Article 18_01

Journal of Engineering Research and Studies E-ISSN 0976-7916

JERS/Vol.I/ Issue II/Oct.-Dec.,2010/177-194

Research Article

USE OF SHAININ TOOLS FOR SIMPLIFYING SIX

SIGMA IMPLEMENTATION IN QMS/ISO CERTIFIED

ENVIRONMENT AN INDIAN SME CASE STUDY Anand K. Bewoor*, Maruti S. Pawar

Address for Correspondence

*1Mechanical Engineering Dept.,Vishwakarama Institute of Information Tech.,Kondhwa

(Bk), Pune 411048, Maharashtra, India 2Professor and Vice-Principal, B. M. I. T., Solapur University, Solapur Maharashtra, India.

E-mail: [email protected], [email protected]

ABSTRACT Six sigma for small- and medium-sized enterprises (SMEs) is an emerging topic among many

academics and Six Sigma practitioners over the last two to three years. Very few studies have been

reported about the successful applications of Six Sigma in SMEs. Main objective of this paper is to

examine the extent of usefulness of a simpler but not very frequently used methodology known as the

Shainin methodology for simplifying the implementing Six Sigma. To confirm whether Six Sigma

implementation is simplified, this paper highlights the comparison of three DOE approaches viz.

Classical, Taguchi and Shainin methodology.

A case study based research work done in ISO certified Indian SME, concludes that, Six Sigma

implementation process can be simplified by using Shainin tools and proper use companys ISO/QMS.

KEYWORDS Six Sigma, Shainin Tools, QMS, Indian SMEs.

1. INTRODUCTION

In recent past, academicians, practitioners

and organizational researchers have

recognized that the Quality Management

System (QMS) process and the Six-Sigma

process are disciplines that have a

powerful potential to affect an

organizations ability to compete within

an increasingly global and dynamic

marketplace (Falshaw et al., 2006). QMS

certification (such as ISO 9000, TS

16949) demonstrates the capability of an

industry to control the processes that

determine the acceptability of the product

or service being produced & sold. These,

traditional QMS are having some

limitations like methodological assistance

etc. (Bewoor and Pawar, 2008). But new

QM methods continue to grow (Xingxing

Zu et. al., 2008) for example, Six Sigma,

which is an organized and systematic

method for strategic process improvement

and new product and service development.

Six Sigma relies on statistical methods and

the scientific method to make dramatic

reductions in customer defined defect

rates (Linderman et al., 2003). Since its

initiation at Motorola in the 1980s, many

companies including GE, Honeywell,

Sony, Caterpillar, Johnson Controls etc.

have adopted Six Sigma and obtained

substantial benefits (Pande et al., 2000).

Spectacular development of an

organizational performance due to Six

Sigma implementation many companies

are reported in the published literature.

Antony and Banuelas (2002) presented the

key ingredients for the effective

introduction and implementation of Six-

Sigma in manufacturing and services

organizations as: Management commit-

ment and involvement, Understanding of

Six Sigma methodology, tools, and

techniques, Linking Six Sigma to business



strategy, to customers, to suppliers, project

selection, reviews and tracking,

organizational infrastructure, Cultural

change, Project management skills,

Training. All these ingredients make the

Six Sigma process as a complex process

and very little efforts has been made for

simplifying the process of Six Sigma

implementation process by making use of

existing QMS and by selecting proper

implementation tools. Some of the

criticisms of the Six Sigma methodology

perhaps stems from the fact that it is

sometimes too statistical and beyond

comprehension of the people involved in

implementing it in practice. Eckes (2001)

is of the opinion that Six Sigma initiatives

can fail if the organization believes that

better quality is possible only through the

use of sophisticated statistical tools. The

objective of this paper is to examine as to

how to simplify and demystify the use of

Shainin tools for Six Sigma

implementation tools. At present, the

impacts of QMS and Six Sigma processes

on an organizations ability to compete

have been examined independently. Very

little emphasis has been given by the

researchers to conceptually examine the

potential impact of the synergistic effects

that might be gained from merging various

quality management principles and those

of Six-Sigma process. After doing clause-

wise analysis Bewoor and Pawar, (2008)

