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Agentic AI use cases COOs can implement in the supply chain
Agentic AI can optimize production schedules based on asset availability and manage parts and supplies inventory. Learn more agentic AI supply chain use cases.
Agentic AI has various applications for different company departments, and COOs and chief supply chain officers (CSCOs) can potentially use agentic AI to improve their organization's supply chain resilience.
Over the past several years, global disruptions have prompted a rebalancing of priorities, with C-suite executives devoting greater attention to diversification and agility. Agentic AI is capable of consuming vast amounts of data, which can help executives improve their decision-making, though having a "human in the loop" for AI is important as well.
In this article, we’ll explore some agentic AI supply chain use cases COOs and CSCOs can implement today to enhance the efficiency, accuracy and agility of their operations.
Use case 1: Dynamic inventory optimization and replenishment
Agentic AI can help improve inventory optimization by using various data for automated decisions about safety stock, reorder points and replenishment. These tools monitor real-time demand signals, supplier performance and external factors that can affect supply networks. They can rebalance inventory and expedite orders.
Agentic AI can help CSCOs and COOs minimize stockouts and excess inventory as well as improve fill rates. AI-driven inventory optimization tools can help companies be more agile, which can be particularly helpful when critical supply chains are disrupted.
Use case 2: Production scheduling
Agentic AI can account for sales and market trends, weather and geopolitics, then deliver forecasts and autonomously triage exceptions. Based on the forecasts, agentic AI can recommend or execute work order adjustments, find alternative sourcing and expedite deliveries.
This capability can lead to greater throughput and less downtime and reduce bullwhip effects.
Use case 3: Logistics and routing optimization
Autonomous agents have uses for various aspects of transportation management, including coordination of carriers and re-planning of routes.
Agents can continuously ingest live data such as information about traffic, weather and carrier capacity, then generate optimized routes and transport modes. Carrier negotiation agents can interact with both traditional carriers and independent contractor networks to identify the fastest and most cost-effective means of getting a product to its destination.
Use case 4: Predictive maintenance and asset optimization
Manufacturers and others have been using predictive maintenance capabilities for quite some time. Predictive maintenance applies machine learning technology to forecast potential equipment failures and recommend maintenance. Predictive maintenance tools continuously analyze sensor data from equipment, vehicles and facilities.
Agentic AI can optimize production schedules based on asset availability, create maintenance work orders and manage parts and supplies inventory.
Implementing these agentic AI supply chain use cases can potentially help COOs and CSCOs achieve greater resilience and efficiency.
James Kofalt spent 16 years at SAP working with SME business applications and was a product manager for integration technology at Microsoft's Business Solutions division. He is currently the president of DX4 Research, a technology advisory practice specializing in ERP and digital transformation.
