贝恩咨询行业分析-overviewonanalysis1.ppt

APrimeronAnalysisOverviewConfidentialDocument,TABLEOFCONTENTS,IntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale,experience,complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand“Discovery”conjointanalysismulti-dimensionalscalingPrice-volumecurvesandelasticityDemandforecastingtechnology/substitutioncurvesWrap-up,LOGICANDANALYSISCRITICALTOSTRATEGYDEVELOPMENT,Keytostrategydevelopmentislayingout“logic”toUnderstandwhatmakesbusinessworkeconomicsinteractionsacrosscompetitors,segments,time,.ConceptuallyorganizeclientgoalsDevisewaystoachieveclientsgoalsHelpclient“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,DONTLIMITYOURSELFTOTHESETOOLS,TheyareasampleofthemostcommonlyusedtoolsOtherswillbeofuseinspecificsituationsValuemanagement(CFROI,assetgrowth,etc.)Additionally,notoolcansubstituteforanewcreativeapproach,TABLEOFCONTENTS,IntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale,experience,complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand“Discovery”conjointanalysismulti-dimensionalscalingPrice-volumecurvesandelasticityDemandforecastingtechnology/substitutioncurvesWrap-up,RELATIONSHIPSHAVEMOSTIMPACTWHENDISPLAYEDVISUALLY,Graphsandchartsshouldbeeasilyunderstandabletoa“nonquantitative”clientDisplayonemainideapergraphMakethepointasdirectlyaspossibleDemonstrateclearrelevancetoaccompanyingmaterialandclientsbusinessClearlylabeltitle,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$)x,Deflator1993Deflator1979,SELECTAPPROPRIATEDEFLATORDEPENDINGONTHEQUESTIONYOURETRYINGTOANSWER,G.N.P.deflatorisbestforexpressingdollarsintermsofaveragerealvaluetotherestoftheeconomyCurrent(variable)weightsMeasuredquarterlyC.P.I.isbestonlyforexpressingvalueinrelationtoconsumerspendingonafixedmarketbasketofgoods(1973base)MeasuredmonthlyIndustryorproduct-specificindicesarebestforconvertingdollarsintomeasuresofphysicaloutputAvailablefromCommerceDept.forbroadindustrycategoriesCanbeconstructedfromclientorindustrydatafornarrowcategories,BECAREFULWHENMIXINGEXCHANGERATESANDINFLATIONACROSSCOUNTRIES,FirstconverteachcountryshistoricaldatatoconstantlocalcurrencyE.g.,Japan1993yenW.Germany1993DMU.S.A.1993dollarsThenconverttosinglecurrency(dollars,forexample)atfixedexchangerate,EXAMPLE:ANINTEGRATEDCIRCUITMANUFACTURER,ReportedSalesG.N.P.DeflatorAverageI.C.AverageI.C.Year($M)(1987=1.00)Price($)TransistorPrice(),19877861.0001.001.0519885951.033.92.7219897301.075.99.49199083419911,06419921,4819931,8381.2271.14.16,Reportedsales$15.2%Realsales$11.4%I.C.unitsales8.9%Transistorsales52.4%,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-statisticmeasureshowwellXexplainsYSimplycalculatedasslopedividedbyitsstandarderrorCloserslopeistozero,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,UseWhenseveralfactorshaveanimpactsimultaneouslyTohelpdistinguishcausefromcorrelationDontuseas“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?AvoidspuriouscorrelationsthinkindependentlyaboutwhatfactorsarelogicaltoincludeAvoidincludingexplanatoryvariablesthatarehighlycorrelatedwitheachotherShouldtheregressionhaveaninterceptterm?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)PredicteffectofonefirmsactionsonCompetitors(shortterm,reaction)ProfitandcashflowofclientNotCostsystemsCorrectingaveragecostingforitsownsake,WHYDOCOSTANALYSIS?,WHICHCOSTS?,CompetitivecostanalysisUseactualcosts,notstandardsUsefullyabsorbedcosts,sinceexpensesareoftenthemostsensitivetoscale/experience,etc.Identifycostsandexpenseswithindividualmodels/productlinesTherefore,competitivecostanalysisinvolvesAllocationofvariancesAllocationofexpensesCapitalizationofnonrecurringcostsandexpenses,INMOSTSUPPLYSIDEANALYSIS,FIRSTLAYOUTTHECLIENTSCOSTSTRUCTUREFocusonKeyCostElements,Profit,Overhead,SellingandDistribution,VariableManufacturing,RawMaterials,FixedManufacturing,8%,8%,16%,18%,40%