Manual Model Selection MRP Forecast Parameters

If you want to select a model manually, then you must first of all analyse past consumption data to determine whether a distinct pattern or trend exists according to which you can manually select a model for the system.

Constant requirements pattern

If your past data represents a constant consumption flow, you can then select either the constant model or the constant model with adaption of the smoothing factors. In both cases, the forecast is carried out by first-order exponential smoothing. When adapting the smoothing parameters, the system calculates different parameter combinations and then selects the optimum parameter combination which is the one which results in the lowest mean absolute deviation.

You have another two possibilities if your past consumption pattern is constant; either the moving average model or the weighted moving average model.

In the weighted moving average model, you can weight individual past consumption values, which means that the system will not give equal value to past data when calculating the forecast values. In so doing, you can influence the calculation so that the most recent consumption values play a more important role in the forecast than the previous periods - as is also the case in exponential smoothing.

Trend requirements pattern

If your past consumption data represents a trend, it makes sense to select either the trend model or the second-order exponential smoothing model. In the trend model, the system calculates the forecast values by means of the first-order exponential smoothing procedure.

In the second-order exponential smoothing models, you can choose between a model with or without parameter optimization.

Seasonal requirements pattern

If your past consumption data represents a seasonal pattern, you can specify the seasonal model you want. The system calculates the forecast values for the seasonal model by means of first-order exponential smoothing.

Seasonal trend requirements pattern

If your past consumption data represents a seasonal trend pattern, you can select the seasonal trend model you want. The system calculates the forecast values by means of first-order exponential smoothing.

Irregular requirements pattern

None of the patterns or trends mentioned in the above examples can be recognized in an irregular consumption flow. If the system is to carry out a forecast for an irregular pattern, then it is usually advisable to select either the moving average model or the weighted moving average model.

Forecast Models for Different Requirements Patterns

Requirements pattern	Forecast model
Constant	constant model
	constant model with smoothing factor adaption
	moving average model
	weighted moving average model
Trend	trend model (first-order exponent smoothing)
Irregular	no forecast
	moving average model
	weighted moving average model
Extended forecasting component used:
Trend	second-order exponential smoothing model (with and without parameter optimization)
Seasonal	seasonal model
Seasonal trend	seasonal trend model

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