Oracle Inventory Optimization Enables Retailers to Navigate Uncharted Demand
Cloud service taps machine learning to help retailers optimize inventory to adapt to changing customer buying behaviors
The global health crisis has created an inventory emergency for many retailers. Today’s unpredictable and volatile customer buying habits are limiting the effectiveness of using historical data to predict purchasing behavior and plan product inventory accordingly. Powered by machine learning, the new Oracle Retail Inventory Optimization Cloud Service can sit between a retailer’s forecasting and supply chain systems to help highlight the next best actions they can take to optimize inventory. This helps retailers get to answers on inventory placement and volume faster so they can better serve customers while maintaining a healthy cash position.
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“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.”
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In today’s climate, the ability to respond to changing customer demands as quickly as possible is critical. Oracle Retail Inventory Optimization Cloud Service comes with pre-built machine learning models that more accurately predict overall inventory levels; recommend inventory re-distribution; balance supply and demand to free up money tied up in excess inventory; and more.
The cloud service easily 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.
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