A Case Study on Cost Estimation and Profitability Analysis at Continental Airlines

ISSUES IN ACCOUNTING EDUCATION Vol. 26, No. 1 2011 pp. 181–200 American Accounting Association DOI: 10. 2308/iace. 2011. 26. 1. 181 A Case Study on Cost Estimation and Pro? tability Analysis at Continental Airlines Francisco J. Roman ABSTRACT: This case exposes students to the application of regression analyses to be used as a tool pursuant to understanding cost behavior and forecasting future costs using publicly available data from Continental Airlines. Speci? cally, the case focuses on the harsh ? nancial situation faced by Continental as a result of the recent ? ancial crisis and the challenges it faces to remain pro? table. It then highlights the importance of reducing and controlling costs as a viable strategy to restore pro? tability and how regression analysis can assist in this pursuit. Students are next presented with quarterly data for various categories of costs and several potential cost drivers, which they must use to perform regressions on operating costs using a variety of cost drivers. They must then use their regression results to forecast operating costs and conduct a pro? tability analysis to project quarterly pro? ts for the upcoming ? scal year.

Finally, students must summarize the main results of their analysis in a memorandum addressed to Continental’s management, providing recommendations to restore pro? ts. In particular, the concept of mixed cost functions is reinforced, as is the understanding of the steps required to perform regression analysis in Excel, interpreting the regression output, and the underlying standard assumptions in regression analysis. The case has been tested and well received in an intermediate cost accounting course and it is suitable for both undergraduate and graduate students. Keywords: cost estimation; pro? ability analysis; cost behavior; regression analyses; cost functions. Data Availability: All data are from public sources and are available in hard copy inside the case. Data are also available in electronic form by the author upon request. INTRODUCTION n 2008, the senior management team at Continental Airlines, commanded by Lawrence Kellner, the Chairman and Chief Executive Of? cer, convened a special meeting to discuss the ? rm’s latest quarterly ? nancial results. A bleak situation lay before them. Continental had incurred an operating loss of $71 million dollars—its second consecutive quarterly earnings de-

I Francisco J. Roman is an Assistant Professor at Texas Tech University. I thank Kent St. Pierre editor , Michael Costa, and two anonymous referees for their suggestions on previous versions of the case. Editor’s note: Accepted by Kent St. Pierre Published Online: February 2011 181 182 Roman cline that year. Likewise, passenger volume was signi? cantly down, dropping by nearly 5 percent from the prior year’s quarter. Continental’s senior management needed to act swiftly to reverse this trend and return to pro? tability. Being the fourth largest airline in the U.

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Concurrent with this revenue decline, the price of jet fuel soared to record levels during 2008. 1 Thus, while revenue was decreasing, Continental was paying almost twice as much in fuel costs. Interestingly, fuel costs surpassed the ? rm’s salaries and wages as the highest cost in Continental’s cost structure. This obviously had a negative impact on the bottom line, squeezing even further the already strained pro? t margins. The outlook for a quick recovery in the U. S. economy and, consequently, an upturn in the demand for air travel in the short term did not seem likely.

Continental’s internal forecasts indicated that a further decline in passenger volume should be anticipated throughout 2009, with a recovery in travel possibly occurring by the middle of 2010. To summarize, adverse economic conditions in the U. S. , coupled with the rise in fuel costs, were dragging down Continental’s pro? ts and relief was unlikely through the foreseeable future. THE DECISION TO REDUCE FLYING CAPACITY AND THE IMPACT ON OPERATING COSTS Given the situation described above, management needed to act swiftly to restore pro? tability. Several strategic options were evaluated.

Since the U. S. and much of the world was facing a severe recession, the prospect for growing revenues by either raising airfares or passenger volume seemed futile. Contrary to raising revenue, Continental’s managers believed that raising fares could potentially erode future revenues beyond the present level. Discounting fares did not seem a plausible solution either, because given the severity of the economic situation a fare cut could fall short in stimulating additional passenger demand and lead to lowering revenues. Thus, because management anticipated that revenues would remain ? t for most of the year, the only viable short-term solution to restoring pro? ts was a substantial and swift reduction in operating costs. This could most effectively be accomplished in two ways. First, through a reduction in ? ying capacity adjusted to match projected passenger demand. With this in mind, Continental’s management agreed to reduce ? ying capacity by 11 percent on domestic and international routes. 2 As a result of this action, Continental would eliminate the least pro? table or unpro? table ? ights and, accordingly, would ground several planes in the ? eet.

