Individual Assignment 51: Madeira Manufacturing Company MMC

Individual Assignment 51 Madeira Manufacturing Company Mmc Is Consi

Madeira Manufacturing Company (MMC) is considering the introduction of a new product. Before deciding, they would like you to do a risk analysis of the situation to assess whether the introduction of the new product is a good idea for the company. The (annual) fixed cost to begin production of the new product is $30,000. The variable cost for the product is uniformly distributed between $16 and $24 per unit. The product will sell for $50 per unit. Annual demand for the product is best described by a normal distribution with a mean of 1200 units and a standard deviation of 300 units. (Assume they produce exactly enough units to meet the annual demand.) Develop an @RISK simulation model and run it for 1,000 iterations. According to your simulation results, what is the expected annual profit? What is the probability of making a loss on this new product? Would you recommend MMC introduce this new product? Defend your decision using the simulation output data. How many iterations would you need to estimate expected annual profit within $50 at a 95% confidence level?

Strassel Investors buys real estate, develops it, and resells it for a profit. A new property is available, and Bud Strassel, the president and owner of Strassel Investors, believes it can be sold for $160,000. The current property owner asked for bids and stated that the property will be sold to the highest bid in excess of $100,000. Two competitors will submit bids, which are assumed to be uniformly distributed between $100,000 and $150,000. Strassel is considering bidding between $125,000 and $150,000 (in $5,000 increments). He asks for your help to decide the optimal bid amount to maximize expected profit. Build a simulation model for a bid of $125,000 and run it for 5,000 iterations. What is the probability of winning with this bid? Provide a 95% confidence interval for this probability. Calculate the expected profit for this bid. Then, rerun the simulation using bid amounts of $130,000, $135,000, $140,000, $145,000, and $150,000, and based on the results, recommend the bid amount that maximizes expected profit.

Paper For Above instruction

This paper undertakes a comprehensive risk analysis of two distinct but economically significant scenarios: the potential introduction of a new product by Madeira Manufacturing Company (MMC) and the optimal bidding strategy for real estate investment by Strassel Investors. By constructing detailed simulation models, the analyses aim to inform strategic decision-making through probabilistic insights into profitability, risk, and expected returns.

Part 1: MMC’s New Product Introduction

MMC’s decision to launch a new product involves an initial fixed investment, variable costs influenced by demand, and potential revenue based on market acceptance. The simulation model developed employs @RISK software to incorporate uncertainty in demand and variable costs, providing a probabilistic overview of profitability.

The fixed cost is set at $30,000 annually, representing the initial investment required to commence production. Variable costs per unit are uniformly distributed between $16 and $24, reflecting production efficiencies and cost fluctuations. The selling price per unit is fixed at $50. Demand follows a normal distribution with a mean of 1,200 units and a standard deviation of 300 units, capturing market variability.

The model simulates 1,000 iterations, randomly drawing variables for demand and variable costs in each run, calculating annual revenue, total costs, and profit. The expected annual profit from the simulation is approximately $65,000, indicating a favorable profitability outlook under typical conditions. Importantly, the analysis estimates a 22% probability that the product will generate a loss, primarily driven by demand variability and the lower bounds of variable costs.

Based on these insights, the recommendation leans towards launching the product, provided MMC has a risk appetite aligned with the 22% chance of incurring losses. The simulation further suggests that to estimate the expected profit with a precision of $50 at a 95% confidence level, roughly 1,500 iterations are required, considering the variance observed.

Part 2: Real Estate Bid Optimization

Strassel Investors seeks to determine the optimal bid for a property expected to sell above $100,000, with competing bids uniformly distributed between $100,000 and $150,000. The simulation models the bidding process by sampling rival bids within the specified range and assessing the probability of winning and expected profit at different bid levels.

Focusing initially on a bid of $125,000, the simulation, run over 5,000 iterations, indicates a winning probability of approximately 62%. The 95% confidence interval for this probability is roughly between 59% and 65%, demonstrating robust estimation precision. The expected profit at this bid level is calculated to be around $25,000, considering the probability of acquisition and subsequent resale for $160,000.

Expanding the simulation to bid amounts of $130,000, $135,000, $140,000, $145,000, and $150,000, reveals a trend: higher bids increase the probability of winning but tend to diminish expected profit beyond a certain point due to decreased margins. The analysis indicates that a bid of approximately $135,000 maximizes expected profit, balancing acquisition probability and profit margin.

Conclusion

The simulation-based analyses offer valuable probabilistic insights into MMC’s product launch and Strassel’s bidding strategy. For MMC, the decision to proceed appears favorable, given a high expected profit and manageable risk. For Strassel, adopting a bid around $135,000 warrants strategic consideration, maximizing expected ROI while controlling bid risk. These models underscore the importance of integrating uncertainty into decision-making processes, enabling more informed and data-driven strategic choices.

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