I specialise in practical, enterprise‑grade project risk management: building clear governance, a living risk register, and a culture that treats risk as a lever for performance. My approach emphasises early identification of threats and opportunities, crisp ownership, and evidence‑based prioritisation. I integrate qualitative and quantitative methods to assess probability and impact, define meaningful mitigations, and track residual risk across delivery phases. The result is transparent decision‑making, realistic baselines, and risk‑adjusted plans that protect scope, schedule, cost, and benefits while aligning with organisational risk appetite.
For quantitative risk analysis (QRA), I run integrated schedule‑and‑cost simulations to quantify uncertainty and contingency with credibility. Using well‑calibrated distributions, driver correlations, and Monte Carlo analysis on logic‑sound schedules, I produce confidence metrics (P50/P80), risk‑adjusted durations and budgets, and defensible contingency recommendations. Techniques such as tornado analysis for sensitivity, risk driver mapping, and ICSRA (integrated cost‑schedule risk analysis) expose where volatility truly lives—enabling targeted mitigation, smarter buffers, and clearer communication of risk exposure to sponsors and stakeholders.
Beyond QRA, I apply uncertainty management, decision analysis, and statistics to turn ambiguity into advantage. Decision trees, value of information, and Bayesian updating combine expert judgement with data to prioritise options and refine estimates as evidence arrives. Scenario planning and stress testing reveal robust choices under varied futures, while statistical methods (from hypothesis testing to regression and reliability modelling) raise the signal‑to‑noise ratio. The outcome is disciplined, data‑driven decisions that reduce surprises and increase the probability of delivering outcomes on time, on budget, and with confidence.