1 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

Analytics and Planning Enhancements

Integration and Interface Updates

This release of Retail Analytics and Planning (RAP) applications includes the following updates to data integrations and commonly used batch programs in the AIF DATA batch schedule:

  • All ad hoc ZIP file processing jobs (such as REPROCESS_ZIP_FILE_UNLOAD_JOB and HIST_ZIP_FILE_UNLOAD_JOB) will now fail if the ZIP file contains any folders, as folders are not allowed in AIF DATA file processing. A folder in an AIF DATA zip file (such as RAP_DATA_HIST.zip) typically means the ZIP is malformed and will not have the files necessary to proceed with your data load activity. Prior to this change, the program would complete without error, but later steps in the process would fail because data files could not be located within the ZIP file.

  • BATCH_RUN_NIGHTLY_JOB should be enabled by default and is a required program for all customers. This job is related batch execution tracking and maintenance.

  • W_RTL_CLSTR_HDR_D_JOB has a new load method. Previously, it was loading data incrementally with no ability to delete or close old records. It now performs full loads with automatic deactivation of old records, which is comparable to how other similar interfaces were already working.

  • W_RTL_PROMO_D_JOB has a new load method. Previously, it was inserting overlapping promotion records if a promotion dropped out of the data but was reintroduced later with the same IDs and start/end dates. The load will now reopen the original record and update it instead of inserting a second record. This only applies to full snapshot loads; incremental loads are not affected.

  • ETLREFRESHGENSDE_JOB has been modified to only consider RDE jobs when checking the status in C_LOAD_DATES table for the latest batch run, instead of checking all jobs regardless of type. This job must be enabled for customers using RDE to extract data from MFCS.

  • ETL_REFRESH_RI_JOB has been added to the schedule to check all non-RDE jobs in the AIF DATA schedule for errors in the C_LOAD_DATES table, and this job is enabled by default and required for all customers. This job replaces ETL_REFRESH_JOB which is no longer in the batch schedule.

  • A new field from MFCS for the Selling Phase Start Date has been incorporated fully into the inventory position integration with RAP, including extract program RDE_EXTRACT_FACT_P7_INVILDSDE_JOB, file load programs W_RTL_INV_IT_LC_DY_FS_JOB and STG_SI_INVENTORY_JOB, and data warehouse programs such as W_RTL_INV_IT_LC_DY_F_JOB. This does not include any downstream usage of the new field, only the integration and data warehouse updates.

  • RDE_EXTRACT_DIM_P1_PRDITMLSDE_JOB is enhanced to include style lists in addition to SKU lists, which is done by spreading the styles down to SKU level and then loading them to RI to be used in existing reporting functionality.

  • RDE_EXTRACT_FACT_P3_POONORDILDSDE_JOB has been enhanced to configure which exchange rate dates are used to convert purchase order currency amounts. By default, it will use the current behavior of converting on the current business date, but changing the configuration option PO_EXCHANGE_DT_TYPE to a value of A will allow it to use the PO approval date instead. W_RTL_PO_ONORD_IT_LC_DY_F_JOB was also modified to support dynamically selecting the exchange rate based on the date specified.

  • RDE_EXTRACT_FACT_P3_POONORDILDSDE_JOB has been enhanced to configure if pack item orders should be extracted at their original item level or exploded down to component item level. By default, the current behavior is maintained, which converts all orders into their component items. If pack items are required for downstream application usage, then the parameter PO_PACK_LEVEL_IND must be updated to a value of Y.

  • W_RTL_PRICE_IT_LC_DY_F_JOB now maintains two new calculated fields for prior base cost (amount and date). These fields are derived from cost changes on the field BASE_COST_AMT_LCL. When the value changes on the incoming data, the prior value in the data warehouse is captured along with the date it changed.

  • W_PDS_PRODUCT_D_JOB and W_PDS_ORGANIZATION_D_JOB have been enhanced with multiple configuration options to control inclusion/exclusion of hierarchy IDs from the hierarchy labels. By default, all new options will maintain the current extract behavior.

