Estimating task execution time is essential for planning and managing engineering projects. Many process scheduling and optimisation tools and methods require precise task execution time estimates. However, estimates are often too optimistic, potentially harming the usefulness of such tools. In this paper, we develop a methodology to aggregate multiple data sources into a Multiple Domain Matrix and show that its structural properties correlate with task execution time. Specifically, using data from a real-world engineering case, we show that the size of a task, the number of people assigned to it, and the number of interfaces directly correlate with task execution time. We discuss how these measures are available during the planning stage of the process and how people can use them to obtain better estimates.

Understanding task execution time in relation to the multilayer project structure: Empirical evidence

Piccolo S. A.
;
2018-01-01

Abstract

Estimating task execution time is essential for planning and managing engineering projects. Many process scheduling and optimisation tools and methods require precise task execution time estimates. However, estimates are often too optimistic, potentially harming the usefulness of such tools. In this paper, we develop a methodology to aggregate multiple data sources into a Multiple Domain Matrix and show that its structural properties correlate with task execution time. Specifically, using data from a real-world engineering case, we show that the size of a task, the number of people assigned to it, and the number of interfaces directly correlate with task execution time. We discuss how these measures are available during the planning stage of the process and how people can use them to obtain better estimates.
2018
978-3-00-057492-4
Data science
Design project
MDM
Multilayer networks
Task execution time estimation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/358725
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