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Lean Six Sigma Project on Sales improvement by Advance Innovation Group
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Lean Six Sigma Project on Sales improvement 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.

A student project on Improvement of Sales submitted for Six Sigma Certification.

Additionally, it is advisable that you also visit and subscribe Advance Innovation Group Blog (http://advanceinnovationgroup.com/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. Voice of the Customer-VOCCustomer Comments Critical to Quality-CTQ’sAnand Manohar- Process Owner The number of sales is below thebenchmark. It should be increased on thedaily basis to sustain the business.SalesKumar Nishant- VP The business is dependent on sales andthe revenue generated for clients for theirproducts sold. If target are not met,customer trust might be lost which willimpact in job cut-offs.SalesRevenueNeeraj Kumar - AVP If we don’t perform as per customer’sexpectations, customers will loose faith onus leading to customer dissatisfaction.SalesClient dissatisfaction
  • 2. Project CharterBusiness caseABC, a BPO having clients based in India from financial sector. Deals indifferent aspects of the financial industry outsourced from variousclients like Citibank, ICICI bank, HDFC bank and IDBI bank.Sell-well process deals in sales of loans and credit cards for Citibank.ICICI bank has been very old client and maximum revenue generator forABC.For the last few months Sell-Well has not been able to meet sales targetfor ICICI bank and that is causing client dissatisfaction.TeamChampion:MBB:BB:GB:Members:Problem StatementAfter analysing daily data for last 4 months from July 2011 to Oct 2011,we see that median is 16.5 against a target of 25. More than 75% of thetime the process has not been able to meet the target . The clients aregetting worried and we might lose one of our most valuable client.This project will help us increase our sales per day and put that lostconfidence back in our clients.Goal StatementTo increase the daily sales to 25 by 29thFebruary,2012 without impacting the call quality andcompliance to regulations.In Scope : Sell – well process floorOut Scope : All other processes in ABC.Milestones Target Date Actual dateD 1thNov, 2011 1thNov, 2011M 10thNov, 2011 10thNov, 2011A 10th Dec, 2011 1st Dec, 2011I 1stJan, 2012 20th Dec, 2011C 1stMar, 2012 1stMar, 2012Define
  • 3. ARMIKey Stakeholders ARMI WorksheetDefine Measure Analyze Improve ControlChampion I & A I & A I & A I & A I & AMBB R, I & A R, I & A R, I & A R, I & A R, I & ABB M M M M MGB R R R R RTeam Members R R R R RA – 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 XXXX E-mails XXXX BI-WeeklyTollgate Review XXXXX E-mails or Meetings XXXX As per Project PlanProject Deliverables or Activities Members Emails, Meetings XXXX WeeklydDefine
  • 4. CTQ TreeTo Increase theno. of sales of theprocess.Sales less than 25a day is a defect.LSL = 25USL = NATarget = 25sales/dayTotal number ofproducts (Loan &Credit Cards) soldin a day,Define
  • 5. SIPOCGreeted theProspective customerInformed about theproduct (Disc andspecial offer)Agreed to purchaseMade outbound /Received inboundcall•Sales Rep.•Contact Number•Product Details•GUI• prospectivecustomers•Sales Pitch•Lead•Customer details•SalesLogged the notes insoftwareSales man sent fordetails.•IT Team•HR•Vendors•Trainers•Customer•Sales Rep•ICICI BankDefine
  • 6. Data Collection Plan MEASUREKPI Operational Definition Defect Def Performance StdSpecification LimitOpportunityLSL USLNo. Of Sales per dayTotal numbers of Credit Cards /Loans sold in a day.Sales less than25 in anyparticular day,>=25 Sales / Day 25 NA Daily SalesKPI 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 theplannedstart date fordatacollection?No. Of Salesper dayDiscreteCount of Sales(daily data)NANo. OfCountExcel Existing NA10th Nov,2011Completedataconsidered
  • 7. Validation Measurement System -Effectiveness &EfficiencyMEASUREEffectivenessVolumeOpportunities 30Errors 0Result 100%Efficiency & Effectiveness is successful, hence the MSA is adequate andwe can further analyze the data.KPI Data TypeNo. Of sales DiscreteMIS Data Client Data13 1314 1411 1112 1224 2413 1317 1725 2529 2912 1225 2524 2423 2312 1210 1020 2016 1616 1610 1022 2216 1610 1018 1830 3018 1825 2525 2530 3026 2617 17
  • 8. Process Sigma LevelDefect Per Million OpportunitiesMEASURETotal Opportunity = 184Total Defects = 142If total opportunity is 1, defect will be 142/184If total opportunity is 10^6, defect will be (142/184)* 10^6Hence, Defect per Million Opportunity (DPMO) = 771739 (Approx.)Hence, current Sigma level of the process is at 0.75Through this project we will improve the sales of the process , thereby increase thesigma level.
