Your SlideShare is downloading. ×
0
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Lean Six Sigma Project on Improving  Process Quality by Advance Innovation Group
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Lean Six Sigma Project on Improving Process Quality by Advance Innovation Group

1,939

Published on

(http://advanceinnovationgroup.com) …

(http://advanceinnovationgroup.com)

This Lean Six Sigma Project done by student from Advance Innovation group which is posted to provide for benchmarking and best practices sharing purposes.
Six Sigma is a process improvement methodology that this project focuses on the improvement of Quality process.

The project is aimed at giving phase by phase documentation and implementation of Six Sigma in the Quality domain and makes it easy for students to understand so as to improve their process and benefit the organization.

This project was initiated to ensure that the Quality deliverables are met as per the agreed Service Levels as the organization was paying heavy penalties and the client satisfaction was also getting effected. The project started with the Voice of Customer from the Senior Management and then taking inputs the Project Charter was developed covering the project storyboard and then defined the Roles and Responsibilities of the Project Team using ARMI and the documented a Communication Plan after which defined the CTQ Drill down tree and then designed a Process Map using the SIPOC and thus moved to the Measure Phase wherein documented a Data Collection Plan and Validated the correctness of data via Measurement System Analysis and then moved to Process Capability using the Z Value, and then checked the stability of the process using the Control Chart and the Run Chart, described the data using the Graphical Summary and then checked the normality of the data.

After which started looking at the Root Causes of the lower Quality using Graphical depiction and the Hypothesis Tests, summarised the findings of Analyze and moved to Improve Phase with the Vital Xs.

In Improve designed the Improvement Plan using the Quality Function Deployment and the improvement plan which was the output of the QFD was implemented, and then the Control Plan was designed using FMEA (Failure Modes and Effects Analysis) and then the improvement and the process control was validated using the Graphical Analysis and the Control Charts.

Additionally, it is advisable that you also visit and subscribe Advance Innovation Group Blog (http://advanceinnovationgroup.com/blog) for more Lean Six Sigma Projects, Case Studies on Lean Six Sigma, Lean Six Sigma Videos, Lean Six Sigma Discussions, Lean Six Sigma Jobs etc.

Published in: Education, Business, Technology
0 Comments
9 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
1,939
On Slideshare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
0
Comments
0
Likes
9
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. 2Customer Comments Critical to Quality-CTQ’sAkhil Tripathi – CIO(Harleysville Insurance Company)Process Quality is a major concern for usin order to plan further business planningand to continue our existing outsourcedbusiness to IBM DakshProcess QualityInternal Senior ManagementProcess Quality is being a key factor toretain our existing business.Process QualityD M A I C
  • 2. Business caseHarleysville insurance company is a largest P & C insurance provider in U.S.and have outsourced its some of large process to IBM Daksh. Considering last4 months data of customer care department for Harleysville insurancecompany, we observed that the average quality of the process is @ 92%against the target of 95.00%. This may result client dissatisfaction, revenuegeneration and majorly contributing to client penalty against processperformance for last 2 months. This may also impact our long tern businessplanning and new business generation from the existing client.TeamBU Head : O.P. Singh• Process Champion :- Prem Singh• Process Owner : Pallavi Gami• Master Black Belt :- Pranay Kumar• Project Leader :- Abhishek Singh• Team Member :- Saubhik Roy, Sunny Gaba, NagendraBaralProblem StatementLast 4 months data shows that the Process Quality has been reduced to 92%against the SLA of 95.00%. It might impact our long term business planningwith the existing client and can contribute to process penalty, clientdissatisfaction and VOC.In Scope : Only the LOBs of Business Engagement ofHarleysville Insurance CompanyOut Scope : Any other lob or business engagement.Goal StatementTo achieve 95% monthly quality score for the entire process. Milestones Target Date Actual dateD Aug 1st, 2010 Aug 15th, 2010M Aug 16th, 2010 Aug 30th , 2010A Sep 1st, 2010 Sep 30th, 2010I Nov 1st, 2010 Dec 31st, 2010C Dec 1st,2010 Dec 31st, 2010Project CharterD M A I C
  • 3. Distribution plan (ARMI)Key StakeholdersARMI WorksheetDefine Measure Analyze Improve ControlBU Head I I I I IProcess Champion I I I I IMaster Black Belt I & A I & A I & A I & A I & AProcess Owner A & I A & I A & I A & I A & IProject Lead I & R I & R I & R I & R I & RTeam Member I & M I & M I & M I & M I & MA – Approval of team decisions I.e., sponsor, business leader, MBB.R – Resource to the team, one whose expertise, skills, may be needed on an ad-hoc basis.M – Member of team – whose expertise will be needed on a regular basis.I – Interested party, one who will need to be kept informed on direction, findings.Communication PlanInformation Or Activity Target Audience Information Channel Who WhenProject Status Leadership E-mailsAbhishekBI-WeeklyTollgate ReviewBB,LBB,MBB &ChampionE-mails or Meetings As per Project PlanProject Deliverables orActivitiesMembers Emails, Meetings WeeklyD M A I C
  • 4. Improvement inprocess QualityCTQs*Documents per day(Project Y Metric)/noof defect occur perday/Total # of auditsdone for the day.Data for differentlocations, gender,complexity, team,manager, gender,shifts etc..Data for(Lower SpecificationLimit)/U SLAny day whendocuments per day isless than 90 would beconsidered adefective day(Defect Definition)*CTQ- Critical To QualityCTQ TreeD M A I C
  • 5. SupplierSSIIInput(Use nouns)PPProcess(Use verbs)OOOutput(Use nouns)CCCustomer12345Client and EndCustomerClient and EndCustomerCalls routingmonitoring S/WAVAYA and otherreporting tools.Variousperformancereports fromLead/AM/ManagersTransaction routed toprocessing queue.Team leads assigns task toprocessors as per their LOBand task prefix.Executives hold the task if anyassistance / clarifications arerequired in order to processthe request.Post assistance/ clearificationexecutive complete processingthe task in the system.Post processing , executivesend the task and pick nexttask to process.Transactionaudit (MasterSheet) sheet hasupdated from allthe auditors.IBM Daksh andClientCOPISD M A I C
  • 6. KPI Operational Definition Defect Def Performance StdSpecification LimitOpportunityLSL USLQuality Total audit done Vs Error receivedQuality score<95.00%Maximum 5 defectsper 100 processedtasks90 100To increase thecurrent Qualityscores to thedesired level of> 95%KPI Data TypeData ItemsNeededFormula to beusedUnitPlan to collect DataPlan tosampleWhat Databaseor Containerwill be used torecord thisdata?Is this anexistingdatabaseor new?If new, Whenwill thedatabase beready for use?When is theplanned startdate for datacollection?Quality DiscreteTotaltransactionsaudited anderror receivedTotal errorsreceived/Totalaudits doneQuality %WeeklydashboardsExisting N/AAugust 15th,2010Monitor themonthlyQualityTracker.Data Collection PlanD M A I C
  • 7. Effectiveness & EfficiencyEffectiveness at 99.4% with 166 samples is good enough, and we conclude that the measurementSystem is adequate.For the purpose of data validation, 166 data points have been re-verified for the timetaken.Procedure Used:• 166 random samples picked up from 1666 data points. The samples have beenrandomly generated by Axus Pro and we asked two Quality auditors to re-verify the 166data points.• On verification, we found that 1 data points were differently captured in the 166samples which were re-verified.Effectiveness % = Number of samples where both measurement were similar * 100Total Number of samples under consideration= 165 * 100 = 99.39769166D M A I C
