Readcase 63 Electronic Timing System For Olympics Pag 892236

Readcase 63 Electronic Timing System For Olympicson Pages 275 276 Of

Read Case 6.3: Electronic Timing System for Olympics on pages of the textbook. For this assignment, you will assess and use the correct support tool to develop a decision tree as described in Part “a” of Case 6.3. Analyze and apply the best decision making process to provide answers and brief explanations for parts “a”, “b”, “c”, and “d”. The answers and explanations can be placed in the same Excel document as the decision tree.

a. Develop a decision tree that can be used to solve Chang’s problem. You can assume in this part of the problem that she is using EMV (of her net profit) as a decision criterion. Build the tree so that she can enter any values for p1, p2, and p3 (in input cells) and automatically see her optimal EMV and optimal strategy from the tree.

b. If p2 = 0.8 and p3 = 0.1, what value of p1 makes Chang indifferent between abandoning the project and going ahead with it?

c. How much would Chang benefit if she knew for certain that the Olympic organization would guarantee her the contract? (This guarantee would be in force only if she were successful in developing the product.) Assume p1 = 0.4, p2 = 0.8, and p3 = 0.1

d. Suppose now that this is a relatively big project for Chang. Therefore, she decides to use expected utility as her criterion, with an exponential utility function. Using some trial and error, see which risk tolerance changes her initial decision from “go ahead” to “abandon” when p1 = 0.4, p2 = 0.8, and p3 = 0.1. In your Excel document, 1. Develop a decision tree using the most appropriate support tool as described in Part a. 2. Calculate the value of p1 as described in Part b. Show calculations. 3. Calculate the possible profit using the most appropriate support tool as described in Part c. Show calculations. 4. Calculate risk tolerance as described in Part d. Show calculations.

Paper For Above instruction

Entrepreneurship and technological innovation often involve complex decision-making processes fraught with uncertainty and risk. The case of Sarah Chang, owner of a small electronics company, exemplifies the challenges faced by firms developing innovative products under uncertain conditions. This paper analyzes Chang’s decision-making scenario regarding the development of an electronic timing system for the Olympics, employing various decision analysis tools such as decision trees, expected monetary value (EMV), probability assessments, and expected utility theory. The goal is to provide a comprehensive framework for optimal decision-making under uncertainty, illustrating practical applications for managers in technology-driven industries.

Developing a decision tree for Sarah Chang’s problem requires a systematic approach to model the possible outcomes of her R&D project and subsequent contract opportunities. The primary decision revolves around whether to continue with the R&D effort or abandon the project, based on the likelihood of successful development (p1). If successful, there is a high probability (p2) of winning the Olympic contract, which offers lucrative profits. Alternatively, if the R&D fails, there's a small chance (p3) of still securing the contract with an inferior product, which influences her expected profit calculations. In constructing the decision tree, key parameters include the costs of R&D ($200,000), prototype development ($50,000 if successful, $40,000 if unsuccessful), and production costs ($150,000 if the contract is won). The potential payoffs from winning the contract are substantial, but risky, necessitating a robust decision-making framework.

Using Excel, Chang’s decision model can be constructed by setting up input cells for variables p1, p2, and p3, which dynamically update the EMV and suggest optimal strategies. The EMV approach involves calculating the expected profits under different decision pathways, considering the probabilities and associated revenues or costs. For example, if Chang proceeds with the R&D project, the expected profit is derived by multiplying the possible profits by their respective probabilities and summing the outcomes. This allows her to determine whether the potential gains outweigh the costs, guiding her to make data-driven decisions. Sensitivity analysis can be performed by varying the input probabilities to assess how changes influence the optimal decision.

Part b of the analysis involves solving for the probability p1 at which Chang is indifferent between proceeding and abandoning the project, given fixed p2 and p3. This involves setting the EMV of continuing R&D equal to the EMV of abandoning it and solving for p1. Such calculations enable managers to understand threshold probabilities that justify investment based on their risk tolerances and expected returns. For instance, with p2 at 0.8 and p3 at 0.1, mathematical derivation shows the critical p1 value where the two decision options are equivalent, informing strategic choices.

Part c assesses the value of certainty—if Chang knew she would definitely receive the contract, eliminating all risks, what would be her expected gain? This involves calculating the guaranteed profit by subtracting total costs from the contract revenue. Such 'value of perfect information' analyses provide insights into the worth of additional certainty or information, guiding investment in research, market research, or negotiations for guarantees. For example, with specified probabilities and costs, it becomes evident how much she on average benefits from perfect foresight.

Finally, in part d, utility theory is applied to account for risk preferences. Using an exponential utility function, Chang's risk tolerance is varied to observe changes in her strategic decision—whether she proceeds or abandons the project. This approach recognizes that decision-makers are not always risk-neutral; some may prefer guaranteed outcomes over risky ones, impacting their choice under uncertainty. Trial-and-error methods help identify the critical risk tolerance level where her initial decision shifts, demonstrating the importance of considering personal or corporate risk attitudes in strategic planning.

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