  • 9. Graphical Summary - SalesDEFECTSAnalyzeAs P < 0.05, datais non-normal.For 95% CI datalies between 14to 20.
  • 10. Normality TestFrom Normality testwe find that P-valueis < 0.05 , hence Datais Non normalAnalyze
  • 11. Box-Plot of SalesDEFECTSAnalyzeAs per the boxplot analysis,more than 75% ofthe times we areunable to meettarget of 25 sales /day.
  • 12. Histogram - SalesDEFECTSAnalyze
  • 13. Graphical Representation – Target Met VsTarget not metOut of 184 days,process met targetonly for 42 days,while 142 data itwas unable tomeet target.
  • 14. Time Series Plot - SalesThe Plot showsthat maximum ofthe times we havebeen performingbelow the target of25 sales/day.Analyze
  • 15. P value is > 0.05Cluster, Trend, Mixture and Oscillation notpresent, i.e., Data is Random and StableAnalyze
  • 16. Fish-bone DiagramSalesManMother-natureMethodMaterialMeasureGenderAgeHeadcountWorkinghoursHolidaysDisasterProduct TypeAd. expensesNo of Discount givenShiftPrintMediaCallTypeTotal Call timeNo of calls takenNoofpeoplecontactedAverageQualityScore
  • 17. Sl. No. Potential Xs Description Data Type Test to be performed1 Sales (Y) Total no. of products sold in a day Discrete NA2 Advertisement Expense (in Rs)Total amount of rupees spent/dayon advertisementContinuous Binary Logistic Regression3 Head Count No. of people calling in a day Discrete Chi Square (Two Way Table)4 Male Count No. of male agents calling in a day Discrete Chi Square (Two Way Table)5 Female Count No. of female agents calling in a day Discrete Chi Square (Two Way Table)6 Print on NewspaperPrint advertisement on paper thereor not on that particular dayDiscrete Chi Square (Goodness of Fit)7 Floor Support AvailabilityAvailability of SME on floor forsupportDiscrete Chi Square (Goodness of Fit)8 Floor SupportPerson who was available for floorsupport on that particular dayDiscrete Chi Square (Goodness of Fit)9 Shift First Shift / Second Shift Discrete Chi Square (Goodness of Fit)10 Product Type Credit Card / Loan Discrete Chi Square (Goodness of Fit)11 Login Hour Total login hour for a particular day Continuous Binary Logistic Regression12 Call Type Inbound / Outbound Discrete Chi Square (Goodness of Fit)13 Avg Quality Score (In %) Average Quality score for the day Discrete Chi Square (Two Way Table)14 Total call time (In Hours) Total time spent on calls Continuous Binary Logistic Regression15 HolidayWas that day a national holiday /WeekendDiscrete Chi Square (Goodness of Fit)16 No. of a/c called Total number of numbers called Discrete Chi Square (Two Way Table)17 No. of people contacted Total number of right party contacts Discrete Chi Square (Two Way Table)18 No. of discounts given Total no. of Discounts given in a day Discrete Chi Square (Two Way Table)19 Special Offer Special offer available or not Discrete Chi Square (Goodness of Fit)20 Location Noida / Delhi Discrete Chi Square (Goodness of Fit)AnalyzePotential X’s Identification
  • 18. Binary Logistic Regression -Sales Vs Advertisement ExpenseLogistic Regression TableOddsPredictor Coef SE Coef Z P RatioConstant 1.68284 0.336110 5.01 0.000Advertisement Expense (in Rs) -0.0001001 0.0000585 -1.71 0.087 1.0095% CIPredictor Lower UpperConstantAdvertisement Expense (in Rs) 1.00 1.00Table of Observed and Expected Frequencies:(See Hosmer - Lemeshow Test for the Pearson Chi-Square Statistic)GroupValue 1 2 3 4 5 6 7 8 9 10Target not metObs 15 12 11 15 15 11 11 18 16 18Exp 12.2 12.6 13.8 13.4 14.7 14.3 14.5 15.6 14.9 15.9Target metObs 3 6 8 3 4 7 7 1 2 1Exp 5.8 5.4 5.2 4.6 4.3 3.7 3.5 3.4 3.1 3.1Total 18 18 19 18 19 18 18 19 18 19Measures of Association:(Between the Response Variable and Predicted Probabilities)Pairs Number Percent Summary MeasuresConcordant 3561 59.7 Somers D 0.21Discordant 2327 39.0 Goodman-Kruskal Gamma 0.21Ties 76 1.3 Kendalls Tau-a 0.07Total 5964 100.0As P-Value is morethan 0.05, hence NullHypothesis is trueand There is nosignificant impact ofAdvertisementexpense on sales.Analyze
