or ongoing projects, nonlinear regression-based growth models allow for re- fined duration and cost estimates at completion. In particular, the Gompertz sigmoidal function has been used in curve fitting and proven suitable in forecast- ing S-shaped cost profiles for projects experiencing overruns. In this paper, we follow the standard approach to both Earned Value Management and Earned Schedule and use the Gompertz function for the planned, earned, and actual cost profiles. A simple, linear expression is derived for the forecast of the du- ration estimate and the theoretical formula is validated by application to many synthetic project data sets. The model’s predictions are shown to be accurate, stable and reliable, thus validating the theoretical concepts and demonstrat- ing their practical relevance. We conclude with practical guidance for project managers.

Improving the accuracy of project estimates at completion using the Gompertz function / Nannini, Giulia; Warburton, Roger D. H.; DE MARCO, Alberto. - STAMPA. - Irnop 2017 The Modern Project: Mindsets, Toolsets, and Theoretical Frameworks:(2017). (Intervento presentato al convegno International Research Network on Organizing by Projects Conference 2017 tenutosi a Boston nel 11-14 June 2017).

Improving the accuracy of project estimates at completion using the Gompertz function

DE MARCO, Alberto
2017

Abstract

or ongoing projects, nonlinear regression-based growth models allow for re- fined duration and cost estimates at completion. In particular, the Gompertz sigmoidal function has been used in curve fitting and proven suitable in forecast- ing S-shaped cost profiles for projects experiencing overruns. In this paper, we follow the standard approach to both Earned Value Management and Earned Schedule and use the Gompertz function for the planned, earned, and actual cost profiles. A simple, linear expression is derived for the forecast of the du- ration estimate and the theoretical formula is validated by application to many synthetic project data sets. The model’s predictions are shown to be accurate, stable and reliable, thus validating the theoretical concepts and demonstrat- ing their practical relevance. We conclude with practical guidance for project managers.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2676527
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