  • W_RTL_PLAN5_PROD5_LC5_T5_FS_SDE_JOB has been enhanced with a configuration option RI_PLAN5_SOURCE that allows you to select the AP export table to use for the integration. By default, it will continue to use AP_PLAN1_EXP, but you may change it to use AP_PLAN2_EXP or AP_PLAN3_EXP if those tables will contain the data you need to extract instead.

This release also includes the following notable additions to common batch processes in the AIF APPS batch schedule.

  • RSE_CHECK_POST_INSTALL_RUN_JOB is a new maintenance program that will be enabled by default for all customers. It is used to monitor certain background activities which occur after application patching and should not be disabled.

  • A new set of location hierarchy jobs has been added with prefix RSE_WH, such as RSE_WH_HIER_LOAD_JOB. These jobs are used to build and maintain certain warehouse hierarchy data used within AIF applications and should be kept enabled for most customers.

  • RSE_PL_PROMO_HIER_LOAD_JOB is a new job for managing the promotion hierarchy data for forecasting usage. It populates table RSE_PL_PROMO_HIER during the nightly batch. The existing RSE_PROMO_OFFER_EXPORT_PROCESS has a new dependency added on this program.

  • RSE_FLEX_GROUP_DTL_DEFAULT_LIFECYCLE_STG_JOB and RSE_FLEX_GROUP_DTL_CUSTOM_STG_JOB are added to the existing flex group load process to provide additional ways to generate flex groups for forecasting. These should be disabled if flex groups are already being loaded from flat files or other means, as they are mutually exclusive with the older load methods and will overwrite the RSE_FLEX_GROUP_DTL_STG table when run.

  • PMO_ACTIVITY_WO_SHIP_LOAD_SETUP_JOB and PMO_ACTIVITY_WO_SHIP_LOAD_PROCESS_JOB are added to process warehouse outbound shipments data for use in certain forecasting methods. They will be enabled by default if you currently use PMO_ACTIVITY_WH_LOAD_PROCESS_JOB in your batches.

  • New jobs are added with the prefix PRO_REG for Regular Price Optimization data processing and execution. Some of these jobs may be enabled by default due to existing PRO_* or PMO_* programs active in your batch schedule, but you may disable them if not using Lifecycle Pricing Optimization for regular pricing decisions.

  • New jobs are added with the prefix RSE_ODA for Oracle Digital Assistant data processing and export activities.

Product and Organization Hierarchy Configurations

The export of product and organization hierarchies from the data warehouse to Planning (PDS) can be configured to change format options for certain identifiers and labels. To change the hierarchy format options, you must update the associated parameter in the C_ODI_PARAM table from the list below.

  • PDS_ORG_INCLUDE_HIER_ID – Include or exclude organization hierarchy IDs from the labels (at District and above).

  • PDS_ORG_INCLUDE_LOC_ID – Include or exclude the location IDs from the labels at store/warehouse level.

  • PDS_PROD_INCLUDE_HIER_ID – Include or exclude product hierarchy IDs from the labels (at Subclass and above)

  • PDS_PROD_INCLUDE_ITEM_ID – Include or exclude item IDs from the labels.

  • ITEM_PARENT_DIFF_SEPARATOR – Change the separator between the item parent ID and differentiator ID.

  • ITEM_PARENT_DIFF_DESC_SEPARATOR – Change the separator between the item parent label and differentiator label.

Calendar Hierarchy Flex Attributes

A new interface has been added for extending the calendar hierarchy exported to PDS with flex attributes and levels. A new input file CALENDAR_ALT.csv has been added to the RAP foundation interfaces and it will function similarly to existing hierarchy alternate files for product/location data. The data warehouse table storing this data is W_RTL_MCAL_DAY_FLEX_D. The export to W_PDS_CALENDAR_D includes all the fields from this new file automatically if there is data available in the data warehouse table. You may alter your interface configuration in PDS to include these added fields to extend your calendar hierarchy.

