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A New Era in Transport Logistics with Big Data and Self-Driving Trucks

Logistics Viewpoints

This is where big data technologies come into play. Big data for real-time optimizations in transport logistics. Logistics and transport service providers create enormous data records as they manage the flow of goods. These data include information such as types of goods, location, weight, size, origin, and destination.

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Infor Nexus is Taking an Interesting Approach to Generative AI

Logistics Viewpoints

A good example is saying “What are my demurrage issues at the Port of Long Beach?” This check involves connecting carrier contract data and shipment dwell times. They look at the data and ask themselves, “is this a problem?” It is data in context. The digital assistant becomes that analyst. It is a visual control.

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Supply Chain Design Crosses the Chasm

Logistics Viewpoints

The supply chain nodes which were once deemed to be relatively static have become far more dynamic in the recent past. The rapid shifts to eCommerce during the pandemic caused retailers and brand owners alike to flex their network nodes (where goods are made and inventories are stocked) significantly.

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Improved Supply Chain Visibility Requires an End-to-End Solutions

Logistics Viewpoints

The devices will improve visibility by transmitting data on a real-time basis from each container. Tracking devices from Nexxiot and ORBCOMM are being installed that will provide location data based on GPS, measure temperature, and monitor any sudden shocks to the container. It has so many data points.”.

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Getting Started with AI in Supply Chain

Logistics Viewpoints

Instead start with the foundation of your AI strategy, which should be an understanding of your company’s supply chain and your data. Consider a planner in Brazil working with the previous lead time prediction example, who has forgotten how to update the parameters. Because it doesn’t understand, we need humans at the helm.

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Active Supply Chain Design: A Key Imperative for De Risking Supply Chains

Logistics Viewpoints

The bullwhip effect is one example of this disruptive effect, when small changes in demand cause huge demand spikes downstream. Table 1 describes a few examples of these types of risks. Examples of disruptive risks are suppliers going out of business or shipwrecks that result in the loss of cargo containers.

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The Future of Ocean Data Sharing and Visibility

Talking Logistics

How are these advancements improving the way data is shared in ocean transportation? Andrew explains that, “Due to regulatory changes, every truck has to have an [ELD] device for tracking data. This data is collected via APIs, eliminating the need for EDI transactions. Timely data is an important factor enabling them to do that.

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