According to Heizer and Render, a good retail layout or design

	a.	Seeks the best personnel and machine utilization in repetitive or continuous production.
	b.	Deals with low-volume, high-variety production.
	c.	Addresses the layout requirements of large, bulky projects such as ships and buildings.
	d.	Groups workers, their equipment, and spaces/offices to provide for efficient movement of information.
	e.	Focuses on maximizing product exposure to customers

A factory manager of a Work Cell is seeking to reduce employee movement and space requirements while

enhancing communication, reducing the number of workers, and facilitating inspection. The manager can

achieve that by setting a new layout of the workstation in form of a:

	a.	Straight line layout
	b.	L shape layout
	c.	U shape layout
	d.	Layout not mentioned here, since these will not make any difference

In a Process-oriented layout work centers are arranged to minimize the costs of material handling.

The basic cost elements for optimizing material handling costs are:

	a.	material costs and labor costs
	b.	number of loads and distance the loads move between the work centers
	c.	setup time and run time at the work centers
	d.	number of shifts and number of working days during the week

Would you rather be given the opportunity to receive incentives based on individual performance or group performance? Please explain your answer. Does it depend on the situation? Why or why not?

1. Which of the following exemplifies location-based advantage for the companies competing on an international basis?

A. Hyundai signs a memorandum of understanding with the government of South Korea to halt exports.
B. Samsung diversifies and ventures into textiles and food processing.
C. RBC Wealth Management closes operations in South Florida.
D. Microsemi Corporation acquires California-based Actel Corporation.
E. De Beers sets up operations in the mining region of South Africa.

2. Which of the following statements regarding multidomestic competition is false?

A. Winning in one country market does not necessarily signal the ability to fare well in other countries.
B. The benefits from global integration and standardization are high.

C. Buyers in different countries are attracted to different product attributes.
D. Industry conditions and competitive forces in each national market differ in important respects.
E. The mix of competitors in each country market varies from country to country.

3. Companies operating in an international marketplace have to respond to all of the following, EXCEPT

A. Whether to buy a struggling competitor at a bargain price or pay a premium to gain entry to the local market.
B. The tensions between market pressures to localize a company's product offerings country by country and the competitive pressures to lower costs through greater product customization.
C. How much to customize their offerings in each different country market to match the tastes and preferences of local buyers.
D. whether to customize their offerings in each different country market to match the tastes and preferences of local buyers.

E. Whether to pursue a strategy of offering a mostly standardized product worldwide

The Steelie Dan Company produces a component that is subsequently used in the aerospace industry. The component consists of three parts (A, B, and C) that are purchased from outside and cost 40, 35, and 15 cents per piece, respectively. Parts A and B are assembled first on assembly line 1, which produces 170 components per hour. Part C undergoes a drilling operation before being finally assembled with the output from assembly line 1. There are in total six drilling machines, but at present only three of them are operational. Each drilling machine drills part C at a rate of 70 parts per hour. In the final assembly, the output from assembly line 1 is assembled with the drilled part C. The final assembly line produces at a rate of 190 components per hour. At present, components are produced eight hours a day and five days a week. Management believes that if need arises, it can add a second shift of eight hours for the assembly lines. The cost of assemble labor is 30 cents per part for each assembly line; the cost of drilling labor is 10 cents per part. For drilling the cost of electricity is two cent per part. The total overhead cost has been calculated as $1,100 per week. The depreciation cost for equipment has been calculated as $30 per week.

a. Create a process flow diagram to determine the process capacity of the entire process.

b. Suppose a second shift of eight hours is run for assembly line 1 and the same is done for the final assembly line. In addition, four of the six drilling machines are made operational. The drilling machines, however, operate for just eight hours a day. What is the new process capacity (number of components produced per week)? Which of the three operations limits the capacity.

c. Management decides to run a second shift of eight hours for assembly line1 plus a second shift of only four hours for the final assembly line. Five of the six drilling machines operate for eight hours a day. What is the new capacity? Which of the three operations limits the capacity?

d. Determine the cost per unit output for questions (b) and (c). Item Calculation Cost Cost of part A Cost of part B Cost of part C Electricity Assembly 1 labor Final assembly labor Drilling labor Overhead Depreciation Total Cost Cost per unit = Item Calculation Cost Cost of part A Cost of part B Cost of part C Electricity Assembly 1 labor Final assembly labor Drilling labor Overhead Depreciation Total Cost per unit=

e. The product is sold at $3.00 per unit. Assume that the cost of a drilling machine (fixed cost) is $30,000 and the company produces 8,000 units per week. Assume that four drilling machines are used for production. If the company had an option to buy the same part at $3.00 per unit, what would be the break-even number of units?

