A Hybrid Monte Carlo-AHP Based Model For Integrated Risk Management of Material Delivery Delays In Construction Supply Chain

Abstract

Material delivery delays in construction supply chains constitute critical disruptions that compromise project schedules and escalate operational costs. Conventional deterministic risk assessment models inadequately capture the stochastic uncertainties and multidimensional complexities inherent in supply chain disruptions, necessitating advanced probabilistic methodologies. This investigation develops an integrated Monte Carlo simulation (MCS) and Analytic Hierarchy Process (AHP) framework for comprehensive risk evaluation and prioritization. AHP methodology systematically derives priority weights for critical risk factors including supplier reliability, transportation constraints, meteorological conditions, and geopolitical variables through structured expert pairwise comparisons. These weights are subsequently integrated with MCS to execute 10,000 probabilistic scenario iterations, quantifying delay impact distributions. The analytical framework identifies supplier reliability and transportation issues as dominant risk contributors, generating mean delays of 1.96 and 1.45 days respectively within the total mean delay of 4.85 days, with 95th percentile delays reaching 8.13 days. This hybrid methodology effectively synthesizes quantitative stochastic modeling with qualitative expert judgment, providing robust decision-support capabilities for supply chain risk management and enhancing project resilience.