Data Analysis and Predictive Model Generation for Delays in Navy Construction Projects
Abstract
Currently, Naval Facilities Engineering Command (NAVFAC) records all data on the process from application to awarding of Military Construction (MILCON) projects. This data is not utilized to increase poor performance and lack of timely results on the completion of MILCON projects. The poor performance leads to delays in deliveries to important facilities and delays in warship deployment and degradation of warfighting capabilities. NAVFAC currently has personnel investigating methods on improving the project timelines to minimize delays. Majority of the delays occur during the pre-award phase of the projects with the post-award phase causing additional delays. The purpose of this thesis is to analyze projects across multiple fiscal years from project initiation to contract award. To accomplish this, data was acquired from NAVFAC's eProjects database and analyzed using machine learning techniques as well as statistical analysis to determine a correlation between the possible causes and the delays that occurred to develop a predictive model for analyzing future project contract delays. This collection will potentially assist NAVFAC in focusing onto ongoing improvements. Reducing the delays in project awarding will further the process for reducing the overall time required to complete MILCON projects. This will shorten the amount of time that ships are in the shipyard further enhancing the Navy's undersea warfare capabilities with more submarines and other assets deployed.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2022
- Accession Number
- AD1201698
Entities
People
- Justin R. Rhea
Organizations
- Naval Postgraduate School