Improvement of quality awareness using six sigma methodology for achieving higher cmmi level

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  • 1. International Journal of Advanced Research in Management (IJARM),Volume 1, Issue 1, June 2010. pp. 20-41 Prabhuswamy & Mamatha. M I J ARM International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.http://www.iaeme.com/ijarm.html © IAEME IMPROVEMENT OF QUALITY AWARENESS USING SIX SIGMA METHODOLOGY FOR ACHIEVING HIGHER CMMI LEVEL B.P. Mahesh Assistant Professor, Department of Industrial Engineering and Management M.S.Ramaiah Institute of Technology, Bangalore-560054, India bpmahesh@gmail.com (+91-9448739040) Dr. M.S. Prabhuswamy Professor, Department of Mechanical Engineering S.J. College of Engineering, Mysore-570006, India msp_sjce@yahoo.com (+91-9886624627) Mamatha. M Project Manager, FINACLE Infosys Technologies Limited, Electronics City, Bangalore- 560100, INDIA mamatha_m@infosys.com (+91-9945529504) ABSTRACT Globalization and increased competition gives rise to new approaches to managing Quality and Productivity. New approaches and frame works such as TQM, Business Process Re-engineering (BPR), Capability Maturity Model (CMM), etc., have been extensively deployed in organizations. Along with these approaches, in the face of a complex dynamic environment, the organizational survival hinges on adaptation and human competence also. Managing the creative and innovative ability of the human capital would make a difference between success and failure of any organization. Six Sigma methodologies provide a highly prescriptive cultural infrastructure and an adaptive framework for obtaining sustainable results in manufacturing as well as service organizations. In this article, the research scholar presents the application of Six Sigma framework for achieving a higher CMMI level through improvement of quality awareness among process users. The pilot implementation of recommendations of the study showed improved awareness, better involvement and enhanced commitment from the process users to follow the standardized processes for achieving the organization’s goal of being a CMMI level 4 assessed organization. KEYWORDS Capability Maturity Model Integration; Six Sigma; Quality Function Deployment; Failure Mode and Effect Analysis; Quality Management System; Critical to Quality. 20
  • 2. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M1. INTRODUCTION Six Sigma methodology has been effectively implemented in manymanufacturing and service sectors. But there is a lot of scope for implementing SixSigma methodology in the various areas of Information Technology sector. SoftwareEngineering Institute – Capability Maturity Model Integration (SEI – CMMI) providesa road map for organizations to achieve excellence in the Information Technologysector. The present study was undertaken at a multinational Research and Developmentcenter located in Bangalore. The organization is currently SEI – CMM level 3 assessedand is striving to achieve CMMI (Capability Maturity Model – Integration) level 4assessment. To achieve CMMI level 4 assessments, all process users must followstandardized processes as specified in the Quality Management System (QMS) of theorganization. The initial observation by the research scholar revealed that the processusers were not strictly adhering to specified standardized processes, thus causing ahindrance for the organization to achieve CMMI level 4. The objective of the study was to increase the awareness, understanding andperceived importance of QMS amongst the process users. The Six Sigma - DMAIC(Define, Measure, Analyze, Improve and Control) methodology was applied to meetthe set objective. The various TQM tools and techniques used in the study wereStructured Survey, Process Mapping, Quality Function Deployment (QFD), ParetoAnalysis, Failure Modes and Effects analysis (FMEA) and Regression Analysis.2. LITERATURE REVIEW Six Sigma is a statistical concept that measures a process in terms of defects.Achieving Six Sigma means processes are delivering 3.4 defects per millionopportunities (DPMO). In other words, they are working almost perfectly. Sigma is a term in statistics that measures standard deviation. In its businessuse, it indicates defects in the outputs of a process, and helps us to understand how farthe process deviates from perfection. One sigma represents 691462.5 DPMO, whichtranslates to a percentage of non-defective outputs of only 30.854%. That’s obviouslyreally poor performance. If we have processes functioning at a three sigma level, thismeans we are allowing 66807.2 errors per million opportunities, or delivering 93.319%non-defective outputs. That is much better, but we are still wasting money anddisappointing our customers. The central idea of Six Sigma management is that if wecan measure the defects in a process, we can systematically figure out ways toeliminate them, to approach a quality level of zero defects, which is the ultimate goalof TQM. DMAIC refers to a data-driven quality strategy for improving processes, and isan integral part of the companys Six Sigma Quality Initiative. This methodology canbe applied to the product or process that is in existence. DMAIC is an acronym for fiveinterconnected phases: Define, Measure, Analyze, Improve, and Control. Each step inthe cyclical DMAIC Process is required to ensure the best possible results (Figure 1). 21
