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.     «

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 f...     »