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Chapter 1

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Chapter: 1

INTRODUCTION AND SUMMARY OF THESIS

1.1 Introduction

Statistics plays a vital role in analysis and decision making in all spears of life.

Researchers in the field of engineering, management, social sciences etc. also use statistics

heavily in their data analysis and make meaningful conclusions. In industrial scenario, as

competition grows every day, it is a challenge for the organizations to survive in the

market. The customers have greatly increased their quality requirements, and this trend is

likely to be intensified by competitive pressures in future.

Generally, all the industrial processes starts from identifying customer requirements

and end with delivery of product or service to the customer. At each stage of this product

or service realization process, there are always challenges and opportunities which can be

explored for further improvement, so that organizations can be more competitive and

successful in the market. Statistical analysis of process/ product/ market related data help

the organizations to succeed in this endeavour.

One objective of this research is to study application of various statistical

techniques at different stages of the organizational processes for performance improvement

in Indian industries. The success of an organization depends on how well it takes care of

the entire stream of activities starting from identifying customer demand to delivering of

products/ services to customers. If any failures occur anywhere in this value stream, the

organization will not be able to survive in the market. Hence identifying the weak areas in

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the process and improving them are at most important for every business organization.

During this study, few important processes from various organizations were selected. The

processes considered for study includes demand forecasting, product design, reliability

analysis, production and engineering process optimisation etc. Methods like Six Sigma,

Taguchi method, Time series forecasting, Reliability analysis and other statistical tools

were considered during this research. The systematic introduction of these methods marks

the start of substantial improvement in quality, productivity and customer satisfaction,

thereby improving the profitability and competitiveness of the organization.

The remaining part of this chapter is arranged as follows. Section 1.2 presents a

basic concepts and literature review and on Six Sigma, Taguchi methods, Time series

forecasting and Reliability. Section 1.3 provides the overview of this thesis followed by list

of publications/ presentations related to this research in section 1.4.

1.2 Basic concepts and literature review

1.2.1 Six Sigma methodology

In the attempt to manage change, many large organisations have pursued formalised

change programmes or quality initiatives such as Six Sigma that can have a significant

impact on the bottom-line and working culture of an organisation. The Six Sigma

methodology is becoming one of the most successful quality management initiatives

(Gutiérrez et al, 2012). This methodology is a breakthrough business strategy used for

quality and process improvement by using a set of structured tools and statistical measures

to evaluate processes (Adams et al, 2003). Six Sigma is a disciplined, project-oriented,

statistically based approach for reducing variability, removing defects and eliminating

Introduction and Summary of Thesis

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waste from products, processes and transactions (Montgomery and Woodall, 2008). Six

Sigma can facilitate in solving complex cross functional problems where the root causes of

a problem are unknown and help to reduce undesirable variations in processes (Breyfogle,

2003). It really takes Total Quality Management (TQM) efforts to the next level and has a

great future even in services (Lazarus and Butler, 2001). It creates a sense of urgency by

emphasizing rapid project completion, typically within six months. Motorola introduced

the concept of Six Sigma in the mid 1980s as a powerful business strategy to improve

quality. It has been claimed to be the best known approach to process improvement (Snee

and Hoerl, 2003). Leading organizations with a track record in quality have adopted Six

Sigma and claimed that it has transformed their organization (Bisgaard and Freiesleben,

2004).

The Six Sigma approach starts with a business strategy and ends with top-down

implementation, having a significant impact on profit if successfully deployed (Adams, et

al, 2003; Mahesh et al, 2006). It takes users away from ‘intuition based decisions’, to ‘fact

based decisions’ (Ellis, 2001). Many papers and books have discussed Six Sigma – the

concept, its ingredients, its relation to other quality concepts and its benefits, its

weaknesses etc. Articles/ books are also available in topics related to: What is Six Sigma?

(Harry, 1997; Harry and Schroeder, 2000; Halliday, 2001); Why do we need Six Sigma?

