The Monte Carlo simulation offered by Wiveez is an advanced tool for estimating the probability of different outcomes for scenarios such as "How Many" and "When" (When a certain number of tickets or points can be completed). It relies on repeating simulations based on historical data and probability distributions, to give users a forecast based on realistic and varied scenarios.
Wiveez allows users to filter input data according to numerous criteria, to refine the simulation and adapt the results to specific situations. This allows more precise modeling of probabilities, taking into account the particularities of the project or team.
“How Many”: The simulation answers the question “How many tickets (or effort points) can be completed” in a certain given time frame. The user can use this simulation to estimate the amount of work that can be completed in a specific Sprint or iteration.
“When”: The simulation answers the question “When a certain number of tickets (or effort points) can be completed”. This makes it possible to predict when the team will be able to reach a specific goal of completed work, taking into account contingencies and the historical capacity of the team.
How the Monte Carlo simulation works
Monte Carlo simulation relies on repeating thousands (or even millions) of simulations to model probable scenarios. Here's how it works in Wiveez:
Historical data: The simulation uses historical project or team data to create a realistic probability distribution based on team velocity, workflow, ticket processing time, and other parameters. These data serve as a basis for the simulations.
Input data filtering: Wiveez offers great flexibility in data filtering.
These filters make it possible to refine the simulation for specific scenarios and obtain results adapted to each particular context.
Repeating simulations: The simulation repeats scenarios several thousand times based on the probability distributions of the input data. Each simulation follows a random distribution of possible outcomes, based on historical data and the chosen criteria.
Estimating Results: Once simulations are completed, Wiveez compiles the results to provide probabilistic estimates. For each scenario, the simulation provides predictions in the form of probability percentages for different outcomes. For example, a typical result would be:
“With a 95% probability, the team will be able to complete between 40 and 50 tickets in the next two weeks.”
“There is an 85% probability that the team will complete 200 effort points before the end of next month.”
Usefulness and Advantages of Monte-Carlo simulation in Wiveez
The Monte Carlo simulation in Wiveez is particularly useful for:
Estimate How Much Work Can Be Done: By simulating multiple scenarios, Wiveez helps teams answer the question “How many tickets or effort points” they can complete in a certain amount of time. This allows for better planning of Sprints and projects.
Predict completion date (“When”): For projects with a specific deadline, Wiveez helps answer the question “When” a certain amount of work will be completed. This makes it possible to better manage stakeholder expectations and anticipate risks related to deadlines.
Adapt simulations to specific scenarios: With the advanced filters offered by Wiveez, users can fine-tune the simulation to adapt to specific situations. This makes it possible to model more precise scenarios and obtain results that take into account the complexity and particularities of each project or team.
Improve risk management: By offering results in the form of probabilities, Wiveez makes it possible to anticipate risks and uncertainties in planning. Users can see the most likely scenarios, as well as less likely (but risky) scenarios, helping to better prepare the team for unforeseen eventualities.
Improve communication with stakeholders: With probability-based forecasting, teams can provide more accurate answers to stakeholders about workload or completion times, improving transparency and planning.
Example
Let's take an example of Monte Carlo simulation in Wiveez for a feature development project.
"How Many" Scenario: If the team wants to know how many tickets they can complete in a two-week Sprint, they can run a Monte Carlo simulation with historical data filtered for similar tickets (e.g. feature tickets ), and based on the team's past velocity. The result could be:
"There is an 85% probability that the team will finish between 45 and 55 tickets in this Sprint."
“When” Scenario: If the team needs to complete 200 effort points for a specific milestone, they can run a simulation to determine when this could be achieved. The simulation could indicate:
“There is a 75% probability that the team will complete 200 points before October 20.”
Based on these results, the team can adjust its schedule, re-prioritize tickets, or adjust commitments to ensure deadlines are met.
Usefulness in Wiveez
The Monte Carlo simulation in Wiveez helps teams:
Plan more accurately by estimating how much work can be accomplished or when a specific goal will be achieved.
Manage risks and expectations by factoring uncertainty and multiple scenarios into their forecasts, and adjusting their commitments based on probabilities.
Make informed decisions using advanced filters to tailor simulations to specific situations and achieve results that precisely match project needs.
Summary
Monte Carlo simulation in Wiveez allows teams to make accurate predictions about work to be completed (“How Much”) and completion times (“When”). Thanks to advanced filtering criteria and repeated simulations, Wiveez provides probabilistic results, allowing you to better plan, manage risks, and adjust commitments according to the team's actual capabilities.