Management Information System MIS 201
Semester 2 (2019-2020)
Assignment Details
Prepare an in-depth analysis of four case studies during the semester. Here are some guidelines:

This is an individual assessment, which is a part from your course score. It requires effort and
critical thinking

This assignment will worth 25 mark (Case Studies Questions 15 Marks/ Presentation 10
Marks)

Answer all the questions listed below for each case.

The ‘answers’ to the questions are best formulated by reviewing the case and the reading
materials up and including the current week in the course.

The questions are worded to help you apply the readings to the case, so don’t limit yourself
to the case’s terminology and perspective. The best analysis will abstract the case content by
applying the reading materials to draw broader lessons about the material

As for the Presentation you should summarize your analysis of only one case study in a set
of PowerPoint slides
Case Study 1: Should a Computer Grade Your Essays?
1) Identify the kinds of systems described in this case. (1 Mark)
2) What are the benefits of automated essay grading? What are the drawbacks? (1
Mark)
3) What management, organization, and technology factor should be considered when
deciding whether to use AES? (1 Mark)
Case Study 2: American Water Keeps Data Flowing
1) How did implementing a data warehouse help American Water move toward a more
centralized organization? (1 Mark)
2) Give some examples of problems that would have occurred at American Water if its
data were not “clean”? (1 Mark)
3) How did American Water’s data warehouse improve operations and management
decision making? (1 Mark)
Case Study 3: Driving Ari Fleet Management with Real-Time Analytics
1) Why was data management so problematic at ARI? (1 Mark)
2) Describe ARI’s earlier capabilities for data analysis and reporting and their impact on
the business. (1 Mark)
3) Was SAP HANA a good solution for ARI? Why or why not? (1 Mark)
4) Describe the changes in the business as a result of adopting HANA. (1 Mark)
Case Study 4: Zappos
1) Define SCM and how it can benefit Zappos. (1 Mark)
2) Explain CRM and why Zappos would benefit from the implementation of a CRM
system. (1 Mark)
3) Demonstrate why Zappos would need to implement SCM, CRM, and ERP for a
connected corporation. (1 Mark)
4) Analyze the merger between Zappos and Amazon and assess potential issues for
Zappos customers. (1 Mark)
5) Propose a plan for how Zappos can use Amazon’s supply chain to increase sales and
customer satisfaction. (1 Mark)
Case Study 1: Should a Computer Grade Your Essays?
Would you like your college essays graded by a computer? Well, you just might find that happening in your
next course. In April 2013, EdX, a Harvard/MIT joint venture to develop massively open online courses
(MOOCs), launched an essay-scoring program. Using artificial intelligence technology, essays and short
answers are immediately scored and feedback tendered, allowing students to revise, resubmit, and improve
their grade as many times as necessary. The non-profit organization is offering the software free to any
institution that wants to use it. From a pedagogical standpoint—if the guidance is sound—immediate
feedback and the ability to directly act on it is an optimal learning environment. But while proponents
trumpet automated essay grading’s superiority to students waiting days or weeks for returned papers—
which they may or may not have the opportunity to revise—as well as the time-saving benefit for
instructors, critics doubt that humans can be replaced.
In 2012, Les Perelman, the former director of writing at MIT, countered a paper touting the proficiency of
automated essay scoring (AES) software. University of Akron College of Education dean, Mark Shermis,
and co-author, data scientist Ben Hamner used AES programs from nine companies, including Pearson and
McGraw-Hill, to rescore over 16,000 middle and high school essays from six different state standardized
tests. Their Hewlett Foundation sponsored study found that machine scoring closely tracked human grading,
and in some cases, produced a more accurate grade. Perelman, however, found that no direct statistical
comparison between the human graders and the programs was performed. While Shermis concedes that
regression analysis was not performed—because the software companies imposed this condition in order
to allow him and Hamner to test their products—he unsurprisingly accuses Perelman of evaluating their
work without performing research of his own.
Perelman has in fact conducted studies on the Electronic Essay Rater (e-rater) developed by the
Educational Testing Service (ETS)—the only organization that would allow him access. The e-rater uses
syntactic variety, discourse structure (like PEG) and content analysis (like IEA) and is based on natural
language processing technology. It applies statistical analysis to linguistic features like argument formation
and syntactic variety to determine scores, but also gives weight to vocabulary and topical content. In the
month granted him, Perelman analyzed the algorithms and toyed with the e-Rater, confirming his prior
critiques. The major problem with AES programs (so far) is that they cannot distinguish fact from fiction.
For example, in response to an essay prompt about the causes for the steep rise in the cost of higher
education, Perelman wrote that the main driver was greedy teaching assistants whose salaries were six times
that of college presidents with exorbitant benefits packages including South Seas vacations, private jets,
and movie contracts. He supplemented the argument with a line from Allen Ginsberg’s “Howl,” and
received the top score of 6. The metrics that merited this score included overall length, paragraph length,
number of words per sentence, word length, and the use of conjunctive adverbs such as “however” and
“moreover.” Since computer programs cannot divine meaning, essay length is a proxy for writing fluency,
conjunctive adverb use for complex thinking, and big words for vocabulary aptitude.
Program vendors such as Pearson and Vantage Learning defend these parameters, asserting that they are
highly correlated. Good writers have acquired skills that enable them to write more under time constraints;
they use more complex vocabulary, and they understand how to introduce, interrupt, connect, and conclude
complex ideas—the jobs of conjunctive adverbs. AES programs also recognize sentence fragments and
dock students for sentences that begin with “and” or “or.” However, professional writers know how to
employ both to great effect. Perelman and a newly formed group of educators, Professionals Against
Machine Scoring of Student Essays in High-Stakes Assessment, warn that writing instruction will be
dumbed down to meet the limited and rigid metrics machines are capable of measuring.
