Timelines

This is a picture:

And this is a picture:

And this is a picture:

The only difference between these pictures is the tool used to draw them: highlighters, PowerPoint or a Gantt Chart generator. The value of these pictures is exactly the same.

Pictures with uncertainties
If this project has duration and cost uncertainties, it is possible to draw other pictures.

Probability Distribution:

Scatter Diagram:

It is NOT the Monte Carlo Simulation Method applied to forecast project delivery, taking project uncertainties into account.

Monte Carlo Simulation process 

In project management Monte Carlo Simulation has to be applied the following way:

• Integrated logically driven dynamic Project Delivery Plan developed and assessed;

• People enter three estimates of initial data that are uncertain (optimistic, most probable and pessimistic) and define what probability distribution each uncertain parameter has;

• Risk events are included in the project risk model with their probabilities and impacts;

• Corresponding corrective actions added if target achievements are endangered;

• The software calculates the model and accompanying parameters over and over, each time using a different set of initial data in accordance with their probability distributions. The number of iterations is usually defined by the risk management software user. Usually, this number is measured in thousands of iterations.

As the result, we get the distributions of possible outcome values.

Top-down and bottom-up estimations

There are two main project estimation methods: top-down and bottom-up. The top-down is extremely inaccurate but quick. The bottom-up is slower but has a higher accuracy.

The above pictures were drawn based on Top-Down estimations and show how long each project phase will likely take.

The top-down approach doesn’t allow understand how inputs (activity uncertainties and project risks) correlate with outputs (duration and cost probability distributions), and such correlations are critical for reliable analysis. The Monte Carlo Simulation applied to Top-Down estimations doesn’t simulate POSSIBLE outputs. It just narrows the range of uncertainties.

Drawing risk distribution pictures in a complex way with the scientific method may create the impression of accuracy, but the reliability of such predictions is extremely low. Usually, it is so low that the P80 prediction has zero chance of being achieved.