Evaluate the capital budgeting project using the traditional Net Present Value (NPV) approach and the Internal Rate of Return (IRR) criterion and present findings.

Find if this new proposal will turn out to be a good investment for his company.

Capital budgeting and investment proposal – a new product line of branded shirts that the committee was considering for launch. What would be the basis for calculating the after-tax operating cash flows for the capital project? How would you arrive at the depreciation and working capital requirements for computing the NPV? What would be the basis for calculating the terminal year cash flows?

Indian Retail Market The Indian retail market is at the cusp of a sweet spot driven by strong GDP (Gross Domestic Product) growth, benign inflation, and rising per capita income and purchasing power of consumers. Currently, the retail industry accounts for more than ten percent of the Indian Gross Domestic Product and approximately eight percent of employment. The industry is expected to nearly double, from US$ 600 billion in 2015 to US$ 1 trillion, by 2020 driven by income growth, urbanization, and attitudinal shifts (Indian Terrain Annual Report, 2015–16). It has been estimated that, by 2030, the Indian apparel market, in particular, is expected to grow at a CAGR (compounded annual growth rate) of approximately 10–12%, backed by increasing affordability on account of an increase in disposable incomes, increase in aspirations, and a shift from unbranded to branded products by the burgeoning middle class. This trend is likely to be further accentuated by the rise of e-commerce companies that enable shopping from anywhere, thereby leading to increased penetration in small cities and towns (Indian Terrain Annual Report, 2015–16).

Company Background: Case

Bhatia Textiles is a small, privately-owned clothing company based in New Delhi, India. It was founded in 1995 by Harish Bhatia, a retired executive. Since then, the company had grown steadily by catering to middle to low income consumers in the Delhi-National Capital Region (NCR). The company recorded stellar growth of 50% in its sales during the last financial year of 2015–16. With a healthy operating margin ratio and low leverage levels, the company had been able to grow its profits at a CAGR of 25% during the last 10 years. With a good brand name and healthy financial metrics, the company was now looking to expand its footprint to new product lines catering to middle to high income customers. Project Investment Proposal Details The project is estimated to be of 10 years duration. It involves setting up new machinery with an estimated cost of as much as INR 500 million, including installation. This amount could be depreciated using the straight-line method (SLM) over a period of 10 years with a resale value of INR 15 million. The project would require an initial working capital of INR 20 million with cumulative investment in net working capital to be maintained at 10% of each year’s projected revenue. With the planned new capacity, the company would be able to produce 240,000 pieces of shirts each year for the next 10 years. In terms of pricing, each shirt can initially be sold at INR 1,300 apiece, which takes into account the target segment and competitor pricing.

The project proposal incorporates an annual increase of 3% in the price of the shirt to compensate for the inflationary impact. With regards to the raw material costs and other expenses, the project estimated the following details: • Raw material cost for manufacturing shirts at INR 400 per shirt, slated to rise by 5% per annum on account of inflation. • Other direct manufacturing costs at INR 125 per shirt with an annual increase of 5% per annum on account of inflation. • Selling, general, and administrative expenses (including employee expenses) at INR 35 million per annum, expected to increase by 10% each year. • Depreciation expense on the basis of SLM. • Tax rate was assumed to be 25%. Funding For funding of the expansion project, the promoters agreed to infuse 50% in the form of equity with the rest (50%) being financed from issue of new debt. Based on the current credit position and market scenario, new debt can be raised by the company at 12% per annum. Cost of equity was assumed to be 15%. The requisite discounting rate or weighted average cost of capital (WACC) for NPV and IRR calculations can now be calculated with the help of the above assumptions.

Although the project proposal estimates maximum annual production of 240,000 shirts, find the capital budgeting analysis under two demand scenarios: Optimistic and Expected. The likely annual demand estimated under each scenario is as follows:

Scenario Annual demand

Optimistic 240,000 Shirts

Expected 200,000 Shirts

Question 1

On the basis of the financial information given in the case, calculate the after-tax operating cash flows, NPV, and IRR under the Optimistic and Expected scenarios. Clearly specify the calculations required for the same.

please no handwriting

Learning Outcome:

Understand the issues involved with transfer pricing in multidivisional companies (question 1)

Provide an appreciation of logistics activities and their relationship to supply chain management, other business functions and enterprises. (Question 2)

ECR in the UK

Dutchman Paul Polman, now CEO of Unilever, did a stint as General Manager of Procter & Gamble UK and Eire from 1995 to 1999. While admiring the UK’s advanced retailing systems, he saw opportunities for all four of the ‘pillars of ECR’ – range, new items, promotions and replenishment. The following is extracted from the text of a speech he made to the Institute of Grocery Distribution.

Range

The average store now holds 35 per cent more than five years ago, yet a typical consumer buys just 18 items on a trip. A quarter of these skus[1] sell less than six units a week!

The number of skus offered by manufacturers and stores has become too large and complex. My company is equally guilty in this area. No question, we make too many skus. I can assure you we are working on it. Actually, our overall sku count in laundry is already down 20 per cent compared to this time last year. What’s more, business is up.

Clearly, we have an opportunity to rationalise our ranges. As long as we do this in an ECR way – focusing on what consumers want – we will all win. The consumer will see a clearer range. Retailers and manufacturers will carry less inventory and less complexity.

The result will be cost savings across the whole supply chain and stronger margins.

