Chapter 3 ForecastingCar buyer- Models & OptionDoes the dealer know!

Basic Managerial function- PlanningAdeyl Khan, Faculty, BBA, NSUForecastA statement about the future value of a variable of interest such as demand.Forecasting is used to make informed decisions.Long-range (Plan system)Short-range (Plan use of system)3-2TrafficWeatherI see that you willget an A this semester.Adeyl Khan, Faculty, BBA, NSUUses of ForecastsAccountingCost/profit estimatesFinanceCash flow and fundingHuman ResourcesHiring/recruiting/trainingMarketingPricing, promotion, strategyMISIT/IS systems, servicesOperationsSchedules, MRP, workloadsProduct/service designNew products and services3-3Example!Adeyl Khan, Faculty, BBA, NSU3-4Elements of a Good ForecastAdeyl Khan, Faculty, BBA, NSUSteps in the Forecasting ProcessAdeyl Khan, Faculty, BBA, NSUTypes of ForecastQualitativeJudgmentalQuantitativeTime seriesAssociative models3-6

Adeyl Khan, Faculty, BBA, NSU1. Judgmental ForecastsExecutive opinionsSales force opinionsConsumer surveysOutside opinion

3-7SubjectiveQualitative inputsAdeyl Khan, Faculty, BBA, NSU72. Time Series ForecastsTrend - long-term movement in dataSeasonality - short-term regular variations in dataSpecific dates, days, timesCycle wavelike variations of more than one years durationEconomic, political, GDP 3-8Uses historical dataAdeyl Khan, Faculty, BBA, NSUForecast Variations3-9Figure 3.1TrendIrregularvariation=SLOPE(B5:B66,A5:A66)=INTERCEPT(B5:B66,A5:A66)

CyclesSeasonal variations908988Adeyl Khan, Faculty, BBA, NSU2. Time Series Forecasts Naive Forecasts

3-10How many wheel are you going to make this week?The forecast for any period equals the previous periods actual value.

Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell....Adeyl Khan, Faculty, BBA, NSUNave ForecastsSimple to useVirtually no costQuick and easy to prepareData analysis is nonexistentEasily understandable

Cannot provide high accuracyCan be a standard for accuracy3-11Adeyl Khan, Faculty, BBA, NSUUses for Nave ForecastsStable time series dataF(t) = A(t-1) | Forecast(Feb) = Actual (Feb -1=Jan)Seasonal variationsF(t) = A(t-n) | Forecast(July) = Actual (July -4=Mar)Data with trendsF(t) = A(t-1) + (A(t-1) A(t-2))3-12TrendIrregularvariation

Seasonal variations908988Adeyl Khan, Faculty, BBA, NSUTechniques for AveragingMoving averageWeighted moving averageExponential smoothing3-13

Adeyl Khan, Faculty, BBA, NSU2. Time Series Forecasts Moving AveragesMoving 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.3-14Ft = MAn= nAt-n + At-2 + At-1Ft = WMAn= wnwnAt-n + wn-1At-2 + w1At-1

Adeyl Khan, Faculty, BBA, NSUFt = MAn= nAt-n + At-2 + At-1Ft = MA3= 3At-3 + At-2 + At-1Ft = MA5= 5At-5 + At-4 + At-3 + At-2 + At-1FDec Adeyl Khan, Faculty, BBA, NSUSimple Moving Average3-16

ActualMA3MA5Ft = MAn= nAt-n + At-2 + At-1

Adeyl Khan, Faculty, BBA, NSUExponential SmoothingPremise--The most recent observations might have the highest predictive value.Therefore, we should give more weight to the more recent time periods when forecasting.

Weighted averaging method based on previous forecast plus a percentage of the forecast errorA-F is the error term, is the % feedback

3-17Ft = Ft-1 + (At-1 - Ft-1)Adeyl Khan, Faculty, BBA, NSUExample 3 - Exponential Smoothing

3-18

Ft = Ft-1 + (At-1 - Ft-1)Adeyl Khan, Faculty, BBA, NSUPicking a Smoothing Constant3-19

.1.4Actual

Adeyl Khan, Faculty, BBA, NSUTime series forecastingCommon Nonlinear Trends3-20ParabolicExponentialGrowthAdeyl Khan, Faculty, BBA, NSULinear Trend EquationFt = Forecast for period tt = Specified number of time periodsa = Value of Ft at t = 0b = Slope of the line3-21Ft = a + bt0 1 2 3 4 5 tFtAdeyl Khan, Faculty, BBA, NSUCalculating a and b3-22b = n (ty) - tynt2 - (t)2a = y - btnAdeyl Khan, Faculty, BBA, NSULinear Trend Equation Example3-23tWeekt2ySalesty111501502415731439162486416166664525177885S t = 15S t2 = 55S y = 812S ty = 2499(S t)2 = 225Adeyl Khan, Faculty, BBA, NSULinear Trend Calculation3-24Ft= y = 143.5 + 6.3t

