The International journal of analytical and experimental modal analysis ISSN NO:0886-9367

A Study on Defect Reduction in IT based on Six Sigma Patterns Prabhu Thangavelu1, Research Scholar, HITS University, Chennai, Tamil Nadu,India Dr M. Rajeswari2, Associate Professor, HITS University, Chennai, TamilNadu, India

ABSTRACT Anything done by human is error prone. Software is not an exception to that. If software development has its buzz, a bisection of the buzz is because of the fault reduction. As long as there is software, there also will be fault reduction with its own hype. Both are interrelated. But getting a bird view of the software won’t suffice when it comes to pointing faults and defects. It is a painstaking task to get a holistic view to reach the quality’s pinnacle. According to GOST ISO 9000-2001, quality is "the degree, to which the inherent characteristics of the product meet the requirements," which may not be interpreted in the field of software development. Each software has its own goal with reaching the perfect stage of functional, operational and technicality. If the product works as per the requirement in the superlative degree of it performing the operations confined to the requirement, then it is said to have quality. Six Sigma is a strategy used for defect reduction. It is a -based, process-focused, data-driven strategy and methodology, coupled with management concepts and lean tools, that aims to improve the quality of process outputs. Six Sigma seeks to identify the causes of failures and minimize variability in key industrial processes in areas, such as healthcare, manufacturing, and service. Keywords: Defect Reduction, on Six Sigma, Tamil nadu. 1. INTRODUCTION: In the last two and half decades, Due to globalization and domestic business scenario Indian industries have faced many challenges to compete in the global market. They have initiated many strategies such as TQM, SQC, Lean Manufacturing, but these approaches did not provide breakthrough improvement given by Six Sigma. Six Sigma is a business-driven, multi- faceted approach for reducing the defects and improving the process capability. Six Sigma is a well-structured methodology that focuses on reducing variation, quantifying non -conformance and make products, process and services defect-free. Standard of the software has its own impact on economic growth of any organization. Errors can occur in any phase of the SDLC, it is the allegiance of the developer to identify those and eliminate them then and there because in typical SDLC the errors would cascade to other

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phases. The chances are it would be bandwidth consuming to rectify them or it would be almost impossible to ascend back to the earlier phases to rectify them. The consensus about fault is that if there aren’t any pores in the software that would allow errors to pass through then it is said to have quality. If the software has lacking functionality or any inconvenience in the interface or if it has bad code then it isn’t error impermeable software. There are 4 parameters into which errors fall into. They are as follows 1. Type (this is determined by the phase in which the defect came into existence) 2. Criticality (the severity of the defect) 3. Priority (whether the error requires immediate attention or can it be rectified later) 4. Complexity (how time-consuming it could be) The above stated consensus makes evaluating a developed software a plain sail. Errors are expressed quantitatively to identify the phases that are paradoxical in order to decrease the burden and combat for quality. 1.1 METHODS FOR FINDING AND PREVENTING DEFECTS An effective defect search strategy consists in applying a combination of several methods, each of which will have its level of efficiency, expressed in percentages. According to the data of, testing has a relatively low efficiency of defect search (30-40%), and to make it high, it is necessary to increase the cost of the testing process at times (the effectiveness of beta testing for the number of testers over 1000 is about 75%). It is hardly possible to develop software without any defects, but it is worth trying to try to reduce the number of defects introduced. We list the most well-known methods for preventing defects.  Prototyping. Creation and testing of the model of the developed system to verify its characteristics and to identify incorrect assumptions and decisions that could lead to serious defects (and modifications) in the development.  The use of standards for all types of products produced during the development of software (requirements, design, code, various documentation, etc.).  Application of the component approach.  Using ready-made components - the less you have to develop new solutions, the fewer errors.  Preliminary development of test cases (before the coding stage) allows you to understand better the requirements for the system being developed and better design it. A particular case

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of this approach is Test-Driven Development, in which unit tests are developed not before but before encoding.  Refactoring the code that is, bringing it into the proper form.  Regular analysis of the causes of the appearance of the most serious defects and the search for ways to eliminate these causes. This can occur at periodic meetings of the development team, or you can conduct such analysis for each serious defect found during the system-testing phase or after implementation. The result of such an analysis should be modifications of the development process aimed at eliminating the causes of defects or, at least, contributing to the early detection of such defects. It is also worth mentioning the human factor; no methods will replace the professionalism and experience of developers and managers. Experienced professionals, as a rule, make fewer mistakes, which gives a good background for quality development. 1.2 QUALITY MANAGEMENT PROCESS For quality management, it is not enough to simply use various methods to improve it- they need to be consciously systematically applied, which would become an integral part of the process of developing software oriented toward quality. It is necessary to continuously monitor the quality of the software being developed through quality metrics (defect density, the size of alterations, the average time between failures, etc.), as well as quality control of individual sub- processes that make up the entire development process. Methods that worked well yesterday today can be a waste of time. Each project can have its specificity, which requires a different set of methods for improving quality. For example, some projects (especially critical ones) may require thorough testing of all test cases, in others (when testing is very laborious), more attention should be paid to inspections, third (innovative) will require pre-prototyping, the fourth (resource-critical) will require stress testing, etc.. If you do not constantly monitor the effectiveness of methods, then in time it can significantly decrease.

