1 Feature Summary
This chapter describes the feature enhancements in this release.
Noteworthy Enhancements
This guide outlines the information you need to know about new or improved functionality in the Oracle Retail Analytics and Planning applications update and describes any tasks you might need to perform for the update. Each section includes a brief description of the feature, the steps you need to take to enable or begin using the feature, any tips or considerations that you should keep in mind, and the resources available to help you.
Column Definitions
-
Feature: Provides a description of the feature being delivered.
-
Module Impacted: Identifies the module impacted associated with the feature, if any.
-
Scale: Identifies the size of the feature. Options are:
-
Small: These UI or process-based features are typically comprised of minor field, validation, or program changes. Therefore, the potential impact to users is minimal.
-
Medium: These UI or process-based features are typically comprised of field, validation, or program changes. Therefore the potential impact on users is moderate
-
Large: These UI or process-based features have more complex designs. Therefore, the potential impact to users is higher.
-
-
Delivered: Is the new feature available for use immediately after upgrade or must the feature be enabled or configured? If no, the feature is non-disruptive to end users and action is required (detailed steps below) to make the feature ready to use.
-
Customer Action Required: You must take action before these features can be used. These features are delivered disabled and you choose if and when to enable them.
Table 1-1 Noteworthy Enhancements
| Feature | Module Impacted | Scale | Delivered | Customer Action Required? |
|---|---|---|---|---|
| Retail Analytics and Planning | ||||
|
RAP Integration |
Small |
Enabled |
No |
|
|
RAP Integration |
Small |
Enabled |
No |
|
|
RAP Integration |
Small |
Enabled |
No |
|
|
RAP Integration |
Small |
Enabled |
No |
|
|
RAP Integration |
Small |
Enabled |
No |
|
| AI Foundation Cloud Service | ||||
|
AI Foundation |
Small |
Enabled |
No |
|
|
AI Foundation |
Small |
Enabled |
No |
|
|
AI Foundation |
Small |
Enabled |
No |
|
|
AI Foundation |
Small |
Enabled |
No |
|
|
AI Foundation |
Small |
Enabled |
No |
|
|
Size Profile Optimization |
Medium |
Disabled |
Yes |
|
|
Size Profile Optimization |
Small |
Enabled |
No |
|
| Inventory Planning Optimization (IPO) Cloud Service | ||||
|
Inventory Planning Optimization (IPO) Cloud Service – Inventory Planning |
All |
Small |
Enabled |
No |
| Retail Predictive Application Server Cloud Edition Server | ||||
|
Integration |
Small |
Disabled |
Yes |
|
|
Operational Utilities |
Small |
Disabled |
YesFoot 1 |
|
|
Batch |
Medium |
Disabled |
YesFoot 2 |
|
|
Retail Predictive Application Cloud Edition Client |
||||
|
Usability |
Small |
Enabled |
No |
|
|
Usability |
Small |
Enabled |
No |
|
|
Usability |
Small |
Enabled |
No |
|
|
Merchandise Financial Planning and Assortment Planning Cloud Service |
||||
|
MFP |
Small |
Enabled |
No |
|
|
MFP |
Small |
Enabled |
No |
|
Footnote 1 See feature description for more details.
Footnote 2 See feature description for more details.
New Feature Description
This section describes the new features.
Analytics and Planning
Purchase Order Dates in PDS Integration
When exporting data from the data warehouse to W_PDS_PO_ONORD_IT_LC_WK_A table, the date used on the export
data is derived from the OTB_EOW_DATE input column. In this release, the derivation of those dates has a
new configuration option PDS_EXPORT_DAILY_ONORD in the C_ODI_PARAM_VW configuration. When
set to Y, the export uses the dates provided by the customer on the input, the same as in prior versions
of RAP. When set to another value such as N (default value), it converts the OTB_EOW_DATE to a week-ending date, regardless of the input value provided. This option provides additional control over the export data
to align the dates with your RPAS base intersection.
CE Promotion Details to RAP
There is a new integration path to load promotion details from Customer Engagement (CE) to AI Foundation when all applications
are on Oracle’s next-generation architecture. There is a new staging table W_RTL_PROMO_CE_IT_LC_DS to accept
item/location level CE promotion details in the same format as existing Pricing Cloud Service integrations. This staging table
combines with any Pricing data on the same target table W_RTL_PROMO_IT_LC_D. This data load must be used
in conjunction with the existing CE data interface W_RTL_PROMO_CE_DS. If CE is on an older version or on-premise,
then it is possible to manually produce and load a flat file into this new interface.
Transformable Item Integration from MFCS
Prior to this release, MFCS transformable items (also known as break-to-sell items) were explicitly filtered out of all integrations and not allowed to come into RAP. Starting in this release, the product dimension interfaces allow such items to come across. This change only applies to the item and product hierarchy data at this time, allowing dimensions to synchronize between MFCS and RAP for the additional item type. All other integrations continue to exclude transformable item data as they do today.
