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Lean Six Sigma Project on Improving Handling time by Advance Innovation Group


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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 Green Belt Project of a student at Advance Innovation Group for Green Belt Certification.

Additionally, it is advisable that you also visit and subscribe Advance Innovation Group Blog ( for more Lean Six Sigma Project, Case Studies on Lean Six Sigma, Lean Six Sigma Videos, Lean Six Sigma Discussions, Lean Six Sigma Jobs etc.

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  • 1. 5/7/20131Project TeamChampion : Niti SethiProcess Owner : Tejinder SinghPuriBlack Belt : RekhaMaster Black Belt: : Pranay Kumar5/7/20131Reduction in AHT for Claims Indexing processDMAIC
  • 2. 5/7/20132Project Progress OverviewD1. Map ProjectD2. Approve ProjectM1. CTQ Characteristics&StandardsM2. Measurement SystemAnalysisM3. Data CollectionA1. Baseline ProcessA2. PerformanceObjectiveA3. Identify Drivers ofVariationI1. Screen for Vital XsI2. Screen for vital XsI3. Define ImprovedProcessC1. MSA on XsC2. Improved Process CapabilityC3. Establish Control PlanKey Deliverables:• List of Customer(s) andProject CTQs• Team Charter• High Level Process Map(COPIS)• CAP Plan (Optional)• Preliminary CBA, ifapplicable• QFD / CTQ Tree• Operational definition,Specification limits,target, defect definitionfor Project Y(s)• Data Collection Plan• Measurement SystemAnalysis• Baseline of CurrentProcess Performance• Normality Test• Statistical GoalStatement for Project• List of StatisticallySignificant Xs• List of Vital Few Xs• Transfer Function(s)• Optimal Settings forXs• ConfirmationRuns/Results• Tolerances on VitalFew Xs• MSA Results on Xs• Post ImprovementCapability• Statistical Confirmationof Improvements• Process Control Plan• Process Owner Signoff• Final CBA, if applicableTollgates:PlannedCompleted15/Feb/2011 22/Feb/2011 01/March/2011 08/March/2011 15/April/2011mm/dd/yyyy mm/dd/yyyy mm/dd/yyyySteps:Tools:  Survey Focus Groups Interviews ARMI, StakeholderAnalysis In/Out of Frame Threat vs. OpportunityMatrix Other ______________ QFD FMEA (1st half) Data Collection Plan Continuous Gage R&R Attribute Gage R&R Sample Size Calculator Other ______________ Basic Statistics Histogram Dot Plots Box and Whisker Plots Run Charts Normality Testing Continuous/Discrete Zst, Zlt Benchmarking Detailed Process Mapping Moments of Truth Nature of Work Flow of Work Fishbone Hypothesis Testing Regression Analysis Other ______________ DOE Pugh Matrix New Process Mapping FMEA on new process Process Modeling /Simulation Other ______________ Continuous Gauge R&R Discrete Gauge R&R Control Charts Hypothesis Testing CAP Plan Control Plan Other ______________Define Measure Analyze Improve ControlOverviewmm/dd/yyyy mm/dd/yyyy
  • 3. 5/7/20133Map Project<How Do My Customers See Me?>Sample CommentsDefineCustomer Sample CommentsKey Output CharacteristicsImportant to Customer (CTQs)Ms Niti Sethi – SDL InsuranceBusiness – ClaimsI am unhappy about the productivitynumbers.AHT
  • 4. 5/7/20134In Scope: All Auto & Home Insurance claims for ClaimsIndexing process for BAU associates (> 3 months in theprocess)Out of Scope: Anything else than Auto & Home insurance claims,Associates in Training & OJT, Any process other than ClaimsIndexingBusiness Case: The productivity of Claims Indexing process is abusiness requirement for continual customer satisfaction andrevenue increment for GOSC. The Claims Indexing processcontributes to 20% of the revenue for GOSC and processes Auto &Home insurance claims for US region. Lower productivity threatensthe end to end claims payment cycle of 13 days thus attracting apenalty of USD 50 per claim. The XYZ process handles 324000claims per year. Even 10% error in TAT would mean 1.6 millionUSD paid annually as penalties.Problem Statement:Median of Project Y is 336 which is way above the Target of 170Sec.Min & Max values range between 2 & 992 indicates huge variation