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TABLEOFCONTENTSIntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale,experience,complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand“Discovery”conjointanalysismulti-dimensionalscalingPrice-volumecurvesandelasticityDemandforecastingtechnology/substitutioncurvesWrap-upLOGICANDANALYSISCRITICALTO

STRATEGYDEVELOPMENTKeytostrategydevelopmentislayingout“l(fā)ogic”toUnderstandwhatmakesbusinessworkeconomicsinteractionsacrosscompetitors,segments,time,......ConceptuallyorganizeclientgoalsDevisewaystoachieveclient’sgoalsHelpclient“makeithappen”AtightlydevelopedpieceofthislogicisanalysisReducingcomplexrealitytoafewsalientpointsIsolatingimportanteconomicelementsANALYSISISMORETHANNUMBERCRUNCHINGAnalysisis......IntegratingquantitativeandqualitativeknowledgeSeeingthebiggerpictureThinkingcreativelyconceptuallyNot...EndlesscalculationsLettingstatisticsdictate/rule“Classic”scientificrigorANALYTICALBIAS“Everythingcanbequantified”Notreally,butMost“qualitative”effectsarebasedineconomicsexplicitoropportunitycostsaccuratelyquantifiableornotClienthiresustoanalyzeandobjectifyQuantitativeanalysisisthebasisCREATIVITYANDANALYTICALPERSEVERANCEARE

IMPORTANTTRAITSFORSUPERIORANALYSTSStrivetoaddressaproblemusingdifferentapproachestotesthypothesesandfindinconsistenciesTriangulateonanswersNeverbelieveadataseriesblindlyNeverstopatfirstobstacleClientsoftenstopshortofgoodanalysisbecausetheyquicklysurrenderintheabsenceofgood,readilyavailabledataWeneversurrendertotheunavailabilityofdataYourcaseleaderdoesnotwanttohearthat“thereisnodata,”butratherwhatcanbedeveloped,inhowmuchtime,andatwhatcostWHERETHISPRIMERFITSNodocumentcanteachyoutobeagreatanalystAnswerslookeasy,butprocessofgettingtherepainfulEachproblemsomewhatdifferentfromexamplesAprimercanGiveflavorofexpectedanalysesShowwhichanalyseshavebeenmostproductivehistoricallyExplainbasictechniquesandwarnofcommonmethodologicalerrorsBesttrainingcomesfromExperienceinprojectteamworkDiscussionswithJohnTangandothersYouareexpectedtolocateknowledgeonyourowninitiativeDON’TLIMITYOURSELFTOTHESETOOLSTheyareasampleofthemostcommonlyusedtools

OtherswillbeofuseinspecificsituationsValuemanagement(CFROI,assetgrowth,etc.)Additionally,notoolcansubstituteforanewcreativeapproachTABLEOFCONTENTSIntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale,experience,complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand“Discovery”conjointanalysismulti-dimensionalscalingPrice-volumecurvesandelasticityDemandforecastingtechnology/substitutioncurvesWrap-upRELATIONSHIPSHAVEMOSTIMPACT

WHENDISPLAYEDVISUALLY

Graphsandchartsshouldbeeasilyunderstandabletoa“nonquantitative”clientDisplayonemainideapergraphMakethepointasdirectlyaspossibleDemonstrateclearrelevancetoaccompanyingmaterialandclient'sbusinessClearlylabeltitle,axes,andsources

TailorgraphtoitsaudienceandpurposeExplorationPersuasionDocumentationCHOOSEGRAPHSCALETHOUGHTFULLYMatchchartboundariestorelevantrangeofthedataascloselyaspossibleSelectscaletofacilitatethinkingaboutproposedrelationshipsUsesamescaleacrosschartsifyouintendtocomparethemLINEARVS.LOGOnalinearscale,agivendifferencebetweentwovaluescoversthesamedistanceanywhereonthescaleOnalogarithmicscale,agivenratiooftwovaluescoversthesamedistanceanywhereonthescale11124816OneCycleLinearLogLog1Theratioofanythingtozeroisinfinite.Zerocannotappearonalogscale.DATARELATIONSHIPDETERMINESSELECTIONOFSCALEThreeScalesMostCommonLinearLogLogLinearLinear(usuallytime)LogLinearSemi-LogLog-LogConstantRateofChangeConstantGrowthRateConstant““Elasticity””Givennopriorexpectationabouttheformofarelationship,plotitlinearlyy=mx+blogy=mx+blogy=mlogx+bWHENSHOULDALINEARGRAPHBEUSED?Lineargraphsarebestwhenthechangeinunittermsisofinterest,e.g.,MarketshareovertimeProfitmarginovertimeForty-fivedegreedownwardslopinglinesonlineargraphrepresentpointswhosexandyvalueshaveconstantsumRaysthroughoriginrepresentpointswithcommonratioMarketShare(%)LinearGraphHardwareSoftwareWHENSHOULDASEMI-LOGPLOTBEUSED?Semi-loggraphsaregenerallyusedtoillustrateconstantgrowthrates,e.g.,VolumeofsalesgrowthovertimeYearSource:AgriculturalStatisticsU.S.CornYield(Bushels/Acre)R2=.95Semi-LogGraphWHENSHOULDALOG-LOGPLOTBEUSED?Log-loggraphsaregenerallyusedtoplot“elasticities,””e.g.,PriceelasticityofdemandScaleslopeForty-fivedegreedownwardslopinglinesshowpointswithcommonproductSalariedandIndirecthourlyEmployees/BillionImpressionsofCapacityPrintingCapacity(BillionsofImpressions)78%ScaleSlopeR2=.6361,00010010CIRCLEORBUBBLECHARTSOFTENUSEDTOSHOWATHIRDDIMENSIONThirddimensionshouldberelatedtoxandyaxesCommonexamplesinclude:MarketsizeAssetsCostflowCirclearea(notdiameter)isproportionalBUBBLECHARTEXAMPLECategoryGrowthVersusGrossMarginVersusSize1980-84RealCAGR(%)GrossMargin(%)=$1BsalesConsumerElectronicsToysHousewares/GiftsJewelrySportingGoodsSmallAppliancesCamera/PhotoSource:DiscountMerchandiserTABLEOFCONTENTSIntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale,experience,complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand““Discovery”conjointanalysismulti-dimensionalscalingPrice-volumecurvesandelasticityDemandforecastingtechnology/substitutioncurvesWrap-upDEFLATORSCORRECTEFFECTSOFINFLATIONConvertsVariablesfrom““Nominal””to““Real”TimeseriesdataindollarswithhighorwidelyfluctuatinginflationratesdistortpictureofgrowthDeflatingdataremovessomeofthedistortionUsingadeflatorindexlist,currencydataaremultipliedbytheratioofthebaseyeardeflatorindextothedatayeardeflatorindex,e.g.,1979sales(1993$)=1979(1979$)xDeflator1993Deflator1979SELECTAPPROPRIATEDEFLATORDEPENDINGONTHEQUESTIONYOU’RETRYINGTOANSWERG.N.P.deflatorisbestforexpressingdollarsintermsofaveragerealvaluetotherestoftheeconomyCurrent(variable)weightsMeasuredquarterlyC.P.I.isbestonlyforexpressingvalueinrelationtoconsumerspendingonafixedmarketbasketofgoods(1973base)MeasuredmonthlyIndustryorproduct-specificindicesarebestforconvertingdollarsintomeasuresofphysicaloutputAvailablefromCommerceDept.forbroadindustrycategoriesCanbeconstructedfromclientorindustrydatafornarrowcategoriesBECAREFULWHENMIXINGEXCHANGERATESANDINFLATIONACROSSCOUNTRIESFirstconverteachcountry’shistoricaldatatoconstantlocalcurrencyE.g.,Japan—1993yenW.Germany—1993DMU.S.A.—1993dollarsThenconverttosinglecurrency(dollars,forexample)atfixedexchangerateEXAMPLE:ANINTEGRATEDCIRCUITMANUFACTURERReportedSales G.N.P.Deflator AverageI.C.AverageI.C.Year ($M)(1987=1.00) Price($)TransistorPrice(￠)19877861.000 1.001.0519885951.033 .92 .7219897301.075 .99 .4919908331.119 .98 .3419911,062 419921,423 819931,838 1.2271.14 .16Reportedsales$15.2%

