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, preferred format is YYYY-MM-DD HH:MM:SS
but most known formats to represent a date and/or time are accepted.
When the parameter is set to “today”, we use today 00:00 / midnight
as the currrent date.
When the parameter is set to “now”, we use the system time as current date.
If the parameter is missing, empty or has an uncognized format, the system
time is also 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.
|
excel_duration_in_days |
Determines whether numbers in spreadsheets are considered
as days or seconds. Default is true for days.
This parameter is only useful for backward compability.
|
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.
The default value is 0 days, which is a just-in-time plan, where we try
to plan all demands in backward scheduling mode from their due date.
Setting this parameter to a high value (eg 999) will result in a plan
where everything is planned ASAP in forward scheduling mode.
|
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.individualPoolResources |
Defines the behavior of aggregate resource.
A operation-resource record with quantity N for an aggregate resource
can mean either:
- Find a member resource with size N. Value false, default.
- Find N member resources of size 1. Value true.
|
plan.minimalBeforeCurrentConstraints |
By default the “why short or late” list for a sales order can include
many operations as lead-time and release-fence constraints.
When setting this option to true, we will limit the list to show only
the most constraining operation. This make the list easier to interpret
by users.
|
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 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.
|
plan.fixBrokenSupplyPath |
When set to true (which is the default), frepple will scan for
items that can’t be replenished any longer with purchase orders,
distribution orders or manufacturing orders.
FrePPLe automatically creates a dummy/fake supplier for such items.
In this way broken supply paths are automatically fixed. Planners
will need to review such dummy purchase orders and update the
master data to replace them with the correct replenishment method.
When this parameter is set to false, broken supply paths will result
in unplanned demand. Analysing the unplanned demand is in most cases
more complex than reviewing the dummy purchase orders.
|
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).
|
COMPLETED.consume_material |
Determines whether completed manufacturing orders consume material
or not.
Default is true.
|
COMPLETED.allow_future |
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.
|
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.
|
WIP.produce_full_quantity |
Controls how material is produced from partially completed
manufacturing orders.
When set to “false” (the default) a partially completed manufacturing
order is producing only the remaining quantity of material. We assume
that the on hand inventory has already been incremented to reflect
the produced material.
When set to “true” a partially completed manufacturing ordre will
still produce the full quantity of the material. We assume that the
produced material will only be booked as inventory when the
manufacturing order is fully finished.
|
Demand forecasting parameters
The recommended default parameters for the demand forecasting module are different for daily, weekly and monthly time buckets. The parameters with a value “default” in the parameters screen can get a different value depending on the configured time bucket.
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.Net_PastDemand |
When this parameter is false (default) only sales orders in the current and
future buckets net from forecast.
When set to true also older demands are used for netting forecast.
|
forecast.Net_IgnoreLocation |
When this parameter is true the forecasting netting doesn’t need a match
between location of the sales order and the forecast.
This can be useful when sales orders are often shipped from a non-standard
location.
|
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
|
inventoryplanning.report_min_horizon |
The top table in the inventory planning screen show forecast and
supply position info computed over the maximum of a) lead time period
and b) the value of this parameter.
If you have items with a short lead time, increasing this parameter will
result in improved and more stable results.
Default: 0 (i.e. only use the lead time)
|
inventoryplanning.replenish_roq_or_max |
When proposing a replenishment for a buffer we can calculate in two ways.
You select an approach that aligns with your planning process and ERP
configuration.
In “roq”-mode (the default) we replenish a (computed) fixed quantity.
In “max”-mode we replenish the stock to a certain (computed) max level.
Allowed values are “roq” (default) and “max”.
|
abc.classes |
Defines the ABC classes as a list of class:threshold pairs.
The list defines the name of the class and the cumulative portion of the
sales value over the most recent time period.
Default A:20 B:80 C
The default value is interpreted as:
|
abc.history |
Demand history upon which the ABC classification is based.
Default: 365
|
abc.future |
Defines the forecasting horizon (in days) over which the ABC
classification is computed.
Default: 0 (i.e. only use the demand history for the calculation)
|
abc.loglevel |
Verbosity of the ABC classificiation.
Possible values: 0 (default, silent) and 1 (verbose)
|
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
|
Report manager parameters
Parameter |
Description |
|---|---|
report_download_limit |
The maximum number of rows that are allowed to be downloaded with a
custom report. The limit protects against inefficient SQL report queries
that download excessive ammounts of data.
Default value: 20000
|