Management anticipated that this decision would reduce several of the ? rm’s operating costs. Apart from this, Continental could achieve further reductions in costs by implementing several cost-cutting initiatives and through operational ef? ciencies. For example, management pro- 1 2 To illustrate, jet fuel is tied to the price of oil and, over the past year, oil prices surged from about $70 to $135 per barrel. Consequently, the price of jet fuel increased markedly, from an average of $1. 77 per gallon to $4. 20 by the mid-summer of 2008. Speci? cally, on June 13, 2008, Continental Airlines announced that it planned to reduce its ? ght capacity by 11 percent. By shrinking capacity, Continental expected to reduce the number of domestic and international ? ights from its three major hubs in Houston, Cleveland, and Newark Maynard 2008 . Issues in Accounting Education American Accounting Association Volume 26, No. 1, 2011 A Case Study on Cost Estimation and Pro? tability Analysis at Continental Airlines 183 jected that it could achieve reductions in Passenger Services expenses by consolidating several tasks during passenger check-in and by reducing food and beverage waste served during ? ights. Additionally, the ? m could reduce various miscellaneous expenses through targeted cuts in discretionary spending. In sum, to close the gap in pro? tability, Continental’s strategy was geared toward slashing operating costs by cutting capacity and through aggressive identi? cation and implementation of cost-cutting initiatives. The next step would be for management to know precisely how their decision to downsize capacity would impact the ? rm’s future operating costs, and also identify speci? c areas in which the ? rm could achieve additional cost reductions. Additionally, the cost analysis would help forecast the ? m’s operating costs and projected pro? ts or losses for the upcoming ? scal year. However, before we can proceed with such analysis, an examination of how the various categories of Continental’s costs behave is in order. Before we begin, let us prepare with an overview of the airline industry and its competitive landscape, and an understanding of why cost behavior bears particular relevance in this case. Relative to other industries, airlines are a very dif? cult business to manage. In particular, they are exposed to tremendous risks brought by volatility inherent in their business model, as they deal with high ? ed costs, labor unions, instability in fuel prices, weather and natural disasters, passenger safety, and security regulations. These aspects bring a large burden to airlines’ cost structures. Moreover, competition within the industry is ? erce; the proliferation of discount carriers, such as Southwest Airlines and, most recently, Jet Blue, and the end of fare regulation in 1978, has hindered airlines’ pricing power and their ability to spur revenues. For these reasons, cost containment is a critically important aspect of pro? tability in this industry.

In order for Continental to restore pro? tability in this harsh environment of weak demand for air travel, it must be able to contain its operating costs, especially its massive ? xed costs, which are visible in several ways. For example, salaries for pilots, ? ight attendants, and mechanics, as well as aircraft leasing costs, are typically ? xed, varying little with shifts in passenger volume. Because ? xed costs typically embody the amount of operating capacity of a ? rm, they are commonly referred as “capacity” costs. Since ? xed costs do not self-adjust to ? ctuations in passenger volume, the only way in which they can be decreased or increased is if management adjusts them in accordance to the level of operating capacity. In contrast, other costs, such as passenger services and reservation and distribution costs, behave as variable and would self-adjust with variations in volume or operating activity. Hence, to assess the impact of this strategic decision to alter Continental’s cost structure, and identify the areas that could achieve the greatest reduction in costs, we must resolve how Continental’s operating costs behave and what drives them.

In what follows, we learn how to apply regression analyses to examine cost behavior and forecast future costs, and then use that knowledge to assess how the reduction in ? ying capacity would affect Continental’s operating costs and pro? tability in the near term. ESTIMATING COSTS USING REGRESSION ANALYSES The previous discussion highlighted the importance of examining the behavior of Continental’s operating costs to pave the way for a cost and pro? tability analysis using regression analysis. Regression analysis is a powerful statistical tool that is frequently used by ? ms to examine cost behavior and predict future costs. The idea behind regression analysis is straightforward: historical data for costs, and the various activities that could potentially drive operating costs, are inserted into a mathematical calculation which yields the average amount of change in that particular cost that has occurred over time. Average values provided by regression calculations may then be applied to estimate future change that will occur in that cost given a one-unit change in one or Issues in Accounting Education Volume 26, No. 1, 2011 American Accounting Association 184 Roman ore of the business activities which drive that cost. 3 More precisely, in a regression model, cost is a function of one or more business activities or factors underlying a business operation. Simply put, the business activities are the drivers of operating costs. Therefore, since activities drive costs, our ? rst step in the estimation of a cost function is to identify the underlying activities or other potential factors that drive the cost in question—the cost drivers. This requires extensive knowledge of the business operation. In the case of Continental Airlines, the potential drivers of operating costs vary greatly.

