Assignment 2: Probability Analysis For General Manager Of Ha

Assignment 2 Probability Analysisa General Manger Of Harley Davidson

Assignment 2: Probability Analysis A General Manger of Harley-Davidson has to decide on the size of a new facility. The GM has narrowed the choices to two: large facility or small facility. The company has collected information on the payoffs. It now has to decide which option is the best using probability analysis, the decision tree model, and expected monetary value. Options: Facility Demand Options Probability Actions Expected Payoffs Large Low Demand 0.4 Do Nothing ($10) Low Demand 0.4 Reduce Prices $50 High Demand 0.6 $70 Small Low Demand 0.4 $40 High Demand 0.6 Do Nothing $40 High Demand 0.6 Overtime $50 High Demand 0.6 Expand $55 Determination of chance probability and respective payoffs: Build Small: Low Demand 0.4($40)=$16 High Demand 0.6($55)=$33 Build Large: Low Demand 0.4($50)=$20 High Demand 0.6($70)=$42 Determination of Expected Value of each alternative Build Small: $16+$33=$49 Build Large: $20+$42=$62 Click here for the Statistical Terms review sheet. Submit your conclusion in a Word document to the M4: Assignment 2 Dropbox by Wednesday, April 13, 2016. Assignment 2 Grading Criteria Maximum Points The diagram is accurate and labeled correctly. The diagram clearly illustrates the sequence of events and their probability of occurrences. 32 A step-by-step breakdown of the calculations for the chance of probability and respective payoff is clearly communicated. The results of the calculations are accurate. 28 A step-by-step breakdown of the calculations for expected value is clearly communicated. The results of the calculations are accurate. 20 Clear and concise statement explaining the decision and a description of elements that lead to the decision. 20 Total:

Paper For Above instruction

The decision of whether to construct a large or small manufacturing facility at Harley-Davidson involves a comprehensive probability analysis that encompasses assessing demand probabilities, payoffs associated with each demand level, calculating expected monetary values, and ultimately making a data-driven decision. This process encapsulates the utilization of decision tree modeling, expected value calculations, and an understanding of probabilistic outcomes to guide managerial choices. In this paper, I will analyze the problem, construct relevant decision trees, perform detailed calculations for expected payoffs, and conclude with a justified recommendation based on the analysis.

Understanding the Problem Context

The Harley-Davidson General Manager faces a strategic investment decision: should the company build a large or small facility in anticipation of future demand? Two uncertain states of demand are considered: low demand and high demand, each with associated probabilities and payoffs. The primary goal is to employ probability analysis to choose the option that maximizes expected monetary value. The decision hinges on an evaluation of probable demand levels, possible strategic responses, and their financial implications.

Construction of Expected Payoffs

The company has gathered data on potential payoffs corresponding to different demand conditions and strategic responses. For the small facility, the payoffs are structured around low and high demand scenarios: $40 and $55 respectively. The probabilities assigned to these demand levels are 0.4 for low demand and 0.6 for high demand. Similarly, for the large facility, payoffs are $50 for low demand and $70 for high demand, with same probabilities.

Calculating the expected payoff for each facility involves multiplying the payoff in each demand scenario by its probability and summing these to derive the expected monetary value:

• Small Facility: (0.4 $40) + (0.6 $55) = $16 + $33 = $49
• Large Facility: (0.4 $50) + (0.6 $70) = $20 + $42 = $62

These calculations indicate that the large facility yields a higher expected payoff than the small facility ($62 vs. $49). Based solely on expected monetary value, the large facility appears to be the preferable option.

Decision Tree Model and Probabilistic Analysis

The decision tree model encapsulates the sequence of decision points and possible outcomes. Starting with the decision to build either the small or large facility, the branches depict demand scenarios (low or high demand) with their probabilities and associated payoffs. The structure of the decision tree provides a visual and quantitative method to weigh options effectively. For instance, the tree for the small facility branches into low and high demand, each with a payoff, multiplied by their respective probabilities. Similarly for the large facility, the branches delineate demand states and payoffs.

Using the decision tree, the expected payoffs are obtained by summing the products of probabilities and payoffs along each branch. This method confirms the earlier calculations and solidifies the choice based on the highest expected monetary value, which favors the large facility.

Strategic Considerations and Sensitivity Analysis

While the expected value analysis favors the large facility, managerial decisions should incorporate additional factors such as risk preferences, capacity constraints, and market conditions. Sensitivity analysis further examines how variations in demand probabilities or payoffs impact the decision. For example, if the probability of high demand decreases below a certain threshold, the expected value for the large facility could diminish, potentially making the small facility more attractive. Therefore, conducting such analyses ensures robustness of the decision.

Conclusion and Recommendation

Based on the probability analysis, expected payoff calculations, and decision tree modeling, the optimal strategic choice for Harley-Davidson is to proceed with constructing the large facility. The expected monetary value ($62) surpasses that of the small facility ($49), indicating a higher probability-weighted financial benefit. Nonetheless, the decision should be supplemented with risk assessments and market considerations to account for potential variability in demand. An evidence-based approach, leveraging probability and expected value analyses, provides a rational and quantitative foundation for this strategic decision, aligning with best practices in managerial decision-making.

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