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Predicting Cost Escalation Pathways and Deviation Severities of Infrastructure Projects Using Risk‐Based Econometric Models and Monte Carlo Simulation
Author(s) -
Bhargava Abhishek,
Labi Samuel,
Chen Sikai,
Saeed Tariq Usman,
Sinha Kumares C.
Publication year - 2017
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12279
Subject(s) - cost overrun , cost estimate , monte carlo method , computer science , econometrics , multinomial distribution , cost contingency , operations research , statistics , economics , engineering , mathematics , cost engineering , systems engineering , construction industry , construction engineering
Abstract In the past decade, infrastructure‐related legislation in the United States has consistently emphasized the need to measure the variation associated with infrastructure project cost estimates. Such cost variability is best viewed from the perspective of the project development phases and how the project cost estimate changes as it evolves across these phases. The article first identifies a few gaps in the cost overrun literature. Then it introduces a methodology that uses risk‐based multinomial models and Monte Carlo simulation involving random draws to predict the probability that a project will follow a particular cost escalation pathway across its development phases and that it will incur a given level of cost deviation severity. The article then uses historical data to demonstrate how infrastructure agencies could apply the proposed methodology. Statistical models are developed to estimate the probability that a highway project will follow any specific cost escalation pathway and ultimately, a given direction and severity of cost deviation. The case study results provided some interesting insights. For a given highway functional class, larger project sizes are associated with lower probability of underestimating the final cost; however, such a trend is not exhibited by very large projects (total cost exceeding $30M). For a given project size, higher class roads were generally observed to have a lower probability of underestimating the final cost, compared to lower class roads and this gap in probability narrows as the project size increases. It was determined that a project's most likely pathway of cost escalation is not a guarantee that it will yield any particular direction of cost deviation. The case study results also confirmed the findings of a few past studies that the probabilities of cost escalation pathways and the cost overruns differ significantly across highway districts, and attributed this to differences in administrative culture and work practices across the districts. Infrastructure managers can use the developed methodology to identify which projects are likely to experience a particular pathway of cost escalation, the direction and severity of cost deviation, and to develop more realistic project contingency estimates.