| # | Risk Name | Distribution | Min (Cr) | Likely (Cr) | Max (Cr) | Prob | EV (Cr) |
|---|
| Analysis Name | -- |
| Iterations | -- |
| Sampling Method | -- |
| Seed (MT19937) | -- |
| PERT Lambda | -- |
| Correlation Mode | -- |
| Risks Loaded / Fired | -- |
| Execution Time | -- |
| Iterations/Second | -- |
| Convergence | -- |
| Timestamp | -- |
| Config Hash | -- |
Calibrated from 489 projects | 87,245 MW | 14,723 risk events | 2015-2026
| Rank | Risk Name | Category | Frequency | Avg Impact (Cr/MW) | Total Impact (Cr) | Probability | Severity |
|---|
A full Monte Carlo dashboard produces a dozen tabs and fifty metrics. You need three for every meeting and three more for audit.
1. KPI Cards -- the headline numbers (P50, P80, P90, CVaR). 2. S-Curve -- the probability map. 3. Tornado Chart -- where the risk lives and where to spend mitigation money.
4. Convergence Plot -- is the number stable? 5. Sensitivity Tab -- which inputs drive which outputs. 6. Engine Config -- seed, iterations, method. The audit trail.
The 60-Second Read: Step 1: Look at P80 -- this is your anchor number. Step 2: Compare P80 to your allocated contingency. If contingency is less than P80, you are underbudgeted at 80% confidence. Step 3: Check P90/P50 ratio. Between 1.5 and 2.0 = well-calibrated model. Above 2.5 = extreme tail risk. Below 1.3 = distributions too tight.
The 3-Chart Meeting Stack: S-Curve first (establishes the gap), Tornado second (names the cause), Options table third (presents the choices). If you reverse this order, you are asking the director to solve a problem they do not yet believe exists.
Stop reading about swimming. Build your first Monte Carlo model in 30 minutes. The project: 50 MW ground-mounted solar PV, Rajasthan, SECI tender, aggressive tariff. Base project cost: 250 Crore. Contingency: 25 Crore (10%).
R1 Land acquisition (2/8/35 Cr). R2 Module price escalation (0/5/22 Cr). R3 Transmission bay delay (1/4/18 Cr). R4 Labour shortage (0.5/3/12 Cr). R5 LD exposure (0/6/28 Cr). These are statistical facts from 489 projects.
R1-R5 (land-LDs): 0.78 -- structural dependency. R2-R4 (module-labour): 0.10 -- nearly independent. An approximate correlation is infinitely better than zero.
10,000 iterations. 3 seconds. The S-curve lands. P50 is roughly 35 Cr, P80 is roughly 52 Cr, P90 is roughly 70 Cr. Your 25 Cr contingency sits below P50. The gap: 27 Crore.
R5 (LDs) is the longest bar but it is the most dependent risk. It correlates at 0.78 with R1, 0.72 with R3. Mitigate R1 (land) and R3 (grid), and R5 shrinks automatically.
The Three Mistakes You Will Make: 1) Setting Min=0 on every risk (creates extreme right tail). 2) Ignoring correlation (zero means independent -- your risks are not independent). 3) Running once and calling it truth (check convergence -- if P80 moves more than 5% between runs, increase iterations).
| Priority | Risk | Mitigation Strategy | Expected Reduction | Timeline |
|---|---|---|---|---|
| 1 | Land Acquisition Delay | SECI pre-identified land parcels. Direct cash at 2x market rate. Parallel Revenue-Forest-Agriculture NOC processing. Dedicated community liaison. | 67% impact reduction | Start 24 months before NTP |
| 2 | Module Supply Chain | Multi-vendor sourcing with BCD-exempt warehousing. Hedged polysilicon contracts. 20% buffer stock at port. | 45% impact reduction | 12 months before procurement |
| 3 | Transmission Bay Delay | Parallel PGCIL bay allocation tracking. Pre-fund bay extension works. Alternative evacuation routing study. | 40% impact reduction | 18 months before COD |
| 4 | Labour Shortage | Multi-season mobilisation calendar. Regional contractor pool. Mechanised installation for pile driving. | 35% impact reduction | 6 months before mobilisation |
| 5 | LD Exposure | Downstream risk -- mitigating R1 and R3 reduces R5 automatically. PPA extension clause negotiation. | Indirect via R1/R3 | Continuous |