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1、 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-1Operations ManagementForecastingChapter 4 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-2What is Forecasting?Process of predicting a future eventUnderlying basis of all business decisionsProductionInventoryPersonnelFacilitiesSales
2、 will be$200 Million!2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-3Types of ForecastsEconomic forecastsAddress business cycle,e.g.,inflation rate,money supply etc.Technological forecastsPredict rate of technological progressPredict acceptance of new productDemand forecastsPredict sales o
3、f existing product 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-4Short-range forecastUp to 1 year;usually less than 3 monthsJob scheduling,worker assignmentsMedium-range forecast3 months to 3 yearsSales&production planning,purchasing,budgetingLong-range forecast3+yearsNew product plannin
4、g,facility locationTypes of Forecasts by Time Horizon 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-5What Do We Forecast-AggregationClustering goods or services that have similar demand requirements and common processing,labor,and materials requirements:Red shirtsWhite shirtsBlue shirtsBi
5、g MacQuarter PounderRegular HamburgerShirts Pounds of Beef$Why do we aggregate?What about units of measurement?2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-6Realities of ForecastingForecasts are seldom perfectMost forecasting methods assume that there is some underlying stability in the
6、systemBoth product family and aggregated product forecasts are more accurate than individual product forecasts 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-7Persistent,overall upward or downward patternDue to population,technology etc.Several years duration Trend ComponentTimeResponse 20
7、04 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-8Regular pattern of up&down fluctuationsDue to weather,customs etc.TimeResponseSeasonal ComponentSummer 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-9Repeating up&down movementsDue to interactions of factors influencing economyUsuall
8、y 2-10 years duration TimeTimeResponseResponseCyclical ComponentCycle 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-10Product DemandYear1Year2Year3Year4Actual demand lineDemand for product or serviceSeasonal peaksTrend componentAverage demand over four yearsRandom variation 2004 by Prenti
9、ce Hall,Inc.,Upper Saddle River,N.J.074584-11Forecasting ApproachesUsed when situation is stable&historical data existExisting productsCurrent technologyInvolves mathematical techniquese.g.,forecasting sales of color televisionsQuantitative MethodsUsed when situation is vague&little data existNew pr
10、oductsNew technologyInvolves intuition,experiencee.g.,forecasting sales on InternetQualitative Methods 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-12Overview of Qualitative MethodsJury of executive opinionPool opinions of high-level executives,sometimes augmented by statistical modelsDe
11、lphi methodPanel of experts,queried iterativelySales force compositeEstimates from individual salespersons are reviewed for reasonableness,then aggregated Consumer Market SurveyAsk the customer 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-13Overview of Quantitative ApproachesNave approac
12、hMoving averagesExponential smoothingTrend projectionSeasonal variationLinear regressionTime-series ModelsAssociative Models 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-14Set of evenly spaced numerical data Observing the response variable at regular time intervalsForecast based only on
13、past values Assumes that factors influencing the past and present will continue to influence the futureExample Year:19992000200120022003 What is a Time Series?2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-15Naive ApproachAssumes demand in next period is the same as demand in most recent p
14、eriode.g.,If May sales were 48,then June sales will be 48Sometimes cost effective&efficient 1995 Corel Corp.2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-16ForecastForecast nnDemand in Previous Demand in Previous PeriodsPeriodsSimple Moving AverageF =A +A +A +AF =A +A +A t t1 t2 t3 t4 t t
15、1 t2 t3 43 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-17ForecastForecast =(Weight for period n)(Demand in period n)(Weight for period n)(Demand in period n)Weights WeightsWeighted Moving AverageF =.4A +.3A +.2A +.1AF =.7A +.2A +.1A t t1 t2 t3 t4 t t1 t2 t3 2004 by Prentice Hall,Inc.,Up
16、per Saddle River,N.J.074584-18Form of weighted moving averageWeights decline exponentiallyMost recent data weighted mostRequires smoothing constant()Ranges from 0 to 1Subjectively chosenInvolves little record keeping of past dataExponential Smoothing Method 2004 by Prentice Hall,Inc.,Upper Saddle Ri
