贝恩分析技能概要（overview+on+analysis）.ppt

APrimeronAnalysisOverviewConfidentialDocument TABLEOFCONTENTS IntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale experience complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand Discovery conjointanalysismulti dimensionalscalingPrice volumecurvesandelasticityDemandforecastingtechnology substitutioncurvesWrap up LOGICANDANALYSISCRITICALTOSTRATEGYDEVELOPMENT Keytostrategydevelopmentislayingout logic toUnderstandwhatmakesbusinessworkeconomicsinteractionsacrosscompetitors segments time ConceptuallyorganizeclientgoalsDevisewaystoachieveclient sgoalsHelpclient makeithappen AtightlydevelopedpieceofthislogicisanalysisReducingcomplexrealitytoafewsalientpointsIsolatingimportanteconomicelements ANALYSISISMORETHANNUMBERCRUNCHING Analysisis IntegratingquantitativeandqualitativeknowledgeSeeingthebiggerpictureThinkingcreativelyconceptuallyNot EndlesscalculationsLettingstatisticsdictate rule Classic scientificrigor ANALYTICALBIAS Everythingcanbequantified Notreally butMost qualitative effectsarebasedineconomicsexplicitoropportunitycostsaccuratelyquantifiableornotClienthiresustoanalyzeandobjectifyQuantitativeanalysisisthebasis CREATIVITYANDANALYTICALPERSEVERANCEAREIMPORTANTTRAITSFORSUPERIORANALYSTS StrivetoaddressaproblemusingdifferentapproachestotesthypothesesandfindinconsistenciesTriangulateonanswersNeverbelieveadataseriesblindlyNeverstopatfirstobstacleClientsoftenstopshortofgoodanalysisbecausetheyquicklysurrenderintheabsenceofgood readilyavailabledataWeneversurrendertotheunavailabilityofdataYourcaseleaderdoesnotwanttohearthat thereisnodata butratherwhatcanbedeveloped inhowmuchtime andatwhatcost WHERETHISPRIMERFITS NodocumentcanteachyoutobeagreatanalystAnswerslookeasy butprocessofgettingtherepainfulEachproblemsomewhatdifferentfromexamplesAprimercanGiveflavorofexpectedanalysesShowwhichanalyseshavebeenmostproductivehistoricallyExplainbasictechniquesandwarnofcommonmethodologicalerrorsBesttrainingcomesfromExperienceinprojectteamworkDiscussionswithJohnTangandothersYouareexpectedtolocateknowledgeonyourowninitiative DON TLIMITYOURSELFTOTHESETOOLS TheyareasampleofthemostcommonlyusedtoolsOtherswillbeofuseinspecificsituationsValuemanagement CFROI assetgrowth etc Additionally notoolcansubstituteforanewcreativeapproach TABLEOFCONTENTS IntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale experience complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand Discovery conjointanalysismulti dimensionalscalingPrice volumecurvesandelasticityDemandforecastingtechnology substitutioncurvesWrap up RELATIONSHIPSHAVEMOSTIMPACTWHENDISPLAYEDVISUALLY Graphsandchartsshouldbeeasilyunderstandabletoa nonquantitative clientDisplayonemainideapergraphMakethepointasdirectlyaspossibleDemonstrateclearrelevancetoaccompanyingmaterialandclient sbusinessClearlylabeltitle axes andsourcesTailorgraphtoitsaudienceandpurposeExplorationPersuasionDocumentation CHOOSEGRAPHSCALETHOUGHTFULLY MatchchartboundariestorelevantrangeofthedataascloselyaspossibleSelectscaletofacilitatethinkingaboutproposedrelationshipsUsesamescaleacrosschartsifyouintendtocomparethem LINEARVS LOG Onalinearscale agivendifferencebetweentwovaluescoversthesamedistanceanywhereonthescaleOnalogarithmicscale agivenratiooftwovaluescoversthesamedistanceanywhereonthescale 1 2 4 8 16 OneCycle Linear Log Log Theratioofanythingtozeroisinfinite Zerocannotappearonalogscale DATARELATIONSHIPDETERMINESSELECTIONOFSCALEThreeScalesMostCommon