JOURNAL ARTICLE

EARNED COST COMPLEMENTS EARNED SCHEDULE, GENERATES AN ESTIMATE OF THE FINAL COST, AND FORMALLY UNIFIES EARNED VALUE MANAGEMENT.

  • Published In: Journal of Modern Project Management, 2025, v. 13, n. 1. P. 20 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Warburton, Roger D. H.; Cioffi, Denis F. 3 of 3

Abstract

This research defines a new quantity called earned cost, and, analogous to the manner in which earned schedule predicts a project’s final duration, earned cost leads to an estimate of the final cost. The earned-cost cost estimate is proven to be identical to the traditional cost estimate at completion. It is proven that the earned schedule-based duration estimate and the earned cost-based cost estimate formulas are universal: they apply at all project stages and to all projects. Although the formal proof of universality provides little project management insight, a graphical method clearly shows the universality of the formulas. Using the graphical method, the paper also shows that the duration estimate determined from the critical path is, in fact, formally identical to that obtained from earned schedule. Factors that determine the accuracy of the estimates of the final duration and cost are defined. The result is a formal unification of Earned Schedule, Earned Cost, and Critical Path into Earned Value Management. This confirmation reassures project managers, who know now that they may use any of the “earned” methods on any project with the confidence that they will obtain timely and reliable estimates for the final duration and cost. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Modern Project Management. 2025/01, Vol. 13, Issue 1, p20
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:2317-3963
  • DOI:10.19255/JMPM3702
  • Accession Number:188082729
  • Copyright Statement:Copyright of Journal of Modern Project Management is the property of JMPM Publishing House and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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