Decision Tree Analysis
Data- Driven Decision Making
Rolanda Martin
Western Governors University
Mr. Tony Pineda
C207 Data – Driven Decision Making
May 15, 2024
Decision Tree Analysis
Market Research Report
Today’s pharmaceutical world is changing. As Chief Operating Officer of Major Pharmaceutical Company (MPC), I deliver a complete market research report to help formulate MPC’s drug line development strategy. Our focus revolves around two key alternatives: developing a new drug (exhibition) or modifying an existing drug (exploitation of new applications). Market conditions are decisive, and our analysis examines both favorable and unfavorable cases. In a real ideal market, our new drug line has a 69% probability of success with a sale of 4133 units per month, and the old drugs have already achieved a probability of 69% (DiMasi, 2020). If the status quo is maintained, there is a 77% probability of success with an order for 657 units.
Meanwhile, in an undesirable market, the demand for a new drug line is 1355 units, the existing drug line is at 1911 units, and simply maintaining the current strategy means a demand of 258. The new drug is $0.65 per unit, the existing drug has new uses but the same price, $0.77, and the current drug is at its present price of $0.87 per unit. In this report, decision tree analysis is used to suggest a set of actions with values (probabilities and payoffs) estimated under different conditions. Limitations identified include uncertainties regarding data and potential shortcomings of the decision tree analysis method. These recommendations will give MPC a clear direction of how to go based on our business and market conditions.
Business Question and Applied Decision Tree Analysis
The central business question derived from the scenario is: Is there a single decision alternative offering the highest expected financial outcome today, given current market research? Should it be new drug development, expanding the established drug line or continuing with existing levels of production and sales? A decision tree analysis was used to answer that question. For each alternative, the probabilities, payoffs and profits under favorable and unfavorable market conditions were systematically worked out. These Expected Monetary Values (EMVs) calculated for each decision alternative form a strategic basis for recommending the most financially feasible course of action (Gupta et al., 2017).
Relevant Data Values Required for Decision Tree Analysis
Relevant data provided in the scenario included every alternative, the probability of a favorable or unfavorable market. For every alternative, there was the expected demand in a favorable and unfavorable market, as well as the profit per unit. Having this information, it is now easy to calculate the EMV.
Favorable Market
|
Alternative
|
Probability
|
Demand
|
Profits
|
Payoff
|
Develop a new drug line
|
69
|
4133
|
0.65
|
2686.45
|
Expand existing drug line
|
61
|
5577
|
0.77
|
4294.29
|
Continue existing line
|
77
|
657
|
0.87
|
597.69
|
Unfavorable Market
|
Develop a new drug line
|
31
|
1355
|
0.65
|
880.75
|
Expand existing drug line
|
39
|
1911
|
0.77
|
1471.47
|
Continue existing line
|
23
|
258
|
0.87
|
224.46
|
Decision Tree
The decision tree analysis tool is used to compare several options and different decisions, with the aim of making a judgment on what course to take based on the results of comparing all possible alternatives. For this case, MPC should consider three alternative options and take the best one. It should consider either developing a new drug line, expanding the existing drug line or continuing with the existing line. Using this kind of analysis tool, it is possible to compare all these options and their respective outcomes. Based on this comparison, we have a chance to choose the option that has the highest positive answer.
Implications of Decision Tree Analysis
Develop a New Drug Line
Probabilities and Demand – Under a probability of 69% favorable market conditions, demand for the new drug line is estimated to be 4133 units per month. On the other side, a 31% vesting probability for an unfavorable market condition lowers demand to 1355 units.
Expected Value Determination – The decision tree analysis yields an expected monetary value (EMV) of $2124.33 for developing a new drug line. Thus, given the uncertainties of market conditions, this choice works out to a positive expected financial bottom line.
Expand Existing Drug Line
Probabilities and Demand – According to the expectation of a favorable market (61% probability), demand for the existing drug line expansion in one month is estimated at 5577 units. If the market turns unfavorable (about a 39% chance), demand drops to 191 units.
Expected Value Determination – If expanding the existing drug line is an option, then EMV is $3194.04. Based on decision tree analysis, compared with other options, this alternative has a higher expected financial outcome.
Continue Existing Line
Probabilities and Demand – When there is a favorable market, the probability of success associated with continuing the drug line is 77%. In such cases, demand will reach 657 units. For an unfavorable market (23% probability), demand drops to 258 units.
Expected Value Determination – The EMV for continuing the current drug line is $512.56. This option has a lower expected financial outcome than the other choices, but it is stable and low-risk.
Limitations
Though it is effective, decision tree analysis has its limitations. One problem is that the accuracy of data elements depends on individual interpretation. Where probabilities, payoffs, or profits are based on unreliable or outdated information, the analysis could produce misleading results. Moreover, decision tree analysis relies on the assumption that events are independent of one another. However, in real-life business problems, this is only sometimes the case. There are also political, social, short-term, and long-term consequences of making business decisions. The decision tree analysis only looks at one-dimensional profit potential (Pauker & Kassirer, 2019; Rennane et al., 2021).
Recommendations
The decision to expand the existing drug line is recommended since it gives the largest EMV of $3194.04. This decision reflects Major Pharmaceutical Company’s desire to strive for maximum profit while attaining a balanced risk-return ratio (Charbuty & Abdulazeez, 2021). The expansion strategy seeks to take advantage of market opportunities and ride on proven success. There is also an option for developing a new drug line that has the second-highest EMV but has a lower profitability. The recommendation intends to direct MPC institutionally toward optimal decision-making in a rapidly changing pharmaceutical market.
References
Charbuty, B., & Abdulazeez, A. (2021). Classification based on decision tree algorithm for machine learning.
Journal of Applied Science and Technology Trends,
2(01), 20-28.
DiMasi, J. A. (2020). Research and development costs of new drugs.
JAMA,
324(5), 517-517.
Gupta, B., Rawat, A., Jain, A., Arora, A., & Dhami, N. (2017). Analysis of various decision tree algorithms for classification in data mining.
International Journal of Computer Applications,
163(8), 15-19.
Pauker, S. G., & Kassirer, J. P. (2019). Decision analysis.
Medical uses of statistics, 159-179.
Rennane, S., Baker, L., & Mulcahy, A. (2021). Estimating the cost of industry investment in drug research and development: a review of methods and results.
INQUIRY: The Journal of Health Care Organization, Provision, and Financing,
58, 00469580211059731.
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