Smart manufacturing has an AI problem — just not the one you think
Summary
A Manufacturing Dive analysis argues that the core challenge in smart manufacturing is not AI capability itself, but the proliferation of ungoverned decisions being made across disconnected systems. As AI tools get embedded in MES, ERP, SCADA, and planning platforms simultaneously, decision logic can conflict, overlap, or operate without accountability. The piece frames this as a governance and architecture problem rather than a technology limitation.
Why It Matters
On the factory floor, this plays out in concrete ways: a machine-learning-driven predictive maintenance system recommends keeping a line running while a separate AI-assisted production scheduler pulls that asset offline for a changeover, and a third system has already committed downstream capacity based on assumed uptime. Without a defined decision hierarchy and clear ownership of each logic layer, these conflicts don't surface cleanly — they surface as unexplained downtime, missed OEE targets, or inventory variances that are hard to trace back to root cause. Manufacturers investing in AI tooling need to treat decision governance as a first-class engineering requirement, not an afterthought. That means mapping which systems hold authority over which operational variables, establishing conflict-resolution protocols between automated agents, and assigning human accountability at each decision tier — before adding more AI, not after.