  • 8. Process CapabilityZ Score of the process is really poor, there is immediate need to improve the process capabilityDenominator ValuesMean 92.290Std Dev 4.617Target 95.00%Zlt 0.586961Zlt 2.086961D M A I C
  • 9. Control chart shows that the process is not in control and there are so many special cause variation.D M A I CControl chart of present quality score
  • 10. The red points on the I-MR chart shows that currently the Quality is out of control in the process andrequires an urgent attention.16611495132911639978316654993331671100908070Obser v at ionIndividualValue_X= 92.29UCL= 103.96LCL= 80.62166114951329116399783166549933316713020100Obser v at ionMovingRange__MR= 4.39UCL= 14.34LCL= 01111111111111111111111111111111111111111111111111111111111111111111111111111111111I -MR Char t of Quality Scor eCurrent Quality in the ProcessD M A I C
  • 11. The RUN chart shows that mixture and oscillation exists in the data while there are noClustering and Trends. This evidently shows that the data is not stable.Data Stability AnalysisD M A I C
  • 12. There is too much variation in the system, Quality varies to 66% to 100%. 25 % data point arebetween 66% to 89% (Minimum to quartile 1) while median is at 92% . Our 75% population isbelow target of 95%.Any data on this sideis consider as adefectData Distribution AnalysisD M A I C
  • 13. Since p -value is less than 0.05, hence the data is not normal.Probability PlotD M A I C
  • 14. The box plot shows that more then 75% population is below the target of 95%.Box Plot Analysis on QualityD M A I C
  • 15. 100908070100908070AjayQualityScore95Kalim RakeshSatendra Shishir95Boxplot of Qualit y Scor ePanel variable: TLTeam leader wise box Plot Analysis for Quality scoreD M A I CThis shows the quality on the basis of Team Leaders. Out of 4 Team Leaders Shishir and Rakesh areperforming better than others with a mean of 94% and 94.5%
  • 16. Box plot for Location shows that there is no effect of the Location(X) on the Quality (Y).Location wise box plot analysis of Quality scoreD M A I C
  • 17. Box plot for Department shows that there is no effect of the Department (X) on the Quality(Y).Department wise box plot analysis of quality scoreD M A I C
  • 18. Box plot for Managers shows that there is no effect of the Managers (X) on the Quality (Y).Manager wise box plot Analysis of quality scoreD M A I C
  • 19. Box plot for Process Complexity Levels shows that there is no effect of the Process ComplexityLevels(X) on the Quality (Y).Process Complexity wise box plot analysis of quality scoreD M A I C
  • 20. Box plot for Educational Qualification shows that there is no effect of the EducationalQualification(X) on the Quality (Y).Education wise box plot analysis of quality scoreD M A I C
  • 21. Box plot for Monthly Quality score shows that there is no effect of the Monthly QualityScore(X) on the Quality (Y) Except for October month.Month wise box plot analysis of quality scoreD M A I C
  • 22. Box plot for Process knowledge shows that there is no effect of the Process knowledge (X) onthe Quality (Y).Process Knowledge wise box plot analysis of quality scoreD M A I C
  • 23. Box plot for Tenure shows that there is no effect of the Tenure (X) on the Quality (Y).Tenure wise box plot analysis of quality scoreD M A I C
  • 24. Box plot for Experience shows that there is no effect of the Experience (X) on the Quality (Y).Experience wise box plot analysis of quality scoreD M A I C
  • 25. Box plot for Age shows that there is no effect of the Age (X) on the Quality (Y).Age wise box plot analysis of quality scoreD M A I C
