Earning value – OPEN

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Topic 11.8: Earning value

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An important question that needs answering here is, when does a task earn its value?

In our extended example, it might be quite simple to say that if the fence was planned to be 100m long, and 50m is complete, then it has earned 50% of its value.

But even that might not be a true reflection of the value earned.

After all, we might have dug 100% of the post holes, put in 70% of the posts, but only fixed palings to 20% of the posts.

How much value have we earned now?

And for more complex, intangible tasks (such as brainstorming), how do we know when we are 20, 40 or 85% complete?

Beyond the statistics, it should be becoming clear that the more detailed your WBS information is – particularly with regard to the timing of costs – the easier it is to accurately monitor project cost and schedule performance.

This way you can divide the labour, too!

Similarly, the more comprehensively tasks are broken down into their component activities, the easier it is to track progress.

To illustrate, if we broke the task ‘Build a fence’ down into three separate tasks – dig post holes, plant posts, and fix palings – we now have a much greater capacity to measure earned value.

By the same logic, the less precise our measures of earned value are, the more margin for error there is likely to be in our indices.

Yet even then, what one person says is ‘42% complete’ another might decide is ‘67% earned’.

Using a highly detailed WBS can also remove subjectivity entirely from the earned value debate, by allowing us to easily apply the 0/100 rule.

The 0/100 rule is an all-or-nothing approach to calculating earned value.

Not to be confused with the previously introduced 100% rule, it states that a task does not earn any value until it is complete.

In other words, until a task is 100% finished, its earned value is zero.

Now this may seem unduly harsh, and local variations to this rule might be 0/50/100.

Nevertheless, a 0/100 earned value policy not only reduces the risk of people overstating completion so as to produce better EVM stats, but encourages a much finer level of detail in the planning process.

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