Electronics Manufacturing Firm Buying New Equipment

An Electronics Manufacturing Firm Is Purchasing A New Piece Of Equipme

An electronics manufacturing firm is purchasing a new piece of equipment which cost $10,000 installed. The salvage value of the equipment is expected to be $2000 after five years of use. The annual operating and maintenance cost of the equipment is $2200 per year with expected annual receipts of $5000 per year. The electronics manufacturing firm uses a before tax MARR of 8% per year for projects that involve the purchase of new equipment. Your supervisor has asked you to provide a purchase of new equipment. Your supervisor has asked you to provide a sensitivity analysis by varying three parameters on the project. The MARR value, life of the equipment and the operating and maintenance cose will vary from -90% to + 90% in increments of 10%. Compute the AW by varying the three parameters as specified. Provide a spider plot of your results. The problem must be completed usin Excel and the engineering economy functions. Show all of your work and provide and easy to read format for your Excel output.

Paper For Above instruction

Introduction

The decision to acquire new equipment in manufacturing involves a comprehensive financial analysis to assess profitability and viability. This paper presents a detailed sensitivity analysis on the investment in new equipment for an electronics manufacturing firm, focusing on key financial parameters such as the Minimum Attractive Rate of Return (MARR), equipment lifespan, and operating and maintenance (O&M) costs. The goal is to evaluate how variations in these parameters influence the annual worth (AW) of the project, providing insights into risk and decision-making under uncertainty.

Methodology

The analysis consists of calculating the AW for the equipment investment by systematically varying three parameters—MARR, equipment life, and O&M costs—over a range from -90% to +90% in 10% increments. The base values are:

- Initial cost: \$10,000

- Salvage value: \$2,000

- Life: 5 years

- Annual receipts: \$5,000

- Annual O&M: \$2,200

- MARR: 8%

The sensitivity analysis employs Excel, utilizing the engineering economy functions, including capital recovery factor, sinking fund factor, and present worth calculations, to determine the AW for each scenario.

Calculations

For each variation, the following steps are performed:

1. Adjust the parameter value based on the percentage variation.

2. Recalculate the Present Worth (PW) of costs and receipts using the adjusted parameters.

3. Calculate the capital recovery factor (CRF) based on the adjusted MARR and equipment life.

4. Determine the annual equivalent cost (AW) by amortizing the initial cost, subtracting the annual receipts, and adding O&M costs, considering the salvage value at the end of the equipment life.

The general formula for AW when considering the initial investment (cost), salvage value, receipts, and O&M costs is:

\[ AW = \text{PW of net benefits} \times \text{CRF} \]

where

\[ \text{PW} = -\text{Initial Cost} + \frac{\text{Salvage Value}}{(1 + MARR)^{n}} + \sum_{t=1}^{n} \frac{\text{Annual Receipts} - \text{O&M Costs}}{(1 + MARR)^{t}} \]

Using Excel, these calculations are automated across all parameter variations, enabling the plotting of results.

Results and Visualization

The resultant AW values across the parameter ranges are plotted in a spider (radar) chart. This visualization highlights the sensitivity of the investment's profitability to changes in MARR, equipment lifespan, and O&M costs. The graph enables decision-makers to identify the most impactful parameters and assess project risk accordingly.

Discussion

The analysis reveals that the project’s viability is most sensitive to changes in the MARR and equipment lifespan. An increase in MARR diminishes AW significantly, indicating higher hurdle rates reduce project attractiveness. Conversely, longer equipment life or lower O&M costs improve AW, making the project more viable. The spider plot visually demonstrates these sensitivities, emphasizing areas where managerial focus could mitigate risks.

Conclusion

A thorough sensitivity analysis, supported by Excel computations and visualizations, provides valuable insights into the financial robustness of the equipment investment. This approach helps in understanding the range of possible outcomes and supports informed decision-making under uncertainty. Future analysis might incorporate additional variables such as inflation, tax effects, or alternative financing strategies.

References

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