Oracle fusion planning central cloud

1. Forecast Methods
The forecast method specifies the statistical method used to generate the forecast.
Oracle Demand Planning offers an Automatic forecast method whereby the forecasting
engine determines the best statistical forecasting method to use based on the historical
performance of each algorithm and the application of decision rules.
You can also choose one of the following statistical forecasting methods:
 Linear regression
 Polynomial regression
 Exponential fit
 Logarithmic fit
 Asymptotic fit
 Exponential Asymptotic fit
 Single Exponential Smoothing
 Double Exponential Smoothing
 Holt-Winters
 Croston's Method
Advanced statistical parameters
When you select a forecasting method, you have the option to specify values for
advanced statistical parameters. This enables you to manually fine-tune the forecast.
Setting advanced parameters is discretionary. If you are using the Automatic method,
the forecasting engine sets parameters for the selected forecast method when it runs
the forecast. If you are using a method other than Automatic, the forecasting engine
uses the defaults for parameters that are relevant to the selected method.
The following section lists the advanced statistical parameters in alphabetic order and
briefly describes each one. The actual parameters that you see depend on the forecast
method that you chose.
Alpha
For the three methods of the exponential smoothing family (Single Exponential
Smoothing, Double Exponential Smoothing, and Holt-Winters), specifies the relative
weighting to give to recent changes in mean value.
 Alpha Max — The minimum value is 0.0; the maximum value is 1.0; the default
is .3.

2.  Alpha Min (available for Double Exponential Smoothing and Holt-Winters) — The
minimum value is 0.0; the maximum value is 1.0; the default is 1.0.
 Alpha Step (available for Holt-Winters) — The incremental value used to go from
Alpha Min to Alpha Max. The minimum value is 0.05; the maximum value is 0.2;
the default is 0.1. The value that you enter must evenly divide the difference
between Alpha Max and Alpha Min.
Beta
For the double exponential smoothing and Holt-Winters forecasting methods, specifies
the relative weighting to give to recent changes in trend.
 Beta Max — The minimum value is 0.0; the maximum value is 1.0; the default is
.3.
 Beta Min — The minimum value is 0.0; the maximum value is 1.0; the default is
1.0.
 Beta Step — The incremental value used to go from Beta Min to Beta Max. The
minimum value is 0.05; the maximum value is 0.2; the default is 0.1. The value
that you enter must evenly divide the difference between Beta Max and Beta
Min.
Cyclical Decay
For the Automatic, linear and non-linear regression forecast methods, indicates how
trends that are calculated based on history will be considered as the forecast time
horizon increases. This parameter is useful when the history is large and some cyclical
component has been identified.
The parameter value indicates how seriously deviations from baseline activity are
considered: a higher value implies slower decay while a lower value implies faster decay
for cyclical components. Note that for less history (for example, less than about 1.5 to 2
years) and in the absence of cyclical activity, this parameter might not have any effect
on the calculated forecasts.
 Cyclical Decay Max — The minimum value is 0.2; the maximum value is 1.0;
the default is 1.0.
 Cyclical Decay Min — The minimum value is 0.2 the maximum value is 1.0;
the default is 0.2.
The difference between the maximum value and the minimum value must be evenly
divisible by 4.
Data Filters

3. You can turn a seasonal or aggregate data filter on or off. You can choose one of the
following options:
 No seasonal filter — This is the default.
 Seasonal filter — Accounts for seasonal patterns in the data.
 Moving periodic total filter — An "aggregation" filter that handles sporadic or
intermittent time series data. This is available for all methods except Holt-
Winters.
Gamma
For the Holt-Winters forecasting method, specifies the relative weighting to give to
recent changes in seasonality.
 Gamma Max — The minimum value is 0.0; the maximum value is 1.0; the
default is .3.
 Gamma Min — The minimum value is 0.0; the maximum value is 1.0; the
default is 1.0.
 Gamma Step — The incremental value used to go from Gamma Min to Gamma
Max. The minimum value is 0.05; the maximum value is 0.2; the default is 0.1.
The value that you enter must evenly divide the difference between Gamma Max
and Gamma Min.
Min/Max Bounds
For all forecasting methods, specifies upper and lower bounds on forecast numbers as a
factor or multiple of the historical values.
 Minimum Forecast Factor — Sets lower bounds on forecast numbers. The
default is 0.
 Maximum Forecast Factor — Sets upper bounds on forecast numbers. The
default is 100.
Over adjustment of forecasts within periods
For all forecasting methods, specifies whether to prevent over-adjustment to the data
by using average, rather than the individual, values of a period. For example, using this
parameter for a forecast at the day level would allocate the forecast to all valid days in
a weeks rather than forecasting individually at the day level.
Smoothing
For all forecasting methods, historical data is smoothed by averaging data with the time
series. You can set the following smoothing parameters:

4.  Median smoothing window — Specifies the length of the median smoothing
period. Larger values result in smoother forecasts. Depending on the nature of
the data, a window size that is too small might be unable to filter outliers while a
window size that is too large might miss data patterns. The minimum value is 1;
the maximum value is 27; the default is 3. The time period is based on the
level/calendar and history start date.
 Do you want to fit the data on smoothed series? — Specifies whether you
want to turn the median smoothing filter on or off. The default is off.
 Do you want to interpolate for missing values? — Specifies whether you
want smooth the data by interpolating for missing values. This is useful for
handling occasional missing values in the time series. The default is no.
Trend decay
For the Automatic, Double Exponential Smoothing and Holt-Winters methods, specifies
parameters that determine how large trends detected from recent data affect the
forecast.
 Trend Min — The minimum value is 0.0; the maximum value is 1.0; the default
is 0.4.
 Trend Max — The minimum value is 0.0; the maximum value is 0.8; the default
is 0.8.
 Trend Step — The incremental value used to go from Trend Min to Trend Max.
The minimum value is 0.05; the maximum value is 0.2; the default is 0.2. The
value that you enter must evenly divide the difference between Min and Max.
 Trend Dampening for erratic data — (Active if you chose "Moving Periodic
Total Filter" as the data option for Data Filters) Specifies whether to apply trend
dampening to erratic data.
Verification Window Size
For all forecasting methods, provides a ratio that specifies the portion of the data used
in the verification phase. This ratio is used to calculate forecast accuracy statistics
(MAD, MAPE, and RMSE). For the Automatic method, this ratio is also used to verify the
best-fit method. Increasing the size means that the forecasting engine will use a larger
portion of the most recent data; decreasing the size means that it will use a smaller
portion of the data. The minimum value is 1/26; the maximum value is 1/2; the default
is 1/3.
Measure Property: Unit of Measure Association
When you enable the Apply Unit of Measure (UOM) when aggregating data property
for a measure, Oracle Demand Planning uses the Unit of Measure (UOM) for leaf level
product items when aggregating product values to higher levels. For example, suppose

5. that the base Unit of Measure (UOM) for the plan is "Each." If product A has a UOM of
DZ (dozen) and a value of 10, and product B has a UOM of Each and a value of 20,
then they would be aggregated to a higher level using the UOM conversion of 1 DZ =
12 Each, so 10*12 + 20*1 = 120 + 20 = 140.
You would almost always want to associate UOM's with a new measure, because for
volume amounts, it makes the most sense to have them aggregate up this way.
An example of when you might not want to aggregate would be for something like a
"Population" stream, for which the UOM's would not make sense.
The Base UOM for the plan is defined in the Demand Planning Server; however, the
UOM for specific products may be unavailable or the conversion rates between the leaf
products' UOM and the Base UOM for the plan may be unavailable. In these cases, the
conversion rates will default to 1, so it will act like a flat aggregate from the leaf level of
Product to higher levels. (In the previous example, it would just mean 10 + 20 = 30 if
no conversion rates were available.)
Effect of Events on Measures
When you apply events to a stored measure, Oracle Demand Planning applies the factor
specified in the Action phase of the event definition process.
For all event types, events are applied at the day level for Time, values are allocated to
the leaf level of the hierarchies, and then aggregated up.
When viewing events and their effects, ensure you are looking at the day level for
Time. If you have qualified your event, then ensure that you select the qualified
dimension value at the level you selected. When viewing events from other levels you
will see an aggregated or allocated value which may not match what you expect to see.
Effect of a promotion event on a measure
When you associate a promotion event with a measure, Oracle Demand Planning
applies the lift value (for each intersection, New Value = Existing Value + Lift) or the lift
percent (for each intersection, New Value = Existing Value * [1 + {Lift/100}]) of the
absolute lift (for each intersection, New Value = Lift).
Effect of a product introduction event on a measure
When you associate a product introduction event with a measure, Oracle Demand
Planning calculates the effect of the event as follows:

6.  For a lifecycle event: Forecast for new product = Sum over all specified base
products (specified weights applied to each base product * History for base
product, with optional lag). Cannibalizations take away from the specified
product a specified fraction of the forecast for the newly introduced product.
 For a supercession event: Forecast for new product = Sum over all specified
base products (specified weights applied to each product * Forecast for that base
product).
Effect of a product phase out event on a measure
When you associate a product phase out event with a measure, Oracle Demand
Planning calculates the effect of the event as linear decay rate, with specified start and
end dates and the factor of the start day's forecast remaining at the end day. The end
day's value is the factor times the start day's value; values between start and end are
linearly interpreted.
Effect of multiple events on a measure
When a measure is associated with multiple events, Oracle Demand Planning applies
the events in the following order:
1. New product introductions
2. Product phase outs
3. Optional promotion events
4. Mandatory promotion events
Optional events are calculated before mandatory events for the following reason:
Assume that you are a store retailer, who offers a storewide 10% discount on
Saturdays. The discount is a mandatory event — it happens to all transactions on that
day. A customer comes in with a $50 voucher. The voucher is an optional event which
may not happen for each transaction. The storewide discount is always taken before
the voucher is used. In addition, its unlikely that the vendor would allow a customer to
use two optional events for one transaction. For example, you couldn't use a $50
voucher and a voucher for 50% off anything at the same time.