(Pande et al., 2000); What makes Six Sigma different from other quality initiatives? (Snee,

2005); Six Sigma deployment (Keller, 2001); Six Sigma and bottom line (Bisgaard and

Freiesleben (2004); Is Six Sigma really different? (Walters, 2005); Does Six Sigma

improves performance? (Foster, 2007) Critical success factors of Six Sigma

implementation (Treichler, 2005); Hurdles in Six Sigma implementation (Hahn, 2005); Six

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Sigma project selection (Pande et al., 2003); Organisational infrastructure required for

implementing Six Sigma (Snee and Hoerl, 2003; Taghizadegan, 2006; Treichler, 2005);

Integrating many facets of Six Sigma (De Mast, 2007) etc. In the later part of 2000, there

were efforts to integrate Six Sigma with various other initiatives/ methodologies. These

include integration of Six Sigma with Lean management (Smith, 2003; Ferng and Price,

2005; Shah et al, 2008; Chen and Lyu, 2009; Chen et al, 2010), Knowledge management

(Gowen et al, 2008), Industrial engineering (Bunce et al, 2008), ISO 9001:2008 (Karthi et

al, 2011), Manufacturing executing systems (Hwang, 2006) etc.

In Six Sigma, broadly two approaches are used – DMAIC (Define-Measure-

Analyze-Improve-Control) and DFSS (Design for Six Sigma). DMAIC is used mostly for

existing processes (Firka, 2010). This approach not only makes use of Six Sigma tools, it

also incorporates other concepts such as financial analysis and project schedule

development (Bisgaard and Freiesleben, 2004). The DMAIC methodology is excellent

when dealing with an existing process in which reaching a defined level of performance

will result in the benefits expected (De Mast, 2007). Six Sigma has been embraced by

companies not only for its robust tool set but also because of its well-defined application

methodology, the DMAIC. When a new process is required, DFSS is used. DFSS consists

of a number of disciplined and rigorous approaches to product, process, and service design

(El-Haik and Roy, 2005). Both DMAIC and DFSS approaches integrates a set of tools and

techniques in a disciplined fashion (Kumar et al., 2006).

The first phase in a DMAIC project is define phase. This phase of the DMAIC

methodology aims to define the scope and goals of the improvement project in terms of

customer requirements and to develop a process that delivers these requirements. The first


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step towards solving any problem in Six Sigma methodology is by formulating a team of

people associated with the process. The team consists of a leader (generally known as

Black Belt) and few members (Green Belt). The primary responsibility of team members

was supporting the Black Belt in executing the project related actions. A person from the

management, termed as Champion, monitors the progress of the project during its

execution. For providing training and giving guidance for completing the project, the

support of a Master Black Belt (MBB) is used. The team first prepares a project charter

with all details of the project. This will help the team members clearly understand the

project objective, project duration, resources available, roles and responsibilities of team

members, project scope and boundaries, expected results from the project etc. This creates

a common vision and sense of ownership for the project, so that the entire team is focused

on the objectives of the project (Park, 2002). After the project charter, a Supplier-Input-

Process-Output (SIPOC) mapping is prepared. SIPOC is similar to process mapping for

defining and understanding process steps, process inputs and process outputs (Breyfogle,

2003). The team with the involvement of people working with the process prepared a

SIPOC mapping along with a basic flow chart of the process. This SIPOC has given a clear

understanding of the process steps needed to create the output of the process. Through this

exercise, the team got the clarity of the project in terms of the scope of the project, inputs,

outputs, suppliers and customers of the process.

The objective of the measure phase in a Six Sigma project is to understand and

establish the baseline performance of the process in terms of process capability or sigma

rating (Rahmqvist and Bara, 2010). During this phase data were collected on the critical to

quality (CTQ) characteristics and the baseline performance is evaluated. The purpose of

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analyze phase in a Six Sigma project is to identify the root causes that are responsible for

high variation in the selected CTQs. A brainstorming session was conducted to identify the

potential causes of high variation in the CTQs. The output of the brainstorming session

depends to a large extent on the quality and creativity of the session and the knowledge

level of the participants (Ishikawa and Lu, 1985; Imai, 1986).

Improve phase of a Six Sigma project is aimed at identifying solutions for all the

root causes selected during analyze phase, implementing them after studying the risk

involved in implementation and observing the results. The idea behind including the

control phase was to make sure that the benefits and knowledge generated from Six Sigma

projects are sustained on a long-term basis (Kumar et al, 2006). Sustainability of the results

requires standardization of the improved methods and introduction of monitoring

mechanisms for the key results achieved. It also requires bringing awareness among the

personnel performing the activities.

