貝恩咨詢(xún)貝恩分析技能概要ppt課件

1、A Primer on AnalysisOverviewConfidential DocumentTABLE OF CONTENTSIntroductionGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale, experience, complexity and utilizationSupply curvesDemand side analysisCustomer underst

2、andingsegmentation and “Discoveryconjoint analysismulti-dimensional scalingPrice-volume curves and elasticityDemand forecastingtechnology/substitution curvesWrap-upLOGIC AND ANALYSIS CRITICAL TOSTRATEGY DEVELOPMENTKey to strategy development is laying out “l(fā)ogic toUnderstand what makes business work

3、economicsinteractions across competitors, segments, time, . Conceptually organize client goalsDevise ways to achieve clients goalsHelp client “make it happenA tightly developed piece of this logic is analysisReducing complex reality to a few salient pointsIsolating important economic elementsANALYSI

4、S IS MORE THAN NUMBER CRUNCHINGAnalysis is. Integrating quantitative and qualitative knowledgeSeeing the bigger pictureThinkingcreativelyconceptuallyNot . . .Endless calculationsLetting statistics dictate/rule“Classic scientific rigorANALYTICAL BIAS“Everything can be quantifiedNot really, butMost “q

5、ualitative effects are based in economicsexplicit or opportunity costsaccurately quantifiable or notClient hires us to analyze and objectifyQuantitative analysis is the basisCREATIVITY AND ANALYTICAL PERSEVERANCE AREIMPORTANT TRAITS FOR SUPERIOR ANALYSTS Strive to address a problem using different a

6、pproaches to test hypotheses and find inconsistenciesTriangulate on answersNever believe a data series blindlyNever stop at first obstacleClients often stop short of good analysis because they quickly surrender in the absence of good, readily available dataWe never surrender to the unavailability of

7、 dataYour case leader does not want to hear that “there is no data, but rather what can be developed, in how much time, and at what costWHERE THIS PRIMER FITSNo document can teach you to be a great analystAnswers look easy, but process of getting there painfulEach problem somewhat different from exa

8、mplesA primer canGive flavor of expected analysesShow which analyses have been most productive historicallyExplain basic techniques and warn of common methodological errorsBest training comes fromExperience in project team workDiscussions with John Tang and othersYou are expected to locate knowledge

9、 on your own initiativeDONT LIMIT YOURSELF TO THESE TOOLSThey are a sample of the most commonly used toolsOthers will be of use in specific situationsValue management (CFROI, asset growth, etc.)Additionally, no tool can substitute for a new creative approachTABLE OF CONTENTSIntroductionGeneral analy

10、tical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale, experience, complexity and utilizationSupply curvesDemand side analysisCustomer understandingsegmentation and “Discoveryconjoint analysismulti-dimensional scalingPrice-volume cur

11、ves and elasticityDemand forecastingtechnology/substitution curvesWrap-upRELATIONSHIPS HAVE MOST IMPACT WHEN DISPLAYED VISUALLYGraphs and charts should be easily understandable to a “nonquantitative clientDisplay one main idea per graphMake the point as directly as possibleDemonstrate clear relevanc

12、e to accompanying material and clients businessClearly label title, axes, and sourcesTailor graph to its audience and purposeExplorationPersuasionDocumentationCHOOSE GRAPH SCALE THOUGHTFULLYMatch chart boundaries to relevant range of the data as closely as possibleSelect scale to facilitate thinking

13、 about proposed relationshipsUse same scale across charts if you intend to compare themLINEAR VS. LOGOn a linear scale, a given difference between two values covers the same distance anywhere on the scaleOn a logarithmic scale, a given ratio of two values covers the same distance anywhere on the sca

14、le124816One CycleLinearLogLogThe ratio of anything to zero is infinite. Zero cannot appear on a log scale.DATA RELATIONSHIP DETERMINES SELECTION OF SCALEThree Scales Most CommonLinearLogLogLinearLinear (usually time)LogLinearSemi-LogLog-LogConstant Rate of ChangeConstant Growth RateConstant “Elastic

15、ityGiven no prior expectation about the form of a relationship, plot it linearlyy = mx + blog y = mx + blog y = mlog x + bWHEN SHOULD A LINEAR GRAPH BE USED?Linear graphs are best when the change in unit terms is of interest, e.g.,Market share over timeProfit margin over timeForty-five degree downwa

16、rd sloping lines on linear graph represent points whose x and y values have constant sumRays through origin represent points with common ratioMarket Share (%)Linear GraphHardwareSoftwareWHEN SHOULD A SEMI-LOG PLOT BE USED?Semi-log graphs are generally used to illustrate constant growth rates, e.g.,V

