Agentic Digital Twins For AI-Native Fulfillment Networks

InnovationAgentic Digital Twins For AI-Native Fulfillment NetworksByAmruth Puppala,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)Jun 12, 2026, 06:00am EDTAmruth Puppala | Senior Engineering Manager | Walmart. gettyFulfillment networks have become far more dynamic than traditional warehouse systems were built to manage. Today’s operations require constant coordination across robotics, inventory movement, transportation scheduling, labor planning and customer delivery commitments. Same-day delivery expectations and demand volatility have increased the complexity even further.From my experience leading fulfillment automation, one challenge appears repeatedly: Organizations invest heavily in automation, but operational coordination still remains fragmented. Warehouse management systems, warehouse control systems, transportation platforms and scheduling engines often operate independently, each optimizing its own area without awareness of broader fulfillment conditions.That disconnect creates familiar problems: dock congestion, robotics traffic conflicts, workload imbalance and delayed disruption response. In many cases, instability does not begin with one major failure. Smaller inefficiencies across unloading, replenishment, robotics movement and transportation coordination compound over time before teams can clearly see them.Digital twins have improved visibility and simulation, but many still function mainly as monitoring tools. I believe the next step is an agentic digital twin framework—one that helps fulfillment networks move from reactive coordination to predictive and continuously adaptive orchestration.Industry ChallengesIf you operate a large fulfillment network, a few challenges are likely consistent:• Fragmented Operational Visibility: Telemetry often lives across isolated systems, making it difficult to respond...