Imagine you’re in a meeting. The CEO leans forward, eyes locked on you, and asks the question that makes every project manager’s heart race: “When will it be done? “
In that moment, your mind starts racing:
- Will I overpromise?
- What if we miss the deadline?
- How can I be realistic without sounding uncertain?
It’s like being asked to predict the weather a month from now – impossible, right?
But what if predicting project timelines could be more science than guesswork
The Magic of Monte Carlo: Like Rolling Dice for Your Project
Imagine if you could roll dice to predict your project’s future. That’s kind of what Monte Carlo simulations do, but with your team’s past data instead of dice.
Here’s how it works:
- Data Collection: Gather historical throughput data from previous sprints or time periods.
- Random Sampling: Randomly select data points from the historical dataset to simulate future sprints.
- Multiple Iterations: Run thousands of simulations to generate a range of possible outcomes.
- Probabilistic Results: Analyse the distribution of outcomes to determine probabilities.
Example: Predicting Sprint Success
Let’s say your team’s completed tasks in the last 6 sprints looked like this:
- Sprint 1: 10 tasks
- Sprint 2: 12 tasks
- Sprint 3: 8 tasks
- Sprint 4: 9 tasks
- Sprint 5: 10 tasks
- Sprint 6: 7 tasks
After running a Monte Carlo simulation, you might find out:
– There’s an 85% chance you’ll finish 20 tasks in the next 10 days.
– You’re 85% sure you can do 50 tasks in the next 6 weeks.
Making Predictions Even Better
- Cycle Time
Origin: Lean manufacturing
Definition: The time it takes to complete a single task from start to finish.
- Throughput
Origin: Manufacturing and operations management
Definition: The number of tasks or units completed in a given time period.
- Work in Progress (WIP) Limits
Origin: Kanban and lean methodologies
Definition: The maximum number of tasks allowed in each stage of the workflow.
- Cumulative Flow
Origin: Lean software development
Definition: A visual representation of work items in various stages over time.
These metrics, originating from manufacturing and lean principles, have been adapted for use in Agile project management to improve efficiency and predictability in software development and other knowledge work domains.
Putting It All Together
Now, instead of guessing, you can say things like:
– “We have a 70% chance of finishing in 8-10 weeks.”
– “There’s an 85% chance we’ll complete 30-35 tasks this quarter.”
Remember, we can’t see the future perfectly. But with these tools, we can make much better informed guesses based on how we’ve done before.