A bike manufacturer has fixed costs of $250,000 and variable costs per bike of $55.00. The bike sells for $150.00 per bike.

A. How many Bikes must be sold to break even?

B.If the fixed cost is increased, would the new breakeven point be higher or lower?

C. If the variable cost per bike decreased, would the new break even point be higher or lower?

Instead of entering into an alliance or partnership, a wireless company, Verizon Wireless opts to merge with a web services company, Yahoo! What are the reasons for preferring a merger to an alliance or partnership? Explain the other organizational mechanisms that are also preferable to alliances.

Explain what the Observe, Orient, Decide, Act (OODA) Model  is and how it is applicable to intelligent agents.

Provide a graphical labeled model for an intelligent agent and explain each component of a proper intelligent agent model.

in rainey a funeral in a public service center what values were the file clerks emphasizing through their behavior

Discuss and Elaborate the following questions with Diversity & Inclusion (Discuss and Elaborate more with Diversity & Inclusion, Organisational Behavior, Management and other related topics.) (Word limit of 600 words)

What is the optimal solution companies can use to approach Diversity & Inclusion?

Would measures (Example: A Quota or Targets) help to reduce bias or discrimination at work? Why or why not?

Use Google & International Business Machines (IBM), or other related organizations and company, as examples to discuss and illustrate the top two concerns of Diversity & Inclusion.

What have you learned from the Diversity & Inclusion? Discuss and Illustrate two learning points, give examples and elaborate when necessary.

*PLEASE TYPE YOUR ANSWER (NO SCREENSHOTS OR IMAGES) IN FULL SENTENCE/PARAGRAPH/REPORT FORMAT, NO POINT FORM. THANK YOU IN ADVANCE

Develop a production plan and calculate the annual cost for a firm whose demand forecast is fall, 10,000; winter, 8,000; spring, 7,000; summer, 12,000. Inventory at the beginning of fall is 500 units. At the beginning of fall you currently have 30 workers, but you plan to hire temporary workers at the beginning of summer and lay them off at the end of summer. In addition, you have negotiated with the union an option to use the regular workforce on overtime during winter or spring only if overtime is necessary to prevent stockouts at the end of those quarters. Overtime is not available during the fall. Relevant costs are hiring, $110 for each temp; layoff, $220 for each worker laid off; inventory holding, $5 per unit-quarter; backorder, $10 per unit; straight time, $5 per hour; overtime, $8 per hour. Assume that the productivity is 0.5 unit per worker hour, with eight hours per day and 60 days per season. In each quarter, produce to the full output of your regular workforce, even if that results in excess production. In Winter and Spring, use overtime only if needed to meet the production required in that quarter. Do not use overtime to build excess inventory in prior seasons expressly for the purpose of reducing the number of temp workers in Summer. (Leave no cells blank - be certain to enter "0" wherever required. Negative values should be indicated by a minus sign. Round up "Number of temp workers, Workers hired and Workers laid off" to the next whole number and all other answers to the nearest whole number.)

Freight Railroads—what are the benefits for using the railroads vs. using Motor Carrier/trucking? What are two major differentiators?

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.

What are steps that leaders should take in adoption of emerging digital technologies?

Exercise 5.3: How will the Digital Revolution Affect Your Organization?

Briefing (2)

• Ok, you are not leaving the organization after all. That briefing was designed to make you think about potential threats to your organization from agile and innovative “out of sector” competitors. Let us assume that the new business model that you have just described is real, and that somebody else has already thought about it---and may already be setting it up. How can your organization respond to that threat? Better still, how can your organization counter that threat before it emerges?