Technology & Tools

Artificial Intelligence in Supply Chain

Definition

Artificial intelligence in supply chain applies machine learning, optimization, and increasingly generative and agentic AI to forecasting, planning, logistics, and exception management tasks that previously required manual analysis.

In Practice

AI shows up across the stack: ML models forecasting demand, optimization engines routing trucks, computer vision inspecting products, and language models summarizing supplier contracts or answering 'why is this order late?' in plain English. The newest wave — agentic AI — executes routine decisions autonomously within guardrails, like rebalancing stock or re-tendering a rejected load.

For planners, the practical shift is from making every decision to managing decision quality: setting the policies, reviewing exceptions the AI escalates, and auditing outcomes. AI is only as good as its data, so master data and integration work usually precede the payoff.

Example: a retailer's AI system autonomously reorders 90 percent of SKUs, escalating only new items and unusual demand patterns; planner headcount stays flat while the assortment doubles and stockouts fall.

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