Southwestern University (SWU), a large state college in Stephenville. Texas, enrolls close to 20,000 students. The school is a dominant force in the small city, with more students during fall and spring than permanent residents.

Always a football powerhouse, SWU is usually in the top 20 in college football rankings. Since the legendary Phil Flamm was hired as its head coach in 2009 (in hopes of reaching the elusive number 1 ranking), attendance at the five Saturday home games each year increased. Prior to Flamm's arrival, attendance generally averaged 25,000 to 29,000 per game. Season ticket sales bumped up by 10,000 just with the announcement of the new coach's arrival. Stephenville and SWU were ready to move to the big time!

The immediate issue facing SWU, however, was not NCAA ranking. It was capacity. The existing SWU stadium, built in 1953, has seating for 54,000 fans. The following table indicates attendance at each game for the past 6 years.

One of Flamm's demands upon joining SWU had been a stadium expansion, or possibly even a new stadium. With attendance increasing, SWU administrators began to face the issue head-on. Flamm had wanted dormitories solely for his athletes in the stadium as an additional feature of any expansion.

SWU's president, Dr. Joel Wisner, decided it was time for his vice president of development to forecast when the existing stadium would "max out." The expansion was, in his mind, a given. But Wisner needed to know how long he could wait. He also sought a revenue projection, assuming an average ticket price of $S 50$ in 2016 and a $5 \%$ increase each year in future prices.

	2010		2011		2012
GAME	ATTENDEES	OPPONENT	ATTENDEES	OPPONENT	ATTENDEES	OPPONENT
1	$34,200$	Rice	$36,100$	Miami	$35,900$	USC
2$^a$	$39,800$	Texas	$40,200$	Nebraska	$46,500$	Texas Tech
3	$38,200$	Duke	$39,100$	Ohio State	$43,100$	Alaska
$4^b$	$26,900$	Arkansas	$25,300$	Nevada	$27,900$	Arizona
5	$35,100$	TCU	$36,200$	Boise State	$39,200$	Baylor

	2013		2014		2015
GAME	ATTENDEES	OPPONENT	ATTENDEES	OPPONENT	ATTENDEES	OPPONENT
1	$41,900$	Arkansas	$42,500$	Indiana	$46,900$	LSU
2$^a$	$46,100$	Missouri	$48,200$	North Texas	$50,100$	Texas
3	$43,900$	Florida	$44,200$	Texas A8M	$45,900$	South Florida
4$^b$	$30,100$	Central Florida	$33,900$	Southern	$36,300$	Montana
5	$40,500$	LSU	$4,800$	Oklahoma	$49,900$	Arizona State

$^a$ Homecoming games. $^b$ During the fourth week of each season, Stephenville hosted a hugely popular southwestern crafts festival. This event brought tens of thousands of tourists to the town, especially on weekends, and had an obvious negative impact on game attendance.

Discuss the school's options.

Trans Systems is a small, information systems consulting firm located in Meridian, Louisiana. Trans has just been hired to design and install a local area network (LAN) for the city of Meridian’s social welfare agency. You are the manager for the project, which includes one Trans professional and two interns from a local university. You have just finished a preliminary scope statement for the project (see below). You are now brainstorming potential risks associated with the project.

1. Identify potential risks associated with this project. Try to come up with at least five different risks.

2. Use a risk assessment form similar to the Figure to analyze identified risks.

3. Develop a risk response matrix similar to the Figure to outline how you would deal with each of the risks

What are some of the potential benefits of working on a special project in one’s firm? What are some of the risks?

University endowments are financial assets that are donated by supporters to be used to provide income to universities. There is a large discrepancy in the size of university endowments. The following table provides a listing of many of the universities that have the largest endowments as reported by the National Association of College and University Business Officers in 2017.

Summarize the data by constructing the following:

What is the largest university endowment and which university holds it?