The productivity gains from using automated essay-grading software will undoubtedly take away some of
the jobs of the graders hired by the standardized test companies. Pearson, for example, ostensibly pays its
graders between $40 and $60 per hour. In that hour, a grader expected to score between 20 and 30 essays—
that is two to three minutes (and dollars) per essay. Clearly graders must use some type of shorthand metrics
in order to score this quickly, but at least they can recognize as false the statement that on July 4, 2013, the
United States observed its 2,013th birthday, even if it is contained in a well-constructed sentence. While
the e-Rater can score 16,000 essays in 20 seconds, it cannot make this distinction. In addition, presumably,
a 716-word essay containing multiple nonsense sentences will not receive a 6 from a human grader while
a 150-word shorter, factual, well-reasoned essay scores a 5, as Perelman was able to demonstrate.
ETS, developer of the SAT, GRE, Praxis, and K-12 standardized tests for multiple states, counters that the
e-Rater is not replacing human graders in high stakes tests; it is supplementing them. Essays are scored by
both human and machine and when the scores do not match, a second human breaks the impasse.
Furthermore, they posit that the test prep course Perelman developed to teach students how to beat AES
software requires higher-order thinking skills—precisely those the tests seek to measure. Thus, if students
can master Perelman’s techniques, they have likely earned their 6. Pearson adds that its Intelligent Essay
Assessor is primarily a classroom tool, allowing students to revise their essays multiple times before turning
them in to a teacher to be graded. However, for many states looking to introduce writing sections to their
battery of K-12 standardized tests, and for those that abandoned the effort due to the cost, eliminating
graders altogether will make them affordable. In addition, the stakes are not insubstantial for failure to
achieve passing grades on state standardized tests, ranging from retesting, to remedial programs, to summer
school, to non-promotion.
ETS, developer of the SAT, GRE, Praxis, and K-12 standardized tests for multiple states, counters that the
e-Rater is not replacing human graders in high stakes tests; it is supplementing them. Essays are scored by
both human and machine and when the scores do not match, a second human breaks the impasse.
Furthermore, they posit that the test prep course Perelman developed to teach students how to beat AES
software requires higher-order thinking skills—precisely those the tests seek to measure. Thus, if students
can master Perelman’s techniques, they have likely earned their 6. Pearson adds that its Intelligent Essay
Assessor is primarily a classroom tool, allowing students to revise their essays multiple times before turning
them in to a teacher to be graded. However, for many states looking to introduce writing sections to their
battery of K-12 standardized tests, and for those that abandoned the effort due to the cost, eliminating
graders altogether will make them affordable. In addition, the stakes are not insubstantial for failure to
achieve passing grades on state standardized tests, ranging from retesting, to remedial programs, to summer
school, to non-promotion. In addition, that provides immediate guidance, is a welcome addition to the
instructional toolbox. However, as demands on instructor’s time decrease, will university administrators
push staff cutbacks to meet budgetary constraints? Will fewer and fewer instructors be teaching more and
more students?
As MOOC and AES proliferate, the answer is: most likely. EdX is quickly becoming controversial in
academic circles. Presently, its course offerings are free and students earn a certificate of completion, but
not course credit. To become self-sustaining, however, the non-profit plans to offer its MOOC platform as
a “self-service” system, which faculty members can use to develop courses specifically branded for their
universities. EdX will then receive the first $50,000 in revenue generated from the course or $10,000 for a
recurring course. Thereafter, revenue will be split 50-50 between the university and EdX. A second
revenue-generating model offers universities “production help” with course development, charging them
$250,000 for a new course and $50,000 each term the course is offered again. If a course is successful, the
university receives 70% of the revenue, as long as EdX has been fully compensated for any self-service
courses. However, in order to generate enough revenue to share with its 12 university partners, which now
include University of California, Berkeley, Wellesley, Georgetown, and the University of Texas, a licensing
model is likely. Tested at no charge at San Jose State University in 2012, an EdX MOOC served as the
basis for a blended online engineering course. The enriched curriculum resulted in an increased passing rate
from 60% to 91 %. If course licensing becomes the key revenue stream, Anant Agarwal, the electrical
engineer president of EdX, foresees this happening in closed classrooms with limited enrollment.
But some members of the San Jose State faculty are nonetheless alarmed. When a second EdX MOOC,
JusticeX, was considered, the Philosophy department sent a sharply-worded letter addressed to Harvard
course developer, Michael Sandel, but actually leveled at university administrators. Asserting that the
department did not have an academic problem in need of remediation and was not lacking faculty to teach
its equivalent course, it did not shy from attacking the economic motives behind public universities’
embrace of MOOCs. The authors further asserted that MOOCs represented a decline in educational quality
and noted the irony involved when a social justice course was the vehicle for perpetrating a social
injustice—a long-term effort to “dismantle departments and replace professors.” Sandel’s conciliatory
response expressed his desire to share free educational resources, his aversion to undercutting colleagues,
and a call for a serious debate at both EdX and in the higher education community.
Other universities are similarly pushing back, against both EdX and other new MOOC ventures such as
Coursera and Udacity, founded by Stanford faculty members. MOOCs and AES are inextricably linked.
Massive online courses require automated assessment systems. In addition, both Coursera and Udacity have
expressed their commitment to using them due to the value of immediate feedback. Amherst College faculty
voted against joining the EdX consortium. Duke University faculty members thwarted administration
attempts to join nine other universities and educational technology company 2U in a venture to develop a
collection of for-credit undergraduate courses.
However, EdX was founded by two of the most prominent universities in the United States, has gathered
prestigious partners, and is already shaping educational standards. Stanford, for one, has decided to get on
board; it adopted the OpenEdX open-source platform and began offering a summer reading program for
freshman and two public courses in the summer of 2013. Stanford will collaborate with EdX on the future
development of OpenEdX and will offer both public and university classes on it.
Therefore, while Professor Perelman jokes that his former computer science major students could develop
an Android app capable of spitting out formulaic essays that would get a 6 from e-Rater, cutting humans
completely out of the equation, he knows that serious issues are in play. What educational outcomes will
result from diminishing human interaction and input? Will AI develop to the point that truth, accuracy,
effective organization, persuasiveness, argumentation and supporting evidence can be evaluated? And how
many more jobs in education will disappear as a result?