  • 19. Chi Square – Two Way TableSales Vs Head CountChi-Square Test: Sales, Head CountExpected counts are printed below observed countsChi-Square contributions are printed below expected countsHeadSales Count Total1 20 8 2816.26 11.740.860 1.1912 30 15 4526.13 18.870.572 0.7933 14 12 2615.10 10.900.080 0.1114 24 15 3922.65 16.350.081 0.1125 7 19 2615.10 10.904.344 6.017Total 3140 2267 5407Chi-Sq = 345.641, DF = 183, P-Value = 0.000As P-Value < 0.05,hence AlternateHypothesis is true.There is a significantimpact of HeadCount on sales.Analyze
  • 20. Box Plot – Head-Count WiseAs per the box plot,when we have 15-20people logged in, thesales is high. 27% ofthe times we are able toachieve target. For 5-10& 10-15 people lessthan 25% of time weachieved target.Hence getting morepeople logged in dailybasis will increaseSales.Analyze
  • 21. Chi Square – Two Way TableSales Vs Male Head CountChi-Square Test: Sales, Male CountExpected counts are printed below observed countsChi-Square contributions are printed below expected countsMaleSales Count Total1 20 4 2417.82 6.180.268 0.7722 30 8 3828.21 9.790.114 0.3283 14 3 1712.62 4.380.151 0.4354 24 6 3022.27 7.730.134 0.3875 7 9 1611.88 4.122.003 5.769Total 3140 1090 4230Chi-Sq = 256.783, DF = 183, P-Value = 0.000As P-Value < 0.05,hence AlternateHypothesis is true.There is a significantimpact of Male HeadCount on sales.Analyze
  • 22. Chi Square – Two Way TableSales Vs Female Head CountChi-Square Test: Sales, Female CountExpected counts are printed below observed countsChi-Square contributions are printed below expected countsFemaleSales Count Total1 20 4 2417.46 6.540.371 0.9892 30 7 3726.91 10.090.354 0.9453 14 9 2316.73 6.270.445 1.1884 24 9 3324.00 9.000.000 0.0005 7 10 1712.37 4.632.328 6.210Total 3140 1177 4317Chi-Sq = 304.810, DF = 183, P-Value = 0.000As P-Value < 0.05,hence AlternateHypothesis is true.There is a significantimpact of FemaleHead Count onsales.Analyze
  • 23. Chi-Square Goodness-of-Fit Test for Categorical Variable:Print on NewspaperTest ContributionCategory Observed Proportion Expected to Chi-SqNo 92 0.5 92 0Yes 92 0.5 92 0N N* DF Chi-Sq P-Value184 0 1 0 1.000As P-Value > 0.05,hence NullHypothesis is true.There is nosignificant impact ofAd Print onNewspaper on sales.Chi Square – Goodness-Of-FitSales Vs Print on NewspaperAnalyze
  • 24. Chi Square – Goodness-Of-FitSales Vs Floor Support AvailabilityChi-Square Goodness-of-Fit Test for Categorical Variable:Floor Support AvailabTest ContributionCategory Observed Proportion Expected to Chi-SqNo 100 0.5 92 0.695652Yes 84 0.5 92 0.695652N N* DF Chi-Sq P-Value184 0 1 1.39130 0.238As P-Value > 0.05, henceNull Hypothesis is true.There is no significantimpact of Floor SupportAvailability on sales.Analyze
  • 25. Chi Square – Goodness-Of-FitSales Vs Floor Support (By Person)Chi-Square Goodness-of-Fit Test for Categorical Variable:Floor SupportTest ContributionCategory Observed Proportion Expected to Chi-SqNo Floor Support 100 0.333333 61.3333 24.3768Ravi 48 0.333333 61.3333 2.8986Vivek 36 0.333333 61.3333 10.4638N N* DF Chi-Sq P-Value184 0 2 37.7391 0.000As P-Value < 0.05,hence AlternateHypothesis is true.There is a significantimpact of FloorSupport on Sales.Analyze