Southwestern University (SWU), a large state college in Stephenville, Texas, 30 miles southwest of the Dallas/Fort Worth metroplex, enrolls close to 20,000 students. In a typical town-gown relationship, the school is a dominant force in the small city, with more students during fall and spring than permanent residents. A longtime football powerhouse, $\mathrm{SWU}$ is a member of the Big Eleven conference and is usually in the top 20 in college footpall rankings. To bolster its chances of reaching the elusive and ong-desired number-one ranking, in 2009, SWU hired the legendary Phil Flamm as its head coach. One of Flamm's demands on joining SWU had been a new stadium. With attendance increasing, SWU administrators began to face the issue head-on. After 6 months of study, much political arm wrestling, and some serious financial analysis, Dr. Joel Wisner, president of Southwestern University, had reached a decision to expand the capacity at its on-campus stadium.

Adding thousands of seats, including dozens of luxury skyboxes, would not please everyone. The influential Flamm had argued the need for a first-class stadium, one with built-in dormitory rooms for his players and a palatial office appropriate for the coach of a future NCAA champion team. But the decision was made, and everyone, including the coach, would learn to live with it.

The job now was to get construction going immediately after the 2015 season ended. This would allow exactly 270 days until the 2016 season opening game. The contractor, Hill Construction (Bob Hill being an alumnus, of course), signed his contract. Bob Hill looked at the tasks his engineers had outlined and looked President Wisner in the eye. "I guarantee the team will be able to take the field on schedule next year," he said with a sense of confidence. "I sure hope so," replied Wisner. "The contract penalty of $\$ 10,000$ per day for running late is nothing compared to what Coach Flamm will do to you if our opening game with Penn State is delayed or canceled." Hill, sweating slightly, did not need to respond. In football-crazy Texas, Hill Construction would be mud if the 270-day target was missed.

Back in his office, Hill again reviewed the data (see Table 3.6) and noted that optimistic time estimates can be used as crash times. He then gathered his foremen. "Folks, if we're not $75 \%$ sure we'll finish this stadium in less than 270 days, I want this project crashed! Give me the cost figures for a target date of 250 days also for 240 days. I want to be early, not just on time!"

Develop a network drawing for Hill Construction and determine the critical path. How long is the project expected to take?

Dr. Lillian Fok, a New Orleans psychologist, specializes in treating patients who are agoraphobic (i.e., afraid to leave their homes). The following table indicates how many patients Dr. Fok has seen each year for the past 10 years. It also indicates what the robbery rate was in New Orleans during the same year:

Using trend (linear regression) analysis, predict the number of patients Dr. Fok will see in years 11 and 12 as a function of time. How well does the model fit the data?

The popularity of Southwestern University's football program under its new coach Phil Flamm surged in each of the 5 years since his arrival at the Stephenville, Texas, college. (See Southwestern University: (A) in Chapter 3 and (B) in Chapter 4.) With a football stadium close to maxing out at 54,000 seats and a vocal coach pushing for a new stadium, SWU president Joel Wisner faced some difficult decisions. After a phenomenal upset victory over its archrival, the University of Texas, at the homecoming game in the fall, Dr. Wisner was not as happy as one would think. Instead of ecstatic alumni, students, and faculty, all Wisner heard were complaints. "The lines at the concession stands were too long"; "Parking was harder to find and farther away than in the old days" (that is, before the team won regularly); "Seats weren't comfortable"; "Traffic was backed up halfway to Dallas"; and on and on. "A college president just can't win," muttered Wisner to himself.

At his staff meeting the following Monday, Wisner turned to his VP of administration, Leslie Gardner. "I wish you would take care of these football complaints, Leslie," he said. "See what the real problems are and let me know how you've resolved them." Gardner wasn't surprised at the request. "I've already got a handle on it, Joel," she replied. "We've been randomly surveying 50 fans per game for the past year to see what's on their minds. It's all part of my campus wide TQM effort. Let me tally things up and I'll get back to you in a week."

When she returned to her office, Gardner pulled out the file her assistant had compiled (see Table 6). "There's a lot of information here," she thought.

Table 6. Fan Satisfaction Survey Results (N) = 250

Open –Ended Comments on Survey Cards:

Using at least two different quality tools, analyze the data and present your conclusions.

CEO John Goodale, at Southern Illinois Power and Light, has been collecting data on demand for electric power in its western subregion for only the past 2 years. Those data are shown in the table below.

