介绍oeraionmanagemen项目管理

3Forecasting3-2LearningObjectivesListtheelementsofagoodforecast.Outlinethestepsintheforecastingprocess.Describeatleastthreequalitativeforecastingtechniquesandtheadvantagesanddisadvantagesofeach.Compareandcontrastqualitativeandquantitativeapproachestoforecasting.3-3LearningObjectivesBrieflydescribeaveragingtechniques,trendandseasonaltechniques,andregressionanalysis,andsolvetypicalproblems.Describetwomeasuresofforecastaccuracy.Describetwowaysofevaluatingandcontrollingforecasts.Identifythemajorfactorstoconsiderwhenchoosingaforecastingtechnique.3-4FORECAST:Astatementaboutthefuturevalueofavariableofinterestsuchasdemand.Forecastingisusedtomakeinformeddecisions.Long-rangeShort-range3-5ForecastsForecastsaffectdecisionsandactivitiesthroughoutanorganizationAccounting,financeHumanresourcesMarketingMISOperationsProduct/servicedesign3-6AccountingCost/profitestimatesFinanceCashflowandfundingHumanresourcesHiring/recruiting/trainingMarketingPricing,promotion,strategyMISIT/ISsystems,servicesOperationsSchedules,MRP,workloadsProduct/servicedesignNewproductsandservicesUsesofForecastsIseethatyouwill

getanAthissemester.3-7Assumescausalsystem

past==>futureForecastsrarelyperfectbecauseofrandomnessForecastsmoreaccuratefor

groupsvs.individualsForecastaccuracydecreases

astimehorizonincreasesFeaturesofForecasts3-8ElementsofaGoodForecastTimelyAccurateReliableMeaningfulWrittenEasytouse3-9StepsintheForecastingProcessStep1DeterminepurposeofforecastStep2EstablishatimehorizonStep3SelectaforecastingtechniqueStep4Obtain,cleanandanalyzedataStep5MaketheforecastStep6Monitortheforecast“Theforecast”3-10TypesofForecastsJudgmental:usessubjectiveinputsTimeseries:

useshistoricaldata,assumingthefuturewillbelikethepastAssociativemodels:

usesexplanatoryvariablestopredictthefuture3-11JudgmentalForecastsExecutiveopinionsSalesforceopinionsConsumersurveysOutsideopinionDelphimethodOpinionsofmanagersandstaffAchievesaconsensusforecast3-12TimeSeriesForecastsTrend:long-termmovementindataSeasonality:short-termregularvariationsindataCycles:wavelikevariationsofmorethanoneyear’sdurationIrregularvariations:causedbyunusualcircumstancesRandomvariations:causedbychance3-13ForecastVariationsTrendIrregular

variationSeasonalvariations908988Figure3.1Cycles3-14NaiveForecastsUh,givemeaminute....Wesold250wheelslastweek....Now,nextweekweshouldsell....Theforecastforanyperiodequalsthepreviousperiod’sactualvalue.3-15SimpletouseVirtuallynocostQuickandeasytoprepareDataanalysisisnonexistentEasilyunderstandableCannotprovidehighaccuracyCanbeastandardforaccuracyNaiveForecasts3-16StabletimeseriesdataF(t)=A(t-1)SeasonalvariationsF(t)=A(t-n)DatawithtrendsF(t)=A(t-1)+(A(t-1)––A(t-2))UsesofNaiveForecasts3-17TechniquesforAveragingMovingaverageWeightedmovingaverageExponentialsmoothing3-18MovingAveragesMovingaverage:Atechniquethataveragesanumberofrecentactualvalues,updatedasnewvaluesbecomeavailable.Weightedmovingaverage:Morerecentvaluesinaseriesaregivenmoreweightincomputingtheforecast.Ft=MAn=

nAt-n

+…At-2+At-1Ft=WMAn=

nwnAt-n

+…wn-1At-2+w1At-13-19SimpleMovingAverageActualMA3MA5Ft=MAn=

nAt-n

+…At-2+At-13-20ExponentialSmoothingPremise:Themostrecentobservationsmighthavethehighestpredictivevalue.Therefore,weshouldgivemoreweighttothemorerecenttimeperiodswhenforecasting.Ft=Ft-1+(At-1-Ft-1)3-21ExponentialSmoothingWeightedaveragingmethodbasedonpreviousforecastplusapercentageoftheforecasterrorA-Fistheerrorterm,isthe%feedbackFt=Ft-1+(At-1-Ft-1)3-22Example3:ExponentialSmoothing3-23PickingaSmoothingConstant.1.4Actual3-24CommonNonlinearTrendsParabolicExponentialGrowthFigure3.53-25LinearTrendEquationFt=Forecastforperiodtt=Specifiednumberoftimeperiodsa=ValueofFtatt=0b=SlopeofthelineFt=a+bt012345tFt3-26Calculatingaandbb=

n(ty)-

tynt2

-(t)2a=

y-btn3-27LinearTrendEquationExample3-28LinearTrendCalculationy=143.5+6.3ta=

812-6.3(15)5

=b=

5(2499)-15(812)5(55)-225

=

12495-12180275-225

=

6.3143.5

3-29TechniquesforSeasonalitySeasonalvariationsRegularlyrepeatingmovementsinseriesvaluesthatcanbetiedtorecurringeventsSeasonalrelativePercentageofaverageortrendCenteredmovingaverageAmovingaveragepositionedatthecenterofthedatathatwereusedtocomputeit3-30AssociativeForecastingPredictorvariables:usedtopredictvaluesofvariableinterestRegression:techniqueforfittingalinetoasetofpointsLeastsquaresline:minimizessumofsquareddeviationsaroundtheline3-31LinearModelSeemsReasonableAstraightlineisfittedtoasetofsamplepoints.Computed

relationship3-32LinearRegressionAssumptionsVariationsaroundthelinearerandomDeviationsaroundthelinenormallydistributedPredictionsarebeingmadeonlywithintherangeofobservedvaluesForbestresults:AlwaysplotthedatatoverifylinearityCheckfordatabeingtime-dependentSmallcorrelationmayimplythatothervariablesareimportant3-33ForecastAccuracyError:differencebetweenactualvalueandpredictedvalueMeanAbsoluteDeviation(MAD)AverageabsoluteerrorMeanSquaredError(MSE)AverageofsquarederrorMeanAbsolutePercentError(MAPE)Averageabsolutepercenterror3-34MAD,MSE,andMAPEMAD=ActualforecastnMSE=

Actualforecast)-12n(MAPE=

Actualforecastn/Actual*100)(3-35MAD,MSE,andMAPEMADEasytocomputeWeightserrorslinearlyMSESquareserrorMoreweighttolargeerrorsMAPEPutserrorsinperspective3-36Example103-37ControllingtheForecastControlchartAvisualtoolformonitoringforecasterrorsUsedtodetectnon-randomnessinerrorsForecastingerrorsareincontrolifAllerrorsarewithinthecontrollimitsNopatterns,suchastrendsorcycles,arepresent3-38SourcesofForecastErrorsModelmaybeinadequateIrregularvariationsIncorrectuseofforecastingtechnique3-39TrackingSignalTrackingsignal=

(Actual-forecast)MADTrackingsignalRatioofcumulativeerrortoMADBias:Persistenttend