Quantitative Analysis of the Impacts of Out-of-Sequence Work on Project Performance

• Published in: Journal of construction engineering and management 2020-08, Vol.146 (8)
• Main Authors: Ibrahim, Michael W, Hanna, Awad S, Russell, Jeffrey S, Abotaleb, Ibrahim S, El-adaway, Islam H
• Format: Article
• Language: English
• Subjects: Confidence intervals; Construction costs; Construction planning; Cost analysis; Enumeration; Productivity; Quantitative analysis; Safety measures; Schedules; Sequences; Statistical analysis; Statistical significance; Technical Papers; Three dimensional models
• ISSN: 0733-9364; 1943-7862
• DOI: 10.1061/(ASCE)CO.1943-7862.0001876

AbstractAlthough out-of-sequence work (OOS) is often cited as a recurring challenge that impacts project performance, it has not been fully addressed in the existing literature as a standalone topic. This paper addresses this gap, defining OOS work as an activity or series of activities that was not performed according to baseline planned logical productive sequencing. By developing a project-based survey and contacting 300 professionals, extensive data were gathered from 42 projects. Using these data, the impacts of OOS were examined. It was found that, on average, approximately 15% of project activities are performed as OOS, resulting in 33% growth in construction schedule and 25% additional construction cost. Applying statistical analysis verified that, at 95% confidence level, OOS has a statistically significant inverse correlation with the productivity index, and a significant direct correlation with cost growth, schedule growth, and value of punchlist items. On average, a 5% increase in OOS was shown to be associated with an 8.5% drop in productivity, 10% increase in construction cost, and 11% increase in construction schedule. Regarding safety, no conclusive statistically significant relationships were found between OOS and the examined safety metrics, possibly because safety measures were followed regardless of sequencing. Investigating 19 project factors, the study highlighted statistically significant relationships between OOS and 15 leading indicators, at 95% confidence level. For example, implementing unplanned overtime, trade stacking, rework, as well as request for information (RFI) enumeration and processing time have significant direct correlation with OOS, while overall collaboration among project team members is inversely correlated with OOS. Project complexity, construction pace (traditional versus phased), percent design completion prior to construction, and the use of second shifts were not found to be statistically correlated with OOS. On investigating the level to which projects used five commonly recommended practices (alignment, front-end planning, constructability, planning for startup, and 3D modeling), the study found that the five practices are inversely correlated with OOS, especially when collaboratively implemented early and often throughout the project. Industry practitioners should use the presented findings to address and mitigate OOS and its negative impacts, ultimately enhancing overall project performance.