  • 26. S No. Potential Cause Operational Definition Data Type Test of be performed1 Team LeaderThe process owner, who is responsible for managing the performance of a setof associates in a given shiftDiscrete Goodness Of Fit2 Department Different Line of Business defined in the organization Discrete Goodness of fit3 Process Complexity The level of reasoning & decision making involved in the process Discrete Goodness of Fit5 Process KnowledgeThe level of knowledge held by an individual associate about the process,gauged through the scores in the process test conductedDiscrete 2-Way Variable8 Gender The sex of the individual associate logged in to take calls Discrete Goodness of Fit9 Location The place where the operations is set up & function from Discrete Goodness of Fit10 Age The number of years an individual associate is old in his / her span of life Discrete Binary Logistic Regression11 Marital Status Married or un married Discrete Goodness of Fit12Mode ofCommunicationLanguage or Dialect. The medium used by an associate to converse Discrete Goodness of Fit13 TenureThe time in months / years which an individual associate has spent in theparticular processContinuous Binary Logistic Regression14 Experience The total work experience of an individual (inside & outside the company) Continuous Binary Logistic RegressionProposed tests according to problem and factor’s data typeD M A I C
  • 27. Chi-Square Goodness-of-Fit Test for CategoricalVariable: QualificationTest ContributionCategory Observed Proportion Expected to Chi-SqB.Com 650 0.333333 555 16.2613B.Sc 573 0.333333 555 0.5838BA 442 0.333333 555 23.0072N N* DF Chi-Sq P-Value1665 0 2 39.8523 0.000The Chi –Square test shows thatsince the P-Value is 0.000,Educational Qualification(X) hasimpact on Quality (Y).Chi-Square Test between quality score & qualificationD M A I C
  • 28. Chi-Square Goodness-of-Fit Test for CategoricalVariable: DeptTest ContributionCategory Observed Proportion Expected to Chi-SqBilling 520 0.25 416.25 25.8596CS 261 0.25 416.25 57.9041Fax 572 0.25 416.25 58.2776Tech 312 0.25 416.25 26.1095N N* DF Chi-Sq P-Value1665 0 3 168.151 0.060The Chi –Square test shows thatsince the P-Value is 0.060,Dept(X) has no impact onQuality (Y).Chi-Square Test between quality score & DepartmentD M A I C
  • 29. Chi-Square 2- Way Variable: Process KnowledgeTest ContributionCategory Observed Proportion Expected to Chi-Sq88.00% 208 0.125 208.125 0.00089.00% 364 0.125 208.125 116.74290.00% 208 0.125 208.125 0.00091.00% 156 0.125 208.125 13.05594.00% 156 0.125 208.125 13.05597.00% 208 0.125 208.125 0.00098.00% 209 0.125 208.125 0.004100.00% 156 0.125 208.125 13.055N N* DF Chi-Sq P-Value1665 0 7 155.911 0.000The Chi –Square test shows thatsince the P-Value is 0.000,Dept(X) has significant impact onQuality (Y).Chi-Square Test between quality score & process knowledgeD M A I C
  • 30. Chi-Square Goodness-of-Fit Test for CategoricalVariable: Gender_1Test ContributionCategory Observed Proportion Expected to Chi-SqFemale 442 0.5 832.5 183.171Male 1223 0.5 832.5 183.171N N* DF Chi-Sq P-Value1665 0 1 366.343 0.560The Chi –Square test shows thatsince the P-Value is 0.560,Dept(X) has no significant impacton Quality (Y).Chi-Square Test between quality score & genderD M A I C
  • 31. Chi-Square Goodness-of-Fit Test for CategoricalVariable: Location_1Test ContributionCategory Observed Proportion Expected to Chi-SqNoida 832 0.5 832.5 0.0003003Pune 833 0.5 832.5 0.0003003N N* DF Chi-Sq P-Value1665 0 1 0.0006006 0.980The Chi –Square test shows thatsince the P-Value is 0.980,Dept(X) has no significantimpact on Quality (Y).Chi-Square Test between quality score & LocationD M A I C
  • 32. Chi-Square Goodness-of-Fit Test for CategoricalVariable: TLTest ContributionCategory Observed Proportion Expected to Chi-SqAjay 364 0.2 333 2.88589Kalim 287 0.2 333 6.35435Rakesh 338 0.2 333 0.07508Satendra 338 0.2 333 0.07508Shishir 338 0.2 333 0.07508N N* DF Chi-Sq P-Value1665 0 4 9.46547 0.055The Chi –Square test shows thatsince the P-Value is 0.055,Dept(X) has no significantimpact on Quality (Y).Chi-Square Test between quality score & team leaderD M A I C
  • 33. Chi-Square Goodness-of-Fit Test for CategoricalVariable: ManagerTest ContributionCategory Observed Proportion Expected to Chi-SqBinny 442 0.5 832.5 183.171Pranay 1223 0.5 832.5 183.171N N* DF Chi-Sq P-Value1665 0 1 366.343 0.055The Chi –Square test shows thatsince the P-Value is 0.055,Dept(X) has no significantimpact on Quality (Y).Chi-Square Test between quality score & team managerD M A I C
