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.
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
|
Plan archiving parameters
Frepple keeps a history of the key metrics of your plan. These metrics are used to display overall trends in your plan, and can also be useful to debug the evolution of certain data elements over time.
Parameter |
Description |
|---|---|
archive.frequency |
Frequency of history snapshot. Accepted values are “week”, “month” and
“none”.
| Default value: week
|
archive.duration |
Archived data older than this parameter in days will be deleted.
Default value: 365
|
Quoting parameters
Parameter |
Description |
|---|---|
quoting.loglevel |
Set to non-zero to get a verbose log of quoting messages. Default is 0. |