Monte Carlo Outcome Probability · 5,000 Iterations
Project Timeline
Runway: -- weeks
Enter realistic output, not theoretical maximum.
-- delivery cycles in window
Risk Factors
Scope CreepHow to estimate: Review your last 2–3 similar projects. What percentage of total effort was added after scope was baselined? Include change requests, discovered dependencies, and regulatory surprises.15%
Percentage of unplanned work likely to emerge.
🔍 0–10%: Mature scope, experienced team · 10–25%: Typical for complex projects · 25%+: Discovery-heavy or poorly defined scope
Vendor DelayHow to estimate: What's the longest realistic delay from third parties — vendors, partner teams, regulatory approvals, procurement? Consider your worst recent experience, not the average.2 wks
Maximum expected delay from external dependencies.
🔍 0–2 wks: Reliable vendors, few dependencies · 2–6 wks: Multiple external parties · 6+: Government or regulatory gates
Resource AttritionHow to estimate: Consider team tenure, market conditions, and contract status. If 1 of 5 members might leave, that's ~20%. Include reassignment risk, not just resignations.45%
Probability of losing a key team member during the project.
🔍 0–15%: Stable team, strong retention · 15–40%: Contract staff or competitive market · 40%+: Known flight risks or restructuring
Delivery ConsistencyHow to estimate: Look at output over the last few delivery cycles. If the team consistently delivers close to the same amount, that's high consistency (80%+). If output swings wildly between cycles, it's low. New teams or unfamiliar domains typically start around 50–65%.75%
How predictable is the team's output cycle to cycle?
🔍 80–100%: Established team, stable process · 50–80%: Normal variation · Below 50%: New team, new domain, or unstable priorities
Team Ramp-UpHow to estimate: How many delivery cycles before the team hits steady-state output? Consider: Is the team new or established? Is the domain familiar? Are tools and processes already in place? A mature team on known ground may need 0–1 cycles. A new team in a new domain could need 4–6.2 cycles
Cycles before the team reaches steady-state output.
🔍 0–1: Mature team, familiar domain · 2–3: Some new members or new technology · 4–6: Entirely new team or unfamiliar domain
Dependency FrictionHow to estimate: What percentage of your team's capacity is typically lost waiting on other teams, shared services, API contracts, design approvals, or external inputs? This is not about your own backlog changing — it's about being blocked by others. Count the hours lost waiting, divided by total available hours.10%
Capacity lost waiting on external teams or shared resources.
🔍 0–5%: Autonomous team, few dependencies · 5–20%: Normal cross-team coordination · 20%+: Heavy reliance on shared services or partner teams
SustainabilityHow to estimate: How much is the team investing in keeping things healthy — addressing technical debt, improving processes, refactoring, writing tests, fixing root causes? At 100%, the team maintains full capacity over time. At lower levels, shortcuts accumulate and later cycles get progressively slower. If the team is consistently told to "just ship it", this is probably 40–60%.70%
Investment in long-term health vs. short-term output.
🔍 80–100%: Dedicated time for quality and improvement · 50–80%: Some investment, some shortcuts · Below 50%: Speed over sustainability — expect drag later
Disruption RateHow to estimate: In any given delivery cycle, what's the probability of a significant disruption — production incident, key person unavailable, emergency re-prioritisation, infrastructure failure? Think about how often the team has a cycle that's materially derailed by something unplanned and urgent.10%
Probability of a significant disruption per delivery cycle.
🔍 0–5%: Stable environment, rare incidents · 5–15%: Occasional production issues or emergencies · 15%+: Frequent firefighting or volatile priorities
Simulating...
--Vitality
Calculating...
P20--
P50--
P90--
Delivery Cycle Forecast (avg. output per cycle)
C1C--
Monte Carlo simulation using triangular distributions · Risk parameters model independent stochastic variables · P-values represent percentile outcomes across 5,000 iterations