  • 34. Chi-Square Goodness-of-Fit Test for CategoricalVariable: Process ComplexityTest ContributionCategory Observed Proportion Expected to Chi-SqL1 572 0.333333 555 0.52072L2 521 0.333333 555 2.08288L3 572 0.333333 555 0.52072N N* DF Chi-Sq P-Value1665 0 2 3.12432 0.210The Chi –Square test shows thatsince the P-Value is 0.210,Dept(X) has no significantimpact on Quality (Y).complexityD M A I C
  • 35. Chi-Square Goodness-of-Fit Test for CategoricalVariable: MonthTest ContributionCategory Observed Proportion Expected to Chi-SqAugust 386 0.25 416.25 2.1983July 348 0.25 416.25 11.1905Oct 542 0.25 416.25 37.9893Sept 389 0.25 416.25 1.7839N N* DF Chi-Sq P-Value1665 0 3 53.1622 0.069The Chi –Square test shows thatsince the P-Value is 0.069,Dept(X) has no significantimpact on Quality (Y).Chi-Square Test between quality score & monthD M A I C
  • 36. Chi-Square Goodness-of-Fit Test for CategoricalVariable: Marital StatusTest ContributionCategory Observed Proportion Expected to Chi-SqM 812 0.5 832.5 0.504805S 853 0.5 832.5 0.504805N N* DF Chi-Sq P-Value1665 0 1 1.00961 0.315The Chi –Square test shows thatsince the P-Value is 0.315, Dept(X)has no significant impact onQuality (Y).Chi-Square Test between quality score & marital statusD M A I C
  • 37. Chi-Square Goodness-of-Fit Test for CategoricalVariable: Mode of CommunicationTest ContributionCategory Observed Proportion Expected to Chi-SqE-Mail 786 0.5 868.5 7.83679Phone 951 0.5 868.5 7.83679N N* DF Chi-Sq P-Value1737 3992 1 15.6736 0.010The Chi –Square test shows thatsince the P-Value is 0.010,Dept(X) has no significantimpact on Quality (Y).Chi-Square Test between quality score & mode of comm.D M A I C
  • 38. Binary Logistic Regression: Quality Score versusExperienceLink Function: LogitResponse InformationVariable Value CountQuality Score 1 971 (Event)0 694Total 1665Logistic Regression TableOdds 95% CIPredictor Coef SE Coef Z P Ratio Lower UpperConstant 0.224632 0.146234 1.54 0.125Experience 0.0449702 0.0556735 0.81 0.419 1.05 0.94 1.17Log-Likelihood = -1130.614Test that all slopes are zero: G = 0.653, DF = 1, P-Value = 0.000Binary Logistic Regression: Quality Score versus AgeLink Function: LogitResponse InformationVariable Value CountQuality Score 1 971 (Event)0 694Total 1665Logistic Regression TableOdds 95% CIPredictor Coef SE Coef Z P Ratio Lower UpperConstant 0.993845 0.517426 1.92 0.055Age -0.0297221 0.0232525 -1.28 0.201 0.97 0.93 1.02Log-Likelihood = -1130.123Test that all slopes are zero: G = 1.636, DF = 1, P-Value = 0.650BLR test shows that both Experience and Age have significant impact on QualityBinary Logistic Regression of quality score experience wise andage wise D M A I C
  • 39. Binary Logistic Regression: Quality Score versus TenureLink Function: LogitResponse InformationVariable Value CountQuality Score 1 971 (Event)0 694Total 1665Logistic Regression TableOdds 95% CIPredictor Coef SE Coef Z P Ratio Lower UpperConstant 0.400270 0.128073 3.13 0.002Tenure -0.0203903 0.0373270 -0.55 0.585 0.98 0.91 1.05Log-Likelihood = -1130.792Test that all slopes are zero: G = 0.298, DF = 1, P-Value =0.000BLR shows that there is a significant impact of Tenure on QualitBinary Logistic Regression between quality score & tenureD M A I C
  • 40. S No. Potential Cause Operational Definition Data Type Test of be performed1 Team LeaderThe process owner, who is responsible for managing the performance of a setof associates in a given shiftDiscrete 0.0552 Department Different Line of Business defined in the organization Discrete 0.0603 Process Complexity The level of reasoning & decision making involved in the process Discrete 0.2105 Process KnowledgeThe level of knowledge held by an individual associate about the process,gauged through the scores in the process test conductedDiscrete 0.0008 Gender The sex of the individual associate logged in to take calls Discrete 0.5609 Location The place where the operations is set up & function from Discrete 0.98010 Age The number of years an individual associate is old in his / her span of life Discrete 0.65011 Marital Status Married or un married Discrete 0.31512Mode ofCommunicationLanguage or Dialect. The medium used by an associate to converse Discrete 0.01013 TenureThe time in months / years which an individual associate has spent in theparticular processContinuous 0.00014 Experience The total work experience of an individual Continuous 0.000Vital X’s that are impacting the quality scoreD M A I C
