Pharma process manufacturing needs foresight — not another ERP schedule.
ERP records what happened on the batch record. RippleFlo simulates what will happen across API synthesis, hold times, blending, fill-finish, and release windows — before QA finds a deviation.
Plans batches. Can't model the process.
ERP handles orders, BOMs, inventory, and batch records. It assumes average cycle times and dedicated equipment — but pharma is campaigns, holds, shared reactors, and release windows.
- Schedules batches on average lead times — not reactor queues or hold-time chains
- Cannot model campaign overlap when two products share validated equipment
- Batch record deviations discovered at QA sign-off, not days earlier
- Changing a release date means re-planning across planning, QA, and ops manually
Simulates the batch journey.
A discrete-event digital twin of your pharma line. Batches flow event by event through reactors, holds, QC gates, and fill-finish — with release risk scored before the window closes.
- Simulates API synthesis → hold → blending → fill-finish as discrete events
- Scores every batch on track, at risk, or critical before release window slips
- Traces reactor delays through hold, QC IPC, and fill-finish in one ripple map
- Pre-commit blast preview shows which lots shift before QA publishes a change
Pharma process manufacturing — ERP vs RippleFlo
| Dimension | ERP / MES | RippleFlo |
|---|---|---|
| Batch scheduling | Fixed lead times per SKU | Campaign windows with queue & hold modeling |
| Equipment sharing | Assumes dedicated lines | Validated train contention & overlap detection |
| Hold times | Static offset in plan | Dynamic hold chains that ripple downstream |
| Release risk | Discovered at sign-off | Amber/red scoring days before deviation |
| Lot splitting | Manual spreadsheet math | Min/max batch sizes simulated in DES |
| Equipment failure | Logged after production stops | MTBF/MTTR injected — measure batch slip |
| Audit trail | Reconstruct from batch records | Deterministic replay + commitment diff |
| What-if cost | Change the real campaign | Re-run the model — zero shop-floor risk |
Watch a pharma campaign ripple through the line.
Reactor R2 builds queue. Hold times extend. Fill-finish slips. Release windows turn amber — all visible before a batch record changes.
How RippleFlo solves what ERP cannot in pharma.
Process manufacturing in regulated environments needs simulation-backed decisions — not spreadsheet hope.
Campaign overlap detection
See when Batch BX-77 and BX-78 compete for Reactor R2 before either misses a release window.
Hold-time ripple chains
A 6-hour hold extension traces the slip through fill-finish and QC release — not just one operation.
QA-ready risk scoring
Flag batches at risk before deviation paperwork. Planning and QA see the same amber alert.
Validated alternate routes
When Reactor R2 is down, DES routes through the next qualified train.
Release window defense
Pegging tree from customer PO → batch → operation → root cause for review or regulator.
Audit-grade replay
Re-run any historical campaign scenario with identical inputs.
Reactor R2 down 18 hours. Five batches. Five different release stories.
ERP reschedules on averages. RippleFlo measures slip per batch against its own release window — BX-77 slips 6 hours while BX-75 only slips 2 because its hold cleared before the queue peaked.
- Per-batch slip with attribution (% Reactor R2 vs downstream hold)
- Stage-to-stage trace: if hold extends, when does fill-finish move?
- Pre-commit preview — see which release windows shift before QA signs off