When trying to make an important decision or plan strategies, it is imperative managers examine all of their options carefully. One tool they can use to do so is a decision tree. Decision trees are simply flowchart graphs or diagrams that help explore all of the decision alternatives and their possible outcomes. Every “branch” of the tree represents one of the possible options that are available when planning a strategy or making decisions.
The branches can be extended when one alternative outcome leads to another decision that must be made. Added into each branch are the costs associated with each choice and the probability each is likely to occur. With these numbers, managers can calculate the value of each set of branches to determine the best choice when planning business strategies.
A decision tree uses a graph or a model of decisions, their consequences (cost, time, etc.) and their possible outcomes. It is a useful method because it is fairly straightforward and easy to do, and gives the manager a graphic representation of the risks and rewards of following various options. To use the decision tree method requires decision making managers to be clear about the various alternative paths that are open to them, estimate what are the possible outcomes that can result from each of the alternative paths they choose, including a probability for each, and estimate a dollar value for each possible outcome. The real payoff of a decision tree comes from calculating the Expected Value for each of the possible decisions, and then comparing the possibilities to see which is the most lucrative.
It is important to remember that decision making techniques, including Decision Trees, are simply exercises to help managers and decision makers analyze a situation and make a decision. Since there are a lot of calculations involved in creating decision trees, many businesses use dedicated decision tree software to help them with the process. Decision tree software helps businesses draw out their trees, assigns value and probabilities to each branch and analyzes each option.
Decision tree software, both free and paid for versions, is available from a variety of vendors, including IBM, TreeAge, SmartDraw, Palisade, Angoss and Edraw. Researchers have shown that while humans excel at decision making based on knowledge of past events, they are poor at making decisions where the consequences have to be calculated based on a forward-looking evaluation of consequences — exactly the type of situation a decision tree addresses.
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Why Managers Make Use of Decision Trees in Planning Business Strategies
Managers and business owners sometimes want to either delay or totally avoid making decisions that have unpleasant emotional consequences, such as firing or reprimanding an employee, closing a failing business or taking decisive actions to shore up a business with a weak balance sheet and an impending cash squeeze. Researchers have also determined that when the decision making finally occurs for emotionally laden matters, decision makers work “harder, yet not smarter” in making these decisions, as they try to avoid explicit trade-offs between emotionally painful alternatives.
Decision trees are a wonderful decision- making tool but are not used often enough by small business owners. Business owners who can adopt them in their decision making have an opportunity to make value-added decisions that are analytically sound and avoid a series of proven biases that may be holding them back from making the best decisions for their businesses. Below are the various reasons why decision trees are useful to managers in this modern age.
A decision tree helps a manager focus on the relationship among various events and thereby, replicates the natural course of events, and as such, remains robust with little scope for errors, provided the inputted data is correct.
Agreeably, a decision tree is the best predictive model. It finds use to make quantitative analysis of business problems, and to validate results of statistical tests. It naturally supports classification problems with more than two classes and by modification, handles regression problems. Sophisticated decision tree models implemented using custom software applications can use historic data to apply a statistical analysis and make predictions regarding the probability of events. Decision trees provide a framework to quantify the values and probability of each possible outcome of a decision, allowing decision makers to make educated choices among the various alternatives.
A key benefit of the decision tree model is its transparent nature. Unlike other decision-making models, the decision tree makes clear and understandable all possible alternatives and traces each alternative to its conclusion in a single view, allowing for easy comparison among the various alternatives. The use of separate nodes to denote user defined decisions, uncertainties, and end of process lends further clarity and transparency to the decision-making process.
With a decision tree, a manager can clearly assign specific values to problem, decisions, and outcomes of each decision. This reduces ambiguity in decision-making. Every possible scenario from a decision finds representation by a clear fork and node, enabling viewing all possible solutions clearly in a single view. Incorporation of monetary values to decision trees help make explicit the costs and benefits of different alternative courses of action.
Other decision-making tools require comprehensive quantitative data, but decision trees remain flexible to handle items with a mixture of real-valued and categorical features, and items with some missing features. Once constructed, they classify new items quickly.
This decision strategy is the best predictive model as it allows for a comprehensive analysis of the consequences of each possible decision, such as what the decision leads to, whether it ends in uncertainty or a definite conclusion, or whether it leads to new issues for which the process needs repetition. It also allows for partitioning data in a much deeper level, not as easily achieved with other decision-making classifiers such as logistic regression or support of vector machines.