To plan for expansion and to arrange to borrow power from neighboring utilities during peak periods, Goodale needs to be able to forecast demand for each month next year. However, the standard forecasting models discussed in this chapter will not fit the data observed for the 2 years.

What are the weaknesses of the standard forecasting techniques as applied to this set of data?

For its first 2 decades of existence, the NBA's Orlando Magic basketball team set seat prices for its 41-game home schedule the same for each game. If a lower-deck seat sold for $\$ 150$, that was the price charged, regardless of the opponent, day of the week, or time of the season. If an upper-deck seat sold for $\$ 10$ in the first game of the year, it likewise sold for $\$ 10$ for every game.

But when Anthony Perez, director of business strategy, finished his $\mathrm{MBA}$ at the University of Florida, he developed a valuable database of ticket sales. Analysis of the data led him to build a forecasting model he hoped would increase ticket revenue. Perez hypothesized that selling a ticket for similar seats should differ based on demand.

Studying individual sales of Magic tickets on the open Stub Hub marketplace during the prior season. Perez determined the additional potential sales revenue the Magic could have made had they charged prices the fans had proven they were willing to pay on Stub Hub. This became his dependent variable. $y$, in a multiple-regression model.

He also found that three variables would help him build the "true market' seat price for every game. With his model, it was possible that the same seat in the arena would have as many as seven different prices created at season onset-sometimes higher than expected on average and sometimes lower.

The major factors he found to be statistically significant in determining how high the demand for a game ticket, and hence. its price, would be were:

• The day of the week $\left(x_1\right)$
• A rating of how popular the opponent was $\left(x_2\right)$
• The time of the year $\left(x_3\right)$ For the day of the week, Perez found that Mondays were the least-favored game days (and he assigned them a value of 1). The rest of the weckdays increased in popularity, up to a Saturday game, which he rated a 6 . Sandays and Fridays received 5 ratings, and holidays a 3 (refer to the footnote in Table 4.3).

His ratings of opponents, done just before the start of the season, were subjective and range from a low of 0 to a high of 8 . A very high-rated team in that particular season may have had one or more superstars on its roster, or have won the $\mathrm{NBA}$ finals the prior season, making it a popular fan draw. Finally, Perez believed that the $\mathrm{NBA}$ season could be divided into four periods in popularity:

• Early games (which he assigned 0 scores)
• Games during the Christmas season (assigned a 3)
• Games until the All-Star break (given a 2)
• Games leading into the play-offs (scored with a 3) The first year Perez built his multiple-regression model. the dependent variable $y$. which was a "potential premium revenue score, "yielded an $r^2=.86$ with this equation:

$$y=14,996+10,801 x_1+23,397 x_2+10.784 x_3$$

Table 4.3 illustrates, for brevity in this case study, a sample of 12 games that year (out of the total 41 home game regular season), including the potential extra revenue per game (y) to be expected using the variable pricing model.

A leader in NBA variable pricing, the Orlando Magic have learned that regression analysis is indeed a profitable forecasting tool.

TABLE $4.3$ Data for Last Year's Magic Ticket Sales Pricing Model

*Day of week rated as $1=$ Monday, $2=$ Tuesday, 3 - Wednesday, $4=$ Thursday, $5=$ Friday, $6=$ Saturday, $5=5$ Sunday, $3=$ holiday.

Using Perez's multiple-regression model, what would be the additional sales potential of a Thursday Miami Heat game played during the Christmas holiday?

Refer to Problem 4.2. Develop a forecast for years 2 through 12 using exponential smoothing with $\alpha=.4$ and a forecast for year 1 of 6 . Plot your new forecast on a graph with the actual data and the naive forecast. Based on a visual inspection, which forecast is better?

The number of disk drives (in millions) made at a plant in Taiwan during the past 5 years follows:

Compute the mean absolute percent error (MAPE).

Which of these is NOT an advantage of outsourcing?

a. cost savings

b. potential creation of future competition

c. accessing outside technology

d. gaining outside expertise

Using PERT, Adam Munson was able to determine that the expected project completion time for the construction of a pleasure yacht is 21 months, and the project variance is 4. What is the probability that the project will be completed in 20 months?

The difference between a moving-average model and an exponential smoothing model is that