1.3 REVIEW OF LITERATURE Hongbo Wang. (2019). Six sigma is an approach that improves quality by analyzing data with statistics. In recent years there has been a significant increase in the use and development of the six sigma methodology in manufacturing industry and others. It is high time to have a review on the six sigma approach. This paper reviews some related literatures to describe methodology,

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implementation and future researches. The present paper summaries four issues within the sub- category of the initial six sigma concepts: basic concept, DMAIC, DFSS and deployment. Then, some sectors that benefit from the implementation of six sigma are listed out, and the key factors influencing the successful six sigma project implementation are identified. At last, some topics for future research are presented. Khawarita Siregar. (2019).The literature review is a review of several published journals on a particular topic. Lean Six Sigma (LSS) is a method that has been widely used in research in various fields and continues to grow, so it is important to review this method to get the most common topics solved. LSS is an ongoing improvement methodology with the aim of reduce costs for producing poor quality, reduce waste of non-value-added activities and increase value for consumers and company. The purpose of this review is to find out the objective of using LSS, the tools used in implementing LSS in the manufacturing industry and to identify the factors that have caused the company's failure to implement LSS. Zhiyi Zhuo. (2019).In the banking industry, which aims to serve customers, management level and service level are one of the criteria for measuring the core competitiveness of banks. An important indicator of management and service levels is to ensure customer satisfaction with the bank used. Six Sigma management is customer-centric, based on data and facts, adopting improvement measures for the process, focusing on preventive control, emphasizing borderless cooperation, continuous improvement, and the pursuit of quality and efficiency management mechanisms. In this paper, we empirically analyze the reasons why banks affect customer satisfaction and design the bank’s Six Sigma service process based on empirical analysis. Finally, in the section, the research suggestions for improving bank customer satisfaction are given. Boby John and Abdulrahiman Areshankar. (2018). Six Sigma is a structured and systematic approach to performance and quality improvement. Six Sigma is a rigorous methodology consists of five major phases, namely definition, measure, analysis, improvement, and control for problem solving. A lot of case studies have been published and many large organizations have reported financial benefits by the application of Six Sigma methodology. This paper is a case study on reducing the bearing end plate reworks in a machining process through the application of Six Sigma methodology. The study focuses on reducing the rework due to thickness and diameter variation. From the list of identified potential causes, two causes, namely tool type and

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coolant pH are shortlisted as root causes. The optimum values of tool type and coolant pH, which would simultaneously optimize the diameter and thickness, are identified using the design of experiments and Taguchi's loss function approach. The implementation of optimum settings shows that the capability of the machining process to meet the customer requirements on thickness and diameter has substantially improved and rework has reduced. J. Hill. (2018). Lean Six Sigma (LSS) has rapidly established itself as the key business process improvement strategy of choice for many companies. The LSS approach provides significant benefits to companies through its dual focus on reducing waste and increasing value whilst resolving Critical to Quality (CTQ) issues that affect consistency and repeatability in a product and process. The implementation of LSS is finding wider application in many different environments. Through a case study approach, this paper describes the novel implementation of an integrated LSS framework and outlines how it was used to identify the factors that affect supply chain performance in an aerospace Maintenance Repair and Overhaul (MRO) facility. The study outlines the application and measures the effectiveness of the integrated LSS framework through its ability to achieve new and enhanced performance through simultaneously reducing late material calls and reducing and stabilizing Order To Receipt (OTR) times. Fernando Filardi. (2015).Several process improvement methodologies have emerged in recent decades, but a lack of studies continues on their applicability and effective results in organizations. This article analyzes the Lean Sigma methodology implementation results in the cost and time allocation process for a multinational oil company IT application in Brazil. The analytical criteria were based on the literature review: a) cost, b) time, c) quality, d) effectiveness, e) efficiency and f) internal customer satisfaction. The action research methodology was used and the findings showed Lean Sigma improved the IT application, however it revealed the methodology has low adherence to processes where the intangibles and people participation has greater influence. 1.4 OBJECTIVES OF THE STUDY 1. To study on Six Sigma and its implementation in IT industries with special reference to Tamilnadu. 2. To analyze the effect of Six sigma techniques implemented in the organization 3. To know the employees’ opinion towards the six sigma factors followed in the organization

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4. To measure the level of satisfaction towards the Six Sigma key roles assigned in the organization 5. To identify the level of usage towards various Six sigma tools and awareness of analysis tools in the organization 6. To identify the issues in following six sigma methods inside the organization 7. To suggest feasible solutions based on the findings analyzed.

