1 Overview

The Oracle Retail AI Foundation Cloud Service (AIF) combines AI, machine learning, and decision science with data captured from Oracle Retail SaaS applications and third-party data. The unique property of AIF Cloud Service, a learning-enabled application, is that it detect trends, learns from results, and increases its accuracy the more it is used, adding massive amounts of contextual data to obtain a clearer picture on what motivates outcomes.

The Oracle Retail AI Foundation Cloud Services are composed of the following Cloud Services:

• Oracle Retail AI Foundation Cloud Service

• Oracle Retail Assortment and Space Optimization Cloud Service

• Oracle Retail Promotion, Markdown and Offer Optimization Cloud Service

• Oracle Retail Inventory Optimization Cloud Service

The Oracle Retail AI Foundation Cloud Service

The Oracle Retail AI Foundation Cloud Service provides retailers with a data science toolkit that supports specific use-cases in planning, operations, and execution and can be expanded to support broader retail uses. This includes Advanced Clustering, Customer Segmentation, and Transference, Customer Decision Tree, Affinity Analysis, Attribute Extraction/Binning and Innovation Workbench capabilities, and Profile Science.

The new Strategy and Policy Management dashboard is the central place for admin users and implementers to manage configurations, rules, and policies for different applications. In this new dashboard, Manage Forecast Configurations can be used for setting up and configuring the forecast runs. The user can set up batch runs, create and submit what-if runs, and manage the configuration parameters that are used by the forecasting method.

Oracle Retail Inventory Optimization Cloud Service

The Oracle Retail Inventory Optimization Cloud Service provides insights into trade-offs between service level and inventory cost and helps retailers set replenishment strategies in terms of safety stock or service level. These data-driven strategies are translated into item-location replenishment policies that are pushed to replenishment systems, such as the Oracle Retail Merchandising System (RMS), or any external system to generate and execute orders. To provide full visibility, the replenishment policies are also leveraged within Inventory Optimization to calculate the optimal transfers and purchase orders.

To support strategic inventory optimization throughout the lifecycle, Inventory Optimization recommends optimal rebalancing transfers between stores to increase sell-through and avoid markdowns. This type of strategy can be turned off when not applicable (for example, for grocery categories).

Inventory optimization leverages historical sales and inventory, business requirements such as lead time and review schedule, and the demand forecast to generate optimal recommendations throughout the life cycle. The demand forecast takes into account different factors such as demand transference, variation across customer segments, holidays and promotions, and returns (primarily for fashion and hardline categories). Alternatively, demand forecast can be provided through an interface by external forecasting system.

Oracle Retail Assortment and Space Optimization Cloud Service

The Oracle Retail Assortment and Space Optimization Cloud Service is used to determine the optimal selection and arrangement of products within stores by optimizing the product assortment and product placement on a virtual planogram.

Oracle Retail Promotion, Markdown and Offer Optimization Cloud Service

The Oracle Retail Promotion, Markdown and Offer Optimization Cloud Service reflects the evolution of our price and promotion optimization capabilities into an integrated life-cycle price optimization offering that enables retailers to engage their customers in an omnichannel environment while maximizing profits. The modular approach to offering life cycle pricing for promotions and markdowns separate from targeted offers enables retailers to innovate at the speed of their customer, while also accounting for the maturity of loyalty data necessary for targeted offers. The combined capabilities provide the following benefits to retailers:

• Drive optimal promotion and pricing decisions for the entire product life cycle

• Engage customers with targeted and contextual offers

• Execute consistently, incorporating price and promotion plans, projected receipts, and returns

• Simplify decision-making through high-automation, exception-driven processes and what-if optimizations

• Maximize accuracy and scale using artificial intelligence, machine learning, and optimization on Oracle Retail's data science infrastructure