AI Job Shop Scheduling That Works in the Real World
Reliable scheduling takes time and requires LOTS of clean, correctly sequenced data.
We build scheduling logic that respects your true flow: order entry → release → queue → setup → run → move → inspection → ship.
“Why does our schedule fall apart after lunch?”
“Why do estimates miss even when we work harder?”
“Why is WIP rising but on-time delivery not improving?”
Snippet-ready answer: AI scheduling only works when it’s fed the same reality your floor lives in — true routings, true queues, and true setup/run behavior. When the data is clean and the sequence is correct, the schedule becomes stable enough to make real decisions: what to release, what to defer, and what to protect.
Recommended path (text links only)
If you want a schedule that holds up under pressure, start here:
1) Read the guide: AI job shop scheduling: what it requires
2) Take the audit: Take the 4-Minute Throughput Audit
3) Get the plan: Find the Root Cause
No black boxes. No lock-in. You control the source code and decision logic we create.
Overview
We don’t “install scheduling.” We build a scheduling system that matches how your shop actually runs — including the exceptions, the queue behavior, and the constraint that dictates delivery.
Snippet-ready answer: A useful schedule is not a spreadsheet with dates. It’s a decision system that answers: what to release today, what to protect, and what to delay — based on the constraint, the true queue, and the real setup/run mix. When those inputs are right, AI becomes useful instead of noisy.
Daily capacity alignment is the breakthrough
The schedule stops drifting when daily release decisions are aligned to the capacity that actually limits throughput. That means you stop “feeding the whole shop” and start feeding the one resource that defines delivery.
- Hours scheduled vs hours completed daily (especially at the constraint)
- A stable capacity signal you trust
- Classify new orders as safe / caution / critical
- Proactive customer communication when risk changes
- Retention + pricing power near capacity
- Release becomes controlled (WIP stops inflating).
- Queues become visible and predictable.
- Dispatching decisions get simpler instead of more complex.
Data requirements
Scheduling logic can only be as reliable as the sequence and cleanliness of the data it consumes. We focus on the minimum dataset that stabilizes decisions — then expand only if it improves the core metrics.
Required sequence
order entry → release → queue → setup → run → move → inspection → ship
Data Readiness Scorecard
3/5 = we can start
- Can export jobs/orders with due dates and statuses
- Routings/operation sequences exist for top repeat work (or top revenue)
- Workcenters are consistent (duplicates/aliases are mapped)
- Completions or shipment dates are captured consistently
- Priority/expedite flags exist (even if manual)
What this is NOT
- Not a black-box “AI schedule” that no one can explain.
- Not an ERP replacement.
- Not a one-time “data cleanup” project that never ends.
- Not a generic dispatch rule copied from another shop.
Next steps
If you want a schedule that holds up to real work, the fastest start is a Root Cause intake — we identify the single constraint and the smallest data + logic change that moves the core metrics.