New items

There were 16,000 new skus last year. Yet 80 per cent lasted less than a year. You don’t need to be an accountant to imagine the costs associated with this kind of activity. And look how this has changed. Since 1975, the number of new sku introductions has increased eightfold. Yet their life expectancy has shrunk from around five years in 1975 to about nine months now. We can hardly call this progress.

Promotions

In promotions it’s the same story. Take laundry detergents. This is a fairly stable market. Yet we’re spending 50 per cent more on promotions than two years ago, with Consumers buying nearly 30 per cent more of their volume on promotions. This not only creates an inefficient supply chain, or in some cases poor in-store availability, but, more importantly, has reduced the value of the category and likely the retailers’ profit. We are all aware of the inefficiencies promotions cause in the system, such as problems in production, inventory and in-store availability. They all create extra costs, which ultimately have to be recouped in price. But there’s a higher cost. As promotions are increasing, they are decreasing customer loyalty to both stores and brands by 16 per cent during the period of the promotion. We commissioned a report by Professor Barwise of the London Business School. He called it ‘Taming the Multi-buy

Dragon’. The report shows us that over 70 per cent of laundry promotional investment goes on multi-buys. The level of investment on multi-buys has increased by 60 per cent over the last three years. There’s been a 50 per cent increase behind brands and a doubling of investment behind own labels. Contrary to what we thought, most of this volume is not going to a broad base of households. It is going to a small minority.

Seventy-one per cent of all multi-buy volume is bought by just 14 per cent of households. Just 2 per cent of multi-buy volume goes to 55 per cent of households.

We really are focusing our spending on influencing and rewarding a very small minority of people indeed.

Replenishment

Based on the escalating activity I’ve just [referred to], costs are unnecessarily high. There are huge cost savings also here, up to 6 per cent, by removing the non-value-added skus and inefficient new brand and promotional activity.

Questions

1 - Cutting down on range, new items and promotions is presumably going to lead to ‘everyday low prices’. Discuss the implications to the trade-off between choice and price. (2 points)

2- Procter & Gamble’s major laundry brand in the US is Tide. This is marketed in some 60 pack presentations, some of which have less than 0.1 per cent share. The proliferation of these pack presentations is considered to have been instrumental in increasing Tide’s market share from 20 to 40 per cent of the US market in recent years. Clearly, this is a major issue within P&G.

What are the logistics pros and cons of sku proliferation? (2points)

please no handwriting

Learning Outcome:

Understand the issues involved with transfer pricing in multidivisional companies (question 1)

Provide an appreciation of logistics activities and their relationship to supply chain management, other business functions and enterprises. (Question 2)

CASE STUDY

ECR in the UK

Dutchman Paul Polman, now CEO of Unilever, did a stint as General Manager of Procter & Gamble UK and Eire from 1995 to 1999. While admiring the UK’s advanced retailing systems, he saw opportunities for all four of the ‘pillars of ECR’ – range, new items, promotions and replenishment. The following is extracted from the text of a speech he made to the Institute of Grocery Distribution.

Range

The average store now holds 35 per cent more than five years ago, yet a typical consumer buys just 18 items on a trip. A quarter of these skus[1] sell less than six units a week!

The number of skus offered by manufacturers and stores has become too large and complex. My company is equally guilty in this area. No question, we make too many skus. I can assure you we are working on it. Actually, our overall sku count in laundry is already down 20 per cent compared to this time last year. What’s more, business is up.

Clearly, we have an opportunity to rationalise our ranges. As long as we do this in an ECR way – focusing on what consumers want – we will all win. The consumer will see a clearer range. Retailers and manufacturers will carry less inventory and less complexity.

The result will be cost savings across the whole supply chain and stronger margins.

New items

There were 16,000 new skus last year. Yet 80 per cent lasted less than a year. You don’t need to be an accountant to imagine the costs associated with this kind of activity. And look how this has changed. Since 1975, the number of new sku introductions has increased eightfold. Yet their life expectancy has shrunk from around five years in 1975 to about nine months now. We can hardly call this progress.

Promotions

In promotions it’s the same story. Take laundry detergents. This is a fairly stable market. Yet we’re spending 50 per cent more on promotions than two years ago, with Consumers buying nearly 30 per cent more of their volume on promotions. This not only creates an inefficient supply chain, or in some cases poor in-store availability, but, more importantly, has reduced the value of the category and likely the retailers’ profit. We are all aware of the inefficiencies promotions cause in the system, such as problems in production, inventory and in-store availability. They all create extra costs, which ultimately have to be recouped in price. But there’s a higher cost. As promotions are increasing, they are decreasing customer loyalty to both stores and brands by 16 per cent during the period of the promotion. We commissioned a report by Professor Barwise of the London Business School. He called it ‘Taming the Multi-buy

Dragon’. The report shows us that over 70 per cent of laundry promotional investment goes on multi-buys. The level of investment on multi-buys has increased by 60 per cent over the last three years. There’s been a 50 per cent increase behind brands and a doubling of investment behind own labels. Contrary to what we thought, most of this volume is not going to a broad base of households. It is going to a small minority.

Seventy-one per cent of all multi-buy volume is bought by just 14 per cent of households. Just 2 per cent of multi-buy volume goes to 55 per cent of households.

We really are focusing our spending on influencing and rewarding a very small minority of people indeed.