  • 41. Actionable Items Responsibility Start Date Close DateProcess Trining Training manager 1-Nov 15-NovHands on practice Training manager 1-Nov 30-NovRefresher Training Training manager 1-Nov 7-DecUpdate Sharing session Team manager 8-Nov 15-DecAssessment Quality Manager 8-Nov 30-NovOnline Support portal MIS team 1-Nov 31-DecCheat Sheet Quality Manager 1-Nov 15-NovMIS around PK by trainer MIS team 1-Nov 30-NovDip-chck of briefing Quality Manager 1-Nov 31-DecPre-shift briefing Team manager 1-Nov 31-DecRegulate Mentor-mentee program Team manager 1-Nov 31-DecHire ppl with specific educational background Hr Team 1-Nov 31-DecProfessional Certification Management/HR 1-Dec 15-DecIn-house domain training Training manager 21-Nov 21-DecBaselining the cutoff % before hiring Process Head 1-Nov 15-NovMIS around the perfromance by specific edu background MIS team/Process head 1-Nov 31-DecInternal Assesment for the in-house domain training Training manager 16-Nov 21-DecInter assement score should be Qualifying factor for certification Process Head 1-Nov 15-NovRnR Process Head 1-Nov 15-NovIntroduce Certification for tenured ppl Hr Team 1-Nov 21-NovIntroduce Loyalty program Hr Team 1-Nov 15-NovIntroduce new Designation Hr Team/Business Head 1-Nov 7-DecIntroduce retention bonus Hr Team/Business Head 1-Nov 15-NovProvide badges, lanyard HR Team 1-Nov 30-NovUse best performers as part-time trainers Training manager 16-Nov 21-DecExtra responsibility sharing Team manager 1-Dec 31-DecGiving an edge to the tenured pp during IJP HR/Business Head 1-Nov 30-NovMixing best performers across all the critical process Business Head 1-Dec 31-DecNomination for best performers for moving next level Process Head 1-Dec 15-DecRe-engineering of process Complexity Business Head 1-Nov 31-DecIncentive plans on the basis of process Complexity Business Head 1-Nov 15-NovImplement Autonomation MIS Team/Business Head 1-Nov 31-DecQFDQuality Function Deployment (QFD)D M A I C
  • 42. Implementation Road MapD M A I C
  • 43. Faliure Mode Effect Analysis (FMEA)Defined "X"s Items Failure Mode Effect on EDR Severity Occurrence Detection RPN RMS RTP ResponsibilityProcess KnowledgeProcess TrainingNon availability of trainer Delay in Process training 10 5 10 500 Reduce Create back up Training ManagerTraining Room and required training resource not available Delay in Process training 10 5 10 500 Trf Transfer to Facility Facility ManagerTTT is not effective Delay in Process training 8 5 10 400 Reduce Assessment of the session Training ManagerTrainers could be ill during the training period Delay in Process training 8 5 10 400 Reduce Create back up Training ManagerHands on practice Required Resource (systems / S/W / Manpower) are not available for trainees Delay in Process training 10 5 10 500 Trf Transfer to IT/Facility Facility ManagerRefresher TrainingRefresher training not conducted. Delay in Process training 10 5 10 500 Reduce Send Reminder Training Team LeadTraining Not effective Delay in Process training 8 5 10 400 Reduce Assessment of the session Training Team LeadTraining material not updated as per current updates received. Delay in Process training 9 5 10 450 Reduce Store Updates in Central Depository Team LeadUpdate Sharing sessionUpdates sharing session not conducted Impact on process quality 10 5 10 500 Reduce Post sharing the updates send MOM Team LeadUpdated folders not updated/stored. Impact on effective update sharing 8 5 10 400 Reduce Review on weekly basis Quality ManagerUpdates not shared with the absent population. Impact on effective update sharing 7 5 10 350 Reduce Post sharing the updates send MOM Team LeadAssessmentassessment sessions not conducted. Impact on process