3Learning ObjectivesSlide 3Slide 4ForecastsUses of ForecastsFeatures of ForecastsElements of a Good ForecastSteps in the Forecasting ProcessTypes of ForecastsJudgmental ForecastsTime Series ForecastsForecast VariationsNaive ForecastsNaïve ForecastsUses for Naïve ForecastsTechniques for AveragingMoving AveragesSimple Moving AverageExponential SmoothingSlide 21Slide 22Picking a Smoothing ConstantCommon Nonlinear TrendsLinear Trend EquationCalculating a and bLinear Trend Equation ExampleLinear Trend CalculationTechniques for SeasonalityAssociative ForecastingLinear Model Seems ReasonableLinear Regression AssumptionsForecast AccuracyMAD, MSE, and MAPEMAD, MSE and MAPEExample 10Controlling the ForecastSources of Forecast errorsTracking SignalChoosing a Forecasting TechniqueOperations StrategySupply Chain ForecastsSlide 43Slide 44Slide 45McGraw-Hill/IrwinCopyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved.33Forecasting3-2Learning ObjectivesLearning ObjectivesList 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 qualitative and quantitative approaches to forecasting.3-3Learning ObjectivesLearning ObjectivesBriefly 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 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-range3-5ForecastsForecastsForecasts affect decisions and activities throughout an organizationAccounting, financeHuman resourcesMarketingMISOperationsProduct / service design3-6Accounting Cost/profit estimatesFinance Cash flow and fundingHuman Resources Hiring/recruiting/trainingMarketing Pricing, promotion, strategyMIS IT/IS systems, servicesOperations Schedules, MRP, workloadsProduct/service design New products and servicesUses of ForecastsUses of Forecasts3-7Assumes causal systempast ==> futureForecasts rarely perfect because of randomnessForecasts more accurate forgroups vs. individualsForecast accuracy decreases as time horizon increasesI see that you willget an A this semester.Features of ForecastsFeatures of Forecasts3-8Elements of a Good ForecastElements of a Good ForecastTimelyAccurateReliableMeaningfulWrittenEasy to use3-9Steps in the Forecasting ProcessSteps in the Forecasting ProcessStep 1 Determine 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”3-10Types of ForecastsTypes of ForecastsJudgmental - uses subjective inputsTime series - uses historical data assuming the future will be like the pastAssociative models - uses explanatory variables to predict the future3-11Judgmental ForecastsJudgmental ForecastsExecutive opinionsSales force opinionsConsumer surveysOutside opinionDelphi methodOpinions of managers and staffAchieves a consensus forecast3-12Time Series ForecastsTime Series ForecastsTrend - long-term movement in dataSeasonality - short-term regular variations in dataCycle – wavelike variations of more than one year’s durationIrregular variations - caused by unusual circumstancesRandom variations - caused by chance3-13Forecast VariationsForecast VariationsTrendIrregularvariationSeasonal variations908988Figure 3.1Cycles3-14Naive ForecastsNaive ForecastsUh, give me a minute.... We sold 250 wheels lastweek.... Now, next week we should sell....The forecast for any period equals the previous period’s 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 accuracyNaïve ForecastsNaïve Forecasts3-16Stable time series dataF(t) = A(t-1)Seasonal variationsF(t) = A(t-n)Data with trendsF(t) = A(t-1) + (A(t-1) – A(t-2))Uses for Naïve ForecastsUses for Naïve Forecasts3-17Techniques for AveragingTechniques for AveragingMoving averageWeighted moving averageExponential smoothing3-18Moving AveragesMoving Averages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 given more weight in computing the forecast.Ft = MAn= nAt-n + … At-2 + At-1Ft = WMAn= nwnAt-n + … wn-1At-2 + w1At-13-19Simple Moving AverageSimple Moving AverageActualMA3MA5Ft = MAn= nAt-n + … At-2 + At-13-20Exponential SmoothingExponential Smoothing•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.Ft = Ft-1 + (At-1 - Ft-1)3-21Exponential SmoothingExponential Smoothing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)3-22Period Actual Alpha = 0.1 Error Alpha = 0.4 Error1 422 40 42 -2.00 42 -23 43 41.8 1.20 41.2 1.84 40 41.92 -1.92 41.92 -1.925 41 41.73 -0.73 41.15 -0.156 39 41.66 -2.66 41.09 -2.097 46 41.39 4.61 40.25 5.758 44 41.85 2.15 42.55 1.459 45 42.07 2.93 43.13 1.8710 38 42.36 -4.36 43.88 -5.8811 40 41.92 -1.92 41.53 -1.5312 41.73 40.92Example 3 - Exponential SmoothingExample 3 - Exponential Smoothing3-23Picking a Smoothing ConstantPicking a Smoothing Constant354045501 2 3 4 5 6 7 8 9 10 11 12PeriodDemand.1.4Actual3-24Common Nonlinear TrendsCommon Nonlinear TrendsParabolicExponentialGrowthFigure 3.53-25Linear Trend EquationLinear Trend EquationFt = Forecast for period tt = Specified number of time periodsa = Value of Ft at t = 0b = Slope of the lineFt = a + bt0 1 2 3 4 5 tFt3-26Calculating a and bCalculating a and bb = n (ty) - t yn t2 - ( t)2a = y - b tn3-27Linear Trend Equation ExampleLinear Trend Equation Examplet yW e e k t2S a l e s t y1 1 1 5 0 1 5 02 4 1 5 7 3 1 43 9 1 6 2 4 8 64 1 6 1 6 6 6 6 45 2 5 1 7