Estimating the time it takes to complete a task or project is one the biggest challenges in task and project planning. Monaco helps you make better task estimations by modeling tasks as random processes.
Defining a Task is easy:
task = Task(name='Task', min=3, mode=4, max=9, estimator='triangular')
Projects are sequences of tasks:
Tasks can be added to Projects:
# initiate a project
project = Project(name='Build Machine Learning App')
# define tasks and duration (in this case: number of days)
task1 = Task(name='Train model', min=1, max=5, estimator='uniform')
task2 = Task(name='Deploy Application', min=1, mode=2, max=3, estimator='uniform')
# define task sequence
project.add_task(task1)
project.add_task(task2)
Monaco can estimate the duration of a project by simulating many project cycles using Monte Carlo Simulation. The central limit theorem establishes that the sum of many independent random variables approximate a normal distribution.
Monte Carlo Simulation can be done with a single line of code:
plot = p.plot(n=10000)
plot.show()
The likelihood of completing a project can be read from the cumulative distribution. In this example there is an 80% chance that the project will be completed under 23 days.
plot = p.plot(n=10000, hist=False)
plot.show()
