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
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Feature: Provides a description of the feature being delivered.
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Module Impacted: Identifies the module impacted associated with the feature, if any.
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Scale: Identifies the size of the feature. Options are:
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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.
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Medium: These UI or process-based features are typically comprised of field, validation, or program changes. Therefore the potential impact on users is moderate
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Large: These UI or process-based features have more complex designs. Therefore, the potential impact to users is higher.
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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.
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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
New Feature Description
This section describes the new features.
Analytics and Planning
Markdown Export to PDS
The PDS integration of Markdown fact data from the data warehouse tables no longer requires inventory data to be present to populate the promotion markdown measures. Instead, if inventory data is not present, the export defaults the promotion markdowns into the "Reg Promo Markdown" measure. If inventory data is present, then it uses the inventory clearance flag to place promotion markdowns into either the Regular-Promo or Clearance-Promo measures accordingly.
Item Hierarchy Export to PDS
A new configuration option has been added to the item hierarchy
export to PDS to control whether item identifiers are included in
the labels or not. Update the parameter PDS_PROD_INCLUDE_ITEM_ID in the C_ODI_PARAM_VW table in Manage System Configurations
to change the behavior. When set to a value of N, the current functionality
is used, where only the product descriptions are included in the labels.
When changed to a value of Y, the item IDs are concatenated in front
of the descriptions, such that the final item labels shown in Planning
apps will be "12345 Product Desc" instead of just "Product Desc".
This change applies to all levels of item (SKU, parent, and grandparent).
The change affects the contents of the W_PDS_PRODUCT_D table so it is applied uniformly for all Planning applications.
Additional Demand from Merchandising
New AIF DATA jobs have been added to integrate the table REPL_ADDITIONAL_DEMAND from Merchandising to RAP. This data
is intended for use in IPO-Optimization and is not exposed or integrated
with any other applications in this release. This integration is only
supported with Merchandising version 23 or greater. The new job is
disabled by default in POM and you must enable it if you wish to extract
and integrate this data.
Purchase Order Alternate Load Method
The RAP foundation interface for purchase order details (ORDER_DETAIL.csv)has been enhanced with a new configuration
that optionally allows the interface to accept a full load of non-zero
order records each day, instead of being required to also send zero-balance
rows for completed order lines. The new parameter PO_FULL_LOAD_IND defaults to a value of N, which maintains the prior behavior of
the interface that requires posting of zero balance order lines. When
set to Y, the interface compares the incoming data with the currently
loaded purchase orders and zero out all order lines not included in
the file. In this way, you may choose to send a full snapshot of all
non-zero order lines every day to update purchase orders in RAP, instead
of being required to track which order lines have had updates.
Product Reclassification Changes
The RAP foundation interface handling of item reclassifications
has been altered in this release. The methods used to automatically
detect item reclassifications has been turned on by default, and the
programs used in earlier versions to get reclassification data from
Merchandising have been removed from the batch schedule. It is now
required for all environments to enable and use the C_ODI_PARAM_VW setting for RI_GEN_PROD_RECLASS_IND. The setting
should be updated to a value of Y on install or upgrade, but it is
still recommended to verify it from the Control & Tactical Center
after this release is applied to your environments.
Additional Jobs for Innovation Workbench (IW) Extensions
The AIF DATA batch schedule has been enhanced with 10 more jobs
that may be linked to Innovation Workbench (Innovation Workbench (IW))
extensions and programs. These jobs are placed near the beginning
of the batch schedule as the final set of jobs to execute after flat
files and staging data are imported to the database, but before any
processing begins to load that data into the data warehouse tables.
The intent of the jobs is to allow for customization of input data
prior to loading it, or creation of input data directly from Innovation
Workbench (IW) that is not coming from an external source. The jobs
have a naming convention of RI_IW_PRELOAD_CUSTOM_#_JOB and are disabled by default in the AIF DATA batch schedule. If you
need to use these jobs, you will need to configure them from the Manage
System Configurations screen first, then enable them in your POM nightly
cycle.
Default Language Codes
Some RAP data warehouse interfaces leverage a language code to
store translatable data. When no language code is specified, a default
language code is used, but it was being sourced from multiple configurations.