17、ver,N.J.074584-19Exponential Smoothing F =A +(1 )(F )=A +F F =F +(A F )t t1 t1 t1 t1 t1t1 t1 t1Forecast =(Demand last period)+(1 )(Last forecast)Forecast =Last forecast +(Last demand Last forecast)2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-20Tt =(Forecast this period Forecast last peri
18、od)+(1-)(Trend estimate last period)=(Ft-Ft-1)+(1-)Tt-1 Exponential Smoothing with Trend AdjustmentForecast =Exponentially smoothed forecast(F)+Exponentially smoothed trend(T )tt 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-21Seasonal VariationQuarterYear 1Year 2Year 3Year 41457010010023
19、35370585725352059083011604100170285215 Total1000120018002200 Average250300450550Seasonal Index=Actual DemandAverage Demand=0.1845250Forecast for Year 5=2600 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-22 Quarter Year 1 Year 2 Year 3 Year 4145/250=0.1870/300=0.23100/450=0.22100/550=0.182
20、335/250=1.34370/300=1.23585/450=1.30725/550=1.323520/250=2.08590/300=1.97830/450=1.841160/550=2.114100/250=0.40170/300=0.57285/450=0.63215/550=0.39QuarterAverage Seasonal Index1(0.18+0.23+0.22+0.18)/4=0.202(1.34+1.23+1.30+1.32)/4=1.303(2.08+1.97+1.84+2.11)/4=2.004(0.40+0.57+0.63+0.39)/4=0.50Seasonal
21、 Variation Forecast 650(0.20)=130650(1.30)=845650(2.00)=1300650(0.50)=325 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-23Overview of Quantitative ApproachesNave approachMoving averagesExponential smoothingTrend projectionSeasonal variationLinear regressionTime-series ModelsAssociative Mo
22、dels 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-24Linear RegressionIndependent Dependent Variables VariableFactors Associated with Our Sales Advertising Pricing Competitors Economy WeatherSales 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-25Scatter DiagramSales vs.Payroll01
23、234012345678Area Payroll(in$hundreds of millions)Sales(in$hundreds of thousands)Regression Line Now What?2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-26Short-range forecastUp to 1 year;usually less than 3 monthsJob scheduling,worker assignmentsMedium-range forecast3 months to 3 yearsSale
24、s&production planning,budgetingLong-range forecast3+yearsNew product planning,facility locationTypes of Forecasts by Time HorizonTime SeriesAssociativeQualitative 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-27Forecast Error 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-28Fore
25、cast Error+5 3E =A F t t t 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-29Forecast Error-CFECFE=EtCFE Cumulative sum of Forecast ErrorsPositive errors offset negative errorsUseful in assessing bias in a forecast 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-30Forecast Error-MS
26、EMSE Mean Squared ErrorAccentuates large deviationsMSE =Etn2 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-31Forecast Error-MAD|Et|n MAD=MAD Mean Absolute DeviationWidely used,well understood measurement of forecast error 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-32Forecast
27、 Error-MAPEMAPE=100|Et|/AtnMAPE Mean Absolute Percent ErrorRelates forecast error to the level of demand 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-33Forecast ErrorEt =At FtCFE =Et100|Et|/AtnMAPE=|Et|nMAD =MSE =Et n2 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-34Monitoring
28、&Controlling ForecastsWe need a TRACKING SIGNAL to measure how well the forecast is predicting actual valuesTS =Running sum of forecast errors(CFE)Mean Absolute Deviation(MAD)=E|E|/ntt 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-35Plot of a Tracking SignalTimeLower control limitUpper co
29、ntrol limitSignal exceeded limitTracking signal CFE/MADAcceptable range+0-2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-36Forecasting in the Service SectorPresents unusual challengesspecial need for short term recordsneeds differ greatly as function of industry and productissues of holida
30、ys and calendarunusual events 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-37Forecast of Sales by Hour for Fast Food Restaurant11-12 12-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 2004 by Prentice Hall,Inc.,Upper Saddle River,N.J.074584-38Summary Demand forecasts drive a firms plans-Pro
31、duction-Capacity-Scheduling Need to find the forecasting method(s)that best fit our pattern of demand no one right tool-Qualitative methods e.g.customer surveys-Time series methods(quantitative)rely on historical demand to predict future demand-Associative models(quantitative)use historical data on independent variables to predict demand e.g.promotional campaign Track forecast error to determine if forecasting model requires change