Replenishment

Based on the escalating activity I’ve just [referred to], costs are unnecessarily high. There are huge cost savings also here, up to 6 per cent, by removing the non-value-added skus and inefficient new brand and promotional activity.

Questions

1 - Cutting down on range, new items and promotions is presumably going to lead to ‘everyday low prices’. Discuss the implications to the trade-off between choice and price. (2 points)

2- Procter & Gamble’s major laundry brand in the US is Tide. This is marketed in some 60 pack presentations, some of which have less than 0.1 per cent share. The proliferation of these pack presentations is considered to have been instrumental in increasing Tide’s market share from 20 to 40 per cent of the US market in recent years. Clearly, this is a major issue within P&G.

What are the logistics pros and cons of sku proliferation? (2points)

please long answer

You've been hired by Water Wonders to write a C++ console application that analyzes lake level data. MichiganHuronLakeLevels.txt. Place the input file in a folder where your development tool can locate it (on Visual Studio, in folder \). The input file may be placed in any folder but a path must be specified to locate it.

MichiganHuronLakeLevels.txt Down below:

Lake Michigan and Lake Huron - Average lake levels - 1860-2015

Year    Average level (meters)

1860    177.3351667

1861    177.3318333

1862    177.316

1863    177.1796667

1864    176.9955833

1865    176.90525

1866    176.80575

1867    176.9365833

1868    176.7891667

1869    176.8250833

1870    177.1

1871    177.0769167

1872    176.7318333

1873    176.9188333

1874    177.0413333

1875    176.9683333

1876    177.2855833

1877    177.1971667

1878    177.1183333

1879    176.85325

1880    176.90425

1881    177.0205

1882    177.1250833

1883    177.2096667

1884    177.2734167

1885    177.3208333

1886    177.3893333

1887    177.1890833

1888    176.9931667

1889    176.8393333

1890    176.788

1891    176.6149167

1892    176.564

1893    176.6204167

1894    176.6811667

1895    176.4201667

1896    176.3256667

1897    176.5094167

1898    176.5564167

1898    176.5595

1900    176.5626667

1901    176.64175

1902    176.5305833

1903    176.5748333

1904    176.7460909

1905    176.7561667

1906    176.7635

1907    176.7844167

1908    176.7670909

1909    176.5988333

1910    176.5025

1911    176.3356667

1912    176.4768333

1913    176.67025

1914    176.5297273

1915    176.3535833

1916    176.5715833

1917    176.7980833

1918    176.8866667

1919    176.745

1920    176.625

1921    176.4883333

1922    176.445

1923    176.2641667

1924    176.1866667

1925    175.9191667

1926    175.885

1927    176.1483333

1928    176.4433333

1929    176.8958333

1930    176.6508333

1931    176.1183333

1932    175.9408333

1933    175.8675

1934    175.7666667

1935    175.8908333

1936    175.9391667

1937    175.9225

1938   176.1408333

1939    176.2691667

1940    176.1416667

1941    176.1216667

1942    176.3341667

1943    176.6266667

1944    176.5966667

1945    176.57

1946    176.6016667

1947    176.5666667

1948    176.5308333

1949    176.2108333

1950    176.2608333

1951    176.7358333

1952    177.085

1953    176.9333333

1954    176.8291667

1955    176.7225

1956    176.44

1957    176.2633333

1958    176.0675

1959    176.0058333

1960    176.4775

1961    176.3766667

1962    176.2225

1963    175.9225

1964    175.6825

1965    175.9158333

1966    176.1608333

1967    176.3008333

1968    176.4466667

1969    176.6958333

1970    176.6783333

1971    176.805

1972    176.8883333

1973    177.1233333

1974    177.0933333

1975    176.9733333

1976    176.8991667

1977    176.505

1978    176.5908333

1979    176.7941667

1980    176.8033333

1981    176.6983333

1982    176.5983333

1983    176.8333333

1984    176.895

1985    177.1266667

1986    177.2925

1987    176.97

1988    176.5641667

1989    176.4008333

1990    176.35

1991    176.4691667

1992    176.4791667

1993    176.6958333

1994    176.6783333

1995    176.5275

1996    176.6541667

1997    176.9841667

1998    176.7166667

1999    176.2358333

2000    175.9783333

2001    175.9508333

2002    176.1183333

2003    175.8916667

2004    176.1108333

2005    176.09

2006    176.0158333

2007    175.9433333

2008    176.005

2009    176.2583333

2010    176.1108333

2011    176.0366

2012    175.9158

2013    175.9

2014    176.3016667

2015    176.59

Within the app, attempt to open the input file and output file MichiganHuronLakeLevelsHighAndLow.txt. If the input file didn't open, print an error message. If the output file didn't open, print an error message. Read the input file by scanning past the two header rows. Each detail row in the input file contains two fields (year, lake level). Read one token at a time from the input file. See sample Canvas app Text file input – one token per read. Determine the maximum, minimum, and average lake levels. One technique to accomplish this is to use a max variable that starts very small and a min variable that starts very large. After all lines of the input file have been read, use formatted output manipulators (setw, left/right) to print the following rows:

          ● Column headers.

          ● Max values.

          ● Min values.

          ● Average value.

And columns:

          ● A left-justified label.

          ● A right-justified year.

          ● A right-justified level.

