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How Can You Improve Value in Your Supply Chain?

Supply Chain Shaman

Each supply chain planning technology at the end of 2024, went through disruption–change in CEO, business model shift, layoffs, re-platforming and acquisitions. To build an outside-in model, and use new forms of analytics, we must start the discussion with the question of, “what drives value?” My advice?

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Beyond Cost Optimization: Building Resilient Supply Chains in an Unstable Trade Environment

Logistics Viewpoints

Lean models alone are no longer sufficient. Sudden tariff increases can quickly make a cost-optimized procurement strategy untenable, leaving companies scrambling to adjust. AI is helping companies better detect risk, model alternatives, and make faster decisions with more confidence. AI also helps with scenario modeling.

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Aligning Supply Chain Metrics to Improve Value

Supply Chain Shaman

In follow-up qualitative interviews, one of the largest issues with organizational alignment was metric definition and a clear definition of supply chain excellence. In my post Mea Culpa, I reference my work with the Gartner Supply Chain Hierarchy of Metrics. Error is error, but is it the most important metric? My answer is no.

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Step Past AI Hype Drive Real Value

Supply Chain Shaman

Venture capitalists are high on Artificial Intelligence (AI), and over-exuberant professors with shiny new models are jockeying into position to get rich. Most of the answers will fall into categories: Engines: The improvement of the math in models to improve decisions. Building a software company is hard work. Ask for use cases.

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Dynamic Inventory Replenishment Optimization Guide

ToolsGroup

This uncertainty makes dynamic inventory replenishment optimization essential for business success. Effective inventory optimization directly impacts customer satisfaction, loyalty, operational costs, and waste reduction making it a critical business function in todays volatile market.

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Executives Exploring AI Need to Understand Data Fabrics

Logistics Viewpoints

Developing Models : Building and scaling AI models in a manner that ensures they are reliable and understandable. These new data fabrics will need to go beyond traditional enterprise data fabrics, which are optimized for cloud environments, to be able to embrace complex supply chain data.

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Please Don’t AI Stupid

Supply Chain Shaman

The issue is that when companies optimize functional metrics, they throw the supply chain out of balance and sub-optimize value. Traditional approaches built optimization on top of relational databases. This shift improves modeling options and the use of disparate data. Today, the bright and shiny object is AI.