quality 10 5 10 500 Reduce Send Reminder and Post assessment publish the score Training ManagerRequired resources (Test papers, Stationary, etc..) not available. Assessment delay 9 5 10 450 Reduce Review one week prior to the assessment date Training ManagerAssessment rooms not available. Assessment delay 8 5 10 400 Trf Transfer to FacilityOnline Support portalLack of complete documentations of the complete process. Portal creation delay 10 5 10 500 Reduce Dip check on Documentation process Operation ManagerBandwidth challenge for the completion of portal. Portal creation delay 8 5 10 400 Trf Transfer to MIS MIS ManagerRequired approval not available. Portal creation delay 10 5 10 500 Accept N/AEducationHire ppl with specific educational backgroundCommunication not sent to HR. Delay In hiring right candidate 10 5 10 500 Reduce Send Reminder Operation ManagerCommunication sent but was not clear (Passing % cut off, Specializations, etc..) People skill enhancement would be impacted 9 5 10 450 Reduce HR is require to send an acknowledge post receiving the requisition HR ManagerUseful resouces were not identified. People skill enhancement would be impacted 9 5 10 450 Accept N/AVerifications were not done or less effective. People skill enhancement would be impacted 8 5 10 400 Trf Transfer to HR HR ManagerProfessional CertificationTie-ups not happended with the universities/ Institute on time. People skill enhancement would be impacted 10 5 10 500 Trf Transfer to HR HR ManagerRequired approval not available. People skill enhancement would be impacted 10 5 10 500 Accept N/ARequired resources (Test papers, Stationary, etc..) not available for in house training. People skill enhancement would be impacted 10 5 10 500 Trf Transfer to Facility Facility ManagerCandidate nomination not rceived on time. People skill enhancement would be impacted 10 5 10 500 Reduce Send Reminder to Team Lead Assistant ManagerIn-house domain trainingSubject matter trainer not available. People skill enhancement would be impacted 10 5 10 500 Reduce Create back up Training ManagerRequired resources (Training Room, training manuals, Stationary, etc..) not available for in house training. People skill enhancement would be impacted 10 5 10 500 Trf Transfer to Facility Facility ManagerCandidate nomination not rceived on time. People skill enhancement would be impacted 10 5 10 500 Reduce Send Reminder to Team Lead Assistant ManagerMIS around the perfromance by specific edu backgroundPopulation has not been sagrigated on the basis of educational qualification People skill enhancement would be impacted 10 5 10 500 Reduce Process Manager is required to update the educational Qualification Operation ManagerReporting tool can not pull reports on the basis of educational qualifications. People skill enhancement would be impacted 9 5 10 450 Accept N/AReports not reported on time. People skill enhancement would be impacted 9 5 10 450 Reduce Send reminders Operation ManagerInternal Assesment for the in-house domain trainingassessment sessions not conducted. People skill enhancement would be impacted 10 5 10 500 Reduce Send Reminder and Post assessment publish the score Training ManagerRequired resources (Test papers, Stationary, etc..) not available. People skill enhancement would be impacted 10 5 10 500 Trf Transfer to Facility Facility ManagerAssessment rooms not available. People skill enhancement would be impacted 10 5 10 500 Trf Transfer to Facility Facility ManagerAssessment papers not prepared. People skill enhancement would be impacted 10 5 10 500 Reduce Send Reminder Training ManagerTests were not uploded on time for the assessment. People skill enhancement would be impacted 10 5 10 500 Reduce Send reminder in 2 days advance from the actual training date Training ManagerTenureRnRRequired approval not available. Employee Retention programme would be impacted 10 5 10 500 Accept N/APopulation tenure data not available. Employee Retention programme would be impacted 9 5 10 450 Reduce Create a database and update the Tenure ffor all the associates Operation