b) Give brief information about NSFNET that form the backbone of the Internet in early 1990s? How many routers were involved?

c) Consider a router that interconnects three subnets: Subnet 1, Subnet 2, and Subnet 3. Suppose all of the interfaces in each of these three subnets are required to have the prefix 223.1.17/24. Also suppose that Subnet 1 is required to support up to 63 interfaces, Subnet 2 is to support up to 95 interfaces, and Subnet 3 is to support up to 16 interfaces. Provide three network addresses (of the form a.b.c.d/x) that satisfy these constraints.

On Target: Leveraging the

Retail Website through Data

Science

In the mid-1990s, Target was a discount superstore behemoth. As a nationally

branded general merchandise retailer, it sells products through digital

channels since 1999.

The retailer had set itself apart from chief rival Walmart with a focus on more

upscale but wallet-friendly fashion and lifestyle lines, spurring double-digit

growth by double-digits each year for more than a decade, from 1996 to 2008.

That fruitful streak came to an abrupt halt with the United States financial

crash in the fall of 2008. Target was hit hard—much harder, in fact, than

Walmart. Five years later (2013) the company was still struggling. Today, the

company hosts over 30 million shoppers at its 1,800+ stores per week. With more than 1,800 stores and a relatively new e-commerce site, Target

was collecting reams of data about its online customers—products purchased,

browsing habits, items abandoned in shopping carts—yet it wasn’t fully

leveraging all that information. The company began to see this huge pile of e-

commerce data as the needle-in-a-haystack key to driving higher sales, says

Harvard Business School Professor Srikant M. Datar.

“Target had to make this big shift from thinking only about retailing to

also thinking about data. And to do that, data had to become the big

asset they needed to develop to provide new opportunities,” Datar says.

Winners in the retail market are putting data to work. In the case of a

national chain the size of Target, that means keeping tabs on an

inventory of around 1 million products, then using data to ensure their

availability.

“Even today, not all retailers have embraced data fully to the point

where they think of themselves as data companies, and it might be why

many companies are suffering.” Several traditional retailers are indeed

tripping over the digital divide. JC Penney lost its market value

significantly, with shares trading at $1.50 mid 2018. And in October

2018, Sears filed for bankruptcy protection, following the dark road

paved by Borders, RadioShack, and Toys ‘R’ Us.

However by 2018, Target managed a startling recovery from its five-

year slump . In third quarter of 2019, total revenue grew 4.7% during the

quarter to $18.67 billion from $17.82 billion a year earlier, beating

expectations for $18.49 billion. Sales at Target stores open for at least

12 months and online were up 4.5%, better than expected growth of

3.6%. The company said digital sales surged 31% during the quarter,

with its same-day delivery options including buy online, pick up in store

and curbside pickup accounting for 80% of digital sales growth.

Fascinated with Target’s stunning turnaround, Datar studied the

calculated steps it took to fuel its success.

Hire data experts

In 2013, the one bright spot in Target’s otherwise bleak financial picture

was its then-small e-commerce arm. Although overall sales declined,

online business soared nearly 30 percent between 2012 and 2013.

Target was awash in customer data from these online sales, but to

make sense of it, the company needed to bring in the right people.

Paritosh Desai joined Target in August 2013 as vice president of

business intelligence, analytics, and testing, and he then went on a

hiring spree, growing the analytics team with data scientists and others

trained in computer science, math, statistics, and physics, including

many who held doctorates.

To attract the best people, Target knew it had to keep at least part of its

data operation in Silicon Valley, even though the company’s corporate

offices were in Minneapolis. “It was a big decision to stay in Silicon

Valley,” Datar says. “The demand for data-science professionals is

through the roof, so you have to go where the experts are. Desai credits

the success of data science at Target to this team.”

Target leverages data science to help improve on-shelf availability,

reduce inventory levels and create operating efficiencies, according to

Paritosh Desai, SVP and chief data and analytics officer, speaking at

The AI Summit in New York 2019.

"We're dealing with problems that are extremely large," said Desai.

"And we're not just solving for today but for next week and next month."

To solve its complex data science problem in the context of retail,

leadership blends traditional data science methods with modern

techniques like neural networks to improve performance of demand-

forecasting models.

Experiment and execute quickly

Desai created an entrepreneurial culture, knowing experimentation

would be critical to discovering how data could be woven into the

company’s business practices. His colleagues followed this mantra:

develop, test, measure.