,10%,8%,10%,35%,11%,18%,18%,GainRawmaterialsSellinganddistributionAdvantageBackwardintegrationRelateddiversificationtofurtherThroughusesalesforce?PurchasingscaleSalesfocus,tools,COSTDATACANBEFOUNDINCLIENTACCOUNTINGSYSTEMS.,ClientaccountingsystemsgoodforControl/auditofshort-termevolutionNotforstrategicanalysisGenerallybrokendownbytypeofcostDirectIndirectOverheadsEmphasisisonefficiency,notonunderstandinglong-termcostdynamicsasafunctionofscale,runlength,etc.,.BUTOFTENREQUIRESRECASTING,Materials30Manufacturingcosts40Direct15Indirect10Overheads15Commercialcosts30Variable10Fixed20Totalcost100,Materials30Manufacturingcosts40Metalworking15Painting8Assembly12Overheads5Distributioncosts7Logistics5Warehousing2Sellingcosts9Salesmen6After-sales3serviceMarketingcosts10Advertising3Overheads7GDealerscopeMerchandising;BCGAnalysis,EXPERIENCEEFFECTCANBEDIFFICULTTOMEASURE,ExperienceeffectnormallyappliesonlytothevaluethefirmaddstotheproductCostallocationinmultiproductplantcreatesproblemsinmeasuringtheexperienceeffectDifferencesinfactorcostsmakecomparisondifficultInflationmustbeeliminatedSignificantchangesinproductdesignmustbetakenintoaccountRelevantexperienceunitnotalwaysobvious,ComplexitygivesrisetounitcoststhatincreasewiththescopeofactivityScopeinmanufacturing:parts,models,productlines,etc.Scopeinadministration:businesses,countries,etc.ComplexityoftenworksagainstscaleExample:thecostofconnectingeverytwopeopleinacommunicationnetworkwithadedicatedconnectionat$1perconnection210.5510210454.5501,22524.51004,95049.5,COMPLEXITYCOSTSARISEFROMPROBLEMSANDCOSTSINVOLVEDINCOORDINATINGMANYACTIVITIES,NumberNumberofConnectionsofPeople()(N)(N-1)/2Cost/Person($),COMPLEXITYARISESININDUSTRYDUETOMANYFACTORS,PlantmakessomanyproductsthatmachinesspendsubstantialtimechangingoverbetweenproductsSalesmenselltoomanyproductstomasteranyoneofthemproperlyMultiproductplanthashighadministrativecostsofcoordinationandtracking,COMPLEXITYEXAMPLES,MachineManufacturing,OtherManufacturing,IndirectCost(%ofTotalCost),139%,IndirectCost(%ofTotalCost),30,20,10,5,5,10,20,40,50,30,20,10,4,5,6,8,10,15,20,30,40,50,#ofProductFamiliesProduced,#ofModels,Source:BCGInterviewsandAnalysis,8Factories,SCALEANDCOMPLEXITYTYPICALLYWORKAGAINSTEACHOTHERSignificantValueinLearningHowtoManageComplexity,OverheadCost/Unit,Volume,CombinedImpact,ComplexityImpact,ReducedComplexityCost,ScaleImpact,UTILIZATIONMEANSUNITCOSTSARELOWERWHENCAPACITYISFULL,UtilizationisimportantwhenCapitalintensityishighEnergyconsumptionismajorpartofcostsStartupcostsarehighLaborforceisnotflexibleDifferentfromscaleFrequentlythetwophenomenainteract,UTILIZATIONEXAMPLES,HealthCareServices,PrintingPresses,Cost/Procedure($),140,40,80,60,100,20,16,12,2,3,4,5,20,NumberofProfessionals/Office,Note:Assumes2,000,000RunLength,FullyLoadedCost($/1,00032s),DailyProcedures/Professional5101520,NewTechnology,StandardGravure,AggressiveGravure,UTILIZATIONANDSCALE-1GlasswoolSmelting,CostIndex/t,30020015010075,35102050,100%,50kt,100%,25kt,50%,50%,Capacity,CapacityUtilization,AnnualProduction(kt),Source:ClientsDatabaseandSimulations,UTILIZATIONANDSCALE-2,CostAdded(/Lb),50,40,30,25,20,15,10,1,000,2,000,3,000,4,000,5,000,6,000,10,000,8,000,TotalMonthlyShipments(000Lbs),Guelph78%,Columbiana78%,Stillwater75%,90%ScaleSlope,Note:Costsadjustedforwageandenergyfactorcostdifferences,TABLEOFCONTENTS,IntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale,experience,complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand“Discovery”conjointanalysismulti-dimensionalscalingPrice-volumecurvesandelasticityDemandforecastingtechnology/substitutioncurvesWrap-up,SUPPLYCURVESDESCRIBETHECASHCOSTPOSITIONOFCOMPETITIONINCOMMODITYINDUSTRIES,UsedtomakepredictionsforanindustryaboutPriceEntryandexitProfitabilityUsedmainlyforcommodityindustrieswhereallproducersmakethesameproductandthereisonemarketpriceDisplaythesum