How can decision trees help you make complex decisions?

It’s a tool that places present value cash flow values on various decision outcomes to show how investment decisions are contingent upon one another. Each tree branch shows a potential outcome as adjusted by probability weightings. Risk-adjusted cash flow values are arrived at by applying an appropriate discount rate. In addition, decision trees are more valuable when kept simple, as multiple branches might make the model difficult to interpret.

Decision trees act like a roadmap for complex choices, breaking them down into step-by-step, visual extensions. Imagine a flowchart with each fork representing a decision point and each path leading to a possible result. By following these offshoots, you systematically consider all possibilities, assess risks and rewards, and ultimately choose the course with the brightest outcome. It's like having a crystal ball for your decision-making, bringing clarity and confidence to even the most intricate quandaries.

A decision tree is a way of communicating potential options and outcomes and the steps required to achieve those outcomes. Each node in a decision tree represents a step, action or consideration. A branch is created when more than one possible choice exists. Each unique path through the decision tree outlines a distinct possible outcome and the steps required to achieve that outcome. Depending on the implementation, a decision tree can be as simple as a non-calculating way to visualize or assess potential outcomes. On the other extreme, new generation FP&A tools use decision-trees as a simplified UI/UX representation layered on top of more complex cause-and-effect financial calculations.

In finance, decision trees assist by structuring complex financial decisions. They help analyze investment options, assess risks, and guide strategic financial planning. The visual representation simplifies intricate scenarios, aiding in informed and strategic financial decision-making.

I love structure. Even if you don’t, having a process rather than starting with a blank page always feel better. A decision tree just gets you to think about the what ifs.

We often use decision trees as a problem-solving tool with out clients. Having completed the problem statement, we create HOW-trees by breaking the issue down into potential solution options. This is closely related to the 5xWHY approach. Ideally, you can create a MECE structure - with solution elements being mutually exclusive, collectively exhaustive (no gaps, no overlaps). This allows you to prioritize between competing solution options.

Constructing a decision tree can be a hand-drawn exercise, using a whiteboard in a collaborative environment, or using a whole host of digital software tools, ranging from tools like Canva or Excalidraw, to flowcharting tools like Lucidcharts or Visio, to calculating decision tree tools like whatifi, Excel or Tableau. It is best to approach the construction of a decision tree thinking about cause-and-effect sequencing. Create nodes for each step. Draw arrows or connectors to the next step. When the next step has more than one possible choice or option, create a branch. Each branch represents a new possible series of steps that results in a new end outcome.

It’s always best to know the expected outcome and begin by drawing plans that possibly lead to the end goal. You have to be strategic in calculating your expected results.

It comes down to better and more informed decision-making. Corporate Finance professionals can use decision trees to illustrate competing approaches or investments to their business counterparts. An example: "How might we improve the performance of our machines on the production line?" A decision tree related to this question could have different branches related to: - Utilizing existing machines (production planning) - Improving existing machines (repairs etc.) - Acquisition of new machines (replacement) By decomposing the problem into sub-components, Finance professionals can help their business stakeholders make more informed decisions.

Decision trees are the most popular tool in financial strategy brainstorming as they break down all alternatives and simplify comparisons for all stakeholders. e.g.; 1. Corporate Restructuring 2. Fundraise 3. CapEx decisions 4. Investing 5. Scale up/down decisions 6. Inhouse/ Outsource Each time we decide on the above, we have multiple alternatives to choose from and multiple ways to proceed. Decision trees help us understand, explain, and build consensus on the best option.

One thing is it helps in structured analysis. A step by step process to achieving the end goal ensuring that one is making an informed decision. Second, risk assessment. Using decision tree is also to assess risk and make provision suitable for such risk.

Despite their usefulness in some circumstances, Decision trees present some limitations: They work well only under mutually exclusive decisions. It is challenging to assign probability affected by exogenous factors. They do not consider the firm's risk approach enough; in some cases, a higher EMV comes with a broader range of possible outcomes, including negative EMV. A decision tree is often an oversimplified model of reality that works well under binary, mutually exclusive scenarios.

Decision trees can assist a business analyst in complex decision-making by providing a structured framework to evaluate options. They visually represent choices, potential outcomes, and decision criteria, aiding in understanding complex scenarios. Through a series of yes/no questions, decision trees help analyze factors influencing decisions, facilitating a systematic approach. This visual clarity enhances communication with stakeholders, fostering consensus. Additionally, decision trees enable the identification of key variables impacting outcomes, aiding in risk assessment and strategy refinement.