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demand forecasting
 

demand forecasting

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all u wanna know about demand forecasting methods

all u wanna know about demand forecasting methods

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    demand forecasting demand forecasting Presentation Transcript

    • DEMAND FORECASTING II
    • 2TREND PROJECTION METHOD1. Graphic method (fitting trend line by observation)2. Algebric (least squares)3. Smoothing methods Moving average method Exponential smoothing
    • 3TimeValuesofDependentVariable(I) GRAPHIC METHODRandom InfluencesCyclical FluctuationSecular Trend
    • 4TimeValuesofDependentVariableActualobservationGRAPHIC METHOD
    • 5DeviationDeviationDeviationDeviationDeviationDeviationDeviationTimeValuesofDependentVariablePoint onthe lineActualobservationGRAPHIC METHOD
    • 6DeviationDeviationDeviationDeviationDeviationDeviationDeviationTimeValuesofDependentVariablebxaYˆPoint onthe lineActualobservationGRAPHIC METHOD
    • 7(II) LEAST SQUARES METHOD• Fitting a trend line to the data• Constant Rate of ChangeSt = So + bt• Where:– St value of time series to be forecasted for period t– So estimated value of time series in the base period– b is the absolute amount of growth per period– t time period for which series is to be forecastedS = nSo + b tS*t = So t + b t2
    • 8PeriodYear (t) Sales (S) S * t t^21991 1 300 300 11992 2 305 610 41993 3 315 945 91994 4 340 1360 161995 5 346 1730 251996 6 352 2112 361997 7 364 2548 491998 8 390 3120 641999 9 397 3573 812000 10 404 4040 1002001 11 418 4598 1212002 12 445 5340 144EXAMPLEAssuming the present trend continues, in which year would youexpect 1992 sales to be doubled?
    • 9PeriodYear (t) Sales (S) S * t t^21991 1 300 300 11992 2 305 610 41993 3 315 945 91994 4 340 1360 161995 5 346 1730 251996 6 352 2112 361997 7 364 2548 491998 8 390 3120 641999 9 397 3573 812000 10 404 4040 1002001 11 418 4598 1212002 12 445 5340 144n = 12 78 4376 30276 650St = So + btS = nSo + b tS*t = So t + b t2St = 281.39 + 12.81tSOLUTIONDouble of 1992 sales = 610So t = 25.65i.e. in 2017
    • 10(III) SMOOTHING TECHNIQUES• Predicting values of a time series on the basis of someaverage of its past values• Used when time series exhibit irregular or random variation– Moving Averages– Exponential Smoothing
    • 11Quarter Actual Market 3 Quarter MovingShare (A) Average(F)1 202 223 234 24 21.675 18 23.006 23 21.677 19 21.678 17 20.009 22 19.6710 23 19.3311 18 20.6712 23 21.00MOVING AVERAGE: EXAMPLE
    • 12Year Actual Market 3 Quarter Moving A - F (A - F)^2 5Share (A) Average(F) Av1 202 22 21.67 0.33 0.10893 23 23 0 04 24 21.67 2.33 5.435 18 21.67 -3.67 13.476 23 20.00 3.00 9.007 19 19.67 -0.67 0.458 17 19.33 -2.33 5.439 22 20.67 1.33 1.7710 23 21.00 2.00 4.0011 18 21.33 -3.33 11.0912 2350.63RMSE 2.05MOVING AVERAGE
    • 13Year Actual Market 3 Quarter Moving A - F (A - F)^2 5 Quarter Moving A - F (A - F)^2Share (A) Average(F) Average(F)1 202 22 21.67 0.33 0.10893 23 23 0 0 21.4 1.6 2.564 24 21.67 2.33 5.43 22.00 2.00 4.005 18 21.67 -3.67 13.47 21.40 -3.40 11.566 23 20.00 3.00 9.00 20.20 2.80 7.847 19 19.67 -0.67 0.45 19.80 -0.80 0.648 17 19.33 -2.33 5.43 20.80 -3.80 14.449 22 20.67 1.33 1.77 19.80 2.20 4.8410 23 21.00 2.00 4.00 20.60 2.40 5.7611 18 21.33 -3.33 11.0912 2350.63 51.64RMSE 2.05 2.07MOVING AVERAGE
    • 14nFA tt2)(RMSE =To decide on the better moving average forecast calculate theroot-mean-square error(RMSE) of each forecast and use themoving average which results in the smallest RMSE
    • 15EXPONENTIAL SMOOTHING• Forecast for next period (ie, t) is a weighted average ofthe actual value in that period and forecasted values ofthe time series in period (t - 1)0 1wSt = w Y t + (1 – w) S t-1
    • 16Year Demand (000 units)1 482 423 204 485 386 347 468 509 4810 5411 4012 44Find the exponentiallysmoothened time series usingsmoothing constant w = 0.1and w = 0.4
    • 17Year Demand(000 units) (A)ExponentiallySmoothenedvalues(w = 0.1)(f t+1)A – (Ft +1){A – (Ft + 1)}21 48 482 42 47.43 20 44.664 48 455 38 44.36 34 43.287 46 43.568 50 44.29 48 44.5810 54 45.5211 40 44.9612 44 44.86FindRMSE