In this release, all integrations and interfaces have been adjusted
to consistently use the LANGUAGE_CODE parameter setup
in the C_ODI_PARAM table. The language code from RA_SRC_CURR_PARAM_G table is no longer used when integrating
Merchandising with RAP
Automatic File Conversion to UTF-8
In this release, all files sent to the RAP foundation interfaces
(such as PRODUCT.csv) are automatically converted
to UTF-8 character encoding before being processed. Some file encodings
are not supported by the interface loaders and caused failures or
data loss if used; this update explicitly converts all data files
to UTF-8 during the ZIP file upload process. If the files are already
in the proper format, then they are not altered; it is recommended
to extract and load the files in UTF-8 encoding from the start to
avoid any additional processing delay.
Inbound Interface Validations
Additional data validations were added to RAP foundation interface loads where the batch fails if they are violated. Input data must be corrected and re-sent when one of these rules are triggered, because these scenarios can cause batch failures in other parts of the platform. You may disable these rules from the Manage System Configurations UI if you want to skip these checks.
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Organization hierarchy data is validated to ensure a single ID/Description pair at the topmost level of the hierarchy.
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Product hierarchy levels having both internal and display IDs (for example, subclass and class) are validated to ensure they're exactly aligned such that the same internal ID cannot be associated with multiple display IDs.
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Product and organization file attributes having ID/Description pairs (such as Brand, Store Format, and Supplier) are validated to ensure every ID has a non-null description value.
POM Schedule Dependency Changes
The POM application now allows for dependencies to be defined between two processes instead of using individual job names, and starting with this release of RAP, the AIF DATA and AIF APPS schedules have been redesigned to leverage this behavior. After this upgrade, individual job dependencies are largely removed from the schedules and, in their place,is the processes that have dependencies on other processes. This change should occur automatically and does not require any manual setup steps. But, if you are actively working on an implementation or plan to make changes to AIF job dependencies after upgrade, be aware that you will see process names instead of job names in dependency screens.
Conversion of Intraday Cycles to Standalone Processes
To take advantage of recent POM enhancements and features, all intraday cycles in the AIF DATA and AIF APPS schedules are converted into new Standalone process flows. As standalone processes, you have the ability to schedule them using any time interval or frequency you want and are not be limited to 12 cycles. This enables faster implementations by allowing you to schedule automated data loads throughout the day without any of the restrictions that intraday cycles have. After upgrade, you are required to configure and schedule the new processes if you were previously using the intraday cycles to load data.
C_BULK_LOAD_STATUS Added in Manage System Configurations
The C_BULK_LOAD_STATUS table was added as a table
that can be managed for self-service purposes through the Manage System
Configuration screen. It has permission to delete rows and update
the Status column only.
Data Model Utility
Innovation Workbench has a new SQL utility for accessing the internal
data model information in RI/AIF without the need to query DBA tables
manually. The utility initially provides commands for getting table
and column definitions, datatypes, comments, and primary/foreign key
constraints about tables in the RADM01 and RABE01USER database schemas.
AI Foundation Cloud Service
Retail AI Assistant
Users are able to access a chat-bot from most AIF screens to ask questions, access data validation reports, and search the public documentation for help. A new icon appears in the corner of the screen which users can click to open the chat window.
Multi-Select Rows in Manage System Configurations
Users are now able to select multiple rows when editing parameters in the Manage System Configurations screen. Once all fields are selected and Edit is pressed, a popup opens, showing the first field to edit. Upon pressing OK on the popup, the next field selected is displayed and can be edited. Changes are saved when OK is clicked prior to moving to the next field to edit. In case of errors or not being able to save the modification made, an error message appears to call out what was not successfully updated. The user must correct the error before they can move to the next field to edit or close the popup.
Run Size Profile Optimization Across Multiple Nodes
Users now have the option to include multiple merchandise/location nodes within Size Profile Optimization runs. This results in better usability/scalability, because fewer optimization runs are needed. It can also result in more accurate size profiles due to data being pooled across different merchandise/locations.
Generate Size Profiles for Warehouses
This enhancement allows the retailer to get a size profile for the warehouse locations and drive the warehouse allocation based on that. The warehouse profiles are generated based on the sales issues (such as, outbound transfers) of the warehouse. Configuration is needed to enable this feature. Refer to the AI Foundation Implementation Guide.
Exclude Stockless Locations from Size Profile Generation
This enhancement filters out the stockless locations from the final output, when there is no allocation/buying for the location.
