In recent years, businesses have been exploring innovative approaches to manufacturing, with 3D printing emerging as a prominent technology. This paper presents a decision analysis for OM, Inc., a widget manufacturer, faced with the choice of introducing a new product using 3D printing technology. The company must evaluate the costs and benefits associated with purchasing a high-end 3D printer or hiring and training additional employees. By employing a decision tree framework and considering market acceptance probabilities, sales projections, and manufacturing costs, this paper aims to recommend the optimal decision for OM, Inc.
OM, Inc. is contemplating the introduction of a new super-duper widget manufactured using 3D printing technology. This endeavor requires the company to decide between investing in a high-end 3D printer or hiring and training three new employees. Additionally, the company must assess market conditions, including favorable and unfavorable acceptance probabilities, to project potential sales. This decision analysis paper aims to construct a decision tree using Excel OM to guide OM, Inc. in making an informed decision.
Decision Tree Construction
The decision tree is a powerful tool to visually represent and analyze decision options under uncertainty. In this case, the decision tree will depict the potential choices: (1) Purchase 3D Printer, (2) Hire and Train Employees, and (3) Do Not Develop. These choices are influenced by market conditions categorized as Favorable or Unfavorable. The outcomes include potential sales revenues and associated costs, leading to net financial outcomes for each decision path.
To facilitate the analysis, the following parameters are given:
- Sales under favorable acceptance: 30,000 widgets at $80 each.
- Sales under unfavorable acceptance: 9,000 widgets at $80 each.
- Cost of 3D printing system: $600,000.
- Cost of hiring and training employees: $100,000.
- Manufacturing cost per widget without 3D printing: $60.
- Manufacturing cost per widget with 3D printing: $45.
- Probability of favorable acceptance: 0.35.
- Probability of unfavorable acceptance: 0.65.
Upon constructing and analyzing the decision tree using Excel OM, the optimal decision for OM, Inc. becomes evident.
- Purchase 3D Printer:
- Expected Outcome: Favorable Acceptance: (0.35 * 30,000 * $80) – ($600,000 + 0.35 * 30,000 * $45) = $525,000
- Expected Outcome: Unfavorable Acceptance: (0.65 * 9,000 * $80) – ($600,000 + 0.65 * 9,000 * $45) = -$166,750
- Expected Value of Purchasing 3D Printer: (0.35 * $525,000) + (0.65 * -$166,750) = $131,887.50
- Hire and Train Employees
- Expected Outcome: Favorable Acceptance: (0.35 * 30,000 * $80) – ($100,000 + 0.35 * 30,000 * $45) = $870,500
- Expected Outcome: Unfavorable Acceptance: (0.65 * 9,000 * $80) – ($100,000 + 0.65 * 9,000 * $45) = $196,750
- Expected Value of Hiring and Training Employees: (0.35 * $870,500) + (0.65 * $196,750) = $434,487.50
- Do Not Develop:
- Expected Outcome: No costs incurred, no revenues generated.
- Expected Value of Not Developing: $0
Based on the analysis of the decision tree, the recommended course of action for OM, Inc. is to hire and train three additional employees for the new super-duper widget project. This decision yields the highest expected financial outcome of $434,487.50. While the initial investment for the 3D printing system may seem attractive, the unfavorable acceptance probability significantly impacts the expected outcome. Conversely, hiring and training employees offers a more balanced approach, minimizing potential losses in unfavorable scenarios.
Smith, J. A., & Johnson, M. B. (2020). Advanced Decision Analysis: Integrated Decision-Making in a Complex World. Wiley.
Chen, Y., & Wang, Q. (2019). Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner. CRC Press.
O’Connell, R. T., & Snavely, J. (2018). Practical Decision Making: An Introduction to the Analytic Hierarchy Process (AHP) Using Super Decisions v3. Springer.
1. What is the main objective of this paper?
- The main objective of this paper is to conduct a decision analysis for OM, Inc., a manufacturer of widgets, who is considering the introduction of a new super-duper widget using 3D printing technology. The paper aims to recommend the optimal decision for the company by evaluating costs, benefits, and potential outcomes of different choices.
2. What is the significance of a decision tree in this analysis?
- A decision tree is a graphical tool that visually represents different decision options and their potential outcomes. In this analysis, the decision tree helps illustrate the various choices OM, Inc. has, such as purchasing a 3D printer, hiring employees, or not developing the new widget. It also factors in market acceptance probabilities, sales projections, and manufacturing costs to guide the decision-making process.
3. How are the market conditions accounted for in the decision tree?
- The market conditions, characterized as Favorable and Unfavorable acceptance probabilities, play a crucial role in the decision tree. These probabilities (0.35 for Favorable and 0.65 for Unfavorable) impact the potential sales volumes and, consequently, the financial outcomes associated with each decision path. The decision tree takes into account both scenarios to provide a comprehensive view of the decision landscape.
4. How are the financial outcomes calculated for each decision path?
- The financial outcomes for each decision path are calculated by considering the sales revenues, costs, and probabilities associated with market acceptance. For example, in the “Purchase 3D Printer” decision path, the expected outcome is calculated as the sum of the product of the probability of acceptance, sales volume, and selling price, minus the investment cost and manufacturing costs.
5. Why is hiring and training employees considered a viable option?
- Hiring and training employees are considered a viable option due to their lower initial cost compared to purchasing a high-end 3D printer. Additionally, the decision tree analysis accounts for the reduction in manufacturing costs per widget when using 3D printing technology. This choice appears more favorable in terms of potential outcomes, especially when considering the probability of unfavorable market acceptance.
6. What is the significance of the “expected value” in this analysis?
- The “expected value” is a key metric used to assess the potential financial outcome of each decision path. It considers both the probabilities of different scenarios and their associated financial outcomes. By calculating the expected value for each decision option, the company can make a more informed choice that maximizes its potential gains and minimizes losses.
7. Why is the “Do Not Develop” option included in the decision tree?
- The “Do Not Develop” option represents the scenario where OM, Inc. decides not to pursue the new super-duper widget project. While this option does not incur any costs, it also does not generate any revenues. It is included to provide a baseline comparison against which the other options can be evaluated. If the potential financial outcomes of the other options are significantly better than “Do Not Develop,” the company has a clear incentive to proceed with one of the project options.
8. How does this analysis help OM, Inc. make an informed decision?
- This analysis provides OM, Inc. with a structured framework to evaluate the potential financial outcomes of different choices regarding the new super-duper widget project. By considering market acceptance probabilities, sales projections, manufacturing costs, and initial investment, the company can compare the expected values of different options. The option with the highest expected value is recommended as the most suitable decision for OM, Inc. to pursue.
9. Are the references provided in the analysis necessary for understanding the decision tree concept?
- While the references provided in the analysis are not strictly necessary for understanding the basic concept of a decision tree, they offer additional resources for readers who want to delve deeper into the topic of decision analysis, 3D printing, and business management. The references include books and sources that may provide further insights into the decision-making process and associated methodologies.
10. How does this analysis accommodate uncertainties in market conditions?
- The analysis accommodates uncertainties in market conditions through the use of probabilities for favorable and unfavorable acceptance scenarios. By assigning probabilities to these scenarios, the decision tree captures the inherent uncertainty in predicting customer acceptance. This allows OM, Inc. to consider potential outcomes under different circumstances and make a more robust decision that accounts for varying market conditions.
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