Warehouse Shipments Export

The PDS export for gross sales used by IPO-DF has been enhanced to optionally include warehouse outbound shipments on table W_PDS_GRS_SLS_IT_LC_WK_A. This requires updating the C_ODI_PARAM table parameter PDS_INCLUDE_SHIPMENT_SALES to a value of Y. The warehouse shipments are combined with store sales data but use a retail type code of S to differentiate them. The export to PDS will include historical shipments the first time the interface runs, and new/changed shipments in every run after that (using the same logic as existing sales exports). The source for this data is the W_RTL_SHIP_DETAILS_D and W_RTL_SHIP_IT_LC_DY_F tables in the data warehouse. Refer to the RAP Implementation Guide for the column mapping from the source tables to the target table.

Intraday Fact Loads

A new standalone flow named RI_INTRADAY_FLOW_ADHOC has been added to the AIF DATA schedule in POM. This process flow enables the intraday loading of a subset of data warehouse subject areas in addition to nightly batch cycles. The intraday flow performs the following high-level steps:

  1. Process a RAP_INTRADAY.zip file containing only the data to be updated intraday (MFCS is not a supported source of intraday data in this release unless implemented as a custom extension).

  2. Stage and load the supported intraday dimension and base fact tables (but not aggregates).

  3. Capture the intraday records in temporary tables to be used again during nightly batch processing.

  4. Capture rejected records from intraday processing for review and correction before the next nightly cycle.

In this release, the following subject areas support intraday processing:

  • Inventory Position (current positions only)

  • Pricing (current positions only)

  • Purchase Orders (dimension and current positions only)

  • Allocations (dimension and base fact)

  • Shipments (dimension and base fact)

Intraday fact processing allows you to make updates to specific data warehouse tables outside of the nightly batch, while also preserving those changes for automatic inclusion in the next nightly batch cycle without providing the same data again. You could also leverage this data in custom extensions in Innovation Workbench that require more frequent updates. The AIF Operations Guide will include complete details on the usage and functionality of the intraday process.

AI Foundation Cloud Service

Portfolio Optimization, a New Module for Better Portfolio Distribution

This new module introduced in the AIF Cloud Service provides an AI-driven recommendation for better portfolio distribution. Based on sales history as wells as current trends, this AI module takes into consideration various risk approaches and constraints, helping retailers make more informed decisions. It provides insights on the growth trade-off between different product areas. The module results can then be used as an input to Merchandise Financial Planning or just as an ad hoc exercise for financial driven planning analysis.

Warehouse Hierarchy Data

New programs in the AI Foundation applications (AIF APPS) batch can create and maintain a warehouse hierarchy structure comparable to the one used in Planning applications. The new jobs have the naming prefix of RSE_WH* and will populate a new hierarchy type (20) within existing tables such as RSE_LOC_HIER and RSE_LOC_SRC_XREF. The new hierarchy data will include the same store data as the standard type (2) plus the added hierarchy levels for warehouses that replicate each warehouse ID and label up to all higher levels of the hierarchy.

Pre-Pack Optimization, a New Module that Recommends Optimal Pre-Pack Configurations

This new module introduced in the AIF Cloud Service includes the following functionality and more:

  • Recommends optimal pre-pack configurations that meet store-specific need while maximizing supply chain efficiencies.

  • Considers vendor constraints, size min/max requirements and other common pre-pack related constraints with scenario modeling support.

  • Users can learn business impact on changes to constraints.

  • Approval process enables users to review and finalize pre-pack configurations with information on benefits from recommended pre-pack configurations.

Using Warehouse Shipments as Part of the Forecasting Process

AI Foundation was enhanced to use Sales and Warehouse outbound shipments as forecast data sources. This allows the forecast of store and warehouse demand in a single run type, thereby speeding up the forecasting generation and reviewing processes, as well as improving performance and avoiding the overhead of multiple run types

Oracle Digital Assistant Updates

The Oracle Digital Assistant (ODA) is an AI-powered platform that enables users to interact with various business applications and services through natural conversations via chat interfaces.