Database Operations Tool in Innovation Workbench
A new Database Operations tool is available in Innovation Workbench for customer administrators to perform certain functions in a self-service manner. They will be able to:
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Create and View AWR reports
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View top active SQL statements
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Configure a custom email server
Data Studio Upgrade
This release upgrades the Data Studio version in Innovation Workbench to 23.4.4.
Expose AIF Retail Home Tiles
AIF applications previously provided a set of tiles and integrations
to Retail Home. In this release, the tiles have all been mapped to
an additional super user role ANALYTIC_EXPERT_JOB (and its pre-production version), to allow some users to see all
tile data in a single dashboard without needing to switch between
multiple Retail Home views.
Store Profile Web Service
Store Profile information is now exposed through WebService. The store profiles are just an extension of the segments API. The segmentProductStoreProfileList is added as an addition to
the response payload. The contents are based on the cis_custseg_store_distr_exp_vw. The store profiles are a JSON array of product
external key and a list store profiles.
Inventory Planning Optimization-Demand Forecasting Integration Allows Multiple Forecast Run Types at Higher Levels
AI Foundation to IPO-DF integration has been enhanced to support any number of forecast run types at any level (leaf or higher nodes).
Life Cycle Forecast Method Supports Re-introduced Fashion Items
The seasonal curve generation step for the Life Cycle forecast method was modified to account for fashion items that are re-introduced vs. new fashion items.
Life Cycle Forecast Method Supports Future New Store Openings
The Base Demand calculation step for the Life Cycle forecast method was modified to account for future new store openings when the forecast level is higher than the store.
New Forecast Method Hierarchical Bayesian
A new Bayesian-based forecast method, named Hierarchical Bayesian, was added to AIF. This method uses regular price as one of the features to generate a forecast. It is useful for retailers focused on optimizing the regular price of items.
Warning Added to Forecast Run Submit Screen When No Data Available
Based on the forecast method selected in the Manage Forecast Configurations
screen and the value of the DFLT_LIFE_CYCLE parameter
set in the RSE_CONFIG table in the Manage System
Configurations screen, it is possible that there may not be available
items to be used by the Forecast Run. A warning message has been added
near the Run Submit button in the Forecast Run Review page
within the Test train stop of the Manage Forecast Configurations screen.
The message specifies the steps needed to make sure that there are
items available before the Forecast Run is submitted.
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.
N-tier Supply Chain Network
Retailers are able to provide data for their distribution/procurement network and different replenishment attributes such as lead time, rounding rules, and so on, through new interfaces. This data flows into IPO-IO and is used in the time-phased optimization of replenishment policies and PO/transfers. Configuration is needed to enable using this data in IPO-IO. Refer to the AIF Implementation Guide for details.
Define Review Schedule in Rules & Strategies
This allows the user to specify the review schedule in the Rules & Strategies UI. This is an alternative way to provide review schedule data if the user prefers to use the UI rather than the N-tier SCN interfaces.
Time Dimensioned Replenishment Attributes
This feature allows users to optimize the time-phased plan with time-dimensioned replenishment attributes (for example the lead time can have a different value for the holiday periods). The time-dimensioned attributes are provided through the N-tier SCN interfaces.
Additional Demand Quantity in ROQ Calculation
This feature allows retailers to provide additional demand values for certain item/locations in Merchandising. The time-phased optimization considers the additional demand when calculating the replenishment policies and order quantity.
Consume Warehouse Forecast in Time-phased Optimization
If a forecast is available for the item/warehouse it is used in the calculation of replenishment policies and ROQ, or the aggregated, unconstrained need of the locations at the lower tier are used instead.
Additional Error Codes and Database View for Time-phased Optimization
This enhancement allows users to know the reason for an item not being optimized; for example, a data error or an unhandled exception in the code. The view can be queried from Innovation Workbench.
Partial Pipeline for Trade-off Analysis
This enhancement allows the user to run the prediction portion of the trade-off analysis without running the training portion; for example, to predict the KPIs of different replenishment policies on new items using the previously trained models.
Run Pre-built Python Functions from Innovation Workbench (IW) Notebook
This support utility allows end-users to review and investigate the output of different types of runs. Currently, it is available for trade-off analysis runs.
Miscellaneous User Interface Enhancements
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Added a Date filter in the Replenishment Policies screen so that users can review recommendations based on the next review date.
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Show the value of safety stock in the PO and Transfer tables in the UI.
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Show the replenishment method in the replenishment policy table in the UI. This allows users to see which item/locations were optimized. The ones with method “Dynamic” are optimized in IO.
