Data Prep for AI Scheduling and Dashboards
AI scheduling fails for one predictable reason: the inputs are inconsistent.
Data prep makes the minimum information trustworthy so decisions stay stable from order entry to ship. We start with exports, fix what changes outcomes, and avoid “clean everything” projects.
Microcopy: Get the smallest next step that moves OTD, lead time, and throughput — or review your constraint + data readiness in 20 minutes.
What data prep means in a job shop
This isn’t data science. It’s operational hygiene: making sure the sequence, workcenters, statuses, and feedback from actuals reflect reality. When the data is trustworthy, dispatching calms down, expediting drops, and delivery becomes predictable.
The minimum dataset we stabilize first
- Orders/jobs: due date, status, priority/expedite flag
- Routings: operation sequence that matches the real flow
- Workcenters: normalized naming and capacity grouping
- Completions: operation completions where available, or ship history as a baseline
- Exceptions: holds, rework flags, engineering change markers (where applicable)
3/5 = we can start (and improve as we go).
What we change in ERP standards
Most shops already have ERP fields. The problem is that the standards don’t match how work actually happens. We align the standards to reality so exports become trustworthy inputs.
Routing standards that match actuals
- Fix sequence order where it’s wrong
- Add missing operations only where they change planning
- Calibrate setup/run assumptions with actual feedback (estimate vs actual)
Workcenter normalization
- Map duplicates and aliases
- Define capacity groups (especially at the constraint)
- Stabilize reporting so hours completed means the same thing every day
Status and priority discipline
- Make released mean released
- Make expedites visible (flagged) instead of verbal
- Reduce priority churn by defining safe/caution/critical rules where data supports it
Why this often reduces WIP without reducing shipments
When release and priorities are based on a stable signal, upstream work stops flooding the floor. Pre-bottleneck work stays moving on the right jobs so parts arrive in sequence and ship. This is how shops reduce expedite rate, stabilize lead times, and improve on-time delivery—without turning the shop into a daily firefight.
What you can expect to improve
- Reduced expedite churn (fewer emergency priority flips)
- Improved schedule stability at the constraint
- More stable lead times (less drift after quoting)
- Lower WIP without sacrificing shipments (when release is controlled)
- Clearer due-date risk earlier in the week (before it becomes a late shipment)
What this is NOT
- Not a rip-and-replace ERP project
- Not “AI fixes dirty data”
- Not a months-long cleanup with no operational payoff
- Not a black box: you control the definitions and decision logic we create
Next step path (text-only links)
If you want the smallest next step:
• Identify the constraint blocking throughput: /root-cause
• Read what AI job shop scheduling requires: /guides/ai-job-shop-scheduling-what-it-requires
• See AI job shop scheduling in the real world: /services/ai-job-shop-scheduling
• Track the right metrics with dashboards: /product/dashboards
FAQ
No. We stabilize the minimum inputs first and improve the rest only when it changes outcomes.
Yes. We can start from exports/CSV and standardize ERP inputs over time.
It means the real operation order from order entry to ship, including inspection/move steps where they matter.
Often, yes—because estimate vs actual becomes visible and routings match reality for repeat work.
When release, priorities, and constraint capacity are visible and consistent, fewer surprises require emergency priority flips.
No. It also improves dashboards, dispatch lists, and day-to-day decision quality.
Sometimes. If the ERP cannot export consistent fields or track key statuses, lightweight add-ons or export tools can be the simplest fix.
Machine shops, CNC job shops, and fabrication teams with high-mix, low-volume work.