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:
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.
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:
The three options—or branches—you’re deciding between are:
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:
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.
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.
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.
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:
Chance nodes from this example:
End nodes from this example:
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.