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As AI becomes embedded across supply chain operations, companies need to plan for heightened expectations around transparency and governance.
A supply chain is a network of interdependent flows. When one node accelerates, it changes the pressure on adjacent ones.
Manhattan Associates Inc. has announced the launch of ‘Solution Design Studio’, a new AI-powered workspace that allows business users to configure complex supply chain systems in t...
New AI innovations make supply chains more adaptive, connected, and responsive.
Retail leaders are increasingly turning to AI-driven forecasting, inventory optimization, and decision-support tools that allow them to respond quickly to changes in consumer behav...
AI systems cannot fully compensate for disconnected operational processes, fragmented data models, and poorly coordinated enterprise architectures. Artificial intelligence is becom...
Artificial intelligence is rapidly moving beyond isolated copilots and narrow automation tools. Across the supply chain technology landscape, a new architectural layer is beginning...
Supply chain technology has traditionally been evaluated by functional category. AI is pushing the market toward a different question: what decisions does the architecture improve,...
ERP, WMS, TMS, OMS, and planning systems remain essential. But AI is introducing a new layer in supply chain technology: systems that evaluate conditions continuously, incorporate...
As supply chains become more dynamic, the way work gets done and operations are run is changing, and agentic AI is at the forefront. The post From intelligence to impact: How agent...
Supply chain AI will not succeed because it can generate answers. It will succeed when it can operate with clean data, business context, governance, action pathways, and closed-loo...
Supply chain AI will not succeed because it can generate answers. It will succeed when it can operate with clean data, business context, governance, action pathways, and closed-loo...
AI can help companies catch supply chain problems earlier, but it also introduces new risks that need to be managed.
AI systems are great tools for repetitive tasks, but eliminating every source of human oversight can have consequences.
For a long time, visibility in the logistics industry has been focused on screens loaded with data, providing an almost real-time view into shipments in transit, anomalies or disru...
Key Takeaways: AI predictive maintenance offers real operational value—but real risk. While these systems can reduce downtime and extend equipment life, a single missed prediction...
Attendees at MODEX heard that, despite the urgency around AI, it will be equally important to ensure that investments in the technology are targeted and strategic.
The next phase of supply chain AI will not be defined by better models alone. It will be defined by whether those models can improve real decisions across planning, logistics, sour...
AI in the supply chain is often approached as an application problem. In practice, it is more often an architectural one. The OSI model offers a useful lens for understanding why....
A multi-part series exploring the displacement of human labor in warehouses and distribution centers. Part One: The Negative Pressure of Increasing Operating Costs on Profit Margin...
For supply chain leaders today, disruptions like port congestion, geopolitical... The post Self-Driving Supply Chains: AI Agents enabling Autonomous Planning, Decisioning, and Exec...
Today, dashboards flag costly return rates, suspicious refund activity, or locations with abnormal behaviors. The insights are helpful, but typically arrive after the fact.
For supply chain leaders evaluating AI, the most important question is whether the organization has done the work required to make that model operative in its real environment.
Global supply chains are going through a profound transformation. For a long time, logistics decisions relied heavily on experience and fragmented data. Today, two very different f...
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