Linear Log Log Linear Linear usuallytime Log Linear Semi Log Log Log ConstantRateofChange ConstantGrowthRate Constant Elasticity Givennopriorexpectationabouttheformofarelationship plotitlinearly y mx b logy mx b logy mlogx b WHENSHOULDALINEARGRAPHBEUSED Lineargraphsarebestwhenthechangeinunittermsisofinterest e g MarketshareovertimeProfitmarginovertimeForty fivedegreedownwardslopinglinesonlineargraphrepresentpointswhosexandyvalueshaveconstantsumRaysthroughoriginrepresentpointswithcommonratio MarketShare LinearGraph Hardware Software WHENSHOULDASEMI LOGPLOTBEUSED Semi loggraphsaregenerallyusedtoillustrateconstantgrowthrates e g Volumeofsalesgrowthovertime Year Source AgriculturalStatistics U S CornYield Bushels Acre R 95 Semi LogGraph WHENSHOULDALOG LOGPLOTBEUSED Log loggraphsaregenerallyusedtoplot elasticities e g PriceelasticityofdemandScaleslopeForty fivedegreedownwardslopinglinesshowpointswithcommonproduct SalariedandIndirecthourlyEmployees BillionImpressionsofCapacity PrintingCapacity BillionsofImpressions 78 ScaleSlopeR 636 1 000 100 10 CIRCLEORBUBBLECHARTSOFTENUSEDTOSHOWATHIRDDIMENSION ThirddimensionshouldberelatedtoxandyaxesCommonexamplesinclude MarketsizeAssetsCostflowCirclearea notdiameter isproportional BUBBLECHARTEXAMPLECategoryGrowthVersusGrossMarginVersusSize 1980 84RealCAGR GrossMargin 1Bsales ConsumerElectronics Toys Housewares Gifts Jewelry SportingGoods SmallAppliances Camera Photo Source DiscountMerchandiser TABLEOFCONTENTS IntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale experience complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand Discovery conjointanalysismulti dimensionalscalingPrice volumecurvesandelasticityDemandforecastingtechnology substitutioncurvesWrap up DEFLATORSCORRECTEFFECTSOFINFLATIONConvertsVariablesfrom Nominal to Real TimeseriesdataindollarswithhighorwidelyfluctuatinginflationratesdistortpictureofgrowthDeflatingdataremovessomeofthedistortionUsingadeflatorindexlist currencydataaremultipliedbytheratioofthebaseyeardeflatorindextothedatayeardeflatorindex e g 1979sales 1993 1979 1979 x Deflator1993Deflator1979 SELECTAPPROPRIATEDEFLATORDEPENDINGONTHEQUESTIONYOU RETRYINGTOANSWER G N P deflatorisbestforexpressingdollarsintermsofaveragerealvaluetotherestoftheeconomyCurrent variable weightsMeasuredquarterlyC P I isbestonlyforexpressingvalueinrelationtoconsumerspendingonafixedmarketbasketofgoods 1973base MeasuredmonthlyIndustryorproduct specificindicesarebestforconvertingdollarsintomeasuresofphysicaloutputAvailablefromCommerceDept forbroadindustrycategoriesCanbeconstructedfromclientorindustrydatafornarrowcategories BECAREFULWHENMIXINGEXCHANGERATESANDINFLATIONACROSSCOUNTRIES Firstconverteachcountry shistoricaldatatoconstantlocalcurrencyE g Japan 1993yenW Germany 1993DMU S A 1993dollarsThenconverttosinglecurrency dollars forexample atfixedexchangerate EXAMPLE ANINTEGRATEDCIRCUITMANUFACTURER ReportedSalesG N P DeflatorAverageI C AverageI C Year M 1987 1 00 Price TransistorPrice 19877861 0001 001 0519885951 033 92 7219897301 075 99 4919908331 119 98 3419911 0621 161 90 2419921 4231 193 98 1819931 8381 2271 14 16 Reportedsales 15 2 Real sales 11 4 I C unitsales8 9 Transistor sales52 4 GrowthRates peryear TABLEOFCONTENTS IntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale experience complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand Discovery conjointanalysismulti dimensionalscalingPrice volumecurvesandelasticityDemandforecastingtechnology