05 for Die temperature

It also effects Scrap.

As P<0.05 for overall regression

It implies input variables have

impact on dependent variables.

26th


is used and experiments were replicated twice for suitable accuracy. So it requires 24

experiments and for generating effective impacts of each factor over the response, random

repetition of 16 experiments or total 32 runs were performed.

Table-4 Two Levels of each Critical Factor

Factors/Levels (A) Die Temp (in

Degrees)

(B) Discharge of Water (Liter Per

Minute)

(C) Delay

(Seconds)

(D) Volume of R & R

(cm3)

Low 250 7 60 260

High 330 10 180 285

Scrap value has been calculated for each run. Figure 5 shows the analysis of the orthogonal

matrix of experiments through Minitab Statistical Software.

Figure-5: DOE Statistics

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%

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In the present case, it has been found that A, B, D, BC and ABD are more critical factors and factor

interactions, that are affecting overall casting scrap substantially. These are dotted in red and lying

far away from the normal plot line of standardized effects. Figure 6 represents the ‘relative

percentage of significance’ for every factor responsible for scrap. The main effect plots have been

drawn to describe the individual effect of each factor on the response. Figure 7 shows four factors

plotted in between their respective two levels and against scrap to show their impact,

independently. The slope of the main effect line in each plot represents the high impact of that

factor on response.

Figure-6: Normal Plot of Effects to Foreground CTQ Factors

Figure-7: Main-Effect Plot (OFAT)

151050-5-10-15-20

99

95

90

80

70

60

50

40

30

20

10

5

1

Standardized Effect

Pe

rce

nt

A Die Temp

B Discharge of water

C Delay

D V olume 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)

330250

16

15

14

13

12

107

18060

16

15

14

13

12

285260

Die Temp

Me

an

Discharge of water

Delay Volume of R&R

Main Effects Plot for Scrap (%)Data Means

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Plots for two-way interactions have been presented in figure 8. Graphically it is obvious that BC (or

combined effect of discharge of cooling water and delay) has vital impact on scrap, as two effects

are crossing with each other.

Similarly other interactions of delay with volume of R&R and die temperature are also affecting

scrap up to a certain level but the main effect of delay was coming out to be less significant. Out of

three two-way interactions of delay, the combination with discharge (C) seems to be more effective.

Figure-8: Two-way Interactions (MFAT)

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

Interaction Plot for Scrap (%)Data Means

Three-way interactions have also been analyzed but only the ABD interaction was significant,

therefore a cube plot has been generated in figure 9 describing all possible three-way interactions of

input process factors in the ABD group.

Figure-9: Cube Plot for Critical Three Way Interaction (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

Cube Plot (data means) for Scrap (%)

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Response Optimizer or Overlaid Contour Plot has been used to obtain a numerical and graphical

analysis. All the factors and their starting lower levels have been quoted in optimizer. The existing

value and required target value of the response are fed with possible upper and lower values. The

optimizer in Minitab software shows results at 99% desirability (see figure 10). The results are placed

in the form of a solution to the problem and quoted in red colour. These optimized values of

respective factors had reduced the piston scrap up to 10% as per the calculations of Minitab’s

response optimizer tool.

Figure-10 Optimization of Process Parameters

CurHigh

Low0.99998D

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

330.0Discharg Delay Volume oDie Temp

[250.0] [9.9602] [136.3636] [281.7172]

Later, it was estimated that an approximate saving of Rs.16,78,000/- per annum has been achieved

by reducing the scrap of H-749 pistons from around 22% to 10% (in first attempt). It is a great

achievement for a medium scale non-ferrous Indian foundry.

Control phase

During 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, p-Chart for overall scrap tracking.

Findings: In order to control the concerned parameters at their improved values, necessary

modification in control plan of casting process has been done as per the output of DOE. After brain

storming, maximum tolerance limits for metal temperature, water temperature and cooling time etc

were fixed. It is recommended to check 5-S condition of casting work station daily by visual

inspection by the Production Supervisor. The overall sigma level has been raised by 0.24. The Runner

& Riser volume has been increased permanently as per recommendations of DOE results. It was also

decided to monitor the overall casting scrap due to other reasons and scrap is further controlled by

drawing p-charts daily by inspection supervisor in general shift. The one month data for p charts has

been collected and shown in figure 11, which predicts the day to day current status of scrap in piston

foundry. These control measures have successfully run the piston foundry for consecutive two

months at around 10.4 % scrap only.