Easy to Use
Decision trees are also very easy to draw and use in planning strategies. The decision tree provides a graphical illustration of the problem and various alternatives in a simple and easy to understand format that requires no explanation. It carefully breaks down data in an easy to understand illustration, based on rules easily understood by humans and SQL programs. Decision trees also allow for classification of data without computation, can handle both continuous and categorical variables, and provide a clear indication of the most important fields for prediction or classification.
How Managers Can Successfully Use a Decision Tree in Planning Strategies
As a manager, you now understand what a decision tree is and why decision tree analysis can be so beneficial to your efforts and planning. Now, let’s take a look at the four steps you need to master to use decision trees successfully.
Identify Each of Your Options
Note that the very first step is to identify each of the options before you. Every idea or strategy has multiple roads to completion. Your initial job is to recognize each of them so that you can add them to your decision tree and make the wise choices about which to take and when. For instance, Austin manages a Cold Pressed Juice plant in Houston. Though they’ve only been in business for a few years, they’re growing rapidly and Austin needs to find a larger vendor to source fruits from. He identifies two legitimate options: a U.S. based farm that sits just a few hours away from Austin’s plant, and a vendor that operates overseas.
Forecast Potential Outcomes for Each Option
After each of the options has been realized, it is time to identify potential outcomes for each of them. This step isn’t full proof. You’ll need to make predictions and best-guesses and estimations — some of which could prove to be inaccurate. Note that this is okay! The point of this exercise is to identify the option with the highest probability of success. Returning to our previous example, Austin needs to decide whether to partner with a U.S. based farm or the one situated overseas. Both options present their fair share of risks and rewards. So, in order to make the right choice, Austin begins crafting a decision tree. Now, Austin needs to evaluate potential outcomes for each choice so that he can correctly predict which product vendor will best suit his growing company’s needs and budget. On the one hand, the U.S. based farmer will allow him to visit more often in person and check up on operations. But it is also the more expensive option. While on the other hand, the overseas vendor is much cheaper and Austin could use the money saved to improve other areas of her business. But there are downsides too. Austin won’t be able to make as many trips to see this vendor, there will be a language barrier, and shipping times will be longer. What’s the best option? Austin takes into account every bit of information he can get and estimates the probability of success for both paths. He then adds these details to his decision tree to help him make the best choice possible.
Thoroughly Analyze Each Potential Result
Here, you should have a full decision tree made. Congratulations! This is a big first step, but the hard work is just getting started. Now you need to analyze each potential result and assess which option will be the best fit for your unique business. If you’re working with monetary amounts, you can use the expected value (EV) formula. Expected value is found by multiplying a potential outcome by the likelihood that it will occur. For example, if you anticipate that your project will earn your company $1,000 and it has a 50% chance of success. Your EV score is 500. Meanwhile, back to Austin and his search for the right vendor. Based on his research, he predicts that working with a U.S. based vendor has an 80% chance of success and will produce a profit of $100,000. Using the formula described above, Austin gets an EV score of 80,000. But he also needs to run the calculations for failure. So Austin multiplies $20,000 by 20% and ends up with 4,000. Finally, he just needs to subtract the failure score from the success score to get his total EV, which is 76,000 for the U.S. based vendor. Austin goes through the same exact process for the overseas vendor and realizes that his EV, should he go that route, amounts to a score of 52,500. Based on EV alone, Austin’s best option is to work with the U.S. based vendor.
Optimize Your Actions Accordingly
The last step is to optimize your actions. Once you know which option provides the greatest chance of success for your project, as well as the one that presents the greatest value, you can confidently make project decisions. In Austin’s case, he decided to go with the U.S. based vendor. Not only did that vendor score a higher EV, but it also represents only $5,000 more dollars in losses should the relationship fail. Those two things plus the fact that Austin can visit the vendor regularly and they both speak the same language made it a very clear choice for him.
Decision tree analysis is an important strategy for managers to learn and utilize, especially when planning strategies. It will help evaluate every option and choose the ones with the highest probability of success. Decision trees are a wonderful decision- making tool but are not used often enough by small business managers. Business managers who can adopt them in their decision making have an opportunity to make value-added decisions that are analytically sound and avoid a series of proven biases that may be holding them back from making the best decisions for their businesses.
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