"Real"sales$ 11.4%I.C.unitsales8.9%"Transistor"sales 52.4%GrowthRates(peryear)TABLEOFCONTENTSIntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale,experience,complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand““Discovery”conjointanalysismulti-dimensionalscalingPrice-volumecurvesandelasticityDemandforecastingtechnology/substitutioncurvesWrap-upREGRESSIONANALYSISISAPOWERFULTOOLFORUNDERSTANDINGRELATIONSHIPBETWEENTWO

ORMOREVARIABLESRegressionanalysis:Explainsvariationinonevariable(dependent)usingvariationinoneormoreothervariables(independent)QuantifiesandvalidatesrelationshipsIsusefulforpredictionandcausalexplanationBut...MustnotsubstituteforclearindependentthinkingaboutaproblemUseassingleelementinportfolioofanalyticaltechniquesCanbemorass“l(fā)oseforestfortrees”ANYRELATIONSHIPBETWEENVARIABLESXANDY?Usedalone,graphicalmethodsprovideonlyqualitativeandgeneralinferencesaboutrelationshipsPercentACV180%70%60%50%40%30%20%10%0%AnnualNumberofPurchasesbyConsumerX: AnnualnumberofpurchasesbybuyerY: PercentACV1PercentACVisthevolumeweightedaveragepercentofgrocerystoreswhichcarrythecategory.Sources:ScanTrack;IRIMarketingFactbook;BCGAnalysisREGRESSIONANALYSISANSWERSTHESEQUESTIONSWhatisrelationshipbetweenXandYHowbiganeffectdoesXhaveonY?Whatisthefunctionalform?Iseffectpositiveornegative?Howstrongisrelationship?HowwelldoesX““explain””Y?Howwelldoesmymodelworkoverall?HowwellhaveIexplainedYingeneral?ArethereothervariablesthatIshouldbeincluding?WHATISRELATIONSHIPBETWEENXANDY?PercentACVAnnualNumberofPurchasesbyCustomerRegressionfitsastraightlinetothedatapointsPercentACV=-0.2790+0.2606annualpurchasesOnemoreannualpurchasewillraisepercentACVby0.2606percentagepointsSlopeofline(here0.2606)indicatessizeofeffect;signofslope(herepositive)indicateswhethereffectispositiveornegativeR2=0.69MultipleR 0.83354RSquare(%)69.48AdjustedRSquare(%) 68.35StandardError 0.10394Observations29RegressionStatisticsRegression 10.664000.6640061.4641.98146E-08Residual270.291680.01080Total 280.95568AnalysisofVariancedfSumofSquaresMeanSquareF SignificantFIntercept (0.27901)0.06286(4.439)0.00013(0.40799) (0.15003)X1 0.26056 0.03324 7.8401.5372E-080.192370.32876CoefficientsStandardErrortStatisticP-valueLower95% Upper95%Sources:Scantrack;IRIMarketingFactbook(1990);BCGAnalysisMicrosoftExcelRegressionOutputHOWSTRONGISRELATIONSHIP?‘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.MultipleR 0.83354RSquare(%)69.48AdjustedRSquare(%) 68.35StandardError 0.10394Observations29Regression 10.664000.6640061.4641.98146E-08Residual270.291680.01080Total 280.95568RegressionStatisticsdf SumofSquares MeanSquare FSignificanceFIntercept (0.27901)0.06286(4.439)0.00013(0.40799) (0.15003)x1 0.26056 0.03324 7.8401.5372E-080.192370.32876CoefficientsStandardErrortStatisticP-valueLower95% Upper95%AnalysisofVarianceHOWWELLDOESMYMODELWORKOVERALL?R2measuresproportionofvariationinYthatisexplainedbythevariablesinthemodel-herejustXIndicatesoverallhowwellmodelexplainsYBasedonhowdispersedthedatapointsarearoundtheregressionlineR2measuredonscaleof0to100%100%indicatesperfectfitofregressionlinetothedatapointsLowR2indicatescurrentmodeldoesnotfitthedatawellsuggeststhereareotherexplanatoryfactors,besidesX,thatwouldhelpexplainYMultipleR 0.83354RSquare(%)69.48AdjustedRSquare(%) 68.35StandardError 0.10394Observations29Regression 10.664000.6640061.4641.98146E-08Residual270.291680.01080Total 280.95568RegressionStatisticsdf SumofSquares MeanSquare FSignificanceFIntercept (0.27901)0.06286(4.439)0.00013(0.40799) (0.15003)x1 0.26056 0.03324 7.8401.5372E-080.192370.32876CoefficientsStandardErrortStatisticP-valueLower95% Upper95%AnalysisofVarianceUSEMULTIPLEREGRESSIONTOSORTOUTEFFECTS