  • 3. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M DEFINE MEASURE ANALYZE IMPROVE CONTROL Figure 1 Six Sigma – DMAIC MethodologyThe DMAIC Methodology is explained in simple terms as follows. Define the Customer, their critical to quality (CTQ) issues, and the core business process involved. Measure the performance of the Core Business Process involved. Analyze the data collected and process map to determine root causes of defects and opportunities for improvement. Improve the target process by designing creative solutions to fix and prevent problems. Control the improvements to keep the process on the new course. Doug Sanders and Cheryl R Hild [1] have stated that process knowledge is veryimportant in obtaining Six Sigma solutions. Also, the metrics associated need notalways be number of people trained in Six Sigma, or savings in cost, but defects perunit, sigma level and rolled-throughput yield. Cherly Hild, Doug Sanders and Tony Copper [2] have opined that to achieveoptimal outcomes in continuous process, non linear and complex relationships amongprocess factors must be managed. The data from continuous processes are oftenplentiful in terms of processing variables and limited with regard to productcharacteristics. With continuous processes, the variation in the main product streamdoes not necessarily reflect the true level of variation exhibited by the process. Goh T.N [3] has brought out an intuitive perspective on the fundamentalmechanics of design of experiments (DOE) in a way that would help enlighten a non-statistician during the course of deployment of DOE related methodologies, regardlessof the context used. He has stated that in most of the experiments involving multifactorprocesses, interactions of 3rd order and higher, often turn out to be insignificant and areimmaterial to subsequent process characterization and optimization. Piere Bayle et al, [4] designed and optimized the braking subsystem for a newproduct. They also stated that focus is placed on the factors that have the strongesteffect on the response, but there is as much information and insight provided aboutdirection of future work by considering the implications of factors with little or noeffect. Spencer Graves [5] has used the tool of forecasted Pareto, which combinedRolled Throughput Yield (RTY) and sales forecast. RTY estimates the probabilitywhether a product passes through a process defect free or not as recommended by SixSigma proponents, because it seems to be a highly correlated scrap rework, warrantyetc. It is relatively easy to compute from data obtainable from many processes. 22
  • 4. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M Goh T.N [6] has explained, in a non mathematical language, the rationale andmechanics of DOE as seen in its deployment in Six Sigma. He has stated theadvantages of DOE over process monitoring techniques. He has described about theshifting emphasis in the deployment of DOE. Dana Rasis et al [7] distinguished between black belt and green belt Six Sigmaprojects on the basis of five criteria. A case study has been discussed presenting thedefinition and measure phases of DMAIC method. The authors identified the CTQ andperformed gauge Repeatability and Reproducibility study on each CTQ. Charles Ribardo and Theodore T Allen [8] have stated that desirability functiondo not explicitly account for the combined effect of the mean and dispersion of quality.The authors have proposed a desirability function that addresses these limitations andestimates the effective yield. They have used an Arc welding application to illustratehow the proposed desirability function can yield a substantially higher level of quality.The proposed desirability function is based on the estimates of yield that is the fractionof confirming units. Goh T.N and M Sie [9] have described some alternative techniques for themonitoring and control of a process that has been successfully implemented. Thetechniques are particularly useful to Six Sigma black belts in dealing with high qualityprocesses. The methodology ensures a smooth transition from a low sigma processmanagement to maintenance of high sigma performance in the closing phase of a SixSigma project. Rick L. Edgeman and David Bigio [10] have stated that the future Six Sigmawill be integrated with other tools, used in nontraditional sectors, more adapted andstrengthened. One can expect new concepts like lean Six Sigma, best Six Sigma, leanbest Six Sigma, Six Sigma in health care, lean design and macro Six Sigma to beapplied in manufacturing and service industries. Mohammed Ramzan and Goyal [11] have stated that Six Sigma provides asystematic, disciplined and quantitative approach to continuous improvement. Throughthe application of statistical thinking, it uncovers the relationship between variation andits effect on waste, operating cost, cycle time, profitability and customer satisfaction.The scope of Six Sigma encompasses all aspects of the organization that is frommarketing to product and process designing to accounting to after sale service.3. OBJECTIVE OF THE STUDY The objective of the study is to measure the current process user’s awarenessabout the organization’s QMS and to improve upon the average awareness level fromthe existing 55% to around 70%. The increased awareness, understanding andperceived importance of QMS enable to have more commitment from the process usersto follow the standardized processes and prepare the necessary documents forachieving the organization’s goal of being a CMMI level 4 assessed organization. 23
