Table of Contents
- Title and Copyright Information
- Send Us Your Comments
- Preface
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1
Introduction
- Oracle Retail AI Foundation Cloud Services and Business Agility
- Oracle Retail AI Foundation Cloud Services Advanced Clustering
- Oracle Retail AI Foundation Assortment and Space Optimization Cloud Service
- Oracle Retail AI Foundation Cloud Services CDT Science and DT 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
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3
Customer Decision Trees
- Input Data
- Using the Calculation Stage
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4
Demand Transference
- DT and CDT
- Demand Transference Model
- Historical Similarity Data
- Historical Sales Data
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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
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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
- 6 Advanced Clustering
- 7 Customer Segmentation
- 8 Affinity Analysis
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9
Assortment and Space Optimization
- Overview
- ASO Data Input Requirements
- Optimization Science
- Monitoring Batch Processes
- 10 Assortment Recommender
- 11 Size Profiles
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12
LifeCycle Pricing
Optimization
- Overview
- Project Planning
- Walkthrough
- Security
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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
- Optimization Science
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13
Control and Tactical Center
- Strategy and Policy Management
- Forecast Configuration for MFP and AP
- Forecast Configuration for IPO-DF and AIF (including IPO-IO and OO)
- Loading and Calculating Event Flags in AIF
- Workflow for IPO-DF Implementation
- Using the Add Multiple Run Types Feature
- Building an Alternate Hierarchy in AIF Applications
- Custom Jobs Through Innovation Workbench (IW)
- Purge Forecast Run and Run Type Data
- Configurations in AIF to Generate Forecasts for FACT Measures
- Data Requirements for AIF
- 14 Inventory Planning Optimization-Inventory Optimization
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15
Configuration
- User Interface Authentication and Authorization
- User Management Configuration: Configuring Users and Roles
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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
- Configurations for Loading Fact Measures into AIF
- Internationalization
- Configuration Updates
- 16 Attribute Processing
- 17 AI Foundation Web Services
- 18 Batch Processing
- 19 Innovation Workbench
- 20 Process Orchestration and Monitoring
- 21 FAQs
- A Appendix: Innovation Workbench Workshop
- Glossary of Acronyms