A General Manager Of Harley Davidson Has To Decide On 021303

A General Manger Of Harley Davidson Has To Decide On The Size Of A New

A Harley Davidson's general manager needs to decide whether to build a large or small facility. The decision hinges on anticipated demand levels, with various probabilities and payoffs associated with each option. The company has collected data on demand probabilities and corresponding payoffs for each facility size, and aims to use probability analysis, decision trees, and expected monetary value (EMV) calculations to determine the optimal choice.

The decision options are as follows:

- For the large facility, demand can be low or high, with respective probabilities of 0.4 and 0.6.

- For the small facility, demand also varies between low and high, with the same probabilities.

- Actions include doing nothing, reducing prices, expanding, or utilizing overtime, depending on demand levels.

The payoff data are summarized as:

- Large facility with low demand results in $10 if no action is taken, or $50 if prices are reduced.

- High demand for the large facility yields $70 if no action is taken.

- Small facility under low demand generates $40.

- High demand at the small facility can be met with no action for $40, overtime for $50, or expansion for $55.

The probabilities and payoffs lead to calculations of expected payoffs:

- The expected payoff for building a small facility is calculated as:

- Low demand: 0.4 × $40 = $16

- High demand: 0.6 × $55 = $33

- Total expected value: $16 + $33 = $49

- The expected payoff for building a large facility is:

- Low demand: 0.4 × $50 = $20

- High demand: 0.6 × $70 = $42

- Total expected value: $20 + $42 = $62

Based on the expected monetary values, the large facility has a higher expected payoff ($62) compared to the small facility ($49), indicating that, from a purely financial standpoint, building the large facility is the better decision.

Decision Analysis and Recommendation

Using probability analysis and expected monetary value calculations, the company should proceed with constructing a large facility. The higher EV suggests it is more likely to yield greater profitability over the alternative. However, additional considerations such as risk tolerance, capacity constraints, and strategic goals should also influence the final decision.

Further refinement of decision-making could involve constructing a decision tree with all possible actions and outcomes, incorporating the probabilities and payoffs, and calculating the overall expected value for each path. Moreover, sensitivity analysis can be performed to assess how variations in demand probabilities impact the decision.

In practice, companies may also consider qualitative factors, such as market growth potential, competitive positioning, and operational flexibility when making such strategic decisions. Despite the quantitative analysis favoring the large facility, a holistic approach often yields the most sustainable decision.

Conclusion

Based on expected monetary value analysis, the Harley Davidson general manager should opt to build the large facility, as it offers a higher expected payoff of $62 compared to the small facility's $49. This data-driven decision provides a rational basis for resource allocation and strategic planning, aligning financial metrics with organizational goals.

References

  • Keeney, R. L., & Raiffa, H. (1993). Decisions with Multiple Objectives: Preferences and Value Trade-offs. Cambridge University Press.
  • Boardman, A. E., Greenberg, D. H., Vining, A. R., & Weimer, D. L. (2018). Cost-Benefit Analysis: Concepts and Practice. Cambridge University Press.
  • Clemen, R. T., & Reilly, T. (2013). Making Hard Decisions with DecisionTools Suite. Cengage Learning.
  • Kirkwood, C. W. (2015). Strategic Decision Making and Support Systems. John Wiley & Sons.
  • Sharda, R., Delen, D., & Turban, E. (2020). Business Intelligence, Analytics, and Data Science: A Management Perspective. Pearson.
  • Hillier, F. S., & Lieberman, G. J. (2010). Introduction to Operations Research. McGraw-Hill Education.
  • Larson, P. D., & Gray, C. F. (2014). Project Management: The Managerial Process. McGraw-Hill Education.
  • Hastie, R., & Dawes, R. M. (2010). Rational Choice in an Uncertain World. Sage Publications.
  • Ross, S. M. (2014). Introduction to Probability and Statistics for Engineers and Scientists. Academic Press.
  • Winston, W. L. (2004). Operations Research: Applications and Algorithms. Cengage Learning.

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