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NRF 2024: Supply Chain Data

Logistics Viewpoints

The usual themes were still very present as solution providers and retailers alike were more than happy to talk about omni-channel, mobility, robotics, and machine learning, to name a few. This year, a recurring theme that I saw was about using supply chain data to improve the customer experience across the entire value chain.

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The Past, Present, and Future of Technology in the Warehouse

Logistics Bureau

For example: Paperwork and data entry: WMS has reduced the need for people to spend time completing paper forms or entering data from documents into spreadsheets and other data-management applications. It also means physical space no longer has to be found for paper storage and archiving.

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LTL in a Post-Yellow World with Curtis Garrett

The Logistics of Logistics

About Understand LTL The Understand LTL education brand is a new initiative that is focused on simplifying the LTL industry and helping people to build the mental models in their mind to think about LTL clearly. For carriers, it presents an opportunity to expand their market share and increase their profitability. The Greenscreens.ai

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Data Everywhere: AI in Logistics

Logistics Business Magazine

Most shippers, carriers and logistics service providers understand the importance of data collection and data-driven decision-making. Data collected over time provides intelligence, enabling companies to enhance long-term decision-making. Artificial intelligence is a potent tool that helps companies get the most from their data.

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Autonomous Robots, Data Intelligence

Logistics Business Magazine

Leading London-based robotics and data intelligence company Dexory today announced the launch of their new, enhanced solution DexoryView – a revolutionary new platform that will change the way that warehouses are managed through the power of real-time data.

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Supply Chain AI: 25 Current Use Cases (and a Handful of Future Ones)

Logistics Viewpoints

Machine Learning occurs when a machine takes the output, observes its accuracy, and updates its model so that better outputs will occur. More recently, demand planning applications based on machine learning have improved forecasting by incorporating competitor pricing data, store traffic, and weather data.

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TGW Presents the Future Fulfilment Centre

Logistics Business Magazine

For example, in the future, staff scheduling need not be handled by employees, but rather can be carried out by intelligent software tools via data processing. Greater flexibility: customers shall be in a position to increase their adaptability and react flexibly to changes in the market or in their business models.