Then write the same information to output file MichiganHuronLakeLevelsHighAndLow.txt. Insure that your code is commented! Provide a complete header comment and body comments. Define constants for the input and output file names and column widths. Format any real numbers to four decimal places. The output should look like this:

Welcome to Wonder Waters

------------------------

Reading lines from file 'MichiganHuronLakeLevels.txt' ...

Writing lines to file 'MichiganHuronLakeLevelsHighAndLow.txt' ...

                      Year    Level (meters)

Max level:               …                 …

Min level:               …                 …

Average level:                             …

158 line(s) read from file 'MichiganHuronLakeLevels.txt'.

4 line(s) written to file 'MichiganHuronLakeLevelsHighAndLow.txt'.

End of Wonder Waters

Case:

Forty years ago, Starbucks was a single store in Seattle’s Pike Place Market selling premium roasted coffee. Today, it is a global roaster and retailer of coffee with some 21,536 stores, 43 percent of which are in 63 countries outside the United States. China (1,716 stores), Canada (1,330 stores),

Japan (1,079 stores), and the United Kingdom (808 stores) are large markets internationally for Starbucks. Starbucks set out on its current course in the 1980s when the company’s director of marketing, Howard Schultz, came back from a trip to Italy enchanted with the Italian coffeehouse experience. Schultz, who later became CEO, persuaded the company’s owners to experiment with the coffeehouse format—and the Starbucks experience was born. The strategy was to sell the company’s own premium roasted coffee and freshly brewed espressostyle coffee beverages, along with a variety of pastries, coffee accessories, teas, and other products, in a tastefully designed coffeehouse setting. From the outset, the company focused on selling “a third place experience,” rather than just the coffee. The formula led to spectacular success in the United States, where Starbucks went from obscurity to one of the best-known brands in the country in a decade. Thanks to Starbucks, coffee stores became places for relaxation, chatting with friends, reading the newspaper, holding business meetings, or (more recently) browsing the web. In 1995, with 700 stores across the United States, Starbucks began exploring foreign market opportunities. The first target market was Japan. The company established a joint venture with a local retailer, Sazaby Inc. Each company held a 50 percent stake in the venture, Starbucks Coffee of Japan. Starbucks initially invested $10 million in this venture, its first foreign direct investment. The Starbucks format was then licensed to the venture, which was charged with taking over responsibility for growing Starbucks’ presence in Japan.

To make sure the Japanese operations replicated the “Starbucks experience” in North America, Starbucks transferred some employees to the Japanese operation. The licensing agreement required all Japanese store managers and employees to attend training classes similar to those given to U.S. employees. The agreement also required that stores adhere to the design parameters established in the United States. In 2001, the company introduced a stock option plan for all Japanese employees, making it the first company in Japan to do so. Skeptics doubted that Starbucks would be able to replicate its North American success overseas, but by June 2015, Starbucks had some 1,079 stores and a profitable business in Japan. After Japan, the company embarked on an aggressive foreign investment program. In 1998, it purchased Seattle Coffee, a British coffee chain with 60 retail stores, for $84 million. An American couple originally from Seattle had started Seattle Coffee with the intention of establishing a Starbucks-like chain in Britain. In the late 1990s, Starbucks opened stores in Taiwan, Singapore, Thailand, New Zealand, South Korea, Malaysia, and—most significantly— China. In Asia, Starbucks’ most common strategy was to license its format to a local operator in return for initial licensing fees and royalties on store revenues. As in Japan, Starbucks insisted on an intensive employee-training program and strict specifications regarding the format and layout of the store. By 2002, Starbucks was pursuing an aggressive expansion in mainland Europe. As its first entry point, Starbucks chose Switzerland. Drawing on its experience in Asia, the company entered into a joint venture with a Swiss company, Bon Appetit Group, Switzerland’s largest food service company. Bon Appetit was to hold a majority stake in the venture, and Starbucks would license its format to the Swiss company using a similar agreement to those it had used successfully in Asia. This was followed by a joint venture in other countries. The United Kingdom leads the charge in Europe with 808 Starbucks stores. By 2014, Starbucks emphasized the rapid growth of its operations in China, where it had 1,716 stores and planned to roll out another 500 in three years. The success of Starbucks in China has been attributed to a smart partnering strategy. China is not one homogeneous market; the culture of northern China is very different from that of the east, and consumer spending power inland is not on par with that of the big coastal cities. To deal with this complexity, Starbucks entered into three different joint ventures: in the north with Beijong Mei Da coffee, in the east with Taiwan-based UniPresident, and in the south with Hong Kong-based Maxim’s Caterers. Each partner brought different strengths and local expertise that helped the company gain insights into the tastes and preferences of local Chinese customers, and to adapt accordingly. Starbucks now believes that China will become its second-largest market after the United States by 2020.

Question:

1. Starbucks prefers a combination approach to foreign market entry: the use of joint ventures and licensing. Do you agree with this approach? Why or why not?

please no handwriting

Learning Outcome:

Understand the issues involved with transfer pricing in multidivisional companies (question 1)

Provide an appreciation of logistics activities and their relationship to supply chain management, other business functions and enterprises. (Question 2)

CASE STUDY

ECR in the UK

Dutchman Paul Polman, now CEO of Unilever, did a stint as General Manager of Procter & Gamble UK and Eire from 1995 to 1999. While admiring the UK’s advanced retailing systems, he saw opportunities for all four of the ‘pillars of ECR’ – range, new items, promotions and replenishment. The following is extracted from the text of a speech he made to the Institute of Grocery Distribution.