inthe process25% of the time AHT lies between 2-164, 25% of the time AHT liesbetween 164-336, 25% of the time AHT lies between 336-639.5,25% of the time AHT lies between 639.5 – 99225% of the times the target AHT of 170 sec is met. A project toreduce the Floor median would ensure better productivity numbersand reduce the risk of TAT of 13 days not being met from the end toend cycle perspectiveGoal Statement:(“SMART”: Specific, Measurable, Attainable, Relevant, Time bound)To reduce the monthly AHT of the floor by 23% by 30th March 2011for Claims Indexing processDefineProject CharterHigh Level Project PlanTarget Date Actual DateStart Date 08/Feb/2011 08/Feb2011Define 15/Feb/2011 15/Feb/2011Measure 22/Feb/2011 22/Feb/2011Analyze 01/March/2011 01/March/2011Improve 08/March/2011 08/March/2011Control 15/April/2011 15/April/2011
  • 5. 5/7/20135Definitions:DefineIndicators DefinitionAHTTotal time taken to process a claim (from the time it comes into the queue to when it is submitted forfurther process from the XYZ process BAU associate)Formula – total time taken to process a claim / total number of claims processed (all categories for Auto& Home insurance claims)Team Leader Name of the TLProcess Complexity Complexity Level of processTrainer The person who is training the associatesShift Shift timingGender Gender of the associateAge Age of the associateTenure Number of months spent in the process by the associateMarital Status Marital Status of the associateMode Of Communication Medium by which the associate received the transactionTyping Speed Associates typing speed
  • 6. 5/7/20136Customer Output ProcessInput Supplier1. WHO are yourprimarycustomers?(From Step A)2. WHAT does thecustomerreceive? (Thinkof their CTQ’s)3. What STEPS areIncluded in the Processtoday? (high level)4. What isprovided toSTART theprocess?5. WhoPROVIDESthe input?(Who) (Nouns) (Verbs) (Nouns) (Who)DefineStep D1COPISEnter steps Here…Highlight the textwith your mouse and begin typing. Thetext will “word wrap” for you.Select Claim from the queueCheck in legacy systemInput the required fields as per the claimrequirementRecheck the claim as per instructions ( asin the Manual - section 2)Submit the claim for approvalEnter Outputs HereClaim for approvalList Customers HereDAVEnter Inputs HereClaim in QueueList Suppliers HereDAV
  • 7. 5/7/20137.DefineStep D1Process MapNYSelect claim from QueueOpen Legacy systemCheck customer detailsFill the application onthe legacy systemGoto the recheck panelRecheck Customer InfoFill info for exceptionEnter the Rechecked Info inscreen 2 of the legacy systemIs there a red alarm postsaving this infoon screen 2?Submit in theexception queueSubmit claim for ApprovalStartEnd
  • 8. 5/7/20138* When Populating the Stakeholder, consider the ARMI:• A= Approver of team decisions• R= Resource or subject matter expert (ad hoc)• M= Member of team• I= Interested Party who will need to be kept informedDefineStep D2ARMI & Communication PlanKey Stakeholders Define Measure Analyze Improve ControlMr. Kush Kamra – MD I I I I IMr. Manu Gautam - MBB A A A A AMr. Manpreet Singh – Dir I I I A AMs Niti Sethi A A A A AMr. Rekha - BB M M M M MMr. Sanjeev Chouhan– TM M M M M MMr. Siddhanth Raj – TM M M M M MMr. Tejinder Singh Puri – Ops Manager M M M M MMessage Audience Media Who WhenProject ProgressKush, Manu, Manpreet,Niti, TejinderE – mail Rekha Bi weekly (Friday )Toll Gate ReviewsE – mail, meetingsRekhaAs pr tollgates definedGroup Activities Sanjeev, Siddhanth, As requiredProject PlansTejinder, Manu, Niti,Sanjeev, SiddhanthBi weekly (Friday )Project Deliverables /To Do ActivitiesE – mail Bi weekly (Friday )Communication PlanARMI Worksheet
  • 9. 5/7/20139MeasureData Collection PlanWhat?How?260260
  • 10. 5/7/201310Gage RnR StudyGage Failed – Part to Part variation < Gage R&R.Part to Part = 5.65, N=1Relook at measurement. Gather more data.Gage R&R & Z Calculation MeasureThe current Mean of the process is towardsRight side of USL, thus there is a hugescope of Improving the Mean of the process.
