A well established fact is that improved forecast accuracy leads to many downstream
improvements not only in operations, but in a variety of business areas such as customer
service and asset management. This white paper concerns itself with the improvement of
forecasts that support the management of a company’s supply chain.
In some cases even the product-level forecast is too random to be useful, which leads
to an aggregated level such as product sub group, group or category level. Even though
forecasting at these summarized levels produces improved statistical results, the challenge
of disaggregation to the prime procurement or manufacturing level (product) exists.
Methods of breaking down aggregated forecasts will be covered in subsequent white papers,
along with the consideration as to the use of units or currency.
The forecast accuracy improvement problem can be approached by examining the underlying
elements that must be considered before the analysis and adjustment process is complete
and a routine production forecasting process is operational.
There are some FORECAST IMPROVEMENT METHODS:
1. Create at least 2 additional demand data measures
2. Filter out each customer’s product sales from DM1 history where not purchases were
made in the last 12 months.
3. Produce a Baseline Forecast using the product elimination DM1 adjustments
4. Create a Stratum Planner Workbench view for analysis