The exponential smoothing it is a way of forecasting the demand for an item for a given period. This method estimates that the demand will be equal to the average of the historical consumption in a given period, giving a greater weight or weight to the values that are closer in time. Additionally, for the following forecasts take into account the existing error of the current forecast.
Demand forecasting is the method of projecting customer demand for a product or service. This process is continuous, where managers use historical data to calculate what they expect to be the sales demand for a good or service..
Information from the company's past is used by adding it to the economic data of the market to see if sales will increase or decrease.
The results of the demand forecast are used to set goals for the sales department, trying to stay in line with the goals of the company.
Article index
Smoothing is a very common statistical process. Smoothed data is often found in various forms of everyday life. Every time an average is used to describe something, a smoothed number is being used.
Suppose the warmest winter on record was experienced this year. To quantify it, we start with the set of daily temperature data for the winter period of each recorded historical year..
This generates a number of numbers with big “jumps”. You need a number that eliminates all these jumps from the data to be able to more easily compare one winter with another.
Eliminating the jump in the data is called smoothing. In this case a simple average can be used to achieve smoothing.
For forecasting demand, smoothing is also used to eliminate variations in historical demand. This allows for better identification of demand patterns, which can be used to estimate future demand..
Variations in demand is the same concept as the "jump" of temperature data. The most common way that variations in demand history are removed is by using an average, or specifically, a moving average..
The moving average uses a predefined number of periods to calculate the average, and those periods move as time passes..
For example, if a four-month moving average is used and today is May 1, the average demand for January, February, March, and April will be used. On June 1, the demand for February, March, April and May will be used.
When using a simple average, the same importance is applied to each value in the data set. Therefore, in a four-month moving average, each month represents 25% of the moving average..
By using demand history to project future demand, it stands to reason that the most recent period has a greater impact on the forecast..
You can adapt the calculation of the moving average to apply different "weights" to each period, in order to obtain the desired results..
These weights are expressed as percentages. The total of all weights for all periods must add up to 100%.
Therefore, if you want to apply 35% as the weight for the closest period in the four-month weighted average, you can subtract 35% from 100%, leaving 65% to divide between the three remaining periods.
For example, you can end up with a weighting of 15%, 20%, 30% and 35% respectively for the four months (15 + 20 + 30 + 35 = 100).
The control input for the exponential smoothing calculation is known as the smoothing factor. Represents the weight applied to the demand for the most recent period.
If 35% is used as the most recent period weight in the weighted moving average calculation, you could also choose to use 35% as the smoothing factor in the exponential smoothing calculation..
The difference in the exponential smoothing calculation is that instead of having to figure out how much weight to apply to each previous period, the smoothing factor is used to do that automatically..
This is the "exponential" part. If 35% is used as the smoothing factor, the demand weight for the most recent period will be 35%. The weighting of the demand from the previous period to the most recent one will be 65% of 35%.
65% comes from subtracting 35% from 100%. This equates to 22.75% weighting for that period. The next most recent period demand will be 65% from 65% from 35%, which equals 14.79%.
The previous period will be weighted as 65% of 65% of 65% of 35%, equivalent to 9.61%. This will be done for all previous periods, up to the first period.
The formula for calculating exponential smoothing is as follows: (D * S) + (P * (1-S)), where,
D = most recent demand of the period.
S = smoothing factor, represented in decimal form (35% would be 0.35).
P = forecast of the most recent period, result of the smoothing calculation of the previous period.
Assuming that we have a smoothing factor of 0.35, we would then have: (D * 0.35) + (P * 0.65).
As you can see, the only data inputs required are the demand and the most recent period forecast..
An insurance company has decided to expand its market to the largest city in the country, providing insurance for vehicles.
As an initial action, the company wants to forecast how much vehicle insurance will be purchased by the inhabitants of this city.
To do this, they will use as initial data the amount of car insurance purchased in another smaller city.
The demand forecast for period 1 is 2,869 contracted vehicle insurance, but the real demand in that period was 3,200.
At the company's discretion, it assigns a smoothing factor of 0.35. The forecast demand for the following period is: P2 = (3200 * 0.35) + 2869 * (1-0.35) = 2984.85.
This same calculation was made for the entire year, obtaining the following comparative table between what was actually obtained and what was forecast for that month.
Compared to averaging techniques, exponential smoothing can predict the trend better. However, it still falls short, as shown in the graph:
It can be seen how the gray line of the forecast can be well below or above the blue line of demand, without being able to fully follow it.
Yet No Comments