04/Simulation
Find out if a redesign works before anyone lives with it.
Run the new process thousands of times before you roll it out.
Build an input set from your real run data or from assumptions you can edit, then run design options at volume and compare what each one does.
Setting up a run
Start from your real numbers, or defaults you can edit
Sapeum constructs a starting input set from live run data where you have it, and from sensible assumptions where you do not. Every value stays editable.
- Inputs derived from live telemetry or stated assumptions
- Volumes, durations and branch rates all adjustable
Comparing options
Compare designs on throughput and cycle time, not opinion
Try the versions you are actually choosing between: one approver or two, parallel review or sequential. Compare what each does to throughput and cycle time.
- Multiple design variants against one input set
- Expected output, throughput and cycle time per variant
How much was tested
See which branches and edge cases were actually covered
Run at scale rather than walking through one happy path, so coverage across branches and edge cases is measured rather than assumed.
- High-volume runs for statistical confidence
- Branch and edge-case coverage reported, not assumed
Know the outcome before you commit to it
The expensive test stops being production.
Choose a design on numbers, not argument
Four options ranked by median cycle time.
Edge cases found before customers find them
Coverage is measured, so gaps show up first.
Related features
All featuresThis workflow
Vendor onboarding
Process improvements
One list of what to change: gaps from analysis, AI suggestions and options proven in simulation.
v2.3 · baseline
v2.4 · candidate
Future-state design
Design the target process from a real baseline, and get an exact list of everything that changes.
Common questions
What if we have no live data to base inputs on?
Sapeum builds a starting input set from reasonable assumptions and shows you every one it made. You adjust the ones you disagree with, which is usually a much faster conversation than starting from a blank model.
How is this different from just estimating?
An estimate gives a single number for a happy path. A simulation runs the branches, the queueing and the edge cases thousands of times, so you get a distribution and a coverage report rather than one optimistic figure.
Can we simulate a design we have not built yet?
Yes, that is the point. Simulation runs against a future-state design, so a variant can be tested and discarded before anyone configures a system or retrains a team.
How many runs is enough?
Enough that the numbers stop moving. Runs are cheap, so simulations are sized for confidence rather than for patience, and the coverage report shows which branches were actually exercised.
Does simulation feed into anything else?
Yes. A variant that wins in simulation becomes a candidate future state, and the result is one of the inputs to process improvements alongside analysis gaps and AI suggestions.