Decision tree analysis involves visually outlining the potential outcomes, costs, and consequences of a complex decision. These trees are particularly helpful for analyzing quantitative data and making a decision based on numbers. In this article, we’ll explain how to use a decision tree to calculate the expected value of each outcome and assess the best course of action. Plus, get an example of what a finished decision tree will look like.
Have you ever made a decision knowing your choice would have major consequences? If you have, you know that it’s especially difficult to determine the best course of action when you aren’t sure what the outcomes will be.
Decision tree analysis can help you visualize the impact your decisions will have so you can find the best course of action. In this article, we’ll show you how to create a decision tree so you can use it throughout the project management process.
A decision tree is a flowchart that starts with one main idea and then branches out based on the consequences of your decisions. It’s called a “decision tree” because the model typically looks like a tree with branches.
These trees are used for decision tree analysis, which involves visually outlining the potential outcomes, costs, and consequences of a complex decision. You can use a decision tree to calculate the expected value of each outcome based on the decisions and consequences that led to it. Then, by comparing the outcomes to one another, you can quickly assess the best course of action. You can also use a decision tree to solve problems, manage costs, and reveal opportunities.
A decision tree includes the following symbols:
Alternative branches: Alternative branches are two lines that branch out from one decision on your decision tree. These branches show two outcomes or decisions that stem from the initial decision on your tree.
Decision nodes: Decision nodes are squares and represent a decision being made on your tree. Every decision tree starts with a decision node.
Chance nodes: Chance nodes are circles that show multiple possible outcomes.
End nodes: End nodes are triangles that show a final outcome.
A decision tree analysis combines these symbols with notes explaining your decisions and outcomes, and any relevant values to explain your profits or losses. You can manually draw your decision tree or use a flowchart tool to map out your tree digitally.Try Lucidchart integration with Asana
You can use decision tree analysis to make decisions in many areas including operations, budget planning, and project management. Where possible, include quantitative data and numbers to create an effective tree. The more data you have, the easier it will be for you to determine expected values and analyze solutions based on numbers.
For example, if you’re trying to determine which project is most cost-effective, you can use a decision tree to analyze the potential outcomes of each project and choose the project that will most likely result in highest earnings.
Follow these five steps to create a decision tree diagram to analyze uncertain outcomes and reach the most logical solution.
Begin your diagram with one main idea or decision. You’ll start your tree with a decision node before adding single branches to the various decisions you’re deciding between.
For example, if you want to create an app but can’t decide whether to build a new one or upgrade an existing one, use a decision tree to assess the possible outcomes of each.
In this case, the initial decision node is:
Create an app
The three options—or branches—you’re deciding between are:
Building a new scheduling app
Upgrading an existing scheduling app
Building a team productivity app
After adding your main idea to the tree, continue adding chance or decision nodes after each decision to expand your tree further. A chance node may need an alternative branch after it because there could be more than one potential outcome for choosing that decision.
For example, if you decide to build a new scheduling app, there’s a chance that your revenue from the app will be large if it’s successful with customers. There’s also a chance the app will be unsuccessful, which could result in a small revenue. Mapping both potential outcomes in your decision tree is key.
Keep adding chance and decision nodes to your decision tree until you can’t expand the tree further. At this point, add end nodes to your tree to signify the completion of the tree creation process.
Once you’ve completed your tree, you can begin analyzing each of the decisions.
Ideally, your decision tree will have quantitative data associated with it. The most common data used in decision trees is monetary value.
For example, it’ll cost your company a specific amount of money to build or upgrade an app. It’ll also cost more or less money to create one app over another. Writing these values in your tree under each decision can help you in the decision-making process.
You can also try to estimate expected value you’ll create, whether large or small, for each decision. Once you know the cost of each outcome and the probability it will occur, you can calculate the expected value of each outcome using the following formula:
Expected value (EV) = (First possible outcome x Likelihood of outcome) + (Second possible outcome x Likelihood of outcome) - Cost
Calculate the expected value by multiplying both possible outcomes by the likelihood that each outcome will occur and then adding those values. You’ll also need to subtract any initial costs from your total.
Once you have your expected outcomes for each decision, determine which decision is best for you based on the amount of risk you’re willing to take. The highest expected value may not always be the one you want to go for. That’s because, even though it could result in a high reward, it also means taking on the highest level of project risk.
Keep in mind that the expected value in decision tree analysis comes from a probability algorithm. It’s up to you and your team to determine how to best evaluate the outcomes of the tree.Read: The project risk management process in 6 clear steps
Used properly, decision tree analysis can help you make better decisions, but it also has its drawbacks. As long as you understand the flaws associated with decision trees, you can reap the benefits of this decision-making tool.
When you’re struggling with a complex decision and juggling a lot of data, decision trees can help you visualize the possible consequences or payoffs associated with each choice.
Transparent: The best part about decision trees is that they provide a focused approach to decision making for you and your team. When you parse out each decision and calculate their expected value, you’ll have a clear idea about which decision makes the most sense for you to move forward with.
Efficient: Decision trees are efficient because they require little time and few resources to create. Other decision-making tools like surveys, user testing, or prototypes can take months and a lot of money to complete. A decision tree is a simple and efficient way to decide what to do.
Flexible: If you come up with a new idea once you’ve created your tree, you can add that decision into the tree with little work. You can also add branches for possible outcomes if you gain information during your analysis.
There are drawbacks to a decision tree that make it a less-than-perfect decision-making tool. By understanding these drawbacks, you can use your tree as part of a larger forecasting process.
Complex: While decision trees often come to definite end points, they can become complex if you add too many decisions to your tree. If your tree branches off in many directions, you may have a hard time keeping the tree under wraps and calculating your expected values. The best way to use a decision tree is to keep it simple so it doesn’t cause confusion or lose its benefits. This may mean using other decision-making tools to narrow down your options, then using a decision tree once you only have a few options left.
Unstable: It’s important to keep the values within your decision tree stable so that your equations stay accurate. If you change even a small part of the data, the larger data can fall apart.
Risky: Because the decision tree uses a probability algorithm, the expected value you calculate is an estimation, not an accurate prediction of each outcome. This means you must take these estimations with a grain of salt. If you don’t sufficiently weigh the probability and payoffs of your outcomes, you could take on a lot of risk with the decision you choose.
In the decision tree analysis example below, you can see how you would map out your tree diagram if you were choosing between building or upgrading a new software app.
As the tree branches out, your outcomes involve large and small revenues and your project costs are taken out of your expected values.
Decision nodes from this example:
Build new scheduling app: $50K
Upgrade existing scheduling app: $25K
Build team productivity app: $75K
Chance nodes from this example:
Large and small revenue for decision one: 40 and 55%
Large and small revenue for decision two: 60 and 38%
Large and small revenue for decision three: 55 and 45%
End nodes from this example:
Potential profits for decision one: $200K or $150K
Potential profits for decision two: $100K or $80K
Potential profits for decision three: $250K or $200K
Although building a new team productivity app would cost the most money for the team, the decision tree analysis shows that this project would also result in the most expected value for the company.
You can draw a decision tree by hand, but using decision tree software to map out possible solutions will make it easier to add various elements to your flowchart, make changes when needed, and calculate tree values. With Asana’s Lucidchart integration, you can build a detailed diagram and share it with your team in a centralized project management tool.
Decision tree software will make you feel confident in your decision-making skills so you can successfully lead your team and manage projects.Try Lucidchart integration with Asana