@Risk task
Budget: $10 – $30 USD
A consumer electronics rm produces a line of portable power banks for mobile
phones. The costs for production of this product line are as follows. There is a xed cost that
is normally distributed with a mean of $30,000 and a standard deviation of $5,000; and a per
item (i.e., per power bank) production cost that is uniformly distributed with a minimum of
$12 and a maximum of $15. The company sells the power bank at a (list) price of $28. The
anticipated demand for the product follows the demand curve formula (10; 000 ? 250 price),
and since they set the price they produce exactly this quantity.
(a) Create an @RISK Simulation Model that will allow you to analyze the distribution of the
pro t associated with this product line. Run this simulation model for 1,000 iterations.
What is the expected pro t? What is the probability of making more than $5,000?
(b) Marcus Boyd, Vice President for Sales and Marketing questions the assumptions of
the simulation model you have just built. He contends that the actual selling price is
not xed at the \list price" of $28. Rather, it varies because of various promotions
between 80% of the list price and the actual list price. Hence he suggests that you use a
triangular distribution to model the actual sales price|with a minimum value of 80% of
list price, most likely value of 90% of list price, and maximum value of actual list price.
He also recognizes that actual demand (sales), may fall short of anticipated demand
(which is calculated using the demand curve formula and the o cial list price of $28).
Speci cally, actual demand will follow a uniform distribution between 90% of anticipated
demand and 100% of anticipated demand. So, the production quantity (and therefore
production costs) will still be based on the anticipated demand (calculated based on the
list price of $28), but revenue will be based on actual demand and the actual sales price.
Create an @RISK Simulation Model that incorporates Mr. Boyd's assumptions, and
use it to analyze the distribution of the pro t associated with this product line. Run
this simulation model for 1,000 iterations. What is the expected pro t? What is the
probability of making more than $5,000?.
(c) What is the impact on the expected pro t when Mr. Boyd's assumptions are incorpo-
rated? What is the impact on the probability of making more than $5,000 when Mr.
Boyd's assumptions are incorporated?
Need this done within 12 hours
phones. The costs for production of this product line are as follows. There is a xed cost that
is normally distributed with a mean of $30,000 and a standard deviation of $5,000; and a per
item (i.e., per power bank) production cost that is uniformly distributed with a minimum of
$12 and a maximum of $15. The company sells the power bank at a (list) price of $28. The
anticipated demand for the product follows the demand curve formula (10; 000 ? 250 price),
and since they set the price they produce exactly this quantity.
(a) Create an @RISK Simulation Model that will allow you to analyze the distribution of the
pro t associated with this product line. Run this simulation model for 1,000 iterations.
What is the expected pro t? What is the probability of making more than $5,000?
(b) Marcus Boyd, Vice President for Sales and Marketing questions the assumptions of
the simulation model you have just built. He contends that the actual selling price is
not xed at the \list price" of $28. Rather, it varies because of various promotions
between 80% of the list price and the actual list price. Hence he suggests that you use a
triangular distribution to model the actual sales price|with a minimum value of 80% of
list price, most likely value of 90% of list price, and maximum value of actual list price.
He also recognizes that actual demand (sales), may fall short of anticipated demand
(which is calculated using the demand curve formula and the o cial list price of $28).
Speci cally, actual demand will follow a uniform distribution between 90% of anticipated
demand and 100% of anticipated demand. So, the production quantity (and therefore
production costs) will still be based on the anticipated demand (calculated based on the
list price of $28), but revenue will be based on actual demand and the actual sales price.
Create an @RISK Simulation Model that incorporates Mr. Boyd's assumptions, and
use it to analyze the distribution of the pro t associated with this product line. Run
this simulation model for 1,000 iterations. What is the expected pro t? What is the
probability of making more than $5,000?.
(c) What is the impact on the expected pro t when Mr. Boyd's assumptions are incorpo-
rated? What is the impact on the probability of making more than $5,000 when Mr.
Boyd's assumptions are incorporated?
Need this done within 12 hours