  • 26. Box Plot Analysis on Floor SupportAs per the box plotanalysis, when we haveVivek as floor support,the sales is relativelyhigher. More than 25%of the times we are ableto achieve target. ForRavi exactly 25% ofpeople meeting target.When we don’t havefloor support we areperforming badly.Analyze
  • 27. Chi Square – Goodness-Of-FitSales Vs ShiftAs P-Value > 0.05, henceNull Hypothesis is true.There is no significantimpact of Shift on Sales.Chi-Square Goodness-of-Fit Test for Categorical Variable:ShiftTest ContributionCategory Observed Proportion Expected to Chi-SqFirst Shift 79 0.5 92 1.83696Second Shift 105 0.5 92 1.83696N N* DF Chi-Sq P-Value184 0 1 3.67391 0.055Analyze
  • 28. Box plot Analysis of Sales Vs ShiftAlso evident from Box plotanalysis that sales in secondshift are slightly better thanfirst shift , since difference isvery small we will not focuson impact of shift on sales.Analyze
  • 29. CChi Square – Goodness Of FitSales Vs Product Typehi Square – Goodness Of FitSales Vs Product TypeChi-Square Goodness-of-Fit Test for Categorical Variable:Product TypeTest ContributionCategory Observed Proportion Expected to Chi-SqCredit Cards 95 0.5 92 0.0978261Loans 89 0.5 92 0.0978261N N* DF Chi-Sq P-Value184 0 1 0.195652 0.658As P-Value > 0.05,hence NullHypothesis is true.There is nosignificant impact ofProduct Type onSales.Analyze
  • 30. Pareto analysis of Sales Vs Product typeSince both Product typesi.e., Credit cards and loansare equally selling.Hence we will notconsider Product type asof now for our analysis .Analyze
  • 31. Binary Logistic Regression (BLR)Sales Vs Login Hour gisticRegression (BLR)Sales Vs Login HourBinary Logistic Regression: Sales (Binary) versus Login HourResponse InformationVariable Value CountSales (Binary) Target not met 142 (Event)Target met 42Total 184Logistic Regression TableOdds 95% CIPredictor Coef SE Coef Z P Ratio LowerUpperConstant 2.14931 0.773693 2.78 0.005Login Hour -0.0124132 0.0099004 -1.25 0.210 0.99 0.97 1.01Log-Likelihood = -98.041Test that all slopes are zero: G = 1.595, DF = 1, P-Value = 0.207Goodness-of-Fit TestsMethod Chi-Square DF PPearson 7.81962 12 0.799Deviance 8.72554 12 0.726Hosmer-Lemeshow 0.74602 5 0.980Table of Observed and Expected Frequencies:GroupValue 1 2 3 4 5 6 7 TotalTarget not metObs 18 28 21 20 28 18 9 142Exp 19.1 27.6 20.7 19.5 27.1 18.9 9.2Target metObs 9 9 6 5 6 5 2 42Exp 7.9 9.4 6.3 5.5 6.9 4.1 1.8Total 27 37 27 25 34 23 11 184As P-Value > 0.05,hence NullHypothesis is true.There is nosignificant impact ofLogin Hour on Sales.Analyze
  • 32. Chi Chi Square – Goodness Of FitSales Vs Call TypeSquare – Goodness Of FitSales Vs Call TypeChi-Square Goodness-of-Fit Test for Categorical Variable:Call TypeTest ContributionCategory Observed Proportion Expected to Chi-SqInbound 73 0.5 92 3.92391Outbound 111 0.5 92 3.92391N N* DF Chi-Sq P-Value184 0 1 7.84783 0.005As P-Value < 0.05,hence AlternateHypothesis is true.There is a significantimpact of Call Typeon Sales.Analyze
  • 33. Box Plot Analysis – Call Type (Inbound / Outbound)As per the box plotanalysis, percentage oftarget achieved byinbound call is slightlymore than outboundcalls. We should tryand get more of ourpeople to dooutbound / Cold callingto boost sales.Analyze
