Algorithm for demand forecasting

In the following sections, each calculation step will be explained.

The demand forecast is based on the historical demand data in the time period between the start date of the scenario and the current plan period.

(*) Only taken into account if the Dependent Demand Forecast check box in the Items - Planning (cprpd1100m000) session is selected.

LN converts the demand history in the current master plan from plan periods to fixed-length forecast periods, based on the Forecast Period Length field in the Scenarios (cprpd4100m000) session. See To distribute master-plan quantities over calendar days.

LN increases the actual demand with a fictitious demand for non-working days, such as holidays, to prevent demand fluctuations. The fictitious demand is equal to the average demand in the forecast period.

The calendar of the item's enterprise unit (see the Items - Planning (cprpd1100m000) session) defines which days are workdays. LN reverses the adjustment in the final step of the calculation, when the demand forecast is converted back to plan periods.

After LN retrieves the available demand history for a plan item, it checks whether the demand history is sufficient to perform a demand forecast. LN counts the forecast periods from the first period with actual demand up to the current period.

The criteria for the minimum number of forecast periods depend on the applied forecast method and the seasonal cycle time. If the history data does not suffice, LN aborts the demand forecast calculation, and reports the reason.

These parameters are located in the Plan Items - Forecast Settings (cpdsp1110m000) session.

If the Type of Trend Influence field is Linear, the trend influence can be described by means of the following formula:

 FD(t) = CS + TF *	t 

Where CS = constant demand TF = trend factor FD(t) = the demand forecast for period t

LN calculates the trend factor (TF) and the constant demand (CS), based on a first degree polynom by means of polynomial regression. See Principle of polynomial regression. If there is a seasonal cycle, LN determines the first degree polynom on the basis of a whole number of seasonal cycles.

If the Type of Trend Influence field is Progressive, the trend influence can be described by means of the following formula:

 FD(t) = BS * (TF ^ (t-1)) 

Where BS = base demand (the estimated demand for period 1) TF = trend factor FD(t) = the demand forecast for period t

If the Type of Trend Influence field is Not Applicable, the trend factor is not used.

If the Automatic Update of Forecast Parameters check box in the Plan Items - Forecast Settings (cpdsp1110m000) session is selected, LN performs an automatic trend detection.

If the Type of Trend Influence field is Linear or Not Applicable, LN calculates a trend factor according to the method for linear trends. If the trend factor is found to be zero, LN sets the Type of Trend Influence field to Not Applicable.

LN reports any automatic change of the Type of Trend Influence field.

A seasonal pattern is defined by a series of seasonal factors: one seasonal factor for each forecast period in a seasonal cycle.

If you specified a fixed seasonal pattern in the Seasonal Pattern for Forecast field in the Items - Ordering (tcibd2500m000) session, LN determines the seasonal factor for each period based on the seasonal factors defined at the execution level. If the Seasonal Pattern for Forecast field is empty, LN determines the seasonal pattern on the basis of the trend-adjusted demand history.

The determination of the seasonal pattern based on the demand history depends on the following fields in the Plan Items - Forecast Settings (cpdsp1110m000) session:

If the Automatic Update of Forecast Parameters check box is selected, LN can update the following fields automatically:

The seasonal factors are calculated as follows:

(1) TD(t) = AV

(2) TD(t) = CS + TF * t

(3) TD(t) = BS * TF ^ (t-1)

Where:

If the Type of Seasonal Influence field is Constant, the seasonal factor is equal to the actual demand reduced by the trend-adjusted average demand for the relevant period. This is calculated as follows:

 SF(t) = AD(t) -	TD(t) 

Where:

If the Type of Seasonal Influence field is Progressive, the seasonal factor is equal to the actual demand divided by the trend-adjusted average demand for the period concerned. This is calculated as follows:

 SF(t) = AD(t) /	TD(t) 

Where:

The above seasonal factor is computed as an average seasonal factor for each period.

A seasonal pattern is assumed to be present, if the value of the Seasonal Correlation Factor field is at least 0.8.

If the Automatic Update of Forecast Parameters check box is selected and one of the above-mentioned situations presents itself, LN changes the Type of Seasonal Influence field accordingly.

If the Automatic Update of Forecast Parameters check box in the Plan Items - Forecast Settings (cpdsp1110m000) session is selected, LN performs an automatic seasonal pattern detection.

If the Type of Seasonal Influence field is Constant or Not Applicable, LN calculates a seasonal pattern according to the method for Constant patterns. If no seasonal pattern is detected, LN sets the Type of Seasonal Influence field to Not Applicable.

If the Type of Seasonal Influence field is Progressive, LN calculates a seasonal pattern according to the method for Progressive patterns. If no pattern is detected, LN sets the Type of Seasonal Influence field to Not Applicable.

LN reports any automatic change of the Type of Seasonal Influence.

LN applies the forecast method that you specified in the Forecast Method field in the Plan Items - Forecast Settings (cpdsp1110m000) session.

After LN forecasts the demand for a plan item, it determines the following error and correlation information:

This information is displayed in the Plan Items - Forecast Settings (cpdsp1110m000) session.

LN converts the computed demand forecast for the forecast periods from a fixed period length to plan periods with a variable period length. This process is the reverse of the first step Retrieve demand history).

LN treats workdays and non-working days differently. The calendar of the company calendar provider defines which days are workdays. Concretely, this means that a demand forecast for a day with zero available capacity is ignored, so that no demand forecast is generated for public holidays and other days off.