For instance, as previously noted, the number of passengers that Continental ? ies may drive the costs related to Passenger Services. Likewise, Aircraft Maintenance and Repairs costs could be driven by the number of aircraft in the ? eet and by the level of ? ying capacity set by Continental i. e. , available seat miles . In synthesis, to predict how Continental’s operating costs would be affected by the decision to reduce capacity, and to identify those areas in which additional room is available for cost cutting, we need to identify which costs in this ? rm’s cost structure behave as variable, ? ed, or mixed in which elements of both variable and ? xed are observable . Equally important, we should also identify the speci? c drivers if any of each cost. Your job is to assist management in their quest to restore pro? tability at Continental Airlines. Speci? cally, you must conduct regression analyses to examine cost behavior and then use this information to forecast operating costs and pro? tability for the upcoming year. As part of your cost analysis, you should investigate how the decision to cut ? ying capacity would impact the ? rm’s future operating costs and, equally important, identify those speci? expense categories or operating areas in which this ? rm could attain additional costs saving by implementing cost-cutting initiatives. Your conclusions should be outlined in a memorandum directed to Continental’s Executive management team. You are provided next with a description of Continental’s operating costs and the potential drivers of costs so you can conduct regression analysis to estimate the corresponding cost functions. To help you in estimating the regressions, a comprehensive set of instructions for performing regression analysis using Microsoft Excel is provided in the Appendix.

Immediately following the description of costs, a series of questions is provided that should help guide your analysis. Additionally, to help you estimate your regressions, Exhibit 1 presents past quarterly data for all of the above expenditures for the period of January 2000 through December 2008, while Exhibit 2 provides quarterly operations data for the same period of time. CONTINENTAL’S OPERATING COSTS AND POTENTIAL COST DRIVERS As shown in Exhibit 1, there are ten categories of operating costs.

These include salaries and wages, aircraft fuel and related taxes, aircraft rentals, airport fees, aircraft maintenance and repairs, depreciation and amortization, distribution costs, passenger services, regional capacity purchases, and other expenses. Of these, some represent a single expense item. For example, the cost of aircraft rentals and airport fees together comprise a single cost item. Other costs represent cost pools comprising several cost items. Such is the case of passenger services and other expenses. The following provides a detailed description of each cost, along with the potential cost drivers. 3 4 For ease in exposition, cost functions and regression analyses are discussed brie? y here. For further insight on cost functions and on the mechanics of regression analyses, I refer the reader to the Appendix. A cost driver represents a particular business activity, which usually tends to have a cause-and-effect relationship with a given cost. For example, for airlines, a typical cost driver for landing fees is the number of daily ? ights carried by the airline, as well as the number of passengers ? own. An increase decrease in the number of ? ights or passengers ? own would increase decrease landing fees.

