Topic: Manufacturing
Agentic AI and the shift handover problem
An agent that runs continuously meets an organisation that stops every eight hours. What the outgoing shift knew and did not write down is where the decisions break.
Supplier lead times are a distribution, not a number
The contract says fourteen days. The mean is sixteen. The ninetieth percentile is twenty-nine. Your safety stock was calculated from the first number.
Agentic AI and the shift-handover problem
Three shifts, three sets of judgement, and a handover conversation that lasts four minutes. Most of what one shift learned never reaches the next.
Scrap rates hide the decision that caused them
Scrap is recorded against a shift and a line. The decision that caused it was made three days earlier, by someone in another department.
Predictive maintenance fails at the work order, not the model
The model predicts the bearing will fail in eleven days. Then nothing happens for nine of them.
Agentic AI in production planning: what actually gets delegated
Not the schedule. The hundred small adjustments to the schedule that a planner currently makes by hand, each one defensible and none of them written down.
Why manufacturers still make inventory decisions reactively
It is rarely a lack of data. It is that the data lives in four systems that were never designed to answer a question together.