  • 11. 5/7/201311Hypothesis Testing – Stability and ShapeData is not stable as there is presence of Clustersand Oscillations in the data – p value for both is <0.05Data is not normal as p value is < 0.05, Hence wewill consider Median as the measure of Centraltendency instead of MeanThere is not much variance amongst theperformance of all TLs, hence we need tolook for CenteringRun Charts Analyze
  • 12. 5/7/201312Hypothesis Testing – Spread and CenteringMedian of Project Y is 336, Mean is 405.65Min & Max values range between 2 & 99225% of the time AHT lies between 2-164, 25% of the time AHT lies between 164-336, 25% of the time AHT liesbetween 336-639.5, 25% of the time AHT lies between 639.5 – 992Inference: which means there is huge variation. BQ improvement is the area of focusGraphical Summary Analyze
  • 13. 5/7/201313ApproachAnalyze
  • 14. 5/7/201314Moods Median : MIS Data & TLAs P Value is greater than 0.05, the medians of all Team Leader performance are more or lessthe same and it does not significantly Impact the AHT.AnalyzeMood Median Test: AHT versus Team LeaderMood median test for AHTChi-Square = 3.30 DF = 4 P = 0.508Individual 95.0% CIsTeam Leader N<= N> Median Q3-Q1 ---------+---------+---------+-------Binny 49 61 391 516 (--------*----------)Jai 58 50 315 401 (----*-------)Ravi 61 51 325 400 (------*---)Shishir 48 55 369 544 (-----------*-------------)Sunny 57 55 292 507 (-------*-------------)---------+---------+---------+-------300 400 500Overall median = 336
  • 15. 5/7/201315Mood Median Test: AHT versus TrainerMood median test for AHTChi-Square = 53.57 DF = 5 P = 0.000Individual 95.0% CIsTrainer N<= N> Median Q3-Q1 +---------+---------+---------+------Amit 56 32 247 367 (--*-----)Atul 56 20 194 247 (--*-)Daniel 69 49 270 459 (---*----)Rashid 21 43 535 517 (-------*------)Ruby 42 91 494 454 (------*------)Sonia 29 37 435 359 (--------*----)+---------+---------+---------+------150 300 450 600Moods Median: MIS Data & TrainerP Value is < than 0.05 hence Median of at least one trainer is not same. Ha is accepted. It issignificantly Impacting AHTAnalyze
  • 16. 5/7/201316Correlation/Regression: MIS Vs Process KnowledgeP Value is > than 0.05 ; Ho accepted. Does not significantly impact AHT.AnalyzeRegression Analysis: AHT versus Process KnowledgeThe regression equation isAHT = 412 - 9.4 Process KnowledgePredictor Coef SE Coef T PConstant 412.32 36.54 11.28 0.000Process Knowledge -9.41 48.86 -0.19 0.847S = 273.659 R-Sq = 0.0% R-Sq(adj) = 0.0%Analysis of VarianceSource DF SS MS F PRegression 1 2777 2777 0.04 0.847Residual Error 543 40664867 74889Total 544 40667643
  • 17. 5/7/201317Mood Median Test: AHT versus ShiftMood median test for AHTChi-Square = 6.80 DF = 2 P = 0.033Individual 95.0% CIsShift N<= N> Median Q3-Q1 +---------+---------+---------+------Evening 129 110 297 483 (------*-------)Morning 81 72 298 440 (------*-----------)Night 63 90 429 441 (-------*--------)+---------+---------+---------+------240 320 400 480Moods Median : MIS Data and ShiftP Value is < than 0.05; Ha is accepted .Shift is significantly impacting AHT.Analyze
  • 18. 5/7/201318Correlation/Regression: MIS Data Vs AgeP Value is > than 0.05; Ho is accepted .Age is not significantly Impacting AHTAnalyzeRegression Analysis: AHT versus AgeThe regression equation isAHT = 377 + 1.03 AgePredictor Coef SE Coef T PConstant 377.02 75.94 4.96 0.000Age 1.025 2.686 0.38 0.703S = 273.632 R-Sq = 0.0% R-Sq(adj) = 0.0%Analysis of VarianceSource DF SS MS F PRegression 1 10905 10905 0.15 0.703Residual Error 543 40656738 74874Total 544 40667643
  • 19. 5/7/201319Moods Median: MIS Data Vs LocationP Value > 0.05, Ho is accepted .Location is not significantly impacting AHT.Median of all thelocations are sameAnalyzeMood Median Test: AHT versus LocationMood median test for AHTChi-Square = 1.80 DF = 1 P = 0.180Individual 95.0% CIsLocation N<= N> Median Q3-Q1 -+---------+---------+---------+-----C5 144 159 363 423 (--------*--------)C6 129 113 299 522 (----------*---------------)-+---------+---------+---------+-----250 300 350 400
