1 Feature Summary
This chapter describes the feature enhancements in this release.
Note:
Oracle Retail has adopted a new numbering system to correlate the release numbers with Major Updates and the calendar for better clarity. The first two digits are the calendar year; the next digit is the Major release number; the third three digits reflect the calendar quarter and the month within that quarter; and the final digit represents the hot fix sequence.
Noteworthy Enhancements
This guide outlines the information you need to know about new or improved functionality in the Oracle Retail Demand Forecasting Cloud Service 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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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? |
|---|---|---|---|---|
|
All |
Large |
Enabled |
Yes |
|
|
All |
Large |
Enabled |
No |
|
|
All |
Large |
Enabled |
No |
|
|
Workflow |
Large |
Enabled |
No |
|
|
Workflow |
Large |
Enabled |
No |
|
|
Workflow |
Large |
Enabled |
No |
|
|
Workflow |
Large |
Enabled |
No |
|
|
Analytics |
Large |
Enabled |
No |
|
|
Integration |
Large |
Enabled |
No |
|
|
Workflow |
Large |
Enabled |
No |
Note:
Because RDF uses the Oracle Retail Predictive Application Server Cloud Edition, Oracle Retail recommends that you review the Oracle Retail Predictive Application Server Cloud Edition Release Readiness Guide for information on the RPAS CE enhancements.
Reference Documents
The following documents are available on My Oracle Support in the Oracle Retail Predictive Application Server (RPAS) Cloud for Planning and Optimization / Supply Chain Cloud Services Documentation Library MOS Doc ID 2492295.1:
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Oracle Retail Analytics Platform System Implementer Handbook
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Oracle Retail Predictive Application Server Cloud Edition User Upgrade to Release 22.x Quick Start Reference
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Oracle Retail Predictive Application Server Cloud Edition Administrator Upgrade to Release 22.x Quick Start Reference
Also refer to the Oracle Retail Analytics Platform Implementation Guide and planning guides available on Oracle Help Center: https://docs.oracle.com/en/industries/retail/index.html
A Note to Existing Customers about 22.1.202.0
This move to the latest Oracle Retail architecture is not automatic and not immediately available for existing customers. MFP and RDF environments already provisioned in the existing architecture will continue to use current processes such as SFTP and Basic authentication for REST services with no changes to your day-to-day activities. For further information or to start discussing your move, contact your Customer Success Manager (CSM) or log an SR. This release is available for Merchandise Financial Planning and Demand Forecasting; additional Planning and Supply Chain Cloud Services will be available in upcoming releases.
New Feature Description
This section describes the new features.
Retail Analytics Platform Capabilities
Oracle’s Platform for Modern Retail provides retailers with the solutions and toolkits to manage their business their way. The Retail Analytics Capabilities, a part of the Platform for Modern Retail, provide an extensible delivery model for analytics and planning solutions, supporting Oracle Retail applications across each of the major analytical categories, including:
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Descriptive and diagnostic functionality with merchandise planning, customer segmentation, and consumer insights.
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Predictive functionality with demand forecasting, along with customer, and location clustering.
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Prescriptive functionality with assortment planning, price optimization, and inventory optimization.
These solutions support business responsiveness through a highly interactive user experience and drive the best outcomes with the application of advanced analytics and artificial intelligence (AI).
With the release of 22.1.202.0 and the unification of our planning, supply chain and analytics capabilities, we provide a centralized data repository, lean integration APIs, and an efficient portfolio of delivery technologies. The data repository reflects a comprehensive data model of retail planning, operations, and execution processes. The integration APIs support right-time interactions: a lean set of bulk, on-demand, and near real-time mechanisms. The delivery technologies represent a portfolio of connected tools to build and extend composite solutions using fit-for-purpose analytical, application, and integration tools.
The Oracle Retail Analytics Capabilities are currently comprised of the following Oracle Retail Cloud Services:
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Oracle Retail Science Cloud Service
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Retail Insights Cloud Service
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Retail Demand Forecasting Cloud Service
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Retail Predictive Application Server Cloud Edition
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Merchandise Financial Planning Cloud Service
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Retail Home
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Retail Process Orchestration and Monitoring
Technical Architecture Enhancements
In release 22.1.202.0, Retail Demand Forecasting Cloud Service will be moving to Oracle Retail’s Next Generation SaaS Architecture.