  • 34. Chi Square – Two Way TableSales Vs Average Quality Scorequare – Two Way TableSales Vs Average Quality ScoreChi-Square Test: Sales, Avg Quality Score (In %)Expected counts are printed below observed countsChi-Square contributions are printed below expected countsAvgQualityScoreSales (In %) Total1 20 76 9615.70 80.301.176 0.2302 30 84 11418.65 95.356.913 1.3523 14 89 10316.85 86.150.481 0.0944 24 82 10617.34 88.662.560 0.5015 7 97 10417.01 86.995.891 1.152Total 3140 16057 19197Chi-Sq = 524.729, DF = 183, P-Value = 0.000As P-Value < 0.05,hence AlternateHypothesis is true.There is a significantimpact of AverageQuality Score onSales.Analyze
  • 35. BiBinary Logistic RegressionSales Vs Total Call Timenary Logistic RegressionSales Vs Total Call TimeBinary Logistic Regression: Sales (Binary) versus Total call timeVariable Value CountSales (Binary) Target not met 142 (Event)Target met 42Total 184Logistic Regression Table95%Odds CIPredictor Coef SE Coef Z P RatioLowerConstant 0.250535 0.420846 0.60 0.552Total call time (In Hours) 0.407829 0.169509 2.41 0.016 1.50 1.08Predictor UpperConstantTotal call time (In Hours) 2.10Log-Likelihood = -95.821Test that all slopes are zero: G = 6.036, DF = 1, P-Value = 0.014Goodness-of-Fit TestsMethod Chi-Square DF PPearson 0.816184 2 0.665Deviance 0.834660 2 0.659Hosmer-Lemeshow 0.816184 2 0.665As P-Value < 0.05,hence AlternateHypothesis is true.There is a significantimpact of Total CallTime on Sales.Analyze
  • 36. Chi Chi Square – Goodness Of FitSales Vs HolidaySquare – Goodness Of FitSales Vs HolidayChi-Square Goodness-of-Fit Test for Categorical Variable:HolidayTest ContributionCategory Observed Proportion Expected to Chi-SqNo 104 0.5 92 1.56522Yes 80 0.5 92 1.56522N N* DF Chi-Sq P-Value184 0 1 3.13043 0.077As P-Value > 0.05,hence NullHypothesis is true.There is nosignificant impact ofHolidays on Sales.Analyze
  • 37. Chi Square – Two Way TableSales Vs No. of Accounts CalledChi-Square Test: Sales, No. of a/c calledExpected counts are printed below observed countsChi-Square contributions are printed below expected countsNo. of a/cSales called Total1 20 87 10711.78 95.225.740 0.7102 30 120 15016.51 133.4911.020 1.3633 14 125 13915.30 123.700.111 0.0144 24 157 18119.92 161.080.834 0.1035 7 175 18220.03 161.978.480 1.049Total 3140 25386 28526Chi-Sq = 825.298, DF = 183, P-Value = 0.000As P-Value < 0.05,hence AlternateHypothesis is true.There is a significantimpact of No. of a/ccalled on Sales.Analyze
  • 38. Chi Square – Two Way TableSales Vs People ContactedChi-Square Test: Sales, No. of people contactedExpected counts are printed below observed countsChi-Square contributions are printed below expected countsNo. ofpeopleSales contacted Total1 20 75 9519.95 75.050.000 0.0002 30 50 8016.80 63.2010.363 2.7563 14 47 6112.81 48.190.110 0.0294 24 81 10522.05 82.950.172 0.0465 7 61 6814.28 53.723.714 0.988Total 3140 11809 14949Chi-Sq = 716.200, DF = 183, P-Value = 0.000As P-Value < 0.05,hence AlternateHypothesis is true.There is a significantimpact of No. ofpeople contacted onSales.Analyze
  • 39. Chi Square – Two Way TableSales Vs No. of Discounts GivenChi-Square Test: Sales, No. of discounts givenExpected counts are printed below observed countsChi-Square contributions are printed below expected countsNo. ofdiscountsSales given Total1 20 8 2822.12 5.880.203 0.7632 30 8 3830.02 7.980.000 0.0003 14 3 1713.43 3.570.024 0.0914 24 4 2822.12 5.880.160 0.6025 7 1 86.32 1.680.073 0.276Total 3140 835 3975Chi-Sq = 428.541, DF = 183, P-Value = 0.000As P-Value < 0.05,hence AlternateHypothesis is true.There is a significantimpact of No. ofDiscounts given onSales.Analyze
