Work-in-Process Bottlenecks
A work cell is starving while the material it needs sits two buildings away.
The decision
Cell 4 is about to run dry. The material it needs is staged two buildings over, another cell is backing up behind it, and the shift changes in ninety minutes. Expedite the move, resequence the line, pull a different order forward, or hold?
Why it's hard today
WIP location is approximate, the schedule is mid-shift, labor is finite, and the bottleneck moves the moment you act on it. The floor knows it's stuck before any system flags it — and an experienced supervisor's instinct is the only thing bridging the gap.
Your MES shows the schedule, not the reality on the floor — where material actually is, how confident that reading is, and which move keeps the most cells running. It reports the bottleneck after it has already formed.
What AgentForgeOS does
It assembles the picture you can't see, and makes the call under your rules.
Move the staged lot to Cell 4 now and resequence two orders behind it — keeping four cells running — under your changeover and labor rules, pending the supervisor.
- WIP location and confidence by cell
- Live schedule and cell status
- Material staging and transit
- Labor available this shift
- Changeover-cost limits
- Labor and safety constraints
- Schedule-adherence rules
- Priority-order protection
⚠ Cell 4 starving — ~22 min to dry, Cell 6 backing up
Evidence assembled
- Required lot staged in Bldg C (move ~12 min)
- Resequencing two orders keeps 4 cells running
- Shift change in 90 min; one mover available
- Priority order due end of shift
Recommendation · expedite the lot, resequence two orders
Policy: within changeover limit · labor ok · priority order protected · awaiting supervisor
What improves
- Higher line utilization
- Fewer starved cells and shift-end misses
- Less reliance on one supervisor's memory
The move and its result are kept, so the floor's recurring bottlenecks — and what actually cleared them — become the model's, not just the veteran supervisor's, sharpening with every plant it runs in.
Underneath, this is the same operating model the rest of the platform runs: verified context is assembled, options are weighed and adversarially challenged, the decision is governed by your policy, and the outcome is learned. The decision changes from one of these to the next. The architecture does not.
See how it worksThis is exactly how your team works today.
Only now the decision is assembled, governed, and remembered — instead of made from memory and lost.