Shainin Tools PDF

Journal of Engineering Research and Studies E-ISSN 0976-7916
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
2
Professor and Vice-Principal, B. M. I. T., Solapur University, Solapur Maharashtra, India.
E-mail: bewooranand@yahoo.com, drmspbmit@rediffmail.com
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 company’s ISO/QMS.
KEYWORDS Six Sigma, Shainin Tools, QMS, Indian SMEs.
1. INTRODUCTION and new product and service development.
In recent past, academicians, practitioners Six Sigma relies on statistical methods and
and organizational researchers have the scientific method to make dramatic
recognized that the Quality Management reductions in customer defined defect
System (QMS) process and the Six-Sigma rates’’ (Linderman et al., 2003). Since its
process are disciplines that have a initiation at Motorola in the 1980s, many
powerful potential to affect an companies including GE, Honeywell,
organization’s ability to compete within Sony, Caterpillar, Johnson Controls etc.
an increasingly global and dynamic have adopted Six Sigma and obtained
marketplace (Falshaw et al., 2006). QMS substantial benefits (Pande et al., 2000).
certification (such as ISO 9000, TS Spectacular development of an
16949) demonstrates the capability of an organizational performance due to Six
industry to control the processes that Sigma implementation many companies
determine the acceptability of the product are reported in the published literature.
or service being produced & sold. These, Antony and Banuelas (2002) presented the
traditional QMS are having some key ingredients for the effective
limitations like methodological assistance introduction and implementation of Six-
etc. (Bewoor and Pawar, 2008). But new Sigma in manufacturing and services
QM methods continue to grow (Xingxing organizations as: Management commit-
Zu et. al., 2008) for example, Six Sigma, ment and involvement, Understanding of
which is ‘‘an organized and systematic Six Sigma methodology, tools, and
method for strategic process improvement techniques, Linking Six Sigma to business
JERS/Vol.I/ Issue II/Oct.-Dec.,2010/177-194

strategy, to customers, to suppliers, project resulted in to more benefit on operational

selection, reviews and tracking, level (Bewoor and Pawar, 2009). This
organizational infrastructure, Cultural case based study helped us to understand
change, Project management skills, that if we use simple to use tools, we can
Training. All these ingredients make the simplify Six Sigma implementation
Six Sigma process as a complex process process. The observations and experiences
and very little efforts has been made for in the above case study leads to question
simplifying the process of Six Sigma about how to simplify the implementation
implementation process by making use of of Six Sigma with or without QMS/ISO
existing QMS and by selecting proper systems. The main complex part of the
implementation tools. Some of the implementation of Six Sigma is the
criticisms of the Six Sigma methodology selection and use of tools for solving
perhaps stems from the fact that it is problems. It is observed that, the efforts to
sometimes too statistical and beyond simplify the implementation of Six Sigma
comprehension of the people involved in are needed in the area of use of tools. One
implementing it in practice. Eckes (2001) of such efforts/studies is presented below.
is of the opinion that Six Sigma initiatives 2.PRESENT METHODOLOGIES FOR
can fail if the organization believes that SIX SIGMA IMPLEMENTATIONS
better quality is possible only through the Pyzdek (2003) has classified Six Sigma
use of sophisticated statistical tools. The tools into three categories (refer table 1),
objective of this paper is to examine as to (i) Basic Six Sigma methods (are further
how to simplify and demystify the use of categorized as problem solving tools, 7M
Shainin tools for Six Sigma tools, and knowledge discovery tools). (ii)
implementation tools. At present, the Intermediate Six Sigma methods include a
impacts of QMS and Six Sigma processes host of enumerative and analytical
on an organization’s ability to compete statistical tools like Distributions,
have been examined independently. Very Statistical inference, Basic control charts,
little emphasis has been given by the exponentially weighted moving average
researchers to conceptually examine the (EWMA) charts etc.). (iii) Advanced Six
potential impact of the synergistic effects Sigma methods are Design of experiments
that might be gained from merging various (DOE) Regression and correlation analysis
quality management principles and those Process capability analysis etc. At the
of Six-Sigma process. After doing clause- heart of the Six Sigma approach is the
wise analysis Bewoor and Pawar, (2008) application of DOE techniques. These
had proposed the ‘Six Sigma+QMS/ISO’ techniques help to identify key factors and
an integrated concept and successfully to subsequently adjust these factors in
validated its applicability with the help of order to achieve sustainable performance
case study based research. This has 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 & Histograms
Tree diagrams
Pareto analysis Interrelationship diagraphs Exploratory data analysis
Cause-and-effect Prioritization matrices
diagrams
Scatter plots Activity network diagrams
(Source: Pyzdek, 2003)
While the basic and intermediate methods identification of the root cause of the
are relatively easier to understand and use, problem out of the potential Xs.
the advanced methods are perceived to be Experimental design is one of the tried
difficult to comprehend and interpret. and tested statistical techniques long used
Design of Experiments (DOE) is one such by industrial engineers to identify the key
tool. The complexity of these DOE variables affecting output. Through
techniques that are often cited by designed experiments, changes are
companies as to the reason why they are deliberately introduced into the process to
unable to employ Six Sigma. A short better understand which of the Xs are
overview of the DOE techniques is affecting the output variable.
presented next. There are two well-known approaches
2.1 Experimental Design using to experimental design. The first approach
Classical and Taguchi Approach is the classical design of experiments
A classical DOE approach would have credited to Sir Ronald Fisher who initially
meant application of factorial designs experimented in the field of agriculture.
requiring much more time and effort, and However, this method is now widely used
above all, it would have required changes in many fields. The second approach is the
in machine settings. Classical DOE Taguchi approach pioneered by Dr
requires large data collection to conduct Genichi Taguchi of Japan who adopted the
the analysis. Six Sigma process classical approach to reintroduce the
improvements consist of analyzing concept of orthogonal arrays used for
relationships between an output variable designing experiments in different fields
(Y) explained wholly or partly by process (Rao, et al.). The commonly used classical
variables (Xs) that affect the output. A key Design of Experiment (DOE) tools are the
step in Six Sigma projects is the family of factorial experiments consisting

