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Lakeside hospital has used a 9-month, average moving average forecasting method to predict drug and surgical dressing inventory requirements. The actual demand for one item is as shown in the table. Using the previous moving – average data, convert to an exponential smoothing forecast for month 33.?
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Lakeside hospital has used a 9-month, average moving average forecasti...
Exponential Smoothing Forecasting Method
Exponential smoothing is a popular forecasting method which involves the use of a weighted average of past observations. The weights decrease exponentially as the observations get older. It is a simple, yet effective method for forecasting time series data.
Calculating Exponential Smoothing Forecast
To calculate the exponential smoothing forecast for month 33, we need to use the following formula:
Ft+1 = αDt + (1-α)Ft
Where,
Ft+1 = Forecast for month 33
α = Smoothing constant (0 < α="" />< />
Dt = Actual demand for month 32
Ft = Forecast for month 32
Calculating Smoothing Constant
To calculate the smoothing constant (α), we need to first determine the smoothing parameter (λ) which is the equivalent of the time constant used in the 9-month moving average method. The formula for λ is:
λ = 2/(N+1)
Where,
N = Number of periods in the moving average (9 in this case)
Using the above formula, we get:
λ = 2/(9+1) = 0.2
Now, we can calculate the smoothing constant (α) using the formula:
α = 1 - λ = 1 - 0.2 = 0.8
Calculating Exponential Smoothing Forecast for Month 33
Using the above values, we can now calculate the exponential smoothing forecast for month 33:
F33 = 0.8 x D32 + 0.2 x F32
Where,
D32 = Actual demand for month 32 (as given in the table)
F32 = Forecast for month 32 (calculated using the 9-month moving average method)
Plugging in the values, we get:
F33 = 0.8 x 360 + 0.2 x 340 = 356
Therefore, the exponential smoothing forecast for month 33 is 356.
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Lakeside hospital has used a 9-month, average moving average forecasting method to predict drug and surgical dressing inventory requirements. The actual demand for one item is as shown in the table. Using the previous moving – average data, convert to an exponential smoothing forecast for month 33.?
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