The "Monte Carlo: When" chart tells you the likelihood (probability) that a specified number of work items will finish by a certain date.
This chart helps you answer the question "When will it be done?" for a set of work. It helps you make forecasts that accurately present the likelihood work will be done by a specific date such as "There's an 85% chance this work will be done on or before June 1 of this year." If you need to forecast when a single item of work will be done, use the Cycle Time Scatterplot.
What you'll find in this article
| Table of Contents |
|---|
Have you checked out our Youtube channel?
We have a Youtube channel with short demo videos for most charts and longer webinars, including a longer introduction to ActionableAgile
Watch the Monte Carlo: When demo
| Widget Connector | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|
How it works
This chart uses your historical throughput data to determine the rate at which you finish work. In the simulation controls, you provide the number of items for which you want to forecast a completion date along with a start date. Using that data, the application runs thousands of simulations taking into account the variation in your historical data to surface all of the potential outcomes and, most importantly, the likelihood that you'll experience any specific outcome when you actually embark on the journey to do the set of work in question.
| Info |
|---|
The start date is very important |
Which historical data is used to forecast?
By default the chart will use all the historical data which has been loaded into ActionableAgile. To refine this search, make sure to select Throughput Data Control in the Layout section of the Controls for this Chart, the Daily Throughput Chart will then appear and allow you to select any date range of your choosing.
In this example, the date range selected goes from 20210801 to 20210815, which means that the chart will only use the historical data from this date rage to forecast.
How to read the chart
The throughput basis and throughput date control provide the data for the histogram and the calendar. All of these areas can be toggled on or off via the Layout chart control.
The throughput date control is similar to that of other charts in that you can select sections of the data with your mouse in order to reduce the data that is used for forecasting your upcoming work. This can be useful if some of your data was generated in conditions unlike that for which your future work will be completed in (for example you now have a different: team size, set of organizational constraints, the balance of work, etc.)
What you select in the throughput date control will be reflected in the throughput basis. This is where you can see what data is used for the histogram and calendar. The throughput basis and throughput date control both show a daily throughput (the number of items finished on a particular calendar day). You can click on the bars or dots in this chart to see the number of items finished on a given day just as you can on the Throughput Histogram.
The histogram is a picture of the various completion dates and how often they occurred. That information provides us a likelihood that you'll achieve any one completion date. In the picture below, you can see that 24% of the trials finished on June 30, 2021, so there is a 24% chance that we'll finish the body of work on or before June 30, 2021.
If you want to see that information on something easier to interpret, toggle on the calendar. Now you can see the data in the format people really care about! Easily go to the date you were hoping for and see what the chances are.
| Info |
|---|
Pro tip |
Key Chart Controls
Simulation Controls
This is where you control the information needed for the Monte Carlo simulation to run:
The start date. Just click on the calendar icon to choose your start date. You can choose a date in the past in order to forecast a body of work that has already begun.
Getting the start date right is very important to the forecast. Often we misjudge when the actual start date will be. If you end up being wrong or it changes for any reason, you'll want to re-forecast!
| Note |
|---|
Start date in the past: If you use a past date you have to include all the work that is left to be done, plus the work that was already done. We recommend that you avoid this approach and instead choose to forecast continually using today’s date or future dates with work that still remains to be done |
The number of items you want to complete. you can also put a range in this field by using this format "100-150". This can be helpful if you aren't quite sure what the actual number of items will be but you have a likely range. When you use a range, not only does the simulation use the variation of your throughput to come up with possible outcomes, it also uses a variation in the number of items you need to complete.
| Info |
|---|
Why might I use a range? Work items often get broken down into multiple smaller stories. If you keep an eye on how many smaller items a larger one becomes, you learn something called your split rate. If you know that an item can do anything from stay the same or break down into up to 3 smaller items, you know that your workload could grow up to 30%. This means you might want to put a range of "100-130" into the simulation control. |
Scale throughput by X. Are you thinking about making an improvement and want to see how it affects your outcomes? Play with this field to see how an improvement in your throughput will impact your forecasts. You can use decimal points here. So, if you think getting a bit of help will improve your throughput by 10% you can update this to 1.1 and see how that changes your forecast.
Number of trials. You can increase the number of trials that the simulation runs to see how that changes your forecast. You do this by clicking on the More button and then clicking again when you want it to stop. (It will keep going until you click the button to stop.)
Surprisingly you'll see that adding more trials doesn't often significantly change the range of outcomes that are likely.
Percentiles
When you choose this option, the chart looks at all of the outcomes that occurred and draws lines at the point at which that percentage of trials have finished. In this chart, these lines tell you the likelihood that the body of work will be finished by specific dates. So, if there is an 85% line on June 1, 2021, that can be translated to "there's an 85% likelihood that we will finish this work on or before June 1, 2021." It means that 85% of the trials finished on or before that date.
Layout
This control tells the chart what information to display.
You can choose to show or hide the following:
Item Filter
You can filter down the items used for your throughput basis by choosing one or more available filters.
This might be useful if you want to forecast a certain type of work, say an issue type of "Story", and you would like the simulation to use only Stories from your past throughput to generate the forecast. Meaning you are filtering out all other issue types like Bugs, Tasks, Epics, etc. This can help you make sure you are comparing apples to apples, as they say.
If you want to clear your filters so that all past work is used, you click the Reset button.
Additional Chart Controls
Throughput Chart Type
This control lets you decide to have a line chart or bar chart to represent the Throughput Basis chart that is located at the top of the chart area. The data doesn't change, only how it is shown.
Workflow Stages
Generally, the chart defines your past throughput as Items that have gone through the entire workflow, all the way to the final stage. However, you can redefine that definition for the purposes of this chart. If you deselect the Done workflow stage(s) at the end of the workflow, you will be redefining throughput as items that made it through the workflow to the Testing stage. This can be useful to find out when a body of work will likely have completed that smaller part of the workflow.
If you have any questions, please submit them to our help desk.
Related Articles
Other Monte Carlo Simulations
Charts related to Throughput