Issues in Accounting Education American Accounting Association Volume 26, No. 1, 2011 A Case Study on Cost Estimation and Pro? tability Analysis at Continental Airlines 185 EXHIBIT 1 REVENUES AND OPERATING COSTS DATA Obs. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Obs. 1 2 3 4 5 6 Period 1Q-2000 2Q-2000 3Q-2000 4Q-2000 1Q-2001 2Q-2001 3Q-2001 4Q-2001 1Q-2002 2Q-2002 3Q-2002 4Q-2002 1Q-2003 2Q-2003 3Q-2003 4Q-2003 1Q-2004 2Q-2004 3Q-2004 4Q-2004 1Q-2005 2Q-2005 3Q-2005 4Q-2005 1Q-2006 2Q-2006 3Q-2006 4Q-2006 1Q-2007 2Q-2007 3Q-2007 4Q-2007 1Q-2008 2Q-2008 3Q-2008 Q-2008 Revenues Fuel Salaries and Wages Capacity Purchases Aircraft Rentals Landing Fees 2,277,000,000 334,000,000 672,000,000 — 206,000,000 2,571,000,000 313,000,000 719,000,000 — 210,000,000 2,622,000,000 354,000,000 748,000,000 — 215,000,000 2,429,000,000 392,000,000 736,000,000 — 213,000,000 2,451,000,000 345,000,000 758,000,000 — 214,000,000 2,556,000,000 349,000,000 800,000,000 — 223,000,000 2,223,000,000 322,000,000 779,000,000 — 230,000,000 1,739,000,000 213,000,000 684,000,000 — 236,000,000 1,993,000,000 208,000,000 732,000,000 — 228,000,000 2,192,000,000 254,000,000 746,000,000 — 231,000,000 2,178,000,000 76,000,000 743,000,000 — 227,000,000 2,039,000,000 285,000,000 738,000,000 — 216,000,000 2,042,000,000 347,000,000 778,000,000 — 223,000,000 2,216,000,000 302,000,000 762,000,000 — 224,000,000 2,365,000,000 316,000,000 778,000,000 — 225,000,000 2,247,000,000 290,000,000 738,000,000 158,000,000 224,000,000 2,307,000,000 333,000,000 688,000,000 317,000,000 220,000,000 2,553,000,000 387,000,000 711,000,000 328,000,000 222,000,000 2,602,000,000 414,000,000 703,000,000 347,000,000 224,000,000 2,437,000,000 453,000,000 717,000,000 359,000,000 225,000,000 2,505,000,000 470,000,000 715,000,000 353,000,000 227,000,000 2,857,000,000 75,000,000 649,000,000 382,000,000 229,000,000 3,001,000,000 684,000,000 646,000,000 406,000,000 234,000,000 2,845,000,000 714,000,000 639,000,000 431,000,000 238,000,000 2,947,000,000 672,000,000 661,000,000 415,000,000 245,000,000 3,507,000,000 744,000,000 791,000,000 454,000,000 248,000,000 3,518,000,000 858,000,000 743,000,000 475,000,000 249,000,000 3,156,000,000 760,000,000 680,000,000 447,000,000 248,000,000 3,179,000,000 684,000,000 726,000,000 430,000,000 248,000,000 3,710,000,000 842,000,000 821,000,000 444,000,000 248,000,000 3,820,000,000 895,000,000 836,000,000 446,000,000 249,000,000 3,523,000,000 33,000,000 744,000,000 473,000,000 249,000,000 3,570,000,000 1,048,000,000 729,000,000 506,000,000 247,000,000 4,044,000,000 1,363,000,000 704,000,000 589,000,000 246,000,000 4,072,000,000 1,501,000,000 765,000,000 553,000,000 244,000,000 3,471,000,000 993,000,000 760,000,000 425,000,000 240,000,000 129,000,000 138,000,000 133,000,000 132,000,000 141,000,000 153,000,000 139,000,000 148,000,000 161,000,000 160,000,000 163,000,000 149,000,000 152,000,000 152,000,000 165,000,000 151,000,000 160,000,000 163,000,000 171,000,000 160,000,000 171,000,000 181,000,000 182,000,000 174,000,000 185,000,000 198,000,000 195,000,000 86,000,000 193,000,000 190,000,000 209,000,000 198,000,000 207,000,000 210,000,000 225,000,000 210,000,000 Period Distribution Costs Aircraft Maintenance Depreciation Passenger Services Other Expenses 1Q-2000 2Q-2000 3Q-2000 4Q-2000 1Q-2001 2Q-2001 248,000,000 261,000,000 255,000,000 217,000,000 243,000,000 230,000,000 159,000,000 171,000,000 167,000,000 149,000,000 160,000,000 162,000,000 95,000,000 98,000,000 102,000,000 107,000,000 105,000,000 111,000,000 85,000,000 91,000,000 97,000,000 89,000,000 91,000,000 96,000,000 286,000,000 284,000,000 288,000,000 277,000,000 318,000,000 295,000,000 (continued on next page)

