AI predicts when shipments arrive—and that's a big deal

January 4, 2024 | 3 minute read
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Tracking shipments on a map was revolutionary in its day. But that day is long gone. Customers of all kinds have now come to expect much more precise estimated time of arrivals (ETAs) for their shipments. In fact, many businesses depend on a precise understanding of arrival times to keep shipping docks clear of congestion, warehouses from overflowing, and production lines from grinding to a halt.

As a supplier, giving a precise ETA is no easy feat, especially when operating large, complex, globally distributed supply chains that are servicing the needs of diverse customers. But it can be critical to maintaining customer satisfaction, and in some cases, compliance with customer agreements and product quality.

Collectively, these capabilities are known as “order visibility and condition monitoring.” Shipment times are usually calculated using ETA models, and AI is creating opportunities to dramatically increase their accuracy.

A different take on AI for ETAs

Oracle’s development team begins with the fundamental assumption that every supply chain is different. So, Oracle Fusion Cloud Transportation Management uses machine learning (ML) to create unique ETA models that account for each organization’s combination of lanes, carriers, distribution centers, and products. These ML-enabled ETA models enable you to significantly improve accuracy—up to 93% in some cases.

One crucial aspect of these models is that they are transparent and accessible during the planning stage. That means that users can adjust the model’s inputs manually to tune the results weeks in advance of the actual ship date. They can also act on predicted ETAs prior to shipping (e.g., swapping carrier or mode) to ensure on-time performance.

For customers working with high-value or perishable items like food and healthcare products, shipments can be monitored in real time using Oracle Fusion Cloud Internet of Things Intelligent Applications. This enables deviation alerts from predefined thresholds such as temperature, humidity, or shock, giving added levels of visibility and assurance that shipments are free of non-obvious damage.

How AI-enabled ETA models benefit you

  • Improved ETA accuracy: significantly improves ETA reliability to better meet customer expectations
  • Reduced shipping costs: optimizes shipment modes for higher efficiency and lower expediting expenses
  • Increased compliance: monitors and documents the condition of goods throughout shipment, including the pre- and post-transit periods
  • Improved customer satisfaction: accelerates response and replacement times related to delays and shipment incidents

Conclusion

Inaccurate ETAs, or delivering shipments with (non-obvious) damage, can have a significant adverse financial impact to suppliers and customers alike. They can result in production delays, revenue loss, penalties, and/or termination relationships between suppliers, customers, and distributors. Oracle's solution for order visibility and condition monitoring can help you meet your customers’ expectations and improve your own bottom line.

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Fusion Development

The Fusion Development team is responsible for building, maintaining, and driving innovation on the Oracle Fusion Cloud Applications Suite, which includes Oracle ERP, EPM, SCM, HCM, and CX. Its members are based throughout the world with central offices in the US, India, Mexico, The Philippines, and Romania.


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