  • 40. Chi Square – Goodness Of FitSales Vs Special Offer Chi Square –Goodness Of FitSales Vs Special OfferChi-Square Goodness-of-Fit Test for Categorical Variable:Special OfferTest ContributionCategory Observed Proportion Expected to Chi-SqNo 116 0.5 92 6.26087Yes 68 0.5 92 6.26087N N* DF Chi-Sq P-Value184 0 1 12.5217 0.000As P-Value is lessthan 0.05, henceAlternateHypothesis is true.There is a significantimpact of SpecialOffers on Sales.Analyze
  • 41. Box Plot Analysis – Special Offers AvailabilityAs per the box plotanalysis, no. of dayswe are able to meettarget is when we havemore special offers forcustomer. Wheneverwe have special offers,we are able to meettarget most of the timewhile when we don’thave special offers, weare unable to meettargets.Analyze
  • 42. SqChi Square – Goodness Of FitSales Vs Locationuare – Goodness Of FitSales Vs LocationAs P-Value > 0.05,hence nullHypothesis is true.There is nosignificant impact ofLocation on Sales.Chi-Square Goodness-of-Fit Test for Categorical Variable:LocationTest ContributionCategory Observed Proportion Expected to Chi-SqDelhi 80 0.5 92 1.56522Gurgaon 104 0.5 92 1.56522N N* DF Chi-Sq P-Value184 0 1 3.13043 0.077Analyze
  • 43. StaFindingstistically Significant X’sSummary of FindingsAnalyzeSl. No. Potential Xs Data Type Test performed P - Value Impact1 Sales (Y) Discrete NA NA NA2 Advertisement Expense (in Rs) Continuous Binary Logistic Regression 0.087 No3 Head Count Discrete Chi Square (Two Way Table) 0 Yes4 Male Count Discrete Chi Square (Two Way Table) 0 Yes5 Female Count Discrete Chi Square (Two Way Table) 0 Yes6 Print on Newspaper Discrete Chi Square (Goodness of Fit) 1 No7 Floor Support Availability Discrete Chi Square (Goodness of Fit) 0.238 No8 Floor Support Discrete Chi Square (Goodness of Fit) 0 Yes9 Shift Discrete Chi Square (Goodness of Fit) 0.055 No10 Product Type Discrete Chi Square (Goodness of Fit) 0.658 No11 Login Hour Continuous Binary Logistic Regression 0.21 No12 Call Type Discrete Chi Square (Goodness of Fit) 0.005 Yes13 Avg Quality Score (In %) Discrete Chi Square (Two Way Table) 0 Yes14 Total call time (In Hours) Continuous Binary Logistic Regression 0.016 Yes15 Holiday Discrete Chi Square (Goodness of Fit) 0.077 No16 No. of a/c called Discrete Chi Square (Two Way Table) 0 Yes17 No. of people contacted Discrete Chi Square (Two Way Table) 0 Yes18 No. of discounts given Discrete Chi Square (Two Way Table) 0 Yes19 Special Offer Discrete Chi Square (Goodness of Fit) 0 Yes20 Location Discrete Chi Square (Goodness of Fit) 0.077 No
  • 44. Sl. No. Vital Xs P-Value Impact1 Head Count 0 Yes2 Male Count 0 Yes3 Female Count 0 Yes4 Floor Support 0 Yes5 Call Type 0.005 Yes6 Avg Quality Score (In %) 0 Yes7 Total call time (In Hours) 0.016 Yes8 No. of a/c called 0 Yes9 No. of people contacted 0 Yes10 No. of discounts given 0 Yes11 Special Offer 0 YesWe need to work on above X’s as these have impact on SalesLisList of Vital X’st of Vital X’sAnalyze
  • 45. Quality Functional DeploymentImprove
  • 46. Quality Function Deployment• QFD helps transform customer needs/VOC intoengineering characteristics for a product or service,prioritizing each product or service characteristic whilesimultaneously setting development targets for productor service.• Acquiring market needs by listening to the Voice ofCustomer (VOC), sorting the needs, and numericallyprioritizing them are the early tasks in QFD.• In present case, number of A/c called is the mostimportant VOC as shown by the maximum value ofPrioritization matrix. Total call time and Head countfollows it in terms of priority.