  • 20. 5/7/201320Correlation/Regression: MIS Data Vs TenureP Value is > than 0.05; Ho is accepted. Tenure is not significantly impacting AHTAnalyzeRegression Analysis: AHT versus TenureThe regression equation isAHT = 405 + 0.06 TenurePredictor Coef SE Coef T PConstant 405.39 31.59 12.83 0.000Tenure 0.058 6.381 0.01 0.993S = 273.668 R-Sq = 0.0% R-Sq(adj) = 0.0%Analysis of VarianceSource DF SS MS F PRegression 1 6 6 0.00 0.993Residual Error 543 40667637 74894Total 544 40667643
  • 21. 5/7/201321Moods Median : MIS Data Vs Mode of Comm.P Value is greater than 0.05; Ho : Medians of the samples are equalAnalyze
  • 22. 5/7/201322Regression Analysis: AHT versus Typing SpeedThe regression equation isAHT = 578 - 5.45 Typing SpeedPredictor Coef SE Coef T PConstant 578.27 51.21 11.29 0.000Typing Speed -5.449 1.575 -3.46 0.001S = 270.700 R-Sq = 2.2% R-Sq(adj) = 2.0%Analysis of VarianceSource DF SS MS F PRegression 1 877472 877472 11.97 0.001Residual Error 543 39790171 73278Total 544 40667643Correlation/Regression : MIS Data & Typing SpeedP Value is less than 0.05; Ha : Typing Speed significantly affects the AHT performanceAnalyze
  • 23. 5/7/201323Moods Median : MIS Data Vs MonthP Value is > than 0.05; Ho is accepted .Month is not Impacting AHTAnalyzeMood Median Test: AHT versus MonthMood median test for AHTChi-Square = 0.01 DF = 2 P = 0.993Individual 95.0% CIsMonth N<= N> Median Q3-Q1 -----+---------+---------+---------+-Feb 81 80 330 481 (-----------*-----------)Jan 94 93 336 513 (-----*---------------------)March 98 99 337 405 (-------*------)-----+---------+---------+---------+-280 350 420 490
  • 24. 5/7/201324Mann Whitney : MIS Data Vs Process complexityP Value > 0.05 as the test is significant at 0.5179.There is no significant difference between themedian of AHT between L1 and L2 process complexities. Process Complexity is not impactingAHTAnalyzeMann-Whitney Test and CI: AHT_L1, AHT_L2N MedianAHT_L1 241 355.00AHT_L2 304 319.00Point estimate for ETA1-ETA2 is -11.0095.0 Percent CI for ETA1-ETA2 is (-55.01,26.01)W = 64612.0Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at0.5179The test is significant at 0.5179 (adjusted for ties)
  • 25. 5/7/201325Mann Whitney : MIS Data Vs GenderP Value > 0.05 as the test is significant at 0.8749.There is no significant difference between themedian of AHT between Female and Male . Gender is not impacting AHTAnalyzeMann-Whitney Test and CI: AHT_F, AHT_MN MedianAHT_F 234 334.50AHT_M 311 344.00Point estimate for ETA1-ETA2 is 3.0095.0 Percent CI for ETA1-ETA2 is (-35.97,41.01)W = 64169.0Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at0.8749The test is significant at 0.8749 (adjusted for ties)
  • 26. 5/7/201326Mann Whitney : MIS Data Vs Marital StatusP Value > 0.05 as the test is significant at 0.9813.There is no significant difference in themedian of AHT between Single and Married. Marital status is not impacting AHTAnalyzeMann-Whitney Test and CI: AHT_M_1, AHT_SN MedianAHT_M_1 227 330.00AHT_S 318 340.50Point estimate for ETA1-ETA2 is -0.0095.0 Percent CI for ETA1-ETA2 is (-40.02,36.97)W = 62014.0Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at0.9813The test is significant at 0.9813 (adjusted for ties)
  • 27. 5/7/201327Mann Whitney : MIS Data Vs Mode of CommunicationP Value > 0.05 as the test is significant at 0.8065.There is no significant difference in themedian of AHT between different mode of communication. Hence it is not impacting AHTAnalyzeMann-Whitney Test and CI: AHT_E, AHT_HN MedianAHT_E 303 366.00AHT_H 242 317.50Point estimate for ETA1-ETA2 is 4.0095.0 Percent CI for ETA1-ETA2 is (-33.00,43.99)W = 83167.0Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at0.8065The test is significant at 0.8065 (adjusted for ties)
  • 28. 5/7/201328Summary Analyze
  • 29. 5/7/201329Quality Function Deployment Improve
  • 30. 5/7/201330Action Items Tracker Improve
  • 31. 5/7/201331ImproveImprovement Plan
  • 32. 5/7/201332ImproveFailure Mode Effect Analysis
  • 33. 5/7/201333Graphical SummaryNew mean for the AHT data is 302 as compared to last mean of 405.65The StDev in the process has reduced from 273.42 to 174.9ImproveDescriptiveStats postimprovement!