Ft=10= ???a = 812 - 6.3(15)5= 143.5b = 5 (2499) - 15(812) 5(55) - 225=12495 - 12180275 - 225= 6.3Adeyl Khan, Faculty, BBA, NSUTechniques for SeasonalitySeasonal variationsRegularly repeating movements in series values that can be tied to recurring events.Seasonal relativePercentage of average or trendCentered moving averageA moving average positioned at the center of the data that were used to compute it.3-25No Maths for Seasonality in the examAdeyl Khan, Faculty, BBA, NSUTypes of Forecasts3. Associative Forecasting3-26Adeyl Khan, Faculty, BBA, NSULinear Model Seems Reasonable3-27A straight line is fitted to a set of sample points.

ComputedrelationshipAdeyl Khan, Faculty, BBA, NSULinear Regression AssumptionsVariations around the line are randomDeviations around the line normally distributedPredictions are being made only within the range of observed valuesFor best results:Always plot the data to verify linearityCheck for data being time-dependentSmall correlation may imply that other variables are important3-28Adeyl Khan, Faculty, BBA, NSUForecast AccuracyError - difference between actual value and predicted valueMean Absolute Deviation (MAD)Average absolute errorMean Squared Error (MSE)Average of squared errorMean Absolute Percent Error (MAPE)Average absolute percent error3-29Adeyl Khan, Faculty, BBA, NSUMAD, MSE, and MAPE3-30MAD = | Actual forecast |nMSE = (Actual forecast ) 2n - 1MAPE = n( | Actual forecast | / Actual )*100Adeyl Khan, Faculty, BBA, NSUExample 103-31

Adeyl Khan, Faculty, BBA, NSUControlling the ForecastControl chartA visual tool for monitoring forecast errorsUsed to detect non-randomness in errorsForecasting errors are in control ifAll errors are within the control limitsNo patterns, such as trends or cycles, are present3-32Adeyl Khan, Faculty, BBA, NSUTracking SignalRatio of cumulative error to MADrunning sum of forecast errorthe number of average deviations (MADs)

Action limits- three to eightthe signal goes beyond this range, corrective action may be required.Bias Persistent tendency for forecasts to be Greater or less than actual values.3-33 Tracking signal =(Actual - forecast)MADAdeyl Khan, Faculty, BBA, NSUChoosing a Forecasting TechniqueNo single technique works in every situationTwo most important (performance) factorsCostAccuracyOther factors include the availability of:Historical dataComputersTime needed to gather and analyze the dataForecast horizon3-34Adeyl Khan, Faculty, BBA, NSUOperations StrategyForecasts are the basis for many decisionsWork to improve short-term forecastsAccurate short-term forecasts improveProfitsLower inventory levelsReduce inventory shortagesImprove customer service levelsEnhance forecasting credibility3-35Adeyl Khan, Faculty, BBA, NSUSupply Chain ForecastsSharing forecasts with supply canImprove forecast quality in the supply chainLower costsShorter lead times

3-36Adeyl Khan, Faculty, BBA, NSUList some examples of how each of these areas of an organization use forecasts: Accounting Finance Human Resources Marketing MIS Product/service design37Adeyl Khan, Faculty, BBA, NSUWhat four features are common to all forecasts? Name the elements of a good forecast: Briefly describe each of these forecast types: Judgmentaltime series associative Briefly describe each of these judgmental forecasting methods: executive opinion sales force opinions consumer surveys Delphi method38Adeyl Khan, Faculty, BBA, NSUWhat is the nave approach in forecasting? Briefly describe each of these techniques for Averagingmoving average weighted moving average exponential smoothing What is a seasonal relative? How is forecast error computed? Briefly describe the three measures are used to summarize forecast error? What two techniques can be used to monitor forecast errors? Which one is generally preferred, and why? 39Adeyl Khan, Faculty, BBA, NSU40Adeyl Khan, Faculty, BBA, NSULearning ObjectivesList 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.3-41Adeyl Khan, Faculty, BBA, NSULearning ObjectivesBriefly 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.3-42Adeyl Khan, Faculty, BBA, NSU3-43Exponential Smoothing

Adeyl Khan, Faculty, BBA, NSU3-44Linear Trend Equation

Adeyl Khan, Faculty, BBA, NSU3-45Simple Linear Regression

Adeyl Khan, Faculty, BBA, NSUChart14240434041.666666666741413941.333333333341.2464040.6444241.84543423845434042.333333333342.44142.6

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