In prior updates, this feature was available for new provisioning only. As of this update, ODA is supported for upgrades as well. ODA appears as a chat icon within the UI and requires a specific user role to access it. Specific to the RPASCE Client, ODA is enabled for Merchandise Financial Planning (MFP) and Assortment Planning (AP) applications. Also in this update, there is expanded support in AIF for additional, data-related use cases and enhanced answer formatting with Generative AI. You can access the digital assistant from the chat icon present on various AI Foundation user interface screens. Generative AI features are only available for customers on database version 23ai. If there is an issue accessing ODA, customers are advised to log a service request (SR), so that Oracle can provide a resolution.

Inventory Planning Optimization (IPO) Cloud Service Enhancements

Simple Pack

Inventory Planning Optimization will now replenish and distribute non-sellable simple packs. A simple pack is a pack with any multiple of a single-sellable SKU. When defined, simple pack items, and inventory will flow into IPO.

A pack requires a complete supply chain definition, but replenishment is driven from component items. Therefore, packs do not require replenishment rules.

Shortfall Priorities by Product/Store

When warehouse inventory is less than the need of its destinations, the inventory is shared according to priority groups and inventory boundaries. Priority groups are flexible and could represent any grouping such as store grade. You will assign product/stores a priority group in Rules Management.

Priority groups and fixed inventory boundaries are used to create a matrix that you will configure to define the order in which to satisfy each priority group’s inventory level. By default, all stores are prioritized together (treated equally) until you assign priority groups.

Excess Push

Inventory above need will be pushed out of a warehouse when cross-docking through the warehouse.

A configured surplus priority matrix defines the order in which to satisfy priority groups and inventory levels allowing you a level of control over which stores are best suited to hold and/or sell the excess inventory.

Purchase Order Approval by Supplier Minimum Constraint

Automatic approval of an IPO-generated purchase order (PO) will now require supplier minimum constraints to be met. When constraints are not met, the PO will require review and manual approval.

When defined, supplier minimum constraints will flow into IPO. Depending on the constraint types, new item/supplier dimension data may be required. UOM conversion factors are also needed when supplier purchase UOMs differ from the supplier minimum constraint UOMs.

GenAI-Powered Features

GenAI-powered capabilities are now integrated into the IPO screens, enhancing the user experience through features such as explainability, data queries, troubleshooting, and human-in-the-loop interaction.

The GenAI chatbot is accessible from any IPO screen. Users can ask natural language questions such as: "What is the total transfer quantity by subclass?", "Why is there no transfer for a specific item and location?", "What are the replenishment policies for a specific item and location?"

  • In addition to answering queries, the chatbot enables users to navigate to different IPO screens, view a summary of the latest recommendations, and approve/export recommendations to Merchandising by selecting appropriate chatbot actions.

  • In the Order Detail screen, a contextual panel displays GenAI-powered explanations of recommended replenishment policies and transfer orders. This explainability feature is available for transfer orders in this release.

Lifecycle Pricing Optimization (LPO) Cloud Service Enhancements

LPO Regular Lite: Rules Based Regular Pricing Optimization

Introducing LPO Lite, a major addition to LPO - Regular that enables retailers to set regular prices without requiring forecast data. LPO Lite empowers users to make informed, rule-based pricing decisions in scenarios where forecast-driven optimization is neither infeasible nor necessary.

Key highlights include:

  • Built as a lightweight pricing mode within the regular pricing workflow, LPO Lite reuses the existing strategy and rules engine while bypassing complex forecast-dependent calculations or inventory projections.

  • Regular Lite returns only the item's prices (Ticket, Regular, Original, and Effective) but does not include projected metrics, as forecasting is not used.

  • LPO Lite runs can be initiated directly from the LPO application by enabling the configuration flag RSE_CONFIG.PRO_LPO_REGULAR_LITE_ENABLED_FLG = Y.

  • All pricing recommendations are validated against strategy rules and business constraints, ensuring no conflicts arise between overlapping rules or strategies.

