AI Data Readiness Scorecard for Job Shops
AI scheduling isn’t smart if the inputs are inconsistent. Before you automate decisions, you need clean, correctly sequenced data from order entry to ship.
This scorecard shows the minimum data you need, how to self-check in minutes, and what to fix first.
Microcopy: Get a clear next step based on how your shop actually runs — or review your constraint + data readiness in 20 minutes.
The minimum sequence you cannot skip
Even the best scheduling logic fails if the flow is unclear. Your data needs to describe the real sequence:
The 5-check data readiness scorecard
3/5 = we can start.
Check 1: Can you export jobs/orders with due dates and statuses?
What yes looks like: a list of open jobs/orders with due date, promised date, status, and a priority/expedite flag (even if manual).
Why it matters: without due dates and a reliable status, you can’t measure on-time delivery or predict risk.
Check 2: Do routings/operation sequences exist for your top repeat work?
What yes looks like: routings exist for the top revenue families or repeat jobs (even if imperfect).
Why it matters: sequence is the foundation. AI can’t guess the path through the shop.
Check 3: Are workcenters consistent (duplicates mapped)?
What yes looks like: you don’t have three names for the same machine group.
Why it matters: inconsistent workcenters turn capacity signals and queue views into noise.
Check 4: Are completions or ship dates captured consistently?
What yes looks like: operation completions, labor reporting, or at least reliable ship history.
Why it matters: you can’t stabilize lead times or improve estimates without feedback from actuals.
Check 5: Do you have a way to tag priority/expedites?
What yes looks like: a field, a flag, or even a spreadsheet marker used consistently.
Why it matters: if priorities are invisible, expediting becomes random and the plan can’t stabilize.
What to fix first (so it actually improves delivery)
Do not try to clean everything. Fix the smallest data issues that change decisions.
- Make due dates and status trustworthy first
- Make routing sequence correct for top repeat work
- Normalize workcenter names
- Capture completions or ship history consistently
- Add a simple expedite flag used the same way every time
Why better data often reduces WIP without reducing shipments
When release and priorities are based on a stable signal, upstream work stops flooding the floor. Pre-bottleneck areas still work — but they work on the right jobs at the right time so parts arrive in sequence and ship.
This is how shops reduce expedite rate, stabilize lead times, and improve on-time delivery.
Next step path (text-only links)
Choose the smallest next step:
• Identify the constraint blocking throughput: /root-cause
• Take the 4-Minute Throughput Audit: /discovery
• How to schedule a job shop step-by-step: /guides/how-to-schedule-a-job-shop
Common pitfalls that make AI look wrong
- Status fields exist but aren’t used consistently
- Workcenters are duplicated or renamed without mapping
- Routings exist but don’t match reality (estimate vs actual)
- Queue time is assumed instead of measured
- Expedites are handled verbally, not tagged