  • 47. Actions identified for achieving ImprovementRoot cause Solution for Implementation ResponsibilityTo IncreaseSalesHead count category of 15-20 sales force is able to achieve moresales hence in each shift minimum 15 people should be logged in.Manager-OperationsOut bound ( Cold calling) call types are to increased Manager-OperationsAs Floor support is having high impact on sales , one more Floorsupport person to be recruited to strengthen the team of Ravi andVivek.Manager –HR RecruitingAs Discounts and special offers are driving factors, Suitablemarketing plan to be prepared to attract customers.AVP-Sales and MarketingEvery day target of no of accounts and no of people to be contactedshould be increased and set as KRAManager-OperationsTraining of Sales person to increase Avg. quality score of calls. Manager-HRSuggestion and Reward scheme to be introduced to increasemorale of the employeesAVP-Operations
  • 48. Post Improvement – Box Plot AnalysisPost improvement boxplot analysis indicatesshift of the no of dayswe are meeting targetwhich is more than75%.The performancepreviously was that wewere unable to meettarget in 75% of days.Improve
  • 49. Sales Then Vs Sales NowPost improvement themean for the processhas increased from 17to 31 which is aboveour target of 25 salesper day. The standarddeviation has alsoreduced (Variation).Previously it was 7.6while now it is 5.5Improve
  • 50. Time Series Plot – Sales Earlier Vs NowPost improvement theperformance of theprocess is lot betterthan what it wasbefore. The Time seriesplot shows the same.Improve
  • 51. Comparison of Sales earlier Vs Sales Post improvementPost improvementthe performance ofthe process is lotbetter than what itwas before. TheI- Chartcomparison of salesshows the same.Improve
  • 52. Graphical Summary – Post ImprovementPost improvement theperformance of theprocess is lot betterthan what it wasbefore.Mean = 31.217Median = 32Standard Dev = 5.56Minimum = 22Maximum = 40Improve
  • 53. Total Opportunity = 184Total Defects = 27When total opportunity is 184, defect is 27If total opportunity is 1, defect will be 27/184If total opportunity is 10^6, defect will be (27/184)* 10^6Hence, Defect per Million Opportunity (DPMO) = 146739 (Approx.)Hence, current Sigma level post improvement of the process is at 2.55 compared to0.75 at the beginning of the process.Sigma Level – Post ImprovementImprove
  • 54. Control & MonitoringDescription Who HowSales call per day – 25 nos ManagerOperationsRun Chart, dailyplottingNo of customer contacted per day per person – 6 nos Sales team Run Chart, dailyplottingDaily Avg Quality Score 90% minimum Manger Sales Team Weekly reviewHead count Minimum 15 Manager-OperationsMonthly ReviewMinimum no of floor support Manager –HRRecruitingMonthly ReviewMinimum 4 Discounts and special offers in month AVP-Sales andMarketingWeekly reviewTraining of Sales person to increase Avg. qualityscore of calls.Manager-HR Training planSuggestion and Reward scheme monitoring – 7suggestion per employeeAVP-Operations Weekly review
  • 55. Project Closure - SynopsisThe Target for the sales process was 25/day which was high that what we were achieving.Business head showed concerns for the same and there was a threat for the process to goaway and company to lose business.Post identification of CTQ, we prepared a Project charter defining the Goal and theperformance of the process.A team for the project was decided and roles and responsibility were assigned. Postpreparation of the process map, an approval for the project was obtained from Business head.A valid verification of the data was done and verified the correctness of data.In the Analyze phase, proper identification for Potential Xs were done and using various testslike Chi Square, Binary Logistic regression and various Data Visualization tools we identifiedthe Vital Xs which were actually hampering our performance.So, we worked on those Vital Xs. Sought for improving each of the vital Xs by using tools likeQFD. Implementation of those improvement plan was done across the process by using fliersand proper documentation, refresher trainings and deploying optimum human resources.Post the changes, the process has shown brilliant improvement and we achieved target. Meanfor the process being 31 against target of 25.