Decision tree analysis

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Abstract

In every persons personal and professional life, they are always faced with decisions. These decisions are both small and large and can have great impact on organizations and their goals. Making the “right” decision can often be difficult, which is why numerous tools have been developed over the years to help with this. One of those tools is decision tree analysis.

Decision tree analysis is a method that uses graphical representation to show the decision-making process given certain conditions. It’s most often used to determine the optimal decision based on data and to estimate the potential consequences of a decision when faced different circumstances. The history of decision tree analysis is complex, with several mathematicians and others developing it over time. However, in the industrial environment, decision making usually involves analysing the data to reach a conclusion. Decision tree analysis has, over the years, become increasingly more used in real life, and numerous case studies show its usefulness in improving efficiency in organizations and customer satisfaction. A common use of decision tree analysis is to help with project-selection, which is a necessary part of decision-making process. A number of different instances call for the use of decision tree analysis, and the procedure has bee shown to provide important benefits in a lot of different instances. [1]

Decision tree analysis can be used in project, program, and portfolio management to help with the decision-making process in relation to project selection, prioritization as well as risk management. Decision tree analysis can assist managers and other people responsible for making decisions in determining the best course of action and effectively allocate resources by examining many scenarios and potential outcomes.

Overview

Decision trees are structures, similar to flowcharts, and are often used in decision analysis for both visual and analytical support tool. When competing options, based on their projected values, decision trees are particularly helpful. In the decision tree, there are nodes which represent different types of events or actions. Connected to the nodes are branches which represent all the possible outcomes for each event or action. In decision trees, there are typically three different types of nodes:[2]

  • The decision node
    • These are used to represent the decision that need to be made based on the information at hand.
    • Typically represented as a square
  • The chance node
    • These are used to represent events that are out of control, for example a coin toss
    • Typically represented as a circle
  • The end node
    • Used to represent the final outcome of the whole decision-making progress
    • Typically represented as a triangle

Decision trees in project management

Using decision tree analysis can be a powerful tool that project managers can use to help them make informed decisions in relation to project management. This method can add value to projects, with thorough analysis of all available options and their possible risks.

There are many ways that decision tree analysis can be used in project management, for example project selection, risk management, resource allocation and project execution.[1] Using it for project selection can involve comparing potential value for each of the options with regards to profits, costs and risks. This allows project managers to make informed decisions and select the project with the most potential to add value to their companies and organizations. An example of this is a business can use decision tree analysis to decide which project to invest in by comparing the expected profit for the different projects.

In risk management, decision tree analysis is useful to identify and assess threats at the same time, make actions to mitigate those threats. By conducting analysis of all the various risk scenarios with the use of decision trees, project managers are able to prioritize the biggest and most critical risks and develop strategies to mitigate them and increase the likelihood of a successful project. This method helps to reduce the negative impacts of risks that can affect the outcome of projects and increase the likelihood of meeting the project objectives. [3]

Another category where decision trees can be applied in project management is resource allocation. Using decision trees allows project managers to evaluate various scenarios with different resource allocation while analysing the expected value of each option as well as the risks involved. This helps project managers in allocating resources to the right places to maximize the value of projects and minimizing the risks.

Lastly, it’s possible to use decision tree analysis to compare various options of project execution. There are often different ways of executing projects, some strategies are more aggressive while others are more conservative. Decision tree analysis can be used to compare the costs involved with these strategies and the potential profits to determine which strategy is the best choice to add the most value to the project.

How to create a decision tree

Creating a decision tree is fairly simple, there are five main steps involved: [4] 

1. What decision has to be made

Begin the decision tree analysis with one idea, or decision that has to be made. That will be the decision node and from that add branches for the different options that have to be chosen. For example, if a project manager is considering three different options for a new software development project. The three different options are:

  • Develop a custom software solution in-house
  • Outsource the software development to a third-party vendor
  • Buy and customize an off-the-shelf software solution 
2. Add chance and decision nodes

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. After the initial idea, the next step is to add chance and decision nodes after every decision to expand the tree. Chance nodes might need more branches after them since there can be more than one outcome. In the example mentioned, that decision tree could start with the initial question: "What is the budget for the project?" and have two branches: one for a budget that is low, and one for a budget that is high. Each branch would then have three sub-branches corresponding to each of the three options for software development. The branches for the high- and low budget could have the same decision nodes, but the other one might be vastly different, since other factors such as cost, timeline, quality and risks differ a lot between low- and high budget projects. 

3. Expand until you reach end points

To create a comprehensive decision tree, continue adding chance and decision nodes until the tree can no longer be expanded. When the tree is fully developed, add end nodes to show that the decision tree creation process is finished. With the completed decision tree, you can start analysing each of the decisions, evaluating their potential outcomes and associated risks. 

4. Calculate tree values

In an ideal scenario, decision trees should have quantifiable data associated with them. The most common data used in decision trees is monetary value, where costs and expected returns are taken into consideration to make the final decision. For instance, outsourcing the software development project has different costs and profits than developing it in house. Quantifying these values under each decision can help in the decision-making process.

