Process campaigns share equipment — ERP schedules them in isolation.
ERP plans each batch independently. RippleFlo simulates shared reactors, distillation queues, hold tanks, and campaign overlap — so planners see the ripple before release slips.
Plans batches. Ignores shared resources.
Process manufacturing means campaigns, min/max batch sizes, and shared reactors. ERP treats each order in isolation — missing the contention that causes real slips.
- Cannot model two campaigns competing for the same reactor
- Hold times applied as static offsets — not dynamic chains
- Material readiness gates checked manually, not in simulation
- Capacity investments justified without DES bottleneck proof
Simulates the campaign network.
Feed prep, reactors, distillation, storage, blending, and pack-out modeled as discrete events — with campaign overlap and resource pool contention visible in real time.
- Campaign windows with min/max batch size simulation
- Shared reactor and storage tank contention modeling
- Hold-time and material gate ripple chains
- Release and ship commitment scoring per campaign
Process manufacturing — ERP vs RippleFlo
| Dimension | ERP / MES | RippleFlo |
|---|---|---|
| Campaign scheduling | Per-order lead times | Shared resource campaign windows |
| Reactor sharing | Assumes dedicated | Contention & overlap detection |
| Hold times | Static offset | Dynamic hold chains |
| Material gates | Manual check | Readiness gates in DES |
| Batch sizing | Spreadsheet math | Min/max lot splitting simulated |
| Equipment failure | Post-event log | MTBF/MTTR — measure campaign slip |
| Audit trail | Batch records only | Simulation archive + replay |
| What-if cost | Change live campaign | Re-run model — zero risk |
Watch a process campaign ripple through the plant.
Reactor A queues. Distillation slips. Pack-out dates shift — all before the campaign meeting.
How RippleFlo solves what ERP cannot in process manufacturing.
Shared equipment and campaign overlap need event-driven simulation.
Campaign overlap detection
See when Campaign C-12 and C-14 compete for Reactor A before either misses ship date.
Reactor contention
Queue depth and utilisation per reactor — the constraint ERP averages away.
Hold-time chains
Storage tank hold extensions ripple through blending and pack-out.
Batch size optimization
Simulate min/max lot splits and pick the throughput winner.
Alternate process routes
Route through secondary trains when primary reactor is down.
Audit-grade replay
Re-run any campaign scenario with identical inputs.
Reactor A down 12 hours. Four campaigns. Four different ship dates.
ERP adds 12 hours to everything. RippleFlo traces per-campaign slip — C-14 slips 2 days while C-11 clears distillation before the queue peaked.
- Per-campaign slip with attribution (% Reactor A vs distillation queue)
- Stage-to-stage trace through storage and blending
- Pre-commit preview before publishing campaign changes