Contents

Title and Copyright Information

Send Us Your Comments

Preface

• Audience
• Documentation Accessibility
• Related Documents
• Customer Support
• Improved Process for Oracle Retail AI Foundation Cloud Services Documentation Corrections
• Oracle Retail AI Foundation Cloud Services Documentation on the Oracle Technology Network
• Conventions

1 Introduction

• Oracle Retail AI Foundation Cloud Services and Business Agility
• Oracle Retail AI Foundation Cloud Services Advanced Clustering
  • Reporting and Analysis
• Oracle Retail AI Foundation Assortment and Space Optimization Cloud Service
  • Dynamic Creation of Space Clusters
  • Conduct Micro-Space Optimization What-if Analysis
  • Preview Results Leveraging Shelf Preview Capabilities
• Oracle Retail AI Foundation Cloud Services CDT Science and DT Science
  • Customer Decision Trees
  • Demand Transference Science
• Oracle Retail AI Foundation Cloud Services Attribute Extraction
• Oracle Retail AI Foundation Cloud Services Affinity Analysis
• Common Workflow
• Interacting with Oracle Retail AI Foundation Cloud Services
• Hardware and Software Requirements
• FAQs

2 Implementation Overview

• Implementation Process
  • Implementation Steps
    • Configure the Application Roles and Users
    • Data Load Overview
    • Edit and Load Common Seed Data
    • Perform Attribute Preprocessing for CDT and DT, as Appropriate
    • Mandatory Configuration Parameters

3 Customer Decision Trees

• Input Data
  • Overview
    • Transactions Data Requirements
    • Attribute Data Requirements
    • Attributes with a Large Number of Values
    • Grouped vs. Raw Attribute Values
    • Attribute Splitting
    • Functional-Fit Attributes
  • Customer Segments
    • Location Hierarchy
    • Setting Up Categories
  • Calculating Customer Decision Trees
    • Setting the Top Attribute
    • Excluding Attributes from the Calculation
    • Handling of the Brand Attribute
    • Limitations of the CDT Calculation
  • Choosing the Time Interval
  • Understanding the Filter Settings
  • Segments vs. Location
  • Setting the Escalation Path
  • How the CDT Score is Calculated
  • Understanding CDT Pruning
  • Overriding the CDT Calculation
• Using the Calculation Stage
  • Setup Stage
  • Data Filtering Stage
  • Calculation Stage
  • Advanced Use

4 Demand Transference

• DT and CDT
• Demand Transference Model
  • An Example
• Historical Similarity Data
• Historical Sales Data
• The Role of Attributes in Calculating Similarities
  • Attribute Data Requirements
  • Guidelines on Number of Attributes and Attribute Values
  • The Effect on Similarity Values
  • Avoiding Attributes with Many Values
  • Functional-Fit Attributes
  • Using Null as an Attribute Value
  • The Effect of Null Attribute Values on Similarity Values
  • Categories Containing Sub-Categories
• Customer Segments
  • Location Hierarchy
  • Setting Up Categories

5 Using Demand Transference

• Seasonality in Historical Sales Data
• Assortment Elasticity
• The Importance of Assortment Changes in Historical Data
• Estimation of Assortment Elasticity
• The Meaning of the Possible Values of Assortment Elasticity
• The Substitutable Demand Percentage
• No Requirement for a Time Interval
• Segments vs. Locations
• Setting the Escalation Path
• Automatic Updating
• Avoiding Categories with Small Assortments
• Implementing DT for Fashion Categories
  • Proper Level for Fashion Categories
  • Seasonality (Life Cycle) Considerations

6 Advanced Clustering

• Overview
• Data Requirements
  • Multiple Hierarchies and Level Support
  • Clustering Criteria Supported in Store Clustering
  • Attributes in Store Clustering
  • Configuration Process
• Copy Clusters Using Like Product Mapping
• Multiple Clustering Approach
  • New Stores or Stores with Poor History
  • Outliers
• Export to Excel

7 Customer Segmentation

• Overview
• Data Requirements
• Multiple Hierarchies and Support
• Supported Segment Criteria
• Customer Segmentation Attributes
• Configuration Process
• Attribute Preprocessing
• Segmenting Approach
• Customer Segment Store Profile Generation
• Preprocessing
• Customer Metrics

