Building Supply Chain Excellence

The drawback of simple exponential smoothing is that it can only see a level (the average value around which the demand varies over time) and is unable to identify and project a trend. Double exponential smoothing brings together "level" and "trend" to generate forecasts. For a given period, "t" with demand, d,  the "Level" and "Trend" can be expressed as: $$ a_{t}, b_{t}$$ The "Level", which is a function of demand and forecast, can be calculated as: $$ a_{t}=\alpha d_{t}+(1-\alpha)(a_{t-1}+b_{t-1})$$ and the "Trend", which is a function of the difference in levels as well as the previous trend, can be calculated as: $$ b_{t}=\beta (a_{t}-a_{t-1}) + (1-\beta) b_{t-1}$$ Forecast = Level + Trend $$f_{t+1}=a_{t}+b_{t}$$ for a generalised forecast, the expression can be written as: $$f_{t+\lambda}=a_{t}+\lambda b_{t}$$ Now moving on to the implementation of the Double Exponential smoothing model. The simplest way to ini...

Exponential smoothing is another simple forecasting model. This model too assumes that future demand is more or less similar to recent demand.  The key concept behind exponential smoothing is that in order to determine the forecast the model assigns highest weight to the most recent period. Older periods are assigned lesser weights. This ensures that outliers and noise have a lesser impact than in the case of the Moving Average model.  The Exponential smoothing model can be mathematically expressed as:$$f_t=\alpha d_{t-1}+(1-\alpha)f_{t-1}$$ $$\ 0<\alpha\le1$$ The Exponential smoothing model is a kind of "learning model" since the previous forecast made by the model, which already included previous demand observations, is used to determine the latest forecast. In other words, this model uses the most recent demand observation as well as the previous forecast it made. Since the forecast for each period is function of "alpha", a learning rate/ratio as well as the p...

The moving average model is the simplest forescasting model. It assumes that the future demand is a function of recent demand and is expressed as: $$f_t = \frac{1}{n} \sum_{i=0}^{n} d_{t-i}$$  where, for a given period, t: f  is the forecast,  d is the demand, and n is the number of periods we take the average for The moving average model gets more "naive" and follows the demand in the latest periods as n gets smaller. It is simplest when n=1 and forecast follows the last observed demand.It will be highly sensitive to any variations in demand as well as to any noise in the demand data. To decrease the sensitivity, one must consider a higher value for n.  While the advantage of the moving average model is that it is highly simple, its drawbacks include:  1. Inability to extrapolate to identify potential trends farther into the future  2. Inability to factor in seasonality, and  3. A flat historical weighing that does not allow you to assign higher wei...