had proposed the Six Sigma+QMS/ISO

an integrated concept and successfully

validated its applicability with the help of

case study based research. This has

resulted in to more benefit on operational

level (Bewoor and Pawar, 2009). This

case based study helped us to understand

that if we use simple to use tools, we can

simplify Six Sigma implementation

process. The observations and experiences

in the above case study leads to question

about how to simplify the implementation

of Six Sigma with or without QMS/ISO

systems. The main complex part of the

implementation of Six Sigma is the

selection and use of tools for solving

problems. It is observed that, the efforts to

simplify the implementation of Six Sigma

are needed in the area of use of tools. One

of such efforts/studies is presented below.

2.PRESENT METHODOLOGIES FOR

SIX SIGMA IMPLEMENTATIONS

Pyzdek (2003) has classified Six Sigma

tools into three categories (refer table 1),

(i) Basic Six Sigma methods (are further

categorized as problem solving tools, 7M

tools, and knowledge discovery tools). (ii)

Intermediate Six Sigma methods include a

host of enumerative and analytical

statistical tools like Distributions,

Statistical inference, Basic control charts,

exponentially weighted moving average

(EWMA) charts etc.). (iii) Advanced Six

Sigma methods are Design of experiments

(DOE) Regression and correlation analysis

Process capability analysis etc. At the

heart of the Six Sigma approach is the

application of DOE techniques. These

techniques help to identify key factors and

to subsequently adjust these factors in

order to achieve sustainable performance

improvements.



Table 1 : Basic Six Sigma Tools

Problem Solving Tools 7M Tools Knowledge Discovery

Tools

Process mapping Affinity diagrams Run charts

Flow charts Process decision program charts Descriptive statistics

Check sheets Matrix diagrams &

Tree diagrams

Histograms

Pareto analysis Interrelationship diagraphs Exploratory data analysis

Cause-and-effect

diagrams

Prioritization matrices

Scatter plots Activity network diagrams

(Source: Pyzdek, 2003)

While the basic and intermediate methods

are relatively easier to understand and use,

the advanced methods are perceived to be

difficult to comprehend and interpret.

Design of Experiments (DOE) is one such

tool. The complexity of these DOE

techniques that are often cited by

companies as to the reason why they are

unable to employ Six Sigma. A short

overview of the DOE techniques is

presented next.

2.1 Experimental Design using

Classical and Taguchi Approach

A classical DOE approach would have

meant application of factorial designs

requiring much more time and effort, and

above all, it would have required changes

in machine settings. Classical DOE

requires large data collection to conduct

the analysis. Six Sigma process

improvements consist of analyzing

relationships between an output variable

(Y) explained wholly or partly by process

variables (Xs) that affect the output. A key

step in Six Sigma projects is the

identification of the root cause of the

problem out of the potential Xs.

Experimental design is one of the tried

and tested statistical techniques long used

by industrial engineers to identify the key

variables affecting output. Through

designed experiments, changes are

deliberately introduced into the process to

better understand which of the Xs are

affecting the output variable.

There are two well-known approaches

to experimental design. The first approach

is the classical design of experiments

credited to Sir Ronald Fisher who initially

experimented in the field of agriculture.

However, this method is now widely used

in many fields. The second approach is the

Taguchi approach pioneered by Dr

Genichi Taguchi of Japan who adopted the

classical approach to reintroduce the

concept of orthogonal arrays used for

designing experiments in different fields

(Rao, et al.). The commonly used classical

Design of Experiment (DOE) tools are the

family of factorial experiments consisting



of full factorial designs and fractional

factorial designs. A full factorial allows us

to test all possible combinations of factors

affecting output in order to identify which

ones are more dominant. A fractional

factorial tests just a fraction of the

possible combinations. Though a very

popular tool, many engineers and quality

practitioners find design of experiments

difficult primarily because of the

complexity of having to create the

conditions for conducting the experiments

in an industrial environment where

interrupting production lines and changing

machine settings may be sometimes

difficult and unproductive.

