Case 6.3 Electronic Timing System For Olympics

Case 6.3 ELECTRONIC TIMING SYSTEM FOR OLYMPICS

Sarah Chang is the owner of a small electronics company, and she faces a decision regarding developing an electronic timing system for the upcoming Olympic Games. Her company has been working on a new microprocessor that could significantly enhance the timing system's performance. However, research and development (R&D) progress has been slow, and she is uncertain whether her staff can develop the microprocessor within the timeframe.

Success in developing the microprocessor (with probability p1) considerably raises the chances (probability p2) of winning a lucrative $1 million contract for the Olympic timing system. Conversely, if the microprocessor development fails (with probability 1 - p1), there remains a small probability p3 that her company can still win the contract by offering an existing, but inferior, timing system. Her key decision involves whether to invest in R&D and how to proceed based on the outcomes of her efforts.

Investment in R&D costs $200,000. If she proceeds, she will decide whether to develop a proposal after observing the R&D outcome. Producing a proposal entails additional costs—$50,000 if the R&D is successful (to develop the new timing system) and $40,000 if unsuccessful (to settle for the older system). Winning the contract includes the additional production costs of $150,000. The critical decision is balancing the investment costs against the probability of securing the contract, considering the success probabilities and alternative options available.

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The decision-making process faced by Sarah Chang involves evaluating the potential benefits and risks associated with investing in the development of a new microprocessor for an Olympic timing system. This case exemplifies strategic investment under uncertainty, integrating probability assessment, cost analysis, and decision theory principles to determine the optimal course of action for her small electronics business.

The primary variable in this scenario is whether the microprocessor development will succeed, which directly influences the likelihood of winning the prestigious Olympic contract. If the microprocessor is successfully developed (probability p1), the probability of victory in securing the contract increases significantly (p2). Conversely, failure to develop the microprocessor (probability 1 - p1) decreases confidence in the company's competitive edge but leaves a small chance (p3) to win with an existing, less advanced system. This uncertainty necessitates a careful assessment of expected values and probabilistic outcomes to inform strategic choices.

The initial investment of $200,000 in R&D is mandatory for progressing the microprocessor development. Should the R&D be successful, additional investment of $50,000 is required to develop a prototype timing system incorporating the new microprocessor. If unsuccessful, a smaller investment of $40,000 is needed to prepare a proposal based on existing technology. These incremental costs are significant, influencing the overall cost-benefit analysis, which must consider the probability-weighted outcomes and potential revenues.

The total costs and potential revenues further complicate the decision. The total cost if the project proceeds includes the R&D expense, prototype development costs, and production costs if the contract is awarded. Specifically, if the company wins the contract, it faces an additional production cost of $150,000. The expected benefit is the potential $1 million revenue from winning the Olympic contract, which must be weighed against the combined costs and the probabilities of winning under different scenarios.

A comprehensive decision model would incorporate the expected monetary value (EMV) of each pathway: continuing with R&D and proposing the new system, or opting for an alternative strategy. The EMV calculation involves multiplying each possible outcome's payoff by its probability and summing across all outcomes. The decision-making framework could employ tools such as decision trees or Bayesian models to integrate the stochastic nature of success, the costs involved, and the strategic importance of the potential contract.

In addition, Chang's decision could be influenced by external factors such as the competitive landscape, technological feasibility within the timeframe, and her company's capacity to scale production rapidly if successful. Risk preferences also play a vital role; a risk-averse owner might prefer a conservative approach, avoiding substantial investments unless the probability of success is very high, whereas a risk-tolerant individual might pursue aggressive R&D efforts to maximize potential gains academically and commercially.

Beyond pure financial considerations, strategic implications include enhancing her company’s reputation through association with an Olympic project, which could lead to future contracts or technological partnerships. Ethical considerations, such as the potential failure consequences and impact on her company's reputation, should also be evaluated.

In conclusion, Sarah Chang’s decision involves balancing the significant upfront investment against uncertain future payoffs influenced by probabilistic outcomes. Employing decision analysis tools, understanding the probabilities involved, and considering strategic and risk factors can guide her in making an informed choice about whether to proceed with R&D, develop the new timing system, or settle for an existing solution.

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