Make better planning decisions, improve customer satisfaction, and lower supply chain risk by adapting a machine learning (ML) approach to transportation planning. Let's take a closer look and see how it works.
Get more accurate estimated time of arrival (ETA) during the shipment planning process. Receive updated ETA based on in-transit events, and obtain the total transportation lead time at order capture. Oracle Transportation Management (OTM) leverages industry-leading ML algorithm and infrastructure for intelligent transit time prediction.
Customers view predicted shipment arrival and transit times in their daily workbenches. Predictions can be incorporated into standard OTM workflow and agents for fast response and action.
Machine learning models are fully configurable and can be fine-tuned to fit business-specific needs and transportation scenarios. A no-code environment makes configuration easy.
Incorporate events, such as GPS updates and disruptions, and external factors, such as weather and traffic, to improve your responsiveness to real-time factors.
Retrain your models with the most recent shipment history. With ML natively integrated into OTM processes, you can trigger conditional logic or set up recurring actions. Over time, your model accumulates a richer set of shipment history and gets periodically retrained with incremental data, becoming more accurate in a lights-out, self-improving process.
Users have full visibility into model accuracy and performance. This helps users further fine-tune the model and build up trust and confidence in AI.
Predict end-to-end transit time for multiple transportation legs and convert this information into actionable insights. Users can easily identify at-risk shipments, make corrections, and improve performance.
Leverage our best-in-class logistics platform to make better planning decisions, improve customer satisfaction, and lower supply chain risks.