  • 5. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M4. DMAIC METHODOLOGY ADOPTED IN THE PRESENT STUDY4.1 DEFINE PHASE The process users of the organization are only 55% aware of the uses/benefits ofthe organizations QMS. This lack of awareness among the process users can lead to bea hurdle for the organization in achieving CMMI Level 4 Assessment as per the setdeadlines. The process users who are well aware about the QMS & its benefits couldcommit themselves to follow the standardized processes and prepare the relevantdocuments which would result in having instances necessary for achieving the CMMILevel 4 Assessment for the organization. The Define Phase consists of Preparation of Project Charter, Collecting the Voiceof Customers (VOC), Identifying the Critical to Quality (CTQs) and Process Mapping.• Preparation of Project Charter The study starts with preparation of a document called Project Charter. Thisdocument clarifies what is expected out of the research team. The major elements ofthis document deals with the questions like, What is the problem for which the study is being carried out? What is the goal of the study? Why the study is worth doing? How the studys goal can be achieved? When the studys goal is supposed to be met? Who all are involved in the study? What are the challenges/risks that are foreseen in the study? Problem Statement Process users are only 55% aware of the uses / benefits of QMS / QI Page as atthe starting of the study and are not fully following the standardized processes (asavailable in the organizations QMS) in their projects.All other issues have been dealt in the project charter in Figure 2.• Collection of the VOC The VOC was collected using a survey questionnaire. The customers for thisstudy are the process users who are the potential users of the organizations QMS. Thequestions used for the purpose of collecting what the customers wanted were openended. Some of the questions included in the survey were like What would you like to have added on the QMS? How do you think Quality can be improved in the organization? These questions were included in the questionnaire as well as were askedverbally in the form of interviews. A standard template was used to collect all therequirements and suggestions of the customers.• Identification of the CTQs The VOC, which was collected in the Define Phase with the help of the survey,is used to identify the CTQs related to the process. These CTQs are used to carry out a 24
  • 6. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. MQFD. The outcome of this application can be used as the suggestions for improving theprocess to make the process users at least 70% aware about the organizations QMS.Goal RisksTo achieve SEI - CMMI level 4 assessment Getting time from the process users for thefrom the existing SEI - CMM level 3. survey. New resources joining the organization, if surveyed, can give inaccurate results.Objective Statement of WorkTo increase the average awareness level of Modifying the process by which theQuality / QMS among the process users Process users are made aware of QMS atfrom the existing 55% to at least 70%. the organization.Value of the study MethodologyIt will ensure increased awareness level The methodology used for the project is Sixabout organizations QMS among the Sigma DMAIC methodology.process users and enable obtaining morecommitment from them to follow thestandardized processes that would result in Background Knowledgehaving instances necessary for achieving The training used for making process usersthe CMMI Level 4 Assessment for the aware of QMS in the organization.organization. Figure 2 Project Charter• Process Mapping The existing process for any process user / employee to be made aware aboutthe organizations QMS or the Quality related activities is mapped by studying thesystem of induction trainings in the organization. This process is clearly depicted inFigure 3. The shaded boxes on the process flow chart indicate where the improvementsin the process may take place.4.2 MEASURE PHASE The measure phase consists of Selecting CTQ characteristics using TQM toolslike QFD, FMEA & Process Mapping, Defining the performance standards andMeasurement system analysis.• Selecting CTQ characteristics using Quality Function Deployment (QFD) QFD may be defined as a systematic process used to integrate the customerrequirements with design, development, engineering, manufacturing and servicefunctions. The CTQs identified in the previous step are used to prepare the first Houseof Quality. Figure 4 shows the VOC on the Y-axis and the requirements of the processfor quality awareness on the X-axis. The Second House of Quality, as shown in the Figure 5 provides us with the“HOWS” that tells us how the process can be more effective and efficient in makingthe process users aware about the organization’s QMS. 25
  • 7. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M New Employee joins the organization Is a batch of 5Employee work on his / her respective No new employeesproject until the batch size reaches 6 waiting for QMS training?Employees go through QMS training in Yes batch of 6. (Induction) Project Manager (PM) /Project Leader(PL) fills up the Templates or just educatethe employee in filling template.Software Quality Analyst (SQA)/ ProjectQuality Analyst (PQA) reviews thedocuments, checks whether the processesare being followed once a week / fortnight (mostly with PM / PL) QMS Awareness among the employees Figure 3 Existing flow process chart of induction process The "Hows" obtained as the suggestions from the Houses of Quality are asfollows.a) Training to be more frequent.b) Instructor to be trained for training.c) Conducting regular quality quiz to evaluate the process users quality awareness.d) Employee scoring below 70% in the quality quiz to be helped by SQA/PQA.e) Search functionality to be added on the QI page.f) QTM and QR of each dept. to come up with dept. specific examples.g) Project knowledge sharing for best practices related to quality to be initiated.h) Training invitee list to be compared with the Training attendee list. From the Pareto Charts as shown in the Figures 6 & 7 for the two Houses ofQuality, we can conclude that Frequency of the QMS training, Conducting regularQuality Quiz and Instructor to be trained for QMS training are the factors that canlargely satisfy the CTQs, and thus result in having higher awareness levels aboutQuality / QMS among the process users. 26
  • 8. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M Process Requirement Experienced employees Refresher Quality training for their dept. Revamping of QI page (training material, search functionality). Department wise categorization of processes on the QI Page. Knowledge sharing related to quality by the projects. Dept. specific examples in the QMS training. Customer Expectation Department-wise QMS training. QMS Training Attendee list. QMS Training Efficiency. Importance. TotalFrequency of QMS Training 5 H L 50QMS training for everyone 5 M M H 75Search Functionality on the QI page 5 M H 60Different links for different departments 4 H L 40Guidance for the usage of templates 4 L H 40Relevance of the training topic 4 H L 40Time lag between joining the org and QMS 4 L L 8trainingAccessibility of QMS training material 2 M L 8More examples in the QMS training 2 L H 20materialTotal 64 57 56 51 45 38 26 4 Figure 4 First House of quality H : High relationship between customer expectation and process requirement. M : Medium relationship between customer expectation and process requirement. L : Low relationship between customer expectation and process requirement. Numerical equivalent of these variables are H = 9, M = 3 and L = 1. 27
  • 9. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M How’s Reward the Project Team following the best quality practices. Invite employees scoring low in quiz for QMS training. Reward experienced PM / PL for training. Instructor to be trained for QMS training. Support from QTM and QR of the dept. QMS training week every 2 months. Process Requirement Conduct regular quality quiz. Importance TotalExperienced employees-refresher Quality 5 H M 60trainings for their dept.Revamping of QI page (training material, search 5 M 15functionality).Department-wise QMS training. 4 L L 8Dept. specific examples in the QMS training. 4 H M 48Knowledge sharing related to quality by the 4 H 36projects.QMS Training Attendee list. 4 H 36QMS Training Efficiency. 4 M H H L 88Department-wise categorization of processes on 3 M 9the QI page.Total 61 51 49 48 40 36 15 Figure 5 Second House of Quality 28
  • 10. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M 1st House – Pareto 80 70 19% 17% 16% 60 15% 50 13% 11% 40 30 08% 20 10 01% 0Legend 1 2 3 4 5 6 71 : Experienced employee – refresher quality trainings for their department.2 : Revamping of QI page (training material, search functionality).3 : QMS Training Efficiency.4 : Department-wise QMS training.5 : QMS Training Attendance list.6 : Department specific examples in the QMS training.7 : Knowledge sharing related to quality by the projects.8 : Department-wise categorization of processes on the QI page. 2nd House - Pareto 80 70 21% 60 18% 16% 15% 50 13% 40 12% 30 20 10 0 1 2 3 4 5 6 29
  • 11. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. MLegend1 : QMS training week every 2 months.2 : Conduct regular quality quiz.3 : Support from QTM and QR of the department.4 : Instructor to be trained for QMS training.5 : Employees scoring low in quiz for QMS training.6 : Reward the Project Team which follows the best quality practices.7 : Reward experienced PM / PL for training.• Failure Modes and Effects Analysis (FMEA) FMEA is a structured approach to identify the ways in which a process can failto meet critical customer requirements. In this study, FMEA is performed to identifythe potential failure modes in the Quality / QMS awareness process. The potentialfailure effects of these failure modes, the causes for these failures and the controls thatcurrently exist over the causes are identified. The severity of the effects of the failure israted on a scale of 1 to 10, with 1 being the case when the failure has no effect on thecustomer requirements and 10 being the case when the failure largely affects thecustomer requirements. The probability of occurrence of the causes of these failures isalso on the same scale, with 1 being the case when these causes are unlikely to occurand 10 being the case when the probability of occurrence of the causes are very high.The detection certainty of the causes is rated on a scale of 1 to 10, with 1 being the casewhen the cause can be easily detectable and 10 being the case when the causes usuallyare not detectable. The performed FMEA is shown in the Figure 8.• Definition of Performance Standards The operational definition for the study is that process users are expected to beat least 55% aware about the organizations QMS. Anyone having an awareness levelbelow 55% is considered as a defect for the current process. The data collectionmethodology that was used for this study is survey. This survey was conducted in aform of questionnaire consisting of QMS-related questions. The data obtained from thesurvey was used for calculating the current Sigma level for the awareness level of theprocess users about the organizations QMS.• Measurement System Analysis -Data Collection Plan The measures used for this study are the scores in the questionnaire. A surveywas conducted in the form of a questionnaire consisting of QMS-related questions.Each question had four options, out of which only one was correct. Each questioncarried different weights, which were arrived at in a discussion with the Quality Teammembers. The designing of the questionnaire involved a brainstorming session with theQuality Team members. The measurement system tool used is MINITAB®Release14.12.0, Statistical software. 30
  • 12. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S. Prabhuswamy & Mamatha. M Potential Potential S O D Occurrences Failure Failure RPN Responsibi- Detection Severity RPN Modes Effects Potential Current Action lity Causes Control Recommended and Target DateQMS No 10 Trainer busy with 1 Stand by trainer 2 20induction awareness other projecttraining not about QMS Trainee not attending 4 None 4 160 Get non-attendee for HR dept. 10 3 2 60happened next training Frequency of QMS 8 Training only 4 320 QMS training week Quality team 10 3 4 120 training very low when batch size every 2 months reaches 6 membersTraining Lack of 9 Poor instructor’s 2 None 6 252 Instructor to be trained Quality team 9 1 6 90not QMS presentation skills for QMS trainingeffective awareness Examples not 4 4 9 1 4 among included attendees Lack of attendee’s 6 None 3 162 Reward highest scorer Quality team 9 3 4 108 interest for quality in quiz Topic irrelevant to 2 Department wise 5 90 Training requested by QRs, QTMs 9 2 3 54 the attendees trainings QR, PM / PLProcess Lack of 9 PM/PL fills all the 8 None 3 216 Initiate project SQAs 9 5 3 135users not QMS templates knowledge sharing forfilling the awareness best practices related totemplates among quality. process usersProcess Lack of 8 QI page structure not 7 None 4 224 Add search EPG 8 5 3 120users not QMS user friendly functionality to QI pagevisiting QI awarenesspage for among Too much data 5 None 3 120 Include and elaborate Instructor 8 4 3 96searching process the QI page duringthe users QMS trainingprocessesor Poor process users 8 None 4 256 Conduct regular quality SQAs 8 7 2 112templates motivation for quality quizavailable inQMS Figure 8 FMEA Table 31
  • 13. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M Even if one person repeatedly measures the awareness level of processusers using the survey questionnaire, there will be no variation in the result andeven if two or more people evaluates the process users awareness revel usingthis questionnaire, there will be no variation. Thus, the questionnaire used asthe measurement system satisfies the Repeatability and Reproducibility (R&R)conditions. The survey is conducted over a number of process users spread throughvarious departments of the organization. This sample size is to be sufficientenough as the organization consists of around 150 process users out of whicharound 30 are students who are not directly involved in the projects.4.3 ANALYZE PHASE The Analyze Phase consists of Establishing Process Capability,Defining the Performance Objectives and Identifying Variation Sources. • Establishment of Process Capability The scores obtained by the process users from the survey which wasconducted during the Define phase is plotted (Figure 9). This graph showspictorially the score obtained by the process users. The red bars are the defects.These bars show the process users scoring below the average score, i.e. below55%. Figure 10 shows the summary of statistics for the score obtained. Thehistogram is shown along with the normal curve fitted to it. The box plot showsthat there are no Outliers. The P-value calculated is 0.038, which is below 0.05(i.e. 5%). This result signifies that the scores are normally distributed. Thus theprocess capability calcu1ations are performed. The current average awareness level of the process users as per thesurvey conducted is found to be only 55%. The defect definition for the processis decided to be "an employee scoring less than the mean score, i e. less than55%". Thus, for the current process, the defects in the process are the processusers scoring below 55%. Score obtained (%) v/s 100 90 80 Score obtained (%) 70 60 50 40 30 20 10 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 Emp. No. Figure 9 Plot of score obtained vs. Emp. No. 32