2.2 Shainin DOE Approach

An alternative to the Classical and

Taguchi experimental design is the lesser-

known but much simpler Shainin DOE

approach developed and perfected by

Dorian Shainin (Bhote and Bhote, 2000),

consultant and advisor to over 750

companies in America and Europe.

Shainins philosophy has been, Dont let

the engineers do the guessing; let the parts

do the talking. Shainin recognized the

value of empirical data in solving real-

world problems. He introduced the

concept of Red X, the dominant source of

variation, among the many sources of

variation of a problem that inevitably

accounts for nearly all the unwanted

effect.

In fact, Shainin (Shainin, 1995; 1993b)

had classified all causes of chronic quality

problems into three Xs, viz., the Red X,

the Pink X- the second most important

cause(s), and the Pale Pink X the third

most important cause(s). According to

him, these three Xs together account for

over 80 per cent of the variation that is

allowed within the specification limit and

when captured, reduced, and controlled,

these can eliminate this variation. Shainin

developed techniques (Shainin and

Shainin, 1990; 1992a; 1992b; 1993a;

1993b; Shainin, Shainin and Nelson,

1997) to track down the dominant source

through a process of elimination (Shainin,

1993b), called progressive search. These

techniques, also referred to as the Shainin

System for quality improvement,

developed over a period of over 40 years,

are simple but at the same time powerful

and easier to interpret and implement in an

industrial environment. In a way, these

may be considered as the non-parametric

equivalent of Taguchis DOE as they do

not make any restrictive assumptions

about population parameters. The Shainin

techniques are primarily known to

produce breakthrough improvements in

eliminating chronic quality problems.

These are highly effective in pinpointing

towards the root cause and validating it.

No statistical software was needed to

analyze the data. In fact, Shainin DOE

does not even require knowledge of

difficult statistical tools. Simple operation

like counts, additions, subtractions, etc.,

makes calculations relatively easy. In



addition, the success of the projects can

lead to a very positive effect on the morale

of the employees in terms of convincing

them that Six Sigma can be implemented

without complex statistics and big jargons.

The subject of the Shainin methods is very

vast and this paper highlights the

applicability of only a few of the Shainin

tools. However, there is a lot of scope for

more research on this methodology

particularly comparative research of some

of the Shainin methods like Paired

Comparison and B Vs C Analysis vis--

vis the more popular statistical tools like

factorial designs and non-parametric

testing. Although these methods are not

necessarily the best, according to Steiner

et al. (2008), the guiding principles of the

Shainin tools are powerful, and at least, in

combination, unique. Also, these tools are

best suited for batch to high volume

production.

3. FINDINGS FROM VARIOUS

CASE STUDIES ABOUT DOE

APPROACHES

Bhote and Bhote (2000) described these

tools in their books, but there have been

many criticisms regarding their claims and

the tools described. Though, Nelson

(1991) and Moore (1993) criticized the

Shainin System as unsubstantiated and

exaggerated, Steiner, et al (2008), are of

the opinion that some of the ideas behind

the Shainin System are genuinely useful.

Goodman and Wyld (2001) offered a case

study involving the use of Shainin DOE in

an industrial operation. Applications of the

Classical and Taguchi methods in various

fields have been extensively researched. In

contrast, the Shainin system has not been

extensively reviewed, academically, and

very limited studies have been carried out

in this area.

3.1 Studies about comparison of

DOE approaches

Bhote (2000) compared Shainin

techniques with Design of Experiments

and Taguchi methods, in the context of the

electronics industry and concluded that the

Shainin techniques are simpler, less

costly, and statistically more powerful

than the other two. Logothetis (1990) also

evaluated the Shainin techniques in

relation to the Taguchi methods and

statistical process control methods.

