# ma14 - For example Let’s say that we start out in Month 1...

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Exponential Smoothing with Trend Adjustment Whenever a trend is present in the data, exponential smoothing has to be modified to account for that trend. We calculate the Forecast Including Trend in time t (FITt) as the exponentially smoothed forecast (Ft) plus the exponentially smoothed trend (Tt). FITt = Ft + Tt The exponentially smoothed forecast and trend are calculated as follows: Ft = alpha(At-1) + (1-alpha)(Ft-1 + Tt-1) Tt= beta(Ft – Ft-1) + (1-beta)Tt-1 Where: Ft = exponentially smoothed forecast Tt= exponentially smoothed trend At= actual demand Alpha=smoothing constant for the average Beta=smoothing constant for the average To determine the trend-adjusted exponentially smoothed forecast, we simply compute Ft and Tt, then add them.

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Unformatted text preview: For example: Let’s say that we start out in Month 1 with an actual demand (At) of 12, a smoothed forecast (Ft) of 11, a smoothed trend (Tt) of 2, and a forecast including trend (FITt) of 13. What is the forecast for Month 2, assuming an alpha of 0.2 and a beta of 0.4? First, we calculate the exponentially smoothed forecast for Month 2: Ft = (0.2)(12) + (1 - 0.2)(11 + 2) = 2.4 + 10.4 = 12.8 Next, we calculate the exponentially smoothed trend for Month 2: Tt = (0.4)(12.8 – 11) + (1 – 0.4)(2) = .72 + 1.2 = 1.92 Finally, we add the exponentially smoothed forecast (Ft) to the exponentially smoothed trend (Tt): FITt = (12.8) + (1.92) = 14.72 units...
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## This note was uploaded on 02/03/2011 for the course MAN 4504 taught by Professor Benson during the Spring '08 term at University of Florida.

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ma14 - For example Let’s say that we start out in Month 1...

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