A Quantitative Schedule Risk Analysis Using the Risk Driver Approach and Monte Carlo Simulation for the Construction Phase of the Seawater Energy and Agriculture System

Abstract

Innovated synergy of water, energy, and food is a current need for the global community and especially for arid regions seeking to have security within these three dimensions. The Seawater Energy and Agriculture System (SEAS) is a new concept that proposes a sustainable water-energyfood cycle, aiming to produce food and generate biomass, while preserving freshwater and arable land. The SEAS has been initially implemented in a 2-hectare (ha) pilot facility that has now been in operation for one year. To develop the concept further, a 200-ha demonstration scale facility needs to be developed. A prior techno-economic evaluation of this scale-up indicates that the construction cost of implementing the SEAS farm is significant, and therefore, a careful appraisal of how to develop such a project is needed. This study uses project management techniques to understand how this project can be constructed by evaluating the main activities that need to be carried out, and by looking at how critical construction risks can affect its development. A scheduling model for the construction of the project was built using project management software. Subsequently, a questionnaire survey was done to collect quantitative risk data to build a Stochastic Quantitative Schedule Risk Analysis, where the Risk Driver approach was used in synchronism with Monte Carlo simulation. Further, a risk sensitivity analysis was performed to identify the most relevant construction risks according to the data collected. The results show that 'shortage of manpower', 'productivity of labor and equipment', and 'extreme weather conditions' are the three most critical risks for the SEAS construction project. A combined reduction of their probability of occurrence and impact could decrease the total project duration by around 12.27% or 67 days, which would translate into a relevant cost reduction.

Date of Award	May 2017
Original language	American English