  • 14. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M Anderson-Darling normality test A- Squared 0.79 P- Value 0.038 Mean 55.477 St. Dev. 22.456 Variance 504.253 Skewness -0.05419 Kurtosis -1.13341 N 65 Minimum 13.000 1st Quartile 36.500 Median 56.000 3rd Quartile 76.000 Maximum 95.000 95% Confidence Interval for Mean 49.913 61.041 95% Confidence Interval for Median 45.121 66.000 95% Confidence Interval for St. Dev. 19.150 27.152 Figure 10 Summary of Statistics for the Quality Awareness ScoreThe calculations of the process capability of the current process are shownbelow.Total number of process users surveyed (o - opportunities) = 65Average Score of the process users = 55%Number of process users on or above the average score (c) = 33Number of employee below the average score (d -defects)= (o)-(c) = 65-33= 32Defects per opportunity (dpo) = (d / o) = (32/65) = 0.49230769Defects per million opportunities (dpmo) = (d/o)*1000000 = 492307.6For the calculated dpmo, the current Sigma Rating† =1.52σProcess Capability of the current process = 1.52σ• Definition of Performance ObjectivesThe goal of the study can be defined statistically as follows.“To increase the average awareness level of process users (process target)from 55% to 70% and the process capability from 1.52σ to 2.1σ”† = The Sigma Rating is obtained from the standard Sigma and DPMO Conversion Table. 33
  • 15. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M• Identification of Variation Sources The P–value calculated signifies that the scores obtained are normallydistributed (for 95% confidence level). P-value may be formally defined as theprobability of being wrong if the alternative hypothesis is selected. The P-valueis calculated here by considering the null hypothesis as “the data followsnormal distribution”. Thus, P-value of less than 0.05 indicates that this nullhypothesis is true. The graphs as shown in Figure 11 show the effects of thecritical ‘X’ on the ‘Y’. This ‘Y’ is the Quality / QMS awareness level of theprocess users. These are the critical ‘X’s which were obtained as a result ofQFD and FMEA.The ‘X’s are: Frequency of training Instructor to be trained for training Conducting regular quality quiz Happening of Project knowledge sharing Search functionality on the QI Page Null Hypothesis statement The present process is better than the new proposed process.4.4 IMPROVE PHASE The Improve Phase consists of Screening the Potential Causes,Discovering Variable Relationships and Establishing Operating Tolerances.• Screening the Potential Causes This step involves determination of the vital few ‘X’s that affect the ‘Y’.In this study, the screening of the potential causes identified in the Measure andAnalyze Phases, using basic tools like QFD and FMEA, is being done in theImprove Phase. Five major factors or ‘X’s that affect the Quality Awarenessamong the process users of the organization have been identified. The Main Effects Plot is used when one have multiple factors. Thepoints in the plot are the means of the Quality / QMS Awareness at variouslevels of each factor (i.e ‘X’s). The plot in Figure 11 is used for comparing themagnitude of effect, various factors have on the Quality / QMS Awareness (i.e‘Y’). The slope of the lines depicts the effect of the factors on the ‘Y’. Thehigher the slope of the line, higher is the effect of the particular ‘X’ on the ‘Y’. In the Figure 11, it can be clearly seen that the slope of the line for‘Frequency of Training’ is highest. Thus it can be concluded that the Quality /QMS Awareness among the process users is largely affected by the ‘Frequencyof Training’. The factor ‘Conducting Quality Quiz’ has the second highestslope, i.e Quality / QMS Awareness among the process users can also be highlyaffected by ‘Conducting Quality Quiz’. The factor ‘Instructor Training’ alsoaffects the Quality / QMS Awareness among the process users. However,adding a ‘QI Page-Search’ and ‘Project Knowledge Sharing’ would not affectthe awareness level among the process users as much as the other 3 factors. 34
  • 16. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M Table 1 Data for Regression AnalysisFrequency Instructor Regular Project QI Page Quality /of Training Training Quality Knowledge Search QMS Quiz Sharing Awareness 1 1 1 1 1 1.00 0 1 1 1 1 0.75 1 0 1 1 1 0.80 1 1 0 1 1 0.79 1 1 1 0 1 0.83 1 1 1 1 0 0.83 0 0 0 0 0 0.00 0 0 1 1 1 0.55 1 0 0 1 1 0.59 1 1 0 0 1 0.62 1 1 1 0 0 0.66 0 1 1 1 0 0.58 0 0 0 1 1 0.34 1 0 0 0 1 0.42 1 1 0 0 0 0.45 0 1 1 0 0 0.41 0 0 1 1 0 0.38 0 0 1 0 1 0.38 1 0 0 1 0 0.42 0 1 0 0 1 0.37 0 1 0 1 0 0.37 1 0 1 0 0 0.46 0 0 0 0 1 0.17 0 0 0 1 0 0.17 0 0 1 0 0 0.21 0 1 0 0 0 0.20 1 0 0 0 0 0.25 Figure 11 Main Effects Plot 35
  • 17. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M Interaction plot (data means) for Quality / QMS Awareness Figure 12 Interaction Plots• Discovering Variable relationships The variable relationships were discovered using the main effects plotand the interaction plots. Interaction plots are useful for judging the presence ofinteraction among the factors. Interaction is present when the response at afactor level depends upon the level(s) of other factors. Parallel lines in aninteractions plot indicate no interaction. The greater the departure of the linesfrom the parallel stage, higher the degree of interaction. Figure 12 shows a matrix of interaction plots for the five factors. It is aplot of means for each level of a factor with the level of a second factor heldconstant. In the full matrix, the transpose of each plot in the upper right isdisplayed in the lower left portion of the matrix. Figure 12 clearly shows that the ‘Frequency of Training’ is not affectedby the factors ‘Conducting Quality Quiz’ and ‘Project Knowledge Sharing’.However, there is an interaction between the ‘Frequency of Training’ with the‘Search functionality on the QI Page’ and ‘Instructor’s training’. Similarly itcan be seen that ‘Project Knowledge Sharing’ has an interaction with the‘Search functionality’ on the ‘QI Page’. From the interaction plots as shown inFigure 12, the variables or the factors affecting the quality awareness do nothave much effect on each other. The prioritization of the factors that affect the awareness ofQuality/QMS among the process users as obtained from the Main Effects Plotis shown in Table 2. 36