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Provide a recommendation detail view and graph view in the contextual area. A user can switch between the two using a new icon.
Option to Migrate Lifecycle Allocation & Replenishment User Setup and Parameters from One Environment to Another
For Inventory Planning Optimization — Lifecycle Allocation and Replenishment to run successfully, some setup activities need to be performed. This is a repetitive action that is very similar for every new environment. This enhancement allows implementers to do the initial setup of a new environment by copying the setup from an existing environment. For instance, the setup can be copied from the stage to the production environment. This helps implementers perform the initial setup task much faster.
Use the Calculated Assortment Start and End Date As Forecast Start and End Dates
One of the key activities in Lifecycle Allocation LAR Replenishment (LAR) is to define the assortment and its start and end dates. This enhancement enables the automation of the forecast start and end date overrides in Demand Forecast, by setting them to be the assortment start and end dates in Lifecycle Allocation LAR Replenishment (LAR).
Option to Load and Override the Lead Time and Supplier Information for Lifecycle Allocation LAR Replenishment (LAR) BOD Setup
One of the key setup activities in Lifecycle Allocation LAR Replenishment (LAR) is to define warehouse and supplier lead times. This happened either by calculating the measure using the business rule engine or by using the default. This enhancement also adds loading the measure and overriding the measure to the available options.
Lifecycle Pricing Optimization (LPO) Cloud Service
Generate Regular Price Recommendations
Users are able to generate regular price recommendations with this release. Refer to the AIF guides for the user and implementation details such as data interfaces, roles, rules, and configurations. Major features to note:
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A new forecasting method named Hierarchical Bayesian has been developed that incorporates regular price as a feature.
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Manage Business Rules have a new subcategory named Regular.
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There is a new tab in Manage Recommendation named Regular.
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There is a new button named What-if in Manage Recommendation to create what-if runs.
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There is the ability to compare up to three runs in LPO Overview for regular runs.
Retail Insights Cloud Service
Lifecycle Pricing Optimization Reporting
Retail Insights has added several new subject areas for reporting on Lifecycle Pricing Optimization results. You can directly access the LPO results in real time with RI reports. The data supported in this release are the LPO Runs, LPO Run Products, LPO Run Metrics, and LPO Recommendations.
Deals Dimension
Retail Insights has added a new integration with Merchandising
for Deal header information, and has also exposed the dimension for
reporting. This dimension is currently a standalone folder of attributes
for Merchandising data, primarily from the DEAL_HEAD table. It is not yet connected to other functional areas of the application.
Sales Metric Updates
Several new sales variance percent metrics (for example, Net Profit YTD % Var LY) have been added, which are not pre-scaled by a factor of 100 like older variance calculations. This is because Data Visualization formatting has built-in scaling when it displays percent values, so the calculation itself does not require it.
Retail Predictive Application Server Cloud Edition
Ability to Launch Data Visualization Report from RPASCE UI
With this release, users can launch Data Visualization reports from the application UI to the browser tab. This enables users to refer to the report in two separate tabs and makes it easier to review and analyze data while planning. This also reduces the log in to Data Visualization step as the reports are directly launched.
Note:
The administrator enables this feature by configuring data visualization reports using the self-service capability.Streamlined Planning UI
The RPASCE UI has been improved to provide more working space for users. Several changes have been implemented around the task menu, workspace page edge, and dimension tile area to enhance the user experience and increase the real estate of the application.
Sort Options for Real Time Alerts
Real Time Alerts can be sorted by alphabetical order. The current system sorts the real time alerts by the priority set in the Configuration Tools. The Admin user can change this setting using the self-service configuration property and choose to sort the alerts either by priority or alphabetical order.
Merchandise Financial Planning and Assortment Planning Cloud Service
Multiple Solution Capabilities
This enhanced platform capability allows the customer to have multiple instances of MFPCS and APCS within a single RAP planning data schema instance. Each instance of MFP or AP can have its own configuration, including business rules and constraints, used to fulfill the business requirements of a specific region. Each instance of MFP or AP can also have its own integration with POM and Retail Home. This capability allows the customer to use a single source of transaction data, which is then aggregated and used at different levels and different rules for each planning instance. The use of PDS also allows instances to share data, through shared measures. Finally, reporting for each instance or across instances is now possible.
Note:
This RAP instance still resides within a specific OCI region and does not span physical geographies.