Verma, et al (2004) used a slightly

different approach to compare the

methods. In their study, three cases of

Taguchi experiments were picked up from

the available literature and the Shainin

method was then re-applied to find out

whether it had an edge over the other DOE

techniques. A comparison between

Taguchi and Shainin techniques in an

aerospace environment was offered by

Thomas and Anthony (2005). A few other

authors who have studied these techniques

are Ledolter and Swersey (1997), De

Mast, et al. (2000) and Steiner and

MacKay (2005). The Classical DOE,

Taguchi DOE, and Shainin DOE are

compared with each other in Table 2.



Table 2:Comparison of Classical, Taguchi, and Shainin DOE Approaches

Items for

compari-

son

Classical DOE Taguchi DOE Shainin DOE

Primary

tools

Factorial experiments

Orthogonal arrays

a. Component search,

b. Multi-vari analysis,

c. Paired comparison,

d. Product/Process Search or,

variable search, e. Full

factorials, f. B vs. C (Better

vs. Current) analysis, Scatter

plots.

Advan-

tage

Effective when

interaction effects are

not present

(20 to 200%

improvements).

Limited possibilities

for optimization.

Effective when

interaction

effects are not present

(20 to 200%

improvements).

Limited possibilities for

optimization.

Very powerful irrespective of

the presence or absence of

interactions. Maximum

optimization possible.

Cost/Tim

e Moderate Moderate Low

Training 3 to 5 days 3 to 10 days 1 to 2 days

Complexi

ty Moderate High

Low (simple & basic

mathematical operations)

Facility &

Scope

Requires use of

statistical software

e.g., SAS, SPSS, etc.

Used mainly in

production.

Requires use of

statistical software e.g.,

SAS, SPSS, etc. Used

mainly in pre-

production & can be

used at the design stage

under certain

constraints.

Software not necessary.

Ease of

Imple-

mentation

Moderate (Requires

knowledge of

statistics. Engineers

find methods

complex to

comprehend and

interpret.)

Poor

High (Almost no knowledge

of statistics required. Easy to

understand at all levels

including shop floor workers,

engineers, and suppliers, thus

creating an overall positive

impact.

(Bothe & Bothe, 2000)



An examination of the three approaches

clearly indicates that the Shainin tools

have an edge over the other two

approaches in terms of cost, time, training,

complexity, scope, and ease of

implementation. The following work

highlights the tools and techniques that

were used by Indian SME, a

manufacturing unit of Gange Industries

(GI) in their development of the six sigma

programme

4. CASE STUDY

This case-study was successfully

completed in the welding unit of GI,

which is a SME was established in 1985,

located at Bhosari M.I.D.C., Pune,

Maharashtra State in India. GI has grown

to become a one of the major player in

processing/manufacturing of automobile

sheet-metal parts. GI is ISO 9001 and TS

16949 certified and has implemented

company wide QM, Kaizen and TPM

initiatives to good effect.

The company from their past experience

found that the QM process and its

associated systems were too slow in

identifying and responding to problems

primarily, since they were developed to

obtain long-term strategic direction and

focus. Therefore, company officials had

accepted and initiated move towards use

of Shainin tools for implementation of

Six Sigma + QMS integrated approach

for increase the process quality,

productivity intern reducing process cost.

Until the introduction of the integrated

strategy, the company attended to quality

problems in an often ad-hoc and

unstructured manner.

The following section followed how

the company followed the proposed

methodology in an attempt to provide a

structured approach to solving critical to

quality (CTQ) problems within the

company and to achieve enhanced process

quality, productivity, customer satisfaction

and internal benefits through a case study

of one particular project undertaken.

Six Sigma DMAIC Process

The six sigma process concentrates on a

simple five phase methodology called

DMAIC (Define, Measure, Analyze,

Improve, Control). The company followed

this approach and each stage is explained

in detail in the following section of the

paper.