  • 18. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M Table 2 Prioritization of factors affecting Quality awareness Factors PriorityFrequency of QMS training 1Conducting regular Quality Quiz 2QMS training instructor’s presentation skills 3Search functionality on QI page 4Project knowledge sharing for best practices related to quality 5 This prioritization is used for arriving at an equation relating variousfactors with the Quality / QMS Awareness among the process users. Thesemagnitudes of effect that the various factors have on the Quality / QMSAwareness (i.e. ‘Y’) can be seen in the Main Effects Plot (Figure 11). Theslope of the lines depicts the effect of the factors on the ‘Y’. The higher theslope of the line, higher is the effect of the particular ‘X’ on the ‘Y’.Regression Analysis was executed for arriving at the equation. (Table 1)Transfer Function between ‘Y’ and the vital few ‘X’s is Y = 0.25X1 + 0.21 X2 + 0.20X3 + 0.17X4 + 0.17X5Where, Y Quality / QMS Awareness among the process users. X1 Frequency of the QMS training. X2 Regular Quality Quiz. X3 Instructor to be trained for QMS training. X4 Project Knowledge Sharing for best practices related to quality. X5 Search functionality on the QI page.• Proposed Process Based on the results of the steps performed above, the proposed processof making the employees aware of the organization’s QMS / Quality relatedactivities, is shown in the Figure 13.4.5 CONTROL PHASE The Control Phase consists of Definition and Validation of MeasurementSystem for the Xs in actual implementation, Determination of ProcessCapability (i.e. Short Term Sigma or σST) and Controlling Long Term Sigma(σLT).• Definition and Validation of Measurement System for the Xs in actual implementation The proposed process needs a pilot study. The need for a pilot study isto better understand the effects of the proposed solution and plan for asuccessful full-scale implementation and to lower the risk of failing to meetimprovement goals when the solution is fully implemented. The measures forthe pilot study stage remains the same as were during the Measure Phase, i.e.scores obtained in the questionnaire. This data collection plan is used toconfirm that the suggested solution meets the improvement goals. 37
  • 19. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S. Prabhuswamy & Mamatha. M New Employee Joins the Instructor is trained for organization QMS training Mention about URL Employee to undergo QMS for QI Page and EPGinduction training, which will especiallyhappen bi-monthly and as per need-basis Department specific examples are included in consultation with the experienced PMs / PLs and QR. Is the score of the employee above Yes 70% in the quiz Employee continues to conducted with work on his / her the QMS training? project and prepare necessary documents Yes No The employee’s name is noted in the Is the employeeinvitee list of the next QMS training / No scoring > 70% in special attention to be given by the the regular SQA / PQA in the project he / she is quality quiz (by working. SQA / PQA)? Figure 13 Proposed Process • Determination of Process Capability During the first few trials, in any process, the variability is small and mean is centered at the target. It is called Short Term Sigma (σST). This is the best the process is capable of. The survey used for measuring the Quality Awareness levels of the process users again after implementing the suggested improvements is the data for calculating the process capability of the new process. 38
  • 20. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M The defect definition for the process is modified as "employee scoringless than the mean score, i. e. less than 70%". This change in defect definitionis due to the goal of this study, which aims at having an average score of 70%in the questionnaire used for survey. Thus, the number of process users scoringbelow 70% is the number of defects for the new process and the number ofprocess users being surveyed is the number of opportunities. Every possibilityof making an error is called an opportunity and in this process, an opportunityis an employee who is being surveyed. The number of defects and the number of opportunities are used tocalculate defects per million opportunities (dpmo). The process capability (σST)of the new process is obtained using the "Sigma and DPMO Conversion Table"corresponding to the calculated dpmo. If this sigma rating is around 2.1σ, thenew process is successful. The new process is then to be documented andfollowed.