Case Study 1: American Water Keeps Data Flowing
American Water, founded in 1886, is the largest public water utility in the United States. Headquartered in
Voorhees, N.J., the company employs more than 7,000 dedicated professionals who provide drinking water,
wastewater and other related services to approximately 16 million people in 35 states, as well as Ontario
and Manitoba, Canada. Most of American Water’s services support locally managed utility subsidiaries that
are regulated by the U.S. state in which each operates as well as the federal government. American Water
also owns subsidiaries that manage municipal drinking water and wastewater systems under contract and
others that supply businesses and residential communities with water management products and services.
Until recently, American water’s systems and business, processes were much localized, and many of these
processes were manual. Over time, this information environment became increasingly difficult to manage.
Many systems were not integrated, so that running any type of report that had to provide information about
more than one region was a heavily manual process. Data had to be extracted from the systems supporting
each region and then combined manually to create the desired output. When the company was preparing to
hold an initial public offering of its stock in 2006, its software systems could not handle the required
regulatory controls, so roughly 80 percent of this work had to be performed manually. It was close to a
nightmare.
Management wanted to change the company from a decentralized group of independent regional businesses
into a more centralized organization with standard company-wide business processes and enterprise-wide
reporting. The first step toward achieving this goal was to implement an enterprise resource planning (ERP)
system designed to replace disparate systems with a single integrated software platform. The company
selected SAP as its ERP system vendor.
An important step of this project was to migrate the data from American Water’s old systems to the new
platform. The company’s data resided in many different systems in various formats. Each regional business
maintained some of its own data in its own systems, and a portion of these data was redundant and
inconsistent. For example, there were duplicate pieces of materials master data because a material might be
called one thing in the company’s Missouri operation and another in its New Jersey business. These names
had to be standardized so that every business unit used the same name for a piece of data. American Water’s
business users had to buy into this new company-wide view of data.
Data migration entails much more than just transferring data between old and new systems. Business users
need to know that data are not just a responsibility of the information systems department: the business
“owns” the data. Business needs determine the rules and standards for managing the data. Therefore, it is
up to business users to inventory and review all the pieces of data in their systems to determine precisely
which pieces of data from the old system will be used in the new system and which data do not need to be
brought over. The data also need to be reviewed to make sure they are accurate and consistent and that
redundant data are eliminated.
Most likely some type of data cleansing will be required. For example, American Water had data on more
than 70,000 vendors in its vendor master data file. Andrew Clarkson, American Water’s Business
Intelligence Lead, asked business users to define an active vendor and to use that definition to identify
which data to migrate. He also worked with various functional groups to standardize how to present address
data.
One of the objectives of American Water’s data management work was to support an enterprise wide
business intelligence program based on a single view of the business. An analytical system and data
warehouse would be able to combine data from the SAP ERP System with data from other sources,
including new customer information and enterprise asset management systems. That meant that American
Water’s business users had to do a lot of thinking about the kinds of reports they wanted. The company had
originally planned to have the system provide 200 reports, but later reduced that number by half. Business
users were trained to generate these reports and customize them. Most financial users initially tried to create
their reports using Microsoft Excel spreadsheet software. Over time, however, they learned to do the same
thing using SAP Business Objects Web Intelligence tools that came with the system. SAP Business Objects
Web Intelligence is a set of tools that enables business users to view, sort, and analyze business intelligence
data. It includes tools for generating queries, reports and interactive dashboards.
At present, American Water is focusing on promoting the idea that data must be “clean” to be effective and
has poured an incredible amount of effort into its data cleansing work—identifying incomplete, incorrect,
inaccurate, and irrelevant pieces of data and then replacing, modifying, or deleting the “dirty” data.
According to Clarkson, just as water treatment plants have measurements and meters to check water quality
as its being treated, data management needs to ensure the quality of data at every step to make sure the final
product will be genuinely useful for the company.
Case Study 1: Driving Ari Fleet Management with Real-Time Analytics
Automotive Resources International®, better known as simply ARI®, is the world’s largest privately-held
company for vehicle fleet management services. ARI is headquartered in Mt. Laurel, New Jersey and has
2,500 employees and offices throughout North America, Europe, the UK, and Hong Kong. The company
manages more than 1,000,000 vehicles in the U.S., Canada, Mexico, Puerto Rico and Europe.
Businesses that need vehicles for shipments (trucks, vans, cars, ships, and rail cars) may choose to manage
their own fleet of vehicles or they may outsource fleet management to companies such as ARI which
specialize in these services. ARI manages the entire life cycle and operation of a fleet of vehicles for its
customers, from up-front specification and acquisition to resale, including financing, maintenance, fuel
management, and risk management services such as driver safety training and accident management. ARI
also maintains six call centers in North America that operate 24/7, 365 days a year to support customers’
fleet operations by providing assistance regarding repairs, breakdowns, accident response, preventive
maintenance, and other driver needs. These call centers handle about 3.5 million calls per year from
customers, drivers, and suppliers who expect access to real-time actionable information.
Providing this information has become increasingly challenging. Operating a single large commercial
vehicle fleet generates high volumes of complex data, such as data on fuel consumption, maintenance,
licensing, and compliance. A fuel transaction, for example, requires data on state taxes paid, fuel grade,
total sale, amount sold, and time and place of purchase. A simple brake job and preventive maintenance
checkup generates dozens of records for each component that is serviced. Each part and service performed
on a vehicle is tracked using American Trucking Association codes. ARI collects and analyzes over 14,000
pieces of data per vehicle. Then multiply the data by hundreds of fleets, some with up to 10,000 vehicles,
all operating simultaneously throughout the globe, and you’ll have an idea of the enormous volume of data
ARI needs to manage, both for itself and for its customers.
ARI provided its customers with detailed information about their fleet operations, but the type of
information it could deliver was very limited. For example, ARI could generate detailed reports on lineitem expenditures, vehicle purchases, maintenance records, and other operational information presented as
simple spreadsheets, charts, or graphs, but it was not possible to analyze all the data to spot trends and make
recommendations. ARI was able to analyze data customer by customer, but it was not able to aggregate
data across its entire customer base.
For instance, if ARI was managing a pharmaceutical company’s vehicle fleet, its information systems could
not benchmark that fleet’s performance against others in the industry. That type of problem required too
much manual work and time, and still didn’t deliver the level of insight management thought was possible.
