Employing Predictive Trend Analysis to Decrease Construction Schedule Delay

• Published in: 2021 Systems and Information Engineering Design Symposium (SIEDS) pp. 1 - 6
• Main Authors: Austin, Vivian, McLane, Zachary, O'Keeffe, Caroline, Rashid, Diyar, Zimmerman, Ariana, Duran, Diana Franco, Heydarian, Arsalan, Bagwell, Todd
• Format: Conference Proceeding
• Language: English
• Published: IEEE 30-04-2021
• Subjects: Construction Projects; Data analysis; Forensics; Job shop scheduling; Predictive models; Probability; Schedule Delay Analysis; Schedules
• DOI: 10.1109/SIEDS52267.2021.9483796

Construction projects of all kinds are plagued by inefficiencies, creating excess risk, and leading to delays and cost overruns. Existing research has focused on analyzing delays in completed construction projects for forensic claims disputes. However, this data could also be used to decrease the risk of future schedule delays through the use of predictive trend modeling and data analysis. This form of data analytics is becoming increasingly prevalent and valuable in the construction industry as a means of identifying and allowing for the prevention of potential delays. An interdisciplinary team at the University of Virginia (referred to as the capstone team) seeks to provide insight into delay causation and prevention for Hourigan, a general contracting and construction firm. This work focuses on the analysis of scheduling data and project teams' input from three medium-sized construction projects recently completed by Hourigan, referred to by the placeholder names projects A, B, and C. These data sets were interpreted using statistical analyses to assess correlations between owner, designer, or contractor-related delays and frequent delays. Interviews with the project team for each Hourigan project were conducted to obtain qualitative data regarding specific delay events. The main causes of delay for Project A were found to be the owner and designer; for Project B the designer and subcontractors; and for Project C the subcontractors, materials, and external factors. The capstone team also identified that Hourigan would benefit from recording more data related to project schedules as well as costs incurred due to specific delays. These findings will allow Hourigan to better manage, avoid, and overcome future challenges due to project delays.