Achieve accurate transit time predictions with machine learning in Oracle Transportation Management Cloud

In a world where supply chains are complex, dynamic and prone to disruptions, providing accurate Estimated Time of Arrival (ETA) predictions is challenging but critical. With potentially hundreds of factors influencing the outcome, a simple and static model can no longer suffice in the world of today. Some shippers and Logistics Service Providers are tempted to experiment with Machine Learning-based ETA prediction methods, but hiring data science talents and building an end-to-end machine learning pipeline can be cost-prohibitive.     

Oracle Transportation Management (OTM) Cloud can help. With the recent 21A update, OTM now has the ability to provide machine learning-based shipment transit time predictions. Leveraging the rich shipment histories in OTM, this capability identifies shipments truly at-risk, and provides customers with accurate ETA predictions.

Through rigorous research using real-world shipment data, Oracle’s machine learning capability has demonstrated a reduction of prediction errors by upwards of 65%. Furthermore, Oracle’s data pipeline is pre-built, the model building process is automated, and the results are actionable.

Watch machine learning in action through this product tour, and for more technical documentation, visit the New Feature Summary page.

Learn more about machine learning in Oracle Transportation Management

I will be presenting more insights and doing a technical deep dive into our new machine learning capabilities in the following Cloud Customer Connect sessions, please register and attend to learn more:

• Logistics Machine Learning in Oracle Transportation Management Cloud – Functional Overview | March 24, 2021 at 8:00am PT | Learn about the latest machine learning-based transit time prediction capability in OTM Cloud.

• Logistics Machine Learning in Oracle Transportation Management Cloud – Technical Deep Dive | March 30, 2021 at 8:00am PT | This is a partner-oriented technical session on the new machine learning-based transit time prediction capability in OTM Cloud.

Benefits of Oracle’s Approach to Machine Learning in Transportation Management

• Identify shipments truly-at-risk and provide customers with accurate ETA predictions
• Configure machine learning models specific to your business scenarios
• Simple one-time setup without the need for a data scientist
• Automated data science pipeline and workflow with Oracle’s proprietary AutoML technology
• Execute immediately on actionable prediction results

To understand Oracle’s strategy and vision around Machine Learning in Logistics, watch the ARC Forum Executive Interviews, and read the recent Logistics Viewpoints article by Derek Gittoes, Vice president, Supply Chain Management Product Strategy at Oracle.