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What Georgia-Pacific Is Doing With Causal AI Is Remarkable

Logistics Viewpoints

However, complex process manufacturing presents a much more difficult ATP problem than is typical in discrete industries. Causal AI utilizes sophisticated causal models to make decisions on multiple levels. Seeing the layers of knowledge modeled in a knowledge graph is more powerful. Before working with GP, Parabole.ai

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Optimizing the food industry: a conversation with UniSoma’s Luis Pinto

AIMMS

The first product of this partnership is TacticalOps, a Planning & Optimization solution for Food Manufacturers. I spoke with Luis Pinto, Partner at UniSoma, to understand the need for new planning and optimization solutions in the global food supply chain. I’ve seen the attitude towards optimization evolving yes.

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Bottom Line: What is the ROI of Supply Chain Network Design Software?

AIMMS

To do this, we built two representative models of a business. When the models are built, running scenarios with these large businesses can be a lot of fun. Having justified the project investment, it’s now time to get into the more substantial opportunities that present themselves in these sample $7.5B

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Optimizing the food industry: a conversation with UniSoma’s Luis Pinto

AIMMS

The first product of this partnership is TacticalOps, a Planning & Optimization solution for Food Manufacturers. I spoke with Luis Pinto, Partner at UniSoma, to understand the need for new planning and optimization solutions in the global food supply chain. I’ve seen the attitude towards optimization evolving yes.

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5 Myths of Making Robots Work with People

Logistics Viewpoints

There are many myths when it comes to the implementation of what some call “ human in the loop ” robotic systems or just optimizing “sharetasking” between humans and robots in the DC. But it is not looking at batch and path optimization without robots. But there is still much to learn. It can be quite static.

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Predictability in a Time of Uncertainty: Machine Learning in Logistics

Logistics Viewpoints

ML looks into historical data (for example, transit time statistics of carriers) and data from impactful external factors (such as port congestion, weather or holidays) and uses this information to develop more accurate transit time estimates. The model learns continuously and can adapt to changing conditions in the network.

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Evaluating Innovative Technology with Warehouse Simulation

Logistics Viewpoints

Compared to traditional forecasting models on spreadsheets, the benefits from simulation software come in the form of a holistic overview of both current and potential operations through a user-friendly interface. Through warehouse simulation, you can accurately visualize all the attributes of the warehouse in a 2D or 3D computer model.