Issues in Accounting Education Volume 26, No. 1, 2011 American Accounting Association 186 Obs. 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Obs. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Roman Period Distribution Costs Aircraft Maintenance Depreciation Passenger Services Other Expenses 3Q-2001 4Q-2001 1Q-2002 2Q-2002 3Q-2002 4Q-2002 1Q-2003 2Q-2003 3Q-2003 4Q-2003 1Q-2004 2Q-2004 3Q-2004 4Q-2004 1Q-2005 2Q-2005 3Q-2005 4Q-2005 1Q-2006 2Q-2006 3Q-2006 4Q-2006 1Q-2007 2Q-2007 3Q-2007 4Q-2007 1Q-2008 2Q-2008 3Q-2008 4Q-2008 194,000,000 142,000,000 172,000,000 158,000,000 138,000,000 124,000,000 27,000,000 138,000,000 131,000,000 135,000,000 137,000,000 140,000,000 139,000,000 136,000,000 138,000,000 154,000,000 154,000,000 142,000,000 160,000,000 178,000,000 157,000,000 155,000,000 161,000,000 176,000,000 171,000,000 174,000,000 182,000,000 194,000,000 182,000,000 159,000,000 142,000,000 104,000,000 114,000,000 119,000,000 119,000,000 124,000,000 133,000,000 126,000,000 135,000,000 115,000,000 112,000,000 102,000,000 107,000,000 93,000,000 112,000,000 106,000,000 116,000,000 121,000,000 127,000,000 140,000,000 140,000,000 140,000,000 144,000,000 169,000,000 166,000,000 142,000,000 159,000,000 167,000,000 52,000,000 135,000,000 120,000,000 131,000,000 106,000,000 112,000,000 112,000,000 114,000,000 116,000,000 110,000,000 110,000,000 108,000,000 104,000,000 105,000,000 104,000,000 102,000,000 99,000,000 98,000,000 97,000,000 95,000,000 96,000,000 97,000,000 99,000,000 99,000,000 99,000,000 101,000,000 106,000,000 107,000,000 106,000,000 108,000,000 112,000,000 111,000,000 89,000,000 71,000,000 77,000,000 73,000,000 78,000,000 68,000,000 70,000,000 73,000,000 81,000,000 73,000,000 69,000,000 76,000,000 84,000,000 77,000,000 77,000,000 84,000,000 91,000,000 80,000,000 82,000,000 90,000,000 97,000,000 87,000,000 90,000,000 9,000,000 105,000,000 95,000,000 96,000,000 107,000,000 113,000,000 91,000,000 121,000,000 166,000,000 382,000,000 454,000,000 276,000,000 277,000,000 320,000,000 91,000,000 250,000,000 455,000,000 304,000,000 279,000,000 287,000,000 278,000,000 316,000,000 280,000,000 282,000,000 305,000,000 293,000,000 323,000,000 313,000,000 333,000,000 340,000,000 357,000,000 357,000,000 328,000,000 356,000,000 427,000,000 461,000,000 372,000,000 Period Total Aircraft 1Q-2000 2Q-2000 3Q-2000 4Q-2000 1Q-2001 2Q-2001 3Q-2001 4Q-2001 1Q-2002 2Q-2002 3Q-2002 4Q-2002 1Q-2003 2Q-2003 514 522 535 522 548 557 501 522 538 570 570 554 562 70 OPERATIONS AND COST DRIVER DATA Leased Aircraft Flights Passengers Available Seat Miles 403 410 414 398 406 416 377 393 400 404 401 410 419 428 98,820 97,871 97,967 98,378 98,590 99,018 98,564 81,109 81,883 82,815 81,737 78,809 75,178 75,617 11,201,000 12,084,000 12,155,000 11,456,000 11,220,000 12,256,000 11,254,000 9,508,000 12,062,000 13,099,000 13,006,000 12,874,000 11,518,000 13,044,000 20,951,000,000 21,384,000,000 22,356,000,000 21,409,000,000 21,459,000,000 22,813,000,000 21,994,000,000 18,219,000,000 20,375,000,000 22,286,000,000 22,626,000,000 21,054,000,000 20,843,000,000 21,241,000,000 Available Seat

Miles Regional — — — — — — — — — — — — 1,767,000,000 2,073,000,000 (continued on next page) Issues in Accounting Education American Accounting Association Volume 26, No. 1, 2011 A Case Study on Cost Estimation and Pro? tability Analysis at Continental Airlines Obs. 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Obs. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Period Total Aircraft 3Q-2003 4Q-2003 1Q-2004 2Q-2004 3Q-2004 4Q-2004 1Q-2005 2Q-2005 3Q-2005 4Q-2005 1Q-2006 2Q-2006 3Q-2006 4Q-2006 1Q-2007 2Q-2007 3Q-2007 4Q-2007 1Q-2008 2Q-2008 3Q-2008 4Q-2008 187 OPERATIONS AND COST DRIVER DATA