substitutioncurvesWrap up REGRESSIONANALYSISISAPOWERFULTOOLFORUNDERSTANDINGRELATIONSHIPBETWEENTWOORMOREVARIABLES Regressionanalysis Explainsvariationinonevariable dependent usingvariationinoneormoreothervariables independent QuantifiesandvalidatesrelationshipsIsusefulforpredictionandcausalexplanationBut MustnotsubstituteforclearindependentthinkingaboutaproblemUseassingleelementinportfolioofanalyticaltechniquesCanbemorass loseforestfortrees ANYRELATIONSHIPBETWEENVARIABLESXANDY Usedalone graphicalmethodsprovideonlyqualitativeandgeneralinferencesaboutrelationships PercentACV 80 70 60 50 40 30 20 10 0 AnnualNumberofPurchasesbyConsumer X AnnualnumberofpurchasesbybuyerY PercentACV PercentACVisthevolumeweightedaveragepercentofgrocerystoreswhichcarrythecategory Sources ScanTrack IRIMarketingFactbook BCGAnalysis REGRESSIONANALYSISANSWERSTHESEQUESTIONS WhatisrelationshipbetweenXandYHowbiganeffectdoesXhaveonY Whatisthefunctionalform Iseffectpositiveornegative Howstrongisrelationship HowwelldoesX explain Y Howwelldoesmymodelworkoverall HowwellhaveIexplainedYingeneral ArethereothervariablesthatIshouldbeincluding WHATISRELATIONSHIPBETWEENXANDY PercentACV AnnualNumberofPurchasesbyCustomer RegressionfitsastraightlinetothedatapointsPercentACV 0 2790 0 2606annualpurchasesOnemoreannualpurchasewillraisepercentACVby0 2606percentagepointsSlopeofline here0 2606 indicatessizeofeffect signofslope herepositive indicateswhethereffectispositiveornegative R2 0 69 MultipleR0 83354RSquare 69 48AdjustedRSquare 68 35StandardError0 10394Observations29 RegressionStatistics Regression10 664000 6640061 4641 98146E 08Residual270 291680 01080Total280 95568 AnalysisofVariancedfSumofSquaresMeanSquareFSignificantF Intercept 0 27901 0 06286 4 439 0 00013 0 40799 0 15003 X10 260560 033247 8401 5372E 080 192370 32876 CoefficientsStandardErrortStatisticP valueLower95 Upper95 Sources Scantrack IRIMarketingFactbook 1990 BCGAnalysis MicrosoftExcelRegressionOutput HOWSTRONGISRELATIONSHIP t statistic measureshowwellXexplainsYSimplycalculatedasslopedividedbyitsstandarderrorCloserslopeistozero and orhigherstandarderror variability theweakertherelationshipAshort cut t statisticgreaterinmagnitudethan2meansrelationshipisverystrong i e roughly95 confidencelevel Between1 5and2 relationshipisrelativelystrong i e roughly85 95 confidencelevel Under1 5 relationshipisweak MultipleR0 83354RSquare 69 48AdjustedRSquare 68 35StandardError0 10394Observations29 Regression10 664000 6640061 4641 98146E 08Residual270 291680 01080Total280 95568 RegressionStatistics dfSumofSquaresMeanSquareFSignificanceF Intercept 0 27901 0 06286 4 439 0 00013 0 40799 0 15003 x10 260560 033247 8401 5372E 080 192370 32876 CoefficientsStandardErrortStatisticP valueLower95 Upper95 AnalysisofVariance HOWWELLDOESMYMODELWORKOVERALL R2measuresproportionofvariationinYthatisexplainedbythevariablesinthemodel herejustXIndicatesoverallhowwellmodelexplainsYBasedonhowdispersedthedatapointsarearoundtheregressionlineR2measuredonscaleof0to100 100 indicatesperfectfitofregressionlinetothedatapointsLowR2indicatescurrentmodeldoesnotfitthedatawellsuggeststhereareotherexplanatoryfactors besidesX thatwouldhelpexplainY MultipleR0 83354RSquare 69 48AdjustedRSquare 68 35StandardError0 10394Observations29 Regression10 664000 6640061 4641 98146E 08Residual270 291680 01080Total280 95568 RegressionStatistics dfSumofSquaresMeanSquareFSignificanceF Intercept 0 27901 0 06286 4 439 0 00013 0 40799 0 15003 x10 260560 