1、McGraw-Hill/IrwinCopyright 2010 by The McGraw-Hill Companies,Inc.All rights reserved.3-2List the elements of a good forecast.Outline the steps in the forecasting process.Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each.Compare and contrast quali
2、tative and quantitative approaches to forecasting.3-3Briefly describe averaging techniques,trend and seasonal techniques,and regression analysis,and solve typical problems.Describe two measures of forecast accuracy.Describe two ways of evaluating and controlling forecasts.Identify the major factors
3、to consider when choosing a forecasting technique.3-4FORECAST:A statement about the future value of a variable of interest such as demand.Forecasting is used to make informed decisions.Long-range Short-range3-5 Forecasts affect decisions and activities throughout an organizationAccounting,financeHum
4、an resourcesMarketingMISOperationsProduct/service design3-6AccountingCost/profit estimatesFinanceCash flow and fundingHuman resourcesHiring/recruiting/trainingMarketingPricing,promotion,strategyMISIT/IS systems,servicesOperationsSchedules,MRP,workloadsProduct/service designNew products and servicesI
5、 see that you willget an A this semester.3-7 Assumes causal systempast=future Forecasts rarely perfect because of randomness Forecasts more accurate forgroups vs.individuals Forecast accuracy decreases as time horizon increases3-8TimelyAccurateReliableMeaningfulWrittenEasy to use3-9Step 1 Determine
6、purpose of forecastStep 2 Establish a time horizonStep 3 Select a forecasting techniqueStep 4 Obtain,clean and analyze dataStep 5 Make the forecastStep 6 Monitor the forecast“The forecast”3-10 Judgmental:uses subjective inputs Time series:uses historical data,assuming the future will be like the pas
7、t Associative models:uses explanatory variables to predict the future3-11 Executive opinions Sales force opinions Consumer surveys Outside opinion Delphi methodOpinions of managers and staffAchieves a consensus forecast3-12 Trend:long-term movement in data Seasonality:short-term regular variations i
8、n data Cycles:wavelike variations of more than one years duration Irregular variations:caused by unusual circumstances Random variations:caused by chance3-13TrendIrregularvariationSeasonal variations908988Figure 3.1Cycles3-14Uh,give me a minute.We sold 250 wheels lastweek.Now,next week we should sel
9、l.The forecast for any period equals the previous periods actual value.3-15 Simple to use Virtually no cost Quick and easy to prepare Data analysis is nonexistent Easily understandable Cannot provide high accuracy Can be a standard for accuracy3-16 Stable time series dataF(t)=A(t-1)Seasonal variatio
10、nsF(t)=A(t-n)Data with trendsF(t)=A(t-1)+(A(t-1)A(t-2)3-17 Moving average Weighted moving average Exponential smoothing3-18 Moving average:A technique that averages a number of recent actual values,updated as new values become available.Weighted moving average:More recent values in a series are give
11、n more weight in computing the forecast.Ft=MAn=nAt-n+At-2+At-1Ft=WMAn=nwnAt-n+wn-1At-2+w1At-13-19ActualMA3MA5Ft=MAn=nAt-n+At-2+At-13-20 Premise:The most recent observations might have the highest predictive value.Therefore,we should give more weight to the more recent time periods when forecasting.F
12、t=Ft-1+(At-1-Ft-1)3-21 Weighted averaging method based on previous forecast plus a percentage of the forecast error A-F is the error term,is the%feedbackFt=Ft-1+(At-1-Ft-1)3-22PeriodActualAlpha=0.1 ErrorAlpha=0.4 Error14224042-2.0042-234341.81.2041.21.844041.92-1.9241.92-1.9254141.73-0.7341.15-0.156
13、3941.66-2.6641.09-2.0974641.394.6140.255.7584441.852.1542.551.4594542.072.9343.131.87103842.36-4.3643.88-5.88114041.92-1.9241.53-1.531241.7340.923-233540455012345678910 11 12PeriodDemand .1 .4Actual3-24ParabolicExponentialGrowthFigure 3.53-25 Ft=Forecast for period t t=Specified number of time perio
14、ds a=Value of Ft at t=0 b=Slope of the lineFt=a+bt0 1 2 3 4 5 tFt3-26b=n(ty)-tynt2-(t)2a=y-btn3-27tyWeekt2Salesty111501502415731439162486416166664525177885 t=15 t2 =55 y=812 ty=2499(t)2 =2253-28y=143.5+6.3t a=812-6.3(15)5=b=5(2499)-15(812)5(55)-225=12495-12180275-225=6.3143.5 3-29 Seasonal variation
15、sRegularly repeating movements in series values that can be tied to recurring events Seasonal relativePercentage of average or trend Centered moving averageA moving average positioned at the center of the data that were used to compute it3-30 Predictor variables:used to predict values of variable in
16、terest Regression:technique for fitting a line to a set of points Least squares line:minimizes sum of squared deviations around the line3-31A straight line is fitted to a set of sample points.010203040500510152025XY7152106134151425152716241220142720441534717Computedrelationship3-32 Variations around
17、 the line are random Deviations around the line normally distributed Predictions are being made only within the range of observed values For best results:Always plot the data to verify linearityCheck for data being time-dependentSmall correlation may imply that other variables are important3-33 Erro
18、r:difference between actual value and predicted value Mean Absolute Deviation(MAD)Average absolute error Mean Squared Error(MSE)Average of squared error Mean Absolute Percent Error(MAPE)Average absolute percent error3-34MAD=ActualforecastnMSE=Actualforecast)-12n(MAPE=Actualforecastn/Actual*100)(3-35
19、 MADEasy to computeWeights errors linearly MSESquares errorMore weight to large errors MAPEPuts errors in perspective3-36PeriodActualForecast(A-F)|A-F|(A-F)2(|A-F|/Actual)*10012172152240.922213216-3391.4132162151110.464210214-44161.9052132112240.94621921455252.287216217-1110.468212216-44161.89-22276
20、10.26MAD=2.75MSE=10.86MAPE=1.283-37 Control chartA visual tool for monitoring forecast errorsUsed to detect non-randomness in errors Forecasting errors are in control ifAll errors are within the control limitsNo patterns,such as trends or cycles,are present3-38 Model may be inadequate Irregular vari
21、ations Incorrect use of forecasting technique3-39Tracking signal=(Actual-forecast)MAD Tracking signalRatio of cumulative error to MADBias:Persistent tendency for forecasts to begreater or less than actual values.3-40 No single technique works in every situation Two most important factorsCostAccuracy
22、 Other factors include the availability of:Historical dataComputersTime needed to gather and analyze the dataForecast horizon3-41 Forecasts are the basis for many decisions Work to improve short-term forecasts Accurate short-term forecasts improveProfitsLower inventory levelsReduce inventory shortagesImprove customer service levelsEnhance forecasting credibility3-42 Sharing forecasts with supply canImprove forecast quality in the supply chainLower costsShorter lead times3-433-443-45