Define Phase: The data available

(collected through QMS) related to type,

frequency and amount of rework done at

GI is analyzed. Our team (which includes

companys management representative,

managers, engineers and author) at GI

confirmed that, parts named Assy-sub

structure with floor (613 LP RUSSIA)

(XXX 6100 0182), which fits into

assembly frame of light commercial

vehicle after welding on Welding M/C

ST-CO2-17 machine was under rejection

because of defective welding (non

uniform welding, weld penetration, dry

welding, weld under cut and spatter etc.),

which resulted in to annual Cost of Poor

Quality (COPQ) about INR 2Lakh/-.

Process stages, where the problem

detected are in-process inspection and

final inspection. This project was

undertaken to achieve certain objectives

viz. productivity improvements in terms of



reduction/elimination of reworks and

reducing process cost [tangible], customer

satisfaction, and increase in confidence on

shop floor [intangible]. Hence,

repeatability and reproducibility study was

required for validating the measurement

system. Process Mapping is carried out

(refer figure 1),

Measure and Analyze Phase: A

brainstorming exercise was carried out by

a multi-disciplinary team of engineers

within the company. The team identified

the factors that could influence the product

quality. A cause-and-effect diagram was

developed (refer figure 2) to identify the

key sources of variation during the

welding process. Two potential

Suspectable Sources of Variations (SSVs)

were finally listed as: Sheet material

thickness, Welding Process itself.

Figure 2: Cause-and-Effect Diagram



Without taking educated guesses as to the

factors of real importance, authors have

suggested to adopt the Shainin

Techniques. The Shainins Techniques

been employed to identify whether the

primary cause of shabby/defective

welding lay within the process itself or

within the components used. This allowed

for a first stage filter to be employed that

cut down the factors to a manageable

number. Key stages, in which Shainin

tools were applied, are explained below.

Initial tool selected for measuring and

analyzing the response was Product

Process Search, as of variations in the

identified suspectable sources of

variations (SSV) i.e. input material

parameter (as compared with their

standard specification) viz. SSV-1.

Material Thickness (Specifications 2.0

mm +/- 0.18), gets changed during

processing. Data was collected for 100

samples.

Observation 1 It has been observed that,

minimum and maximum value of sheet

metal (raw material) thickness as an

important input to production process

belongs to same category of response.

Therefore, as per Product Process Search

method the end-count is zero. Hence, it

has been concluded that, SSV-1: Input

material parameter (i.e. Thickness) is not

creating problem. Next another

brainstorming session has concluded for

characterization of CO2-Welding process

as process itself is yielding in to

variations, which is required to be

analysed. Hence, relation can be written

as; BigY (Response i.e. Defective welding)

= f [X (Sources of variations i.e. CO2

Welding process)]. Therefore, new SSVs

are now related to CO2-Welding process

are listed viz. Voltage, Current, Gas Flow

and Wire Feed Rate. To check whether

any relationship exists within the

identified parameters or not; data related

to all these parameters are collected (refer

table 3), regression analysis is carried out

and Graphs are plotted. Graph of Wire

Feed Rate vs Current clearly shows the

positive relationship (refer figure no. 3).

Hence, new SSVs identified parameters

related to CO2-Welding process are now

limited to: Voltage, Wire Feed Rate and

Gas Flow.

As the identified parameters were design

parameters of process and number of

parameters are equal to 3 hence, it has

been decided that, process characterization

analysis i.e. Full Factorial Analysis tool is

to be used. All stages of full factorial

method are explained as follows,

Stage 0: As the response is attribute in

nature, consider current setting as the

setting and identify + setting on the basis

of experience on domain expert for each

parameters (refer table 4).