17、olume of sales growth over timeYearSource: Agricultural StatisticsU.S. Corn Yield (Bushels/ Acre)R=.95Semi-Log GraphWHEN SHOULD A LOG-LOG PLOT BE USED?Log-log graphs are generally used to plot “elasticities, e.g.,Price elasticity of demandScale slopeForty-five degree downward sloping lines show poin

18、ts with common productSalaried and Indirect hourly Employees/ Billion Impressions of CapacityPrinting Capacity (Billions of Impressions)78% Scale SlopeR=.6361,00010010CIRCLE OR BUBBLE CHARTS OFTEN USED TO SHOW A THIRD DIMENSIONThird dimension should be related to x and y axesCommon examples include:

19、Market sizeAssetsCost flowCircle area (not diameter) is proportionalBUBBLE CHART EXAMPLECategory Growth Versus Gross Margin Versus Size1980-84Real CAGR (%)Gross Margin (%)= $1B salesConsumer ElectronicsToysHousewares/GiftsJewelrySportingGoodsSmallAppliancesCamera/PhotoSource: Discount MerchandiserTA

20、BLE OF CONTENTSIntroductionGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale, experience, complexity and utilizationSupply curvesDemand side analysisCustomer understandingsegmentation and “Discoveryconjoint analysism

21、ulti-dimensional scalingPrice-volume curves and elasticityDemand forecastingtechnology/substitution curvesWrap-upDEFLATORS CORRECT EFFECTS OF INFLATIONConverts Variables from “Nominal to “RealTime series data in dollars with high or widely fluctuating inflation rates distort picture of growthDeflati

22、ng data removes some of the distortionUsing a deflator index list, currency data are multiplied by the ratio of the base year deflator index to the data year deflator index, e.g.,1979 sales (1993 $) = 1979 (1979 $) x Deflator 1993Deflator 1979SELECT APPROPRIATE DEFLATOR DEPENDING ONTHE QUESTION YOUR

23、E TRYING TO ANSWERG.N.P. deflator is best for expressing dollars in terms of average real value to the rest of the economyCurrent (variable) weightsMeasured quarterlyC.P.I. is best only for expressing value in relation to consumer spending on a fixed market basket of goods (1973 base)Measured monthl

24、yIndustry or product-specific indices are best for converting dollars into measures of physical outputAvailable from Commerce Dept. for broad industry categoriesCan be constructed from client or industry data for narrow categoriesBE CAREFUL WHEN MIXING EXCHANGERATES AND INFLATION ACROSS COUNTRIESFir

25、st convert each countrys historical data to constant local currencyE.g., Japan1993 yenW. Germany1993 DMU.S.A.1993 dollarsThen convert to single currency (dollars, for example) at fixed exchange rateEXAMPLE: AN INTEGRATED CIRCUIT MANUFACTURERReported SalesG.N.P. DeflatorAverage I.C.Average I.C.Year($

26、M)(1987 = 1.00)Price ($)Transistor Price ()19877861.0001.001.0519885951.033.92.7219897301.075.99.4919908331.119.98.3419911,0621.161.90.2419921,4231.193.98.1819931,8381.2271.14.16Reported sales $15.2%Real sales $11.4%I.C. unit sales8.9%Transistor sales52.4%Growth Rates (per year)TABLE OF CONTENTSIntr

27、oductionGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale, experience, complexity and utilizationSupply curvesDemand side analysisCustomer understandingsegmentation and “Discoveryconjoint analysismulti-dimensional sc

28、alingPrice-volume curves and elasticityDemand forecastingtechnology/substitution curvesWrap-upREGRESSION ANALYSIS IS A POWERFUL TOOL FORUNDERSTANDING RELATIONSHIP BETWEEN TWOOR MORE VARIABLESRegression analysis:Explains variation in one variable (dependent) using variation in one or more other varia

29、bles (independent)Quantifies and validates relationshipsIs useful for prediction and causal explanationBut . . .Must not substitute for clear independent thinking about a problemUse as single element in portfolio of analytical techniquesCan be morass“l(fā)ose forest for treesANY RELATIONSHIP BETWEEN VAR

30、IABLES X AND Y?Used alone, graphical methods provide only qualitative and general inferences about relationshipsPercentACV80%70%60%50%40%30%20%10%0%Annual Number of Purchases by ConsumerX:Annual number of purchases by buyerY:Percent ACVPercent ACV is the volume weighted average percent of grocery st

31、ores which carry the category.Sources: ScanTrack; IRI Marketing Factbook; BCG AnalysisREGRESSION ANALYSIS ANSWERS THESE QUESTIONSWhat is relationship between X and YHow big an effect does X have on Y?What is the functional form?Is effect positive or negative?How strong is relationship?How well does