ManagerMaterial (Gifts, certificates) not available. Employee Retention programme would be impacted 10 5 10 500 Trf Transfer to Facility Facility ManagerChief guest not available on proposed date. Employee Retention programme would be impacted 10 5 10 500 Reduce Follow up Chief guests for the available slot HR ManagerIntroduce Certification for tenured pplFormat and Quotation have not been proposed. Employee Retention programme would be impacted 9 5 10 450 Trf Transfer to HR HR ManagerRequired approval not available. Employee Retention programme would be impacted 10 5 10 500Accept N/ATenure Certification critaria not defined. Employee Retention programme would be impacted 10 5 10 500Trf Transfer to HR HR ManagerIntroduce Loyalty programRequired approval not available. Employee Retention programme would be impacted 10 5 10 500Accept N/ALoyalty Bonus amount and critaria not been defined. Employee Retention programme would be impacted 9 5 10 450Trf Transfer to HR HR ManagerIntroduce new DesignationNew Designations not formed. Employee Retention programme would be impacted 10 5 10 500Reduce Follow up with HR Process ManagerRequired approval not available. Employee Retention programme would be impacted 10 5 10 500Accept N/AFormal Communication not sent across the organization. Employee Retention programme would be impacted 10 5 10 500Trf Transfer to HR HR ManagerIntroduce retention bonusRretention bonus not defined. Employee Retention programme would be impacted 10 5 10 500Trf Transfer to HR HR ManagerRequired approval not available. Employee Retention programme would be impacted 10 5 10 500Accept N/AProvide badges, lanyardThe Badge and lanyard order has not been sent to vendors. Employee Retention programme would be impacted 10 5 10 500Trf Transfer to Facility Facility ManagerBadges and Lanyard not designed on time. Employee Retention programme would be impacted 10 5 10 500trf Transfer to Facility Facility ManagerThe list of eligible candidates not received. Employee Retention programme would be impacted 9 5 10 450Reduce Send Reminders Assistant ManagerFMEAD M A I C
  • 44. The Control chart shows that the process Median has shifted to 96.22% while special causevariations have been reduced.Post Project Process Control AnalysisD M A I C
  • 45. Earlier the range was from 75% to 100% while after project the range has been reduced to from89% to 100 %. Also the process is more stable now in comparison to pre project.Post Project Process Control AnalysisD M A I C
  • 46. Box plot of quality shows that the more then 50% population is above target.planD M A I C
  • 47. Box plot for departments shows that post project all the department are above targets.Department wise box plot after improvementD M A I C
  • 48. The box plot for location shows that both the locations are above targets post project.improvementD M A I C
  • 49. The box plot shows that all teams are performing above target post project.Team leader wise box plot of quality score after improvementD M A I C
  • 50. The box plot shows that both the managers ‘s Teams are performing well in comparison to thetarget.improvementD M A I C
  • 51. The box plot also shows that the Quality across all the three centers is moving perfectly fineProcess complexity wise box plot of quality afterimprovementD M A I C
  • 52. This graph shows the bell curve for ‘Pre-Project Quality ‘ v/s ‘Improved Quality’. Theimprovement is evident in both the bell-curvesDiffernece in quality score between pre & post improvementD M A I C
  • 53. Average of qualityscore has beenshifted to 96%This chart shows the comparison of both ‘Improved Quality and Pre-project Quality’. In Pre-Project Quality the mean was of 92% and post project the mean is 96%. There is animprovement of 4%Differene between the mean of pre & post quality scoreD M A I C
  • 54. This chart shows the Data Distribution of Quality Pre and Post the Project. Pre-Project theminimum was 66% and post project it is 89%. Now 75% of the population is able to achievethe target.Differene between pre & post quality scoreD M A I C
  • 55. Thank You

×