Use case · AI LAB · Nuclear
Hundreds of supplier schedules, one reliable master schedule.
A major nuclear player receives schedules from several hundred suppliers. Before integrating them, it has to check them. OFFOLIO automates that check and proposes the corrections.
Nuclear
+95%
measured accuracy of deep learning models applied to schedule defect correction.
The problem
What you deal with today.
- 01
Internal and supplier schedules of uneven quality.
- 02
A check that combines recognized standards (DCMA) with company‑specific in‑house rules.
- 03
Back‑and‑forth with suppliers too slow to keep the master schedule up to date.
Interactive demo
AI makes a hand‑typed schedule reliable
OFFOLIO predicts the work of every activity; durations and the Gantt recalculate, changes in green.
Chapters
- A supplier schedule to make reliable
- Predict the work
- Work predicted, schedule recalculated
At the last step: “See it on your data” → demo request.
How it works
What you do with OFFOLIO.
- 01
Receive
Every schedule, internal or supplier, enters the schedule quality check pipeline.
- 02
Check
The DCMA 14-point assessment and your custom internal checks, applied automatically.
- 03
Correct
The AI proposes a fix for every anomaly; feedback to the supplier happens in real time.
- 04
Integrate
Only compliant schedules make it into the master schedule.
Standardized checks across hundreds of suppliers
Internal rules applied effortlessly
Real‑time supplier feedback
A more reliable master schedule
“Accuracy of proprietary deep learning models measured at +95%. Applied to schedule defect correction and generative scheduling.”
Module:AI LAB
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