Supply Chain Simulation and Digital Twins
Test the change before it touches the floor.
- Typical duration
- 6 to 12 weeks
- Deliverables
- 5 core artifacts
Overview
Optimization tells you what the best plan looks like when the world behaves. Simulation tells you what happens when it does not: when the truck is late, the line goes down, the picker calls out, and demand arrives in a lump rather than a curve.
We build discrete event and agent based models of warehouses, production lines, and end to end flows, then run thousands of replications to characterize the distribution of outcomes rather than a single average. Digital twin engagements keep that model synchronized with live operational data so it stays useful after go live.
What you get out of it
- Throughput and bottleneck analysis under realistic variability
- Confidence intervals on service level, cycle time, and utilization
- Stress tests for disruption, surge, and ramp scenarios
- Validated equipment, labor, and automation sizing before capital commitment
Capabilities
What the work actually involves.
Discrete event simulation
Warehouse, dock, line, and network models with realistic arrival processes, resource contention, downtime distributions, and shift calendars.
Automation and layout validation
Sizing storage and retrieval systems, goods to person stations, sortation, and mobile robot fleets against a demand profile before signing, including the failure modes vendors tend not to model.
Disruption stress testing
Port closures, single source supplier loss, demand spikes, and weather events run as structured shocks so you can see where the network bends and where it breaks.
Digital twin operations
A maintained model wired to live ERP and WMS feeds, used for rolling what if analysis on staffing, wave design, and commitment decisions.
Labor and shift modeling
Staffing plans validated against hourly volume curves, learning curves, absenteeism, and overtime policy rather than a daily average.
Questions
Things people ask first.
- Is simulation worth it if we already have an optimization model?
- They answer different questions. Optimization finds the best plan under assumed conditions; simulation measures how that plan degrades under variability. Most costly surprises live in the gap between the two.
- What does a digital twin actually require operationally?
- A reliable daily or hourly data feed, a named owner on your side, and a quarterly revalidation cadence. Twins that are not maintained quietly stop matching reality within a couple of quarters.
Next step
Have a simulation question?
Send the decision you are facing. A first conversation is a working session, and it usually clarifies scope more than a proposal would.
Other practice areas