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Sustainable Manufacturing for Sustainable Supply Chains

Logistics Viewpoints

Sustainable manufacturing is one step in the process of building sustainable supply chains. This means that supply chain emissions can account for up to 90 percent of a company’s carbon footprint. The first example was the Planetary Equilibrium Tee , which was a collaboration between the North Face and Spiber.

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Baltimore Bridge Collapse: An Opportunity to Reinforce the Importance of Supply Chain Resilience

Logistics Viewpoints

Supply chain resilience is a hot topic the last few years since post-Covid issues and newsworthy incidents like the Houthi attacks in the red sea have raised awareness. The bridge collapse is a stark reminder of the vulnerability of the nation’s aging infrastructure and the importance of supply chain resilience.

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Energy Transition in Supply Chain

Logistics Viewpoints

Supply chain sustainability has become a very hot topic, but for a lot of people, there is uncertainty around its meaning. There are three responses in this question that address supply chain sustainability: align with our goals, mission, and values; meet consumers expectations; and increase profitability.

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Exiger Has Taken the Supply Chain Risk Market by Storm

Logistics Viewpoints

Supply chain risk solutions are hot. Exiger’s CEO, Brandon Daniels, told me they were larger than their three largest supply chain risk competitors combined. Modern supply chain risk solutions are marvelous pieces of technology. And a company called Exiger seemingly came out of nowhere to capitalize on this.

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Supply Chain Planning Maturity – How Do You Compare to Peers?

Today's supply chains are networked, global ecosystems. The COVID-19 pandemic is an extreme example of how this unfolds in practice. How prepared are supply chain teams to react and recover from a planning maturity stance? Time allocated to data collection: Data quality is a considerable pain point.

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

Logistics Viewpoints

Supply chain resiliency and sustainability are top priorities for CEOs today. To achieve these goals, corporate leadership must focus on two key areas: shift from internally focused supply chains to collaborative supply networks and actively design their supply chains.

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Getting Started with AI in Supply Chain

Logistics Viewpoints

Getting started with AI in supply chain might not start where you think. Instead start with the foundation of your AI strategy, which should be an understanding of your company’s supply chain and your data. Since this advice on getting started with AI in supply chain may seem counterintuitive, let me explain.

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Common Use Cases for Mathematical Optimization

How can this type of prescriptive analytics be applied to lower costs, reduce carbon emissions and build more resilient supply chains? Want examples where optimization is used in combination with other types of AI techniques, such as machine learning. Want ballpark estimates of value and benefits achieved through optimization.

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5 Powerful Prescriptive Analytics Examples in Supply Chain

Supply chain, with its complex planning questions, is typically an area where optimization technology is required. Supply Chain Network Design. Prescriptive analytics is a type of advanced analytics that optimizes decision-making by providing a recommended action. Read about 5 use cases. Inventory optimization.

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No Fluff ROI: The Top 5 ROI Drivers for Supply Chain Visibility

Speaker: Glenn Koepke, Andy Johnson, and Steve Rotter

To help you chart your supply chain transformation, we gathered best practices from 1,300 of the world’s most recognized brands and we’re sharing them with you!

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Top 5 Barriers to Supply Chain Network Design Adoption and How to Overcome Them

Speaker: Brian Dooley, Director SC Navigator, AIMMS, and Paul van Nierop, Supply Chain Planning Specialist, AIMMS

This on-demand webinar shares research findings from Supply Chain Insights, including the top 5 obstacles that bog you down when trying to improve your network design efforts: Poor data quality. When you finally have the analysis, everything has changed, and it is no longer relevant. Lack of skilled resources.