Yet they couldn’t just continue with experiment after experiment without

applying what they learned—and quickly. “If you just keep

experimenting, people [in the company] will say, when do the sales

come in? You don’t have that much time to keep trying,” Datar says.

“The only solution is to learn fast, take action, and continue to build on

your learning.”

Deliver a mobile response in milliseconds

Desai knew from previous experience leading data science at Gap Inc.

that consumers get frustrated with slow mobile apps. To him, the most

important engineering requirement was providing users with a response

to their search in milliseconds consistently.

Just as important, the response had to be relevant to that customer. If a

consumer searched for “sneakers,” the site should not only provide a list

of sneaker-like shoes, but at the top of the list should be the particular

brand the user purchased in the past.

Measure success in narrow terms

A successful customer interaction on Target.com was narrowly defined:

Only when a customer searched for a product, received

recommendations, and actually purchased the product would the

company bother to drill deeper to test which banner designs worked

best to push a sale through.

And if a customer didn’t buy after browsing, the team asked questions:

What information was the customer missing? Was there something in

the customer’s purchase history that Target should have known that

would have blocked the sale?

“Only searches that led to sales of recommended products in the same

session were considered statistically significant and quantifiable,” Datar

says. “By setting the bar that high, you remain humble about what you

can learn from both successful and unsuccessful transactions.”

Managers should ask data-related questions

Desai wanted the team to help managers in the field make smart

business decisions based on data. Those managers were encouraged

to develop questions that could produce value if analysts could

massage the data to provide accurate answers. For example, a

manager might ask: Did a dish detergent promotion boost sales, or

would customers have bought the detergent anyway?

“The folks who had the data did not go to the managers and say, ‘I have

all this data. Let me tell you how to run your business now.’ They said,

‘Look, let me understand the key questions you would like to have

answered with data,’” Datar says.

As the team worked with analysts and managers, the questions became

so sophisticated that an engineer might be required to develop a tool to

answer them. The engineer had to work quickly. “Retail is always

changing so fast that you can’t wait three weeks to get an answer,”

Datar says. “The engineer might have to build the tool and provide the

answer in an hour.”

Allow managers to analyze data

The alliance between data experts and managers was a good start. But

Desai knew that if managers had to wait in a queue for an expert every

time they had a question, they might grow weary of asking. The

solution: allow managers themselves to work with the data. That meant

creating a flexible analytics system that could not only adapt to real-time

business changes but one that managers felt comfortable using.

“He didn’t want to be the bottleneck. It took vision and humility to say,

‘The answer doesn’t have to come through me and my data science

team,’” Datar says. “It was a bold decision because it was much costlier

and more complicated to design flexible architecture that managers

could easily interact with.”

Take a calculated approach to using data

In the early days of e-commerce many retailers made the mistake of

treating their online unit as a mere add-on to the store. Target, by

contrast, spent much time focusing on how data could be used

specifically to help build its web arm. And the retailer was careful to

establish the value of data, analytics, and algorithms for the e-

commerce site first before scaling up its capabilities to make decisions

and solve problems in other areas of the business, such as marketing,

store sales, and the supply chain.

In its approach to modernization, Target has learned to lean on the

physicality of retail to achieve better results. But when the time was

right, this data-analytic approach would help dictate a variety of

decisions for Target, including which products earned precious shelf

space inside its brick and mortar stores.

"A lot of people have predicted that stores will not have a place in the

future with online shopping and e-commerce taking over," Desai said.

"What we say is that stores are our most important asset."

With 70% to 80% of Americans living within 10 miles of a Target store,

the chain opted to rely on the stores as hubs to help fulfill digital

demand. Services such as buy online, pick-up in store (or BOPIS, per

industry speak), and Drive Up add complexity and size to the math the

data science team is solving for.

"When you have so many ways to fulfill demand, it's important to have

the product in the right place at right time," Desai said. In 2017, Target

began testing algorithms to increase fulfillment velocity in its supply

chain. With initial improvements proving valuable, the company is

planning further efforts around distribution center automation and

design, said John Mulligan, Target EVP and COO, during its Q1 2018

earnings call.

“Across our supply chain, we're testing and rolling out new processes

designed to make us faster, more nimble, more accurate and reliable.

Last year, we told you about our new facility in Perth Amboy, New

Jersey, which provides a clean slate where you could develop and

refine a completely new way to replenish stores, and the results have

been impressive. Out-of-stocks on items in the store served by the

Perth Amboy facility have been running about 40% lower than our

previous benchmark. These results were accomplished by applying a

new inventory positioning logic, developed by our data and analytics

team that allows us to send the right quantity in the right unit measure

much faster than our other facilities.

