Retail Demand Planning

Maximize forecast accuracy with Retail Demand Forecasting

Oracle Retail Demand Forecasting, our demand forecasting engine, has been integrated into Retail Inventory Planning Optimization Cloud Service and includes Oracle Retail AI Foundation (PDF), which provides analytical insights to drive planning, buying, moving of inventory, and selling decisions. These capabilities help retailers increase profits and remain flexible amid the changing retail environment.

Get accurate forecasts to coordinate demand-driven outcomes

With a single view of demand, Retail Demand Forecasting provides value across retail processes, including driving optimal strategies in planning, increasing inventory productivity in retail supply chains, decreasing operational costs, and driving customer satisfaction from engagement to sale to fulfillment.

With Retail Demand Forecasting, Oracle distilled more than 15 years of forecasting experience across hundreds of retailers worldwide into a comprehensive solution that maximizes forecast accuracy for the entire product lifecycle. Retail Demand Forecasting has added additional capabilities and evolved into Retail Inventory Planning Optimization Cloud Service.

Understand how your customers shop and the uniqueness of your assortment to drive incremental revenue

Gain customer insights with decision trees

Oracle Retail Customer Decision Trees and Demand Transference enables you to create customer decision trees using transaction-level data. These decision trees are specific to their customer segments and respective geographies. They help you understand the most important products and product attributes as customers see them. Armed with these details, you can effectively analyze assortment coverage, identify duplication of item types, and prevent the removal of core items that customers seek.

Identify customer purchase and switching patterns

Oracle Retail Customer Decision Trees and Demand Transference mines customer purchase history to identify shopping and switching patterns. These help you understand what attributes are driving customer purchases, when customers would walk away without making a purchase, and when they're willing to switch products. These key insights can help you make better assortment, pricing, and promotion decisions.

Identify/eliminate national influences and manufacturer biases

Leveraging retailer-specific transaction-level data provides a true view of the customer during the customer decision tree creation process, eliminating bias that an external customer decision tree may have. Using Oracle Retail Customer Decision Trees and Demand Transference, you can compare customer decision trees you’ve generated with ones provided externally. Then you can make adjustments to confirm and approve usage within your assortment processes.

Oracle Retail Data Store helps leverage retail data

Oracle Retail Data Store is a low-cost, low-code environment that enables retailers to innovate, take control of their data, and extend the capabilities of their Oracle Retail cloud services.

Oracle Retail Data Store is highly scalable and configurable to support the constantly changing retail landscape. It consolidates data on sales, inventory, pricing, promotions, customers, orders, demand, fulfillment, items, suppliers, consumers, and channels to support a retailer’s unique business processes and journeys.

Retail Data Store includes powerful tools to leverage all of this data

• Oracle Retail Home
• Oracle Analytics
• Oracle APEX
• Oracle REST Data Services

Oracle Retail AI Foundation

Leverage core retail AI and ML to make decisions on assortments, offers, inventory placement, forecasts, planning, buying, pricing, etc.

Forecasting engine

Provide an intelligent starting point for your planners to increase automation and accuracy.

Customer segmentation

Group customers based on attributes, behaviors, and transactions to tailor offers, pricing, and assortments.

Advanced clustering (PDF)

Cluster your stores based on traditional approaches of volume, square footage, and region, or leverage ML to cluster stores based on similar selling patterns.

Profile science

Determine the best size ratio for your buys by understanding the true demand of your sizes, considering stockouts.

Attribute extraction and binning

Extract item attributes from free-form descriptions, correcting short forms, misspellings, and other inconsistencies, and apply them to demand transference, customer decision trees, advanced clustering, and more.

Affinity analysis (PDF)

Determine how items interact with each other for an effective promotional strategy within your financial planning process.

Innovation workbench

Leverage open source along with your data science team to create your own AI and ML models.