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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.
The table below shows the parameters that are recognized by the standard application.
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.
Current date of the plan, preferred format is YYYY-MM-DD HH:MM:SS but most known formats to represent a date and/or time are accepted. If the parameter is missing or empty the system time is used as current date.
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.
Time units to be used for the resource report.
Accepted values are: hours, days, weeks.
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.
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)
Name of a calendar to align the end date of new manufacturing orders, purchase orders, distribution orders and delivery orders 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.
Controls the verbosity of the planning log file.
Accepted values are 0 (silent – default), 1 (minimal) and 2 (verbose).
Specifies a minimum delay the algorithm applies when the requested date isn’t feasible.
The default value is 3600. 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.
Controls whether safety stock is planned before or after the demand.
Accepted values are false (default) and true.
When set to true, the algorithm will better distribute the demand across alternate suboperations instead of using the preferred operation.
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).
Determines whether completed manufacturing orders consume material or not.
Default is true.
We assume that completed operations are always ending in the past. The planning engine will automatically adjust the end date to enforce this rule, unless this parameter is set to true.
Default is false.
Determines whether confirmed manufacturing orders consume material or not.
Default is true.
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.
|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|
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
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.|
Holding cost percentage to compute economic reorder quantity.
Default value: 20
Fixed order cost to compute the economic reorder quantity.
Default value: 0.05
Specifies the number of days in the future for which we generate safety stock and reorder quantity values.
|inventoryplanning.horizon_start||Specifies the number of days in the past for which we generate safety stock and reorder quantity values. Default: 0|
Controls the verbosity of the inventory planning solver.
Accepted values are 0(silent - default), 1 and 2 (verbose)
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
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
The minimum part cost threshold used to trigger a rebalancing. Parts with a cost below the threshold will not be rebalanced.
Default value: 100000
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