Coca Cola Cost Analysis Essay

COCA COLA COMPANY Research Project For ACC 412 Presented to: Overview of Coca-Cola Leading the beverage industry for the third consecutive year, Coca-Cola, a common household name known around the world, climbs to the 4th spot in Fortune’s 50 Most Admired Companies in the world for year 2012. When it comes to a refreshing cold soda, who does not know of Coca-Cola? The company was established in 1886 in Atlanta, Georgia at the Jacobs’ Pharmacy soda fountain by pharmacist John Pemberton.

In its humble beginning, a glass of this drink costed only five cents and only 9 glasses of Coca-Cola were sold each day. Since then, Coca-Cola has grown to be a multi-billion dollar company. Employing approximately 139,600 workers worldwide, it operates in more than 200 countries with more than 300 bottling partners around the world to produce more than 3,500 types of regular, diet and caffeine-free drinks, water, and mixers such as A&W, Bacardi Mixers, Cherry Coke Zero, Dasani, Eva Water, Fanta, Glaceau Smartwater, Hi-C, Minute Maid Light, Nestea, Powerade, and the list goes on.

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In expanding its empire, Coca-Cola currently has headquarters all over the world leaving footsteps in the continents of North America, Latin America, Europe, Eurasia, Africa, and the Middle-East. Marking the 49th consecutive year of increased dividends, in his March 31, 2011 letter to shareholders, Chairman and Chief Executive Officer Muhtar Kent announced that the company has “added more than 1 billion incremental unit cases of volume to [the] business in 2010. Our unit case volume grew 5 percent and we exceeded our long-term growth target for unit case volume and operating income. In 2010 alone the company generated $9. 5 billion in cash from operations, an increase of 16 percent since 2009, and the drink, Minute Maid Pulpy, which was developed only five years ago in China achieved the status of the 14th brand to earn an annual retail sales of more than $1 billion. Kent happily exclaimed that in 2010, the company returned $7. 2 billion back to shareholders by repurchasing $3. 1 billion in company stock and paying $4. 1 billion in dividends. Although the company continues to grow, CEO Kent explains that they will not stop there.

With even greater goals set for 2020, the 2020 Vision lays out more milestones the company plans to achieve and actions already underway to achieve doubled revenues, to develop new beverage products, to save on manufacturing costs while maintaining quality, and to bring innovations to the market faster; this is to name only a few of the goals set for 2020. For a more detailed roadmap, look to Figure 1 below for a comprehensive list of visions, goals, priorities, and a metric system to measure accomplishments along the way. Figure 1: Goals for 2020

The company’s effort to maintain its stand in the worldwide market through the deliverance of quality innovations to the world has gained a great return for Coca-Cola as well as its investors and partners. To gain even greater interest and support from old and new investors, Coca-Cola must demonstrate the ability to meet its goals which can be attained through actions to achieve higher revenues and lower costs. In the following we examine Coca-Cola’s financial results for years 2007 to 2011, each with four quarters to determine the predictability of future revenues and the probability that these figures will grow in future years.

Data and Analysis In our first analysis, we begin by determining the equation for projection of sales using data of sales and GDP through a variety of methods- High-Low, Regression, and Multiple Regression. Figure 2: Sales Revenue and GDP Data Net Sales (Net revenue) GDP (in billions) Year 2011 QTR 4 10,292,000 15,320. 8 QTR 3 12,248,000 15,176. 1 QTR 2 12,737,000 15,012. 8 QTR 1 10,517,000 14,867. 8 Year 2010 QTR 4 10,494,000 14,755. 0 QTR 3 8,426,000 14,605. 5 QTR 2 8,674,000 14,467. 8 QTR 1 7,525,000 14,277. 9 Year 2009 QTR 4 7,510,000 14,087. 4 QTR 3 8,044,000 13,920. 5