  • LPO Lite supports the same Pricing Ladder, Rounding, and Price Points logic used in full LPO, ensuring consistency in pricing decisions across modes.

  • Auto-approval rules can be configured, allowing qualified recommendations to be automatically approved after the batch process is executed and sent to pricing execution system.

  • LPO Lite uses the same UI, workflow, and approval processes as full LPO, offering a familiar and consistent experience. This ensures a seamless transition for users when switching from LPO Lite to LPO.

LPO Regular: Forecast and Non-Forecast Rules

Regular Pricing Optimization rules are now labeled as F (Forecast) or N/F (Non-Forecast) to indicate whether they require forecast data. For example, the Revenue rules require forecast data, while the Competitor rules does not. LPO Regular Lite uses only Non-Forecast-based rules, whereas LPO Regular Full uses both Forecast and Non-Forecast-based rules. If a strategy used in a Regular Lite run includes a mix of both, the Forecast-based rules will be ignored.

LPO Lite List

LPO Regular: Applied Rules and Violation Info in Contextual Panel

If a rule is applied to an item, selecting that item and opening the Rules tab in the Contextual Panel for LPO Regular will display the applied rules along with their values, priorities, constraint types (hard/soft), and any violations.

violations and info panel

LPO Regular: Pack Pricing Rules

Full and Lite modes. This enhancement ensures that simple and complex sellable packs are priced based on predefined value rules, offering better value than the sum of individual item prices.

Pack Pricing Rules:

  • RPO_SIMPLE_PACK_SELLABLE - Minimum fixed value for simple pack pricing.

  • RPO_SIMPLE_PACK_SELLABLE_PCT - Minimum % value for simple pack pricing.

  • RPO_COMPLEX_PACK_SELLABLE - Minimum fixed discount for complex packs.

  • RPO_COMPLEX_PACK_SELLABLE_PCT - Minimum % discount for complex pack.

Manage LPO Recommendation: GenAI - Explainability

GenAI-powered explanations are now available in the Manage LPO Recommendation screen to help users get answers to key questions like “What are the projected metrics for the item?”, “What rules are applied for this item?”, “Which rules have been violated?”, or “What is the impact of user override actions?”

Note:

GenAI Explainability currently takes up to 10 seconds for LLMs to generate a response.

LPO GenAI Explainability

LPO Run Overview: Compare Runs

The Compare Runs feature is now available for both Promo/Mkdn and Regular runs, is now extended to Promotion and Markdown runs. Users can initiate comparisons directly from the table toolbar by clicking the Compare icon. In the new workflow, users can select a run in ‘Ready for Review’ status from the ‘Compare Runs’ window, and the subsequent comparison options are automatically filtered to show only runs from the same department for comparison. The Gen AI explanation includes details like, The Item Information, the price recommendation summary, projected metrics, and execution status for the selected item.

Compare Runs Window

Compare Runs Window

Promo/Mkdn Run: ‘Allocate Budget’ Moved to Rules Screen

The ‘Allocate Budget’ functionality has been moved from the Scope screen to a new tab within the Rules screen for improved visibility and usability.

LPO Promo Markdown

QBE in Strategy Overview and Rules Management

Query By Example (QBE) has been added to all tables across the Control and Tactical Center, including the Strategy Overview and Rules Management screens. This enhancement enables users to easily filter and search within tables for faster navigation and improved usability.

Enhanced LPO User Guide

The LPO User Guide has been extensively revised to enhance clarity, completeness, and alignment with the current product experience. New screenshots have been added, and existing ones updated to reflect the latest JET UI. Content has been reorganized to support a more logical and seamless flow. The updated guide includes detailed explanations, end-to-end workflow diagrams, UI walkthroughs, and table definitions, providing a comprehensive and user-friendly reference for all users.

Retail Insights Cloud Service Enhancements

Variance Percent Metrics

New metrics have been added to RI for calculating variance between two measures and numerous existing variance measures have had formula updates. The format of the new and existing measures has been set specifically for Data Visualization’s percent format option. DV will apply an additional scaling factor of 100 automatically when a value is formatted as a percent, so these variance metrics do not have any built-in scaling and use a normal range of 0-1 to represent 0-100%. RI metrics of this type will have the “%” sign in the name, indicating that they should be formatted as a percent in the report.

