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Mrac [35]
3 years ago
6

High Plains Inc. manufacturers furniture in North Dakota. High Plains receives its wood from a lumber yard in Calgary. The lead

time for orders is 3 weeks. One of their board costs $14 per unit and the holding cost for this board is $0.5 per week. They manage their inventory to achieve a 96 percent in-stock probability. Weekly demand is for 150 boards with a standard deviation of 200.a. How many boards do they have on on order on average? boardsb. How many boards do they have on hand on average? boardsc. For this board what is the total holding cost incurred per week? per weekd. What is the holding cost they incur per board? per board
Business
1 answer:
ArbitrLikvidat [17]3 years ago
5 0

Answer:

a. On average, the number of boards they have on order  = 1,056 boards.

b. On average, the number of boards they have =560 boards.

c. Total holding cost per week = $140.

d. Holding cost incurred per board = $ 0.25.

Explanation:

In the question, the details given are:

Service level =96 %

Lead time =3 weeks

Weekly demand =150

Standard deviation=200

This is a case of variable demand and constant lead time

a. Reorder point =Demand during lead time +Safety stock

=Average weekly demand*lead time+z*sqrt(lead time)*standard deviation of weekly demand

=150*3+NORMSINV(0.99)*sqrt(3)*200

=450+1.7507*sqrt(3)*200

=450+606.46=1,056.46

=1,056 (nearest whole number).

On average, the number of boards they have on order  = 1,056 boards.

b. For a normal distribution,

z=x-mean/std deviation

z-value for a 96% confidence level = 2.05

2.05=x-150/200

x = 150+2.05*200=560

On average, the number of boards they have =560 boards.

c.Total holding cost per week=Average inventory *holding cost per week=560/2 *0.5=280*0.5 =$140

d.Holding cost incurred per board =Total holding cost /Number of boards =140/560 = $ 0.25.

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Suppose that the S&P 500, with a beta of 1.0, has an expected return of 13% and T-bills provide a risk-free return of 4%. a.
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Answer:

a. The answers are as follows:

(i) Expected of Return of Portfolio = 4%; and Beta of Portfolio = 0

(ii) Expected of Return of Portfolio = 6.25%; and Beta of Portfolio = 0.25

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b. Change in expected return = 9% increase

Explanation:

Note: This question is not complete as part b of it is omitted. The complete question is therefore provided before answering the question as follows:

Suppose that the S&P 500, with a beta of 1.0, has an expected return of 13% and T-bills provide a risk-free return of 4%.

a. What would be the expected return and beta of portfolios constructed from these two assets with weights in the S&P 500 of (i) 0; (ii) 0.25; (iii) 0.50; (iv) 0.75; (v) 1.0

b. How does expected return vary with beta? (Do not round intermediate calculations.)

The explanation to the answers are now provided as follows:

a. What would be the expected return and beta of portfolios constructed from these two assets with weights in the S&P 500 of (i) 0; (ii) 0.25; (iii) 0.50; (iv) 0.75; (v) 1.0

To calculate these, we use the following formula:

Expected of Return of Portfolio = (WS&P * RS&P) + (WT * RT) ………… (1)

Beta of Portfolio = (WS&P * BS&P) + (WT * BT) ………………..………………. (2)

Where;

WS&P = Weight of S&P = (1) – (1v)

RS&P = Return of S&P = 13%, or 0.13

WT = Weight of T-bills = 1 – WS&P

RT = Return of T-bills = 4%, or 0.04

BS&P = 1.0

BT = 0

After substituting the values into equation (1) & (2), we therefore have:

(i) Expected return and beta of portfolios with weights in the S&P 500 of 0 (i.e. WS&P = 0)

Using equation (1), we have:

Expected of Return of Portfolio = (0 * 0.13) + ((1 - 0) * 0.04) = 0.04, or 4%

Using equation (2), we have:

Beta of Portfolio = (0 * 1.0) + ((1 - 0) * 0) = 0

(ii) Expected return and beta of portfolios with weights in the S&P 500 of 0.25 (i.e. WS&P = 0.25)

Using equation (1), we have:

Expected of Return of Portfolio = (0.25 * 0.13) + ((1 - 0.25) * 0.04) = 0.0625, or 6.25%

Using equation (2), we have:

Beta of Portfolio = (0.25 * 1.0) + ((1 - 0.25) * 0) = 0.25

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Using equation (1), we have:

Expected of Return of Portfolio = (0.50 * 0.13) + ((1 - 0.50) * 0.04) = 0.0850, or 8.50%

Using equation (2), we have:

Beta of Portfolio = (0.50 * 1.0) + ((1 - 0.50) * 0) = 0.50

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Using equation (1), we have:

Expected of Return of Portfolio = (0.75 * 0.13) + ((1 - 0.75) * 0.04) = 0.1075, or 10.75%

Using equation (2), we have:

Beta of Portfolio = (0.75 * 1.0) + ((1 - 0.75) * 0) = 0.75

(v) Expected return and beta of portfolios with weights in the S&P 500 of 1.0 (i.e. WS&P = 1.0)

Using equation (1), we have:

Expected of Return of Portfolio = (1.0 * 0.13) + ((1 – 1.0) * 0.04) = 0.13, or 13%

Using equation (2), we have:

Beta of Portfolio = (1.0 * 1.0) + (1 – 1.0) * 0) = 1.0

b. How does expected return vary with beta? (Do not round intermediate calculations.)

There expected return will increase by the percentage of the difference between Expected Return and Risk free rate. That is;

Change in expected return = Expected Return - Risk free rate = 13% - 4% = 9% increase

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Explanation:

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Market division makes it simpler for advertisers to customize their promoting efforts.  

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Gathering comparable shoppers enables advertisers to target explicit spectators in a financially savvy way.   Market division additionally lessens the danger of a fruitless or ineffectual showcasing effort.

At the point when advertisers separate a market dependent on key attributes and customize their procedures dependent on that data, there is an a lot higher possibility of progress than if they somehow managed to make a conventional battle and attempt to actualize it over all sections.

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Answer: Please refer to Explanation.

Explanation:

Your question was incomplete so I attached the missing details.

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<u>Answers:</u>

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<u>Explanation:</u>

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