of full factorial designs and fractional problems into three X’s, viz., the Red X,
factorial designs. A full factorial allows us the Pink X- the second most important
to test all possible combinations of factors cause(s), and the Pale Pink X – the third
affecting output in order to identify which most important cause(s). According to
ones are more dominant. A fractional him, these three Xs together account for
factorial tests just a fraction of the over 80 per cent of the variation that is
possible combinations. Though a very allowed within the specification limit and
popular tool, many engineers and quality when captured, reduced, and controlled,
practitioners find design of experiments these can eliminate this variation. Shainin
difficult primarily because of the developed techniques (Shainin and
complexity of having to create the Shainin, 1990; 1992a; 1992b; 1993a;
conditions for conducting the experiments 1993b; Shainin, Shainin and Nelson,
in an industrial environment where 1997) to track down the dominant source
interrupting production lines and changing through a process of elimination (Shainin,
machine settings may be sometimes 1993b), called progressive search. These
difficult and unproductive. techniques, also referred to as the Shainin
2.2 Shainin DOE Approach System for quality improvement,
An alternative to the Classical and developed over a period of over 40 years,
Taguchi experimental design is the lesser- are simple but at the same time powerful
known but much simpler Shainin DOE and easier to interpret and implement in an
approach developed and perfected by industrial environment. In a way, these
Dorian Shainin (Bhote and Bhote, 2000), may be considered as the non-parametric
consultant and advisor to over 750 equivalent of Taguchi’s DOE as they do
companies in America and Europe. not make any restrictive assumptions
Shainin’s philosophy has been, “Don’t let about population parameters. The Shainin
the engineers do the guessing; let the parts techniques are primarily known to
do the talking.” Shainin recognized the produce breakthrough improvements in
value of empirical data in solving real- eliminating chronic quality problems.
world problems. He introduced the These are highly effective in pinpointing
concept of Red X, the dominant source of towards the root cause and validating it.
variation, among the many sources of No statistical software was needed to
variation of a problem that inevitably analyze the data. In fact, Shainin DOE
accounts for nearly all the unwanted does not even require knowledge of
effect. difficult statistical tools. Simple operation
In fact, Shainin (Shainin, 1995; 1993b) like counts, additions, subtractions, etc.,
had classified all causes of chronic quality makes calculations relatively easy. In

