If you’re a soccer fan, you probably know all these websites that give you the probability of how a game will end.

The approach that they use is pretty smart. As the game progresses, and teams score or don’t score, the chances of your team winning will change as well. Every time a new event happens, the model is updated and a new prediction of how the game will end takes place.

Now, imagine you are the coach of a team and you observe the probability of your team winning going down.

If that’s the case, you’d probably want to make some changes, switch the positions of certain players, let new players enter the game, or swap the initial strategy altogether, to increase your chances of winning.

Either way, when provided with that information, it would be simply unreasonable not to make any changes. Your tactics have to adjust based on the new information you’re gathering.

This is a classic example of continuous forecasting.

The Little-Known Fact About Forecasting That Every Agile Coach Should Be Aware Of

Let’s say that you used the Monte Carlo simulation to come up with a project delivery date and have already made your commitment.

Once you have your initial forecast, you need to continuously reevaluate it and adjust your course accordingly.

Your forecast will change as you deliver more work.

Remember, Monte Carlo simulation uses your past throughput as a base. Your throughput will vary based on any changes in the scope, your team, or the efficiency of your workflow.

All these factors will affect your initial prediction. That’s why continuous forecasting is essential – to be able to deliver on your commitment, you need to make sure that you have your finger on the pulse of the work.

Introducing the Continuous Forecasting Dashboard

Now, let’s put this strategy into action.

If we say that, hypothetically, our project started on Nov 1st, we have 44 items in our backlog, and we’ve committed to delivering the work before the 19th of April. That outcome comes with an 85% certainty of us meeting our goal.

Monte Carlo Delivery Date by Nave | Image 1

Now, I want to show you something that we call the Continuous Forecasting Dashboard.

Continuous Forecasting Dashboard by Nave | Image 1

Here we have the start date of our project, today’s date, as well as our planned release date: 19th April. The scope is 44 items and the probability % of us meeting our goal is initially set to 85%.

We also have the rest of the probabilities that come with our forecast – 30%, 50%, 70%, 85%, 95% and 98%.

This is our starting point. This is the initial forecast that we’ve made at the beginning of our project, on Nov 1st.

We’re going to track the Continuous Forecasting Dashboard every 2 weeks.

Monte Carlo Delivery Date by Nave | Image 2

From here on, we’re going to evaluate our progress on a regular basis. So, we start working on Nov 1st and as we deliver work, we want to apply the continuous forecasting strategy to reevaluate our initial commitment.

We run the simulation again on Nov 15th. We change the start date of the simulation to Nov 15th and extend the throughput date range to account for the work we’ve completed so far. Let’s say the remaining scope is 35 work items.

The simulation says that, based on our performance in the past 2 weeks, the probability that comes with our initial commitment has moved to 62% which means that we’re off schedule and the work is not moving smoothly through the process.

Now, if you observe that the probability of hitting your initial commitment is dropping down, this is a red flag and you need to act accordingly, in order to avoid delays.

You need to continuously reevaluate your forecast and take actions accordingly, in order to get back on track.

Continuous Forecasting Dashboard by Nave | Image 2

After a few weeks, your release tracking dashboard will look something like this.

We still have the start date of the project set to Nov 1st, let’s assume that today’s date is Jan 24th, and our initial target date is still Apr 19th. Here on the right side, you’ll see that as time passes, we keep delivering work and the remaining number of items goes down, the probability of our initial commitment changes as well.

On Nov 15th, it was 62%, on Nov 29th it went up to 73%, and today, the chance of hitting that target is already 95%. There are still 14 remaining tasks but the probability of delivering on time is really high now.

The most important thing is to stay on top of the progress you make and ensure you take action accordingly. In this instance, we have an example where your project goes off track. So, a solution could be to reduce your WIP limits, which would enable you to stop multitasking and focus on the outstanding work, a decision that will ultimately result in an increase in your throughput.

And even if the adjustments you make don’t pay off, you’ve become aware of the situation and you can communicate it to your customers or stakeholders early, so they can react on time. Acting like this builds credibility and shapes our reputation as professionals.

Here’s your action item: Go ahead and give the continuous forecasting dashboard a try right away. It’s free for 30 days, and no credit card is required

Use the dashboard to understand whether the risk you’ve taken is increasing or decreasing as you work through your project.

The Monte Carlo simulation is there at your disposal to provide real-time feedback on whether you will make it on time or you need to adjust your course accordingly to mitigate the risk of failure.

Take advantage of it! That is the most effective approach to managing realistic expectations.

I wish you a productive day ahead, and I’ll see you next week, same time and place for more managerial insights. Bye for now!

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