Leased Aircraft Flights Passengers Available Seat Miles 570 579 586 587 592 594 598 604 611 622 630 634 648 648 630 625 631 628 641 630 653 632 428 434 437 440 445 448 453 459 466 477 483 484 482 480 446 418 415 415 414 390 412 397 76,297 75,650 74,859 75,816 74,211 74,443 71,494 74,651 74,630 75,886 74,962 77,729 77,468 79,030 78,601 82,582 81,118 80,850 76,719 76,096 78,599 76,000 Available Seat Miles Regional 13,727,000 13,769,000 12,810,000 14,558,000 14,862,000 14,252,000 14,122,000 15,540,000 15,905,000 15,448,000 15,594,000 17,596,000 17,328,000 16,601,000 16,176,000 18,120,000 17,901,000 16,733,000 16,440,000 7,108,000 17,962,000 15,183,000 22,819,000,000 21,907,000,000 22,670,000,000 24,150,000,000 24,674,000,000 23,588,000,000 23,585,000,000 25,482,000,000 26,833,000,000 25,720,000,000 26,117,000,000 28,259,000,000 29,262,000,000 27,280,000,000 27,250,000,000 29,592,000,000 30,346,000,000 28,550,000,000 28,376,000,000 30,304,000,000 30,383,000,000 26,448,000,000 1,605,000,000 2,980,000,000 2,400,000,000 2,603,000,000 1,999,000,000 3,408,000,000 2,740,000,000 3,026,000,000 3,112,000,000 3,095,000,000 3,082,000,000 3,374,000,000 3,503,000,000 3,292,000,000 3,126,000,000 3,177,000,000 3,193,000,000 3,104,000,000 3,098,000,000 ,450,000,000 3,390,000,000 3,046,000,000 Period Passenger Miles Flown Employees Fuel Price Fuel Consumed 1Q-2000 2Q-2000 3Q-2000 4Q-2000 1Q-2001 2Q-2001 3Q-2001 4Q-2001 1Q-2002 2Q-2002 3Q-2002 4Q-2002 1Q-2003 2Q-2003 3Q-2003 4Q-2003 1Q-2004 2Q-2004 3Q-2004 4Q-2004 1Q-2005 2Q-2005 15,005,000,000 16,491,000,000 17,325,000,000 15,340,000,000 15,114,000,000 17,053,000,000 16,206,000,000 12,767,000,000 14,867,000,000 16,489,000,000 16,960,000,000 17,252,000,000 14,352,000,000 16,129,000,000 18,041,000,000 16,412,000,000 16,255,000,000 18,735,000,000 19,922,000,000 18,239,000,000 18,112,000,000 20,292,000,000 45,000 45,500 46,000 5,944 38,396 39,000 39,500 39,461 40,229 41,011 41,809 40,244 38,960 39,000 39,500 39,000 38,240 37,496 36,766 38,255 41,831 45,742 $0. 829 $0. 797 $0. 865 $0. 885 $0. 856 $0. 815 $0. 824 $0. 826 $0. 644 $0. 723 $0. 760 $0. 740 $1. 029 $0. 881 $0. 857 $0. 872 $1. 041 $1. 787 $1. 199 $1. 190 $1. 453 $1. 670 377,000,000 386,000,000 398,000,000 372,000,000 369,000,000 391,000,000 373,000,000 369,000,000 308,000,000 332,000,000 340,000,000 316,000,000 305,000,000 308,000,000 330,000,000 314,000,000 320,000,000 347,000,000 345,000,000 321,000,000 324,000,000 344,000,000 (continued on next page) Issues in Accounting Education

Volume 26, No. 1, 2011 American Accounting Association 188 Roman Period 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Passenger Miles Flown Employees Fuel Price Fuel Consumed 3Q-2005 4Q-2005 1Q-2006 2Q-2006 3Q-2006 4Q-2006 1Q-2007 2Q-2007 3Q-2007 4Q-2007 1Q-2008 2Q-2008 3Q-2008 4Q-2008 Obs. 21,762,000,000 20,033,000,000 20,336,000,000 23,367,000,000 24,042,000,000 21,772,000,000 21,450,000,000 24,623,000,000 25,422,000,000 22,670,000,000 22,280,000,000 24,836,000,000 24,746,000,000 20,825,000,000 50,018 42,200 42,600 43,450 41,500 38,033 41,800 43,300 41,400 39,640 43,000 40,100 43,500 42,490 $1. 880 $1. 776 $1. 904 $2. 10 $2. 215 $2. 064 $1. 895 $2. 079 $2. 206 $2. 499 $2. 797 $3. 856 $3. 450 $2. 925 364,000,000 344,000,000 347,000,000 375,000,000 387,000,000 362,000,000 361,000,000 395,000,000 406,000,000 380,000,000 375,000,000 389,000,000 395,000,000 339,000,000 EXHIBIT 2 PROJECTIONS OF REVENUES AND OPERATING ACTIVITY FOR YEAR 2009 Variable Revenues Available seat miles Available regional seat miles Number of passengers Number of planes Number leased planes Price of fuel per gallon Gallons of fuel consumed Quarter 1 Quarter 2 Quarter 3 Quarter 4 $2,962,000,000 26,323,000,000 2,971,000,000 14,408,000 634 398 $1. 82 403,000,000 2,767,000,000 28,007,000,000 3,044,000,000 16,348,000 617 394 $2. 07 430,000,000 $2,947,000,000 28,933,000,000 3,130,000,000 16,795,000 604 380 $1. 99 369,000,000 $2,462,000,000 26,291,000,000 3,002,000,000 15,258,000 601 379 $1. 98 479,000,000 All ? nancial and operational data represent quarterly data for the quarter beginning January 2000 Observation 1 through December 2008. Data have been compiled from Continental’s 8-K and10-K reports, submitted to the Securities and Exchange Commission. De? nitions of Operations Variables: Available seat miles the number of seats available multiplied by the number of miles ? wn; Available regional seat miles available seat miles on regional routes; Number of passengers number of paying passengers ? own; Number of planes number of planes in the ? eet, including regional routes aircraft; Number of leased planes number of leased planes; Price of jet fuel average price per gallon of jet fuel in the respective quarter; and Gallons of fuel consumed number of gallons of fuel consumed in the respective quarter. Salaries and Wages This account represents costs related to salaries and wages, as well as fringe bene? ts, of Continental’s workers. These include salaries for pilots and wages for ? ght attendants and ground crew, as well as wages for Continental’s mechanics. Additionally, a signi? cant portion of this salary pool represents wages of reservation specialists, customer service representatives at airports, and the salaries for administrative and support personnel e. g. , ? ight schedulers, technology Issues in Accounting Education American Accounting Association Volume 26, No. 1, 2011 A Case Study on Cost Estimation and Pro? tability Analysis at Continental Airlines 189 personnel, accountants, and division managers . A possible cost driver of salaries is the available seat miles. Aircraft Fuel and Related Taxes This represents the cost of jet fuel and related fuel taxes. Jet fuel cost tends to be driven by the current price of jet fuel and gallons of jet fuel consumed. Aircraft Rentals These are expenses for capital leases of aircraft. The main driver is the number of leased planes in Continental’s ? eet, including regional jets operated on behalf of Continental by four regional airlines under various capacity purchase agreements. Airport Fees Represents landing fees and passenger security fees paid to the various domestic and international airports where Continental ? ies.

