Since people started coordinating their activities within communities, the first outlines of project work appeared. Planning actions, distributing participants, and estimating how much time and resources the work can take became necessary to procure food, protect the community, and migrate to better locations.
In the prehistoric world, the “projects” people ran were different but the estimation techniques were basically the same – with allowance for state of the art, of course. How did ancient people estimate and plan things they needed to get done?
This estimation method is based on expertise and opinions of experienced specialists, not necessarily from the project team. The judgment is based on theoretical approaches, existing practical knowledge, a set of criteria, or a summary of previous experience.
Comparative method consists in using the experience from previous similar projects to analyze work scope, forecast possible risks, and estimate delivery timelines. Whether the previous project was a success or a failure, it can provide decision makers with relevant data for estimation and important insights.
This type of estimation works as a quick method for providing ballpark estimates for prospective projects and assessing whether they are viable. In it, a high-level breakdown of project steps is used. Later, more specifics can be added to the rough estimate.
As opposed to the previous method, bottom-up estimation implies a detailed breakdown of project tasks and estimating each of them separately. The next step is usually calculating the totals at higher levels and seeing the big picture. This method allows managers to obtain more accurate results than the previous one.
In this method, measurable variables are used to forecast and estimate upcoming work. Parametric model method is a more scientific technique and it ensures maximum accuracy of the results. With special tools, it’s possible to automate calculation and estimation process.
In this method, a mathematical approach is used. It is based on producing the weighted average of three estimates: optimistic, most likely, and pessimistic estimates. This method allows automation and is quick, but it requires significant amounts of data and detail levels.
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