Forecast module

Main > frePPLe Manual > Extension modules > Forecast module

The forecast module provides the following functionality for representing forecasted future demand:

  • A new demand type to model forecasts.
    A calendar model is used to divide the time horizon into a number of time buckets. A demand is automatically created for each time bucket.
    See the example below.
  • Functionality for distributing / profiling forecast numbers into time buckets used for planning.
    This functionality allows to translate between different time granularities.
    The forecast entered by the sales department could for instance be in monthly buckets, while the manufacturing department requires the forecast to be in weekly or even daily buckets to generate accurate manufacturing and procurement plans.
    Another usage is to model a delivery date profile of the customers. Each bucket has a weight that is used to model situations where the demand is not evenly spread across buckets: e.g. when more orders are expected due on a monday than on a friday, or when a peak of orders is expected for delivery near the end of a month.
  • A solver for netting orders from the forecast.
    As customer orders are being received they need to be deducted from the forecast to avoid double-counting it. The netting solver will for each order search for a matching forecast and reduce the remaining net quantity of the forecast.
    For example, assume the forecast for customer A in January is 100 pieces, and we have already received orders of 20 from the customer.
    Without the netting algorithm the demand in January will be 120 pieces, which is (very likely) not correct.
    The netting solver will deduct the orders of 20 from the forecast. The total demand that is planned in January will then be equal to 100: 80 remaining forecast + 20 orders.
    The solver algorithm has logic to match a demand with the most appropriate forecast, and can also consider netting in previous and subsequent time buckets.
  • A forecasting algorithm to extrapolate historical demand data to the future.
    The following classical forecasting methods are implemented:
    • single exponential smoothing, which is applicable for constant demands
    • double exponential smoothing, which is applicable for trended demands
    • moving average, which is applicable when there is little demand history to rely on
    The forecast method giving the smallest mean absolute deviation (aka "mad"-error) will be automatically picked to produce the forecast.
    The algorithm will automatically tune the parameters for the forecasting methods (i.e. alfa for the single exponential smoothing, or alfa and gamma for the double exponential smoothing) to their optimal value. The user can specify minimum and maximum bounaries for the parameters and the maximum allowed number of iterations for the algorithm.

The module enables the following new objects:

Module configuration

The module support the following configuration parameters:

  • Net.CustomerThenItemHierarchy:
    As part of the forecast netting a demand is assiociated with a certain forecast. When no matching forecast is found for the customer and item of the demand, frePPLe looks for forecast at higher level customers and items.
    This flag allows us to control whether we first search the customer hierarchy and then the item hierarchy, or the other way around.
    The default value is true, ie search higher customer levels before searching higher levels of the item.
  • Net.MatchUsingDeliveryOperation:
    Specifies whether or not a demand and a forecast require to have the same delivery operation to be a match.
    The default value is true.
  • Net.NetEarly:
    Defines how much time before the due date of an order we are allowed to search for a forecast bucket to net from.
    The default value is 0, meaning that we can net only from the bucket where the demand is due.
  • Net.NetLate:
    Defines how much time after the due date of an order we are allowed to search for a forecast bucket to net from.
    The default value is 0, meaning that we can net only from the bucket where the demand is due.
  • Forecast.Iterations:
    Specifies the maximum number of iterations allowed for a forecast method to tune its parameters.
    Only positive values are allowed and the default value is 10.
    Set the parameter to 1 to disable the tuning and generate a forecast based on the user-supplied parameters.
  • Forecast.madAlfa:
    Specifies how the MAD forecast error is weighted for different time buckets. The MAD value in the most recent bucket is 1.0, and the weight decreases exponentially for earlier buckets.
    Acceptable values are in the interval 0.5 and 1.0, and the default is 0.95.
  • 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.MovingAverage.buckets
    This parameter controls the number of buckets to be averaged by the moving average forecast method.
  • Forecast.SingleExponential.initialAlfa,
    Forecast.SingleExponential.minAlfa,
    Forecast.SingleExponential.maxAlfa:
    Specifies the initial value and the allowed range of the smoothing parameter in the single exponential forecasting method.
    The allowed range is between 0 and 1. Values lower than about 0.05 are not advisible.
  • Forecast.DoubleExponential.initialAlfa,
    Forecast.DoubleExponential.minAlfa,
    Forecast.DoubleExponential.maxAlfa:
    Specifies the initial value and the allowed range of the smoothing parameter in the double exponential forecasting method.
    The allowed range is between 0 and 1. Values lower than about 0.05 are not advisible.
  • Forecast.DoubleExponential.initialGamma,
    Forecast.DoubleExponential.minGamma,
    Forecast.DoubleExponential.maxGamma:
    Specifies the initial value and the allowed range of the trend smoothing parameter in the double exponential forecasting method.
    The allowed range is between 0 and 1.

