Decision Support System to Manage and Forecast Final Project Costs

Managing construction projects can be a challenging task as they are subject to change and comprised of numerous work packages. Understanding project outputs in the early phases of a project can assist project managers to make informed decisions and take timely corrective actions to deliver projects on-time and on-budget. In the applicant’s doctoral research project, a Markov model was developed to enhance the accuracy of project cost forecasting through the integration of historical performance data. While this previous research has improved forecasting accuracy of a project’s ongoing cost performance, it cannot provide information regarding the impact that actions designed to mitigate poor cost performance (e.g., time crashing, duration extension) will have on project outcomes.
The objective of the current proposal is to develop a simulation-based framework that can predict the impact of various potential actions taken in response to poor project forecasts and suggest optimal actions. Monte Carlo simulation will be added to the previously developed Markov model to simulate various decision-making scenarios that can be taken by project managers. Outcomes of these analyses will be ranked and optimal decisions selected. Results of this research can be used as a supplementary tool to aid practitioners in the decision-making process.

Faculty Supervisor:

Simaan Abourizk

Student:

Amin Amini Khafri

Partner:

Graham Industrial Services Ltd

Discipline:

Engineering - civil

Sector:

Construction and infrastructure

University:

Program: