WGU C207 Task 2 Decision Tree | Data-Driven Decision Making.
MPC Drug Line Development Report
A1. Business Question
MPC has engaged Drug Markets Analysts Inc. (DMA) to analyze the competitive pharmaceutical
landscape. Given the emergence of new FDA-approved competitor drugs, MPC must determine the
most profitable action. The three available options include:
1. Developing a new drug line (exploration)
2. Modifying the existing drug for new FDA-approved applications (exploitation)
3. Continuing with the current drug line without changes
The business question is: Given market probabilities and profit per unit, does investing, changing
strategy, or doing nothing yield the highest expected return on investment for MPC’s drug options
according to the competitive pharmaceutical analysis by DMA?
A2. Justification for a Business Tree Analysis
Decision tree analysis is an effective approach for this scenario because MPC must evaluate multiple
decision paths under conditions of uncertainty. Given the competitive pharmaceutical landscape and the
availability of market research data with probabilities and projected payoffs, a decision tree allows for a
structured assessment of potential outcomes (Ryder et al., 2013). Key reasons why decision tree analysis
is appropriate in this scenario include:
Managing Uncertainty: The decision involves distinct points and uncertain market conditions.
A decision tree enables MPC to systematically evaluate each path, considering potential
consequences and identifying the most optimal course of action.
Visualizing Decision Paths and Outcomes: The decision tree clearly represents possible
strategies for developing a new drug, modifying the existing drug, or continuing with the
current product, along with their respective probabilities and payoffs. This visualization helps in
making a well- informed recommendation.
Assessing Risks and Rewards: By incorporating probabilities of success and failure, the
decision tree allows MPC to weigh potential risks and rewards associated with each option,
ensuring a balanced and data-driven decision.
Calculating Expected Values: Assigning numerical values to different outcomes and computing
expected values (EV) helps MPC determine which action offers the highest potential return.
This quantitative approach ensures the decision is based on measurable financial implications
rather than intuition alone.
Given these factors, decision tree analysis provides MPC with a structured, data-driven framework to
evaluate its options, mitigate risks, and maximize profitability in a competitive market (Ryder et al.,
2013).
B. Relevant Data Values
DMA has provided market research indicating the probability of success and projected demand in
favorable and unfavorable market conditions. The key values are:
, C. Data Analysis Using Decision Tree
Decision tree analysis was conducted to determine the most financially viable option. The EV was
calculated using the formula:
EV = (Favorable Payoff * Favorable Probability) + (Unfavorable Payoff * Unfavorable Probability)
This visualization allows for a clearer comparison of the financial implications of each decision:
MPC Drug Line Development Report
A1. Business Question
MPC has engaged Drug Markets Analysts Inc. (DMA) to analyze the competitive pharmaceutical
landscape. Given the emergence of new FDA-approved competitor drugs, MPC must determine the
most profitable action. The three available options include:
1. Developing a new drug line (exploration)
2. Modifying the existing drug for new FDA-approved applications (exploitation)
3. Continuing with the current drug line without changes
The business question is: Given market probabilities and profit per unit, does investing, changing
strategy, or doing nothing yield the highest expected return on investment for MPC’s drug options
according to the competitive pharmaceutical analysis by DMA?
A2. Justification for a Business Tree Analysis
Decision tree analysis is an effective approach for this scenario because MPC must evaluate multiple
decision paths under conditions of uncertainty. Given the competitive pharmaceutical landscape and the
availability of market research data with probabilities and projected payoffs, a decision tree allows for a
structured assessment of potential outcomes (Ryder et al., 2013). Key reasons why decision tree analysis
is appropriate in this scenario include:
Managing Uncertainty: The decision involves distinct points and uncertain market conditions.
A decision tree enables MPC to systematically evaluate each path, considering potential
consequences and identifying the most optimal course of action.
Visualizing Decision Paths and Outcomes: The decision tree clearly represents possible
strategies for developing a new drug, modifying the existing drug, or continuing with the
current product, along with their respective probabilities and payoffs. This visualization helps in
making a well- informed recommendation.
Assessing Risks and Rewards: By incorporating probabilities of success and failure, the
decision tree allows MPC to weigh potential risks and rewards associated with each option,
ensuring a balanced and data-driven decision.
Calculating Expected Values: Assigning numerical values to different outcomes and computing
expected values (EV) helps MPC determine which action offers the highest potential return.
This quantitative approach ensures the decision is based on measurable financial implications
rather than intuition alone.
Given these factors, decision tree analysis provides MPC with a structured, data-driven framework to
evaluate its options, mitigate risks, and maximize profitability in a competitive market (Ryder et al.,
2013).
B. Relevant Data Values
DMA has provided market research indicating the probability of success and projected demand in
favorable and unfavorable market conditions. The key values are:
, C. Data Analysis Using Decision Tree
Decision tree analysis was conducted to determine the most financially viable option. The EV was
calculated using the formula:
EV = (Favorable Payoff * Favorable Probability) + (Unfavorable Payoff * Unfavorable Probability)
This visualization allows for a clearer comparison of the financial implications of each decision:
