1Z0-931 Autonomous Database

Service Operations Management Final Exam

Instructions:

  1. Please complete all thirteen questions; points are indicated on each question. (85 points total)
  2. Write your answer to each question in the space provided. Show your reasoning and explain all assumptions. If you use a spreadsheet, make sure you label and explain everything.  Do not submit your spreadsheet model, copy and paste relevant part of the results in this document and provide explanation.
  3. Be succinct and to the point; brevity & clarity of your argument will be valued. Write the most important point first.  If your answer is long and covers a lot of points, many irrelevant, it will be valued less.
  4. Partial credit will be given for partially complete or incorrect answers; however, to receive partial credit you must explain the assumptions and reasoning behind your answers.
  5. The exam is open book, open notes, open web. There is no time limit to complete this exam. You may not seek help from anyone else, nor can you discuss this exam with anyone. Use of class notes from previous offering of this course or examination from previous years will be considered a violation of Honor Code.

Question

Possible

Your Score

1

2

2

2

3

5

4(a)

3

4(b)

3

5

4

6 (a)

10

6 (b)

5

6 (c)

5

6 (d)

2

7

7

8

3

9

3

10

3

11(a)

7

11(b)

5

11(c)

3

12

5

13

4

14

4

Total

85

Q1         Based upon case data, estimate the effective transfer charge (fees collected as a percentage of cash transfer) M-Pesa imposes on its customers. (2 points)

Q2         Based on the case data, what percentage of Safaricom revenue can be directly attributed to M-Pesa? (2 points)

Q3       Outline the limitations of the proposed incentive plan in the TD Canada Trust (B) case.  Indicate the extent to which these limitations have been addressed in the incentive plan proposed in the TD Canada Trust (C) case.  Refer to specific case exhibits to support your answer. (5 points)

Q4 (a)  A consultant looks at the compensation scheme in TD Canada Trust (C) case, and states that it would damage the organization irreparably by not rewarding high performers for their model behavior. Is he right? Why or why not? (3 points)

Q4 (b) Compare the incentive system proposed in TD Canada (c) case to Amazon’s rank-and-yank system (as reported in “Inside Amazon: Wrestling Big Ideas in a Bruising Workplace,” New York Times, August 15, 2015, http://www.nytimes.com/2015/08/16/technology/inside-amazon-wrestling-big-ideas-in-a-bruising-workplace.html ).

(3 points)

Q5    Do you agree with the following assertion?

“The main thrust of Factor Analysis in the TD Canada Trust (B) case is to reduce the number of variables such that, going forward, only a subset of data needs to be gathered.” 

Why (or why not)? 

What does “Makes Me Feel Comfortable” mean in the Exhibit 7 of TD Canada Trust (B) case?
(4 points)

Q6     Adcock Breast-care Clinic has ten radiologists on its staff.  The Clinic tracks doctors’ performance diligently in an effort to improve the quality of care.  For the first quarter of 2018, the table below summarizes the performance of the ten radiologists at the Clinic; each working for roughly the same hours with similar compensation.  For example, Radiologist #1 read a total of 450 mammograms, out of which he found 38 positive cases, i.e. cases that appeared to show lesions.  All these cases were referred for biopsies.  Fortunately, the biopsy results indicated that 30 of these cases were false positives, i.e. they were normal cells, not tumors.  The Clinic also tracks false negatives (actual cancer missed).  For example, out of all the cases that Radiologist #1 considered negative (or, not positive), 10 developed breast cancer, and in fact, the tumor could be traced back to missed lesions on the mammograms read by Radiologist #1 during the first quarter of 2018. 

Radiologist

Mammograms Read

Positives

False Positives

False Negatives

1

450

38

30

10

2

150

8

3

1

3

1500

70

60

50

4

1000

50

35

25

5

250

40

35

5

6

750

35

15

10

7

300

15

10

7

8

775

25

5

11

9

425

20

18

15

10

200

45

40

3

  • Design a DEA study to compare the performance of radiologists. Clearly indicate what are the input and output variables that you will use in your model.  For each input and output variable, explain (i) why it should be included in the model, and (ii) how you would compute it.  Note: You do not need to develop the Linear Programming or the Solver model.  However, you do need to list the values of input and output variables that can be readily used in such a model  (10 points).
  • Based on your model, which radiologists would you guess have 100% productivity (DEA efficiency)? Why?  (Please note: “Because I ran my model” is not a valid explanation).
    Which radiologists can you rule out as a candidate for the reference set?   (5 points)
  • Some of the radiologists have blamed their lower productivity on the ethnic mix of their patient population. In particular, they claim that their patient population depicts a higher incidence rate of breast cancer compared to the average, and that slows them down.   Do you agree? Provide clear justification for your opinion. (5 points)
  • The Chief Radiologist of Adcock Breast-care Clinic proposes to use the relative productivity of radiologists as a basis for their compensation structure. She states that as a productivity-driven organization, it is appropriate to transparently use DEA to reward high performers and penalize poor ones. Is she correct? Why or why not? (2 points)

Q7     The Uber Switchback Experiment Data has an indicator for “rush-hour” that we did not use during the class analysis.  Redo the analysis by separating the commute from the non-commute hours. What is the impact of increasing the wait-time from 2 min (control) to 5 min (treatment) on the cancelation rate and on driver payout per trip? What explains the difference between the commute and non-commute periods when you compare the “control” and “treatment” cancellation rates and driver payouts per trip? (7 points)

Q8     The New Uber Pool:  Uber incorporated the Express into its POOL product and dropped the explicit distinction between Express and POOL service offerings. Riders who chose POOL were then given a choice of whether they would be willing to walk and wait for a price discount.  Explicit wait time (e.g. 2 min or 5 min) were dropped.  Riders who chose the option to walk, were given a reduced price and a pick-up point to walk to, after a flexible wait time.  They could accept the lower price and walk to the pick-up point or choose the regular POOL.

Compare the New Uber Pool service to the Express Pool service discussed in the case.  Why would Uber choose the New Uber Pool? (3 points) 

Q9   Is Infosys Technologies, Ltd. a technology company according the metrics discussed in the class?

Why (not)? (3 points)

Q10   Why M-Pesa has not been successful anywhere else except Kenya? (3 points)

Q11     In 2007, Brian Chesky and Joe Gebbia came up with a wheeze to rent out two air beds in their San Francisco apartment, because a conference had left the local hotels full-to-overflowing. Thus, Airbnb was born. Since then more than 500m stays have been booked through its platform, which now offers more than 7m properties (including 4,900 castles and 2,400 tree-houses) in over 100,000 cities. Each night, around 2m people around the world stay in an Airbnb. Airbnb was already profitable (to be precise, EBITDA-positive) in 2017 and 2018*(see S-1 filing). The firm has grand designs to move beyond accommodation, and provide the entire trip: where to go, what to do and how to get there, not just where to stay. The first day price values Airbnb at $86 billion (as of Thursday, December 10, 2020), more than twice the market cap of Marriott, the world’s largest hotel chain.

Airbnb’s founders started as complete outsiders to the hospitality business and indeed, to commerce. Brian Chesky and Joe Gebbia had no previous business experience or technical expertise. They were so untutored in investing that when an early adviser suggested raising money from small investors known as “angels”, Mr Chesky thought people in Silicon Valley actually believed in celestial beings.

https://www.sec.gov/Archives/edgar/data/1559720/000119312520311265/d81668ds1a.htm#toc81668_24

  • What are the similarities and differences between Airbnb’s and Uber’s business models? (7 points)

Similarities

Differences

Airbnb

Uber

Q11 (Contd.) 

Airbnb belatedly knocks on the door in China. Will the American firm do better in China than Uber?

“WE HAVE not focused on building our community in China,” reads a peculiar announcement posted recently by Airbnb on its official blog. Despite the firm’s apparent lack of enthusiasm for the Chinese, the world’s biggest group of travelers, intrepid locals have still discovered the American home-sharing site. Tourists from the mainland have used the platform more than 3.5m times; Airbnb members in China have hosted nearly 1m visitors.

