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In-transit ETA Prediction with Logistics Machine Learning by Brian Ramos (Oracle)
Building on the recently released Logistics Machine Learning (LML) predictive ETA capability, you are now able to configure LML to generate an updated delivery estimate for in-transit tracking events. Inform your internal and external stakeholders of potential delays with increased accuracy, improving coordination across your supply chain. In this session we provide a brief overview of LML and highlight the key enhancement areas in the product. A fully autonomous LML configuration example will be described and demonstrated.
Presentation slides and video recording will be published shortly
Building on the recently released Logistics Machine Learning (LML) predictive ETA capability, you are now able to configure LML to generate an updated delivery estimate for in-transit tracking events. Inform your internal and external stakeholders of potential delays with increased accuracy, improving coordination across your supply chain. In this session we provide a brief overview of LML and highlight the key enhancement areas in the product. A fully autonomous LML configuration example will be described and demonstrated.
Presentation slides and video recording will be published shortly
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