• Controlling the Long Term Sigma (σLT) Over a period of time, assignable causes creep in and the capability ofthe process to meet the requirements diminishes. This sigma which representsthe capability of the process to meet the requirements over a period of timeconsidering those extraneous conditions causes process shifts from that atwhich it was set is called the Long Term Sigma. Normally, the short termsigma is higher than long term sigma. Unless otherwise specified, long termsigma is calculated as σLT = σST – 1.5. There are various mechanisms that can be used to control a processnamely, Risk Management, Mistake Proofing, Statistical Process Control(SPC) and Control Plans. The key to controlling the process is frequent interval monitoring. Theongoing measurements of the process variation and/or process capability are tobe used for monitoring. The ongoing measurements in this study are the regularquality quizzes that need to be conducted by the Quality Team. Even randomauditing of the documents prepared by the process users for their projects cangive an idea of how much the process users are aware of the organizationsQMS. The responses obtained by these measurement systems indicate thesuccess of the new process.5. SOLUTIONS FOR IMPROVING QUALITY AWARENESS The first four phases -Define, Measure, Analyze, and Improve -of theDMAIC methodology have been applied successfully to this study. Theimprovements suggested were planned for implementation, which essentiallyforms the Control Phase. Rigorous efforts were made to get the requiredapprovals from the top management and co-operation from the process usersthemselves to improve the Quality Awareness levels in the organization.Some of the improvements suggested were • To have QMS trainings every 2 months or on the need basis. • To conduct regular Quality Quiz for all the process users of the organization. • To train the instructor who conducts QMS training. 39
  • 21. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S.Prabhuswamy & Mamatha. M• To add a search functionality on the QI Page on the organizations intranet.• To initiate regular project knowledge sharing sessions by the SQAs/PQAs highlighting the best practices related to quality.• To involve QRs and experienced PMs/PLs of all the departments to suggest good examples that can be included in the QMS training material.• To involve experienced PMs/PLs to conduct refresher QMS/Quality- related trainings for their departments.• To welcome constructive comments, so that the Quality Awareness process can be improved continuously.6. POST IMPLEMENTATION RESULTS In a span of three months, all solutions recommended wereimplemented. Then, the research scholar repeated the Measure and Analyzephases. The scores obtained by the process users in the post implementationstudy are plotted (Figure 14). The red bars are the defects. These bars show theprocess users scoring below the average score, i.e. below 70%. In the improved process, for 17 defects out of 65 opportunities, thedpmo is found out to be 261538. i.e. the sigma rating or the process capabilityof the improved process is found to be 2.13σ. Score obtained (%) v/s Emp.No. 100 90 80 Score obtained (%) 70 60 50 40 30 20 10 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 Emp. No. Figure 14 Plot of score obtained vs. Emp. No.7. CONCLUSION All the phases - Define, Measure, Analyze, Improve and Control - of theDMAIC methodology have been successfully applied to the study. Thesolutions implemented resulted in increasing the awareness level of the processuser’s form 55% to 70% and increasing the sigma level from 1.52σ to 2.13σabout the organizations QMS. Similarly, efforts can be put for achievinghigher and higher level of Sigma, until the organization reaches Six Sigmalevel. 40
  • 22. International Journal of Advanced Research in Management (IJARM), B.P. Mahesh, Dr. M.S. Prabhuswamy & Mamatha. M REFERENCES1. Doug Sanders and Cheryl R Hild (2000-01), “Common Myths about Six Sigma”, Quality Engineering, Vol 13, No 2, pp 269-276.2. Cheryl Hild, Doug Sanders and Tony Cooper (2000-02), “Six Sigma on continuous processes: How & why it differs?” Quality Engineering, Vol 13, No1, pp 1-9.3. Goh T.N (2001), “Information Transformation Perspective on Experimental Design in Six Sigma”, Quality Engineering, Vol 13, No 3, pp 349-355.4. Piere Bayle, Mike Farrington, Brenner Sharp, Cheryl Hild & Doug Sanders (2001), “Illustration of Six Sigma Assistance on a Design Project”, Quality Engineering, Vol 13, No 3, pp 341-348.5. Spencer Graves (2001-02), “Six Sigma Rolled Throughput Yield”, Quality Engineering, Vol 14, No. 2, pp 257-266.6. Goh T.N (2002), “The role of Statistical Design of Experiments in Six Sigma: Perspectives of a Practitioner”, Quality Engineering, Vol 14, No 4, pp 659 – 671.7. Dana Rasis, Howard. S. Gitlow & Edward Popouich (2002-2003), “A fictitious Six Sigma Green Belt, case study”, Quality Engg; Vol 15, No 1, 127-145.8. Charles Ribardo and Theodore T Allen (2003), “An Alternative Desirability Function for achieving Six Sigma Quality”, Quality and Reliability Engineering International, Vol 19, pp 227-240.9. Goh T N and M Sie (2003), “Statistical control of a Six Sigma Process”, Quality Engineering, Vol 15, No 4, pp 587-592.10. Rick L. Edgeman & David Bigio (Jan 2004), “Six Sigma in Metaphor: Heresy or Holy Writ?” Quality Progress, pp 25 -31.11. Mohammed Ramzan and Goyal (Jan 2006), “Six Sigma: An introduction for Industrial Engineers”, IIIE Journal, Vol 35, No. 1, pp 13-15.12. Peter S. Pande & Larry Holpp (2001), “What is Six Sigma?” Tata Mc Graw Hill Company Limited 1st edition.13. Peter S. Pande, Robert P. Neuman, Roland R. Cavanagh (2000), “The Six Sigma Way: How GE, Motorola, and Other Top Companies are Honing Their Performance”, McGraw- Hill Companies.14. Greg Brue (2002), “Six Sigma for Managers” Tata McGraw-Hill. 41