Stage 1: To find out whether the

parameters and the levels identified in

stage 0 are correct or not. Then, we have

to produced 3 batches in setting and 3

batches in + setting. Calculate D/d ratio,

if D/d ratio is >=1.25 and



tabulated in table 5. D/d ratio is 0.4, which

indicates that, the levels identified in stage

0 are not correct. Hence, new parameters

levels are identified by considering earlier

+ ve setting as new - setting and new

+ ve settings for all parameters are

identified and set (refer table 6). Again

new trials are conducted and the results

are tabulated in table 7. D/d ratio is 10,

which indicates that the levels identified in

2nd settings are acceptable for further

consideration.

Stage 2: Construct factorial table and

collect the data for each combination in

the factorial table and quantify the

contributions of the interactions.

Table 8 shows factorial design and plan

of experimentation. Accordingly

experiments were performed, which

resulted in to following important

conclusions.

Parameter- A: As if we change from +

level to - level then response increases by

2.5 points.

Parameter- B: As if we change from +

level to - level then response decreases by

1.5 points.

Parameter- C: As if we change from +

level to - level then response decreases by

5 points.

Stage 3: Make a simple mathematical

equation based on the contribution of

significant parameters and arrive at the

optimal setting.

Y= 84.875 3.125 A + 14.162B + 4.875C

+2.625 AB 4.375 BC 7.125 CA +

7.625ABC

As response Y considered is

shabby/defective welding hence, our

objective is lower the better. Using above

equation, offline iterations are done.

While doing iterations +ve settings are

refereed as 1, - settings are referred as

-1. Values some of the offline iterations

and its calculated responses are tabulated

in table 9. Then, experiments are carried

out using the levels of the parameters for

which responses are zero or less than zero

and physical outputs are analyzed.

Response for setting in case of experiment

no. 9 (shown in same table) resulted in to

proper welding (considered as an optimum

output).

Improvement Phase: Conclusions of

earlier phase (identified optimum levels of

the parameters as shown in table 10) are

used as an input to this phase. Once

optimum settings are set then, it is

necessary to validate it. This was done, by

using the Shainin B vs. C analysis, which

is a confirmation tool to verify whether

the actions taken have actually improved

the process (Bhote and Bhote, 2000). In

this case, 6B vs. 6C, i.e., 6 batches (10

units per batch) with modification and 6

(10 units per batch) without modification

(B with modification and C without

modification) was analyzed to validate the

improvement action, i.e., the modification

of CO2 machine operating parameters

(table 11).

The data in table 12 exhibited the

responses with B and C conditions. As per

rule of this technique, the final analysis is

done based on the end-count scheme. In

this case, end count is 8 (greater than 6),

which confirms that identified root causes

are correct.



Further, the result clearly validates the

improvement against the criteria

mentioned in table 13. The data has

exhibited no overlaps of the responses

with B condition and C condition. The

conclusion being that the process has been

improved by changing the CO2 welding

machine operational specifications as

mentioned in table 10. New specifications

not only helped to improve the quality

level but also productivity by reducing

defect/rework rate and optimizing the use

of resource and time (e.g. Wire Feed Rate

from 10 Min/min to 6.5 Min/min and Gas

Flow from15 Lit/min to 14 Lit/min).

Table 3: Data related to all these interactions among identified parameters

Sr. No. wire feed voltage current

1 50 27 40

2 55 13 90

3 55 16 100

4 55 18 80

5 55 20 100

6 55 22 110

7 55 22 110

7 55 25 100

9 55 28 90

10 55 30 90

11 65 17 100

12 65 19 100

13 65 23 100

14 75 30 160

15 80 20 150

16 80 27 140

17 100 26 190

Table 4: First Setting of levels of each parameter

Sr. No. Parameter UOM Existing Setting (- ve ) Modified Setting (+ ve )