2. RESEARCH DESIGN As the objectives of the study are clear and well defined, the study adopted is Descriptive Research Design. Descriptive research, also known as statistical research, described data and characteristics about the population or phenomenon being studied. Descriptive research answers the question who, what, where, when and how. Therefore, our type of research is the descriptive type of research in which data collected describes the characteristics of the population that has been studied. 2.1 SAMPLING TECHNIQUE Sampling technique is a definite plan for a given population. It refers to the procedure the researcher adopts for selecting the items for sampling. The technique adopted for our study is Non-Probability sampling technique. 2.2 NON-PROBABILITY SAMPLING TECHNIQUE Non-probability sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected. 2.3 CONVENIENCE SAMPLING Convenience sampling is probably the most common of all sampling techniques. With convenience sampling, the samples are selected because they are accessible to the researcher. Subjects are chosen simply because they are easy to recruit. This technique is considered easiest, cheapest and least time consuming. 2.4 SAMPLING SIZE In the present study, the sample is collected from 117 respondentswho are working in IT industries from Tamilnadu.

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2.5 TOOLS FOR DATA COLLECTION Data refers to information or facts. It not only refers to numerical figures but also includes descriptive facts. The method of data collection includes two types of study, such as primary data and secondary data. 2.5.1 PRIMARY DATA In this method, preprinted list of questions arranged in a sequence which is used by the researcher for collecting data. The questionnaire is filled by the respondents. The questionnaire is considered as the heart of the survey. The questions were in close ended. 2.5.2 SECONDARY DATA It was used mainly to support the primary data. In this study, the secondary data has been collected from the company records, text records, and websites.

3. ANALYSIS AND INTERPRETATION I. SIMPLE PERCENTAGE ANALYSIS TABLE 4.1 AGE Age Group No. of Respondents Percent Below 25 years 29 24.8 25 to 35 years 49 41.9 35 to 45 years 21 17.9 Above 45 years 18 15.4 Total 117 100.0 Source: Primary Data INTERPRETATION: From the above table out of 117 respondents, it is clearly stated that 24.8% of the respondents are ‘Below 25 years’ whereas 41.9% of the respondents are between ’25 to 35 years of age’, 17.9% of the respondents are between ‘35 to 45 years of age’ and the remaining 15.4% of the respondents are ‘Above 45 years’. The result inferred that most (41.9%) of the respondents are between ’25 to 35 years’. TABLE 4.2 GENDER Gender No. of Respondents Percent Male 57 48.7 Female 60 51.3

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Total 117 100.0 Source: Primary Data INTERPRETATION: From the above table out of 117 respondents, it is clearly stated that 48.7% of the respondents are ‘Male’ and 51.3% of the respondents are ‘Female’. The result inferred that majority (51.3%) of the respondents are ‘Female’ only. TABLE 4.3 MARITAL STATUS Marital Status No. of Respondents Percent Married 69 59.0 Unmarried 48 41.0 Total 117 100.0 Source: Primary Data INTERPRETATION: From the above table out of 117 respondents, it is clearly stated that 59% of the respondents are 'Married’ and the remaining 41% of the respondents are ‘Unmarried’. The result inferred that majority (59%) of the respondents are ‘Married’ only. TABLE 4.4 EDUCATIONAL QUALIFICATION Educational Qualification No. of Respondents Percent School Level 35 29.9 Diploma 36 30.8 Doctrate 13 11.1 Under graduate 7 6.0 Post graduate 14 12.0 Others 12 10.3 Total 117 100.0 Source: Primary Data INTERPRETATION: From the above table out of 117 respondents, it is clearly stated that 29.9% of the respondents are in ‘School Level’ whereas 30.8% of the respondents are ‘Diploma’, 11.1% of the respondents are ‘Doctrate’, 6% of the respondents are ‘Under Graduate’, 12% of the respondents ‘Post Graduate’ and the remaining 10.3% of the respondents ‘Others’. The result inferred that most (30.8%) of the respondents are ‘Diploma’ level only. TABLE 4.5 DESIGNATION Designation No. of Respondents Percent