Existing metrics which have been altered to have the correct scaling factors (removing any built-in scaling) include:

  • Gross Profit MTD Var LY (renamed as Gross Profit MTD % Var LY for consistency)

  • Gross Profit QTD Var LY (renamed as Gross Profit QTD % Var LY for consistency)

  • Net Clr Sales Amt % Var LY

  • Store Traffic % Var LY

  • EOH Clr Cost % Var LY

  • EOH Clr Qty % Var LY

  • EOH Clr Retail % Var LY

  • EOH Comp Clr Retail % Var LY

  • Return Amt % (and all time-based variations)

Other metrics having “%” in their name that are not part of this list already had the correct scaling factor for Data Visualization’s formatting options. Variance metrics without “%” in the name will have a scaling factor in the base formula and should not be displayed as a percent in DV. These metrics are meant to be displayed as numbers (or as a percent in Analytics Classic with no added scaling).

In addition to these changes, the following table summarizes the new metrics added

Functional Area Summary of Changes

Inventory Position

21 new metrics added, such as In-Transit Qty % Var LY and BOH Qty % Var LY

Inventory Receipts

12 new metrics added, such as Receipts Qty % Var LY and Receipts Retail YTD % Var LY

Inventory Unavailable

18 new metrics added, such as Unavailable EOH Qty % Var LY and Unavailable BOH Qty % Var LY

Markdown

12 new metrics added, such as Pmt Mkdn Qty % Var LY and Emp Mkdn Qty % Var LY

Purchase On Order

1 new metric added for PO Avg Item Unit IMU %

Sales

9 new metrics added, such as Net Profit LW % Var LY and Net Sales Amt LW % Var LY

Regular Price Optimization Rules

The Regular Price Optimization Rules fact is a new reporting area that displays metrics relating to rules and constraints applied in Regular Price Optimization runs. These metrics specifically focus on rule violations, such as the count of products that exceeded a threshold on revenue or margin impact. The dimensions supported on the fact metrics are Price Optimization Run, Regular Price Optimization Products, and Clusters (which includes price zone data).

Assortment Groups

A new dimension has been added to RI for Assortment Groups. The Assortment Group dimension defines the combinations of merchandise, stores, and selling periods that make up a specific product assortment. These groups can be integrated directly from the output of the Assortment Planning (AP) application or loaded manually into the input interface. A single assortment group is defined as a level of the merchandise hierarchy (for example, Womenswear Department) combined with a store cluster (for example, Northeast Stores) and a selling period (FY2025 Feb through FY2025 July). Retail Insights will take this information and spread it down to the item/location/week level of detail, and then join it with specific fact data. Assortment groups can currently be used only with Sales and Sales Optimization facts. You may use them to compare and analyze the differences between your historical sales in the data warehouse and the optimized sales produced by AI Foundation.

This update includes both the data warehouse interfaces and associated reporting objects to access the data. New AIF DATA programs in POM have been added to extract assortment groups from AP_ASSORT_GROUP_EXP to RI tables W_RTL_ASSORT_GRP_D and W_RTL_ASSORT_GRP_LC_D. A new ad hoc process LOAD_ASSORT_GROUP_DATA_ADHOC has been created to run the AP data extract and RI load programs outside of the nightly batch. If the data comes from non-Oracle sources, then files can be loaded to these tables using W_RTL_ASSORT_GRP_DS and W_RTL_ASSORT_GRP_LC_DS staging table interfaces. The dimensions use Type 1 SCD merge logic for the loads, meaning that incoming data can contain only new/updated records and they will be merged on top of existing data without dropping or closing records.

