Parameter

Global settings and parameters are stored here.

Some of these parameters are used by the planning algorithm, others are used by the web application. Extension modules also add additional configuration parameters to this table.

Standard parameters

The table below shows the parameters that are recognized by the standard application.

Parameter Description
currentdate
Current date of the plan, formatted as YYYY-MM-DD HH:MM:SS. If the parameter is missing or empty the system time is used as current date.
currency
Currency symbol.
This parameter may be only set on the default database and will be globally applied, including in all the scenarios.
If the parameter is missing or empty the currency symbol will be the $.
By default the symbol will show after the value, i.e. 123 $.
For the symbol to show before the value a , should be added after the symbol, i.e. $,, resulting in $ 123.
loading_time_units
Time units to be used for the resource report.
Accepted values are: hours, days, weeks.
plan.administrativeLeadtime
Specifies an administrative lead time in days.
FrePPLe will plan the sales orders one administrative lead time ahead of the due date.
Accepted values : Any positive decimal number.
plan.autoFenceOperations
The number of days the solver should wait for a confirmed replenishment before generating a proposed order.
Default:0 (deactivated).
plan.calendar
Name of a calendar to align new operationplans with.
When this parameter is used, the plan results are effectively grouped in the time buckets defined in this calendar.
This feature is typically used for medium and long term plans.
Such plans are reviewed in monthly or weekly buckets rather than at individual dates.
plan.loglevel
Controls the verbosity of the planning log file.
Accepted values are 0 (silent – default), 1 (minimal)
and 2 (verbose).
plan.planSafetyStockFirst
Controls whether safety stock is planned before or after the demand.
Accepted values are false (default) and true.
plan.rotateResources
When set to true, the algorithm will better distribute the demand across alternate suboperations instead of using the preferred operation.
plan.webservice
Specifies whether we keep the plan in memory as a web service for quick incremental planning. This functionality is only available in the Enterprise and Cloud Editions.
Accepted values are false and true (default).

Demand forecasting parameters

The recommended default parameters for the demand forecasting module are different for weekly and monthly time buckets. The datasets parameters_month_forecast and parameters_week_forecast allow you to reset the defaults values applicable to your configuration.