Price History Load Changes
The RAP price history load program has been altered in this release in ways that make it incompatible with past releases. Price history data that is only partially loaded at the time of upgrade cannot be resumed with the new program without first raising a Service Request to Oracle for assistance converting your data to the new format required. This does not affect price history data which is already loaded in full, or price data being loaded through nightly batches.
Conversion of Processes to Flows
The main ad hoc data load processes for AIF DATA are converted into POM Flows in this release. Flows are a relatively new concept in POM that allows for parallel execution of jobs and multiple processes all organized within a single executable flow. The following processes will be affected by this change:
-
RDE_EXTRACT_DIM_INITIAL_ADHOC
-
RDE_EXTRACT_CE_DIM_INITIAL_ADHOC
-
RDE_EXTRACT_FACT_INITIAL_ADHOC
-
LOAD_DIM_INITIAL_ADHOC
-
LOAD_DIM_INITIAL_STAGE_ADHOC
After upgrade, if you are using any of these processes you should review which jobs are enabled or disabled in POM before running them again. Also, be aware that the ordering of the jobs in the UI may change because of new process and dependency configurations that are specific to flows.
AI Foundation Cloud Service
Oracle Digital Assistant Usage
The Oracle Digital Assistant (ODA) that provides chatbot functionality in some AI Foundation screens is disabled in non-production environments. If you want to interact with ODA, you must use production environments.
Data Studio Upgrade
This release of AI Foundation will include an upgrade to Data Studio to version 23.4.7 that is automatically applied as part of the upgrade process.
Configure Like Item Calculation and New Item Forecasting
The forecast configuration user interface in AI Foundation will be enhanced with new screens and options for setting up the like item calculations and new item forecasting process as part of creating new runs.
Forecast Override Source
Forecast setup (trainstop 3) will have a new field for Override Source that indicates the source of overrides for parameters, such as Manual, Forecast Run, or Estimation Run. This column is intended to show which run ID is applied to override the value of a particular parameter.
CDT Node Dynamic Grouping
The CDT calculation process will be enhanced with a method for automatically grouping together nodes that only represent a small portion of the data within the tree, based on a configurable threshold to determine when nodes should be merged.
Size Profile Escalation with Multiple Attributes
When creating runs in SPO, the Attribute field is now configurable to be either a single-select or multi-select
dropdown. Numerous screens throughout the user interface have also been updated to display the additional attributes as new
columns when they are used. A new configuration MULTI_ATTR_ESC_ENABLED_FLG determines the behavior. When
it is set to Y, the dropdown will allow up to three attributes to be selected instead of only one. The default
value is N, where it only allows a single attribute selection.
Inventory Planning Optimization (IPO) Cloud Service
With this update, Oracle is announcing the new IPO Cloud Service module: IPO Cloud Service – Inventory Planning, which provides visibility to the purchase and placement of inventory in a time-phased plan.
Inventory Planning Optimization (IPO) Cloud Service – Inventory Planning
This Cloud Service generates demand forecasts and inventory plans for the supply chain network. Demand forecasting allows retailers to accurately predict sales. Demand forecasts are the foundation of inventory planning that drives placement of product where it is most needed.
Retail Predictive Application Server Cloud Edition
Enhanced Export Measure Utility
The Export Measure utility can now be executed with an additional optional parameter to have a header in the exported files.
The new parameter H can be specified in the batch_exportmeas_list.txt file to either export
the header as the measure name or the fact name.
Enhanced Workbook Build Timeout
Long-running workbooks were originally designed to fail due to timeout after 24 hours. This behavior could potentially
block others from completing PDS tasks. To allow the customer finer-grained control over this behavior, an optional parameter
to specify the workbook timeout has been introduced. An administrator can set the RPAS_WORKBOOK_BUILD_TIMEOUT parameter using the List/Set/Unset PDS Environment Variables OAT task to specify their desired value, in seconds. If this
is unset, the default of 24 hours will be used.
Note:
Customer action is only required to take full advantage of the extended functionality. Otherwise, the product will continue to work with its current behavior.Improved RPASCE Batch Exports
Batch exports were originally designed to run sequentially. To further reduce batch runtimes, this has been improved to now run batch exports in parallel. To take advantage of this, the customer should update their batch control files to use the new format.
Note:
Customer action is only required to take full advantage of the extended functionality. Otherwise, the product will continue to work with its current behavior.Retail Predictive Application Cloud Edition Client Changes
Ability to Add Comments to Cell and Row Column Headers
This enhancement allows the users to add comments to cell and row column headers. The comment feature helps in keeping notes for quick reference during meetings and discussions. These comments can also be used as reminders for the user or others.
Merchandise Financial Planning and Assortment Planning Cloud Service
New Metrics Added
The following metrics have been added in this version:
-
An editable Week over Week Build measure for sales has been added which will help users quickly investigate current week sales data to previous week.
-
Location Count and Average Sales per Location Measure have been added in MFP. Location Count provides the total number of stores and Average Sales per Location Measure is defined by dividing the total sales by location count.