OFSEVERALINFLUENCESUseWhenseveralfactorshaveanimpactsimultaneouslyTohelpdistinguishcausefromcorrelationDon’tuseas““fishingexpedition””MULTIPLEREGRESSIONCANENHANCE

PREDICTIVEABILITY%ACVwithFeaturesand/orDisplaysBrandSizePercentofHouseholdsBuyingAnnualNumberofPurchasesperYear%ACVwithFeaturesand/orDisplays%ACVwithFeaturesand/orDisplaysBrandSize($M)PercentofHouseholdsBuyingAnnualNumberofPurchases/YearR2=.67R2=.51R2=.69R2=.87Predicted%ACVwithFeaturesand/orDisplaysActual%ACVwithFeaturesand/orDisplaysBrandSize,Reach,and

PurchaseFreqencySources:Scantrack;IRIMarketingFactbook1990;BCGAnalysisOTHERREGRESSIONEXAMPLESVeryLowR2*PercentACVU.S.CornYield(Bushels/Acre)U.S.CornYield(Bushels/Acre)RetailerMarginonDealAverageNumberofDaysonDealTotalAnnualPurchases(M)NegativeSlope*NonlinearRawData**AfterLogTransformation***Sources:IRIMarketingFactbook;CertifiedPriceBook;Nielsen;BCGAnalysis** Source:AgriculturalStatisticsR2=.64R2=.002R2=.95QUESTIONSTOASKBEFORERUNNINGAREGRESSIONWhichvariableisthepredictive(ordependent)variable?OftenstraightforwardbutsometimesrequiresthoughtConsiderdirectionofcausationWhatexplanatoryvariablesdoIbelieveareappropriatetoinclude?Avoidspuriouscorrelations—thinkindependentlyaboutwhatfactorsarelogicaltoincludeAvoidincludingexplanatoryvariablesthatarehighlycorrelatedwitheachotherShouldtheregressionhaveaninterceptterm?Howfarcanthedatabereasonablyextrapolated?Shouldtheregressionlinecutthroughtheorigin?Doesazerovalueofexplanatoryvariableimplyazerovalueforpredictivevariable?HaveIplottedthedata?WatchoutforoutliersLookforformofdata(linear,exponential,power,etc.)DoIhaveenoughobservations?Roughruleofthumb:10observationsforeachexplanatoryvariableTABLEOFCONTENTSIntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale,experience,complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand““Discovery”conjointanalysismulti-dimensionalscalingPrice-volumecurvesandelasticityDemandforecastingtechnology/substitutioncurvesWrap-upDefinerelevantcompetitiveenvironmentBasisofadvantage(profitlevers)Relativestrengths/weaknessesofcompetitorsBarriertonewcompetitorsEffectofchangesovertime(technology,scale)Predicteffectofonefirm’sactionsonCompetitors(shortterm,reaction)ProfitandcashflowofclientNotCostsystemsCorrectingaveragecostingforitsownsakeWHYDOCOSTANALYSIS?WHICHCOSTS?CompetitivecostanalysisUseactualcosts,notstandardsUsefullyabsorbedcosts,sinceexpensesareoftenthemostsensitivetoscale/experience,etc.Identifycostsandexpenseswithindividualmodels/productlinesTherefore,competitivecostanalysisinvolvesAllocationofvariancesAllocationofexpensesCapitalizationofnonrecurringcostsandexpensesINMOSTSUPPLYSIDEANALYSIS,FIRSTLAYOUTTHECLIENT’SCOSTSTRUCTUREFocusonKeyCostElementsProfitOverheadSellingandDistributionVariableManufacturingRawMaterialsFixedManufacturing8%8%16%18%40%10%8%10%35%11%18%18%GainRawmaterials SellinganddistributionAdvantage ?Backwardintegration ?RelateddiversificationtofurtherThroughusesalesforce?? Purchasingscale? Salesfocus,toolsCOSTDATACANBEFOUNDINCLIENT

ACCOUNTINGSYSTEMS...ClientaccountingsystemsgoodforControl/auditofshort-termevolutionNotforstrategicanalysisGenerallybrokendownbytypeofcostDirectIndirectOverheadsEmphasisisonefficiency,notonunderstandinglong-termcostdynamicsasafunctionofscale,runlength,etc....BUTOFTENREQUIRESRECASTINGMaterials 30Manufacturingcosts40Direct 15Indirect10Overheads 15Commercialcosts30Variable10Fixed 20Totalcost100Materials 30Manufacturingcosts40Metalworking15Painting8Assembly12Overheads 5Distributioncosts7Logistics 5Warehousing2Sellingcosts 9Salesmen6After-sales3serviceMarketingcosts10Advertising3Overheads 7G&A4Totalcost100AccountingSystemStrategicCostElementsMANYVARIABLESAFFECTCOSTSMaterialsVolumeLocationofsuppliersDesignManufacturingPlantoutputTechnologyExperienceDesignRunlengthComplexityFactorcostsLogisticsVolumeDropsizeSellingVolumeNumberofoutletsMarketingVolumeVolume/brandTABLEOFCONTENTSIntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale,experience,complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand““Discovery”conjointanalysismulti-dimensionalscalingPrice-volumecurvesandelasticityDemandforecastingtechnology/substitutioncurvesWrap-upDESIGNDIFFERENCESCANBEAMAJORDRIVER

OFPRODUCTCOSTDIFFERENCESAffectrawmaterialcostsaswellasmanufacturingvalueaddedUsuallyrequiresa“teardown””ofcompetitorproductstounderstandrealdifferencesRequiresclientinvolvementdesignengineersmanufacturingengineerspurchasingagentsFIRSTSTEPISTOIDENTIFYDESIGNDIFFERENCES-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.MiscDataTurbineStatorPumpDamperCoverModelAModelBDesigndifferencestranslateintocostdifferencesFIRSTSTEPISTOIDENTIFYDESIGNDIFFERENCES-2Example:DigitalLineCardComparisons8ports2transformers2customICs(DCPFs)NostandardTTLICs2layerPWB1253discretesSM/TH2Time-slotinterchangingConferencingGaincontrolParallel——serialconversionSanityscanningControlchannelinterface16ports1transformerNocustomICs11standardTTLICs2layerPWB(foreignsourced)150discretesAllTH"Off-board"(Morecentralized)16ports1transformer1hybridIC3customICs46standardTTLICs6layerPWB210discretesSM/THGoldfingersattachedtoPWB(noseparateconnector)"Off-board"(Morecentralized)PortinterfacewithterminalsControlswitchingBoardoverheadOther“on-board”functionality1Printedwiringboard2SurfacemountandthroughholeMajorFunction Client CompetitorX CompetitorYNEXT,WORKWITHCLIENTPURCHASINGAGENTS

TODETERMINEMATERIALCOSTSExample:ClientMaterialCostsperDigitalPortAreHighFunctionClient(8/board)CompetitorX(16/board)CompetitorY(16/board)1Additionalfunctionalityassumed2Only32.65/cardifredesigneddigitalcardisassumedPortControlOverheadAdditionalfunctionalityTotalmaterialcostperboardPortsTotalmaterialcostperportCostindexCostindexexcludingfunctionality47.7624.5834.7951.852158.98819.8710010068.604.9439.49—113.03167.07365396.2863.82176.06—236.161614.7674110DESIGNDIFFERENCESMAYSUGGESTFOCUSFORCOSTREDUCTIONEFFORTSExample:CostReductionofAdditionalOpportunity

AppearsinControlUnit,DigitalLineControlunitTrunkmodulesAnaloglineDigitallineSwitchTotalTelsetsTotalSystem5,2351,3211,0802,1609,7965,44115,237ComponentClientCompetitor3,0461,7701,1401,3767,3326,07213,404Cost($/Component)TABLEOFCONTENTSIntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale,experience,complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand““Discovery”conjointanalysismulti-dimensionalscalingPrice-volumecurvesandelasticityDemandforecastingtechnology/substitutioncurvesWrap-upFACTORCOSTSUSUALLYDON’TREQUIRE