Parameter Description
forecast.calendar Name of a calendar model to define the granularity of the time buckets for forecasting.
forecast.Croston_initialAlfa Initial parameter for the Croston forecast method.
forecast.Croston_maxAlfa Maximum parameter for the Croston forecast method.
forecast.Croston_minAlfa Minimum parameter for the Croston forecast method.
forecast.Croston_minIntermittence Minimum intermittence (defined as the percentage of zero demand buckets) before the Croston method is applied.
forecast.DeadAfterInactivity Number of days of inactivity before a forecast is marked dead and it’s baseline forecast will be 0. Default is 365.
forecast.DoubleExponential_dampenTrend Dampening factor applied to the trend in future periods.
forecast.DoubleExponential_initialAlfa Initial smoothing constant.
forecast.DoubleExponential_initialGamma Initial trend smoothing constant.
forecast.DoubleExponential_maxAlfa Maximum smoothing constant.
forecast.DoubleExponential_maxGamma Maximum trend smoothing constant.
forecast.DoubleExponential_minAlfa Minimum smoothing constant.
forecast.DoubleExponential_minGamma Minimum trend smoothing constant.
forecast.DueWithinBucket Specifies whether forecasted demand is due at the ‘start’, ‘middle’ (default value) or ‘end’ of the bucket.
forecast.Horizon_future Specifies the number of days in the future we generate a forecast for.
forecast.Horizon_history Specifies the number of days in the past we use to compute a statistical forecast.
forecast.Iterations Specifies the maximum number of iterations allowed for a forecast method to tune its parameters.
forecast.loglevel Verbosity of the forecast solver
forecast.MovingAverage_order This parameter controls the number of buckets to be averaged by the moving average forecast method.
forecast.Net_CustomerThenItemHierarchy This flag allows us to control whether we first search the customer hierarchy and then the item hierarchy, or the other way around.
forecast.Net_MatchUsingDeliveryOperation Specifies whether or not a demand and a forecast require to have the same delivery operation to be a match.
forecast.Net_NetEarly Defines how much time (expressed in days) before the due date of an order we are allowed to search for a forecast bucket to net from.
forecast.Net_NetLate Defines how much time (expressed in days) after the due date of an order we are allowed to search for a forecast bucket to net from.
forecast.Outlier_maxDeviation Multiple of the standard deviation used to detect outliers
forecast.populateForecastTable
Populates automatically the forecast table based on the item/location combinations found in the demand table using parent customer when available.
Default : true
forecast.Seasonal_dampenTrend Dampening factor applied to the trend in future periods.
forecast.Seasonal_gamma Value of the seasonal parameter
forecast.Seasonal_initialAlfa Initial value for the constant parameter
forecast.Seasonal_initialBeta Initial value for the trend parameter
forecast.Seasonal_maxAlfa Maximum value for the constant parameter
forecast.Seasonal_maxBeta Maximum value for the trend parameter
forecast.Seasonal_maxPeriod Maximum seasonal cycle to be checked.
forecast.Seasonal_minAlfa Minimum value for the constant parameter
forecast.Seasonal_minBeta Initial value for the trend parameter
forecast.Seasonal_minPeriod Minimum seasonal cycle to be checked.
forecast.Seasonal_minAutocorrelation Minimum autocorrelation below which the seasonal forecast method is never selected.
forecast.Seasonal_maxAutocorrelation Maximum autocorrelation above which the seasonal forecast method is always selected.
forecast.SingleExponential_initialAlfa Initial smoothing constant.
forecast.SingleExponential_maxAlfa Maximum smoothing constant.
forecast.SingleExponential_minAlfa Minimum smoothing constant.
forecast.Skip Specifies the number of time series values used to initialize the forecasting method. The forecast error in these bucket isn’t counted.
forecast.SmapeAlfa Specifies how the sMAPE forecast error is weighted for different time buckets.

Inventory planning parameters

Parameter Description
inventoryplanning.average_window_duration
The number of days used to average the demand to limit reorder quantity and safety stock variability over periods.
Default value : 180
inventoryplanning.calendar Name of a calendar model to define the granularity of the time buckets for inventory planning.
inventoryplanning.fixed_order_cost
Holding cost percentage to compute economic reorder quantity.
Default value: 20
inventoryplanning.holding_cost
Fixed order cost to compute the economic reorder quantity.
Default value: 0.05
inventoryplanning.horizon_end
Specifies the number of days in the future for which we generate safety stock and reorder quantity values.
Default: 365
inventoryplanning.horizon_start Specifies the number of days in the past for which we generate safety stock and reorder quantity values. Default: 0
inventoryplanning.loglevel
Controls the verbosity of the inventory planning solver.
Accepted values are 0(silent - default), 1 and 2 (verbose)
inventoryplanning.service_level_on_average_inventory
Flag whether the service level is computed based on the expected average inventory. When set to false the service level estimation is based only on the safety stock.
Default value: false

Inventory rebalancing parameters

Parameter Description
inventoryplanning.rebalancing_burnout_threshold
The minimum time to burn up excess inventory (compared to forecast) that can be rebalanced (in days). If the burn out period (Excess Quantity / Forecast) is less than the threshold, the rebalancing will not occur.
Default value: 60
inventoryplanning.rebalancing_part_cost_threshold
The minimum part cost threshold used to trigger a rebalancing. Parts with a cost below the threshold will not be rebalanced.
Default value: 100000
inventoryplanning.rebalancing_total_cost_threshold
The minimum total cost threshold to trigger a rebalancing (equals to rebalanced qty multiplied by item cost). Rebalancing requests with total cost below the threshold will not be created.
Default value: 1000000