How to outsmart peers in estimation - PERT

Most of us will do estimation as part of the job in building software.

Estimation is not just limited to the job, it’s happening in our life as well.

When your mom or spouse asks: when you’ll reach home — you’re mentioning a time based on looking into traffic on road; THAT’S THE ESTIMATE

Why does the estimation fail?

The estimation fails when there is a major difference between the actual time and estimated time.

Let’s say you are building a feature in the software.

You are estimating it to complete it in 5 days.

  • Estimate fails if it takes 10 days
  • At the same time, estimate fails if you complete in 2 days.

Why overestimate or underestimate?

An estimate should be realistic than an imaginary one.

We should hide behind overestimation or breaking the head with underestimation.

While estimating, one should consider all the internal and external factors to be considered in the part of the process.

What if I missed the deadline even after proper estimation?

Estimation is a skill that makes you feel perfect.

It like machine learning, you will keep on learning by doing multiple iterations.

But each iteration should bring the results better compared to the earlier one. 

How can I estimate better?

Yes, that’s the question we trying to discuss now.

Earlier, in one of the projects I have worked — we have experimented PERT principle to estimate our tasks.

PERT — Project Evaluation and Review Technique

I usually call it with another term – 3 point estimation, because it sounds cooler than the previous one.

Short story

PERT is an estimation technique that helps you calculate the estimate by considering the uncertainties that may happen in the tasks.

So one should bring a 3 estimation for one single task. Let’s see that in detail in the long story. 

Long story

For every task, one should prepare 3 estimates by considering all the risks and uncertainties that may occur.

And using a weighted average of that three numbers to come up with a final estimate.

Pessimistic (P) —  when everything goes wrong

Optimistic (O) — when everything goes right

Most likely (M) — common problems and difficulties — similar to life 🤔

Mostly these estimated are measured in hours or days(6 hours a day). So one should prepare 3 estimates for all the above-mentioned cases.

The program (or project) evaluation and review technique (PERT) is a statistical tool used in project management, which was designed to analyze and represent the tasks involved in completing a given project.

Wikipedia says

Process of estimation

Let’s say, I’m working on the task to build a console application to do some operations.

Now I need to go through the requirements and check the technical impediments in it and decide the workflow. And the come up with these 3 estimates like

If everything goes wrong; Pessimistic — I will complete the task in 8 hours

If everything goes right; Optimistic — I will complete the task in 3 hours

If I have some real problem; Most likely— I will complete the task in 5 hours

That’s it. They can start and continue their work in full swing.

From a project management view

I manage this project and I got three estimates for one single task.

Which one I should track now?

And how can I estimate the overall timeline of the project?

The answer is — we have formula now 🤣

Don’t scare! I do scare when I learned it first. But it’s straight forward, nothing to do with algebra or integral calculus.

The resulting PERT estimate is calculated as 

(O + 4M + P)/6

This is called a “weighted average” since the most likely estimate is weighted four times as much as the other two values. You’ll notice that the final PERT estimate is moved slightly toward either the optimistic or pessimistic value – depending on which one is furthest from the most likely.

Estimation is a skill that makes you feel perfect.

It like machine learning, you will keep on learning by doing multiple iterations.

But each iteration should bring the results better compared to the earlier one.

Try out and leave your feedback in comment section. 

Credits:

Photo by Kelly Sikkema on Unsplash

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