Simulation & Prediction
Scenario Simulation Workspace
Compare current state, no action, recommended, upside and downside scenarios for one journey or issue.
Journey
Network Performance & Risk
KPI under simulation
Network availability
Current
99.62%
Target
99.7%
Scenario outcome cards
Base case
Current trajectory with in-flight initiatives only.
- Stated outcome
- SLA penalty exposure: $0.6M
- Risk / trade-off
- —
- Network availability in 6 mo
- 99.62% ACTUAL
Permanent power upgrade
Lithium batteries plus hybrid generators.
- Stated outcome
- 99.75% availability
- Risk / trade-off
- Capex $0.9M
- Network availability in 6 mo
- 99.7% SCENARIO
- Expected value
- Loss avoided: USD 600k expected
Mobile generators only
Short-term rental for CBD sites.
- Stated outcome
- 99.66% availability
- Risk / trade-off
- Recurring cost $40k/month
- Network availability in 6 mo
- not modelled
Upgrade + capacity
Power upgrade plus top-20 capacity.
- Stated outcome
- 99.76% + lower churn
- Risk / trade-off
- Capex $1.7M
- Network availability in 6 mo
- not modelled
Not stored for this journey: No Action, Upside, Downside. Shown as a gap rather than invented.
Scenario comparison — stated value vs cost/risk (USD M)
Amounts parsed from each scenario's stored results; ★ = recommended. Non-monetary outcomes plot as zero.
Risk / return matrix
Top-left = high value for low stated cost/risk.
Network availability — actual vs scenario paths
Same projection as the journey Predict stage and the Forecasting page. Open full forecast →
Comparison table
| Scenario | Role | Assumptions | Projected KPI (6 mo) | Stated impact | Risk / trade-off |
|---|---|---|---|---|---|
| Base case | Current State | Current trajectory with in-flight initiatives only. | 99.62% | Baseline | — |
| Permanent power upgrade | Recommended | Lithium batteries plus hybrid generators. | 99.7% | 99.75% availability | Capex $0.9M |
| Mobile generators only | Alternative | Short-term rental for CBD sites. | — | 99.66% availability | Recurring cost $40k/month |
| Upgrade + capacity | Alternative | Power upgrade plus top-20 capacity. | — | 99.76% + lower churn | Capex $1.7M |
Scenario → decision
Recommended scenario: Permanent power upgrade — 99.75% availability; trade-off: Capex $0.9M.
No decision is saved here; the decision is recorded in the journey at Decide.
Source data, assumptions, methodology & limitations
Source data
- Telecom KPI data (current period, target, prior)
- Historical KPI trend data (monthly history Apr–Sep 2026)
- Scenario library (assumptions, results, recommended flag)
- Value realization records (expected / actual value)
- Evidence records
Assumptions
- No action: continues the 6-month stored trend (-0.02 per month).
- Recommended action: closes the gap to the stored target (99.7%) over 6 months.
- Scenario outcomes, costs and risks are as stored for each scenario.
Methodology
- Scenarios of the selected journey are ordered Current State → No Action → Recommended → Upside → Downside → Alternatives.
- With an originating issue, alternatives sharing no words with the issue move to the bottom of the table.
- Projected KPI per scenario uses the shared deterministic forecast; only Current State, No Action and Recommended are modelled.
Limitations
- Scenario outcomes are governed figures stored with each scenario — they are not re-computed by a simulation engine.
- Forecasts are straight-line projections of six stored months; they ignore seasonality, shocks and interactions between measures.
- Many KPIs have no stored target; where absent, the recommended path reverses the trend at the same monthly rate.
- No downside scenario is stored for any journey; downside coverage is shown as a gap rather than invented.
| Evidence type | Item | Source |
|---|---|---|
| model output | Congestion forecast 214 sites forecast to exceed 85% utilisation within 90 days. | OSS network data |
| methodology | Network Performance & Risk methodology Aggregate availability and KPIs from network counters; Classify outages by root cause; Score sites by risk and revenue exposure. | Finance Planning Dataset |