article · ABUAD Journal of Engineering Research and Development (AJERD)
Infrastructure and industrial Engineering, Procurement and Construction (EPC) projects in Nigeria frequently encounter schedule delays and budget overruns driven by procurement uncertainty, engineering complexity, regulatory constraints, and macroeconomic volatility. An assessment of Monte Carlo Simulation applied to an actual completed EPC project in Ikeja, Lagos State, examined how probabilistic methods compare against traditional deterministic planning. Using data from 199 professionals alongside project records, 10,000 simulation iterations were conducted in spreadsheet-based risk software. The baseline schedule of 180 days showed only a 35% likelihood of achievement, shifting to a median duration of 212 days at P50 and 225 days at P80. Key drivers of uncertainty included procurement activities, construction duration, imported material costs, inflation, and foreign exchange volatility. The resulting framework links empirical risk assessment with probabilistic simulation to generate more realistic schedules, budgets, and contingency plans.
Large engineering and infrastructure projects frequently exceed their deadlines and budgets, wasting capital. Demonstrating that deterministic baselines carry very low probabilities of success allows project owners, financiers, and contractors to adopt probabilistic tools. These methods account for real-world market volatility, currency fluctuations, and procurement complexities, helping stakeholders budget more accurately, plan contingency reserves effectively, and minimise costly project disruptions.
The framework represents an applied and tested risk-modelling workflow implemented in commercial spreadsheet software. It directly serves EPC contractors, project management consultancies, and infrastructure developers seeking improved contingency planning, risk registers, and bid pricing. Because the workflow was tested using real project records alongside professional survey data, it appears near-market and ready for immediate operational adoption by project scheduling and financial risk teams.
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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.
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DOI: 10.53982/ajerd.2026.0902.19-j
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