  • 34. 5/7/201334Process Capability (Pre & Post)Overall Standard Deviation in theprocess has reduced from 273.42 to174.9ControlPrePost
  • 35. 5/7/201335Two-Sample T-Test and CI: AHT_Pre, AHT_PostTwo-sample T for AHT_Pre vs AHT_PostN Mean StDev SE MeanAHT_Pre 545 406 273 12AHT_Post 545 302 175 7.5Difference = mu (AHT_Pre) - mu (AHT_Post)Estimate for difference: 103.795% CI for difference: (76.4, 130.9)T-Test of difference = 0 (vs not =): T-Value = 7.46 P-Value = 0.000DF = 9252 Sample T-testP Value is less than 0.05; Ha : Medians of the pre and post AHT data are significantly differentControlPrePost
  • 36. 5/7/201336Control Chart ControlPrePost
  • 37. 5/7/201337Lessons Learnt ControlFinalBenefitsLessons Learnt• A 30% improvement is the Average Handling Time• Increased personal understanding and belief in the Six Sigma Process• Greater understanding of the provider lifecycle• Gained a greater understanding of business targets and drivers• A greater appreciation for the need to statistically prove factors that are affecting the problems, rather thantaking problems on face value.• Communication – timely and efficient communication internally has had a great impact on the project, andsolution• Presentation skills – both in preparation and delivery
  • 38. 5/7/201338Annexure
  • 39. 5/7/201339Pre DMAICMedian is 336.Mean 405.65STEDV 273.416Range between 2 to 992Q1 638- count 272Q3 164- 273
  • 40. 5/7/201340Pre DMAICTarget is 170 sec.Sunny’s performance is better than the rest as he is closer to the target of170 although even he is way beyond.The median of the overall data is 336 which is way higher than the target.
  • 41. 5/7/201341Pre DMAIC-Sunny and Jai’s performance is better than the rest with L2process as their median is lower than the floor median.Sunny’s performance is the closest to the target for L2 processRavi and shishir’s performance is better with L1 althoughBinny’s performance is above median with both L1 AND L2 processes.
  • 42. 5/7/201342Pre DMAICTeam leader with ShiftOverall performance is better during evening andworst during nightSunny and Binnys performance best duringeveningJai, Ravi and sunny performing good duringmorningBinny is the only one who is closer to overallmedian during night, others are way above.Shishir’s performance worst in all 3 shiftsfollowed by Binny
  • 43. 5/7/201343Pre DMAICGender with ShiftBoth Male and Female performance during Nightis not satisfactory.
  • 44. 5/7/201344Pre DMAICGender with ShiftBoth Males and Females in Jai and Ravi’s team areperforming well in Morning Shift Followed byFemales in Sunny.
  • 45. 5/7/201345Pre DMAICGender with ShiftFemales in Binny, Shishir and Sunny’s teamperforming well in evening shift. Overallperformance of males not good in evening shiftexcept for Sunny’s Team.
  • 46. 5/7/201346Screening of Vital Xs AnalyzeMedian of Rashid , Ruby and Sonia above the floor Median.Median of Atul is the lowest followed by Amit and Daniel
  • 47. 5/7/201347Screening of Vital Xs AnalyzeMedian of Binny , Jai, Shishir and Sunny more than floor Median.Median of Ravi is lower than floor Median but is still higher than 260
  • 48. 5/7/201348Screening of Vital Xs AnalyzeMedian of only Binny is lower than floor median but is higher than 260 .The difference is of 10 sec.
  • 49. 5/7/201349Screening of Vital Xs AnalyzeMedian of only Sunny is less than floor Median , others are higher than floor median
  • 50. 5/7/201350Screening of Vital Xs AnalyzeFor trainers Amit, Atul and Daniel floor medians of all the TLsmostly below floor median.
  • 51. 5/7/201351Screening of Vital Xs AnalyzeMedians of Rashid, Ruby and Sonia are above thefloor Median in all the shifts. The only exceptionis Sonia’s performance in evening shift.
