A General Manager Of Harley Davidson Has To Decide On 708668

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

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

The Harley-Davidson company's general manager faces a strategic decision regarding the size of a new facility. The options are to choose either a large or small facility, with subsequent decisions based on demand levels and associated payoffs. The company has gathered data on potential demand, actions, probabilities, and payoffs, and now aims to evaluate these options using probability analysis, decision tree modelling, and expected monetary value (EMV) calculations to select the optimal choice.

Assignment Instructions

Analyze the decision-making scenario for Harley-Davidson's new facility by calculating the expected monetary value for each option. Build a decision tree to visualize possible outcomes, probabilities, and payoffs. Determine which facility size—large or small—maximizes expected value based on the given data, considering different demand scenarios, actions, and their respective probabilities and payoffs.

Paper For Above instruction

Decision-making in business involves assessing various options under uncertainty and selecting the one that maximizes expected benefits. In the context of Harley-Davidson's expansion, the management must decide between constructing a large or small facility, considering demand fluctuations and associated payoffs. This case exemplifies decision analysis principles, including probability assessment, expected monetary value calculations, and the construction of decision trees to guide strategic decisions.

Understanding the Scenario

The company's decision depends on demand forecasts: low demand and high demand. The probability of low demand is 0.4, while high demand has a probability of 0.6. The management also has specific actions associated with each demand level, with corresponding payoffs. For the large facility, the payoffs are:

  • Low demand: Do nothing with a payoff of -$10 (a loss due to underutilization or fixed costs)
  • Low demand: Reduce prices with a payoff of $50
  • High demand: Continue with the current setup, earning $70

For the small facility, options are:

  • Low demand: Do nothing with a payoff of $40
  • High demand: Continue with the current setup, earning $40
  • High demand: Overtime work, earning $50
  • High demand: Expand, earning $55

This multi-faceted scenario requires constructing a decision tree that captures these alternatives and their probabilities, then calculating the expected monetary value for each option to inform the choice.

Building the Decision Tree and Calculations

The first step involves calculating expected payoffs based on demand probabilities for each facility size. For the small facility, the payoffs weighted by demand probabilities are:

  • Low demand (probability 0.4): $40
  • High demand (probability 0.6): $55 (assuming expansion as the preferred action)

Thus, the expected payoff for building a small facility is:

Expected payoff = (0.4 x $40) + (0.6 x $55) = $16 + $33 = $49

Similarly, for the large facility, the payoffs are:

  • Low demand (probability 0.4): $50 (reduction in prices)
  • High demand (probability 0.6): $70 (no action needed)

Expected payoff for building a large facility is:

Expected payoff = (0.4 x $50) + (0.6 x $70) = $20 + $42 = $62

These calculations suggest that the larger facility has a higher expected monetary value, indicating a potentially better strategic choice under the current assumptions.

Implications and Decision-Making

Based on the EMV analysis, the company should favor the large facility, as it offers a higher expected payoff ($62) compared to the small facility ($49). However, this analysis assumes the payoffs and probabilities are accurate and stable over time. In real-world applications, additional factors like risk tolerance, capacity constraints, and long-term strategic goals should also influence the final decision.

Conclusion

Employing probability analysis, decision trees, and expected monetary value calculations enables businesses like Harley-Davidson to make informed, data-driven decisions under uncertainty. In this case, the analysis indicates that constructing a large facility would maximize expected value, aligning with strategic objectives to meet demand fluctuations effectively. Integrating such quantitative tools into decision-making processes can significantly improve outcomes by providing clear, objective guidance amid complexity and uncertainty.

References

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