Hidden-time tasks are invisible in most software
Even highly automated factories miss a big part of their operational workload:
❌ Quality checks are never accounted for in operator workload
❌ Unplanned tool changes disrupt production
❌ Operators rely on paper to manage quality checks and other tasks
❌ No traceability of what was actually done.
💬 Result: a constant gap between planned efficiency and real performance.
Smart modeling of manual interventions
JITbase allows you to create operator tasks within your production plan —
tasks that can run in parallel with machine cycles or trigger automatically based on real events.
✅ Schedule recurring quality checks (e.g., every 20 parts)
✅ Trigger tasks automatically based on machine events
✅ Display tasks in the operator interface
✅ Confirm and track execution for traceability
💡 Human work becomes predictable, measurable, and part of the plan.
How it works?
1️⃣ Supervisors define recurring or event-based tasks (inspection, tool change, cleaning, etc.)
2️⃣ JITbase automatically inserts them into the live production schedule
3️⃣ Operators see them on their screens and confirm completion
4️⃣ Each task is time-stamped and integrated into OEE and workload analytics
All data updates automatically — no manual entry, no forgotten steps.
Why it matters
Human work represents up to 30% of total production time in most factories.
Ignoring it distorts workload calculations and OEE accuracy.
With JITbase:
✅ Team leaders can view operators’ anticipated real workloads directly in the schedule
✅ Operators see upcoming tasks in real time
✅ You ensure full traceability of operations
Customer Story
“Before, quality checks and robot reloads were never planned, which led to machine downtime.
Now, they automatically appear in the schedule and on operator screens. We’re much more proactive and avoid production losses.”
— Daniel, Production Supervisor
Pro Tip
✅ Operator tasks (quality checks, tool changes, etc.) are not unexpected disruptions — they are predictable and measurable.
By integrating them into scheduling, operator workload calculations become more accurate, enabling smarter, data-driven decisions on machine assignments.