Decision trees are a pivotal tool in the realm of business decision-making, providing a structured and systematic approach to evaluating the consequences of various actions under conditions of uncertainty. This analytical framework aids businesses in navigating the complexities of decision-making by quantifying potential outcomes, incorporating the likelihood of their occurrence, and calculating expected values to guide strategic choices. This detailed exposition aims to elucidate the components and application of decision trees in business, enriched with an industry example, to offer IB Business & Management students comprehensive insights into this critical decision-making tool.
Components of Decision Trees
Decision Points
Overview: Decision points are junctures at which a business must choose between multiple courses of action. These are graphically represented by squares and signify the start of different branches that illustrate potential strategies or actions.
Chance Nodes
Overview: Following a decision point, the tree branches out into chance nodes, depicted by circles. These nodes represent the uncertain outcomes associated with each decision. Each branch from a chance node illustrates a possible result of the decision made at the preceding decision point.
Probability or Chance
Overview: Each outcome branch extending from a chance node is assigned a probability value, indicating the likelihood of that particular outcome occurring. Probabilities are expressed in decimal form, and the sum of probabilities for all possible outcomes of a decision or chance node must equal 1, ensuring that all potential scenarios are accounted for.
Expected Values
Overview: Expected values quantify the financial outcomes associated with each decision under consideration. They are calculated by multiplying the financial value of each outcome by its probability and summing these products for all outcomes associated with a decision. Expected values enable decision-makers to compare the potential financial implications of different actions quantitatively.
Application of Decision Trees in Business Decision-Making
Decision trees are utilized across various business domains, including marketing, finance, operations, and strategic planning, to evaluate decisions involving investments, product launches, market entry, and other scenarios characterized by uncertainty.
Industry Example: Pharmaceutical Company’s Drug Development Decision
Consider a pharmaceutical company faced with the decision of whether to proceed with the development of a new drug. The development process involves significant investment and uncertainty regarding regulatory approval and market acceptance.
Decision Points:
- Proceed with development.
- Halt development.
Chance Nodes and Probabilities:
- If the company proceeds, there are two possible outcomes: regulatory approval (probability of 0.6) and regulatory rejection (probability of 0.4).
- If approved, there are further outcomes based on market acceptance: high market acceptance (probability of 0.7) and low market acceptance (probability of 0.3).
Expected Values Calculation:
- The expected financial return from proceeding with development and achieving regulatory approval and high market acceptance might be calculated as follows:
- Investment cost: -$100 million.
- Revenue in the case of high market acceptance: $300 million.
- Expected value of proceeding with development: (0.6 probability of approval) * [(0.7 probability of high acceptance * $300 million revenue) + (0.3 probability of low acceptance * $100 million revenue)] – $100 million investment cost.
This simplified example illustrates how decision trees enable businesses to break down complex decisions into manageable components, assess the financial implications of various scenarios, and make informed choices by considering both the probabilities of different outcomes and their associated financial impacts.
Steps to completing a decision tree
- Based on the information given to you, draw the layout of the tree diagram.
- Fill in the nodes with numbers.
- Write the forecasted costs on the first branch.
- Write the successful revenue and the unsuccessful revenue on each final branch.
- Write the probability of success or not success on each final branch.
- Complete the calculations for each strand:
(probability of success × forecast revenue if successful) +
(probability of not success × forecast revenue if not successful) − forecast costs. - The strand with the highest expected earnings is the one that yields the highest forecasted returns.
- Based on your choice, put a line through the strands not taken.
- Draw a key, this is necessary for full marks.
- Label your decision tree.
Advantages
Visualization of Decision Paths and Outcomes
One of the foremost benefits of decision trees is their ability to visually map out various courses of action and their potential outcomes. This graphical representation can be instrumental in revealing strategic alternatives that may not have been initially considered by decision-makers.
Advantages:
- Clarity in Decision-Making: The visual layout of a decision tree simplifies complex decision scenarios, making it easier for management to understand the sequence of decisions and potential outcomes.