QTR 2 8,267,000 13,854. 1 QTR 1 7,169,000 13,893. 7 Year 2008 QTR 4 7,126,000 14,081. 7 QTR 3 8,393,000 14,395. 1 QTR 2 9,046,000 14415. 5 QTR 1 7,379,000 14,273. 9 Year 2007 QTR 4 7,331,000 14,253. 2 QTR 3 7,690,000 14,126. 2 QTR 2 7,733,000 13,976. 8 QTR 1 6,103,000 13,758. 5 Graph 1: Net Sales and GDP Forecasting equations for sales (revenue): 1. High-Low Method: The highest revenue is $12,737,000, and the lowest is $6,103,000. The highest GDP is $ 15,321,000, and the lowest is $13,759,000. From the y = mx + b formula (where “y” is Revenue and “x” is GDP), we have m = 4. 47 and b = -52,331,287. Projection of Sales Revenue Equation: Revenue = 4. 247 * GDP – 52,331,287 According to the High-Low method, sales revenue and GDP has a positive relationship. This translates to revenue is increased when GDP is increased. For every dollar increase in GDP, sales revenue will increase by 4. 247 dollars. 2. Simple Linear Regression Equation: Projection of Sales Revenue Equation: Revenue = 3. 3679 * GDP – (3. 978*107) The relationship between sales revenue and GDP is also positive: For each additional GDP in the country, the amount of sales revenue increases 3. 679 dollars. In other words, as GDP increases, the spending in the country also increases which results in the increase of sales revenue. Next we examine the relationship between sales revenue and real interest rate: Revenue = (4. 169*107) – 2,791,339 * real interest rate There is a negative relationship between revenue and real interest rate. For each additional 1 percent of real interest rate in the country, the amount of sales revenue decreases by 2,791,339 dollars. The reason might be due to people spending less money when the interest rate increases in the market. . Multiple Regression Method In this analysis, we are using multiple independent variables and they are: GDP, median household, and real interest rates. Figure 3: Revenue, GDP, Median Household and Real Interest Rates Data Year Net Sales (Net revenue) GDP Median Household Real Interest Rate (%) 2011 45,794,000 60,377,500 51,422 1. 20 2010 35,119,000 58,106,200 49,445 2. 40 2009 30,990,000 55,755,700 49,777 1. 40 2008 31,944,000 57,166,200 52,029 2. 80 2007 28,857,000 56,114,700 52,673 5. 00 Projection of Sales Revenue Equation: Revenue = [2. 94249 * GDP] + [525. 29 * median household] – [1363776 * interest rate] – 1. 58*108 There is a positive correlation between median household and GDP. That is, for each additional GDP in the country, there is an increase of sales revenue by 2. 94249 dollars and for each additional median household, the amount of sales revenue increases by 525. 429 dollars. On the contrary, a negative relationship exists between real interest rate and revenue. In other words, for each additional percent of interest rate in the US, the amount of sales revenue decreases by 1,363,776 dollars.

This is the same result we obtained when we apply the Simple Regression Method. Graph 2: The Relationship between GDP on Sales Revenue Graph 2 is a model of sales revenue and GDP for years 2007 to 2011; the standard deviation of sales revenue and series mean are $1,713,000 and $8,635,000, respectively. As the graph shows, sales has decreased due to the recent decrease of GDP. In our second analysis, we begin by deriving the relational equation for revenue, cost of goods sold, and general and administrative expenses using data obtained from Coca-Cola’s financial records for years 2007 to 2011.

Here we implement the High-Low method to figure out the effects of the independent variable, COGS on net sales and General and Administrative Expenses on net sales. Figure 4: Revenue, COGS and General ; Administrative Expenses Net Sales (Net revenue) COGS General ; Administrative Expenses Year 2011 QTR 4 10,292,000 4,279,000 5,056,000 QTR 3 12,248,000 4,279,000 4,623,000 QTR 2 12,737,000 4,989,000 4,574,000 QTR 1 10,517,000 3,949,000 4,289,000 Year 2010 QTR 4 10,494,000 4,279,000 4,511,000 QTR 3 8,426,000 2,918,000 3,064,000 QTR 2 8,674,000 2,955,000 2,878,000 QTR 1 7,525,000 2,541,000 ,705,000 Year 2009 QTR 4 7,510,000 2,651,000 2,978,000 QTR 3 8,044,000 2,934,000 2,912,000 QTR 2 8,267,000 2,913,000 2,844,000 QTR 1 7,169,000 2,590,000 2,624,000 Year 2008 QTR 4 7,126,000 2,568,000 2,568,000 QTR 3 8,393,000 3,020,000 3,020,000 QTR 2 9,046,000 3,162,000 3,162,000 QTR 1 7,379,000 2,624,000 2,624,000 Year 2007 QTR 4 7,331000 2,641,000 3,039,000 QTR 3 7,690,000 2,884,000 2,896,000 QTR 2 733,000 2,736,000 2,685,000 QTR 1 6,103,000 2,145,000 2,325,000 Graph 3 below demonstrates the relationships between revenue, cost of goods sold and general and administrative expenses.

