Prioritizing collaborative scheduling practices based on their impact on project performance

Chuanni He, Min Liu, Thais da C.L. Alves, Natalie M. Scala, Simon M. Hsiang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Collaborative scheduling (CS) and related practices impact the performance of construction projects which can be measured by key performance indicators (KPIs). However, little is known about which of these practices are implemented and how their combined implementation might impact KPIs. The objectives of this research are to identify perceptions of CS practices that drive project performance, define CS practices used by industry that impact KPIs, and establish practices that are more commonly implemented and have a higher potential to positively impact KPIs. The research team conducted a nationwide survey in the United States and obtained 241 usable responses. Utilizing an information theory approach to measure the uncertainty of implementation and impact of each practice, the research built a set of Chow–Liu tree models to determine the most efficient sequence of actions to improve CS. Results indicate that meeting owners’ expectation throughout the life-cycle of the project from design through construction and commissioning, using the schedule to support a strong project culture, and an effective communication plan were the top CS levers for overall KPI improvement. An innovative method was developed to help construction project managers discover the value of each CS practice, the relation between CS practices, and CS practices’ influence to project KPIs so that managers can improve KPIs efficiently by prioritizing their CS practices according to their own project needs.

Original languageEnglish (US)
Pages (from-to)618-637
Number of pages20
JournalConstruction Management and Economics
Volume40
Issue number7-8
DOIs
StatePublished - 2022

Keywords

  • Collaborative scheduling
  • information theory
  • practice
  • survey
  • uncertainty

ASJC Scopus subject areas

  • Management Information Systems
  • Building and Construction
  • Industrial and Manufacturing Engineering

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