Answer: $7,500
Explanation:
In calculating the Incremental income we will add the amount of variable Manufacturing costs Rory Company will save as well as the income they will get from selling the old machine and then subtract the cost price of the new machine.
Starting off we will calculate the amount of savings they will make by using the new machine,
= $12,000 x 5 years
= $60,000
Calculating the Incremental income therefore we have,
= 60,000 + 60,000(from selling old machine) - 112,500 (cost of new machine)
= $7,500
The incremental income of buying the new machine is $7,500.
If you need any clarification do comment.
Answer:
$31,104
Explanation:
EBIT / 12,000
= [EBIT - ($120,000 × .072)] / [12,000 - ($120,000 / $36)]
EBIT = $31,104
Therefore the minimum level of earnings before interest and taxes that the firm is expecting will be $31,104
Answer:
13%
Explanation:
As per the situation the solution of required rate of return first we need to find out the beta which is shown below:-
Expected rate of return = Risk-free rate of return + Beta × (Market rate of return - Risk-free rate of return)
11% = 7% + Beta × 6%
Beta = 1
now If the market risk premium increased to 6% so,
The required rate of return = 7% + 1 × 6%
= 13%
Therefore for computing the required rate of return we simply applied the above formula.
Answer:
$165
Explanation:
The working capital of organization is the difference between the current assets and the current liabilities of the organization. It shows if a company has enough short term assets or asset that can be converted quickly to cash to settle obligations that will arise in the short term.
Working capital as at December 31, 2015
=$1,105 - $915
=$190
Working capital as at December 31, 2016
=$1,320 - $955
=$365
Change in working capital in 2016
= $365 - $190
= $165
In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product. Optimization of process parameters is one of the ways to achieve desired quality of a part.
In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product.
Optimization of process parameters is one of the ways to achieve desired quality of a part. Finite Element Method (FEM) and machine learning techniques are applied to evaluate and optimize AM process parameters. While FEM requires specific information, Powder Bed Fusion Machine Learning is based on big amounts of data. This paper provides a conceptual framework on combination of mathematical modelling and Machine Learning to avoid these issues.
Learn more about Powder Bed Fusion here
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