1.2.2 Taguchi method

In the 1980s, Dr. Genichi Taguchi received international attention for his ideas on

variation reduction, starting with the translation of his work published in Taguchi and Wu

(1979). Later on many publications were available with further illustrations in Taguchi

methods and its applications in industries. The publications that focused in this area were

Hunter (1985), Kackar (1985), Park (1996), Ross (1996), Taguchi et al (2000) etc. An

overview of different methods to achieve robust design is also provided by Park et al

(2006). Taguchi breaks down the design process into three stages: system design,

parameter design and tolerance design. According to the description in Taguchi (1986), in

system design different concepts and choices of technology are considered at different


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levels. Parameter design aims at determining appropriate levels of parameters that result in

least sensitivity to sources of variation (Taguchi et al, 2000). Finally, in the last stage,

tolerances are set to further minimize the effects of noise factors (Taguchi, 1988).

Taguchi’s experimental design method is a well-known quality improvement

technique for carrying out the analysis of experiments with the least experimental effort

(Taguchi, 1988; Ross, 1996). It is also been widely used for process optimisation (Hossein

et al, 2010). This is due to the advantage of Taguchi’s approach, which includes

simplification of experimental plan by using orthogonal arrays as a basis of experiments,

and feasibility of interaction study between different parameters (Taguchi and Wu, 1979).

Fewer experiments imply that time and cost is reduced. This is extremely vital for any

industrial process where the cost involved is very high (Phadke, 1989).

Taguchi recommends use of the loss function to measure the performance

characteristic deviating from the desired value (Taguchi and Wu, 1979). The value of the

loss function is further transformed into a signal-to-noise (S/N) ratio (Phadke, 1989).

Usually, there are three categories of the performance characteristic in the analysis of the

S/N ratio, that is, lower-the-better, higher-the-better, and nominal-the-best (Ross, 1996).

Regardless of the category of the performance characteristic, the larger S/N ratio

corresponds to a better performance characteristic (Roy, 1990). The formula for calculation

of S/N ratio is defined as follows.

S/N 10 ∑ , for lower-the-better type

10 ∑ , for nominal-the-best type

10 , for larger-the-better type. (1.1)

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where Yi is the response measured during experimentation with average and standard

deviation s.

Around 1990s there was discussion on the statistical validity of methods proposed

by Taguchi. The articles published in this direction were Leon et al (1987), Box et al

(1988), Benton (1991), Shoemaker et al (1991), Box and Jones (1992), Myers et al (1992),

Goh (1993), Lucas (1994), Grize (1995), Ford (1996), Shainin and Shainin (1988), Scibilia

et al (2001), Robinson et al (2006a; 2006b) etc. Although many authors - for example,

Tribus and Szonyi (1989), Pignatiello and Ramberg (1991) and the contributors in Nair

(1992) - are critical to the statistical methods advocated by Taguchi, they give him credit

for his work on communicating the importance of reducing variation to industry. Quite a

few publications are available describing alternate methods for achieving robustness or

extending the scope of application of the methodology. The publications available in these

directions are Vining and Myers (1990), Chan and Xiao (1995), Engel and Huele (1996),

Andersson (1997), Borkowski and Lucas (1997), Box and Bisgaard (1998), Myers and

Montgomery (2002), Lee and Nelder (2003), Myers et al (2004), Myers et al (2005), Smith

and Clarkson (2005) etc.

1.2.3 Time series forecasting

Time series analysis and its application in generating forecasts have become

increasingly important in various fields of research, such as business, economics,

engineering, medicine, and others. The forecaster uses past data to build a model that

explains the behavior of specific variables. This model is then used to make the statement

about the future on the assumption that the future will be like the past (Stekler, 2007). De

Gooijer and Hyndman (2006) have surveyed the development of time series models over


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the past 25 years. They explain that each and every particular time series method works,

primarily because it is based on solid statistical foundations. A wide variety of models,

varying in the complexity of functional form and estimation procedures, has been proposed

for forecasting. They include; multiple regression, decomposition, exponential smoothing,

auto regressive (AR), moving average (MA), auto regressive moving average (ARMA),

auto regressive integrated moving average (ARIMA), neural networks etc models (Box et

al, 2008; Weckman et al., 2008).

Auto Regressive Integrated Moving Average (ARIMA) models have been studied

extensively by Box and Jenkins (1970), and their names have been synonymous with

general ARIMA process applied to time series analysis and forecasting. The Box and

Jenkins (1970) define a general multiplicative seasonal ARIMA model in the form:

Φ Θ (1.2)

where,

B is the backward shift operator, ϕ ,Φ , θ ,Θ are polynomials of order

, , , respectively, denotes a purely random process (Bowerman and O’Connell,

1993).

