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Interoperability in the Supply Chain: Leveraging the OSI Model for Seamless Logistics

Logistics Viewpoints

By applying the ISO OSI (Open Systems Interconnection) seven layer model, traditionally used in networking, to logistics, businesses can achieve a structured framework that enhances communication, reduces friction, and improves collaboration throughout the supply chain. Physical Layer: Transmits data over a physical connection.

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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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Using Data to Improve Supply Chain Operations

Material Handling and Logistics

Learn how to organize your data operations in alignment with supply chain strategy. Complex supply chains generate more data, which companies can use to drive greater efficiency or engage in innovation that disrupts an entire industry—think Amazon. More data is coming in than ever before.

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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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Modeling brewing capacity to drive cost savings at HEINEKEN

AIMMS

Can you tell me about HEINEKEN’s AIMMS-based Brewing Capacity Model? I understand your team took ownership of this model. Yeah, so there was an existing Brewing Capacity Model which lived in an Excel file. There was no global master data in place either. I’m curious to learn more about your vision for the model.

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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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Predictability in a Time of Uncertainty: Machine Learning in Logistics

Logistics Viewpoints

Machine learning (ML) techniques can be applied to provide more accurate transit information and estimated arrival times (ETAs) by analyzing the historical shipment data in your transportation management systems. The model learns continuously and can adapt to changing conditions in the network.