The analytics in Kanbanize work per board. Click the Analytics icon on the top of each board to access the Analytics page.
The Analytics screen presents 5 categories: Cycle Time, Throughput, WIP, Flow, Forecasting, each available as a tab item on the page. Click on a tab button to view the charts for each of the respective
metrics. The "Monte Carlo - When" simulation belongs to the Forecasting tab.
Introduction to "Monte Carlo - When" simulation
The "Monte Carlo - When" simulation is the other way around compared to "Monte Carlo- How Many".
The "Monte Carlo - When" chart aims to tell you when you can expect the team to finish a specific number of tasks that you are yet to start working on.
Based on how you have performed in the past, the "When" simulation can estimate how long it is gonna take you to get a certain number of items (for example 100) done in the future. For example, you can use it if you’ve got a product update scheduled for March 30th and you wish to know how many features you can finish by then.
How to set up the "Monte Carlo - When"?
From the left side of the panel, you can control which data will be used for the simulation as well as to determine the time frame. Add single or multiple workflows.
In addition, you can apply other filters. For example, you may want to see only cards of a certain size, etc.
The Monte Carlo simulation When consists of three charts:
- Throughput basis (1)
- Throughput Navigation (2)
- Monte Carlo (3)
The horizontal axis of the "Throughput Basis" (1) is a representation of time, while the vertical one shows the daily throughput. The high points of the charts represent the maximum throughput of any of the days in the chart, while the low points represent the minimum number of tasks that were completed. Hovering over the dots will give you summarized information about the number of tasks that have been completed that day. The line between the dots visualizes the rises and drops of your team productivity.
The Throughput Navigator (2) allows you to zoom in and out of a specific interval within the selected time frame. This way you can generate different Monte Carlo simulation without having to reset the general time frame every time.
Monte Carlo (3), which is the bottom chart is direct visualization of the results of the simulation in the form of a probabilistic distribution. The horizontal axis visualizes the total work items that will probably be completed within the selected future date. The vertical axis shows how many instances of a certain result occurred during the trials.
How does this chart work?
It has an identical structure to "Monte Carlo How Many" and works in the same way.
(1) Imagine you have a backlog of 100 items. You have to enter this number in the dedicated field.
(2) Activate the percentiles to get the probabilistic view of the third chart.
(3) Run the Trials (up to 1 million)
What's that telling us?
The third chart will be able to tell you with a probability of 50%, 70% 85% and 90%, an approximate end date when your team will complete 100 items. The simulation shows you that there is 85% probability that your team will complete the items by July 4th. If you want to be more certain go to the 95% percentile, where the date is July 7th.
Assign a probability to your estimations or deadlines. If you communicate the date without the probability you commit to a fix date delivery and in knowledge work that is very often unrealistic.
Practical Tip:
- Note that if the characteristics of the team that generated the historical data changes (for example, a new team member joins the team), the simulation produced from the data might be no longer valid.
- You may doubt the results if your work items are different in size or some of them are too complex.
That's why it is so important to keep your cycle time smaller. You have to do right-sizing and try finishing work within a certain period of time. If a task is too big, break it down into a smaller chunk of work and flow it through the system. If you do this consistently, the forecast will be more accurate and your process much more predictable.
Controls for this chart - customize your view
You can use the controls to change the "Monte Carlo When" simulation view and to apply additional filters. Please, check the short video below.
- Throughput Chart type - some users prefer Bar chart to visualize the throughput instead of a Line chart.
- Percentiles - select and deselect percentiles to get the probabilistic view of the chart.
- Layout - you can remove some of the charts and make only the simulation visible. Moreover, you can add a calendar that visualizes the begin and end date for your prediction.
- Item Filter - apply additional filters to run the simulation for tasks that have a specific location or a certain property: tag, type, etc. Choose values to select which items are available for the prediction of this chart. For example, let’s say that you are about to launch a new marketing campaign and have already made the breakdown of the tasks that need to be completed. You’ll probably want to know when the content for the campaign will be ready. To find it out, select the specific card type in the filter and the platform will exclude the rest from the simulation, thus giving you a more precise prediction based only on your previous content performance.
