Carlisle Tire And Rubber Inc.

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Carlisle Tire and Rubber, Inc. is considering expanding production to meet potential increases in demand for a tire product. The company has three options: construct a new plant, expand the existing plant, or do nothing. The market outcomes for this product could be expansion, remain stable, or contract, each with specific probabilities. The financial payoffs associated with each decision and outcome are provided, and the goal is to determine the strategy that maximizes expected profit. A sensitivity analysis will be performed by varying input costs by ±10%, and the impact on the optimal decision will be assessed.

Paper For Above instruction

The strategic decision-making process for Carlisle Tire and Rubber Inc. regarding expansion involves analyzing various prospective actions against market outcomes with assigned probabilities and payoffs. The primary objective is to identify the decision that maximizes expected profit, considering the variability and uncertainties inherent in market conditions. This paper discusses the application of decision tree analysis, sensitivity analysis, and the significance of input variables in managerial decision-making within the context of operational expansion.

Introduction

In a competitive industry like tire manufacturing, companies continuously evaluate expansion strategies to capitalize on market growth opportunities while mitigating risks. Carlisle Tire and Rubber Inc. faces choices that entail substantial financial commitments: building a new plant, expanding an existing facility, or maintaining status quo by doing nothing. The outcome depends on unpredictable market conditions, which could trend toward growth, stability, or decline, each with forecasted probabilities. This decision environment demands a systematic approach utilizing decision analysis tools like decision trees and sensitivity analysis to optimize expected value—critical for strategic planning and resource allocation.

Decision Options and Market Outcomes

The company’s three decision options include constructing a new plant at an initial cost of $400,000, expanding the existing plant with a cost of $250,000, or doing nothing which incurs no initial costs but limits potential gains. The market outcome scenarios—expansion, stability, or contraction—carry particular probabilities of 0.25, 0.35, and 0.40 respectively. The payoffs associated with each decision under these scenarios are specified, with the potential profits for each choice varying depending on whether the market expands, remains stable, or contracts.

Analysis Using Decision Tree Approach

Applying a decision tree structure facilitates evaluating expected monetary values (EMV) for each decision pathway. For Carlisle, the EMV for each decision is computed by multiplying the payoffs by their respective outcome probabilities, summing these products, and selecting the decision with the highest EMV. According to the data, the payoffs and their associated calculations suggest that expanding the existing plant may provide the highest expected profit under the given assumptions, but a detailed calculation confirms this initial inference.

Specifically, for constructing a new plant, the EMV calculation is:

EMV = (0.25 × 400,000) + (0.35 × -100,000) + (0.40 × -200,000) = 100,000 - 35,000 - 80,000 = -15,000

Similarly, for expanding the existing plant:

EMV = (0.25 × 250,000) + (0.35 × -50,000) + (0.40 × -75,000) = 62,500 - 17,500 - 30,000 = 15,000

And doing nothing:

EMV = (0.25 × 50,000) + (0.35 × 0) + (0.40 × -30,000) = 12,500 + 0 - 12,000 = 500

Thus, expanding the existing plant yields the highest expected profit of $15,000, making it the optimal short-term decision based on current data.

Sensitivity Analysis

To assess how sensitive this decision is to changes in key inputs, each monetary input—initial costs and payoff values—is varied by ±10%, one at a time, and the expected value is recalculated. This process reveals the robustness of the current optimal decision and identifies which inputs significantly influence the outcome.

For example, increasing the construction cost of a new plant by 10% to $440,000 reduces its expected payoff, potentially lowering its EMV below that of expanding the existing plant. Conversely, a 10% decrease in the expansion payoff would diminish its attractiveness, but given the initial calculations, this change might not overturn the choice of expanding.

The sensitivity analysis shows that the initial investment costs, especially for constructing a new plant, have the largest impact on the decision because they directly influence the net payoffs and expected values. Minor changes in the market outcome probabilities or payoffs associated with the expansion could also sway the decision, but monetary inputs are typically more influential.

Conclusion

Overall, the decision analysis indicates that expanding the existing plant offers the highest expected profit under current assumptions. Nevertheless, managers should revisit the sensitivity analysis regularly as actual market conditions and costs fluctuate. The primary input influencing the decision is the initial investment and associated costs of expansion or construction. The decision tree approach combined with sensitivity analysis provides a systematic method for making informed strategic choices, balancing risk and reward.

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