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.
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Damnjanovic, I., Reinschmidt, K. (2020). Forecasting Project Completion. In: Data Analytics for Engineering and Construction Project Risk Management. Risk, Systems and Decisions. Springer, Cham. https://doi.org/10.1007/978-3-030-14251-3_13
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DOI: https://doi.org/10.1007/978-3-030-14251-3_13
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