Range

The average store now holds 35 per cent more than five years ago, yet a typical consumer buys just 18 items on a trip. A quarter of these skus[1] sell less than six units a week!

The number of skus offered by manufacturers and stores has become too large and complex. My company is equally guilty in this area. No question, we make too many skus. I can assure you we are working on it. Actually, our overall sku count in laundry is already down 20 per cent compared to this time last year. What’s more, business is up.

Clearly, we have an opportunity to rationalise our ranges. As long as we do this in an ECR way – focusing on what consumers want – we will all win. The consumer will see a clearer range. Retailers and manufacturers will carry less inventory and less complexity.

The result will be cost savings across the whole supply chain and stronger margins.

New items

There were 16,000 new skus last year. Yet 80 per cent lasted less than a year. You don’t need to be an accountant to imagine the costs associated with this kind of activity. And look how this has changed. Since 1975, the number of new sku introductions has increased eightfold. Yet their life expectancy has shrunk from around five years in 1975 to about nine months now. We can hardly call this progress.

Promotions

In promotions it’s the same story. Take laundry detergents. This is a fairly stable market. Yet we’re spending 50 per cent more on promotions than two years ago, with Consumers buying nearly 30 per cent more of their volume on promotions. This not only creates an inefficient supply chain, or in some cases poor in-store availability, but, more importantly, has reduced the value of the category and likely the retailers’ profit. We are all aware of the inefficiencies promotions cause in the system, such as problems in production, inventory and in-store availability. They all create extra costs, which ultimately have to be recouped in price. But there’s a higher cost. As promotions are increasing, they are decreasing customer loyalty to both stores and brands by 16 per cent during the period of the promotion. We commissioned a report by Professor Barwise of the London Business School. He called it ‘Taming the Multi-buy

Dragon’. The report shows us that over 70 per cent of laundry promotional investment goes on multi-buys. The level of investment on multi-buys has increased by 60 per cent over the last three years. There’s been a 50 per cent increase behind brands and a doubling of investment behind own labels. Contrary to what we thought, most of this volume is not going to a broad base of households. It is going to a small minority.

Seventy-one per cent of all multi-buy volume is bought by just 14 per cent of households. Just 2 per cent of multi-buy volume goes to 55 per cent of households.

We really are focusing our spending on influencing and rewarding a very small minority of people indeed.

Replenishment

Based on the escalating activity I’ve just [referred to], costs are unnecessarily high. There are huge cost savings also here, up to 6 per cent, by removing the non-value-added skus and inefficient new brand and promotional activity.

Questions

1 - Cutting down on range, new items and promotions is presumably going to lead to ‘everyday low prices’. Discuss the implications to the trade-off between choice and price. (2 points)

2- Procter & Gamble’s major laundry brand in the US is Tide. This is marketed in some 60 pack presentations, some of which have less than 0.1 per cent share. The proliferation of these pack presentations is considered to have been instrumental in increasing Tide’s market share from 20 to 40 per cent of the US market in recent years. Clearly, this is a major issue within P&G.

What are the logistics pros and cons of sku proliferation? (2points)

please long answer

You've been hired by Water Wonders to write a C++ console application that analyzes lake level data. Place the input file in a folder where your development tool can locate it (on Visual Studio, in folder <project-name>\<project-name>). The input file may be placed in any folder but a path must be specified to locate it. The input file has 158 lines and looks like this:

Lake Michigan and Lake Huron - Average lake levels - 1860-2015

Year    Average level (meters)

1860    177.3351667

1861    177.3318333

…

2014    176.3016667

2015    176.59

Within the app, attempt to open the input file and output file MichiganHuronLakeLevelsHighAndLow.txt. If the input file didn't open, print an error message. If the output file didn't open, print an error message. Read the input file by scanning past the two header rows. Each detail row in the input file contains two fields (year, lake level). Read one token at a time from the input file. See sample Canvas app Text file input – one token per read. Determine the maximum, minimum, and average lake levels. One technique to accomplish this is to use a max variable that starts very small and a min variable that starts very large. After all lines of the input file have been read, use formatted output manipulators (setw, left/right) to print the following rows:

          ● Column headers.

          ● Max values.

          ● Min values.

          ● Average value.

And columns:

          ● A left-justified label.

          ● A right-justified year.

          ● A right-justified level.

Then write the same information to output file MichiganHuronLakeLevelsHighAndLow.txt. Insure that your code is commented! Provide a complete header comment and body comments. Define constants for the input and output file names and column widths. Format any real numbers to four decimal places.

