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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

allowsplits

When set to true (default value), a sales order or forecast is allowed to be planned in multiple manufacturing orders. An order of eg 100 pieces can be planned with 2 manufacturing of 50 pieces.
When the parameter is set to false, this splitting is disabled. This will result in a plan with less manufacturing orders. The plan generation will be considerably faster, but can have additional delivery delays of the customer orders and forecasts.

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 this amount of time ahead of their due date. This creates extra safety in the delivery schedule and also moves all material and capacity needs early.
Default value: 0 days
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: 999 (wait indefinitely)
Default before release 5.0.0: 0 (don’t wait)

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.minimumdelay

Specifies a minimum delay the algorithm applies when the requested date isn’t feasible.
The default value is 0. This value should only be changed when the planning run is taking a long time and the log file shows that demands take many iterations to be planned - where the requested delivery date for each iteration is advancing only in tiny increments.

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).

WIP.consume_material

Determines whether confirmed manufacturing orders consume material or not.
Default is true.

WIP.consume_capacity

Determines whether confirmed manufacturing orders, purchase orders and distribution orders consume capacity or not.
Default is true.

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