Construction projects suffer from diverse
uncertainties that hinder the key objectives’ achievement. These
uncertainties represent risks that may appear through the project
life cycle. This paper introduces a quantitative model to estimate
and rank risks dynamically during the risk planning phase. Such
ranking would help decision-makers appropriately respond to
and/or control construction risks. The model provides proper risk
contingency reserves for both project time and cost that meet
decision-makers' selected confidence levels using Monte Carlo
Simulation (MCS). In order to quantify the project uncertainty,
severities of residual risks are determined and allocated at the
project's activities-level using a planning/scheduling spreadsheet
model and a MCS tool suitable for spreadsheets. The model is able
to calculate the contribution of each risk from the determined
contingency at both the project level for both the time and cost at
the decision-maker confidence level.The model represents a direct
implementation for a Risk Planning Contingency Model (RPCM);
which involves four modules as follows: (1) Risk Register (RR),
(2) Risk Allocator (RA), (3) Risk Simulator (RS), and (4)
Contingency Calculator (CC). These modules are hosted in a
critical path model scheduling spreadsheet to facilitate risk
management. In addition, a simulation engine add-in is used for
analyzing the probability distribution for the project time and cost
outcomes. In order to verify the proposed model, the process and
analysis have been applied to a case study project. The results
show that the RPCM is capable to rank and estimate the residual
risks in an easy, fast, and effective way.
Remuneration Review: Risk, Planning, and Modelling beyond the Covid Crisis