Demand subclass demand_forecast

All fields available on the demand model are allowed on a forecast.
In particular the item and customer field are important, since these are used to match a demand with a certain forecast for netting.

The following fields are available in addition to the demand fields.

Field Type Description
calendar non-empty string Name of the calendar used to define time buckets for distributing the forecast numbers.
discrete boolean Specifies whether forecast should be rounded to integer numbers.
The default value is true.
bucket
buckets 		xml Speficies the forecast value for a date range.
See example below.

Solver solver_forecast

This solver runs the forecast netting calculations.
The solver loops through the demands in order of their priority, and for each demand a matching forecast is searched. When a matching forecast is identified, the solver looks for a time bucket to net from: first in the bucket where it is due, then in earlier buckets within the chosen time window, and finally in later buckets within the chosen time window. The net forecast in the forecast buckets is decreased.

Note the the profiling of the forecast is not handled by this solver. The profiling happens during the data load, i.e. when the forecast demand is read in.

In addition to the regular solver fields, the following fields are available.

Field Type Description
loglevel 0 - 2 Amount of logging and debugging messages:
  • 0: Silent operation. Default logging level.
  • 1: Log demands being netted and the matching forecast.
  • 2: Same as 1, plus details on forecast buckets being netted.

Example XML structures:

  • Loading the module 
 <plan>
   <commands>
     <command xsi:type="command_loadlib" filename="mod_forecast.so">
       <parameter name="Net.CustomerThenItemHierarchy" value="true" />
       <parameter name="Net.MatchUsingDeliveryOperation" value="true" />
       <parameter name="Net.NetEarly" value="P7D" />
       <parameter name="Net.NetLate" value="P7D" />
     </command>
   </commands>
 </plan>
  • Forecast input 
 <plan>
   <demands>
      <demand name="Forecast 1" xsi:type="demand_forecast">
        <item name="Product 1" />
        <customer name="Customer 1" />
        <calendar name="planningbuckets" />
        <discrete>true</discrete>
        <buckets>
          <bucket>
            <start>2007-01-01T00:00:00</start>
            <end>2007-02-01T00:00:00</end>
            <total>200</total>
          </bucket>
          <bucket>
            <start>2007-02-01T00:00:00</start>
            <end>2007-03-01T00:00:00</end>
            <total>200</total>
          </bucket>
        </buckets>
      </demand>
   </demands>
 </plan>
  • Netting customer orders from the forecast 
 <plan>
   <commands>
      <command xsi:type="command_solve">
        <solver name="Netting" xsi:type="solver_forecast">
          <loglevel>1</loglevel>
        </solver>
      </command>
   </commands>
 </plan>

Example Python code:

  • Adding or changing a forecast 
    it = frepple.item(name="item")
    cust = frepple.customer(name="customer")
    cal = frepple.calendar(name="planningbuckets")
    fcst = frepple.demand_forecast(name="My forecast", 
      item=it, customer=cust, calendar=cal)
  • Creating a time series forecast
    The first argument is the demand history in previous buckets.
    The second argument are the time buckets where we want to create a forecast value. 
    thebuckets = [ i.start for i in thecalendar.buckets ]
    fcst.timeseries([10,12,9,11,8,15,19,11], thebuckets)
  • Netting customer orders from the forecast 
   frepple_forecast.solver_forecast(name="Netting", loglevel=1).solve()