- Identification of New Opportunities: By mapping out different paths, decision trees can highlight alternative strategies or actions that hadn’t been previously contemplated, offering a broader perspective on strategic options.
Numerical and Financial Modeling
Decision trees go beyond mere visual aids by incorporating numerical values into the decision-making process. This quantitative aspect involves assigning probabilities to different outcomes and attaching financial values to each path, leading to a more objective and financially sound decision-making process.
Advantages:
- Quantitative Analysis: Attaching numerical values to decisions and outcomes allows for a more objective analysis, reducing the subjectivity that often clouds complex decision-making.
- Profit Maximization: By calculating the expected values of different courses of action, decision trees help ensure that the most financially advantageous path is chosen, aligning decisions with the goal of profit maximization.
Industry Example: ABC Ltd.’s Advertising Campaign Decision
Consider ABC Ltd., a company facing a decision regarding its advertising strategy. The management is deliberating whether to continue with the current advertising campaign, launch a new campaign, or modify the existing one.
- Decision Points: Continue with the current campaign, launch a new campaign, or modify the existing campaign.
- Chance Nodes: For each decision point, there are outcomes related to market reception, ranging from high acceptance to low acceptance, with assigned probabilities.
- Expected Values: Financial returns are estimated for each outcome, allowing ABC Ltd. to quantify the expected profitability of each advertising strategy.
Through constructing a decision tree, ABC Ltd.’s management visualizes the different strategies and their outcomes. This process reveals the option of modifying the current campaign as a potential course of action, previously unconsidered. By attaching probabilities and financial estimates to each outcome, the decision tree enables ABC Ltd. to quantitatively assess the profitability of continuing, launching a new, or modifying the advertising campaign. Ultimately, the decision tree guides ABC Ltd. toward the most profitable choice, demonstrating the decision tree’s utility in combining visual strategy mapping with numerical analysis to enhance business decision-making.
Disadvantages
Reliance on Estimated Figures
Overview: Decision trees often require input data in the form of estimated figures for probabilities, costs, and revenues associated with different outcomes. These estimates, while necessary for quantitative analysis, introduce a level of uncertainty that can affect the accuracy of the decision-making process.
Challenges:
- Subjectivity in Estimates: The process of estimating probabilities and financial figures can be highly subjective, influenced by the decision-maker’s biases or incomplete information.
- Risk of Misestimation: Incorrect or overly optimistic/pessimistic estimates can lead to misleading conclusions, potentially guiding businesses toward suboptimal decisions.
Inability to Account for Dynamic Business Environments
Overview: Decision trees, by their nature, provide a static analysis based on the information available at the time of their creation. They may not effectively capture the dynamic changes in the business environment, such as economic fluctuations, market trends, or competitive actions, which can significantly impact the validity of the decision analysis.
Challenges:
- Lack of Flexibility: Once a decision tree is constructed, it does not automatically adjust to reflect changes in the external environment, such as economic downturns or shifts in consumer behavior.
- Need for Continuous Revision: To remain relevant, decision trees require frequent updates and revisions, which can be time-consuming and may not be feasible for fast-paced business decisions.
Industry Example: The Impact of COVID-19 on the Hospitality Industry
Consider the case of a global hotel chain planning its expansion strategy using a decision tree analysis. The decision tree might have outlined various expansion options, such as opening new hotels in targeted cities, renovating existing properties, or enhancing digital marketing efforts to attract more guests, with estimated probabilities and financial outcomes attached to each option.
However, the sudden onset of the COVID-19 pandemic and the resulting travel restrictions and economic downturn rendered many of these estimates obsolete:
- Demand Shift: The decrease in global travel demand drastically affected the hotel chain’s occupancy rates and revenue projections, invalidating the original financial estimates used in the decision tree.
- Economic Climate Change: The economic crisis required a complete reassessment of the expansion strategy, taking into account the reduced demand, increased operational costs due to health and safety measures, and the uncertain duration of the pandemic’s impact.
This scenario illustrates how decision trees, while useful for initial planning, might fail to account for sudden, significant changes in the economic climate, necessitating the creation of a new decision tree based on revised estimates and assumptions.
Example:
