Every data-driven improvement hits the same wall.
The methods work in the lighthouse. The outcomes never reach the network. The lighthouse plant proved what modern manufacturing can do. The second plant started from zero. The third never started at all. The models work. The data exists. The foundation underneath does not.
Why standardization-first is the wrong path.
The consulting answer is to consolidate MES and ERP before doing AI. By the time that program finishes, the competitive landscape has moved. The foundation that actually delivers improvement still has to be built. Data-driven improvement cannot wait that long.
Why every plant becomes a new integration project.
Each plant has its own systems, its own naming, its own quirks. Every rollout is a new integration project, hand-built and non-transferable. The lighthouse worked on unreasonable attention. The rest of the network cannot get the same. Scale requires a foundation that generalizes. Attention does not.
The foundation that scales.
context/fab is built in three layers. Data connected to the systems you already run. A continuously updated model of how your production runs. Process mining, AI, and manufacturing agents running on live operations. One foundation. Every improvement. Plant by plant, across your network.
What the right architecture unlocks.
Every quarter on the wrong architecture is throughput, scrap, on-time delivery, and working capital improvements left on the table. The right one carries the first outcome and every one after it on the same foundation. New use cases extend the impact without rebuild. The advantage compounds with every plant and every use case added.
Three stages from workshop to scale.
The engagement starts with a value workshop where the highest-impact outcome is identified, the KPI and baseline agreed, the business case quantified. First-plant deployment delivers the named outcome on real production. Scale carries the same model across the network, with every rollout faster than the last on the same foundation.