Do you hear people throwing around words like probabilistic and deterministic forecasting, and you aren't sure exactly what they mean? Well, I'm writing this blog post specifically for you. Spoiler alert: it has to do with uncertainty vs. certainty.

Forecasting is the process of making predictions based on past and present data *(**Wikipedia**)*. Historically the type of forecasting used for business planning was deterministic (or point) forecasting. Increasingly, however, companies are embracing probabilistic forecasting as a way to help understand risk.

## What is deterministic forecasting?

Just like fight club, people don't really talk about deterministic forecasting. It is just what they do, and they don't question it - at least until recently. I mean, if it is all someone knows, why would they even think to question it or explore the pros and cons? But what is it really?

Deterministic forecasting** **is when only one possible outcome is given without any context around the likelihood of that outcome occurring. Statements like these are deterministic forecasts:

It will rain at 1 P.M.

Seventy people will cross this intersection today.

My team will finish ten work items this week.

This project will be done on June 3rd.

For each of those statements, we know that something else *could* happen. But we have picked a specific possible outcome to communicate. Now, when someone hears or reads these statements, they do what comes naturally to humans... they fill in the gaps of information with what they want to be true. Usually, what they see or hear is that these statements are absolutely certain to happen. It makes sense. We've given them no alternative information.

So, the problem with giving a deterministic forecast when more than one possible outcome really exists is that we aren't giving anyone, including ourselves, any information about the risk associated with the forecast we provided. How likely is it truly to happen?

Deterministic forecasts communicate a single outcome with no information about risk.

If there are factors that could come into play that could change the outcome, say external risks or sick employees, then deterministic forecasting doesn't work for us. It doesn't allow us to give that information to others. Fortunately, there's an alternative - probabilistic forecasting.

## What is probabilistic forecasting?

A probabilistic forecast is one that acknowledges the range of possible outcomes and assigns a probability, or likelihood of happening, to each.

The image above is a histogram showing the range of possible outcomes from a Monte Carlo simulation I ran. The question I effectively asked it was "How many items we can complete in 13 days?" Now, there are a lot of possible answers to that question. In fact, each bar on the histogram represents a different option - anywhere from 1 to 75 or more.

We can, and probably should, work to make that range tighter. But, in the meantime, we can create a forecast by understanding the risk we are willing to take on. In the image above we see that in approx 85% of the 10,000 trials we finished at least 19 items in 13 days. This means we can say that, if our conditions stay roughly similar, there's an 85% chance that we can finish at least 19 items in 13 days. That means that there's a 15% chance we could finish 18 or less. Now I can discuss that with my team and my stakeholders and make decisions to move forward or to see what we can do to improve the likelihood of the answer we'd rather have.

Here are some more probabilistic forecasts:

There is a 70% chance of rain between now and 1 P.M.

There's an 85% chance that at least seventy people will cross this intersection today.

There's a 90% chance that my team will finish ten or more work items this week.

There's only a 50% chance that this project will be done on or before June 3rd.

Every probabilistic forecast has two components: a range and a probability, allowing you to make informed decisions.

## Which should I use?

To answer this question you have to answer another: Can you be sure that there's a single possible outcome or are there factors that could cause other possibilities? In other words, do you have certainty or uncertainty?

If the answer is certainty, then deterministic forecasts are right for you. However, that is rarely, if ever, the case. It is easy to give into the allure of the single answer provided by a deterministic forecast. It feels confident. Safe. Easy. Unfortunately, those feelings are an illusion. Deterministic forecasts are often created using qualitative information and estimates but, historically, humans are really bad at estimating. Our brains just can't account for all the possible factors. Even if you were to use data to create a deterministic forecast you still have to pick an outcome to use and often people choose the average. Is it ok being wrong half the time?

It is better to be vaguely right than exactly wrong.Carveth Read (1920)

If the answer is uncertainty (like the rest of us) then probabilistic forecasts are the smart choice. By providing the range of outcomes and the probability of each (or a set) happening, you give significantly more information about the risk involved with any forecast, allowing people to make more informed decisions. Yes, it's not the tidy single answer that people want but its your truth. Carveth Read said it well: "It is better to be vaguely right than exactly wrong."

Remember that the point of forecasting is to manage risk. So, use the technique that provides as much information about risk as possible.

## How can I get started?

First, gather data about when work items start and finish. If you're using work management tools like Jira or Azure DevOps then you are already capturing that data. With that information you can use charts and simulations to forecast how long it takes to finish a single work item, how many work items you can finish in a fixed time period, or even how long it can take you to finish a fixed scope of work. These are things we get asked to do all the time. You don't even need a lot of data. If you. have at least 10 work items, preferably a representative mix, then you have enough data to create probabilistic forecasts.

Once you have the data you need, tools like __ActionableAgile™️__ and __Portfolio Forecaster__ from 55 Degrees help you determine the forecast that matches your risk tolerance with ease. You can also use our tools to improve the predictability of your process. When you do that you are happier with your forecasts because you get higher probability with a narrower range of outcomes.

If you're interested in chatting with us or other users on this topic, __join us in our community__ and create a post! See you there!

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