Forecasting. Lecture Outline   Strategic Role of Forecasting in Supply Chain Management and...

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Transcript of Forecasting. Lecture Outline   Strategic Role of Forecasting in Supply Chain Management and...

  • Forecasting

  • Lecture OutlineStrategic Role of Forecasting in Supply Chain Management and TQMComponents of Forecasting DemandTime Series MethodsForecast AccuracyRegression Methods

  • ForecastingPredicting the FutureQualitative forecast methodssubjectiveQuantitative forecast methodsbased on mathematical formulas

  • Forecasting and Supply Chain ManagementAccurate forecasting determines how much inventory a company must keep at various points along its supply chainContinuous replenishmentsupplier and customer share continuously updated datatypically managed by the supplierreduces inventory for the companyspeeds customer deliveryVariations of continuous replenishmentquick responseJIT (just-in-time)VMI (vendor-managed inventory)stockless inventory

  • Forecasting and TQMAccurate forecasting customer demand is a key to providing good quality serviceContinuous replenishment and JIT complement TQMeliminates the need for buffer inventory, which, in turn, reduces both waste and inventory costs, a primary goal of TQMsmoothes process flow with no defective itemsmeets expectations about on-time delivery, which is perceived as good-quality service

  • Types of Forecasting MethodsDepend ontime framedemand behaviorcauses of behavior

  • Time FrameIndicates how far into the future is forecastShort- to mid-range forecasttypically encompasses the immediate futuredaily up to two yearsLong-range forecastusually encompasses a period of time longer than two years

  • Demand BehaviorTrenda gradual, long-term up or down movement of demandRandom variationsmovements in demand that do not follow a patternCyclean up-and-down repetitive movement in demandSeasonal patternan up-and-down repetitive movement in demand occurring periodically

  • Forms of Forecast Movement

  • Forecasting MethodsQualitativeuse management judgment, expertise, and opinion to predict future demandTime seriesstatistical techniques that use historical demand data to predict future demandRegression methodsattempt to develop a mathematical relationship between demand and factors that cause its behavior

  • Qualitative MethodsManagement, marketing, purchasing, and engineering are sources for internal qualitative forecastsDelphi methodinvolves soliciting forecasts about technological advances from experts

  • Forecasting ProcessNoYes

  • Time SeriesAssume that what has occurred in the past will continue to occur in the futureRelate the forecast to only one factor - timeIncludemoving averageexponential smoothinglinear trend line

  • Moving AverageNaive forecastdemand the current period is used as next periods forecastSimple moving averagestable demand with no pronounced behavioral patternsWeighted moving averageweights are assigned to most recent data

  • Moving Average:Nave Approach

  • Simple Moving Average

  • 3-month Simple Moving Average

  • 5-month Simple Moving Average

  • Smoothing Effects

  • Weighted Moving AverageAdjusts moving average method to more closely reflect data fluctuations

  • Weighted Moving Average ExampleMONTH WEIGHT DATA

    August 17%130September 33%110October 50%90

  • Exponential SmoothingAveraging method Weights most recent data more stronglyReacts more to recent changesWidely used, accurate method

  • Exponential Smoothing (cont.)Ft +1 = Dt + (1 - )Ftwhere:Ft +1 =forecast for next periodDt =actual demand for present periodFt =previously determined forecast for present period=weighting factor, smoothing constant

  • Effect of Smoothing Constant0.0 1.0 If = 0.20, then Ft +1 = 0.20Dt + 0.80 FtIf = 0, then Ft +1 = 0Dt + 1 Ft 0 = Ft Forecast does not reflect recent dataIf = 1, then Ft +1 = 1Dt + 0 Ft =Dt Forecast based only on most recent data

  • Exponential Smoothing (=0.30)

  • Exponential Smoothing (cont.)

  • Exponential Smoothing (cont.)

  • Adjusted Exponential SmoothingAFt +1= Ft +1 + Tt +1whereT = an exponentially smoothed trend factor

    Tt +1 = (Ft +1 - Ft) + (1 - ) TtwhereTt = the last period trend factor= a smoothing constant for trend

  • Adjusted Exponential Smoothing (=0.30)

  • Adjusted Exponential Smoothing: Example

  • Adjusted Exponential Smoothing Forecasts

  • Linear Trend Liney = a + bx

    wherea = interceptb = slope of the linex = time periody = forecast for demand for period x

  • Least Squares Example

  • Least Squares Example (cont.)

  • Seasonal AdjustmentsRepetitive increase/ decrease in demandUse seasonal factor to adjust forecast

  • Seasonal Adjustment (cont.)

  • Seasonal Adjustment (cont.)

  • Forecast AccuracyForecast errordifference between forecast and actual demandMADmean absolute deviationMAPDmean absolute percent deviationCumulative errorAverage error or bias

  • Mean Absolute Deviation (MAD)where t= period number Dt= demand in period t Ft= forecast for period t n= total number of periods= absolute value

  • MAD Example

  • Other Accuracy Measures

  • Comparison of Forecasts

  • Forecast ControlTracking signalmonitors the forecast to see if it is biased high or low

    1 MAD 0.8 Control limits of 2 to 5 MADs are used most frequently

  • Tracking Signal Values

  • Tracking Signal Plot3 2 1 0 -1 -2 -3 |||||||||||||0123456789101112Tracking signal (MAD)Period

  • Statistical Control ChartsUsing we can calculate statistical control limits for the forecast errorControl limits are typically set at 3

  • Statistical Control Charts

  • Regression MethodsLinear regressiona mathematical technique that relates a dependent variable to an independent variable in the form of a linear equationCorrelationa measure of the strength of the relationship between independent and dependent variables

  • Linear Regression

  • Linear Regression Examplexy(WINS)(ATTENDANCE) xyx2

    436.3145.216640.1240.636641.2247.236853.0424.064644.0264.036745.6319.249539.0195.025747.5332.549

    49346.72167.7311

  • Linear Regression Example (cont.)

  • Linear Regression Example (cont.)

  • Correlation and Coefficient of DeterminationCorrelation, rMeasure of strength of relationshipVaries between -1.00 and +1.00Coefficient of determination, r2Percentage of variation in dependent variable resulting from changes in the independent variable

  • Computing Correlation