We’re working on connecting our AI-powered vision inspection systems to our MES and SPC platforms, and the conversation keeps circling back to architecture decisions. The vision systems themselves are performing well in isolation—detecting surface defects with over 95 percent accuracy during pilots—but we’re struggling with how to structure the data flows and decision logic once everything is live across multiple lines.
Right now, every defect the cameras catch gets logged, but there’s no automatic linkage to batch IDs, machine parameters, or shift data unless an operator manually enters context. We want the MES to act as the backbone so that every inspection result is tied to production state in real time, and we want SPC analytics to trigger alerts when defect rates drift even before they breach hard limits. The question is whether to route all vision inference results through the MES first, or to let the vision system write directly to both MES and SPC in parallel, or to build a middleware layer that orchestrates everything.
Another open issue is whether to keep all models running on edge devices at each inspection station, or centralize inference on a server with cameras streaming images over the network. Edge gives us low latency and resilience if the network goes down, but centralized makes model updates and retraining much simpler. We’re also not sure how much operator override and feedback should flow back into the training pipeline versus staying as audit log only.
Would appreciate hearing how others have structured this—what architectural patterns worked, where you ran into bottlenecks, and how you handled the balance between real-time responsiveness and keeping models accurate as production conditions change.