Mitigating Schedule and Cost Overrun Risks in EPC Projects Using Monte Carlo Simulation in Ikeja

Engineering, Procurement and Construction (EPC) projects play a pivotal role in infrastructure and industrial development in Nigeria but continue to experience significant schedule delays and cost overruns arising from engineering complexity, procurement uncertainty, macroeconomic volatility, and regulatory constraints. This study evaluates the application of Monte Carlo Simulation (MCS) as a probabilistic risk analysis technique for improving the prediction of schedule and cost uncertainties in EPC projects within Ikeja, Lagos State, Nigeria. A quantitative research design was adopted. Primary data were collected from 199 EPC professionals using structured questionnaires, while secondary data were obtained from project schedules, procurement records, cost reports, and risk registers. Descriptive statistics, Kendall's Tau-b correlation, multiple regression analysis, and Monte Carlo Simulation were employed for data analysis at a 5% significance level. Simulation modelling was implemented in Frontline Risk Solver for Microsoft Excel using 10,000 iterations with appropriate probability distributions assigned to project duration and cost variables. The deterministic baseline schedule of 180 days and baseline budget of ₦210 million (2024 price basis) were derived from an actual completed EPC project. The simulation produced a P50 completion duration of 212 days, a P80 duration of 225 days, and only a 35% probability of achieving the deterministic schedule. Sensitivity analysis identified procurement activities, construction duration, imported material costs, inflation, and foreign exchange volatility as the dominant drivers of schedule and cost uncertainty. The findings demonstrate that probabilistic modelling provides more realistic forecasting, supports contingency planning, and improves risk-informed decision-making compared with conventional deterministic planning approaches. The study contributes an integrated framework combining empirical risk assessment, statistical analysis, and Monte Carlo Simulation to improve schedule and cost forecasting in Nigerian EPC projects.