Oracle intros inventory optimization platform to meet COVID changes

Dan Ochwat
Executive Editor
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Oracle Retail has unveiled a new cloud service to help retailers optimize product inventory that has been upended by the COVID-19 pandemic, especially in the case of using historical data to help predict purchasing behavior.

The machine learning-powered cloud service is called Oracle Retail Inventory Optimization Cloud Service and it is working to automate product inventory based on new consumer patterns as they’re occurring. The platform sits between a retailer’s forecasting and supply chain systems to help highlight the next best actions they can take to optimize inventory.

“Retailers are struggling to adjust decades of well-defined inventory and traditional supply chain management processes that have been thrown a curveball by COVID-19,” said Jeff Warren, Oracle Retail vice president of strategy and solution management. “With the ability to be deployed in just weeks, Oracle Retail Inventory Optimization Cloud Service does the heavy lifting and modeling to rebalance and optimize inventory so retailers can invest in the right products and automatically adapt to new consumer patterns as they occur.”  

Oracle Retail Inventory Optimization Cloud Service comes with pre-built machine learning models that more accurately predict overall inventory levels; recommend inventory redistribution; balance supply and demand to free up money tied up in excess inventory; and more. The cloud service integrates with existing forecasting and supply chain solutions and can be deployed quickly to reduce the burden on a retailer’s IT and development teams. 

Oracle said an average grocery retailer, with 30,000 SKUs and at least 1,000 stores, will have millions of SKU-store combinations so it can be tough to determine optimal replenishment plans when things are changing so quickly during the pandemic, and not as patterns have been in years past. Oracle said it’s new platform adds value to a retailer by:

  • Performing continuous optimization of replenishment parameters; 
  • Informing replenishment strategies with service-to-inventory trade-offs; 
  • Translating objectives into machine learning-driven replenishment policies down to the item-location; 
  • Recommending inventory re-distribution to serve customers and avoid markdowns; 
  • Enriching the inventory movement processes with time-phased inventory projections;    
  • Helping increase employee productivity to maximize a constrained workforce; and   
  • Interacting with Oracle Retail Offer Optimization to drive better outcomes through simultaneous manipulation of supply and demand.