Φ 1 … (1.3)

where, αi , i = 1, 2, … are constants.

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The variables are formed from the original series not only by simple

differencing (to remove the trend) but also by seasonal differencing, , to remove the

seasonality. For example, if 1 and 12, then,

The model stated above is said to be a seasonal ARIMA model of order

. The values of and do not usually need to exceed one (Montgomery

et al., 2008).

1.2.4 Reliability modeling

In industrial life testing and medical survival analysis, it is very common that the

object under study or observation is lost or withdrawn before failure or the life time of the

object is only known within an interval. Data obtained from such experiments are called

censored data (or an incomplete data). Reducing the total test time and the associated cost

is one of the major reasons for censoring. A censoring scheme, which can balance between

total time spent for the experiment; number of units used in the experiment; and the

efficiency of statistical inference based on the results of the experiment, is desirable. The

most common censoring schemes are type-I censoring and type-II censoring. Under type-I

censoring, the testing is continued until a pre-specified time point. In type-II censoring, the

testing is continued up to a pre-specified number of failures (Nelson, 1982). However, the

conventional type-I and type-II censoring schemes do not have the flexibility of allowing

removal of units at points other than the terminal point of the experiment. Because of this


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lack of flexibility, a more general censoring scheme called progressive censoring was

introduced. In the progressive censoring scheme, the tested items are allowed to be

withdrawn at some other times before the end of life testing (Balakrishnan and Aggarwala,

2000). A properly planned progressively censored life testing experiment can save both the

total test time and the cost induced by failure of the units and increase the efficiency of

statistical analysis.

Aggarwala (2001) introduced type-I interval and progressive censoring scheme and

developed the statistical inference for the exponential distribution based on progressively

type-I interval censored data. Ng and Wang (2009) discussed statistical inference for

Weibull distribution under progressive type-I interval censoring. They have compared

different estimation methods for the parameters of Weibull distribution via simulation.

Chen and Lio (2010) considered parameter estimation for generalized exponential

distribution under progressive type-I interval censoring. They have obtained maximum

likelihood estimate of unknown parameters using EM algorithm, midpoint approximation

methods and method of moments. Huang and Wu (2008) considered determination of

reliability sampling plan under progressive type-I interval censoring using cost function.

Lin et al (2009) considered determination of optimum life testing plan with progressively

type-I interval censored data from the log-normal distribution.

1.3 Methodology and objectives of the study

Exploring application of various statistical techniques for improving the quality and

performance of processes in Indian industries for enhancing the customer satisfaction was

the objective of this research. Towards this endeavour, existing process improvement

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methods available in literature were studied first. Based on this information, an attempt

was made to address various process problems of different Indian industries. The processes

considered for study includes demand pattern of customers, product design, product

manufacturing, reliability estimation etc. The main objectives of the current research work

are as follows.

• To study the effectiveness and shortcomings during implementation of Six Sigma

methodology in Indian industries.

• To understand and study the practical application of Taguchi method for product

design and process optimization.

• To evolve a general approach for problem solving in small and medium size

organizations where systematic approaches like Six Sigma and TQM were not

practiced.

• To explore the feasibility of application of time series forecasting in the demand

pattern of organizations.

• To estimate the parameters of well known probability distributions under

progressive type-I interval censoring scheme.

1.4 Thesis at a glance

The main objectives of the present research are to study application of various

statistical techniques at different stages of the organizational processes for improvement in

Indian scenario. This thesis consists of six chapters. Chapter 1 contains the introduction,

basic concepts with literature review, objectives, chapter-wise overview and list of

publications in journals/ conferences.


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In Chapter 2, the effectiveness of implementation of Six Sigma methodology for

improving the processes and its shortcomings are considered. This methodology was tried

for organizations varying from manufacturing, engineering, services and non-conventional

energy to healthcare. Integration of techniques like Beta correction along with Six Sigma

methodology was also discussed during this study. Six different applications of the

methodology are demonstrated in this chapter. The discussions of this chapter are based on

the articles, Gijo and Scaria (2010; 2013b), Gijo et al (2011), Gijo et al (2013), Gijo and

Antony (2013) and Gijo and Sarkar (2013). Information on Six Sigma projects from

various organizations was gathered during this study. The shortcomings in Six Sigma

implementation under Indian scenario were identified through these projects. The

weaknesses in project selection, defect definition, conduct of training, validation of causes,

sustainability of results, estimation of project benefits etc are discussed in this chapter. The

recent articles, Gijo (2011) and Gijo (2012) are based on the results of this study.