As a cloud-native service, built on Oracle Cloud Infrastructure, this provides higher availability, redundancy, and scalability. The services are underpinned by Oracle Autonomous Data Warehouse (ADW) to deliver improved database processing capabilities and superior performance.
This new architecture will yield the following benefits:
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Solution entirely persisted within ADW-hosted Planning Data Store.
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Significantly reduced downtime due to fully automated deployment of all updates and patches.
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Full adoption of OAuth 2.0 for all REST services.
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Significant architectural improvements in middle-tier and application-tier scalability.
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Operational improvements for application support and diagnosis.
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Centralized Oracle Retail BI instance for easier reporting and administration.
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Enhanced integration capabilities with other Oracle Retail products.
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Retirement of SFTP in favor of a service-based approach.
Updated Solution URLs
If you are provisioned with a release 22.1.202.0 environment in the new architecture, the URLs used to access the services and some of the associated tools will change. The basic structure of the URLs is as follows:
https://<service>.retail.<region>.ocs.oraclecloud.com/<customer_instance>/<application_context_root>
The components that vary by customer are the region and customer instance portions. Region will be based on the data center where your environment is located and the instance portion will contain an acronym for your company name along with type of environment (for example, prd, stg, and so on). The application context roots will remain the same, for example you will use “analytics” to access Retail Insights.
Review Forecasts for Lifecycle Merchandise
The forecasts for long lifecycle and short lifecycle merchandise can now be reviewed in the same workspace.
Prior to this release, the forecasts for long lifecycle and short lifecycle items had to be reviewed in separate workspaces. This was not ideal, since some departments or even categories carry a mix of long and short lifecycle items. With the separate workspaces, the user could not have the department or category information in one place.
The user can now review any items in the same workspace.
Specification for Lifecycle Merchandise
Long and short lifecycle items have different demand characteristics, which require different forecasting methodologies. Prior to this release, the information about an item’s lifecycle had to be loaded, or manually managed. This process has been improved by continuously monitoring demand characteristics to determine the lifecycle type of an item, and applying the relevant forecast methodology.
Business Rule Engine
The business rule engine is a new addition to RDF, which can be used to streamline many processes.
Approval Exceptions
After forecasts are generated, they undergo a rigorous check implemented by approval exceptions. The business rule engine is very helpful in defining approval criteria, and allowing for actions that previously required environment patching.
Navigation Exceptions
After the forecasts are run through the approval exception management, many are approved, while some are flagged for the user for review. However, not all items are equally important. The business rule engine can be used to assign unapproved items to different review buckets. For example, high margin items should always be reviewed first, while reviewing low margin, low selling items is lower priority.
Custom Tasks
Not all processes need to be forecasting related. For example, upon determining demand patterns, the business rule engine can be used to set up replenishment strategies. Or using item attributes, the items can be assigned to flexible groupings, which can then be used to estimate demand parameters, such as promotion impact or seasonality.
Override Preprocessing Results
Users can now display and override preprocessing results in the Forecast Review workspace.
Preprocessing sales is the task that transforms sales into unconstrained demand. It can be highly automated, but sometimes it requires manual override. For this release, the override is happening in the Forecast Results view, where the user also has access to important supporting information, such as demand and promotion information.
Splitting of Workflow and Analytics
Splitting workflow and analytics allows the tasks to evolve separately. Starting with this release, there is one forecast that is powering all retail activities, from merchandising and planning to forecasting and replenishment. The clean cut between workflow and science enables the tasks to evolve separately. The user roles are also better defined, allowing for more focused actions.
Common Promo Data Model
The solutions participating in RAP have a common promo data model. This has several advantages:
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The common promo data model enables seamless integrations between the RAP solutions.
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For RDF specifically, it presents the promotions in a way the users can relate to, as opposed to an event, which is turned ON/OFF for certain periods.
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It summarizes how campaigns/promotions/offers have performed historically, enabling users to make decisions on what has worked well and should be repeated in the future, and what should be steered clear off.