RippleFlo
ContactBook a demo
Industry
PharmaCriticalAerospaceProcessDiscreteMedical

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.

ERP / MES in process

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
RippleFlo for process

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

DimensionERP / MESRippleFlo
Campaign schedulingPer-order lead timesShared resource campaign windows
Reactor sharingAssumes dedicatedContention & overlap detection
Hold timesStatic offsetDynamic hold chains
Material gatesManual checkReadiness gates in DES
Batch sizingSpreadsheet mathMin/max lot splitting simulated
Equipment failurePost-event logMTBF/MTTR — measure campaign slip
Audit trailBatch records onlySimulation archive + replay
What-if costChange live campaignRe-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.

Campaign C-14 · Feed → Pack-out· DES tick 14:05:22
Feed Prep
1h/batch · buf 3
In process
Reactor A
12h/batch · buf 5
Queue build
Distillation
4h/batch · buf 4
Ripple slip
Storage
8h hold · buf 2
In process
Blending
2h/batch · buf 3
In process
QC Lab
3h/batch · buf 2
In process
Pack-out
2h/batch · buf 2
On hold
Ship
Due Nov 8
At risk
Ripple trace · Reactor A
12h downtime · distillation backlog
C-14 slip +2d · Ship Nov 6 → Nov 8
Ship risk
At risk
3 campaigns queued · 1 critical
Campaign WIP
4 active
+1 vs plan
Reactor util
91%
bottleneck
Distill queue
14h
+5h ripple
Ship date
Nov 8
2d slip risk
Campaign slip · Reactor A delay
C-11
+0d
C-12
+6h
C-13
+1d
C-14
+2d
C-15
+3d

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
Demo a process campaign
pegging.trace · C-14
PO #3301 · Customer ChemCo
└─ Campaign C-14 · Ship Nov 8
└─ Batch 440 · 2d slip
└─ Reactor A down 720m
└─ Distillation queue +14h
└─ Reroute → Reactor B
└─ Audit entry #77821 ✓