Date of Graduation


Document Type


Degree Name

Master of Science in Industrial Engineering (MSIE)

Degree Level



Industrial Engineering


Kim LaScola Needy

Committee Member

Gregory Parnell

Second Committee Member

Edward Pohl


The construction industry is a complex and evolving industry, making the project manager’s job of planning, organizing, and making decisions a difficult one. One of the most difficult decisions throughout a project is determining what resources are needed to complete a task by the deadline. Uncertainties cause risks within the schedule often creating delays for which the project manager must develop a mitigation plan once risks are identified. After conducting a review of the literature, no research was found examining the use of an analytical model to estimate the delays caused within the resource allocation process. If an analytical model could be developed to identify potential risks within the three largest resource categories of equipment, materials, and labor, the project manager could combine this information with his experience to help ensure the project is successfully completed. This in-depth case study focuses on creating a model using Microsoft Project, Microsoft Excel, and Palisade @Risk software to perform Monte Carlo simulation to predict potential delays prior to the start of the construction project. Interviews were conducted with a group of subject matter experts (SMEs), with varying levels of experience, to help gather insight into how the model should be developed to benefit the entire construction industry. Once the analysis was conducted, the model was validated by comparing the critical path of the schedule to an actual completed project where a historic brewery was converted into office space. An interview was conducted with the project manager who oversaw the project to determine if the results seemed reasonable and to see if the model results would have been useful at the start of the project. Because this research only performed a case study, no general conclusions about the entire industry can be made until it is tested on additional projects. In the future, the research can be expanded by incorporating the cost portion of the schedule, creating the model using other Monte Carlo software such as Probability Management’s SIPmath or Oracle’s Crystal Ball, and applying the analytical model to other industries such as software development, manufacturing, or defense by changing the names of the resource categories.