What’s more, in order to create reports, ARI had to go through internal subject matter experts in various
aspects of fleet operations, who were called “reporting power users.” Every request for information was
passed to these power users. A request for a report would take 5 days to fill. If the report was unsatisfactory,
it would go back to the report writer to make changes. ARI’s process for analyzing its data was extremely
drawn out.
In mid-2011, ARI implemented SAP BusinessObjects Explorer to give customers the enhanced ability to
access data and run their own reports. SAP BusinessObjects Explorer is a business intelligence tool that
enables business users to view, sort and analyze business intelligence data. Users search through data
sources using an iTunes like interface. They do not have to create queries to search the data and results are
shown with a chart that indicates the best information match. The graphical representation of results
changes as the user asks further questions of the data.
In early 2012, ARI integrated SAP BusinessObjects Explorer with HANA, SAP’s in-memory computing
platform that is deployable as an on-premise appliance (hardware and software) or in the cloud. HANA is
optimized for performing real-time analytics and handling very high volumes of operational and
transactional data in real time. HANA’s in-memory analytics queries data stored in random access memory
(RAM) instead of on a hard disk or flash storage.
Things started happening quickly after that. When ARI’s controller wanted an impact analysis of the
company’s top 10 customers, SAP HANA produced the result in 3 to 3 1/2 seconds. In ARI’s old systems
environment, this task would have been assigned to a power user versed in using reporting tools,
specifications would have to be drawn up and a program designed for that specific query, a process that
would have taken about 36 hours.
Using HANA, ARI is now able to quickly mine its vast data resources and generate predictions based on
the results. For example, the company can produce precise figures on what it costs to operate a fleet of a
certain size over a particular route across specific industries during a certain type of weather and predict
what the impact of changes in any of these variables. And it can do so nearly as easily as providing
customers with a simple history of their expenditures on fuel. With such helpful information ARI provides
more value to its customers.
HANA has also reduced the time required for each transaction handled by ARI’s call centers—from the
time a call center staffer takes a call to retrieving and delivering the requested information—by 5 percent.
Since call center staff account for 40 percent of ARI’s direct overhead, that time reduction translates into
major cost savings.
ARI plans to make some of these real-time reporting and analytic capabilities available on mobile devices,
which will enable customers to instantly approve a variety of operational procedures, such as authorizing
maintenance repairs. Customers will also be able to use the mobile tools for instant insight into their fleet
operations, down to a level of detail such as a specific vehicle’s tire history.
Case Study 4: Zappos
Tony Hsieh’s first entrepreneurial effort began at the age of 12 when he started his own custom button
business. Realizing the importance of advertising, Hsieh began marketing his business to other kids
through directories, and soon his profits soared to a few hundred dollars a month. Throughout his
adolescence, Hsieh started several businesses, and by the time he was in college he was making money
selling pizzas out of his Harvard dorm room. Another entrepreneurial student, Alfred Lin,bought pizzas
from Hsieh and resold them by the slice, making a nice profit. Hsieh and Lin quickly became friends.
After Harvard, Hsieh founded Link Exchange in 1996, a company that helped small businesses exchange
banner ads. A mere two years later, Hsieh sold Link Exchange to Microsoft for $265 million. Using the
profits from the sale, Hsieh and Lin formed a venture capital company that invested in start-up businesses.
One investment that caught their attention was Zappos, an online retailer of shoes. Both entrepreneurs
viewed the $40 billion shoe market as an opportunity they could not miss, and in 2000 Hsieh took over as
Zappos’ CEO with Lin as his chief financial officer.
Today, Zappos is leading its market and offering an enormous selection of more than 90,000 styles of
handbags, clothing, and accessories for more than 500 brands. One reason for Zappos’ incredible success
was Hsieh’s decision to use the advertising and marketing budget for customer service, a tactic that would
not have worked before the Internet. Zappos’ passionate customer service strategy encourages customers
to order as many sizes and styles of products as they want, ships them for free, and offers free return
shipping. Zappos encourages customer communication, and its call center receives more than 5,000 calls
a day with the longest call to date lasting more than four hours. Zappos’ extensive inventory is stored in a
warehouse in Kentucky right next to a UPS shipping center. Only available stock is listed on the website,
and orders as late as 11 p.m. are still guaranteed next-day delivery. To facilitate supplier and partner
relationships, Zappos built an extranet that provides its vendors with all kinds of product information,
such as items sold, times sold, price, customer, and so on. Armed with these kinds of details, suppliers
can quickly change manufacturing schedules to meet demand.
Zappos Culture
Along with valuing its partners and suppliers, Zappos also places a great deal of value on its employee
relationships. Zappos employees have fun, and walking through the offices you will see all kinds of
things not normally seen in business environments—bottle-cap pyramids, cotton-candy machines, and
bouncing balls. Building loyal employee relationships is a critical success factor at Zappos, and to
facilitate this relationship the corporate headquarters are located in the same building as the call center
(where most employees work) in Las Vegas. All employees receive 100 percent company-paid health
insurance along with a daily free lunch.
Of course, the Zappos culture does not work for everyone, and the company pays to find the right
employees through “The Offer,” which extends to new employees the option of quitting and receiving
payment for time worked plus an additional $1,000 bonus. Why the $1,000 bonus for quitting? Zappos
management believes that is a small price to pay to find those employees who do not have the sense of
commitment Zappos requires. Less than 10 percent of new hires take The Offer.
Zappos’ unique culture stresses the following:
1. Delivering WOW through service
2. Embracing and driving change
3. Creating fun and a little weirdness
4. Being adventurous, creative, and open-minded
5. Pursuing growth and learning
6. Building open and honest relationships with communication
7. Building a positive team and family spirit
8. Doing more with less
9. Being passionate and determined
10. Being humble
Zappos’ Sale to Amazon
Amazon.com purchased Zappos for $880 million. Zappos employees shared $40 million in cash and
stock, and the Zappos management team remained in place. Having access to Amazon’s world-class
warehouses and supply chain is sure to catapult Zappos’ revenues, though many wonder whether the
Zappos culture will remain. It’ll be interesting to watch!19
Management Information System MIS 201
Semester 2 (2019-2020)
Assignment Details
Prepare an in-depth analysis of four case studies during the semester. Here are some guidelines:

This is an individual assessment, which is a part from your course score. It requires effort and
critical thinking

This assignment will worth 25 mark (Case Studies Questions 15 Marks/ Presentation 10
Marks)

Answer all the questions listed below for each case.