addition, the success of the projects can an industrial operation. Applications of the
lead to a very positive effect on the morale Classical and Taguchi methods in various
of the employees in terms of convincing fields have been extensively researched. In
them that Six Sigma can be implemented contrast, the Shainin system has not been
without complex statistics and big jargons. extensively reviewed, academically, and
The subject of the Shainin methods is very very limited studies have been carried out
vast and this paper highlights the in this area.
applicability of only a few of the Shainin 3.1 Studies about comparison of
tools. However, there is a lot of scope for DOE approaches
more research on this methodology Bhote (2000) compared Shainin
particularly comparative research of some techniques with Design of Experiments
of the Shainin methods like Paired and Taguchi methods, in the context of the
Comparison and B Vs C Analysis vis-à- electronics industry and concluded that the
vis the more popular statistical tools like Shainin techniques are simpler, less
factorial designs and non-parametric costly, and statistically more powerful
testing. Although these methods are not than the other two. Logothetis (1990) also
necessarily the best, according to Steiner evaluated the Shainin techniques in
et al. (2008), the guiding principles of the relation to the Taguchi methods and
Shainin tools are powerful, and at least, in statistical process control methods.
combination, unique. Also, these tools are Verma, et al (2004) used a slightly
best suited for batch to high volume different approach to compare the
production. methods. In their study, three cases of
3. FINDINGS FROM VARIOUS Taguchi experiments were picked up from
CASE STUDIES ABOUT DOE the available literature and the Shainin
APPROACHES method was then re-applied to find out
Bhote and Bhote (2000) described these whether it had an edge over the other DOE
tools in their books, but there have been techniques. A comparison between
many criticisms regarding their claims and Taguchi and Shainin techniques in an
the tools described. Though, Nelson aerospace environment was offered by
(1991) and Moore (1993) criticized the Thomas and Anthony (2005). A few other
Shainin System as unsubstantiated and authors who have studied these techniques
exaggerated, Steiner, et al (2008), are of are Ledolter and Swersey (1997), De
the opinion that some of the ideas behind Mast, et al. (2000) and Steiner and
the Shainin System are genuinely useful. MacKay (2005). The Classical DOE,
Goodman and Wyld (2001) offered a case Taguchi DOE, and Shainin DOE are
study involving the use of Shainin DOE in compared with each other in Table 2.

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

Items for
compari- Classical DOE Taguchi DOE Shainin DOE
son
a. Component search,
b. Multi-vari analysis,
c. Paired comparison,
Primary
Factorial experiments d. Product/Process Search or,
tools Orthogonal arrays
variable search, e. Full
factorials, f. B vs. C (Better
vs. Current) analysis, Scatter
plots.
Effective when Effective when
interaction effects are interaction Very powerful irrespective of
not present effects are not present the presence or absence of
Advan-
(20 to 200% (20 to 200% interactions. Maximum
tage
improvements). improvements). optimization possible.
Limited possibilities Limited possibilities for
for optimization. optimization.
Cost/Tim
Moderate Moderate Low
e
Training 3 to 5 days 3 to 10 days 1 to 2 days
Complexi Low (simple & basic
Moderate High
ty mathematical operations)
Requires use of
statistical software e.g.,
Requires use of
SAS, SPSS, etc. Used
statistical software
Facility & mainly in pre- Software not necessary.
e.g., SAS, SPSS, etc.
Scope production & can be
Used mainly in
used at the design stage
production.
under certain
constraints.
Moderate (Requires High (Almost no knowledge
Ease of knowledge of of statistics required. Easy to
Imple- statistics. Engineers understand at all levels
mentation find methods Poor including shop floor workers,
complex to engineers, and suppliers, thus
comprehend and creating an overall positive
interpret.) impact.
(Bothe & Bothe, 2000)

An examination of the three approaches The following section followed how

clearly indicates that the Shainin tools the company followed the proposed
have an edge over the other two methodology in an attempt to provide a
approaches in terms of cost, time, training, structured approach to solving critical to
complexity, scope, and ease of quality (CTQ) problems within the
implementation. The following work company and to achieve enhanced process
highlights the tools and techniques that quality, productivity, customer satisfaction
were used by Indian SME, a and internal benefits through a case study
manufacturing unit of Gange Industries of one particular project undertaken.
(GI) in their development of the six sigma Six Sigma DMAIC Process
programme The six sigma process concentrates on a
4. CASE STUDY simple five phase methodology called
This case-study was successfully DMAIC (Define, Measure, Analyze,
completed in the welding unit of GI, Improve, Control). The company followed
which is a SME was established in 1985, this approach and each stage is explained
located at Bhosari M.I.D.C., Pune, in detail in the following section of the
Maharashtra State in India. GI has grown paper.
to become a one of the major player in Define Phase: The data available
processing/manufacturing of automobile (collected through QMS) related to type,
sheet-metal parts. GI is ISO 9001 and TS frequency and amount of rework done at
16949 certified and has implemented GI is analyzed. Our team (which includes
company wide QM, Kaizen and TPM company’s management representative,
initiatives to good effect. managers, engineers and author) at GI
The company from their past experience confirmed that, parts named Assy-sub
found that the QM process and its structure with floor (613 LP RUSSIA)
associated systems were too slow in (XXX 6100 0182), which fits into
identifying and responding to problems assembly frame of light commercial
primarily, since they were developed to vehicle after welding on Welding M/C
obtain long-term strategic direction and ST-CO2-17 machine was under rejection
focus. Therefore, company officials had because of defective welding (non
accepted and initiated move towards use uniform welding, weld penetration, dry
of Shainin tools for implementation of welding, weld under cut and spatter etc.),
‘Six Sigma + QMS’ integrated approach which resulted in to annual Cost of Poor
for increase the process quality, Quality (COPQ) about INR 2Lakh/-.
productivity intern reducing process cost. Process stages, where the problem
Until the introduction of the integrated detected are in-process inspection and
strategy, the company attended to quality final inspection. This project was
problems in an often ad-hoc and undertaken to achieve certain objectives
unstructured manner. viz. productivity improvements in terms of

reduction/elimination of reworks and a multi-disciplinary team of engineers

reducing process cost [tangible], customer within the company. The team identified
satisfaction, and increase in confidence on the factors that could influence the product
shop floor [intangible]. Hence, quality. A cause-and-effect diagram was
repeatability and reproducibility study was developed (refer figure 2) to identify the
required for validating the measurement key sources of variation during the
system. Process Mapping is carried out welding process. Two potential
(refer figure 1), Suspectable Sources of Variations (SSVs)
Measure and Analyze Phase: A were finally listed as: Sheet material
brainstorming exercise was carried out by thickness, Welding Process itself.
Figure 2: Cause-and-Effect Diagram

Without taking educated guesses as to the as; BigY (Response i.e. Defective welding)
factors of real importance, authors have = f [X (Sources of variations i.e. CO2
suggested to adopt the Shainin Welding process)]. Therefore, new SSVs
Techniques. The Shainin’s Techniques are now related to CO2-Welding process
been employed to identify whether the are listed viz. Voltage, Current, Gas Flow
primary cause of shabby/defective and Wire Feed Rate. To check whether
welding lay within the process itself or any relationship exists within the
within the components used. This allowed identified parameters or not; data related
for a first stage filter to be employed that to all these parameters are collected (refer
cut down the factors to a manageable table 3), regression analysis is carried out
number. Key stages, in which Shainin and Graphs are plotted. Graph of Wire
tools were applied, are explained below. Feed Rate vs Current clearly shows the
Initial tool selected for measuring and positive relationship (refer figure no. 3).
analyzing the response was Product Hence, new SSVs identified parameters
Process Search, as of variations in the related to CO2-Welding process are now
identified suspectable sources of limited to: Voltage, Wire Feed Rate and
variations (SSV) i.e. input material Gas Flow.
parameter (as compared with their As the identified parameters were design
standard specification) viz. SSV-1. parameters of process and number of
Material Thickness (Specifications – 2.0 parameters are equal to 3 hence, it has
mm +/- 0.18), gets changed during been decided that, process characterization
processing. Data was collected for 100 analysis i.e. Full Factorial Analysis tool is
samples. to be used. All stages of full factorial
Observation 1 – It has been observed that, method are explained as follows,
minimum and maximum value of sheet Stage 0: As the response is attribute in
metal (raw material) thickness as an nature, consider current setting as the ‘–’
important input to production process setting and identify ‘+’ setting on the basis
belongs to same category of response. of experience on domain expert for each
Therefore, as per Product Process Search parameters (refer table 4).
method the end-count is zero. Hence, it Stage 1: To find out whether the
has been concluded that, SSV-1: Input parameters and the levels identified in
material parameter (i.e. Thickness) is not stage 0 are correct or not. Then, we have
creating problem. Next another to produced 3 batches in ‘–’ setting and 3
brainstorming session has concluded for batches in ‘+’ setting. Calculate D/d ratio,
characterization of CO2-Welding process if D/d ratio is >=1.25 and <3 then the
as process itself is yielding in to settings identified in Stage # 0 are correct
variations, which is required to be and we can go for Stage # 2. Accordingly
analysed. Hence, relation can be written trials are conducted; the results are

tabulated in table 5. D/d ratio is 0.4, which objective is lower the better. Using above
indicates that, the levels identified in stage equation, offline iterations are done.
0 are not correct. Hence, new parameters While doing iterations ‘+’ve settings are
levels are identified by considering earlier refereed as ‘1’, ‘-’ settings are referred as
‘+’ ve setting as new ‘-’ setting and new ‘-1’. Values some of the offline iterations
‘+’ ve settings for all parameters are and its calculated responses are tabulated
identified and set (refer table 6). Again in table 9. Then, experiments are carried
new trials are conducted and the results out using the levels of the parameters for
are tabulated in table 7. D/d ratio is 10, which responses are zero or less than zero
which indicates that the levels identified in and physical outputs are analyzed.
2nd settings are acceptable for further Response for setting in case of experiment
consideration. no. 9 (shown in same table) resulted in to
Stage 2: Construct factorial table and proper welding (considered as an optimum
collect the data for each combination in output).
the factorial table and quantify the Improvement Phase: Conclusions of
contributions of the interactions. earlier phase (identified optimum levels of
Table 8 shows factorial design and plan the parameters as shown in table 10) are
of experimentation. Accordingly used as an input to this phase. Once
experiments were performed, which optimum settings are set then, it is
resulted in to following important necessary to validate it. This was done, by
conclusions. using the Shainin B vs. C analysis, which
Parameter- A: As if we change from + is a confirmation tool to verify whether
level to - level then response increases by the actions taken have actually improved
2.5 points. the process (Bhote and Bhote, 2000). In
Parameter- B: As if we change from + this case, 6B vs. 6C, i.e., 6 batches (10
level to - level then response decreases by units per batch) with modification and 6
1.5 points. (10 units per batch) without modification
Parameter- C: As if we change from + (B – with modification and C – without
level to - level then response decreases by modification) was analyzed to validate the
5 points. improvement action, i.e., the modification
Stage 3: Make a simple mathematical of CO2 machine operating parameters
equation based on the contribution of (table 11).
significant parameters and arrive at the The data in table 12 exhibited the
optimal setting. responses with B and C conditions. As per
Y= 84.875 –3.125 A + 14.162B + 4.875C rule of this technique, the final analysis is
+2.625 AB – 4.375 BC – 7.125 CA + done based on the ‘end-count scheme’. In
7.625ABC this case, end count is 8 (greater than 6),
As response ‘Y’ considered is which confirms that identified root causes
shabby/defective welding hence, our are correct.

Further, the result clearly validates the mentioned in table 10. New specifications
improvement against the criteria not only helped to improve the quality
mentioned in table 13. The data has level but also productivity by reducing
exhibited no overlaps of the responses defect/rework rate and optimizing the use
with B condition and C condition. The of resource and time (e.g. Wire Feed Rate
conclusion being that the process has been from 10 Min/min to 6.5 Min/min and Gas
improved by changing the CO2 welding Flow from15 Lit/min to 14 Lit/min).
machine operational specifications as
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
Response Median
A B C AB BC CA ABC
7 "-" 20 "-" 8 "-" "+" "+" "+" "-" 50 , 50, 60 52
4 " + " 20 "-" 8 "-" "-" "+" "-" "+" 70 70
Parameters 7 "-" 18 "+" 8 "-" "-" "-" "+" "+" 100 100
Settings 4 "+" 18 "+" 8 "-" "+" "-" "-" "-" 70 98
7 "-" 20 "-" 6 "+" "+" "-" "-" "+" 100 100
4 "+" 20 "-" 6 "+" "-" "-" "+" "-" 60 59
7 "-" 18 "+" 6 "+" "-" "+" "-" "-" 100 100
100, 100,
4 " + " 18 "+" 6 "+" "+" "+" "+" "+" 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.
Wire Feed Voltage Gas Flow Constant Response Remark
No.
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.
Existing Setting Optimum Setting
No. Parameter UOM
( -) ve (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
2 Better Condition Optimum Settings (Refer table 10 )
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
Is [Xb - Xc] greater than [K x Sigma (b)]
Yes [(28.33 > 19.78]
5 (Where K is std value = K = 2.96 @ 95%
Confidence Level )