ANALYTICALTOOLS,BUTCANRESULTINDIFFERENTCOSTPOSITIONSFactorcostdifferencescanaffectmostelementsofthecoststructureRawmaterialsEnergyLabor(directandoverhead)CapitalFactorcostdifferencesaregenerallyadditiveormultiplicativeandcanbeincorporateddirectlyintothecostanalysisFACTORCOSTEXAMPLEForestProductsIndustry,1981UnitedStates 14.954.733.11 2.24 1.113.30Canada 13.952.97 2.54 2.240.962.77Sweden 11.514.81 ——— —4.81France 10.514.59 5.21 ——— 4.60Brazil 5.504.544.54 2.72 2.273.73LaborRate($/Hour)OilGasCoal OtherAverageEnergyPrices($/MMBTU)TABLEOFCONTENTSIntroductionGeneralanalyticaltechniquesGraphsDeflatorsRegressionanalysisSupplysideanalysisCoststructuresDesigndifferencesFactorcostsScale,experience,complexityandutilizationSupplycurvesDemandsideanalysisCustomerunderstandingsegmentationand““Discovery”conjointanalysismulti-dimensionalscalingPrice-volumecurvesandelasticityDemandforecastingtechnology/substitutioncurvesWrap-upSCALE,EXPERIENCE,COMPLEXITY,ANDUTILIZATION

HAVEDISTINCTCOSTEFFECTS-1Scale,experienceandutilizationtendtobeconfusedAllareconceptuallyseparateScaleRelatesunitcostorpricetoproductionvolumeGenerallyappliestomachinesorfacilitiesofdifferentsizesatapointintimeExperienceRelatesunitcostorpricetocumulativeproductionBesttothinkintermsofentireindustryexperienceoverlongperiodsArisesforavarietyofeconomicreasonsIsusedalotlessfrequentlythanyoumaythinkComplexityRelatesunitcosttosomemeasureofcomplexityEitherovertimeoverdifferentfacilitiesatapointintimeUtilizationRelatesunitcostorprofitabilitytoutilizationasapercentageofcapacityAppliestodifferentvolumesoroutputfromgivenfacilitiesovertimeSCALE,EXPERIENCE,COMPLEXITY,ANDUTILIZATION

HAVEDISTINCTCOSTEFFECTS-2BCGSLOPEDESCRIBESTHERELATIONSHIPBETWEENUNITCOSTANDVOLUMEBCGSlopeEqualsPercentofBaseRemainingWhenIndependentVariableDoubledScaleTwosimilarfacilitieswithcomparableutilization,butonefourtimestheproductionoftheotherUnitcostofsmallerfacility$10.00andlargerfacility$8.10“Slope”=90%ExperienceCumulativeoutputincreasesfrom100Kunitsto200KunitsUnitcostfallsfrom$1.20to$.97“Slope”=81%UtilizationOutputincreasesfrom750to1,500Amortizablefixedcostsof$5MFixedcostperunitfallsfrom$6,667to$3,333“Slope”=50%bisSlope(negative)oflogY=a-bLogXElasticity(negative)ofYwithrespecttoXBCGslope=2-bThereforelogBCGslopelog2FROMMATHEMATICALSLOPETO

BCG’SSLOPEANDVICEVERSABCGSlope Valueofb90 .15280 .32270 .515Note:Youcanuselog(base10)orln(basee).Answersareunaffected.BCGSlopeTakesLog-LogFormBCGSlopeMathematicallyImpliesbValue-b=CALCULATINGNEWCOSTFROMOLDCOSTANDVOLUMESExampleOldCostBCGSlopeOldVolumeNewVolumeNewCost10070%47?OrYt+1=YtXt+1XtlogBCGSlopelog2Yt+1=YtXt+1Xt(-b)CALCULATINGBCGSLOPEFROMCOSTSANDVOLUMESExampleOldCost100NewCost60OldSVolume4NewSVolume10Slope?BCGSlope=AntilogYt+1Ytlog2*Xt+1XtloglogSCALEMEANSCOSTPERUNITIS