Moreover, it is also possible to estimate the expected value of each decision, which can be helpful in selecting the best possible outcome. Expected value is calculated based on the likelihood of each possible outcome and its associated cost. This calculation can be performed using the following formula: Expected value (EV) = (First possible outcome x Likelihood of outcome) + (Second possible outcome x Likelihood of outcome) - Cost

In order to calculate the expected value, multiply both possible outcomes by the likelihood that each outcome will occur, and then add those values. Finally, subtract any initial costs from the total value to get the expected value of each outcome. This can help to identify the most cost-effective and beneficial option among the various available choices in a decision-making process. 

5. Evaluate outcomes

After calculating the expected values for each decision in the decision tree, the next step is to evaluate the level of risk option has. It is necessary to understand that the decision with the highest expected value may not necessarily be the best option. The level of risk associated with the decision should also be taken into account.

Risk is an inherent aspect in project management, and each decision has its own level of risk. Therefore, it is crucial to evaluate the risk level associated with each decision to determine if it aligns with the project's goals and objectives. If a decision has a high expected value but also comes with high project risk, it may not be the best choice for the project.

Application

Real life examples

Finance: In the banking and financial sector, decision tree analysis is used for credit scoring, risk management, and fraud discovery [5] [6].

Medicine: decision tree analysis is used to make clinical decisions, diagnose diseases, and determine the fate of patients [7] [8].

Marketing: Customer segmentation, marketing targeting, and product recommendation all use decision tree analysis [9] [10].

Engineering: In production and engineering, decision tree analysis is used for fault diagnosis, quality control, and process optimization [11] [12].

Environmental science: decision tree analysis is used in species distribution modeling, land use planning, and environmental impact evaluation [13] [14].

Limitations

Overfitting: Decision trees are susceptible to overfitting, which happens when the model interprets training data as noise rather than the underlying pattern [15] [16].

Instability: Decision trees are sensitive to small changes in the data or model parameters and can be unstable. This can lead to various tree structures or predictions[17] [18].

Bias: Decision trees may be biased toward factors with a high complexity or number of categories, which could lead to an over- or under-representation of particular categories [19] [20].

Interpretability: When dealing with big or complex trees that have numerous branches or nodes, decision trees can be challenging to understand and interpret [21] [22].

References

  1. ↑ 1.0 1.1 Tewari, Puran & Mittal, Kapil & Khanduja, Dinesh. (2017). An Insight into “Decision Tree Analysis”. World Wide Journal of Multidisciplinary Research and Development. 3. 111-115.
  2. ↑ Kamiński, B., Jakubczyk, M. & Szufel, P. A framework for sensitivity analysis of decision trees. Cent Eur J Oper Res 26, 135–159 (2018). https://doi.org/10.1007/s10100-017-0479-6
  3. ↑ Prasanta Kumar Dey (2012) Project risk management using multiple criteria decision-making technique and decision tree analysis: a case study of Indian oil refinery, Production Planning & Control, 23:12, 903-921, DOI: 10.1080/09537287.2011.586379
  4. ↑ What is decision tree analysis? 5 steps to make better decisions (2021, December 10). Asana. Retrieved from https://asana.com/resources/decision-tree-analysis.
  5. ↑ Kohavi, R., & Provost, F. (1998). Glossary of terms. Machine learning
  6. ↑ Tang, F., Zeng, G., Deng, L., Huang, G., Li, X., & Wang, X. (2015). Decision tree models for effective credit scoring in peer-to-peer online microloan platforms. Decision Support Systems
  7. ↑ Chen, J., Guo, Y., Li, S., Li, J., & Li, J. (2019). A decision tree approach to predicting the survival of gastric cancer patients. Journal of Cellular Biochemistry
  8. ↑ Leite, F. N., Oliveira, C. A., Cunha, A. M., Körbes, D., Fumagalli, F., & Leite, J. S. (2018). Decision trees for predicting breast cancer recurrence using clinical data. Expert Systems with Applications
  9. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named Kotsiantis
  10. ↑ Verbeke, W., Dejaeger, K., Martens, D., Hur, J., Baesens, B., & Vanthienen, J. (2014). A novel profit-based classification model for customer base analysis
  11. ↑ Al-Marwani, A., Ramachandran, M., & Subramanian, R. (2020). A review on the application of decision tree and random forest algorithms in engineering. Journal of Advanced Research in Dynamical and Control Systems
  12. ↑ Chen, G., Gao, X., & Li, C. (2015). Decision tree-based quality control for ultrasonic welding of lithium-ion battery. Journal of Materials Processing Technology.
  13. ↑ Figueiredo, R. O., Rocha, J. C. V., & Tavares, R. A. (2019). Decision tree models for environmental impact assessment. Environmental Modelling & Software
  14. ↑ Pu, J., Tang, Q., & Yao, X. (2020). A comparative study of decision tree algorithms for modeling the spatial distribution of forest soil nutrients. Science of The Total Environment
  15. ↑ Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer
  16. ↑ Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann
  17. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named Breiman
  18. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named Hastie
  19. ↑ James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer
  20. ↑ Zadrozny, B., & Elkan, C. (2002). Transforming classifier scores into accurate multiclass probability estimates. Journal of Machine Learning Research
  21. ↑ Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
  22. ↑ Lakkaraju, H., Bach, S. H., & Leskovec, J. (2016). Interpretable decision sets: A joint framework for description and prediction. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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