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NOTE: Forecast150 is released in v15, and the Forecast special expression is decommissioned as of v15.0.1.

 

Prior versions of Oracle Retail Demand Forecasting (RDF) use only the Forecast procedure both to generate the forecast and to estimate the promotion effects. RDF v15 introduces two procedures:

  • Forecast150 procedure — generates the forecast for all forecast methods (the focus of this blog)
  • CausalEstimate procedure — calculates the promotion effects at both final or source level.

 

While the main purpose of the Forecast150 procedure is to support aggregate causal modeling, there are additional advantages over the previous special expression, Forecast, which are described in the v15 Release Notes:

 

Usability

The new forecast special expression is more flexible. Building and maintaining rules is easier because it accepts the input parameter measures at different intersections as long as the input can be mapped to the forecast level. For instance, for an item/store/week forecast level, the new forecast expression can accept the forecast start date as scalar, at class/region or department level.

 

Performance

The new special expression is coded in a new high performance I/O caching framework, which leads to better performance.

 

Modularization

In 15.0, the causal effects estimation is split from forecasting. This modularization allows users to specify only the meaningful inputs for specific steps in the forecasting process. This simplifies the RPAS rules and makes them easier to write and maintain.

 

Accuracy

The new forecast expression together with the new causal estimation special expression are the backbone of the Aggregated Causal Modeling approach discussed previously. This has been proven to improve the promotional forecast accuracy and robustness.

 

Please review the v15 and 15.0.1 Release Notes and Configuration Guide for RDF v15 and v15.0.1 for details on configuring and using Forecast150.

 

Customers upgrading to v15.0.1 should take particular note of the new expression with its new signature and plan to update their configuration accordingly.

 

Take advantage of the new and improved forecasting special expression, Forecast150, today!

Highly available infrastructure has risen in popularity in recent years, as companies strive to match their critical computing environment’s availability to near 7x24x365 requirements.  A new case study has been published to the Oracle Retail knowledge base that describes how an Oracle Linux high-availability environment can be configured to support Oracle Retail applications. Using Oracle Fusion Middleware active-active clustering and Oracle Real Application Clusters (RAC) databases, Oracle Retail applications can be deployed in a fault tolerant computing environment to provide high availability and scalability.

 

This case study includes the following documents for Release 15.0:

 

  • Oracle Retail High-Availability Case Study Introduction
  • Oracle Retail High-Availability Case Study Oracle Linux
  • Oracle Retail High-Availability Case Study RAC (Real Application Clusters Environment)
  • Oracle Retail High-Availability Case Study Fusion Middleware Cluster
  • Oracle Retail High-Availability Case Study - Retail Applications Installation
  • Oracle Retail High-Availability Case Study - Retail Integration Suite Installation

 

For all the details, please visit Document 2151366.1 - Case Study: Creating a High Availability Environment for Oracle Retail 15.0.

 

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