LOWERFORLARGERSUPPLIERSTypicallyChartedonLogvs.LogCost/UnitVolume100%Slope75%Slope50%SlopeEconomiesofScaleObservedinMostCostStructureElementsElementManufacturingscaleAutomatedlineJobshopAdvertisingTelevisionDirectmailSellingFragmentedcustomersConcentratedcustomersEngineeringStandardproductCustomproductEffectofScaleHighLowHighLowHighLowHighLowExampleEngineblocksAssembledcomponentsFoodMail-orderspecialtyapparelBookstobookstoreTruckcomponentsAutomobilesHybridmicroelectronicsSCALEEXAMPLESPaperMachineLaborAdvertisingCostsMan-hours/Ton1.00.2406010020040080058%BCGSlopeAdvertising/Sales0.050.040.030.020.018162432MachineCapacity(TPD)Sales($M)60%BCGSlopeLARGESCALEPLANTSHAVEASIGNIFICANT

UNITCOSTADVANTAGEOverheadCost/000Equivalent32’s($)2520151075001,0002,0004,000AnnualCapacity(000Equivalent32’s)DanvilleOldSaybrookLosAngelesGlasgowMattoon70%SlopeHOWEVER,THINKBEYONDTHEDATAFirstCutofDataWouldShowWideDispersionSalariedandIndirectEmployees/Sales($M)10.05.03.02.01.00.52510205010020010030PlantSales/ProductFamily($M)=TraditionalApproach=Cost-basedManagementApproach=Time-basedManagementApproachSource:BCGAnalysis70AutomotiveComponentSuppliersDIFFERENTVOLUMELEVELSCANJUSTIFYSUPERIORTECHNOLOGIES-1HydraulicComponentCastingPrice/Unit402010864501005001,0005,000MonthlyVolumeNote:IncludesfullamortizationoftoolingcostsSlopeacrosstechnologies75%Slopewithintechnologies90-95%SandMoldGravityDieCast/SingleDieHighPressureDieCastTwinDiesDIFFERENTVOLUMELEVELSCANJUSTIFYSUPERIORTECHNOLOGIES-2ConventionalLathesbyMechanicalDesignMachiningCost/PieceManualUniversalMachineManualCopyLatheManualChuckerAutomaticSingleSpindleAutomaticMultispindleLotSizeEXPERIENCERELATESUNITCOSTTOCUMULATIVEVOLUME-1Experienceisanempiricalobservationaboutverylong-termpricebehaviorformanufacturedgoodsandservicesTrendlinearoundwhichthereissignificantdeviationDrivenbytechnologyimprovementsandchangesInbothprimaryproductionprocessesandsecondaryprocesses(converting)NecessarytounderstandcomponentsofcosttoprojectevolutionofpricesIndicatorofcompetitivecostdifferencesExperienceandscaleofteninteract,butarenotthesameProperexperienceanalysisshouldadjustforscaleeffectsEXPERIENCERELATESUNITCOSTTOCUMULATIVEVOLUME-2Experiencecurvesaremostoftenmodeledbyalogarithmicrelation:Log[UC]=blog[V]+awhereV=cumulativevolumeUC=unitcostThe““slope”ofanexperiencecurveisinterpretedas““BCGslope”CalculatedinasimilarwaytoscaleslopeBCGslope=AntiloglogUCUC*log2logVV2121WHYTHERELATIONSHIPWORKSManyfactorsworktogethertoreducerealcostsovertimeIncreasedpurchasingscale(quantitydiscounts)IncreasedproductivityIncreasedscaleoffacilityIncreasedsubstitutionofcapitalforlaborTechnologyevolutionCostsdon’tjust““comedown,””theyaremanageddownIMPLICATIONSOFEXPERIENCEIfpricesdecline,thencostsmustalsodeclineovertimeThedynamicofconstantchangeinbusinesscompetitionAsaresultofchangingcosts,differentcompetitorswillhavedifferentcostsatanygiventimeDifferentcostpositionswillgeneratedifferentlevelsofprofitabilityAlsoinfluencedbypricerealizationProjectcompetitiveimplicationsofaboveWHOSEGROWTHDETERMINESCOST/PRICEEVOLUTION:

CHOICEOFEXPERIENCEBASESPricedatacanbeplottedagainstdifferentexperiencebases:Theindustr