While last year was about developing and testing these algorithms, this

year is focused on beginning to scale up the physical movement of

inventory. And by early next year, another 50 Target stores will be

served by this new model”

“Target realized the importance of devoting the time, attention, skill, and

strategy to developing data and analytics competencies in a critical part

of the business—its e-commerce site—before rolling out these

capabilities more broadly,” Datar says. “I think that’s a big reason why

Target’s adoption of a data-driven approach has been so successful.”

CASE REVIEW QUESTIONS

1. When the growth of Target sales began suffering and how long

did the slowdown of last? Which year was the turning point and

what was one of the reasons for the recovery of the company?

2. Based on the study of Harvard Business School Professor Srikant

M. Datar, please list and elaborate on the strategic moves Target

has put into action for its turnaround.

A researcher claims that the variation in the salaries of elementary school teachers is greater than the variation in the salaries of secondary school teachers. A random sample of the salaries of 30 elementary school teachers has a variance of $8,208, and a random sample of the salaries of 30 secondary school teachers has a variance of $3,817. At α = 0.05, can the researcher conclude that the variation in the elementary school teachers’ salaries is greater than the variation in the secondary school teachers’ salaries? Use the P-value method.

Listed below are amounts of court income and salaries paid to the town justices for a certain town. All amounts are in thousands of dollars. Find the​ (a) explained​ variation, (b) unexplained​ variation, and​ (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear​ correlation, so it is reasonable to use the regression equation when making predictions. For the prediction​ interval, use a 99​% confidence level with a court income of ​$800​,000. Court Income ​$61 ​$411 ​$1568 ​$1113 ​$271 ​$252 ​$110 ​$152 ​$32 Justice Salary ​$33 ​$41 ​$90 ​$50 ​$41 ​$59 ​$29 ​$25 ​$19

. A certain town has 25,000 families. The average number of children per family is 2.4 with an SD of 1.1 . Also, 20% of the families have no children at all. A random sample of 400 families from that town is drawn.

a) Find the expected value and the standard error for the percentage of families chosen without children?

b) Estimate the chance that somewhere between 17% and 19% of the sample families will have no children?

c) Find the expected value and the standard error for the average number of children per family in the sample

d) Estimate the chance that the average number of kids per family in the sample is greater than 2.5

Listed below are amounts of court income and salaries paid to the town justices for a certain town. All amounts are in thousands of dollars. Find the​ (a) explained​ variation, (b) unexplained​ variation, and​ (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear​ correlation, so it is reasonable to use the regression equation when making predictions. For the prediction​ interval, use a 99 99​% confidence level with a court income of ​$800,000.

Court_Income   Justice_Salary
69   27
412   42
1587   94
1104   57
258   41
252   59
101   21
152   20
28   23

. A certain town has 25,000 families. The average number of children per family is 2.4 with an SD of 1.1 . Also, 20% of the families have no children at all. A random sample of 400 families from that town is drawn.

a) Find the expected value and the standard error for the percentage of families chosen without children?

b) Estimate the chance that somewhere between 17% and 19% of the sample families will have no children?

c) Find the expected value and the standard error for the average number of children per family in the sample

d) Estimate the chance that the average number of kids per family in the sample is greater than 2.5

1a.) If two events A and B are non-overlapping, then the formula for computing the probability of theirunion is: ?(?) + ?(?) . This statement is true or false?

1b.) A survey of 1,000 adults in a small town gave the following data. Round answers to the nearest tenth of a percent.

If an adult in the town is selected at random, find the probability that: a.) The adult is either female or employed.

b.) The adult is neither male nor employed.

c.) The adult is female, given the adult is unemployed.

d.) The adult is unemployed, given the adult is female.

	Employed	Unemployed	Total
Male	510	40	550
Female	190	260	450
Total	700	300	1,000

(law question). Fred Maxson owned Canadian Equipment Company. To operate the business, Maxson borrowed funds from Cross Town Bank & Trust. For each loan, Cross Town filed a financing statement that included Maxson’s signature and address, the bank’s name and address, and a description of the collateral. The first loan covered all of Maxson’s equipment, including “any after-acquired property.” The second loan covered a truck crane “whether owned now or acquired later.” The third loan covered a “Bobcat mini-excavator.” Did these financing statements perfect Cross Town’s security interests? Explain.