The improvements identified were also • Procedure has been developed for
used to set the action plan for other periodic monitoring of CO2 welding
varieties of such components for machine operational specifications w. r. to
horizontal deployment. quality level of output.
Control Phase: • Implemented controls to make sure
The focus of the control phase is to sustain that the actions taken in Phase-III are done
the gains of the improvement phase. This forever.
is usually achieved by documentation and • All these modifications have been
standardization of the control measures. included as a part of Company-QMS
For controlling the process at Six Sigma procedure to ensure the reliability of Six
level, following actions were suggested. Sigma level quality of the process.
• Appropriate modifications have been The operational framework developed and
done in CO2 welding machine operating used in this research-work is described in
and training manuals. figure 4.

It clearly shows the major stages in the delivering certain objectives set by ISO
process integration and implementation. It such as: prevention of defects at all stages
shows initially the sequential nature of the from design through servicing; techniques
stages whereby the Six Sigma phases are required for establishing, controlling and
using appropriate imputes from company verifying process capability and product
QMS database to continently execute the characterization; investigation of the cause
project. The operational framework also of defects relating to product, process and
shows the stages in sequence whereby the quality system; continuous improvement
six sigma DMAIC phases are using of the quality of products/services.
accurately Shainin quality tools. From the results of case study based
5. DISCUSSION AND CONCLUSIONS research work we draw following
The aim of this project is to defeat the conclusions,
biggest “excuses” cited by SMEs as the i. The key phase of the DMAIC
reasons Six Sigma is not feasible, incurs methodology is the measure and
high costs and involve complexity of analysis phase. The tools and
implementation. In addition, it helps to techniques used in this phase
break down so many of the barriers that determine the success or failure of
stand in the way of individuals using the project to a large extent. In
statistical and/or unfamiliar problem both the projects, the Shainin tools
solving methods by acting as a step-by- have been very effectively used to
step guide. This research work focus on pinpoint the root causes and
use of Shainin tools specifically, as they validate the improvement actions.
are easy to understand, involves simple ii. No statistical software was needed
mathematical calculations (so that bottom- to be used to analyse the data. In
line people can also understand it very fact, Shainin DOE does not even
easily) and time required for training is require knowledge of difficult
also less, which is one of the important statistical tools. Simple operation
requirements of SMEs. During this case like counts, additions, subtractions
study, during use of Shainin tools, small etc., makes calculations relatively
samples of BOB and WOW pieces were easy. Therefore the training
sufficient to analyse the data as reported required for application of Shainin
earlier. A very important factor is that data tools is simple and requires less
collection was done for the project time (1-2 days).
undertaken online without disturbing the iii. In addition, the success of the
regular production. projects had a very positive effect
Thus in short, we can understand that, use on the morale of the employees in
of Shainin tools for simplifying Six terms of convincing them that Six
Sigma implementation can provides an Sigma works without complex
appropriate methodology for SMEs for statistics and big jargons.