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Team Leader 59 50.4 Junior Developers 42 35.9 Senior Developers 16 13.7 Total 117 100.0 Source: Primary Data INTERPRETATION: From the above table out of 117 respondents, it is clearly stated that 50.4% of the respondents are designated as ‘Team Leader’ whereas 35.9% of the respondents are ‘Junior Developers’ and the remaining 13.7% of the respondents are ‘Senior Developers’. The result inferred that most (50.4%) of the respondents are designated as ‘Team Leader’. TABLE 4.6 MONTHLY INCOME Monthly Income No. of Respondents Percent Rs 10001 – Rs15000 27 23.1 Rs15001- Rs 22000 48 41.0 Rs 22001 – Rs 30000 21 17.9 Above Rs 30001 21 17.9 Total 117 100.0 Source: Primary Data INTERPRETATION: From the above table out of 117 respondents, it is clearly stated that 23.1% of the respondents’ monthly income are between ‘Rs.10001 to Rs.15000’ whereas 41% of the respondents’ monthly income are between ‘Rs.15001 to Rs.22000’, 17.9% of the respondents’ monthly income are between ‘Rs.22001 to Rs.30000’ and the remaining 17.9% of the respondents’ monthly income is ‘Above Rs.30001’. The result inferred that most (41%) of the respondents are between ‘Rs.15001 to Rs.22000’. TABLE 4.7 DEPARTMENT Department No. of Respondents Percent Testing 68 58.1 Development 12 10.3 Research 27 23.1 Human resource 10 8.5

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Total 117 100.0 Source: Primary Data INTERPRETATION: From the above table out of 117 respondents, it is clearly stated that 58.1% of the respondents are in ‘Testing’ department whereas 10.3% of the respondents are from ‘Development’ department, 23.1% of the respondents are from ‘Research’ department and the remaining 8.5% of the respondents are from ‘Human Resource’ department. The result inferred that majority (58.1%) of the respondents are from ‘Testing’ department. TABLE 4.8 WORK SHIFT TIMINGS WORK SHIFT TIMINGS No. of Respondents Percent 8.00am-5.00pm 45 38.5 9.00am-6.00pm 55 47.0 10.00am-8.00pm 17 14.5 Total 117 100.0 Source: Primary Data INTERPRETATION: From the above table out of 117 respondents, it is clearly stated that 38.5% of the respondents are working in ‘8 am to 5 pm’ whereas 47% of the respondents are working in ‘9 am to 6 pm’ and the remaining 14.5% of the respondents are working in ’10 am to 8 pm’. The result inferred that most (47%) of the respondents are working in ‘9 am to 6 pm’. TABLE 4.9 WORKING EXPERIENCE IN YEARS Years of experience No. of Respondents Percent Less than two years 36 30.8 1-5years 39 33.3 5-10Years 16 13.7 10-15years 7 6.0 Above 15 years 19 16.2 Total 117 100.0 Source: Primary Data INTERPRETATION: From the above table out of 117 respondents, it is clearly stated that 30.8% of the respondents’ are having ‘Less than two years’ whereas 33.3% of the respondents’ are having experience between ‘1 to 5 years’, 13.7% of the respondents’ are having experience between ‘5

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to 10 years’, 6% of the respondents’ are having experience between ’10 to 15 years’ and the remainining 16.2% of the respondents’ are ‘Above 15 years’. The result inferred that most (33.3%) of the respondents are having experience between ‘1 to 5 years’. TABLE 4.10 AWARENESS TOWARDS SIX SIGMA IN YOUR ORGANIZATION Awareness towards Six Sigma In Your Organization No. of Respondents Percent Yes 66 56.4 No 51 43.6 Total 117 100.0 Source: Primary Data INTERPRETATION: From the above table out of 117 respondents, it is clearly stated that 56.4% of the respondents stated ‘Yes’ whereas 43.6% of the respondents stated ‘No’. The result inferred that majority (56.4%) of the respondents stated that they aware of Six Sigma in the organization. TABLE 4.11 RATE TOWARDS SIX SIGMA IN THE ORGANIZATION Rate towards Six Sigma in the organization No. of Respondents Percent One 31 26.5 Two 33 28.2 Three 15 12.8 Four 6 5.1 Five 19 16.2 None of the above 13 11.1 Total 117 100.0 Source: Primary Data INTERPRETATION: From the above table out of 117 respondents, it is clearly stated that 26.5% of the respondents rated ‘One’ whereas 28.2% of the respondents rated ‘Two’, 12.8% of the respondents rated ‘Three’, 5.1% of the respondents rated ‘Four’, 16.2% of the respondents rated ‘Five’ and the remaining 11.1% of the respondents rated ‘None’. The result inferred that most (28.2%) of the respondents rated ‘Two’ towards the Six sigma in the organization.