32、X “explain Y?How well does my model work overall?How well have I explained Y in general?Are there other variables that I should be including?WHAT IS RELATIONSHIP BETWEEN X AND Y?PercentACVAnnual Number of Purchases by CustomerRegression fits a straight line to the data pointsPercent ACV = -0.2790 +

33、0.2606 annual purchasesOne more annual purchase will raise percent ACV by 0.2606 percentage pointsSlope of line (here 0.2606) indicates size of effect; sign of slope (here positive) indicates whether effect is positive or negativeR2 = 0.69Multiple R0.83354R Square (%)69.48Adjusted R Square (%)68.35S

34、tandard Error0.10394Observations29Regression StatisticsRegression10.664000.6640061.4641.98146E-08Residual270.291680.01080Total280.95568Analysis of VariancedfSum of SquaresMean SquareFSignificant FIntercept(0.27901)0.06286(4.439)0.00013(0.40799)(0.15003)X10.260560.033247.8401.5372E-080.192370.32876Co

35、efficientsStandard Errort StatisticP-valueLower 95%Upper 95%Sources: Scantrack; IRI Marketing Factbook (1990); BCG AnalysisMicrosoft Excel Regression OutputHOW STRONG IS RELATIONSHIP?t-statistic measures how well X explains YSimply calculated as slope divided by its standard error Closer slope is to

36、 zero, and/or higher standard error (variability), the weaker the relationshipA short-cut: t-statistic greater in magnitude than 2 means relationship is very strong (i.e., roughly 95% confidence level). Between 1.5 and 2, relationship is relatively strong (i.e., roughly 85-95% confidence level). Und

37、er 1.5, relationship is weak.Multiple R0.83354R Square (%)69.48Adjusted R Square (%)68.35Standard Error0.10394Observations29Regression10.664000.6640061.4641.98146E-08Residual270.291680.01080Total280.95568Regression StatisticsdfSum of SquaresMean SquareFSignificance FIntercept(0.27901)0.06286(4.439)0

38、.00013(0.40799)(0.15003)x10.260560.033247.8401.5372E-080.192370.32876CoefficientsStandard Errort StatisticP-valueLower 95%Upper 95%Analysis of VarianceHOW WELL DOES MY MODEL WORK OVERALL?R2 measures proportion of variation in Y that is explained by the variables in the model - here just XIndicates o

39、verall how well model explains YBased on how dispersed the data points are around the regression lineR2 measured on scale of 0 to 100% 100% indicates perfect fit of regression line to the data pointsLow R2 indicates current model does not fit the data wellsuggests there are other explanatory factors

40、, besides X, that would help explain YMultiple R0.83354R Square (%)69.48Adjusted R Square (%)68.35Standard Error0.10394Observations29Regression10.664000.6640061.4641.98146E-08Residual270.291680.01080Total280.95568Regression StatisticsdfSum of SquaresMean SquareFSignificance FIntercept(0.27901)0.0628

41、6(4.439)0.00013(0.40799)(0.15003)x10.260560.033247.8401.5372E-080.192370.32876CoefficientsStandard Errort StatisticP-valueLower 95%Upper 95%Analysis of VarianceUSE MULTIPLE REGRESSION TO SORT OUT EFFECTSOF SEVERAL INFLUENCESUseWhen several factors have an impact simultaneouslyTo help distinguish cau

42、se from correlationDont use as “fishing expeditionMULTIPLE REGRESSION CAN ENHANCEPREDICTIVE ABILITY% ACV with Features and/or DisplaysBrand SizePercent of Households BuyingAnnual Number of Purchases per Year% ACV with Features and/or Displays% ACV with Features and/or DisplaysBrand Size ($M)Percent

43、of Households BuyingAnnual Number of Purchases/YearR=.67R=.51R=.69R=.87Predicted % ACV with Features and/or DisplaysActual % ACV with Features and/or DisplaysBrand Size, Reach, andPurchase FreqencySources: Scantrack; IRI Marketing Factbook 1990; BCG AnalysisOTHER REGRESSION EXAMPLESVery Low R*Percen

44、tACVU.S. Corn Yield (Bushels/ Acre)U.S. Corn Yield (Bushels/ Acre)Retailer Margin on DealAverage Number of Days on DealTotal Annual Purchases (M)Negative Slope*Nonlinear Raw Data*After Log Transformation*Sources: IRI Marketing Factbook; Certified Price Book; Nielsen; BCG Analysis*Source: Agricultura

45、l StatisticsR=.64R=.002R=.95QUESTIONS TO ASK BEFORE RUNNING A REGRESSIONWhich variable is the predictive (or dependent) variable?Often straightforward but sometimes requires thoughtConsider direction of causationWhat explanatory variables do I believe are appropriate to include?Avoid spurious correl