iv. Existing company QMS program’ Measuring Business

Excellence, Vol. 6, No. 4, pp. 20–27.
procedures has assisted 2. Bewoor A. K., Pawar M. S., (2008),
/complimented in all stages of ‘Developing Integrated Model of Six-
implementation of Six Sigma. Sigma Methodology and Quality
Management System for Improving
v. Operational framework developed Quality, Productivity and
and used in this research-work has Competitiveness’.
3. 12th Annual Conference of the
validated for its implementation Society of Operations Management
and found to be a useful concept December 19-21, 2008, IIT Kanpur,
India.
for improving quality and
4. Bewoor A. K., Pawar M. S. (2009)
productivity/performance of SME. ‘Developing and Implementing
vi. The project was completed within Quality Six Sigma(QSS) – an
Integrated QMS and Six Sigma
a span of almost three months. For Methodology for Improving Quality
the company, the estimated and Productivity/ Performance of
SME – An Indian Case Study’, Inl. J.
savings from this project was of Emerging Technologies and
more than INR 2 lakhs per annum. Applications. in Engineering
Technology and Sciences, Vol. 2,
The guiding principles of the Shainin tools No. 2, pp. 222-228.
are powerful, and at least, in combination, 5. Bhote, K R and Bhote, A. K. (2000)
unique. Therefore, we conclude that, World Class Quality, 2nd Edition.
New York: American Management
applying simplified Shainin tools based Association.
Six Sigma methodology to the existing 6. De Mast, J; Schippers, W. A. J.;
Does, R. J. M. M. and Van den,
company QMS process is the best way for Heuvel E. (2000) ‘Steps and
SMEs to achieve the optimal results in Strategies in Process Improvement’,
Quality and Reliability Engineering
quality progress towards TQM in International, Vol. 16, pp. 301-311.
customer satisfaction. 7. Eckes, George (2001)The Six Sigma
This paper highlights the applicability of Revolution, New York: John Wiley &
Sons.
only a few of the Shainin tools. There is a 8. Falshaw, J.R., Glaister, K.W. and
lot of scope for more research on this Tatoglu, E. (2006) “Evidence on
formal strategic planning and
methodology as its most of the company performance’, Management
terminology is trademarked and legally Decision, Vol. 44 No. 1, pp. 9-30.
9. Goodman, J. and Wyld, David C.
protected, limiting academic debate and (2001) ‘The Hunt for Red X: A Case
discussion on this system of problem Study in the Use of Shainin Design of
solving, which despite many criticisms Experiments (DOE) in an Industrial
Honing Operation,” Management
and having been largely overshadowed by Research News, Vol.4, No.8/9, pp. 1-
the classical and Taguchi techniques in the 17.
10. Ledolter, J. and Swersey, A. (1997)
past, is now gradually being given its due ‘Dorian Shainin’s Variable Search
recognition. Procedure: A Critical Assessment’,
Journal of Quality Technology, Vol.
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‘Key ingredients for the effective Zaheer, S. and Choo, A.S. (2003)
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perspective’, Journal of Operations 23. Shainin, R D (1993b). ‘Strategies for

Management, Vol. 21, No.2, pp. 193– Technical Problem Solving,” Quality
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Improvement’, Quality and Congress Proceedings, ASQC, pp.
Reliability Engineering International, 1163-1169.
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13. Moore, D. (1993) Review of World Ramberg, J S (2008). ‘An Overview
Class Quality, by K Bhote-1991 of the Shainin System for Quality
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21, pp. 76-79. Vol. 20, No. 1, pp. 6-99.
14. Nelson, L S (1991)‘Review of World 26. Thomas, A J and Anthony, J (2005).
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Journal of Quality Technology, Vol. Taguchi and Shainin DOE
25, pp. 152-153. Techniques in an Aerospace
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Cavanagh, R.R. (2000) The Six Sigma of Productivity and Performance
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Top Companies are Honing their 678.
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Journal of Engineering Research and Studies E-ISSN 0976-7916

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strategy, to customers, to suppliers, project resulted in to more benefit on operational

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Table 1 : Basic Six Sigma Tools

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

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An examination of the three approaches The following section followed how

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reduction/elimination of reworks and a multi-disciplinary team of engineers

Figure 2: Cause-and-Effect Diagram

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Table 4: First Setting of levels of each parameter

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Table 5: First Trial

Table 6: Second Setting of levels of each parameter

Table 7: Second Trial

Table 8:Factorial Table

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Table 9:Offline iterations, its calculated and actual responses

Table 10: Existing and Optimum Settings

Table 11: B vs. C analysis

Table 13: Criteria for validating improvements and results

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iv. Existing company QMS program’ Measuring Business

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perspective’, Journal of Operations 23. Shainin, R D (1993b). ‘Strategies for

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