Lake Michigan and Lake Huron - Average lake levels - 1860-2015
Year Average level (meters)
1860 177.3351667
1861 177.3318333
1862 177.316
1863 177.1796667
1864 176.9955833
1865 176.90525
1866 176.80575
1867 176.9365833
1868 176.7891667
1869 176.8250833
1870 177.1
1871 177.0769167
1872 176.7318333
1873 176.9188333
1874 177.0413333
1875 176.9683333
1876 177.2855833
1877 177.1971667
1878 177.1183333
1879 176.85325
1880 176.90425
1881 177.0205
1882 177.1250833
1883 177.2096667
1884 177.2734167
1885 177.3208333
1886 177.3893333
1887 177.1890833
1888 176.9931667
1889 176.8393333
1890 176.788
1891 176.6149167
1892 176.564
1893 176.6204167
1894 176.6811667
1895 176.4201667
1896 176.3256667
1897 176.5094167
1898 176.5564167
1898 176.5595
1900 176.5626667
1901 176.64175
1902 176.5305833
1903 176.5748333
1904 176.7460909
1905 176.7561667
1906 176.7635
1907 176.7844167
1908 176.7670909
1909 176.5988333
1910 176.5025
1911 176.3356667
1912 176.4768333
1913 176.67025
1914 176.5297273
1915 176.3535833
1916 176.5715833
1917 176.7980833
1918 176.8866667
1919 176.745
1920 176.625
1921 176.4883333
1922 176.445
1923 176.2641667
1924 176.1866667
1925 175.9191667
1926 175.885
1927 176.1483333
1928 176.4433333
1929 176.8958333
1930 176.6508333
1931 176.1183333
1932 175.9408333
1933 175.8675
1934 175.7666667
1935 175.8908333
1936 175.9391667
1937 175.9225
1938 176.1408333
1939 176.2691667
1940 176.1416667
1941 176.1216667
1942 176.3341667
1943 176.6266667
1944 176.5966667
1945 176.57
1946 176.6016667
1947 176.5666667
1948 176.5308333
1949 176.2108333
1950 176.2608333
1951 176.7358333
1952 177.085
1953 176.9333333
1954 176.8291667
1955 176.7225
1956 176.44
1957 176.2633333
1958 176.0675
1959 176.0058333
1960 176.4775
1961 176.3766667
1962 176.2225
1963 175.9225
1964 175.6825
1965 175.9158333
1966 176.1608333
1967 176.3008333
1968 176.4466667
1969 176.6958333
1970 176.6783333
1971 176.805
1972 176.8883333
1973 177.1233333
1974 177.0933333
1975 176.9733333
1976 176.8991667
1977 176.505
1978 176.5908333
1979 176.7941667
1980 176.8033333
1981 176.6983333
1982 176.5983333
1983 176.8333333
1984 176.895
1985 177.1266667
1986 177.2925
1987 176.97
1988 176.5641667
1989 176.4008333
1990 176.35
1991 176.4691667
1992 176.4791667
1993 176.6958333
1994 176.6783333
1995 176.5275
1996 176.6541667
1997 176.9841667
1998 176.7166667
1999 176.2358333
2000 175.9783333
2001 175.9508333
2002 176.1183333
2003 175.8916667
2004 176.1108333
2005 176.09
2006 176.0158333
2007 175.9433333
2008 176.005
2009 176.2583333
2010 176.1108333
2011 176.0366
2012 175.9158
2013 175.9
2014 176.3016667
2015 176.59

Toyota (GB) have implemented the Entropy System at their head office in Epsom, two training centres in Salford and Nottingham, and two vehicle distribution centres in Portbury and Burnaston. It provides a centralised system for data capture and is used for HSEQ task management. It is also used to manage a range of internal audits for HSEQ regulatory compliance, HSEQ standards compliance and internal process verification/validation.

Toyota (GB) have built their need for compliance systems over a number of years. In 1991 it was felt that for Toyota to compete within the UK fleet market, it would be beneficial to achieve compliance with the quality standard BS5750, which later became ISO 9001. In 1995 a quality department was set up and the various departmental and site systems created over the past 4 years were amalgamated into one centralised management system.

In 1999, as part of their commitment to the environment, Toyota Motor Corporation announced the requirement for all manufacturing sites to achieve compliance with the environmental standard ISO 14001; a second directive soon followed requesting all overseas distributors to follow suit.

In 1999, a new health & safety standard -OHSAS 18001- was also launched and, upon review, Toyota (GB) soon realised that their system for managing health & safety compliance was not as effective as

it could have been.

Both the above events provided the catalyst for a new business objective to develop an integrated (quality, environment, health & safety) management system in accordance with the requirements of ISO 9001, ISO 14001 and OHSAS 18001.

During the development of the integrated management system, it became apparent that their document management system was unable to record business risks, produce audit checklists, manage and report incidents, provide notification of outstanding tasks, handle objectives or manage audits and non-conformances. In 2001 a team of Toyota managers attended “the annual Royal Society for the Prevention of Accidents conference” to review the range of software solutions available and the Entropy System was concluded to be the most suitable application.

The complete Entropy System was purchased in July 2001 and after an intense period of data inputting, triple certification was achieved in September 2001.

The Entropy System is both a user- friendly and cost-effective solution backed up by Entropy International’s willingness to support customer requirements. Working with Entropy International, Toyota (GB) have adapted some aspects of the System for their specific needs and found Entropy particularly helpful in this regard. One such area was the creation of a register for licences and certificates per site, such as fire certificates and forklift truck licences, to aid the management team in easily identifying expiry dates.

Toyota (GB) believe in establishing strong working relationships with their business partners; to this end they also work closely with Entropy International to develop their user- specific requirements in order to benefit other users. These include improvements such as quick identification of active/completed action plans, development of the risk assessment (particularly environmental aspect) methodology, the ability to activate/de-activate sites, removing the ability to assign responsibilities to personnel no longer employed.

Both the vehicle distribution centres employ a large number of contract staff. Despite not being direct employees, the Entropy System has allowed Toyota (GB) to track information pertinent to these contractors for corporate compliance purposes. Toyota (GB) are currently working towards providing their business partners with access to the Entropy System so they can review and input compliance data.