Landing fees are driven by the number of passengers. Aircraft Maintenance and Repairs These are expenses associated with the service and maintenance of planes. These include expenses related to scheduled maintenance, spare parts and materials, and airframe and engine overhauls. The main drivers of these costs are the number of planes in the ? eet and the number of miles ? own. Depreciation and Amortization This represents depreciation and amortization expenses of aircraft, ground equipment, buildings, and other property. It must be emphasized that the largest portion of depreciation expense relates to the depreciation of aircraft.

Although depreciation expenses are driven by the acquisition cost of Continental’s capital assets, depreciation is greatly in? uenced by both company policy and accounting principles, such as the depreciation method, that a ? rm adopts. Distribution Costs These expenses represent credit card discount fees, booking fees, and travel agency commissions, all of which are affected by passenger revenue. Therefore, the driver of these costs is total revenue. Passenger Services This is also a cost pool that includes expenses related to processing and servicing passengers prior to take-off, during ? ight, and after arrival at their destination.

A signi? cant portion of these costs is generated by Continental’s Field Services Division, the main function of which is to provide service to planes prior to take-off. Some of these expenses relate to checking in passengers, handling luggage on and off planes, cleaning planes, stocking planes with beverage and food, and refueling the aircraft prior to take-off. The potential cost driver of these costs is the number of passengers. Regional Capacity Purchases These are costs related to the purchase of regional routes served by several regional airlines on behalf of Continental ExpressJet, Chautauqua, CommutAir, and Cogan .

These costs are 5 Available seat miles is calculated as the number of seats available for passengers multiplied by the number of scheduled miles those seats are ? own. Issues in Accounting Education Volume 26, No. 1, 2011 American Accounting Association 190 Roman driven by the combined ? ying capacity of the four airlines: available regional seat miles. Other Expenses This is a cost pool that comprises many ancillary and discretionary expenditures, including technology expenses, security and outside services, general supplies, and advertising and promotional expenses.

Further, this cost pool contains various special charges for gains and losses from the sale of retired aircraft and costs of future leases. Given the large variety of miscellaneous items, there is no clear driver of these expenses; however, a large portion of them, such as advertising and promotional expenses, are driven by total revenue. DISCUSSION QUESTIONS 1. 2. 3. 4. 5. 6 Using the quarterly data for operating costs and the various cost drivers of costs provided by Exhibits 1 and 2, estimate regression for cost category of costs.

Then, write the appropriate cost function for each category of cost and then interpret your regression results. Based on your regression results, where do you see the largest reductions in costs if ? ying capacity is lowered by 11 percent? Also, in which areas do you see opportunities to achieve further cost reductions and why? Exhibit 2 provides a quarterly forecast of revenues, jet fuel prices,6 and the projected operating activity for 2009. Using the information from your regressions and the forecast information provided in Exhibit 2, estimate Continental’s operating costs and expected pro? for the upcoming ? scal year. Based on the results of your pro? tability analysis, what can you say about the ? rm’s ? nancial outlook? Would Continental be earning an operating pro? t in 2009? If not, what should Continental’s management do to restore pro? tability in 2009? Summarize your conclusions in a memorandum addressed to Continental’s CEO. In the memo, you must clearly communicate your main ? ndings, emphasizing speci? c areas in which you see the greatest potential to achieve further reductions in costs and, based on your pro? tability analysis, sum up the ? nancial outlook for 2009.