033247 8401 5372E 080 192370 32876 CoefficientsStandardErrortStatisticP valueLower95 Upper95 AnalysisofVariance USEMULTIPLEREGRESSIONTOSORTOUTEFFECTSOFSEVERALINFLUENCES UseWhenseveralfactorshaveanimpactsimultaneouslyTohelpdistinguishcausefromcorrelationDon tuseas fishingexpedition MULTIPLEREGRESSIONCANENHANCEPREDICTIVEABILITY ACVwithFeaturesand orDisplays BrandSize PercentofHouseholdsBuying AnnualNumberofPurchasesperYear ACVwithFeaturesand orDisplays ACVwithFeaturesand orDisplays BrandSize M PercentofHouseholdsBuying AnnualNumberofPurchases Year R 67 R 51 R 69 R 87 Predicted ACVwithFeaturesand orDisplays Actual ACVwithFeaturesand orDisplays BrandSize Reach andPurchaseFreqency Sources Scantrack IRIMarketingFactbook1990 BCGAnalysis OTHERREGRESSIONEXAMPLES VeryLowR PercentACV U S CornYield Bushels Acre U S CornYield Bushels Acre RetailerMarginonDeal AverageNumberofDaysonDeal TotalAnnualPurchases M NegativeSlope NonlinearRawData AfterLogTransformation Sources IRIMarketingFactbook CertifiedPriceBook Nielsen BCGAnalysis Source AgriculturalStatistics R 64 R 002 R 95 QUESTIONSTOASKBEFORERUNNINGAREGRESSION Whichvariableisthepredictive ordependent variable OftenstraightforwardbutsometimesrequiresthoughtConsiderdirectionofcausationWhatexplanatoryvariablesdoIbelieveareappropriatetoinclude Avoidspuriouscorrelations thinkindependentlyaboutwhatfactorsarelogicaltoincludeAvoidincludingexplanatoryvariablesthatarehighlycorrelatedwitheachotherShouldtheregressionhaveaninterceptterm Howfarcanthedatabereasonablyextrapolated Shouldtheregressionlinecutthroughtheorigin Doesazerovalueofexplanatoryvariableimplyazerovalueforpredictivevariable HaveIplottedthedata WatchoutforoutliersLookforformofdata linear exponential power etc DoIhaveenoughobservations Roughruleofthumb 10observationsforeachexplanatoryvariable TABLEOFCONTENTS IntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale experience complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand Discovery conjointanalysismulti dimensionalscalingPrice volumecurvesandelasticityDemandforecastingtechnology substitutioncurvesWrap up DefinerelevantcompetitiveenvironmentBasisofadvantage profitlevers Relativestrengths weaknessesofcompetitorsBarriertonewcompetitorsEffectofchangesovertime technology scale Predicteffectofonefirm sactionsonCompetitors shortterm reaction ProfitandcashflowofclientNotCostsystemsCorrectingaveragecostingforitsownsake WHYDOCOSTANALYSIS WHICHCOSTS CompetitivecostanalysisUseactualcosts notstandardsUsefullyabsorbedcosts sinceexpensesareoftenthemostsensitivetoscale experience etc Identifycostsandexpenseswithindividualmodels productlinesTherefore competitivecostanalysisinvolvesAllocationofvariancesAllocationofexpensesCapitalizationofnonrecurringcostsandexpenses INMOSTSUPPLYSIDEANALYSIS FIRSTLAYOUTTHECLIENT SCOSTSTRUCTUREFocusonKeyCostElements Profit Overhead SellingandDistribution VariableManufacturing RawMaterials FixedManufacturing 8 8 16 18 40 10 8 10 35 11 18 18 GainRawmaterialsSellinganddistributionAdvantage Backwardintegration RelateddiversificationtofurtherThroughusesalesforce Purchasingscale Salesfocus tools COSTDATACANBEFOUNDINCLIENTACCOUNTINGSYSTEMS ClientaccountingsystemsgoodforControl auditofshort termevolutionNotforstrategicanalysisGenerallybrokendownbytypeofcostDirectIndirectOverheadsEmphasisisonefficiency notonunderstandinglong termcostdynamicsasafunctionofscale runlength etc BUTOFTENREQUIRESRECASTING