The ‘answers’ to the questions are best formulated by reviewing the case and the reading
materials up and including the current week in the course.

The questions are worded to help you apply the readings to the case, so don’t limit yourself
to the case’s terminology and perspective. The best analysis will abstract the case content by
applying the reading materials to draw broader lessons about the material

As for the Presentation you should summarize your analysis of only one case study in a set
of PowerPoint slides
Case Study 1: Should a Computer Grade Your Essays?
1) Identify the kinds of systems described in this case. (1 Mark)
2) What are the benefits of automated essay grading? What are the drawbacks? (1
Mark)
3) What management, organization, and technology factor should be considered when
deciding whether to use AES? (1 Mark)
Case Study 2: American Water Keeps Data Flowing
1) How did implementing a data warehouse help American Water move toward a more
centralized organization? (1 Mark)
2) Give some examples of problems that would have occurred at American Water if its
data were not “clean”? (1 Mark)
3) How did American Water’s data warehouse improve operations and management
decision making? (1 Mark)
Case Study 3: Driving Ari Fleet Management with Real-Time Analytics
1) Why was data management so problematic at ARI? (1 Mark)
2) Describe ARI’s earlier capabilities for data analysis and reporting and their impact on
the business. (1 Mark)
3) Was SAP HANA a good solution for ARI? Why or why not? (1 Mark)
4) Describe the changes in the business as a result of adopting HANA. (1 Mark)
Case Study 4: Zappos
1) Define SCM and how it can benefit Zappos. (1 Mark)
2) Explain CRM and why Zappos would benefit from the implementation of a CRM
system. (1 Mark)
3) Demonstrate why Zappos would need to implement SCM, CRM, and ERP for a
connected corporation. (1 Mark)
4) Analyze the merger between Zappos and Amazon and assess potential issues for
Zappos customers. (1 Mark)
5) Propose a plan for how Zappos can use Amazon’s supply chain to increase sales and
customer satisfaction. (1 Mark)
Case Study 1: Should a Computer Grade Your Essays?
Would you like your college essays graded by a computer? Well, you just might find that happening in your
next course. In April 2013, EdX, a Harvard/MIT joint venture to develop massively open online courses
(MOOCs), launched an essay-scoring program. Using artificial intelligence technology, essays and short
answers are immediately scored and feedback tendered, allowing students to revise, resubmit, and improve
their grade as many times as necessary. The non-profit organization is offering the software free to any
institution that wants to use it. From a pedagogical standpoint—if the guidance is sound—immediate
feedback and the ability to directly act on it is an optimal learning environment. But while proponents
trumpet automated essay grading’s superiority to students waiting days or weeks for returned papers—
which they may or may not have the opportunity to revise—as well as the time-saving benefit for
instructors, critics doubt that humans can be replaced.
In 2012, Les Perelman, the former director of writing at MIT, countered a paper touting the proficiency of
automated essay scoring (AES) software. University of Akron College of Education dean, Mark Shermis,
and co-author, data scientist Ben Hamner used AES programs from nine companies, including Pearson and
McGraw-Hill, to rescore over 16,000 middle and high school essays from six different state standardized
tests. Their Hewlett Foundation sponsored study found that machine scoring closely tracked human grading,
and in some cases, produced a more accurate grade. Perelman, however, found that no direct statistical
comparison between the human graders and the programs was performed. While Shermis concedes that
regression analysis was not performed—because the software companies imposed this condition in order
to allow him and Hamner to test their products—he unsurprisingly accuses Perelman of evaluating their
work without performing research of his own.
Perelman has in fact conducted studies on the Electronic Essay Rater (e-rater) developed by the
Educational Testing Service (ETS)—the only organization that would allow him access. The e-rater uses
syntactic variety, discourse structure (like PEG) and content analysis (like IEA) and is based on natural
language processing technology. It applies statistical analysis to linguistic features like argument formation
and syntactic variety to determine scores, but also gives weight to vocabulary and topical content. In the
month granted him, Perelman analyzed the algorithms and toyed with the e-Rater, confirming his prior
critiques. The major problem with AES programs (so far) is that they cannot distinguish fact from fiction.
For example, in response to an essay prompt about the causes for the steep rise in the cost of higher
education, Perelman wrote that the main driver was greedy teaching assistants whose salaries were six times
that of college presidents with exorbitant benefits packages including South Seas vacations, private jets,
and movie contracts. He supplemented the argument with a line from Allen Ginsberg’s “Howl,” and
received the top score of 6. The metrics that merited this score included overall length, paragraph length,
number of words per sentence, word length, and the use of conjunctive adverbs such as “however” and
“moreover.” Since computer programs cannot divine meaning, essay length is a proxy for writing fluency,
conjunctive adverb use for complex thinking, and big words for vocabulary aptitude.
Program vendors such as Pearson and Vantage Learning defend these parameters, asserting that they are
highly correlated. Good writers have acquired skills that enable them to write more under time constraints;
they use more complex vocabulary, and they understand how to introduce, interrupt, connect, and conclude
complex ideas—the jobs of conjunctive adverbs. AES programs also recognize sentence fragments and
dock students for sentences that begin with “and” or “or.” However, professional writers know how to
employ both to great effect. Perelman and a newly formed group of educators, Professionals Against
Machine Scoring of Student Essays in High-Stakes Assessment, warn that writing instruction will be
dumbed down to meet the limited and rigid metrics machines are capable of measuring.