You should note that Continental has entered into several future contracts to hedge the exposed risks of rising fuel prices. The projected costs for jet fuel on exhibit re? ects the value of the various future contracts which guarantee Continental a ? xed price for jet fuel at various maturity dates in 2009, as well the estimated gallons of fuel that Continental plans to use during the year. Issues in Accounting Education American Accounting Association Volume 26, No. 1, 2011 A Case Study on Cost Estimation and Pro? tability Analysis at Continental Airlines 191 CASE LEARNING OBJECTIVES AND IMPLEMENTATION GUIDANCE

Cost estimation is a fundamental aspect of managerial/cost accounting Datar et al. 2008; Eldenburg and Wolcott 2005 . For example, cost estimation is critical for developing budgets, setting up cost standards, inventory valuation, product costing, and many other applications. Ultimately, ? rms’ ability to accurately predict production and operating costs has a profound impact on decision-making. Additionally, given the frequency with which ? rms downsize or expand their operations in response to economic or market-wide conditions, knowing how this strategic decision of scaling output impacts ? ms’ future operating costs, and which tools can facilitate this task, has become increasingly relevant for ? rms. Nonetheless, despite its importance, cost estimation is a topic that merits further discussion in accounting textbooks. Although several managerial/cost accounting textbooks provide rich theoretical discussions of cost estimation, including cost behavior, cost functions, and, to some extent, regression analyses, the examples that are typically used to illustrate such an important concept often lack a sense of realism. Either ? titious data are commonly used in cost estimation, or the examples covered fail to capture realistic situations faced by ? rms in a “real world” context. Accordingly, this case aims to close this gap. The objective is to support students in learning how to apply regression analyses to understand cost behavior and forecast future costs using real data from ? rms. The case focuses on the harsh ? nancial situation faced by Continental Airlines as a result of the recent ? nancial crisis and the challenges it faces to remain pro? table.

It then highlights the importance of reducing and controlling costs as a viable strategy to restore pro? tability, and how regression analysis can assist in this pursuit. Students are next presented with quarterly data for various categories of costs and several potential cost drivers, which they must analyze and then perform regressions on operating costs using a variety of cost drivers. Based on these results, students have to examine how costs behave and then use the regression output to forecast the ? rm’s operating costs for year 2009. As part of the cost analysis, students must also identify speci? areas in which Continental could achieve the largest cost savings as a result of cutting capacity and implementing other cost-cutting measures. Apart from this, they must conduct a pro? tability analysis to project quarterly pro? ts for the upcoming ? scal year. The learning objectives of the case are as follows: 1. 2. 3. Students learn to conduct regression analysis in Excel and use this technique to study cost behavior and forecast future costs. Students also learn how to use actual ? rm-level data from public sources for estimating costs, and apply cost estimation in a “real world” context that involves a widespread decision among ? ms: downsizing capacity. Moreover, learning to use public ? nancial information in cost estimation could have implications that reach beyond accounting; learning to access public ? nancial information exposes students to the possibilities of applying regression analysis for business analysis in general, including cost and pro? tability analyses. The case requires students to synthesize their ? ndings in a memorandum addressed to Continental’s CEO; thus, students are also exposed to re? ning their writing skills in a business setting. Implementation Guidance

This case is primarily designed for use in an intermediate managerial/cost accounting undergraduate class; however, it could also work well in a graduate-level managerial accounting course, at either the master’s level or M. B. A. Issues in Accounting Education Volume 26, No. 1, 2011 American Accounting Association 192 Roman The realistic nature of the setting everyone can easily identify with the business model of airlines makes a particularly appealing environment for students to learn how regression analyses can be applied in cost estimation in a real-world context.

The questions presented in the case include both practical and theoretical questions. As an augmentation of the principles contained in the application of this case, instructors could enhance the student experience by devoting time to reviewing the concepts of cost functions and cost estimation, as well as discussing the fundamentals of regression analyses, so students can be exposed to these concepts prior to receiving the case. Alternatively, students can review these concepts on their own.

The Appendix provides a detailed explanation of cost functions and regression analysis and describes the steps to perform regression analysis in Excel. Additionally, it provides students with broad guidelines to write an effective memorandum. Student Feedback The case was administered to two sections of an upper-level intermediate undergraduate cost accounting class at a major U. S. university. Seventy-seven students responded to an evaluation survey to assess whether they improved their understanding of the concepts illustrated in the case, as well as to whether the case illustrated a “real world” application in predicting operating costs.

As shown in Table 1, students agreed that the case enhanced their understanding of the use of regression analyses in predicting future costs mean of 4. 17, based on a ? ve-point scale , the case encouraged them to think critically about the behavior of operating costs in a “real world” context mean of 4. 03, based on a ? ve-point scale ; plus, they found the case interesting and recommended it for use in teaching cost estimation via regression analyses mean of 4. 07, based on a ? ve-point scale; see also Table 2 . Similar positive responses are shown in Table 2. For example, Table 2 reports students’ knowledge on the use of regression

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