Recent changes in Toyota’s structure have meant that the parts logistics operation has now been transferred from Toyota (GB’s) immediate control to become part of Toyota’s integrated Europe-wide organisation. Whilst it was agreed that the system should still be managed by the Toyota (GB) compliance team, in order to provide Pan-European access, it was no longer viable to host the parts logistics data on the Toyota (GB) web-server. The data was migrated to a hosting environment at Entropy International, which now provides access to the parts logistics management team regardless of their location. Toyota (GB) is dedicated to fulfilling the expectations of their stakeholders, and achievement of the highest health, safety, environment and quality standards is central to Toyota’s philosophy. Kaizen (the Japanese philosophy of continuous business improvement) is used in every area of the business to search for a better way to increase performance, quality and efficiency and thus reduce costs. Toyota’s brand reputation, and protection thereof, is also of paramount importance throughout the organisation and their commitment to compliance is an active part of this. The company constantly review their legal obligations and commitments via the compliance team at Toyota (GB) who manage the impact to the businesses as a result of an ever-changing environment. The Entropy System has been found to deliver on all their expectations.

Question 1

According to the case’s contents, please identify the business challenges as facing by the TOYOTA.

The focus of the answer:

1. The challenging business environment for sustainability

2. which challenges that relate to car industry (Toyota). (refer to the case TOYATA facing those quality standards to meet the quality of Toyota. )

3. The legal aspect for safety  to meet Customers  expectation.

4. conclusion to link to CSR concepts for sustainability.

Jack, Jills and the Buffalo Bills

Before the 2014 season, Cailin Ferrari had conflicting thoughts about continuing her dream of being a member of the Buffalo Jills, (the Buffalo Bill’s cheerleading team) or to seek employment elsewhere. For the past 48 years, the Jills were an important part of the Bills organization, entertaining fans both on and off the playing field. However, after some careful research, the Jills found themselves wondering if they should continue to entertain fans under tense circumstances.

Buffalo Jills

Established in 1967, the Jills began as a permanent replacement for the cheerleaders from Buffalo State College who previously cheered from the Buffalo Bills sidelines. The Jills cheerleaders recognized for their high spirit, dedication, and humanitarian nature, had become a favorite for the city of Buffalo. After 42 seasons of entertaining Bills fans, the Jills established the Buffalo Jills Alumni Association.

Buffalo Bills

The Buffalo Bills, located in Buffalo NY, is currently owned by Terrence and Kim Pegula. In 2016, Forbes reported the team value at one billion, five-hundred million dollars (see exhibit 1). New Era Field, formally Rich Stadium and later Ralph Stadium, has been the home for the Buffalo Bills since 1973. The stadium has a capacity seating for 71,870 Bills fans. NEF is currently within the top 15 in capacity in the National Football League.

Exhibit 1: Bills Value Breakdown

Financial Data
Sport	$1,118M
Market	$179M
Stadium	$139M
Brand	$63M

Legal Issues

In April 2014, five former Bills cheerleaders sued the team over a pay system that had them working hundreds of hours for free at games and at mandatory public appearances. Soon after, management suspended the dance team.

The class action lawsuit claimed the Jills cheerleaders were paid below minimum wage and were required to attend unpaid events. The former cheerleaders also alleged that the Jills were wrongly classified as independent contractors and were subjected to policies that violate the state's $8 per hour minimum wage law and other workplace rules (Rodak, 2014). The Jills were not paid for games or practices and had to make 20 to 35 community and charity events each season.

The Jills stated that at some of these sponsored events, they were made to feel uncomfortable by male attendees. They were forced to adhere to strict dress codes and behavioral guidelines set by the team. According to the Jills, the Buffalo Bills controlled everything from their physical appearance to music selection (Garcia, 2016). The Bills organization claimed the Jills were not traditional employees but independent contractors.

In a 1995 ruling by the National Labor Relations Board, the Jills were classified as non-exempt employees. A former employee of Cumulus Broadcasting Co. (formally Citadel Broadcasting Co), named Stephanie E. Mateczun, managed the Jills. The contracts gave Citadel/Cumulus the exclusive rights to run the Jills, and required each member of the cheerleading squad to sign independent contractor agreements that the Jills would not be paid for working Bills games (Davis, 2017).

National Football Association

Currently, only six teams in the National Football Association (NFL) do not have a cheerleading team, either by personal choice or in the Jills case, suspension: Buffalo Bills, Cleveland Browns, New York Giant, Pittsburgh Steelers, Green Bay Packers, and Chicago Bears.

The NFL has remained quiet with this issue. Rodger Goodell, the commissioner of the NFL stated, he had no knowledge of the Jills’ selection, training, compensation and/or pay practices. According to the NFLPA (National Football League Players Association), the NFL protects its players but has no mention of its cheerleader teams. As reported by the NFLPA website, the National Football League Players Association:

Represents all players in matters about wages, hours and working conditions.

Protects their rights as professional football players

Assures that all the terms of the Collective Bargaining Agreement are met.

Decision

New York State Supreme Court Justice Mark A. Montour decided the cheerleaders' 2005 agreement they signed were unenforceable, and that the plaintiffs were non-exempt employees and they were misclassified as independent contractors.