As revenue increases, it is natural that these costs these costs will also increase. However, the real Graph 3: Relationship between revenue, COGS, and G;A expens A. Cost of Goods Sold and Revenue The lowest revenue is $6,103,000 and COGS is $2,145,000. The highest revenue is $12,737,000 and COGS is $4,989,000 Estimated COGS Equation: m = 0. 429 and b = – 473,187 COGS = 0. 429 * Revenue – 473,187 B. General ; Administrative Expenses and Revenue The lowest revenue is $6,103,000 and S;A is $2,325,000. The highest revenue is $12,737,000 and G;A is $5,056,000. Estimated G;A Equation: = 0. 412 and b = – 189,436 G;A = 0. 412 * Revenue – 189,436 The relationships between revenue and COGS and revenue and G;A are both positive. For each additional dollar of revenue earned, the amount of COGS increases by 0. 429 dollars and the amount of S;A increases by 0. 412 dollars. Moving on to our third analysis, to predict future earnings, we use equations previously calculated in our first analysis to project future sales revenue using the High-Low, Simple Linear Regression, and Multiple Regression methods. 1. High-Low method Projection of Sales Revenue Equation:Revenue = 4. 47 * GDP – 52,331,287 In the last quarter of 2011, GDP is $15,320. 8 billion which is equivalent to $15,320,800. Therefore, revenue is 4. 247 * 15,320,800 – 52,331,287 = $12,736,151 2. Simple Linear Regression Projection of Sales Revenue Equation: Revenue = 3. 3679 * GDP – 3. 978*107 Similarly, plugging in $15,320,800 into the equation, we have: Revenue is 3. 3679 * 15,320,800 – 3. 978*107= $11,818,922 3. Multiple Regression Method Projection of Sales Revenue Equation: Revenue = [2. 94249 * GDP] + [525. 429 * median household] – [1363776 * interest rate] – 1. 8*108 During the last quarter of year 2011, we have: GDP is $15,320,800; Median household income is $51,422; and Real interest rate is 1. 2%. Replacing the above values in the appropriate slots in the equation, the revenue for the fourth quarter of 2011 is: [2. 94249 * 15,320,800] + [525. 429 * 51,422] – [1363776 * 1. 2%] – 1. 58*108 = $21,522,130 Actual revenue in the fourth quarter of 2011 is $10,292,000. Comparing the above calculations, the Simple Linear Regression method provides the most accurate forecast of sales revenue of the three methods used.

Although the Simple Linear Regression method produces a revenue amount that is closest to the actual number, the Multiple Regression is the best method because it takes into account all factors that affect sales revenue. GDP, median household income, and real interest rates are three economic indicators that pose real effects on sales revenue. In our next analysis, we break down the frequency of the range of sales revenue, cost of goods sold as well as selling and administrative expenses. Graph 4: The Frequency of Different Sales Revenue Range

The graph of frequency distribution for sales revenue indicates that sales revenue for the five-year period is within the $7,000,001 to $8,000,000 range. Graph 5: The Frequency of Different Cost of Goods Sold Range When using the High-Low method of the estimated cost of goods sold and selling and administrative expenses, we find that the cost of goods sold frequency distribution has the highest frequency of 12 with the range of $2,500,001 to $3,000,000. This means that even though cost of goods sold is a variable cost, its predictability is still relatively stable from month to month.

Graph 6: The Frequency of Different Selling and Administrative Expenses Range Selling and administrative expenses’ frequency distribution has the highest frequency at the range of $2,501,000 to $ 3,000,000. Next we calculate the means and standard deviations for sales revenue, cost of goods sold, and selling and administrative expenses. Using the complied data listed in the appendix, we were able to determine the means as follows: Sales Revenue: $8,672,600 Cost of goods sold: $3,188,850 Selling and administrative expenses: $3,233,750 The standard deviations are as follows: Sales revenue: $ 1,802,201. 6769

Cost of goods sold: $ 829,783. 37535 Selling and administrative expenses: $ 713,925. 53607 In our final analysis, we calculate the break-even point of sales revenue using the mean values previously calculated for cost of goods sold and selling and administrative expenses. In this scenario, cost of goods sold is our variable cost and selling and administrative expenses are our fixed costs. The formula to calculate the break-even point for revenue is: Breakeven revenues = Fixed cost / contribution margin % For fixed cost, we used the mean S;A expense over the course of 20 periods (4 quarters for 5 years), which is $3,233,750.?

