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Analyzing Billing Patterns in The Construction Industry Using Earned Value Analysis

9 pagesPublished: May 26, 2024

Abstract

Earned Value Analysis is an effective tool to manage project schedules and cost controls. However, it is not widely used because of the time resources required from construction managers. This research considered whether a central tendency exists in construction projects with same, or similar durations regarding cumulative earned value. An analysis was performed of a dataset containing 967 projects with durations primarily spanning 12 to 14 months. By examining the monthly billing percentages, average monthly billing percentages were calculated alongside their standard deviations, leading to the determination of minimum and maximum confidence intervals. An Earned Value Analysis (EVA) curve was then constructed based on these findings. The research found that a generalizable EVA curve can be established with relatively minimal variation from the mean. Key findings include an established average monthly billing percentage and a predictive EVA curve, which aids in identifying minimum and maximum confidence intervals for project billings. The study contributes to EVA knowledge by laying a foundation for predictive modeling in project billing, with implications for future research.

Keyphrases: Construction Finance, Earned Value Analysis, predictive model

In: Tom Leathem, Wesley Collins and Anthony J. Perrenoud (editors). Proceedings of 60th Annual Associated Schools of Construction International Conference, vol 5, pages 668--676

Links:
BibTeX entry
@inproceedings{ASC2024:Analyzing_Billing_Patterns_in,
  author    = {Bharath Chandrasekar and John Killingsworth and Kevin Bright},
  title     = {Analyzing Billing Patterns in The Construction Industry Using Earned Value Analysis},
  booktitle = {Proceedings of 60th Annual Associated Schools of Construction International Conference},
  editor    = {Tom Leathem and Wes Collins and Anthony Perrenoud},
  series    = {EPiC Series in Built Environment},
  volume    = {5},
  pages     = {668--676},
  year      = {2024},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2632-881X},
  url       = {https://easychair.org/publications/paper/PpSq},
  doi       = {10.29007/r6dc}}
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