Perhaps abashed by this show of grassroots support, Airbnb is now making a big push in China. From December 7th a new legal entity (Airbnb China) will cater to all those neglected hosts and guests. To satisfy Chinese regulators the unit will store their data on local servers. The firm has also struck new agreements with the governments of Shanghai, Shenzhen, Chongqing and Guangzhou, which suggests that these big cities welcome its formal arrival. In addition to these developments, there are rumors that Airbnb is about to take over Xiaozhu, a mid-sized local rival that recently raised $65m of venture funding.

The mainland is certainly an attractive prize, with a big sharing economy that is projected to grow by 40% a year for each of the next five years. Local travelers made four billion trips inside China last year. The market for individual leisure lodgings inside the country could reach 10.3bn yuan ($1.7bn) next year, up from 6.8bn yuan this year, reckons iResearch, a market-research firm. Airbnb sees its rivals in China as parochial outfits. None of them have a global network or the means to build one, says Nick Papas, the firm’s spokesman in Washington, DC.

But the American firm is late to the party, and local rivals are by now established. The strongest is Tujia, a venture-backed firm that is valued at more than $1bn and offers some 440,000 homes in over 300 cities. Unlike Airbnb’s model, which connects homeowners with travelers, Tujia’s also helps developers let out vacant properties—taking advantage of China’s property glut—and also offers services to potential buyers of homes.

Other foreign tech firms have stumbled in China in the recent past. “The past decade has shown that it’s very hard for American companies to use their own approach to do business in China,” says Chen Chi, Xiaozhu’s chief executive officer. He previously worked at the local divisions of Yahoo and TripAdvisor, two American internet firms which struggled to localize. This year Uber, a ride-hailing firm, had to retreat after spending a fortune trying to compete against Didi Chuxing, a well-funded and inventive local rival.

Even if foreign firms manage to hire savvy mainlanders, they are held back by having to report to faraway bosses with patchy knowledge of the market. “They end up behaving like rabbits, while we are a pack of wolves,” says Mr Chen (in an interview before the news of Airbnb’s interest in Xiaozhu). One Xiaozhu customer says he far prefers its cheaper prices and greater array of listings to Airbnb’s offering. With over 100,000 listings in about 300 cities across the country (Airbnb has around 70,000 in fewer places), it would be a useful addition to Airbnb’s empire.

A combined firm would still have to contend with regulatory confusion. Tujia’s boss, Luo Jun, laments that there is “no clear national law supervising this industry.” The requirements for special licenses, police checks and identity verification vary widely by region. Doing business often means lengthy one-off negotiations.

Airbnb may reckon it is in the clear with its deals with four cities, but Tujia has made over 200 agreements with local authorities across the country. One businesswoman who rents out eight grand flats in Shanghai’s old French Concession from landlords, and re-lets them on home-sharing sites, says that the police fine landlords as a matter of course every once in a while. A strong relationship with the government is a must for any sharing site, whether local or foreign, she says. It would also help to avoid being dismissive of local companies. Airbnb is still a long way off building its Chinese home from home.   (Economist, December 3, 2016)

  • What should Airbnb learn from Uber’s China experience? Be specific and give examples to illustrate your points. (5 points)

Q11 (Contd.) 

  • Expedia generated $12 billion in revenue in 2019 compared to $4.8 billion for Airbnb in the same year. Net income for Expedia in 2019 was $565 million compared to a net loss of $674 million for Airbnb.  Market Cap for Expedia is approximately $18 billion, compared to that for Airbnb of $86 billion.  In your opinion, what makes Airbnb so valuable in the eyes of investors, compared to Expedia?  (3 points)

Q12      Compare the launch of M-Pesa in Kenya to the launch of Uber in the US.  What are the similarities and differences among them?  (5 points)

Similarities

Differences

M-Pesa

Uber

Q13      Pricing in Two-sided Platform:  C2K, a dance club in Las Vegas, is free for local women while the cover charge is $10 for out-of-state women and $15 for men.  The Buddha Lounge in Chicago charges men an entry fee of $15 at all times.  For women, entry is free until 10:00pm and $5 after that.   Explain the guiding principles behind these pricing. (4 points)

Q14      Explain two surprising facts that you learned from the Q&A with Chris Armstrong (video) that was not evident from the TD Canada Trust cases and explain the significance of those facts. (4 points)

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