Plant fingerprints hold decades of deep production knowledge: how materials behave, how machines are tuned, how quality issues emerge, how processes fail and recover. Yet much of this knowledge is tribal in nature and typically applied through spreadsheets or during the morning production hot list meeting.
Factory-based AI systems are evolving rapidly. General-purpose LLM’s are trained using public text, images, and code, but they lack industrial context. They do not understand manufacturing constraints, process tradeoffs, or the realities of production planning, scheduling, and execution. Purpose-built industrial software systems solve this gap, given they are configured with a deep representation that can leverage real manufacturing knowledge.
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