In response to the lawsuit, the Cheerleaders' Fair Pay Act would force team owners to treat the Jills as employees rather than independent contractors. The change would mean teams like the Buffalo Bills would have to comply with much stricter New York labor laws when it comes to cheerleaders' wages and workplace protections. Was the contract negotiable between both parties? Was the contract by the Jills signed under duress? What employment laws did the Buffalo Bills violate? Should the NFL create a regulated pay scale for all NFL cheerleaders?

1.) Discuss the social responsbility (if any) for the NFL and the Buffalo Bills.

2.) Should the NFL creat a regulated pay scale for all NFL Cheerleaders? Or a union for the cheerleading team? Why or why not?

3.) Was the contract negotiable between both parties?

C Programming question part 4: The following program is an emergency hospital patient admitting process. This program needs a few changes to be sufficient for the hospital. Some patients who have been arriving at the Hospital have been providing social security numbers that are not valid. Create a function to scan all of the patient's social security numbers and detect if any of them are invalid. A Social Security Number (SSN) consists of nine digits, commonly written as three fields separated by hyphens: AAA-GG-SSSS. The first three-digit field is called the "area number". The central, two-digit field is called the "group number". The final, four-digit field is called the "serial number". Any SSN conforming to one of the following criteria is an invalid number: Any field all zeroes (no field of zeroes is ever assigned)., First three digits above 740

a. If you detect an invalid social security number, print the patient's name, their social security number, and then either "area number", "group number", or "serial number" to indicate where the problem with the social security number was detected.

#include
#include
#include
#include

using namespace std;

int main() {

char lname[][10]= {"Johnson","Williams","Ling","Albin","Anderson","Baca","Birner","Dominguez","Aimino","Armstrong","Beard","Calderon","Carter","Chaname"," Chaney"}; char fname[][10] = {"Fred","Betty","Hector","Ross","Jason","Elisa","Dalton","Javier","Ann","Addison","Cindy","Yamil","Thomas","Bryan","Kris"}; char middle[] = {'N','L','X','L','O','L','M','B','S','T','J','C','P','D','Z'}; char addr[][50] = {"2763 Filibuster Drive","701 Collage Avenue","1500 Raceway Lane","207 Daisy Avenue","1527 Lewis Road","25 Hunters Lane","851 Applebe Court","1410 Waterford Blvd","2379 Runners Way","46 Hawthorne Drive","1814 Constitution Ct","345 Cigar Row","896 Pine Avenue","24 Blue Belt Drive","2589 College Court"}; cities[]={"Lakeland","Orlando","Tampa","Lakeland","Tampa","Lakeland","Orlando","Orlando","Lakeland","Lakeland","Orlando","Tampa","Tampa" ,"Lakeland","Orlando"};int zip[] = {37643,31234,32785,32643,32785,32643,31234,31234,32643,32643,31234,32785,32785,32643,31234}; char gender[] = {'M','F','M','M','M','F','M','M','F','M','F','M','M','M','F'}; char dob[][11] = {"05/27/1935","11/27/1971","10/17/2003","12/08/1990","11/25/1991","10/30/1992","09/22/1993","08/04/1994","07/11/1995","06/18/1996","05/2 8/1997","04/07/1998","03/12/1999","02/23/2000","01/15/2001"}; char social[][12] = {"164-55-0726","948-44-1038","193-74-0274","458-57-2867","093-00-1093","159-56-9731","695-21-2340","753-66- 6482","852-73-4196","648-81-1456","879-61-1829","123-87-0000","000-65-3197","741-85-9632","963-25-7418"};
vector fullName;
vector zombies_zip;
std::map dups;

char buffer[16];
char buffer2[16];

// for (int i = 0; i < 15; ++i)
// {
// //char buffer[16]; // large enough
// //char buffer2[16];
// strcpy(buffer, lName[i]);
// strcat(buffer, fName[i]);
// cout << lName[i] << " " << fName[i] << " " << buffer << endl;
// cout << buffer << middleInitial[i] << endl;
//
// }

for(int i =0; i < 15; i++){
if(zombie[i] == 'Y'){
zombies_zip.push_back(zip[i]);
}
}

sort(zombies_zip.begin(), zombies_zip.end());

// for (int i=0; i // cout << zombies_zip[i] << "\n";

for(int i : zombies_zip)
++dups[i];

for(auto& dup : dups)
cout << dup.first << " has " << dup.second << " zombies\n";

//cout<<"Last Name: "<< last_name << ", "<< "First Name: " << first_name << " ," << "Middle name: " << middle_name << " ," << "Street address: "<< street_address << " ," << "City: " << city << " ," << "State: " << state <<" ," << "Zip: " << zip << endl;

//printf("Zombie: %c, ""Gender: %c, Date of Birth: %d-%d-%d, Insurance: %c, Social Security Number %s", gender, date_of_birth[0], date_of_birth[1], date_of_birth[2], insurance, social_security_number);

//cout<<"Zombie?: "<< zombie << ", "<< "Gender: " << gender << " ," << "Date of Birth: " << date_of_birth[0] << "/" << "/" << date_of_birth[1] << date_of_birth[2] << " ," << "Insurance?: "<< insurance << " ," << "Social Security: " << social_security_number<< endl;

cout << "Number of patients: " << sizeof(lName)/sizeof(lName[0]) << endl;
cout<<"Number of zombies: " << num_of_zombies << endl;

}

New format for patient record: Last Name, First Name, Middle Initial, Address, City, State, Zip, Sex, Date of Birth, SS #, Zombie?