Chapter 3 explains the details of Taguchi method and its applications in product

design, process optimisation and improvement. Taguchi’s robust parameter design concept

was used to design products to achieve consistent level of performance. Three different

areas of application of Taguchi methods are discussed in this chapter. Two of these

applications are related to product design and the third one is related process optimization.

These cases have reinforced the applicability of Taguchi method in industrial scenario and

are published in Gijo and Scaria, (2011, 2012), Acharya, Gijo and Antony (2010).

Chapter 4 presents the approach for general problem solving in industry. These

were applied in small and medium sized industries in India where they were not able to

implement approaches like Six Sigma and TQM due to many organizational constraints.

Chapter 1

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Three different problems in various industries are selected and the problem solving

methodology was demonstrated with good results. In this case, quite a few statistical

techniques were logically linked to arrive at solutions for process problems. The recent

work, Gijo and Scaria (2013a) along with early works Gijo (2005) and Gijo and Perumallu

(2003) were based on this approach of problem solving.

Every organization plan its activities based on the projected demand for its products

and services in future. Hence, organizations adopt different types of forecasting models to

predict the demand from the market. Chapter 5 explains the forecasting of monthly demand

of products based on Box Jenkin’s seasonal autoregressive integrated moving average

(SARIMA) models. The results are available in Gijo (2011).

In Chapter 6, we estimate the parameters of lognormal distribution based on

progressive type-I interval censored data. Since the maximum likelihood estimates (MLEs)

were not obtained in a closed form, the expectation-maximization (EM) algorithm, mid-

point approximation and method of moments were used to obtain the estimates. The

Bayesian estimates also were tried with Lindley’s approximation and Metropolis-Hastings

algorithm. A real life data analysis also is demonstrated in this chapter.

Thus, the thesis explores various applications of statistical techniques for improving

the business processes in Indian industry. The results of these studies have been published

or communicated to leading international journals.

1.5 Publications/ presentations

The results of the thesis have been published/ presented/ communicated in the form

of research papers as listed below.


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1.5.1 Papers in journals

1. Acharya, U. H., Gijo, E. V. and Antony, J. (2010). Quality engineering of a traction

alternator by robust design, Proceedings of the Institution of Mechanical Engineers,

Part B: Journal of Engineering Manufacture, 224(2), 297-304.

2. Gijo, E. V. (2005). Improving process capability of manufacturing process by

application of statistical techniques, Quality Engineering, 17(2), 309-315.

3. Gijo, E.V. (2011). 11 ways to sink your Six Sigma project, Six Sigma Forum

Magazine, 11(1), 27-29.

4. Gijo, E.V. (2011). Demand forecasting of tea by Seasonal ARIMA model,

International Journal of Business Excellence, 4(1), 111-124.

5. Gijo, E.V. (2012). Shortcomings in Six Sigma implementation: an experience in

Indian industries, Science & Society, 10(2), 109-116.

6. Gijo, E.V. and Antony, J. (2013). Reducing patient waiting time in outpatient

department using lean six sigma methodology, Quality and Reliability Engineering

International, (wileyonlinelibrary.com) DOI: 10.1002/qre.1552.

7. Gijo, E.V., Antony, J., Hernandez, J. and Scaria, J. (2013). Reducing patient

waiting time in a Pathology department using the Six Sigma methodology,

Leadership in Health Services, 26(4), (in press).

8. Gijo, E.V. and Perumallu, P.K. (2003). Quality improvement by reducing variation:

a case study, Total Quality Management & Business Excellence, 14(9), 1023-1031.

9. Gijo, E.V and Rao, T.S. (2005). Six Sigma implementation - hurdles and more

hurdles, Total Quality Management & Business Excellence, 16(6), 721-725.

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10. Gijo, E.V. and Ravindran, G. (2008). Quality in the construction industry: an

application of DOE with goal programming, Total Quality Management & Business

Excellence, 19(12), 1249-1255.

11. Gijo, E.V. and Sarkar, A. (2013). Application of Six Sigma to improve the quality

of the road for wind turbine installation, The TQM Journal, 25(3), 244-258.

12. Gijo, E.V. and Scaria, J. (2010). Reducing rejection and rework by application of

Six Sigma methodology in manufacturing process, International Journal of Six

Sigma and Competitive Advantage, 6(1/2), 77-90.