Optimized History

A new fact has been added for reporting on the results of the Optimized History process in AI Foundation (AIF). AIF forecasting processes will take the raw sales history data stored in RI and perform optimizations on it to account for various effects on your selling patterns, arriving at an optimized history that shows what could have sold in ideal conditions. The optimized sales data can be passed to AP to improve your plans, but it can also be reviewed in RI alongside your sales history to see what changes were made by AIF to the data. The sales optimization outputs are stored at an intersection of assortment group cluster/item/week. You may use the following RI dimensions with this data: Item As-Is, Business Calendar, Assortment Group. The data is visible only to users having the AP Insights or Science Insights user roles.

Generic Catalog Management Groups

Retail Insights now supports up to 15 additional generic OCI IAM groups for the purpose of managing catalog security within Oracle Analytics (OAS). These groups are not added to existing OCI IAM tenants. You should create the groups as-needed or raise a service request to Oracle to request that they be added for you. The group names use the format RI_CATALOG1_JOB through RI_CATALOG15_JOB. Once created and assigned to users, you can use them in OAS to control the access/share permissions on folders and workbooks at group level.

Retail Predictive Application Server Cloud Edition Server Enhancements

Improved Ranksort Functionality

The Ranksort procedure now seamlessly integrates output ranks into subsequent calculations, streamlining the process of ranking and sorting input data in ascending or descending order without requiring additional action.

Calendar Hierarchy: Alternate Rollup Support using Flex Fields

The calendar hierarchy now supports alternate rollups with the addition of flex fields. These fields enable calendar positions to aggregate into alternate rollups, such as events or seasons.

Retail Predictive Application Server Client Enhancements

Excel Import Errors: Enhanced Feedback

When importing an Excel file to upload data into the UI, if faulty records are detected, enhanced error messages now specify the actual label and the mismatched label. This enables users to swiftly identify and correct errors, facilitating a seamless reimport process.

Sort Icon Moved to the Left of the Header

The Sort icon has been relocated to the left of the header, aligning with the latest Oracle UI standards across all Oracle applications.

ODA Show/Hide – User Preference

Users can now choose to show or hide Oracle Digital Assistant by using the Oracle Assistant Hide/Show Menu as a custom menu in the global header menu.

Configuration Tools Enhancements

Configuration Tools TaskList: Flags Unpublished GA Measures in Custom Rules/Solutions

The Configuration Tools TaskList Pane now enhances error detection by flagging and displaying issues when unpublished GA measures are used incorrectly. Specifically, it identifies whether such measures are

Apply Pattern Dialog: Resizable for Better Usability

The Advanced | Apply Pattern window in the Rule Tool, previously non-resizable, has been updated to allow resizing, addressing usability concerns and providing improved flexibility when working with Configuration Tools.

Assortment Planning Cloud Service Enhancement

Enhanced Assortment Planning: Smarter, More Intuitive, and Powered by AI

The enhanced Assortment Planning solution leverages cutting-edge AI-driven insights to revolutionize assortment precision and profitability. By analyzing historical sales data, it predicts trends and refines product selection, ensuring retailers stock the right mix of items at the right time. The system intelligently determines optimal option counts, preventing oversaturation while maintaining a diverse, customer-focused assortment based on attribute driven analysis. It also provides attribute recommendations, identifying key product characteristics - such as color, size, and material - that resonate with shoppers. Tailored to meet the unique needs of each store, the solution analyzes store group characteristics, regional demand, and customer preferences to curate location-specific assortments. By incorporating demographic insights and localized trends, it optimizes the assortment for maximum engagement and sales conversions, delivering a curated shopping experience that aligns with specific market demands.

With its advanced capabilities, the Assortment Planning solution empowers retailers to make smarter, data-driven decisions, ensuring they stay ahead in a competitive market.

Merchandise Financial Planning (MFP) Cloud Service and Assortment Planning Cloud Service Enhancements

Merchandise Financial Planning Integration with Portfolio Optimization

With this release, users can now utilize Portfolio Optimization output in MFP to make decisions for business growth. Users can seed the MFP plans with Portfolio Optimization output to have a base line for initiating the plan. This option is available for the Merch Target and Merch Plan workspace templates.