Materials30Manufacturingcosts40Direct15Indirect10Overheads15Commercialcosts30Variable10Fixed20Totalcost100 Materials30Manufacturingcosts40Metalworking15Painting8Assembly12Overheads5Distributioncosts7Logistics5Warehousing2Sellingcosts9Salesmen6After sales3serviceMarketingcosts10Advertising3Overheads7G A4Totalcost100 AccountingSystem StrategicCostElements MANYVARIABLESAFFECTCOSTS MaterialsVolumeLocationofsuppliersDesignManufacturingPlantoutputTechnologyExperienceDesignRunlengthComplexityFactorcosts LogisticsVolumeDropsizeSellingVolumeNumberofoutletsMarketingVolumeVolume brand TABLEOFCONTENTS IntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale experience complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand Discovery conjointanalysismulti dimensionalscalingPrice volumecurvesandelasticityDemandforecastingtechnology substitutioncurvesWrap up DESIGNDIFFERENCESCANBEAMAJORDRIVEROFPRODUCTCOSTDIFFERENCES AffectrawmaterialcostsaswellasmanufacturingvalueaddedUsuallyrequiresa teardown ofcompetitorproductstounderstandrealdifferencesRequiresclientinvolvementdesignengineersmanufacturingengineerspurchasingagents FIRSTSTEPISTOIDENTIFYDESIGNDIFFERENCES 1Example DesignAnalysis TorqueConverters 29blades 77mmthick E beamweldhubtoshell Rolltabbed 18blades Diecasting Rollerclutch 2needlethrustbearing 31blades longerandthinner Rolltabbedandstaked Hubpartofstamping 82mm 8springs 4big 4medium nested Closetocenter 3lugswelded 245MM 23 0lbs 27blades 82mmthick Rivethubtoshell 10rivets Rolltabbed 15blades Plastic Rollerclutch 31blades shorterandfatter Rolltabbed Hubpartofstamping 1 04mm 12springs Attacheddirectlytocover 4studswelded 235MM 22 8lbs MiscData Turbine Stator Pump Damper Cover ModelAModelB Designdifferencestranslateintocostdifferences FIRSTSTEPISTOIDENTIFYDESIGNDIFFERENCES 2Example DigitalLineCardComparisons 8ports2transformers2customICs DCPFs NostandardTTLICs2layerPWB1253discretesSM TH2Time slotinterchangingConferencingGaincontrolParallel serialconversionSanityscanningControlchannelinterface 16ports1transformerNocustomICs11standardTTLICs2layerPWB foreignsourced 150discretesAllTH Off board Morecentralized 16ports1transformer1hybridIC3customICs46standardTTLICs6layerPWB210discretesSM THGoldfingersattachedtoPWB noseparateconnector Off board Morecentralized PortinterfacewithterminalsControlswitchingBoardoverheadOther on board functionality 1Printedwiringboard2Surfacemountandthroughhole MajorFunctionClientCompetitorXCompetitorY NEXT WORKWITHCLIENTPURCHASINGAGENTSTODETERMINEMATERIALCOSTSExample ClientMaterialCostsperDigitalPortAreHigh Function Client 8 board CompetitorX 16 board CompetitorY 16 board Additionalfunctionalityassumed Only32 65 cardifredesigneddigitalcardisassumed PortControlOverheadAdditionalfunctionalityTotalmaterialcostperboardPortsTotalmaterialcostperportCostindexCostindexexcludingfunctionality 47 7624 5834 7951 85 158 98819 87100100 68 604 9439 49 113 03167 073653 96 2863 82 76 06 236 161614 7674110 DESIGNDIFFERENCESMAYSUGGESTFOCUSFORCOSTREDUCTIONEFFORTSExample CostReductionofAdditionalOpportunityAppearsinControlUnit DigitalLine ControlunitTrunkmodulesAnaloglineDigitallineSwitchTotalTelsetsTotalSystem 5 2351 3211 0802 1609 7965 44115 237 Component Client Competitor 3 0461 7701 1401 3767 3326 07213 404 Cost Component TABLEOFCONTENTS IntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale experience complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand Discovery conjointanalysismulti dimensionalscalingPrice volumec