1st order exponential smoothing is a time series analysis technique that can be used in materials management to forecast future demand. In 1st order exponential smoothing, the forecast value of the next time period (P1,n+1) is calculated from the forecast value of the old time period (P1,n) plus the difference between the forecast value of the previous period (P1,n) and the actual consumption of the previous period (Vn), weighted with the aid of a present factor α.
If the value of α is “0”, the 1st order exponential smoothing does not take into account the deviation between the forecast and the actual value in the previous period at all and the new forecast corresponds to the old forecast; the actual (current) consumption therefore does not influence the forecast.
If α = “1”, the forecast value of the new time period corresponds to the actual consumption of the preceding time period. In this case, the actual (current) consumption determines the forecast. In practice, values between 0.1 and 0.5 are usually selected for α.
The 1st order exponential smoothing can only be used for items whose consumption does not show any trends or seasonalities and whose fluctuations are classified as chaotic, i.e. not following any regularity.
For time series that include trends and/or seasonality, 2nd order exponential smoothing is classically used.
The general problem of 1st order exponential smoothing, like all classical forecasting methods, is that it assumes a normally distributed demand, which is mostly not the case in practice.