Next we need to determine the contribution margin percentage and the formula is: Contribution margin % = (Revenue-COGS) / Revenue? Contribution margin percentage is, in other words, a percentage gathered from gross profit out of sales ? The average revenue quarterly over the 5 years period was $8,762,600 and the average COGS quarterly over the 5 years period was $3,188,850. Therefore: Contribution Margin % = ($8,762,600 – $3,188,850) / $8,762,600 = . 6361 or 63. 61%? Therefore, the break-even point of sales revenue is $5,083,713. In other words, the amount of sales needed to recover apital spent on costs of goods sold and other selling and administrative expenses is $5,083,713. Coca-Cola must earn this amount before any profit is realized. To reduce the break-even point and start earning profits faster, the company must figure out ways to reduce costs even further. Next we calculate the z-score to determine the probability that the company will break-even (even though in reality it has already been proven that Coca-Cola is doing very well as a whole). Assuming a normal distribution, we use the following formula to compute the z-score: z-score = (break-even revenue – mean of revenue) / (standard deviation of revenue).

Breakeven revenue = $5,083,713; mean of revenue = $8,672,600; and standard deviation of revenue = $1,802,202? z-score= ($5,083,713 – $8,672,600) / $1,802,202Z = -1. 99 Based on a z-score of -1. 99 and assuming a normal distribution, the probability that Coca-Cola will achieve enough sales to break-even is 97. 7%; this is known as a p-value. This figure makes sense since the company’s actual revenue data over the past 5 years does reflect earnings that exceed the break-even point. Appendix Year Sales Revenue 2007 Quarter 1 $6,103,000 Quarter 2 $7,733,000 Quarter 3 7,690,000 Quarter 4 $7,331,000 2008 Quarter 1 $7,379,000 Quarter 2 $9,046,000 Quarter 3 $8,393,000 Quarter 4 $7,126,000 2009 Quarter 1 $7,169,000 Quarter 2 $8,267,000 Quarter 3 $8,044,000 Quarter 4 $7,510,000 2010 Quarter 1 $7,525,000 Quarter 2 $8,674,000 Quarter 3 $8,426,000 Quarter 4 $10,494,000 2011 Quarter 1 $10,517,000 Quarter 2 $12,737,000 Quarter 3 $12,248,000 Quarter 4 $11,040,000 Year Cost of Good Sold 2007 Quarter 1 $2,145,000 Quarter 2 $2,736,000 Quarter 3 $2,884,000 Quarter 4 $2,641,000 2008 Quarter 1 $2,624,000 Quarter 2 $3,162,000 Quarter 3 $3,020,000

Quarter 4 $2,568,000 2009 Quarter 1 $2,590,000 Quarter 2 $2,913,000 Quarter 3 $2,934,000 Quarter 4 $2,651,000 2010 Quarter 1 $2,541,000 Quarter 2 $2,955,000 Quarter 3 $2,918,000 Quarter 4 $4,279,000 2011 Quarter 1 $3,949,000 Quarter 2 $4,989,000 Quarter 3 $4,875,000 Quarter 4 $4,403,000 Year Selling and Administrative Expenses 2007 Quarter 1 $2,325,000 Quarter 2 $2,685,000 Quarter 3 $2,896,000 Quarter 4 $3,039,000 2008 Quarter 1 $2,796,000 Quarter 2 $3,095,000 Quarter 3 $3,139,000 Quarter 4 $2,744,000 2009 Quarter 1 $2,624,000 Quarter 2 $2,844,000 Quarter 3 $2,912,000

Quarter 4 $2,978,000 2010 Quarter 1 $2,705,000 Quarter 2 $2,878,000 Quarter 3 $3,064,000 Quarter 4 $4,511,000 2011 Quarter 1 $4,080,000 Quarter 2 $4,422,000 Quarter 3 $4,527,000 Quarter 4 $4,411,000 Reference Bureau of Economic Analysis. “National income and products accounts table. ” March 1. 2012.? Google finance, 2007-2011. Income statement. March 1, 2012. Manuel, D. (2012, March 02). DaveManuel. com. March 3, 2012. The Coca-Cola Company. 2007-2011. SEC Filing – Annual Filing. March 1, 2012. The Coca-Cola Company. From Our Chairman and Chief Executive Officer.

March 31, 2011. The Coca-Cola Company. Heritage Timeline. 2011. < http://heritage. coca-cola. com/> The Coca-Cola Company. The Coca-Cola Company. Jun 28, 2007. SCE Filing – Quarter Filing. March 3, 2012 U. S. Census Bureau. September 27, 2011. “Income, Expenditures, Poverty, & Wealth: Household Income. The 2012 Statistical Abstract” March 3, 2012. U. S. Department of Commerce Bureau of Economic Analysis. February 29, 2012. “gross domestic product: fourth quarter and annual 2011” March 3, 2012. Yahoo finance, 2007-2011, Income statement. March 1, 2012