AI is moving from insights to execution in the supply chain. In this Supply Chain Byte, Lisa Anderson discusses how AI is being integrated into real supply chain operations. Examples include HappyRobot supporting DHL with AI workers for tasks such as appointment handling, SAP integrating AI into production and work-order processes and Oracle deploying agentic AI across supply chain applications.
The opportunity is significant, but the foundation must be right. To avoid “garbage in, garbage out” at scale, companies need good data, good processes, integrated systems and clear guardrails before AI becomes embedded in operations.
AI is moving from concept to execution. Examples are popping up in the news. For example, HappyRobot just raised $150 million at a $1.2 billion valuation to focus on just this topic. HappyRobot builds AI agents that run phone calls, emails, and scheduling tasks that keep supply chains moving. Their concept is to replace suggestions by doing work such as negotiating with truck drivers, scheduling appointments, and handling customer support. They work with customers like DHL and Uber to take over these tasks and help the companies scale faster.
SAP announced a new set of AI-driven assistants and agents at its recent conference that also demonstrates AI moving from concept to execution. According to SAP, they are moving toward an autonomous operating model, where planning, manufacturing, logistics, and asset operations increasingly anticipate, coordinate, and resolve without manual intervention at every step. In essence, it will do the planners work for them from an 80/20 standpoint and respond to changing conditions, predictive insights and potential likely disruptions.
Oracle is on a similar path. They are embedding AI agents directly in their Fusion enterprise applications. According to Oracle, these agents can access the underlying business data, reason about what is happening, and then take actions within the applications and workflows where the work actually occurs. For example, Oracle uses autonomous inventory and supplier re-routing. When an unexpected logistical disruption occurs, it goes beyond alerting the planner. Instead, the system independently executes multi-step corrections across the network.
What should leaders take away? Focus on your data. Similar to MRP, AI will only be as good as the data that feeds it. Garbage in will create garbage out. On the other hand, one of the largest mistakes clients make it waiting for data perfection. As we roll out SIOP (Sales Inventory Operations Planning) programs, we recommend that you utilize AI to improve data integrity while forming cross-functional teams to fix gaps. However, we start with what’s available and focus these efforts on getting directionally correct results and evolve/ improve over time. Combine this data with upgraded processes such as SIOP, planning process upgrades and inventory optimization techniques and fuel with integrated systems (ERP, CRM, CPQ, sales forecasting, APS) to achieve results.
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Advanced Technologies in Supply Chain
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