| Oracle® Retail Demand Forecasting Implementation Guide Release 16.0 |
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This chapter describes the interaction between RDF and other applications and the script used to load demand data.
RDF and RDF Cloud Service Integrations
The supported integrations differ between RDF and RDF Cloud Service. Table 3-1 shows the supported integrations.
Table 3-1 RDF and RDF Cloud Service Supported Integrations
| Integration | RDF | RDF Cloud Service |
|---|---|---|
|
RMS to RDF |
Yes |
Yes |
|
RDF to RMS |
Yes |
Yes |
|
APC-RPO to RDF |
Yes |
No |
|
RPO to RDF |
Yes |
No |
|
RDF to RPO |
Yes |
No |
|
RDF to APC-RO |
Yes |
Yes |
|
RDF to AIP |
Yes |
Yes |
Oracle Retail Application Integration
RDF is integrated with the following Oracle Retail applications listed by group:
Figure 3-1 shows the integration of the Integrated Inventory Planning Suite applications and the flow of data among those applications. Note that Figure 3-1 shows a replenishment system. This can be AIP or any other replenishment system. The demand forecasting application can be RDF or any other forecasting system. RDF forecasts are used as input to RO for simulation-determined replenishment parameters. RDF forecasts and associated statistics are used by AIP to plan time-phased replenishment
This solution supports data sharing among these applications. Note that the data sharing functionality is not dependent on the presence of all these applications. The defined data sharing between any of the applications works for the entire suite as well as for a subset of the applications.
The data flow from RDF to AIP includes weekly and daily forecasts and cumulative intervals.
For detailed information about the RDF to AIP interface, see the following:
Oracle Retail Advanced Inventory Planning Implementation Guide
RDF's forecasts are important inputs for APC-RO. Forecasts are provided for initial load into APC-RO. A rolling 52 forecasts are generated and exported to APC-RO, each of the forecasts starts one week after another. The main purpose of the integration is to generate RDF forecast in RDF GA domain and export the forecasts from a RDF domain and covert the forecasts into the format required by APC-RO.
APC-RO provides to RDF a list of item/stores and RDF only exports the forecasts for those item/stores to APC-RO.
For detailed information about the RDF to APC-RO interface, see the following:
Oracle Retail Analytic Parameter Calculator for Replenishment Optimization Implementation Guide
APC-RO provides to RDF a list of item/stores and RDF only exports the forecasts for those item/stores to APC-RO.
For detailed information about the APC-RO to RDF interface, see the following:
Oracle Retail Analytic Parameter Calculator for Replenishment Optimization Implementation Guide
RDF integrates with Retail Merchandising System (RMS) to receive foundation data. In addition, it also sends weekly and daily forecasts to RMS (replenishment and allocation). These descriptions explain the data flows between RMS and RDF.
For detailed information about the RMS and RDF interface, see the following:
Appendix A, "RPAS and RDF Integration with RMS"
Oracle Retail Merchandising System Operations Guide, Volume 1
The data flow from RMS to RDF includes:
Product hierarchy
Location hierarchy
Calendar hierarchy
The data flow from RDF to RMS includes:
Weekly and daily forecasts and cumulative intervals
This section describes the integration and data flow between APC-RPO, RPO, and RDF.
The data flow from APC-RPO to RPO sends:
Item and cross item elasticities of items. RPO uses these elasticities to optimize prices.
Maximum and minimum historical prices of items. RPO uses this data to optimize prices.
Anchor prices of items. Anchor prices are the baseline prices that APC-RPO uses to calculate price elasticity. RPO uses the anchor prices to calculate price drift metrics.
Maximum price increase and decrease percentages, self-item elasticity standard errors, and self-item elasticities t-statistics. RPO uses the maximum price increase and decrease percentages to setup up the default minimum and maximum price percentage change. Meanwhile, the RPO user can refer to the self-item elasticity standard error and t-statistics to adjust the price constraint.
The data flow from APC-RPO to RDF sends:
Regular price self elasticities to RDF. The self elasticities, together with the price plan, allow RDF to calculate the item elasticity lift.
Regular price cross-item elasticities to RDF. There are two types of cross-item elasticities: halo and cannibalization. These cross elasticities, together with the price plan, allow RDF to calculate the cross-item lift for both halo and cannibalization effects related to the corresponding elasticities.
Anchor prices to RDF for reference purposes.
Historical prices. RDF uses these to calculate the regular price lifts.
The data flow from RPO to RDF sends the price plan that allows RDF to calculate the three components of the regular price lift: regular price self effect, regular price cannibalization effect, and regular price halo effect.
The data flow from RDF to RPO sends forecasts to RPO. These forecasts represent the base demand at the item/store level. RPO aggregates the forecasts to the item/price zone level and uses that data to optimize prices.