A Wire Feed Rate Min/min 10 7

B Voltage V 26 20

C Gas Flow Lit/min 15 8



Table 5: First Trial

Trial - Setting + Setting

1st Trial 10 50

2nd Trial 50 50

3rd Trial 40 60

Median 40 50

Range 40 10

D = Difference Between Two Medians 10

d = Average of Two Ranges 25

D/d 0.4

Table 6: Second Setting of levels of each parameter

S. N. Parameter UOM Existing Setting ( - ve ) Modified Setting (+ ve )

A Wire Feed Rate Min/min 7 4

B Voltage V 20 18

C Gas Flow Lit/min 8 6

Table 7: Second Trial

Trial - Setting + Setting

1st Trial 50 100

2nd Trial 50 100

3rd Trial 60 100

Median 50 100

Range 10 0

D = Difference Between Two Medians 50

d = Average of Two Ranges 5

D/d 10

Table 8:Factorial Table

Factors (Main Effects) Factor interaction

A B C AB BC CA ABC Response Median

7 " - " 20 " - " 8 " - " " + " " + " " + " " - " 50 , 50, 60 52

4 " + " 20 " - " 8 " - " " - " " + " " - " " + " 70 70

7 " - " 18 " + " 8 " - " " - " " - " " + " " + " 100 100

4 " + " 18 " + " 8 " - " " + " " - " " - " " - " 70 98

7 " - " 20 " - " 6 " + " " + " " - " " - " " + " 100 100

4 " + " 20 " - " 6 " + " " - " " - " " + " " - " 60 59

7 " - " 18 " + " 6 " + " " - " " + " " - " " - " 100 100

Parameters

Settings

4 " + " 18 " + " 6 " + " " + " " + " " + " " + " 100, 100,

100 100

" - " 88 70.25 80 82.25 89.25 92 77.25

" + " 81.75 99.5 89.75 87.5 80.5 77.75 92.5

Sign " - " " + " " + " " + " "-" " - " " + "

Difference 6.25 29.25 9.75 5.25 8.75 14.25 15.25

Coeff. 3.125 14.625 4.875 2.625 4.375 7.125 7.625 84.874



Table 9:Offline iterations, its calculated and actual responses

Expt.

No. Wire Feed Voltage Gas Flow Constant Response Remark

1 0 0 0 84.875 84.88

2 -1 -1 -1 84.875 52

3 1 1 1 84.875 100

4 -.5 -2 -2 84.875 10.19 Poor adhesion

5 -0.45 -2 -2.5 84.875 0.0

6 -0.5 -3 -3 84.875 -52.50 Poor adhesion

7 -0.6 -5 -5 84.875 -248

8 -0.6 -9 -7 84.875 -507.20 Poor adhesion

9 -0.65 -9 -7 84.875 -684 OK 10 -2 -11 -7 84.875 -1652 High Penetration

Table 10: Existing and Optimum Settings

Sr.

No.

Parameter UOM Existing Setting

( -) ve

Optimum Setting

(0 - Target )

A Voltage V 26 28

B Current A 200 150

C Wire Feed Rate Min/min 10 6.5 D Gas Flow Lit/min 15 14

Table 11: B vs. C analysis

1 Part number selected for validation ASSY substructure with floor

Better Condition Optimum Settings (Refer table 10 ) 2 Current Condition -

3 Sample size 6B,6C

4 Sample type Batches

5 Response decided for monitoring % Rejection

6 Lot quantity (for batches) 10

Table 12:B vs. C Response

Lot no. Better ( B ) Current ( C )

1 0 40

2 0 30

3 10 10

4 10 40

5 0 30

6 0 40

Table 13: Criteria for validating improvements and results

Sr. no. Criteria for validating improvements Results

1 Part selected for validation Sub structure assembly with floor

Average of B 3.33 2

Average of C 31.66

3 Xb Xc (Amount of Improvement) 28.33

4 Sigma (B) 4.71

5

Is [Xb - Xc] greater than [K x Sigma (b)]

(Where K is std value = K = 2.96 @ 95%

Confidence Level )

Yes [(28.33 > 19.78]



The improvements identified were also

used to set the action plan for other

varieties of such components for

horizontal deployment.