8 Affinity Analysis

• Overview
• Data Requirements
  • MBA_ARM_SRVC_LOC_STG
  • MBA_ARM_SRVC_CONFIG_STG
• Science Algorithms/Services
  • ARM_PH
  • ARM_PH_PROMO
  • ARM_PH_CS
• Configurations
• Data Output

9 Assortment and Space Optimization

• Overview
• ASO Data Input Requirements
  • Assortment Data
  • Planogram Data
  • Assortment-to-Planogram Mapping
  • Assortment to POG Mapping Process
    • Input Data
    • Automated Process
    • Mapping Errors
  • Product Images Data
  • Replenishment Data
• Optimization Science
  • Optimization Algorithm Overview
  • Sales and Inventory Model
    • Sales and Inventory Modeling Considers All Possibilities
    • Inventory Levels After Replenishment
    • Calculating the Facing Capacity for a Product/Fixture Combination
    • Maximum Capacity of a Product
    • Replenishment Parameters
    • Sales Inventory Model Output
  • Objective Function
  • Constraints
  • Product Family Group Constraints
  • Diagnosis of Visual Guidelines
  • Product Groups
  • Validation Tool (Sanity Checker)
  • Diagnosis of Dropped Products
  • Checklist for Optimization Results Diagnosis
• Monitoring Batch Processes
  • Overview
  • Batch Process Failure
  • Global Validation Issues
  • Sending Data in Data Files
    • Assortment-Related Files
    • POG-Related Files
    • Display-Style Files
    • POG Historical Data and Store CDAs
    • Mapping, Replenishment, and Other Files

10 Assortment Recommender

• Prerequisites
• Producing Better Assortments
• Run Groups and Run Frequency
• The Run Group Parameters
• Data Used by the Assortment Recommender
• Halo Effects
• Troubleshooting

11 Size Profiles

• Overview
• Data Requirements
  • Hierarchy Data
  • Sales Data
  • Inventory Data
  • Product Images Data
  • Season
  • Size Range and Sub Size Range Definition
  • Size Definition and Mapping Between Size and Sub Size Range
  • Mapping Between SKU and Size
  • Mapping Between Style-Color/Store and Sub Size Range
• Configurations
• Data Output
• Batch and Ad-Hoc Jobs

12 Offer Optimization

• Overview
• Project Planning
  • Functional
  • Data Interfaces
  • Forecasting Parameters
  • Optimization
• Walkthrough
• Security
  • Data Filtering
  • User Roles
• Data Input Requirements
  • Hierarchy Data
  • Inventory Data
  • Price History Data
  • Retail Code
  • Retail Sales Data
  • Promotions
  • Warehouse Inventory Allocation
  • Competitor Prices
  • Product Attributes
  • Product Images Data
  • Season
  • Season Periods
  • Season Products
  • Price Ladder
  • Currency
  • Holidays
  • Pricing Product Groups
  • Budget
  • Strategy and Business Rules
  • Custom Columns
  • External Forecast Adjustment
  • Configuration and Expected Levels for Interfaces
  • Optional* Interfaces
• Integration with Retail Pricing Cloud Service
• POM Jobs
• Forecasting Science
  • Parameter Estimation
    • Overview of the Parameter Estimation Stages
    • Data Preparation
    • Preprocessing
    • Elasticity
    • Seasonality
    • Promotion
    • Output and Review of Parameters
  • Day Level and Returns Metrics
    • Day of the Week Profiles
  • Returns Metrics
  • Demand Forecasting
  • New Stores
• Optimization Science
  • Business Rules
  • Optimization Algorithm Overview
  • Model Apply
  • Objective Function
  • Constraints

13 Control and Tactical Center

• Strategy and Policy Management
• Forecast Configuration for MFP and AP
  • Run Type Configurations for MFP and AP to Set Up GA Runs
  • Batch and Ad-Hoc jobs for MFP and AP Forecasting
• Forecast Configuration for RDF and AIF (including Inventory Optimization and Offer Optimization)
  • Batch and Ad-Hoc Jobs for RDF Forecasting
• Workflow for RDF Implementation
• Using the Add Multiple Run Types feature
• Building Alternate Hierarchy in AIF
• Custom Jobs Through Innovation Workbench (IW)