46、ationsthink independently about what factors are logical to includeAvoid including explanatory variables that are highly correlated with each otherShould the regression have an intercept term?How far can the data be reasonably extrapolated?Should the regression line cut through the origin?Does a zer

47、o value of explanatory variable imply a zero value for predictive variable?Have I plotted the data?Watch out for outliersLook for form of data (linear, exponential, power, etc.)Do I have enough observations?Rough rule of thumb: 10 observations for each explanatory variableTABLE OF CONTENTSIntroducti

48、onGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale, experience, complexity and utilizationSupply curvesDemand side analysisCustomer understandingsegmentation and “Discoveryconjoint analysismulti-dimensional scalingP

49、rice-volume curves and elasticityDemand forecastingtechnology/substitution curvesWrap-upDefine relevant competitive environmentBasis of advantage (profit levers)Relative strengths/weaknesses of competitorsBarrier to new competitorsEffect of changes over time (technology, scale)Predict effect of one

50、firms actions onCompetitors (short term, reaction)Profit and cash flow of clientNotCost systemsCorrecting average costing for its own sakeWHY DO COST ANALYSIS?WHICH COSTS?Competitive cost analysisUse actual costs, not standardsUse fully absorbed costs, since expenses are often the most sensitive to

51、scale/experience, etc.Identify costs and expenses with individual models/product linesTherefore, competitive cost analysis involvesAllocation of variancesAllocation of expensesCapitalization of nonrecurring costs and expensesIN MOST SUPPLY SIDE ANALYSIS, FIRSTLAY OUT THE CLIENTS COST STRUCTUREFocus

52、on Key Cost ElementsProfitOverheadSelling and DistributionVariable ManufacturingRaw MaterialsFixed Manufacturing8%8%16%18%40%10%8%10%35%11%18%18%GainRaw materialsSelling and distributionAdvantageBackward integrationRelated diversification to further Throughuse sales force?Purchasing scaleSales focus

53、, toolsCOST DATA CAN BE FOUND IN CLIENTACCOUNTING SYSTEMS . . .Client accounting systems good forControl/audit of short-term evolutionNot for strategic analysisGenerally broken down by type of costDirectIndirectOverheadsEmphasis is on efficiency, not on understanding long-term cost dynamics as a fun

54、ction of scale, run length, etc. . . BUT OFTEN REQUIRES RECASTINGMaterials30Manufacturing costs40Direct15Indirect10Overheads15Commercial costs30Variable10Fixed20Total cost100Materials30Manufacturing costs40Metalworking15Painting8Assembly12Overheads5Distribution costs7Logistics5Warehousing2Selling co

55、sts9Salesmen6After-sales3 serviceMarketing costs10Advertising3Overheads7G&A4Total cost100Accounting SystemStrategic Cost ElementsMANY VARIABLES AFFECT COSTSMaterialsVolumeLocation of suppliersDesignManufacturingPlant outputTechnologyExperienceDesignRun lengthComplexityFactor costsLogisticsVolumeDrop

56、 sizeSellingVolumeNumber of outletsMarketingVolumeVolume/brandTABLE OF CONTENTSIntroductionGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale, experience, complexity and utilizationSupply curvesDemand side analysisCus

57、tomer understandingsegmentation and “Discoveryconjoint analysismulti-dimensional scalingPrice-volume curves and elasticityDemand forecastingtechnology/substitution curvesWrap-upDESIGN DIFFERENCES CAN BE A MAJOR DRIVEROF PRODUCT COST DIFFERENCESAffect raw material costs as well as manufacturing value

58、 addedUsually requires a “teardown of competitor products to understand real differencesRequires client involvementdesign engineersmanufacturing engineerspurchasing agentsFIRST STEP IS TO IDENTIFY DESIGN DIFFERENCES - 1Example: Design AnalysisTorque Converters29 blades, .77mm thickE-beam weld hub to

59、 shellRoll tabbed18 bladesDie castingRoller clutch2 needle thrust bearing31 bladeslonger and thinnerRoll tabbed and stakedHub part of stamping.82mm8 springs4 big, 4 medium (nested)Close to center3 lugs welded245 MM23.0 lbs.27 blades, .82mm thickRivet hub to shell (10 rivets)Roll tabbed15 bladesPlast

60、icRoller clutch31 bladesshorter and fatterRoll tabbedHub part of stamping1.04mm12 springsAttached directly to cover4 studs welded235 MM22.8 lbs.Misc DataTurbineStatorPumpDamperCoverModel AModel BDesign differences translate into cost differencesFIRST STEP IS TO IDENTIFY DESIGN DIFFERENCES - 2Example