Control Phase:

The focus of the control phase is to sustain

the gains of the improvement phase. This

is usually achieved by documentation and

standardization of the control measures.

For controlling the process at Six Sigma

level, following actions were suggested.

Appropriate modifications have been

done in CO2 welding machine operating

and training manuals.

Procedure has been developed for

periodic monitoring of CO2 welding

machine operational specifications w. r. to

quality level of output.

Implemented controls to make sure

that the actions taken in Phase-III are done

forever.

All these modifications have been

included as a part of Company-QMS

procedure to ensure the reliability of Six

Sigma level quality of the process.

The operational framework developed and

used in this research-work is described in

figure 4.



It clearly shows the major stages in the

process integration and implementation. It

shows initially the sequential nature of the

stages whereby the Six Sigma phases are

using appropriate imputes from company

QMS database to continently execute the

project. The operational framework also

shows the stages in sequence whereby the

six sigma DMAIC phases are using

accurately Shainin quality tools.

5. DISCUSSION AND CONCLUSIONS

The aim of this project is to defeat the

biggest excuses cited by SMEs as the

reasons Six Sigma is not feasible, incurs

high costs and involve complexity of

implementation. In addition, it helps to

break down so many of the barriers that

stand in the way of individuals using

statistical and/or unfamiliar problem

solving methods by acting as a step-by-

step guide. This research work focus on

use of Shainin tools specifically, as they

are easy to understand, involves simple

mathematical calculations (so that bottom-

line people can also understand it very

easily) and time required for training is

also less, which is one of the important

requirements of SMEs. During this case

study, during use of Shainin tools, small

samples of BOB and WOW pieces were

sufficient to analyse the data as reported

earlier. A very important factor is that data

collection was done for the project

undertaken online without disturbing the

regular production.

Thus in short, we can understand that, use

of Shainin tools for simplifying Six

Sigma implementation can provides an

appropriate methodology for SMEs for

delivering certain objectives set by ISO

such as: prevention of defects at all stages

from design through servicing; techniques

required for establishing, controlling and

verifying process capability and product

characterization; investigation of the cause

of defects relating to product, process and

quality system; continuous improvement

of the quality of products/services.

From the results of case study based

research work we draw following

conclusions,

i. The key phase of the DMAIC

methodology is the measure and

analysis phase. The tools and

techniques used in this phase

determine the success or failure of

the project to a large extent. In

both the projects, the Shainin tools

have been very effectively used to

pinpoint the root causes and

validate the improvement actions.

ii. No statistical software was needed

to be used to analyse the data. In

fact, Shainin DOE does not even

require knowledge of difficult

statistical tools. Simple operation

like counts, additions, subtractions

etc., makes calculations relatively

easy. Therefore the training

required for application of Shainin

tools is simple and requires less

time (1-2 days).

iii. In addition, the success of the

projects had a very positive effect

on the morale of the employees in

terms of convincing them that Six

Sigma works without complex

statistics and big jargons.



iv. Existing company QMS

procedures has assisted

/complimented in all stages of

implementation of Six Sigma.

v. Operational framework developed

and used in this research-work has

validated for its implementation

and found to be a useful concept

for improving quality and

productivity/performance of SME.

vi. The project was completed within

a span of almost three months. For

the company, the estimated

savings from this project was

more than INR 2 lakhs per annum.

The guiding principles of the Shainin tools

are powerful, and at least, in combination,

unique. Therefore, we conclude that,

applying simplified Shainin tools based

Six Sigma methodology to the existing

company QMS process is the best way for

SMEs to achieve the optimal results in

quality progress towards TQM in

customer satisfaction.

This paper highlights the applicability of

only a few of the Shainin tools. There is a

lot of scope for more research on this

methodology as its most of the

terminology is trademarked and legally

protected, limiting academic debate and

discussion on this system of problem

solving, which despite many criticisms

and having been largely overshadowed by

the classical and Taguchi techniques in the

past, is now gradually being given its due

recognition.

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