The NextGen Supply Chain Conference has revamped its 2026 awards to emphasize real-world execution, introduced a new Partnership in Execution category, and opened submissions through May 15 for organizations delivering measurable supply chain results.
Layered artificial intelligence combined with behavioral data and network detection strategies is becoming essential for securing modern industrial supply chains against increasingly sophisticated, AI-enabled cyber threats.
AI-driven forecasting only delivers real business value when organizations rigorously measure forecast value add (FVA) to ensure every model, agent, and human intervention improves operational decision-making.
Warehouse physical AI is closing the long-standing gap between digital systems and on-the-ground operations by passively capturing real-time inventory data, enabling higher accuracy, improved OTIF performance, and more efficient labor utilization without heavy capital investment.
Thursday, March 19, 2026 · Pierfrancesco Manenti, VP Analyst, Gartner Supply Chain Practice
AI is enabling CSCOs to shift from reactive cost cutting to proactive, data-driven cost management by uncovering hidden cost drivers, optimizing decisions in real time, and modeling financial trade-offs across the supply chain.
Agentic AI is transforming supply chains from deterministic, rule-based systems into adaptive, insight-driven networks that prioritize real-time decision-making, root-cause analysis, and capital-efficient innovation.
In this webinar, Elenna Dugundji, Director, Deep Knowledge Lab for Supply Chain and Logistics Research Scientist with MIT explores why data discipline remains the foundational driver of supply chain optimization. Our discussion will examine the critical role of data…
True supply chain visibility in 2026 depends less on tracking shipments and more on synchronizing data across systems, ensuring a trusted single source of truth, and building AI-driven decision tools on high-quality, interoperable freight data.
Supply chain leaders implementing warehouse automation should avoid overly customized systems and instead prioritize modular, composable architectures that improve scalability, reduce operational risk, and adapt to changing fulfillment demands.
While enthusiasm for generative AI in supply chains is high, most companies remain trapped in pilot programs because successful deployment requires workflow-level problem definition, embedded agents, and disciplined governance rather than simply applying new AI models.
Wednesday, March 4, 2026 · Amanda Dyson, VP of marketing, FourKites
As AI agents increasingly automate supply chain execution, companies must redesign talent strategies to prioritize relationship management, critical thinking, and organizational influence rather than traditional process-based operational skills.
Tuesday, March 3, 2026 · Tom Davis and Dennis Oates
As generative AI reshapes knowledge work, supply chain leaders must orchestrate people, processes, and intelligent systems, shifting from automation to integration to unlock real performance gains.
APQC research shows that while organizations pursue aggressive AI adoption and Net Zero emissions goals, most fail to account for AI’s energy use and GHG impact—creating a growing disconnect between digital transformation and climate commitments
Supply chains are expanding the use of AI across functions, and that expansion means more data storage and more computation, which all require more electricity use and potentially more greenhouse gas (GHG) emissions during electricity production.
Monday, March 2, 2026 · Dean Alms, chief product officer, Aravo
A review of 2025’s AI predictions shows that while agentic AI and automation advanced in supply chains, data readiness, governance gaps, and third-party risk oversight will determine whether organizations realize real AI ROI in 2026.
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