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II. DESCRIPTIVE STATISTICS TABLE 4.12 LEVEL OF OPINION TOWARDS THE EFFECT OF SIX SIGMA TECHNIQUES IMPLEMENTED IN THE ORGANIZATION Descriptive Statistics N Minimum Maximum Mean Std. Deviation Reduce process cycle 117 1.00 4.00 2.9744 .84549 time Achieving sustained 117 1.00 5.00 2.9744 1.02941 quality improvement Evaluates process 117 1.00 5.00 3.0085 1.00426 capability Continuous efforts to achieve stable and 117 1.00 5.00 3.1111 .98941 predictable process results Produce a very high proportion of output 117 1.00 5.00 3.0427 1.07794 within specification Increase customer 117 1.00 5.00 2.8034 .97591 satisfaction Focused on 117 1.00 5.00 4.3333 1.06674 eliminating waste A clear commitment 117 1.00 5.00 2.6239 1.30468 to making decisions Minimizing impact 117 1.00 5.00 3.4530 1.06266 variability Valid N (listwise) 117

INTERPRETATION: ‘Focused on eliminating waste’ has taken the first stance with a mean value of 4.33, ‘Minimizing impact variability’ has taken the second stance with a mean value of 3.45, ‘Continuous efforts to achieve stable and predictable process results’ has taken the third stance a mean value of 3.11, ‘Produce a very high proportion of output within specification’ has taken the fourth stance with a mean value of 3.04, ‘Evaluates process capability’ has taken the fifth stance with a mean value of 3.008. ‘Reduce process cycle time’ and ‘Achieving sustained quality

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improvement’ has taken the sixth stance with a mean value of 2.97. ‘Increase customer satisfaction’ has taken the seventh stance with a mean value of 2.80 and finally ‘A clear commitment to making decisions’ has taken the eighth stance with the value 2.62. It is inferred from the above table that ‘Focused on eliminating waste’ has taken the first stance with a mean value of 4.33 and ‘A clear commitment to making decisions’ has taken the eighth stance with the value 2.62. TABLE 4.13 LEVEL OF OPINION TOWARDS THE SIX SIGMA FACTORS FOLLOWED IN YOUR ORGANIZATION

Descriptive Statistics N Minimum Maximum Mean Std. Deviation Lean Management 117 2.00 5.00 3.9145 .93378 System Measure and identify Critical To Quality 117 1.00 5.00 4.5470 .91440 (CTQ) Complementary 117 1.00 5.00 3.5128 .98799 disciplines Target organizational 117 1.00 5.00 2.9573 1.00338 efficiencies Mistake proofing 117 1.00 5.00 3.2308 .92275 Control production 117 1.00 5.00 2.8718 .95175 boards Design of 117 1.00 5.00 3.1282 .98732 experiments Tools to Measure 117 1.00 5.00 3.4530 .93307 Process Capability Valid N (listwise) 117 INTERPRETATION: ‘Measure and identify Critical To Quality (CTQ)’ has taken the first stance with a mean value of 4.54, ‘Lean Management System’ has taken the second stance with a mean value of 3.91, ‘Complementary disciplines’ has taken the third stance a mean value of 3.51, ‘Tools to Measure Process Capability’ has taken the fourth stance with a mean value of 3.45, ‘Mistake proofing’ has taken the fifth stance with a mean value of 3.23. ‘Design of experiments’ has taken the sixth stance with a mean value of 3.12. ‘Target organizational efficiencies’ has taken the

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seventh stance with a mean value of 2.95 and finally ‘Control production boards’ has taken the eighth stance with the value 2.87. It is inferred from the above table that ‘Measure and identify Critical To Quality (CTQ)’ has taken the first stance with a mean value of 4.54 and ‘Control production boards’ has taken the eighth stance with the value 2.87. TABLE 4.14 LEVEL OF SATISFACTION TOWARDS THE SIX SIGMA KEY ROLES ASSIGNED IN YOUR ORGANIZATION Descriptive Statistics N Minimum Maximum Mean Std. Deviation Executive Leadership 117 1.00 5.00 3.2393 .97953 Champions 117 1.00 5.00 3.2735 .95253 Master Black Belts 117 1.00 5.00 3.1624 1.17413 Black Belts operate 117 1.00 5.00 2.7949 1.18568 Green Belts 117 1.00 5.00 4.3248 1.09725 Yellow Belts 117 1.00 5.00 2.4957 1.42408 White belts 117 1.00 5.00 3.6239 1.33084 belts 117 1.00 5.00 4.2137 .83902 Valid N (listwise) 117 INTERPRETATION: ‘Green Belts’ has taken the first stance with a mean value of 4.32, ‘Orange belts’ has taken the second stance with a mean value of 4.21, ‘White belts’ has taken the third stance a mean value of 3.62, ‘Champions’ has taken the fourth stance with a mean value of 3.27, ‘Executive Leadership’ has taken the fifth stance with a mean value of 3.23. ‘Master Black Belts’ has taken the sixth stance with a mean value of 3.16. ‘Black Belts’ has taken the seventh stance with a mean value of 2.79 and finally ‘Yellow Belts’ has taken the eighth stance with the value 2.49. It is inferred from the above table that ‘Green Belts’ has taken the first stance with a mean value of 4.32 and ‘Yellow Belts’ has taken the eighth stance with the value 2.49. TABLE 4.15 LEVEL OF USAGE TOWARDS THE FOLLOWING TOOLS USED IN YOUR ORGANIZATION

Descriptive Statistics N Minimum Maximum Mean Std. Deviation

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Analysis tools 117 2.00 5.00 4.6752 .53866 Program management 117 1.00 5.00 3.2564 1.02683 tools DMAIC and Lean online project 117 1.00 5.00 3.0684 .99764 collaboration tools Data Collection tools 117 1.00 5.00 3.3504 .93141 Valid N (listwise) 117 INTERPRETATION: ‘Analysis tools’ has taken the first stance with a mean value of 4.67, ‘Data Collection tools’ has taken the second stance with a mean value of 3.35, ‘Program management tools’ has taken the third stance a mean value of 3.25 and finally ‘DMAIC and Lean online project collaboration tools’ has taken the fourth stance with the value 3.06. It is inferred from the above table that ‘Analysis tools’ has taken the first stance with a mean value of 4.67 and ‘DMAIC and Lean online project collaboration tools’ has taken the eighth stance with the value 3.06. TABLE 4.16 LEVEL OF USAGE TOWARDS THE FOLLOWING TOOLS USED IN YOUR ORGANIZATION

Descriptive Statistics N Minimum Maximum Mean Std. Deviation ARIS Six Sigma 117 1.00 5.00 2.8718 .92418 IBM WebSphere 117 1.00 5.00 3.1282 .98732 Business Modeler JMP 117 1.00 5.00 3.4530 .93307 LQATS 117 1.00 5.00 3.2393 .97953 Oracle Crystal Ball 117 1.00 5.00 3.2735 .95253 Microsoft Visio 117 1.00 5.00 3.1624 1.17413 117 1.00 5.00 2.9487 .99867 NCSS Statistical 117 1.00 5.00 3.3419 .90175 Software QPR ProcessGuide 117 1.00 5.00 3.3248 .98985 by QPR Software

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Quality Companion 117 1.00 5.00 3.2137 1.04905 by Minitab RCASE 117 1.00 5.00 3.2308 .95036 R language 117 1.00 5.00 3.2650 1.02042 SDI Tools 117 1.00 5.00 3.3333 .90972 SigmaXL 117 1.00 5.00 3.1795 1.16423 Software AG webMethods BPM 117 1.00 5.00 2.7949 1.14122 Suite Statgraphics 117 1.00 5.00 4.4274 1.05304 117 1.00 5.00 2.4188 1.40344 Telelogic System 117 1.00 5.00 3.7436 1.32062 Architect Valid N (listwise) 117 INTERPRETATION: ‘Statgraphics’ has taken the first stance with a mean value of 4.42, ‘Telelogic System Architect’ has taken the second stance with a mean value of 3.74, ‘JMP’ has taken the third stance a mean value of 3.45, ‘NCSS Statistical Software’ has taken the fourth stance with a mean value of 3.34, ‘SDI Tools’ has taken the fifth stance with a mean value of 3.33. ‘Oracle Crystal Ball’ has taken the sixth stance with a mean value of 3.27. ‘R language’ has taken the seventh stance with a mean value of 3.26, ‘LQATS’ and ‘RCASE’ has taken the eighth stance with the value 3.23, ‘Quality Companion by Minitab’ has taken the ninth stance with the value 3.21, ‘SigmaXL’ has taken the tenth stance with the value 3.17, ‘Microsoft Visio’ has taken the eleventh stance with the value 3.16, ‘IBM WebSphere Business Modeler’ has taken the twelfth stance with the value 3.12, ‘Minitab’ has taken the thirteenth stance with the value 2.94, ‘ARIS Six Sigma’ has taken the fourteenth stance with the value 2.87, ‘Software AG webMethods BPM Suite’ has taken the fifteenth stance with the value 2.79 and finally ‘STATISTICA’ has taken the Sixteenth stance with the value 2.41. It is inferred from the above table that ‘Statgraphic’ has taken the first stance with a mean value of 4.42 and ‘STATISTICA’ has taken the Sixteenth stance with the value 2.41. TABLE 4.17 LEVEL OF OPINION TOWARDS THE VARIOUS ISSUES IN SIX SIGMA METHODS

