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2021 | OriginalPaper | Buchkapitel

14. Statistical Learning Techniques for Project Control

verfasst von : Fernando Acebes, Javier Pajares, Adolfo López-Paredes

Erschienen in: Project Management and Engineering Research

Verlag: Springer International Publishing

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Abstract

There are proven techniques and tools that project managers use to monitor and control the project. Some of them are as common as the Earned Value methodology (EVM). Due to the limitations of the previous methodology, procedures arise that incorporate uncertainty in the project control. However, the advance in research on advanced statistical techniques allows the development of more current tools that determine, on the one hand, the status of the project, and on the other, predict the future situation at the end of the project. In this article, we show how to use these advanced classification and regression techniques applied to project management, focused on the supervision and control of the time and the cost of the project. An example is included where we apply Monte Carlo simulation to obtain a set of data that will be treated with statistical tools and algorithms, which will allow us to determine, for any project control point, the probability of compliance with the planned objectives of time and cost and, where appropriate, predict what will be the deviation from such planning.

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Vorheriges Kapitel Design Thinking: Virtues and Defects of a Project Methodology for Business, Academic Training, and Professional Design

Nächstes Kapitel Program and Project Management Articulation: Evidences from the Infrastructure Sector

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Titel: Statistical Learning Techniques for Project Control
verfasst von: Fernando Acebes
Javier Pajares
Adolfo López-Paredes
Verlag: Springer International Publishing
Buch: Project Management and Engineering Research
Print ISBN: 978-3-030-54409-6

Electronic ISBN: 978-3-030-54410-2

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-54410-2_14

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