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 2Professor and Vice-Principal, B. M. I. T., Solapur University, Solapur Maharashtra, India. E-mail: [email protected], [email protected] ABSTRACT Six sigma for small- and medium-sized enterprises (SMEs) is an emerging topic among many academics and Six Sigma practitioners over the last two to three years. Very few studies have been reported about the successful applications of Six Sigma in SMEs. Main objective of this paper is to examine the extent of usefulness of a simpler but not very frequently used methodology known as the Shainin methodology for simplifying the implementing Six Sigma. To confirm whether Six Sigma implementation is simplified, this paper highlights the comparison of three DOE approaches viz. Classical, Taguchi and Shainin methodology. A case study based research work done in ISO certified Indian SME, concludes that, Six Sigma implementation process can be simplified by using Shainin tools and proper use 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 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

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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.

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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 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

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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

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addition, the success of the projects can an industrial operation. Applications of the lead to a very positive effect on the morale Classical and 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.

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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)

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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

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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

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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. CO 2 suggested to adopt the Shainin Welding process)] . Therefore, new SSVs

Techniques. The Shainin’s Techniques are now related to CO 2-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 CO 2-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 CO 2-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

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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 CO 2 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.

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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 CO 2 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

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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

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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 )

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The improvements identified were also • Procedure has been developed for used to set the action plan for other periodic monitoring of CO 2 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 CO 2 welding machine operating used in this research-work is described in and training manuals. figure 4.

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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.

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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) nd unique. Therefore, we conclude that, World Class Quality , 2 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 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. REFERENCES 29, pp. 237-247. 1. Antony, J., Banuelas, R., (2002), 11. Linderman, K., Schroeder, R.G., ‘Key ingredients for the effective Zaheer, S. and Choo, A.S. (2003) implementation of Six Sigma ‘Six Sigma: a goal-theoretic

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

perspective’, Journal of Operations 23. Shainin, R D (1993b). ‘Strategies for Management , Vol. 21, No.2, pp. 193– Technical Problem Solving,” Quality 203. Engineering, Vol. 5, pp. 433-438. 12. Logothetis, N. (1990) ‘A Perspective 24. Shainin, R D (1995). ‘A Common on Shainin’s Approach to Sense Approach to Quality Experimental Design for Quality Management,” 49th Annual Quality Improvement’, Quality and Congress Proceedings, ASQC , pp. Reliability Engineering International , 1163-1169. Vol. 6, No. 3, pp. 195-202. 25. Steiner, S H; MacKay, R J and 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 Journal of Quality Technology , Vol. Improvement’, Quality Engineering , 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). Class Quality- by K Bhote (1991)’ ‘A Comparative Analysis of the Journal of Quality Technology , Vol. Taguchi and Shainin DOE 25, pp. 152-153. Techniques in an Aerospace 15. Pande, P.S., Neuman, R.P., Environment,” International Journal Cavanagh, R.R. (2000) The Six Sigma of Productivity and Performance Way: How GE, Motorola, & Other Management , Vol. 54, No. 8, pp. 658- Top Companies are Honing their 678. Performance , New York: McGraw- 27. Verma, A K; Srividya, A; Mannikar, Hill. A V; Pankhawala, V A and 16. Pyzdek, T. (2001) The Six Sigma Rathanraj, K J (2004). ‘Shainin Handbook , USA: McGraw- Method: Edge over other DOE Hill/Quality Publishing Tucson. techniques’, Engineering 17. Rao, R. S., Kumar, C. G., Prakasham, Management Conf. 2004 R. S. and Hobbs, P. J. (2008) ‘The Proceedings, IEEE International , Taguchi Methodology as a Statistical Vol. 3, No. 18-21, pp. 1110- 1113. Tool for Biotechnological 28. Xingxing Zu, Lawrence D. Applications: A Critical Appraisal’, Fredendall, Thomas J. Douglas, Biotechnology Journal , Vol. 3, No. 4, (2008), ‘The evolving theory of pp. 510-523. quality management: The role of Six 18. Shainin, D. and Shainin, P. D. (1990) Sigma’, Journal of Operations ‘Analysis of Experiments’. 45th Management , Vol. 26 pp. 630–650. Annual Quality Congress Proceedings , ASQC, pp. 1071- 1077. 19. Shainin, P D (1992a) ‘Managing SPC -A Critical Quality System Element’. 46th Annual Quality Congress Proceedings , ASQC, pp. 251-257. 20. Shainin, P D (1993a). ‘Managing Quality Improvement’. 47 th Annual Quality Congress Proceedings, ASQC , pp. 554-560. 21. Shainin, P D; Shainin, R D and Nelson, M T (1997). ‘Managing Statistical Engineering,” 51st Annual Quality Congress Proceedings, ASQC , pp. 818-832. 22. Shainin, R D (1992b). ‘Technical Problem Solving Strategies, A Case Study,” 46th Annual Quality Congress Proceedings, ASQC , pp. 876-882.

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