WGU C207 Data Driven Decisions Decision Tree Analysis Scenario


  1. Business Question:

The business question to be applied in the presented scenario would be: What would be the most effective strategy for a Major Pharmaceutical Company (MPC) to develop drug lines considering the uncertain market conditions and potential payoffs?   By conducting a decision tree analysis, various action alternatives, such as developing a new drug or making adjustments to an existing one, can be evaluated to determine the base of the expected outcomes on probabilities and payoffs in the market research report. This analysis process helps in identifying the most suitable plan of action for MPC, given the unpredictable and risky nature of future events in the drug marketplace.

  • Relevant data values:

Table 1: Relevant Data values required for decision tree analysis.

 Favourable/ UnfavourableProbabilityPayoffs ( Demand * Profit per unit)Profit per unitDemand (units per month)
Developing new drugHighly favourable0.69$2,686.45$0.654133
Modification of existing drugHighly favourable0.61$4,294.29$0.775577
no changesHighly favourable0.77$571.59$0.87657
Developing new drugUnfavourable0.31$880.75$0.651355
Modification of existing drugUnfavourable0.39$1,471.47$0.771911
no changesUnfavourable0.23$224.46$0.87258
  • Analyzing the Data:
  1. Figure 1: Decision tree

Table 2: Calculation of Expected Values for Each Node

Calculate the Expected Value of each node.  
 State of Nature 
 Developing new drug 
Calculation for Expected Value (EV)$2,126.68 
(Payoff at highly favourable x probability) + (Payoff at unfavourable x probability)  
 State of Nature 
 Modification of  existing drug 
Calculation for Expected Value (EV)$3,193.39 
(Payoff at highly favourable x probability) + (Payoff at unfavourable x probability)  
 State of Nature 
 No changes 
Calculation for Expected Value (EV)$491.75 
(Payoff at highly favourable x probability) + (Payoff at unfavourable x probability)  
  • Why decision tree analysis is an appropriate analysis technique:

Decision tree analysis is a suitable technique for evaluating different decision alternatives in uncertain situations. The scenario provides probabilities and payoffs for different options, which can be incorporated into decision-making using this method (Slovic et al., 2005). Three active alternatives have been presented: developing a new drug, modifying an existing one or not making any changes. Decision tree analysis can evaluate these options and provide a recommended course of action based on calculated expected values which are a merger of profits and the probabilities of achieving them (Xia et al., 2017).

  • Implications of the decision tree analysis:
  1. The role of probabilities and demand for each branch;

In analyzing decision trees, probabilities and demand are vital factors in assessing the potential outcomes of each branch. Probability indicates the chances of a specific market condition, such as a highly favorable or unfavorable market, occurring. These probabilities help measure the uncertainty and risk associated with each branch (Slovic et al., 2005). In this case, a branch refers to the course of action the company should take to remain competitive, such as developing a new drug, modifying an existing one, or maintaining the status quo. Meanwhile, demand represents the estimated quantity of drug units each market condition will demand. It provides insight into each decision alternative’s potential market size and revenue generation.

  1. Determining the expected value of each node based on payoffs:

In a decision tree developed for this scenario, the expected value of each node is determined by multiplying payoffs by their probabilities of favorable and unfavorable scenarios, and then adding them up. This helps compare decision alternatives and choose the one with the highest expected payoff, considering uncertainties and potential benefits.

  1. Limitations of:
  • Limitation of the data elements:

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