13. Gijo, E.V. and Scaria, J. (2011). Application of Taguchi method to optimise the

characteristics of green sand in a foundry, International Journal of Business

Excellence, 4(2), 191-201.

14. Gijo, E.V. and Scaria, J. (2012). Product design by application of Taguchi's robust

engineering using computer simulation, International Journal of Computer

Integrated Manufacturing, 25(9), 761-773.

15. Gijo, E.V. and Scaria, J. (2013a). Application of statistical techniques for

improving yield of a manufacturing process, International Journal of Business

Excellence, 6(3), 361-375.

16. Gijo, E.V. and Scaria, J. (2013b). Simultaneous implementation of Six Sigma

methodology with Beta correction technique in automotive supplier process,

International Journal of Advanced Manufacturing Technology, (Under revision).

17. Gijo, E.V., Scaria, J. and Antony, J. (2011). Application of Six Sigma

methodology to reduce defects of a grinding process, Quality and Reliability

Engineering International, 27(8), 1221-1234.


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18. Pradhan, B. and Gijo, E.V. (2013). Parameter estimation of lognormal distribution

under progressive type-I interval censoring, Journal of Statistical Theory and

Practice, (Submitted).

19. Saravanan, S., Meera, M. and Prakash, S. and Gijo, E.V. (2012). Efficiency

improvement on the multicrystalline silicon wafer through Six Sigma methodology,

International Journal of Sustainable Energy, 31(3), 143-153.

1.5.2 Presentations in conferences/ workshops

1. Delivered Invited talk on “National Workshop on Recent Developments in

Statistics with Special Emphasis on Computational Statistics” organized by

University of Kerala, Trivandrum during March 1 – 7, 2013.

2. Presented a paper titled ‘Application of Seasonal ARIMA model in demand

forecasting’ at the ‘IISA 2013 Conference’ held at Chennai during January 2 -5,

2013.

3. Presented a paper titled ‘Quality improvement in manufacturing process: an

application of statistical techniques.’ at the ‘National Conference on Statistics for

Twenty First Century - 2012’ Organized by University of Kerala, Trivandrum,

during December 10 - 12, 2012.

4. Presented a paper titled ‘Quality improvement of automotive supplier process:

application of Six Sigma methodology’ at the ‘2011 Joint Statistical Meeting’ held

at Miami, Florida, US, during 30 July - 04 August, 2011.

5. Delivered talks on ‘Three-day Lecture Workshop on Statistical Applications in

Industry, Business, Agriculture and Ecology’ jointly sponsored by Indian Academy

of Sciences - Bangalore, Indian National Science Academy - New Delhi and The

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National Academy of Sciences India – Allahabad, at St. Thomas College, Pala,

Kottayam, Kerala, during March 26 – 28, 2011.

6. Presented a paper titled ‘Six Sigma Methodology in manufacturing process - an

application’ at the ‘National Seminar on Stochastic Modelling and Analysis’

organized by Cochin University of Science and Technology, during March 24 – 25,

2011.

7. Presented a paper titled ‘Improving the performance of CNC machine using

reliability models’ at the ‘Annual Conference of Kerala Statistical Association’ at

University of Kerala, during March 17 – 19, 2011.

8. Delivered a talk on UGC sponsored workshop on ‘Applied Statistical Methods in

Quality Control’ organized by Nirmala College, Muvattupuzha, Kerala on March 3,

2011.

9. Presented a paper titled ‘Process capability improvement of a manufacturing

process through design of experiments’ at ‘International Congress on Productivity,

Quality, Reliability, Optimization and Modelling’ organized by Indian Statistical

Institute, Delhi, during February 7 - 8, 2011.

10. Presented a paper titled ‘What is happening in Six Sigma implementation? An

experience in Indian industries’ at ‘13th International Conference on Quality’

organized by National Institution for Quality and Reliability, Bangalore, during

January 6 – 7, 2011.

11. Presented a paper titled ‘Product design by application of Taguchi’s robust

engineering’ at the ‘International conference on Mathematical Sciences 2011’ at St.

Thomas College Pala, Kottayam, Kerala, during January 3 – 5, 2011.


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12. Presented a paper titled ‘Comparison of univariate and multivariate models in

forecasting: an application’ at the ‘Annual Conference of Kerala Statistical

Association’ at Nirmala College, Muvattupuzha, during February 25 – 27, 2010.

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