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Figure-11: p-Chart for Scrap Monitoring

31st D

ec

28th D

ec

25th

Dec

22nd

Dec

19th D

ec

16th

Dec

13th

Dec

10th D

ec

7th

Dec

4th Dec

1st D

ec

0.20

0.18

0.16

0.14

0.12

0.10

0.08

0.06

Time

ind

ivid

ua

l p v

alu

es

_X=0.1043

UCL=0.1380

LCL=0.0707111

1

1

11

1

1

1

p-value (np/n) chart

The Control phase has been integrated to configure infrastructure (hardware and software) and to

with-hold the settings, improvements, adjustments and optimizations performed during Six Sigma

case- study, with passage of time. This phase may also include some Value-Engineering proposals for

input parameters to resist changes in improved process variables, like:

Proper On-the-job training schedule for awareness of all the concerned shop floor staff, so that their

code of conduct becomes positive and responsive to tackle responsibilities. For more awareness and

to understand the significance of Six Sigma improvements, a ‘Six Sigma Corner’ should be developed

in the middle of production floor that should usually be operated by a black belt champion.

A comprehensive check list to cover every factor of all phases could be helpful to related work force

in reminding vital steps at the right time.

A Process-Indicator board (look figure 12) has been specifically designed to mitigate the execution

of all the casting activities/set up activities of H-749 pistons in a desired sequence like die

installation, setting of cooling time and emergency timer, installing of top frame of die with ram of

machine, alignment of top and bottom frame of die, level of molten metal in the holding-furnace,

metal temperature reading and checking of proper water supply connections with die top etc.,

before commencing regular production runs. It also supports important process variables like die

temperature, cooling water temperature and control over cycle time with series of successive

indicators, once the relevant check or activity has been performed or ensured successfully. Proper

hooters have also been installed with the board to warn the machine operators in case of missing of

any casting activity or its parameters settings. It really makes the whole die casting process well

sustained and ensures optimisation of relevant variables efficiently and as per process requirements.

Three patrol teams of expert casting operators are constituted to vigil the critical foundry processes

and their vital parameters, while patrolling in each shift.

To sustain the overall improvements acquired by DMAIC process, one executive level person to be

deputed to have a round at least once a week on the production shop floor, so that value adding

activities can be performed in a more controlled and consistent manner in future.

26th


Audit sheets and work instructions to be displayed for sustaining proper execution of various

foundry processes.

Figure-12: Process Indicator Board

Process to be supported suitably by an appropriate implementation of principle of 5-S, safety rules

and good practices in the foundry.

The Control phase tends to support the improved process and ensures the process execution in

between the controlled limits by total cultural improvement of given production environment. It

leads to stability of the system, which ultimately causes the customer satisfaction. The Control phase

has also raised future hopes for much more profitability by implementing such Six Sigma cases for

other parts like; for domestic gasoline and diesel vehicle pistons etc.

CONCLUSIONS

Manuscript has significantly reaffirmed the efficacy of Six Sigma strategy in Indian foundry industry

by reducing scrap/waste from the operations, thus greatly improving the production efficiency.

‘Project based’ approach for Six Sigma implementation (rather planning, training or investing in

different phases of Six Sigma approach) is more motivating and helps a lot to demystify various fears

on Six Sigma. A cadre with sound theoretical knowledge on different statistical tools and software

needs to be built up in the management, so as to bridge the gap between the theory and practice of

Six Sigma and appreciate its potential while bringing in business excellence (Singh and Khanduja,

2012b).

Beside non-ferrous foundries, Six Sigma approach can be explored for ferrous foundries to bring

breakthrough in rejections and increase yield per annum. It can also be used in energy intensive

units, as it not only enhances productivity by process improvement but also it is a step to create

‘zero defect units’ which indirectly reap huge energy/power savings. Apart from foundry industries,

other manufacturing sectors like forging, forming, welding and machining industries can also take

benefits to lean their respective business operations. Six Sigma should further be explored in service

sector like; hospitals, offices, banking, traffic etc. Through an extensive literature search, it was

observed that very little documentation exists in the application of Six Sigma to education sector.

This approach will bring paradigm shifts in enterprises by inculcating high skill levels among their

management personnel, managers, engineers, practitioners or even workers. 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. Six Sigma, a systematic framework for quality improvement and

business excellence, has been widely publicized in recent years as the most effective means to

26th


combat quality problems and win customer satisfaction but it is still at its infancy stage as far as

Indian industries are concerned.

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Acknowledgement - We acknowledge concerned industrial personnel for their time to time

technical help while conducting the Case study. We are also grateful to Reviewers of SEAAZ-2013 for

giving us valuable suggestions to formulate the manuscript in present form.

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