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Forecasting Project Completion

  • Ivan Damnjanovic
  • Kenneth Reinschmidt
Chapter
Part of the Risk, Systems and Decisions book series (RSD)

Abstract

In this chapter we discuss methods for forecasting future job progress. More specifically we focus on forecasting two important project performance criteria – completion time and cost-at-completion, on the basis of past progress data. We introduce a class of S-curves that is suitable for representing job progress as well as discuss how to develop the confidence intervals around the forecasts. In addition we show how Bayesian methods can be used to update the parameters of the S-curve models.

Keywords

Earned value Forecasting S-curves 

References

  1. Gelman A, Stern HS, Carlin JB, Dunson DB, Vehtari A, Rubin DB (2013) Bayesian data analysis. Chapman and Hall/CRC, Boca RatonzbMATHGoogle Scholar
  2. Nelder JA (1961) The fitting of a generalization of the logistic curve. Biometrics 17(1):89–110MathSciNetCrossRefGoogle Scholar
  3. Pindyck RS, Rubinfeld DL (1976) Econometric models and economic forecasts. McGraw-Hill, New YorkGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ivan Damnjanovic
    • 1
  • Kenneth Reinschmidt
    • 2
  1. 1.Texas A&M UniversityCollege StationUSA
  2. 2.College StationUSA

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