14 Inventory Optimization

• Overview
• Inventory Optimization Runs
• Inventory Optimization UI Workflow
• Security
• Data Load Requirements
  • RAP Foundation Data (CSV and W_ interfaces)
  • RSP Foundation Data (RSE_% Tables)
  • Inventory Optimization Data (IO_% Tables)
• Data Input Requirements
  • Hierarchy Data
  • Retail Sales Data
  • Inventory Data
  • Product Attributes
  • Product Images
  • Replenishment Attributes
  • Price and Cost
  • Season
  • Global Configurations
• Inventory Optimization Integration with RMS
• Forecast Engine Setup for IO
  • Batch and Ad Hoc Jobs for IO

15 Configuration

• User Interface Authentication and Authorization
• User Management Configuration: Configuring Users and Roles
  • User Roles
• Configuration
  • RSE_CONFIG Table
  • Generic Configuration
  • Advanced Clustering Configurations
  • Assortment and Space Optimization Configurations
  • Customer Decision Tree Configurations
  • Demand Transference Configurations
  • Returns Logistics Configurations
  • Affinity Analysis Configurations
  • Resource Bundles
  • Manage Notebooks
• Internationalization
• Configuration Updates
  • Configuration Tables
  • Email Notification Configuration

16 Attribute Processing

• Attribute Preprocessing
  • Process Overview
• Product Attribute Loading
  • Introduce New Attribute
  • Determine the Attribute Source and Define in the Tables
    • W_PRODUCT_D or W_PRODUCT_ATTR_D
    • W_RTL_ITEM_GRP1_D or W_RTL_ITEM_GRP2_D
    • Populate RSE_PROD_ATTR_GRP_VALUE_STG Interface
    • Populate RSE_PROD_ATTR_VALUE_XREF_STG Interface

17 AI Foundation Web Services

• AI Foundation Web Services
  • Authentication and Authorization
  • Summary of Web Services
    • Access to RSE_CONFIG Table
    • Access to RSE_CONFIG_CODE Table
    • Advanced Clustering Export
    • Customer Segment Export
    • ASO Exports
• Retail Science Integration with XStore
  • Endpoint
  • Authentication
  • Retail Science and Retail Insight Integration
  • Configuration
    • POM Server Detail Configuration
    • POM Server Credentials

18 Batch Processing

• Overview
• Custom Batch Requests
  • Managing Custom Batch Requests
  • Handling Data Files
  • Supported PROCESS_QUEUE Trigger Values
• Incremental Exports
• Batch Process Flow
  • Configuring Additional Data Files
  • File Transmissions

19 Innovation Workbench

• Components
  • Retailer Workspace Schema
  • Oracle APEX
    • Workspace
    • Users and Roles
  • Oracle Advanced Analytics
    • Oracle Data Mining
    • How to Invoke Oracle Data Mining
• Notebooks
• Scheduling Innovation Workbench Python Notebook
  • IDCS Setup
    • Scheduling Jobs
    • REST API Documentation
    • Example
  • Notebook and Paragraph IDs
    • Considerations
    • REST API
• Restful Service
• Integration
  • Custom Data Loads
    • Required Files
    • Zip File Contents
    • Context File Details
    • Data Load Feedback
    • Invoking the Data Load
  • Custom Data Exports
    • Required Configuration
    • Invoking the Export
• Sample Extensibility Use Case
• Troubleshooting
• DBMS Scheduler
  • Schema Objects

20 Process Orchestration and Monitoring

• Batch Administration
• Batch Monitoring
  • Monitoring a Batch Cycle
• Triggering Process from POAM
  • Running a Nightly Batch Process
    • Monitoring the Process Executions
  • Running AdHoc Batch Processes
    • Enabling AdHoc
    • Monitoring Process
    • Disabling AdHoc
    • Intraday Cycle
• Error Handling

21 FAQs

• General Questions
• DT Questions
• ASO Questions
• Attribute Processing Questions
• Offer Optimization Questions

A Appendix: Innovation Workbench Workshop

• Overview
• Lab Innovation Workbench
• Lab Analysis
  • Browse Existing Data
  • Using ReST to Load Data
  • Other Methods for Loading Data
• Explore Data Analysis
  • Review Customer Behavior Analysis using Text Mining
• Innovate
  • Churn Analysis using ODM
  • Decision Tree Model Details
  • Display Decision Tree Rules
• Lab Share Insights - ReST API
• Lab Share Insights - Task Navigation
• Database Tools
  • How to Allocate Privileges
  • How to Execute a Job using DBMS Scheduler

Glossary of Acronyms