  • 52. 5/7/201352Screening of Vital Xs AnalyzeMood Median Test: Process Knowledge versus TrainerMood median test for Process KnowledgeChi-Square = 4.13 DF = 5 P = 0.531Individual 95.0% CIsTrainer N<= N> Median Q3-Q1 --------+---------+---------+--------Amit 44 44 0.762 0.370 (--------------*-----------)Atul 38 38 0.762 0.349 (--------------*-----------)Daniel 62 56 0.752 0.352 (------------*------------)Rashid 27 37 0.831 0.289 (-----------*-------)Ruby 71 62 0.750 0.342 (------------*---------)Sonia 39 27 0.660 0.412 (-*----------------------)--------+---------+---------+--------0.700 0.770 0.840P value >0.05, H0 is accepted. No significant variation in the process knowledge between different trainers
  • 53. 5/7/201353Screening of Vital Xs AnalyzeMood Median Test: Typing Speed versus TrainerMood median test for Typing SpeedChi-Square = 38.56 DF = 5 P = 0.000Individual 95.0% CIsTrainer N<= N> Median Q3-Q1 ------+---------+---------+---------+Amit 31 57 34.00 10.75 (------*--)Atul 42 34 30.00 9.75 (-------*---------------)Daniel 49 69 34.00 11.00 (---------------*Rashid 43 21 26.00 8.75 *----)Ruby 90 43 27.00 8.50 (---*---)Sonia 26 40 34.00 11.25 (---------------*-)------+---------+---------+---------+27.5 30.0 32.5 35.0Overall median = 30.00P < 0.05 , Ha accepted, Trainers significantly impacting typing speed . Typing speed is also a vital x, impactingthe AHT
  • 54. 5/7/201354Screening of Vital Xs AnalyzeFor Complexity L1 Rashid’s performance is better. Ruby and Sonia’s Median is above floor median in bothL1 and L2.However their Median is higher in case of L2 as against L1
  • 55. 5/7/201355Screening of Vital Xs AnalyzeMedian of Rashid, Ruby and Sonia above floor Median for both the Genders
  • 56. 5/7/201356Screening of Vital Xs AnalyzeMedian of Rashid, Ruby and Sonia above Floor Median for both the modes of communication
  • 57. 5/7/201357Screening of Vital Xs AnalyzeProcess knowledge of associates trained by Rashid & Rubyin the morning is better as compared to any other shift
  • 58. 5/7/201358Screening of Vital Xs AnalyzeRegression Analysis: Typing Speed versus ProcessKnowledgeThe regression equation isTyping Speed = 29.1 + 3.71 Process KnowledgePredictor Coef SE Coef T PConstant 29.0543 0.9778 29.71 0.000Process Knowledge 3.708 1.308 2.84 0.005S = 7.32375 R-Sq = 1.5% R-Sq(adj) = 1.3%Analysis of VarianceSource DF SS MS F PRegression 1 431.38 431.38 8.04 0.005Residual Error 543 29125.06 53.64Total 544 29556.45P <0.05 , Ha accepted. Process Knowledge significantly impacts Typing Speed which in turn is impactingthe AHT
  • 59. 5/7/201359Screening of Vital Xs AnalyzeNo major variation in process knowledge between shifts.Typing speed lowest in Morning
  • 60. 5/7/201360Screening of Vital Xs AnalyzeMann-Whitney Test and CI: Typing Speed_F, Typing Speed_MN MedianTyping Speed_F 234 32.000Typing Speed_M 311 30.000Point estimate for ETA1-ETA2 is 1.00095.0 Percent CI for ETA1-ETA2 is (0.000,1.999)W = 67556.5Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at0.0435The test is significant at 0.0428 (adjusted for ties)Test of significance is >0.05, Hence Gender not impacting typing speed
  • 61. 5/7/201361Screening of Vital Xs AnalyzeMann-Whitney Test and CI: Typing Speed_L1, Typing Speed_L2N MedianTyping Speed_L1 241 34.000Typing Speed_L2 304 27.000Point estimate for ETA1-ETA2 is 3.00095.0 Percent CI for ETA1-ETA2 is (1.000,4.000)W = 74269.5Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0000The test is significant at 0.0000 (adjusted for ties)Process complexity is impacting typing speedas test of significance is <0.05No significant difference in the processknowledge % between L1 and L2 processesSignificant difference in the Typing Speedbetween L1 and L2 processes
  • 62. 5/7/201362Screening of Vital Xs AnalyzeProcess knowledge is impacting typing speed in case of L1 but not in case of L2