In August, Walton Bookstore must decide how many of next year’s nature calendars should be ordered.  Each calendar costs the bookstore $2 and is sold for $4.50.  After January 1, any unsold calendars are returned to the publisher for a refund of $0.75 per calendar.  Walton believes that the number of calendars sold by January 1 follows the probability distribution shown in the table below.  Demand (d) f (d) 100 0.3 150 0.2 200 0.3 250 0.15 300 0.05 Compute the marginal cost increase/decrease in profit as a result of ordering additional units. Using the table in part b, calculate the per-unit marginal increase and decrease in profit. Verify the solution obtained in part a is the same solution obtained using the newsvendor critical fractile solution and the cost of understocking and overstocking.

Based on Marcus (1990). The Balboa mutual fund has beaten the Standard and Poors 500 during 11 of the last 13 years. People use this as an argument that you can beat the market. Here is another way to look at it that shows that Balboas beating the market 11 out of 13 times is not unusual. Consider 50 mutual funds, each of which has a 50% chance of beating the market during a given year. Use simulation to estimate the probability that over a 13-year period the best of the 50 mutual funds will beat the market for at least 11 out of 13 years. This probability turns out to exceed 40%, which means that the best mutual fund beating the market 11 out of 13 years is not an unusual occurrence after all.

A martingale betting strategy works as follows. You begin with a certain amount of money and repeatedly play a game in which you have a 40% chance of winning any bet. In the first game, you bet 1. From then on, every time you win a bet, you bet 1 the next time. Each time you lose, you double your previous bet. Currently you have 63. Assuming you have unlimited credit, so that you can bet more money than you have, use simulation to estimate the profit or loss you will have after playing the game 50 times.

Use @RISK to analyze the sweatshirt situation in Problem 14 of the previous section. Do this for the discrete distributions given in the problem. Then do it for normal distributions. For the normal case, assume that the regular demand is normally distributed with mean 9800 and standard deviation 1300 and that the demand at the reduced price is normally distributed with mean 3800 and standard deviation 1400.

Six months before its annual convention, the American Medical Association must determine how many rooms to reserve. At this time, the AMA can reserve rooms at a cost of 150 per room. The AMA believes the number of doctors attending the convention will be normally distributed with a mean of 5000 and a standard deviation of 1000. If the number of people attending the convention exceeds the number of rooms reserved, extra rooms must be reserved at a cost of 250 per room. a. Use simulation with @RISK to determine the number of rooms that should be reserved to minimize the expected cost to the AMA. Try possible values from 4100 to 4900 in increments of 100. b. Redo part a for the case where the number attending has a triangular distribution with minimum value 2000, maximum value 7000, and most likely value 5000. Does this change the substantive results from part a?

Play Things is developing a new Lady Gaga doll. The company has made the following assumptions: The doll will sell for a random number of years from 1 to 10. Each of these 10 possibilities is equally likely. At the beginning of year 1, the potential market for the doll is two million. The potential market grows by an average of 4% per year. The company is 95% sure that the growth in the potential market during any year will be between 2.5% and 5.5%. It uses a normal distribution to model this. The company believes its share of the potential market during year 1 will be at worst 30%, most likely 50%, and at best 60%. It uses a triangular distribution to model this. The variable cost of producing a doll during year 1 has a triangular distribution with parameters 15, 17, and 20. The current selling price is 45. Each year, the variable cost of producing the doll will increase by an amount that is triangularly distributed with parameters 2.5%, 3%, and 3.5%. You can assume that once this change is generated, it will be the same for each year. You can also assume that the company will change its selling price by the same percentage each year. The fixed cost of developing the doll (which is incurred right away, at time 0) has a triangular distribution with parameters 5 million, 7.5 million, and 12 million. Right now there is one competitor in the market. During each year that begins with four or fewer competitors, there is a 25% chance that a new competitor will enter the market. Year t sales (for t 1) are determined as follows. Suppose that at the end of year t 1, n competitors are present (including Play Things). Then during year t, a fraction 0.9 0.1n of the company's loyal customers (last year's purchasers) will buy a doll from Play Things this year, and a fraction 0.2 0.04n of customers currently in the market ho did not purchase a doll last year will purchase a doll from Play Things this year. Adding these two provides the mean sales for this year. Then the actual sales this year is normally distributed with this mean and standard deviation equal to 7.5% of the mean. a. Use @RISK to estimate the expected NPV of this project. b. Use the percentiles in @ RISKs output to find an interval such that you are 95% certain that the companys actual NPV will be within this interval.

For a given perishable product, a retailer pays $5 for each unit, then sells them for $10 each. At the end of the day, units not sold at the store are disposed of, and the retailer receives just $1 for each. Given the following probability distribution describing daily demand, how many units should be stocked? x    Prob. (Demand = x)   x    Prob. (Demand = x)  0     0.01                          7         0.18 1     0.02                          8         0.14 2     0.04                          9         0.09 3      0.07                        10        0.05 4     0.09                         11        0.02 5      0.12                        12        0.01 6       0.15                       13         0.01

A warehouse manager at Mary Beth Marrs Corp. needs to simulate the demand placed on a product that does not fit standard models. The concept being measured is "demand during lead time," where both lead time and daily demand are variable. The historical record for this product, along with the cumulative distribution, appear in the table. Demand During Lead Time Cumulaive Probability Probability 120 0.01 0.01 140 0.12 0.13 160 0.35 0.48 180 0.20 0.68 200 0.04 0.72 220 0.08 0.80 240 0.20 1.00 The following random numbers have been generated: 9, 78, 40, 41, and 99. (Note: Assume the random number interval begins at 01 and ends at 00.) Based on the given probabilty distribution, for the given random number the demand during the lead time is: Random Number 9 78 40 41 99 Demand The average demand during the lead time is (enter your response as an integer). The total demand during the lead time based on the five simulations is (enter your response as an integer).

25. Billy’s Bakery bakes fresh bagels each morning. The daily demand for bagels is a random variable with a distribution estimated from prior experience given by # of bagels sold in one day probability 0 .05 5 .10 10 .10 15 .20 20 .25 25 .15 30 .10 35 .05 The bagels cost Billy’s 8 cents to make, and they are sold for 35 cents each. Bagels unsold at the end of the day are purchased by a nearby charity soup kitchen for 3 cents each a) Based on the discrete distribution above, how many bagels should Billy’s bake at the start of each day? b) Determine the optimal number of bagels to bake each day using a normal distribution approximation. (Hint: You must compute the mean and the variance of the demand from the discrete distribution above.)26. Weiss’s paint store uses a reorder point inventory system to control its stock levels. For a particularly popular white latex paint, historical data show that the distribution of monthly demand is approximately normal, with mean 28 and standard…

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