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Scenario Modeling Is Now Crucial for Supply Chain Network Design

AIMMS

How are companies leveraging scenario modeling for network design and optimization ? The good news is many of the survey’s respondents recognize the potential of more advanced optimization solutions. In the context of disruptions like COVID-19, scenario modeling can make considerable difference – Tweet this.

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Alleviating the Uncertainty of Peak Season: The Role of Home Delivery in 2024

Logistics Viewpoints

The onus is on ecommerce retailers to control the controllables, and focusing on eliminating uncertainty from the consumer fulfillment process and optimizing the last mile is a smart approach. By mapping customer delivery personas to the delivery choices they offer, retailers can improve fulfillment certainty to protect margins.

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Tata Steel Makes Strides Towards an Autonomous Supply Chain Powered by Optimization Technology

AIMMS

Today, the steel manufacturing leader has an ambitious digital transformation agenda and is leveraging AIMMS technology to optimize operations in its home country. I belong to this second division and work mostly on mathematical modeling, simulation and supply chain analytics. . When did you join Tata Steel? .

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Navigating M&A Trends in the Transportation, Logistics, and Energy Storage Space

Logistics Viewpoints

As supply chains transition to a more circular and sustainable model, M&A activity in this domain is expected to intensify. For example, the global logistics automation market is expected to grow from $50 billion in 2023 to $120 billion by 2030, according to Allied Market Research.

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Advanced Matrix Applications in Supply Chains

Log-hub

By leveraging these technologies, businesses can optimize operations, reduce costs, and make smarter, data-driven decisions. The Future of Matrix-Based Optimization The Future of Matrix-Based Optimization AI and machine learning (ML) take matrix-based analysis to new heights.

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The Power of Decile Data: Why It’s a Game-Changer for the Green Industry

WorkWave

By ranking prospects and customers into ten groups, from least likely to buy to most likely, green industry businesses can pinpoint high-value clients, optimize marketing campaigns and allocate resources more efficiently. For example , let’s consider a dataset of 100 lawn measurements in a given town. Heres another example.

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Celanese Leads the Pack When it Comes to Agentic AI

Logistics Viewpoints

ARC has been actively studying industrial AI for over two years. What Celanese has accomplished is the single best example ARC is aware of employing agentic AI and copilots at scale. We needed to model the data in a way that we can do simple searching. Data must be modeled consistently across the organization.

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