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Descriptive Statistics N Minimum Maximum Mean Std. Deviation Lack of originality 117 1.00 5.00 4.2308 .84471 Inadequate for complex 117 2.00 5.00 4.6752 .53866 manufacturing Role of consultants 117 1.00 5.00 3.2991 1.01934 Potential negative 117 1.00 5.00 3.2051 .96970 effects Over-reliance on 117 1.00 5.00 3.4274 .91271 statistical tools Stifling creativity in research 117 1.00 5.00 3.5043 .90615 environments Lack of systematic 117 1.00 5.00 2.9060 .90956 documentation Valid N (listwise) 117 INTERPRETATION: ‘Inadequate for complex manufacturing’ has taken the first stance with a mean value of 4.67, ‘Lack of originality’ has taken the second stance with a mean value of 4.23, ‘Stifling creativity in research environments’ has taken the third stance a mean value of 3.50, ‘Over- reliance on statistical tools’ has taken the fourth stance with a mean value of 3.42, ‘Role of consultants’ has taken the fifth stance with a mean value of 3.29. ‘Potential negative effects’ has taken the sixth stance with a mean value of 3.20 and finally ‘Lack of systematic documentation’ has taken the seventh stance with the value 2.90. It is inferred from the above table that ‘Inadequate for complex manufacturing’ has taken the first stance with a mean value of 4.67 and ‘Lack of systematic documentation’ has taken the Seventh stance with the value 2.90. III.CHI-SQUARE ANALYSIS A. AGE AND FOCUSED ON ELIMINATING WASTE

Null Hypothesis: H0: There is no significant association between Age and Focused on Eliminating Waste (µ0 = µ1 = µn) Alternative Hypothesis Ha: There is a significant association between Age and Focused on Eliminating Waste (µ0 != µ1 != µn)

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Age Group Very Low Low Neutral High Very High Total Below 25 years 0 1 0 11 17 29 25 to 35 years 3 0 4 9 33 49 35 to 45 years 1 2 2 4 12 21 Above 45 years 1 2 2 3 10 18 Total 5 5 8 27 72 117

CHI SQUARE TEST RESULT CALCULATED VALUE TABLE VALUE D.F

14.179 21.026 12

In the above analysis, the calculated value 14.179 is less than the table value 21.026 at the level of 5% significance. Hence the null hypothesis is accepted. Thus it can be inferred that there is no significant association between Age and Focused on Eliminating Waste. B. GENDER AND EVALUATES PROCESS CAPABILITY

Null Hypothesis: H0: There is no significant association between Gender and Evaluates Process Capability (µ0 = µ1 = µn) Alternative Hypothesis Ha: There is a significant association between Gender and Evaluates Process Capability (µ0 != µ1 != µn)

Gender Very Low Low Neutral High Very High Total Male 4 7 20 25 1 57 Female 8 13 22 16 1 60 Total 12 20 42 41 2 117

CHI SQUARE TEST RESULT CALCULATED VALUE TABLE VALUE D.F

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5.131 9.488 4

In the above analysis, the calculated value 5.131 is less than the table value 9.488 at the level of 5% significance. Hence the null hypothesis is accepted. Thus it can be inferred that there is no significant association between Gender and Evaluates Process Capability. FINDINGS I. SIMPLE PERCENTAGE ANALYSIS • Most (41.9%) of the respondents are between ’25 to 35 years’. • Majority (51.3%) of the respondents are ‘Female’ only. • Majority (59%) of the respondents are ‘Married’ only. • Most (30.8%) of the respondents are ‘Diploma’ level only. • Most (50.4%) of the respondents are designated as ‘Team Leader’. • Most (41%) of the respondents are between ‘Rs.15001 to Rs.22000’. • Majority (58.1%) of the respondents are from ‘Testing’ department. • Most (47%) of the respondents are working in ‘9 am to 6 pm’. • Most (33.3%) of the respondents are having experience between ‘1 to 5 years’. • Majority (56.4%) of the respondents stated that they aware of Six Sigma in the organization. • Most (28.2%) of the respondents rated ‘Two’ towards the Six sigma in the organization. II. DESCRIPTIVE STATISTICS LEVEL OF OPINION TOWARDS THE EFFECT OF SIX SIGMA TECHNIQUES IMPLEMENTED IN THE ORGANIZATION • ‘Focused on eliminating waste’ has taken the first stance with a mean value of 4.33 and ‘A clear commitment to making decisions’ has taken the eighth stance with the value 2.62. LEVEL OF OPINION TOWARDS THE SIX SIGMA FACTORS FOLLOWED IN YOUR ORGANIZATION • ‘Measure and identify Critical to Quality (CTQ)’ has taken the first stance with a mean value of 4.54 and ‘Control production boards’ has taken the eighth stance with the value 2.87. LEVEL OF SATISFACTION TOWARDS THE SIX SIGMA KEY ROLES ASSIGNED IN YOUR ORGANIZATION

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• ‘Green Belts’ has taken the first stance with a mean value of 4.32 and ‘Yellow Belts’ has taken the eighth stance with the value 2.49. LEVEL OF USAGE TOWARDS THE FOLLOWING TOOLS USED IN YOUR ORGANIZATION • ‘Analysis tools’ has taken the first stance with a mean value of 4.67 and ‘DMAIC and Lean online project collaboration tools’ has taken the eighth stance with the value 3.06. LEVEL OF USAGE TOWARDS THE FOLLOWING TOOLS USED IN YOUR ORGANIZATION • ‘Statgraphic’ has taken the first stance with a mean value of 4.42 and ‘STATISTICA’ has taken the Sixteenth stance with the value 2.41. LEVEL OF OPINION TOWARDS THE VARIOUS ISSUES IN SIX SIGMA METHODS • ‘Inadequate for complex manufacturing’ has taken the first stance with a mean value of 4.67 and ‘Lack of systematic documentation’ has taken the Seventh stance with the value 2.90. III.CHI-SQUARE ANALYSIS • There is no significant association between Age and Focused on Eliminating Waste. • There is no significant association between Gender and Evaluates Process Capability. SUGGESTIONS 1. Employees/operators those who are not aware of the effective methodology should be given awareness of six sigma methodology usefulness. 2. Awareness for anything is important today. As no six sigma project is approved unless the bottom-line impact has been clearly identified and defined, involvement of bottom line employee/labor should be increased in such training programme and awareness regarding six sigma should be given to them. 3. Company should organize more training and development programme for their operators as they are the main factor of product and profit. Training may be given in the form of group discussions, seminars, conferences, giving specific tasks to check their attentiveness, also can show PPT on the project done. 4. Managers and officers should constantly take review on operators work. Should conduct meetings, discussions with the weak operators and make them aware of importance of doing work in order. More on job training should be given so the defect rate will be controlled.

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5. The six sigma methodology has been applied only in two sections but it should be applied to other departments also to see the loop holes in the overall working of the company. 6. Planning project work well. 7. Determining the exact scope of the work and the required/desired outcomes. 8. Developing a proper fact-based understanding of the problem. 9. Leveraging creative tools to develop the highest quality imaginative ideas. 10. Leveraging selection tools and decision making tools to identify the most appropriate solutions. 11. Managing stakeholders well, involving them and planning their involvement. 12. Planning and executing implementation with great care. 13. Ensuring that benefits are calculated and extracted. 14. Handing over a complete sustainable finished product to the business. CONCLUSION The Present study will help to understand the contribution of Six Sigma methodology in improving product quality and ultimately will help in the growth and development of the company. Six Sigma methodology is important from the view point of the company’s customers, as customers are considered boss in today’s market. A genuine effort has been made which can be benefited to the companies who do not use the problem solving tool. Number of Black Belts should be more as they are the people behind the success of six sigma in any organization. More Executives and supervisors should be actively involved in giving training to the operators. Operators and supervisors should be encouraged more for further educational courses; training, seminars, and conferences related to six sigma and make them capable of meeting the current as well as future organizational needs. Training should be provided on communication skills and personality development as it boosts the employee morale and they are motivated as well. Few minutes should be given for meditation, as it was observed that the work process is too hectic and tedious, employees get rid of it. Mediation will somehow be a remedial measure in improving labor productivity. One can’t expect significant reduction in costs and increase in sales using six sigma without investing in training, organizational infrastructure and Cultural Revolution. So such type of training programme should be given on continuous basis. Focus shall be given on, on the job training. Team Head should give the concerned employees proper

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instruction about the development and operational so that optimum utilization of Man, Machine and Material will be on the place. Future research can be conducted using the defect reduction areas. Due to the time constraint the study has been limited to only 117 respondents in Tamilnadu. Further the research will be carried for a National or South India level.

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10. Naresh Motiani and Abhay Kulkarni. (2019). Sustainability and Impact of Lean Six Sigma Practices: Learnings From Some KPO/BPO Organizations. International Journal of Innovative Technology and Exploring Engineering . 8 (11), 52-60. 11. Tarek M. Ibrahim. (2019). Implementation of Lean Six Sigma in the Yarn Manufacturing: a case study. International Journal of Scientific & Engineering Research. 10 (12), 557-561. 12. Abdur Rahman. (2017). A Case Study of Six Sigma Define-Measure-Analyze-Improve- Control (Dmaic) Methodology in Garment Sector. Independent Journal of Management & Production. 8 (4), 1-9. 13. Mr. Mohit Chhikara. (2017). Implementation of Six Sigma in Indian Manufacturing Industries. Journal of Management. 3 (1), 73-78. 14. Murilo Riyuzo Vendrame Takao. (2017). Six Sigma methodology advantages for small- and medium-sized enterprises. Advances in Mechanical Engineering. 9 (10), 1-10. 15. Andrea Sujova. (2016). Sustainable Process Performance by Application of Six Sigma Concepts: The Research Study of Two Industrial Cases. Pacific Business Review International. 4 (12), 52-60. 16. L. Gutierrez Gutierrez. (2016). Logistics services and Lean Six Sigma implementation: a case study. Pacific Business Review International. 7 (3), 1-8.

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