Calculation Setup for Forecast Methods
The Calculation Setup is used to specify formulas for forecast calculation. To set up the calculation methods, go to Forecast Method Property dialog.
To set up calculation parameters, specify the following:
- Formula.
Available formulas are listed below
Formula Description Moving average Forecast calculation with Moving Average formula projects the values in the forecast period based upon the average demand (sales) over a specific number of preceding periods, i.e. weeks or months. The number of preceding periods used in the forecast calculation is refereed to as n. The number of periods used determines how quickly the forecast will react to changes in actual trends and how sensitive it will be to random variations. The more periods included will result more stable method for random variations but will also react slowly to real trends. In M3 BE a number of hybrids of Moving Average exist. In M3 DMP one formula replaces these due to a more dynamic and flexible set up feature. The concept of 'offset periods' and 'offset years enables to determine the first historical period to be used in the forecast calculation when calculating the Moving Average based on n number of calculation periods.
Centered Weighted Moving Averages (CWMA) Centered Weighted Moving Averages (CWMA) is any average that has multiplying factors to give different weights to different data points.
M3 DMP uses a table of pre-defined smoothing constants depending on the selected Weight Function. M3 DMP includes the most used variations including Spencers and Hendersons weight functions.
Centered Moving Average Centered Moving Average is a specific form of Centered Weighted Moving Average where the smoothing constants are the same, except for the start and end when the length is an even number.
The weight function for Centered Moving Average is defined by specifying the number of calculation periods (>=2).
Constant This is used for forecasts as a fixed value or per record using a numerical key as input. Exponential Smoothing Exponential Smoothing calculates the forecast based upon the demand and forecast for the previous periods by setting Offset periods to 1. To use different Offset periods, choose Smoothing constant - Alpha (α) with a value between zero (0) and one (1). The higher the value for α, the faster the method reacts to changes in demand.
This model is good for non-seasonal data that is fairly level (i.e. not trend).
Exponential Smoothing of two period values The formula Exponential Smoothing of two period values weights the average demand of the last 25% of the total number of calculation periods with the average demand for n number of periods. The formula uses the smoothing constant α which determines how strongly the forecast responds to changes in demand. Values of 0.2 and 0.3 are reasonable smoothing constants. These values indicate that the current forecast should be should be adjusted to 20 to 30% for the error in the prior forecast.
Adaptive Exponential Smoothing In Adaptive Exponential Smoothing the smoothing constant is calculated every time a new forecast is made. In order to control and follow up forecast precision, two types of measurements are used: Mean Absolute Deviation (MAD) and Mean Error (Forecast error, AVER). These measure the deviation between forecast and actual demand.
Adaptive-response-rate SES In Adaptive-response-rate single exponential smoothing (also known as Trigg & Leachs method) calculation for α is based upon the smoothed forecast error and the smoothed absolute forecast error. Parameter Beta is the smoothing constant applied to the trend in forecast vs demand.
Alpha is adaptive and calculated automatically. Only Beta needs to be defined.
Calculation Initialization Point defined on Data Set Properties is where the adoption starts from, giving the formula s a change to get a better starting estimate for Alpha.
Applying additional trend is not common since this method already uses the Beta trend smoothing constant to estimate adaptivity. Seasonality can be applied when appropriate.
Forecast as demand from preceding period In Forecast as demand from preceding period formula, the forecast for the next period is equal to the actual demand n periods back in time. It is possible to use either Offset Periods or Offset Years or a combination of the two.
For example, if Offset Periods is equal to zero (0) the forecast for the preceding period is equal to demand for the previous period. If Offset Period is equal to eleven (11) the forecast preceding period is equal to the demand for the same period last year. In last example it is also possible to set Offset Years = 1 and Offset Periods = 0.
Intermittent Croston Exponential smoothing is often used to forecast demand for inventory in stock management. However, if demand is sporadic or intermittent, the exponent smoothing often produce stock level that are too low. The Intermittent Croston Method improves the exponential smoothing by estimating the average size of demand (Z) first and then in a second step (X), the average interval between demands. This formula is very useful when forecasting spare parts or equipment when the demand appears random or with irregular patterns and when there are in many periods with zero demand.
Croston Modified In some cases the forecast can be estimated better by using the Croston Modified method that considers the effects of the last two periods of demands especially if the interval between demands is changing.
Period Relation The Period Relation forecast formula calculates the forecast equal to the relation between previous x number of periods and the same x number of periods last year multiplied with the average demand for y number of periods last starting with the current period previous year.
Holts Linear Method The Holts Linear Method is an extension of exponential smoothing to take into account a possible linear trend. This model is good for non-seasonal data with a trend.
Holt-Winters trend and seasonality method The Holt-Winters trend and seasonality method takes both trend and seasonality into consideration. A seasonal index has been added to the equation.
- Parameters.
The available parameters depend on the selected formula.