The productivity gains from using automated essay-grading software will undoubtedly take away some of
the jobs of the graders hired by the standardized test companies. Pearson, for example, ostensibly pays its
graders between $40 and $60 per hour. In that hour, a grader expected to score between 20 and 30 essays—
that is two to three minutes (and dollars) per essay. Clearly graders must use some type of shorthand metrics
in order to score this quickly, but at least they can recognize as false the statement that on July 4, 2013, the
United States observed its 2,013th birthday, even if it is contained in a well-constructed sentence. While
the e-Rater can score 16,000 essays in 20 seconds, it cannot make this distinction. In addition, presumably,
a 716-word essay containing multiple nonsense sentences will not receive a 6 from a human grader while
a 150-word shorter, factual, well-reasoned essay scores a 5, as Perelman was able to demonstrate.
ETS, developer of the SAT, GRE, Praxis, and K-12 standardized tests for multiple states, counters that the
e-Rater is not replacing human graders in high stakes tests; it is supplementing them. Essays are scored by
both human and machine and when the scores do not match, a second human breaks the impasse.
Furthermore, they posit that the test prep course Perelman developed to teach students how to beat AES
software requires higher-order thinking skills—precisely those the tests seek to measure. Thus, if students
can master Perelman’s techniques, they have likely earned their 6. Pearson adds that its Intelligent Essay
Assessor is primarily a classroom tool, allowing students to revise their essays multiple times before turning
them in to a teacher to be graded. However, for many states looking to introduce writing sections to their
battery of K-12 standardized tests, and for those that abandoned the effort due to the cost, eliminating
graders altogether will make them affordable. In addition, the stakes are not insubstantial for failure to
achieve passing grades on state standardized tests, ranging from retesting, to remedial programs, to summer
school, to non-promotion.
ETS, developer of the SAT, GRE, Praxis, and K-12 standardized tests for multiple states, counters that the
e-Rater is not replacing human graders in high stakes tests; it is supplementing them. Essays are scored by
both human and machine and when the scores do not match, a second human breaks the impasse.
Furthermore, they posit that the test prep course Perelman developed to teach students how to beat AES
software requires higher-order thinking skills—precisely those the tests seek to measure. Thus, if students
can master Perelman’s techniques, they have likely earned their 6. Pearson adds that its Intelligent Essay
Assessor is primarily a classroom tool, allowing students to revise their essays multiple times before turning
them in to a teacher to be graded. However, for many states looking to introduce writing sections to their
battery of K-12 standardized tests, and for those that abandoned the effort due to the cost, eliminating
graders altogether will make them affordable. In addition, the stakes are not insubstantial for failure to
achieve passing grades on state standardized tests, ranging from retesting, to remedial programs, to summer
school, to non-promotion. In addition, that provides immediate guidance, is a welcome addition to the
instructional toolbox. However, as demands on instructor’s time decrease, will university administrators
push staff cutbacks to meet budgetary constraints? Will fewer and fewer instructors be teaching more and
more students?
As MOOC and AES proliferate, the answer is: most likely. EdX is quickly becoming controversial in
academic circles. Presently, its course offerings are free and students earn a certificate of completion, but
not course credit. To become self-sustaining, however, the non-profit plans to offer its MOOC platform as
a “self-service” system, which faculty members can use to develop courses specifically branded for their
universities. EdX will then receive the first $50,000 in revenue generated from the course or $10,000 for a
recurring course. Thereafter, revenue will be split 50-50 between the university and EdX. A second
revenue-generating model offers universities “production help” with course development, charging them
$250,000 for a new course and $50,000 each term the course is offered again. If a course is successful, the
university receives 70% of the revenue, as long as EdX has been fully compensated for any self-service
courses. However, in order to generate enough revenue to share with its 12 university partners, which now
include University of California, Berkeley, Wellesley, Georgetown, and the University of Texas, a licensing
model is likely. Tested at no charge at San Jose State University in 2012, an EdX MOOC served as the
basis for a blended online engineering course. The enriched curriculum resulted in an increased passing rate
from 60% to 91 %. If course licensing becomes the key revenue stream, Anant Agarwal, the electrical
engineer president of EdX, foresees this happening in closed classrooms with limited enrollment.
But some members of the San Jose State faculty are nonetheless alarmed. When a second EdX MOOC,
JusticeX, was considered, the Philosophy department sent a sharply-worded letter addressed to Harvard
course developer, Michael Sandel, but actually leveled at university administrators. Asserting that the
department did not have an academic problem in need of remediation and was not lacking faculty to teach
its equivalent course, it did not shy from attacking the economic motives behind public universities’
embrace of MOOCs. The authors further asserted that MOOCs represented a decline in educational quality
and noted the irony involved when a social justice course was the vehicle for perpetrating a social
injustice—a long-term effort to “dismantle departments and replace professors.” Sandel’s conciliatory
response expressed his desire to share free educational resources, his aversion to undercutting colleagues,
and a call for a serious debate at both EdX and in the higher education community.
Other universities are similarly pushing back, against both EdX and other new MOOC ventures such as
Coursera and Udacity, founded by Stanford faculty members. MOOCs and AES are inextricably linked.
Massive online courses require automated assessment systems. In addition, both Coursera and Udacity have
expressed their commitment to using them due to the value of immediate feedback. Amherst College faculty
voted against joining the EdX consortium. Duke University faculty members thwarted administration
attempts to join nine other universities and educational technology company 2U in a venture to develop a
collection of for-credit undergraduate courses.
However, EdX was founded by two of the most prominent universities in the United States, has gathered
prestigious partners, and is already shaping educational standards. Stanford, for one, has decided to get on
board; it adopted the OpenEdX open-source platform and began offering a summer reading program for
freshman and two public courses in the summer of 2013. Stanford will collaborate with EdX on the future
development of OpenEdX and will offer both public and university classes on it.
Therefore, while Professor Perelman jokes that his former computer science major students could develop
an Android app capable of spitting out formulaic essays that would get a 6 from e-Rater, cutting humans
completely out of the equation, he knows that serious issues are in play. What educational outcomes will
result from diminishing human interaction and input? Will AI develop to the point that truth, accuracy,
effective organization, persuasiveness, argumentation and supporting evidence can be evaluated? And how
many more jobs in education will disappear as a result?
Case Study 1: American Water Keeps Data Flowing
American Water, founded in 1886, is the largest public water utility in the United States. Headquartered in
Voorhees, N.J., the company employs more than 7,000 dedicated professionals who provide drinking water,
wastewater and other related services to approximately 16 million people in 35 states, as well as Ontario
and Manitoba, Canada. Most of American Water’s services support locally managed utility subsidiaries that
are regulated by the U.S. state in which each operates as well as the federal government. American Water
also owns subsidiaries that manage municipal drinking water and wastewater systems under contract and
others that supply businesses and residential communities with water management products and services.
Until recently, American water’s systems and business, processes were much localized, and many of these
processes were manual. Over time, this information environment became increasingly difficult to manage.
Many systems were not integrated, so that running any type of report that had to provide information about
more than one region was a heavily manual process. Data had to be extracted from the systems supporting
each region and then combined manually to create the desired output. When the company was preparing to
hold an initial public offering of its stock in 2006, its software systems could not handle the required
regulatory controls, so roughly 80 percent of this work had to be performed manually. It was close to a
nightmare.
Management wanted to change the company from a decentralized group of independent regional businesses
into a more centralized organization with standard company-wide business processes and enterprise-wide
reporting. The first step toward achieving this goal was to implement an enterprise resource planning (ERP)
system designed to replace disparate systems with a single integrated software platform. The company
selected SAP as its ERP system vendor.
An important step of this project was to migrate the data from American Water’s old systems to the new
platform. The company’s data resided in many different systems in various formats. Each regional business
maintained some of its own data in its own systems, and a portion of these data was redundant and
inconsistent. For example, there were duplicate pieces of materials master data because a material might be
called one thing in the company’s Missouri operation and another in its New Jersey business. These names
had to be standardized so that every business unit used the same name for a piece of data. American Water’s
business users had to buy into this new company-wide view of data.
Data migration entails much more than just transferring data between old and new systems. Business users
need to know that data are not just a responsibility of the information systems department: the business
“owns” the data. Business needs determine the rules and standards for managing the data. Therefore, it is
up to business users to inventory and review all the pieces of data in their systems to determine precisely
which pieces of data from the old system will be used in the new system and which data do not need to be
brought over. The data also need to be reviewed to make sure they are accurate and consistent and that
redundant data are eliminated.
Most likely some type of data cleansing will be required. For example, American Water had data on more
than 70,000 vendors in its vendor master data file. Andrew Clarkson, American Water’s Business
Intelligence Lead, asked business users to define an active vendor and to use that definition to identify
which data to migrate. He also worked with various functional groups to standardize how to present address
data.
One of the objectives of American Water’s data management work was to support an enterprise wide
business intelligence program based on a single view of the business. An analytical system and data
warehouse would be able to combine data from the SAP ERP System with data from other sources,
including new customer information and enterprise asset management systems. That meant that American
Water’s business users had to do a lot of thinking about the kinds of reports they wanted. The company had
originally planned to have the system provide 200 reports, but later reduced that number by half. Business
users were trained to generate these reports and customize them. Most financial users initially tried to create
their reports using Microsoft Excel spreadsheet software. Over time, however, they learned to do the same
thing using SAP Business Objects Web Intelligence tools that came with the system. SAP Business Objects
Web Intelligence is a set of tools that enables business users to view, sort, and analyze business intelligence
data. It includes tools for generating queries, reports and interactive dashboards.
At present, American Water is focusing on promoting the idea that data must be “clean” to be effective and
has poured an incredible amount of effort into its data cleansing work—identifying incomplete, incorrect,
inaccurate, and irrelevant pieces of data and then replacing, modifying, or deleting the “dirty” data.
According to Clarkson, just as water treatment plants have measurements and meters to check water quality
as its being treated, data management needs to ensure the quality of data at every step to make sure the final
product will be genuinely useful for the company.
Case Study 1: Driving Ari Fleet Management with Real-Time Analytics
Automotive Resources International®, better known as simply ARI®, is the world’s largest privately-held
company for vehicle fleet management services. ARI is headquartered in Mt. Laurel, New Jersey and has
2,500 employees and offices throughout North America, Europe, the UK, and Hong Kong. The company
manages more than 1,000,000 vehicles in the U.S., Canada, Mexico, Puerto Rico and Europe.
Businesses that need vehicles for shipments (trucks, vans, cars, ships, and rail cars) may choose to manage
their own fleet of vehicles or they may outsource fleet management to companies such as ARI which
specialize in these services. ARI manages the entire life cycle and operation of a fleet of vehicles for its
customers, from up-front specification and acquisition to resale, including financing, maintenance, fuel
management, and risk management services such as driver safety training and accident management. ARI
also maintains six call centers in North America that operate 24/7, 365 days a year to support customers’
fleet operations by providing assistance regarding repairs, breakdowns, accident response, preventive
maintenance, and other driver needs. These call centers handle about 3.5 million calls per year from
customers, drivers, and suppliers who expect access to real-time actionable information.
Providing this information has become increasingly challenging. Operating a single large commercial
vehicle fleet generates high volumes of complex data, such as data on fuel consumption, maintenance,
licensing, and compliance. A fuel transaction, for example, requires data on state taxes paid, fuel grade,
total sale, amount sold, and time and place of purchase. A simple brake job and preventive maintenance
checkup generates dozens of records for each component that is serviced. Each part and service performed
on a vehicle is tracked using American Trucking Association codes. ARI collects and analyzes over 14,000
pieces of data per vehicle. Then multiply the data by hundreds of fleets, some with up to 10,000 vehicles,
all operating simultaneously throughout the globe, and you’ll have an idea of the enormous volume of data
ARI needs to manage, both for itself and for its customers.
ARI provided its customers with detailed information about their fleet operations, but the type of
information it could deliver was very limited. For example, ARI could generate detailed reports on lineitem expenditures, vehicle purchases, maintenance records, and other operational information presented as
simple spreadsheets, charts, or graphs, but it was not possible to analyze all the data to spot trends and make
recommendations. ARI was able to analyze data customer by customer, but it was not able to aggregate
data across its entire customer base.
For instance, if ARI was managing a pharmaceutical company’s vehicle fleet, its information systems could
not benchmark that fleet’s performance against others in the industry. That type of problem required too
much manual work and time, and still didn’t deliver the level of insight management thought was possible.
What’s more, in order to create reports, ARI had to go through internal subject matter experts in various
aspects of fleet operations, who were called “reporting power users.” Every request for information was
passed to these power users. A request for a report would take 5 days to fill. If the report was unsatisfactory,
it would go back to the report writer to make changes. ARI’s process for analyzing its data was extremely
drawn out.
In mid-2011, ARI implemented SAP BusinessObjects Explorer to give customers the enhanced ability to
access data and run their own reports. SAP BusinessObjects Explorer is a business intelligence tool that
enables business users to view, sort and analyze business intelligence data. Users search through data
sources using an iTunes like interface. They do not have to create queries to search the data and results are
shown with a chart that indicates the best information match. The graphical representation of results
changes as the user asks further questions of the data.
In early 2012, ARI integrated SAP BusinessObjects Explorer with HANA, SAP’s in-memory computing
platform that is deployable as an on-premise appliance (hardware and software) or in the cloud. HANA is
optimized for performing real-time analytics and handling very high volumes of operational and
transactional data in real time. HANA’s in-memory analytics queries data stored in random access memory
(RAM) instead of on a hard disk or flash storage.
Things started happening quickly after that. When ARI’s controller wanted an impact analysis of the
company’s top 10 customers, SAP HANA produced the result in 3 to 3 1/2 seconds. In ARI’s old systems
environment, this task would have been assigned to a power user versed in using reporting tools,
specifications would have to be drawn up and a program designed for that specific query, a process that
would have taken about 36 hours.
Using HANA, ARI is now able to quickly mine its vast data resources and generate predictions based on
the results. For example, the company can produce precise figures on what it costs to operate a fleet of a
certain size over a particular route across specific industries during a certain type of weather and predict
what the impact of changes in any of these variables. And it can do so nearly as easily as providing
customers with a simple history of their expenditures on fuel. With such helpful information ARI provides
more value to its customers.
HANA has also reduced the time required for each transaction handled by ARI’s call centers—from the
time a call center staffer takes a call to retrieving and delivering the requested information—by 5 percent.
Since call center staff account for 40 percent of ARI’s direct overhead, that time reduction translates into
major cost savings.
ARI plans to make some of these real-time reporting and analytic capabilities available on mobile devices,
which will enable customers to instantly approve a variety of operational procedures, such as authorizing
maintenance repairs. Customers will also be able to use the mobile tools for instant insight into their fleet
operations, down to a level of detail such as a specific vehicle’s tire history.
Case Study 4: Zappos
Tony Hsieh’s first entrepreneurial effort began at the age of 12 when he started his own custom button
business. Realizing the importance of advertising, Hsieh began marketing his business to other kids
through directories, and soon his profits soared to a few hundred dollars a month. Throughout his
adolescence, Hsieh started several businesses, and by the time he was in college he was making money
selling pizzas out of his Harvard dorm room. Another entrepreneurial student, Alfred Lin,bought pizzas
from Hsieh and resold them by the slice, making a nice profit. Hsieh and Lin quickly became friends.
After Harvard, Hsieh founded Link Exchange in 1996, a company that helped small businesses exchange
banner ads. A mere two years later, Hsieh sold Link Exchange to Microsoft for $265 million. Using the
profits from the sale, Hsieh and Lin formed a venture capital company that invested in start-up businesses.
One investment that caught their attention was Zappos, an online retailer of shoes. Both entrepreneurs
viewed the $40 billion shoe market as an opportunity they could not miss, and in 2000 Hsieh took over as
Zappos’ CEO with Lin as his chief financial officer.
Today, Zappos is leading its market and offering an enormous selection of more than 90,000 styles of
handbags, clothing, and accessories for more than 500 brands. One reason for Zappos’ incredible success
was Hsieh’s decision to use the advertising and marketing budget for customer service, a tactic that would
not have worked before the Internet. Zappos’ passionate customer service strategy encourages customers
to order as many sizes and styles of products as they want, ships them for free, and offers free return
shipping. Zappos encourages customer communication, and its call center receives more than 5,000 calls
a day with the longest call to date lasting more than four hours. Zappos’ extensive inventory is stored in a
warehouse in Kentucky right next to a UPS shipping center. Only available stock is listed on the website,
and orders as late as 11 p.m. are still guaranteed next-day delivery. To facilitate supplier and partner
relationships, Zappos built an extranet that provides its vendors with all kinds of product information,
such as items sold, times sold, price, customer, and so on. Armed with these kinds of details, suppliers
can quickly change manufacturing schedules to meet demand.
Zappos Culture
Along with valuing its partners and suppliers, Zappos also places a great deal of value on its employee
relationships. Zappos employees have fun, and walking through the offices you will see all kinds of
things not normally seen in business environments—bottle-cap pyramids, cotton-candy machines, and
bouncing balls. Building loyal employee relationships is a critical success factor at Zappos, and to
facilitate this relationship the corporate headquarters are located in the same building as the call center
(where most employees work) in Las Vegas. All employees receive 100 percent company-paid health
insurance along with a daily free lunch.
Of course, the Zappos culture does not work for everyone, and the company pays to find the right
employees through “The Offer,” which extends to new employees the option of quitting and receiving
payment for time worked plus an additional $1,000 bonus. Why the $1,000 bonus for quitting? Zappos
management believes that is a small price to pay to find those employees who do not have the sense of
commitment Zappos requires. Less than 10 percent of new hires take The Offer.
Zappos’ unique culture stresses the following:
1. Delivering WOW through service
2. Embracing and driving change
3. Creating fun and a little weirdness
4. Being adventurous, creative, and open-minded
5. Pursuing growth and learning
6. Building open and honest relationships with communication
7. Building a positive team and family spirit
8. Doing more with less
9. Being passionate and determined
10. Being humble
Zappos’ Sale to Amazon
Amazon.com purchased Zappos for $880 million. Zappos employees shared $40 million in cash and
stock, and the Zappos management team remained in place. Having access to Amazon’s world-class
warehouses and supply chain is sure to catapult Zappos’ revenues, though many wonder whether the
Zappos culture will remain. It’ll be interesting to watch!19

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