Supply Chain News: AI In Operations

AI is rapidly moving from pilot projects to integral parts of operations in supply chains. Companies are applying AI in warehouses, fulfilment, risk detection, and planning to drive efficiency, reduce errors, and respond faster to disruptions. The latest supply chain news shows that operations functions can no longer ignore AI—they must adopt it to stay competitive.

Key Recent Developments & Examples

• Starbucks’ Inventory Counting Rollout
  Starbucks is deploying a system across its >11,000 North American company-owned stores that uses AI plus computer vision, 3D spatial intelligence, and AR to enable workers to count inventory more often and more accurately. The system detects low stock of high-demand items (like oat milk) and improves store operations by reducing time spent in stockrooms.

• Japan’s Fulfillment Centers & Warehouse Automation
  In Japan, operations are under strain due to demographic shifts and labor shortages. Amazon’s Chiba fulfillment center has more robots than people and is using automated packing, advanced sorting systems, and robotics to make up for reduced human labor and maintain service levels.

• Walmart’s Global AI-Driven Operations Playbook
  Walmart is integrating AI deeply into its operations. From predictive warehouse/transport management, route optimization, to “Self-Healing Inventory” systems that reroute excess stock proactively, the company is scaling AI tools across multiple countries to optimize fulfilment and store replenishment.

• Manhattan Associates & Agentic AI
  At the Manhattan Momentum 2025 conference, “Agentic AI” was a major theme. Manhattan’s platform now includes AI agents embedded in its warehouse management systems (WMS), forecasting, labour optimization, inventory planning, etc. These agents make autonomous or semi-autonomous decisions to support real-time operations.

• Blue Yonder’s AI Agents for Operational Decisions
  Blue Yonder has introduced AI agents and a supply chain knowledge graph at its ICON 2025 event. These tools help customers with detection of disruptions, prompt decision-making, minimizing delays in operations, and identifying opportunities for operational enhancement.

What AI Is Doing in Operations: Key Use Cases

From the cases above and broader industry trends, here are the primary operational use cases where AI is having impact:

1. Predictive & Real-Time Inventory Management
   AI can forecast demand, identify slow-moving or overstocked items, and help reallocate or reroute stock among warehouses to avoid waste and shortage. Walmart’s self-healing inventory is a clear example.

2. Warehouse Task Automation & Orchestration
   Using robotics, automation, and AI agents to manage and optimize picking, packing, sorting, and the internal flow of goods. AI is also being used to manage labor allocation in complex warehouse environments.

3. Risk & Disruption Detection / Operational Resilience
   AI models monitoring supplier risk, weather, transportation delays, and regulatory signals help operations teams prepare contingency plans or switch paths to avoid bottlenecks.

4. Forecasting & Planning
   Demand forecasting, capacity planning, replenishment scheduling—using AI to factor in seasonality, external economic indicators, promotional events, etc. This improves precision and reduces guesswork.

5. Autonomous / Agentic Decision-Making
   AI agents that don’t just suggest actions but can trigger certain operational workflows automatically—e.g., reroute inventory, reorder when thresholds are met, shift labour or resources in response to real-time conditions.

Benefits & Impacts

• Greater Operational Efficiency: Less manual work, fewer inefficiencies in warehousing and fulfilment, faster processing of orders.

• Cost Savings: Lower labour costs, reduced overstock / holding costs, fewer emergency shipments or expedited freight.

• Improved Reliability & Speed: Better stock availability, less risk of downtime or fulfilment errors, enhanced ability to meet customer expectations.

• Scalability: Operations that can adjust in real time when demand spikes, disruptions occur, or supply conditions change.

• Better Resource Utilization: More intelligent use of space, labour, and transport; reduce waste and inefficiencies.

Challenges & Risks

• Data Quality & Integration: AI models need accurate, timely data from many sources. Legacy systems, manual entry, silos hurt effectiveness.

• Change Management: Workers and management need to adapt; AI may shift roles; resistance or skill gaps can slow adoption.

• Upfront Investment: Automation, robotics, AI tools cost money and time; ROI may take time to materialize.

• Over-automation Risk: In some environments, over-reliance can reduce flexibility or create single points of failure.

• Ethical / Safety / Regulatory Concerns: Especially with autonomous robots or systems making decisions, permissions, safety oversight matter.

Strategic Takeaways for Operational Leaders

To leverage AI well in operations, companies should consider:

• Start with high-impact areas: Choose operations where gains are large—warehouses with high error rates, labour-intensive fulfillment, frequent stockouts.

• Ensure good data foundations: Clean, unified data across warehouses, transport, demand signals, supplier status.

• Adopt human-in-the-loop models: Let AI suggest or trigger, but keep oversight especially for edge or critical cases.

• Invest in pilot projects and scale carefully: Test AI agents, robotics, or automation in controlled settings; measure results; then scale.

• Align incentives: Operations, procurement, inventory, and finance need to share goals (e.g. fewer stockouts, lower costs, faster fulfilment).

• Monitor and iterate: Operations are dynamic. AI models must be updated as demand, supply constraints, or external disruptions change.

What to Watch Next

• Adoption of agentic AI across more operational functions—moving from suggestions to more autonomous workflows.

• More case studies from non-tech industries (e.g. food, apparel, industrial goods) showing real cost/performance improvements.

• Advances in robotics + AI for smaller, flexible fulfilment centers or last-mile hubs.

• Ethical, safety, and regulatory frameworks for AI in operations (robot safety, decision accountability, etc.).

• How AI helps operations deal with climate or weather disruptions—forecasted supply chain hazard signals, rerouting, etc.

Conclusion

AI in operations is no longer optional for modern supply chains—it’s becoming central. The latest supply chain news shows that companies like Walmart, Starbucks, Manhattan Associates are not just experimenting—they’re building AI-driven operational systems that anticipate, adapt, and act.

For operations leaders, the message is clear: invest in good data, choose high leverage areas, pilot intelligently, and create feedback loops. Those who do will be better equipped to handle volatility, meet customer demands, and run leaner, more resilient supply chains.