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Southwest Airlines' Battle to Conquer Philly Market
- 1. CC20 Appendix 1 Company Cases Company Case 9 Southwest Airlines: Waging War in Philly BAT TLE STATIONS! In March 2004, US Airways CEO David Siegel addressed his employees via a Webcast. “They’re coming for one reason: They’re coming to kill us. They beat us on the West Coast, they beat us in Baltimore, but if they beat us in Philadelphia, they are going to kill us.” Siegel exhorted his employees on, emphasiz- ing that US Airways had to repel Southwest Airlines when the no-frills carrier began operations at the Philadelphia International Airport in May—or die. On Sunday, May 9, 2004, at 5:05 A.M. (yes, A.M.), leisure passengers and some thrift-minded business people lined up to secure seats on Southwest’s 7 A.M. flight from Philadelphia to Chicago—its inaugural flight from the new market. Other pas- sengers scurried to get in line for a flight to Orlando. And why not? One family of six indicated it bought tickets for $49 each way, or $98 round trip. An equivalent round-trip ticket on US Airways would have cost $200. Southwest employees, dressed in golf shirts and khaki pants or shorts, had decorated the ticket counters with lavender, red, and gold balloons and hustled to assist the throng of passengers. As the crowd blew noisemakers and hurled confetti, Herb Kelleher, Southwest’s quirky CEO, shouted, “I hereby declare Philadelphia free from the tyranny of high fares!” At 6:59 A.M., Southwest flight 741 departed for Chicago.
- 2. WAR ON! Was Southwest’s entry into the Philadelphia market worth all this fuss? After all, US Airways was firmly entrenched in Philadelphia, the nation’s eighth-largest market, offering more than 375 flights per day and controlling two-thirds of the air- port’s 120 gates. Further, in 2004, little Southwest served a to- tal of 58 cities and 59 airports in 30 states and was offering only 14 flights a day from Philly out of only two gates. And until its entry into Philadelphia, Southwest had a history of entering smaller, less expensive, more out-of-the-way airports where it didn’t pose a direct threat to the major airlines like US Airways. Did Southwest really have a chance? Southwest was used to that question. In 1971, when Kelleher and a partner concocted a business plan on a cocktail napkin, most people didn’t give Southwest much chance. Its strategy completely countered the industry’s conventional wisdom. Southwest’s planes flew from “point-to-point” rather than using the “hub-and-spoke” pattern that is the backbone of the major air- lines. This allowed more flexibility to move planes around based on demand. Southwest served no meals, only snacks. It did not charge passengers a fee to change same-fare tickets. It had no as- signed seats. It had no electronic entertainment, relying on comic flight attendants to entertain passengers. The airline did not offer a retirement plan; rather, it offered its employees a profit- sharing plan. Because of all this, Southwest had much lower costs than its competitors and was able to crush the competition with low
- 3. fares. For 32 years, Southwest achieved unbelievable success by sticking to this basic no-frills, low-price strategy. Since it began operations in 1972, it was the only airline to post a profit every year. In 2003, just prior to taking the plunge in Philly, the com- pany earned $442 million—more than all the other U.S. airlines combined. In the three prior years, Southwest had earned $1.2 billion, while its competitors lost a combined $22 billion. In May 2003, for the first time, Southwest boarded more do- mestic customers than any other airline. From 1972 through 2002, Southwest had the nation’s best-performing stock—grow- ing at a compound annual rate of 26 percent over the period. Moreover, whereas competing airlines laid off thousands of workers following the September 11 tragedy, Southwest didn’t lay off a single employee. In 2004, its cost per average seat mile (CASM—the cost of flying one seat one mile) was 8.09 cents, as compared with between 9.42 to 11.18 for the big carriers. THE MAJORS: LOW ON AMMUNITION In the early 2000s, the major (or legacy) airlines, such as US Airways, Delta, United, American, and Continental, faced three major problems. First, “little” Southwest was no longer little. Second, other airlines, such as JetBlue, AirTran, ATA, and Virgin Atlantic, had adopted Southwest-like strategies. In fact, JetBlue and America West had CASMs of 5.90 and 7.72 cents, respectively. In 1990, discount airlines flew on just 159 of the nation’s top 1,000 routes. By 2004, that number had risen to 754. As a result, the majors, who had always believed they could earn a 30 percent price premium, were finding it hard to get a 10 per- cent premium, if that. Third, and most importantly, the major
- 4. air- lines had high cost structures that were difficult to change. They had more long-service employees who earned higher pay and re- ceived expensive pension and health benefits. Many had unions, which worked hard to protect employee pay and benefits. AT TACK AND COUNTER AT TACK US Airways had experienced Southwest’s attacks before. In the late 1980s, Southwest entered the California market, where US Airways had a 58 percent market share on its routes. By the mid- 90s, Southwest had forced US Airways to abandon those routes. On the Oakland-to-Burbank route, average one-way fares fell from $104 to just $42 and traffic tripled. In the early ’90s, Southwest entered Baltimore Washington International Airport, where US Airways had a significant hub and a 55 percent mar- ket share. By 2004, US Airways had only 4.9 percent of BWI traffic, with Southwest ranking number one at 47 percent. Knowing it was in for a fight in Philly, US Airways reluc- tantly started to make changes. In preparation for Southwest’s ar- rival, it began to reshape its image as a high-fare, uncooperative carrier. It spread out its scheduling to reduce congestion and the resulting delays and started using two seldom-used runways to reduce bottlenecks. The company also lowered fares to match Southwest’s and dropped its requirement for a Saturday-night stayover on discounted flights. USAirways also began some new promotion tactics. It launched local TV spots on popular shows to promote free massages, movie tickets, pizza, and flowers. Z01_ARMS1131_09_SE_APP1.QXD 1/9/08 6:35 PM Page CC20
- 5. Appendix 1 Company Cases CC21 On the other side, Southwest knew that Philadelphia posed a big challenge. Philadelphia International was one of the biggest airports it had ever attempted to enter. And with US Airways’ strong presence, it was also one of the most heavily guarded. Finally, the airport was known for its delays, conges- tion, bureaucracy, and baggage snafus, making Southwest’s strategy of 20-minute turnarounds very difficult. Therefore, Southwest unveiled a new promotion plan for Philly. Ditching its tried-and-true cookie-cutter approach, the airline held focus groups with local travelers to get their ideas on how it should promote its service—a first for Southwest. As a result, the airline developed a more intense ad campaign and assigned 50 percent more employees to the airport than it typi- cally had for other launches. Southwest also recruited volun- teers to stand on local street corners handing out free inflatable airline hats, luggage tags, and antenna toppers. The airline used billboards, TV, and radio to trumpet the accessibility of its low fares as well as its generous frequent flier program. Two short years after Southwest began service to Philadelphia, the market took on a dramatically different look. Southwest had boosted daily nonstop flights from 14 to 53. It had added service to 11 new cities and quadrupled its number of gates from two to eight, with its eye on four more. The number of Southwest employees in Philly approached 200, a huge in- crease over its postlaunch total of less than 30. But the external impact of Southwest’s first two years in Philadelphia was a classic example of what has come to be known as “the Southwest effect”—a phenomenon in which all carriers’ fares drop and more people fly. In Philadelphia, the in- tense competition brought on by Southwest’s arrival caused air- fares on some routes to drop by as much as 70 percent. In 2005,
- 6. airline passenger traffic for Philadelphia International was up 15 percent over 2004. The airport attributed much of that in- crease to Southwest. In all, by the end of 2005, Southwest had captured 10 percent of total passenger traffic. In turn, US Airways’ share fell about five percentage points. Just as US Airways was absorbing Southwest’s blows in Philadelphia, the underdog airline struck again. In May of 2005, Southwest started service to Pittsburgh, another major US Airways hub. Shortly thereafter, Southwest announced that it would also soon enter Charlotte, North Carolina, US Airways’ last stronghold. THE END OF AN ER A? Today, although it appears that Southwest is on cloud nine, many factors are forcing the nation’s most profitable air carrier to change its flight plans. First, the best-known discount airline has more competition than ever before. Upstarts such as Frontier, AirTran, and JetBlue are doing very well with Southwest’s model. And they are trumping Southwest’s low fares by adding amenities such as free TV and XM satellite radio at each seat. Even the legacy carriers are now in better positions to take on Southwest’s lower fares. All of the major airlines have ruth- lessly slashed costs, mostly in the areas of wages and pensions. Some, like US Airways, have used bankruptcy to force steep union concessions. In fact, Southwest now has some of the highest paid employees in the industry. At the same time that competition is becoming leaner and meaner, Southwest’s cost structure is actually on the rise. Because Southwest already has such a lean cost structure, it has much less
- 7. room for improvement. For example, travel agent commissions have been at zero for some time (Southwest doesn’t work through agents). Sixty-five percent of Southwest customers already buy their tickets online, minimizing its call center expense. And Southwest is quickly losing another of its traditional cost advan- tages. For years, through some smartly negotiated fuel-hedging contracts, Southwest has enjoyed fuel prices far below those paid by the rest of the industry. But with those contracts expiring, Southwest paid 47 percent more for jet fuel in 2006 than it did in 2005. For others, the cost increase was much less. Although Southwest still holds a cost advantage over the major carriers, the gap is narrowing. Southwest’s CASM for 2006 was 8.8 cents, up 17 percent from 7.5 cents four years ear- lier. With the cost structure of the big airlines decreasing, Southwest’s cost advantage has narrowed from 42 percent to 31 percent. With the momentum on both sides, this gap could soon be as little as 20 percent. As these factors have quickly turned the tables on Southwest, some analysts are questioning the company’s current strategic di- rection. “Slowly, Southwest is becoming what its competitors used to be,” says industry consultant Steven Casley. Serving congested hub airports, linking with rivals through code-sharing, and hunting the big boys on their own turf are all things that Southwest would previously have never considered.
- 8. But Gary Kelly, Southwest’s new CEO, defends the com- pany’s actions. “Hey, I can admit it, our competitors are getting better,” says Kelly. “Sure, we have an enormous cost advantage. Sure, we’re the most efficient. The problem is, I just don’t see how that can be indefinitely sustained without some sacrifice.” Kelly has his eye on change, in the areas of both cost and rev- enue. Although costs are already low at Southwest, the low-cost carrier is doing all that it can on that front. But the revenue side poses some greater potential. In 2006, Southwest raised ticket prices, boosting average fares by 11.4 percent over 2005. But fares can only go up so much before customers stop jumping on board. So Kelly is con- sidering many tactics that Southwest long avoided. Some of the possibilities include an assigned seating system and an in-flight entertainment system. By 2009, Southwest will be booking in- ternational flights through ATA Airlines. And in another first, Southwest has begun selling tickets through third-party distri- bution agents Galileo and Sabre Holdings. When asked if he was worried about Southwest losing its competitive advantage, Mr. Kelly responded confidently: We know people shop first for fares, and we’ve got the fares. [But] ultimately, our industry is a customer-service business, and we have the best people to provide that spe- cial customer service . . . that’s our core advantage. Since the U.S. Department of Transportation began collecting and publishing operating statistics, we’ve excelled at on- time performance, baggage handling, fewest complaints, and fewest canceled flights. Besides, we’re still the low- cost producer and the low-fare leader in the United States. We have no intention of conceding that position. Z01_ARMS1131_09_SE_APP1.QXD 1/9/08 6:35 PM Page CC21
- 9. CC22 Appendix 1 Company Cases By almost any measure, Southwest is still the healthiest air- line in the business. However, that might be like saying it’s the least sick patient in the hospital. As the industry as a whole has suffered in the post-September 11th world, Southwest’s 2005 earnings of $313 million were half of what the company made in 2000. The airline’s stock prices continue to hover somewhere between $11 and $14 a share, more than 30 percent below 2001 levels. As the other patients get better, Southwest can only hope that its future initiatives will be the new medicine that it needs. Questions for Discussion 1. How do Southwest’s marketing objectives and its marketing-mix strategy affect its pricing decisions? 2. Discuss factors that have affected the nature of costs in the airline industry since the year 2000. How have these factors affected pricing decisions? 3. How do the nature of the airline market and the demand for airline service affect Southwest’s decisions? 4. What general pricing approaches have airlines pursued? 5. Do you think that Southwest will be able to continue to maintain a competitive advantage based on price? What will happen if others carriers match the low-price leader? Sources: Melanie Trottman, “As Competition Rebounds, Southwest
- 10. Faces Squeeze,” Wall Street Journal, June 27, 2007; “Southwest Airlines Adds Sales Outlet,” Wall Street Journal, May 17, 2007; Chris Walsh, “A Philadelphia Success Story: Southwest’s Quick Growth in City Shows Its Potential in Denver,” Rocky Mountain News, December 30, 2005; Susan Warren, “Keeping Ahead of the Pack,” Wall Street Journal, December 19, 2005; Barney Gimbel, “Southwest’s New Flight Plan,” Fortune, May 16, 2005, accessed at www.money.cnn.com; “Let the Battle Begin,” Air Transport World, May 2004, p. 9; Micheline Maynard, “Southwest Comes Calling, and a Race Begins,” New York Times, May 10, 2004; Melanie Trottman, “Destination: Philadelphia,” Wall Street Journal, May 4, 2004; Andy Serwer and Kate Bonamici, “Southwest Airlines: The Hottest Thing in the Sky,” Fortune, March 8, 2004, p. 86. Z01_ARMS1131_09_SE_APP1.QXD 1/9/08 6:35 PM Page CC22 Static MethodStatic MethodSalesYear 1Year 2Year 3Year 4Year 5JAN20003000200050005000FEB30004000500040002000MAR 30003000500040003000APR30005000300020002000MAY4000 5000400050007000JUN60008000600070006000JUL7000300070 00100008000AUG60008000100001400010000SEP10000120001 50001600020000OCT1200012000150001600020000NOV140001 6000180002000022000DEC8000100008000120008000Total780 008900098000115000113000p = 12 (even)Deseasonalized Demand RegressionYearMonthPeriodDemand DtDeseasonalized Demand Dt Dt (based on regression)Seasonal Factor StForecastEtAtbiasMSEMADPercent ErrorMAPETSSUMMARY
- 11. OUTPUT1JAN1200060680.33258858858858834627358829291. 001FEB2300061380.492913- 87875011769523383161.48Regression Statistics1MAR3300062080.482872- 1281283731234332684121.39Multiple R0.97492561931APR4300062780.482500-500500- 1271550753261713-0.39R Square0.95047996311MAY5400063480.633944-5656- 183124678272111-0.67Adjusted R Square0.94940344061JUN6600064190.935356-644644- 8271730863341111-2.48Standard Error226.90147155071JUL77000654264891.085534-14661466- 22924552404962112- 4.63Observations481AUG86000662565590.91755115511551- 7426989116282614- 1.181SEP910000666766291.51114891489148974786756072315 141.03ANOVA1OCT1012000675067001.7911913- 87876607815676601131.00dfSSMSFSignificance F1NOV1114000687567702.071437737737710377234546343121 .64Regression145456339.830369245456339.8303692882.91691 716281.14477843987011E-311DEC128000700068401.177488- 5125125256850026246110.84Residual462368276.77842709514 84.27779189332JAN133000691769100.432948- 52524736325175802110.82Total4747824616.60879632FEB1440 00683369810.573313-687687-2146210825871711- 0.362MAR153000700070510.433262262262485842505669110.0 8CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%2APR165000708371210.702836- 21642164-21178405076664313- 3.18Intercept5997.260517682279.193407475775.72928996046. 12849862068368E- 505837.85260875486156.66842660975837.85260875486156.66 842660972MAY175000716771910.704468-532532- 26488077046581113-4.03X Variable 170.2457844842.36407008729.71391790331.14477843986988E -
- 12. 3165.48716276175.00440620765.48716276175.0044062072JUN 188000733372621.106059-19411941-45899721287292413- 6.292JUL193000737573320.41625332533253- 1336147800586210818- 1.552AUG208000737574021.088521521521- 8151417679845718- 0.962SEP2112000750074721.6112950950950135139312085081 70.16Average of Seasonal Factor StAvg. Seasonal Factors2OCT2212000750075431.5913411141114111546142035 187512171.77MonthTotal2NOV2316000737576132.1016167167 167171313598158451162.03JAN0.427JAN0.4270.4272DEC241 0000725076831.308411- 15891589124140837487616160.14FEB0.475FEB0.4750.4753JA N252000733377530.263308130813081432142043889365181.60 MAR0.463MAR0.4630.4633FEB265000758378240.643713- 12871287145142954490826190.16APR0.398APR0.3980.3983M AR275000779278940.633652-13481348-120314439099242719- 1.30MAY0.621MAY0.6210.6213APR283000804279640.383171 171171-10321393389898619- 1.15JUN0.834JUN0.8340.8343MAY294000825080340.5049929 92992-4013792669012519- 0.04JUL0.853JUL0.8530.8533JUN306000825081050.74676276 2762722135266589613190.81AUG1.151AUG1.1511.1513JUL31 7000829281750.866972- 282869413090568680180.80SEP1.733SEP1.7331.7333AUG321 0000837582451.219491- 50950918612762318575180.22OCT1.778OCT1.7781.7783SEP3 315000829283151.8014411-589589-4041248087849417- 0.48NOV2.124NOV2.1242.1243OCT3415000820883861.791491 0-9090-4931211615826117- 0.60DEC1.095DEC1.0951.0953NOV3518000820884562.131795 8-4242-5361177049804016- 0.673DEC368000829285260.94933413341334798119376381917 160.974JAN375000845885960.583667-13331333- 53512095038332716- 0.644FEB384000875086670.464113113113-
- 14. 94076NOV71233286DEC7212102 Comparison between Demand and Forecast demand 2000 3000 3000 3000 4000 6000 7000 6000 10000 12000 14000 8000 3000 4000 3000 5000 5000 8000 3000 8000 12000 12000 16000 10000 2000 5000 5000 3000 4000 6000 7000 10000 15000 15000 18000 8000 5000 4000 4000 2000 5000 7000 10000 14000 16000 16000 20000 12000 5000 2000 3000 2000 7000 6000 8000 10000 20000 20000 22000 8000 forecast 2588.4499246394294 2912.6473251781267 2871.9604542679253 2499.9982399114092 3944.414885023808 5355.6958629717747 5534.3376596267271 7550.6784685009907 11488.87589707732 11912.623965724744 14377.252362364457 7488.1150605929442 2948.0593321924757 3312.6658206674965 3261.9279496921486 2835.6609134950813 4468.1524207820976 6059.0401403511869 6253.2743739548123 8521.0428405838247 12949.707220950269 13411.454042671929 16167.409029055985 8410.9086976413073 3307.6687397455216 3712.6843161568668 3651.8954451163718 3171.3235870787535 4991.8899565403872 6762.3844177305991 6972.2110882828974 9491.4072126666561 14410.538544823219 14910.284119619109 17957.565695747511 9333.7023346896694 3667.278147298568 4112.7028116462361 4041.8629405405945 3506.9862606624265 5515.6274922986768 7465.7286951100123 7691.1478026109826 10461.771584749491 15871.369868696169 16409.114196566294 19747.722362439035 10256.495971738032 4026.8875548516139 4512.7213071356064 4431.8304359648182 3842.6489342460982 6039.3650280569655
- 15. 8169.0729724894254 8410.0845169390686 11432.135956832324 17332.201192569122 17907.944273513476 21537.87902913056 11179.289608786394 4386.4969624046598 4912.7398026249757 4821.797931389041 4178.3116078297708 6563.1025638152551 8872.4172498688386 9129.0212312671538 12402.500328915157 18793.032516442072 19406.774350460659 23328.035695822087 12102.083245834756 1. Start by reformatting data Calculate Errors Graph Using Winter's Model MovingAverageMoving AverageSalesYear 1Year 2Year 3Year 4Year 5JAN20003000200050005000FEB30004000500040002000MAR 30003000500040003000APR30005000300020002000MAY4000 5000400050007000JUN60008000600070006000JUL7000300070 00100008000AUG60008000100001400010000SEP10000120001 50001600020000OCT1200012000150001600020000NOV140001 6000180002000022000DEC8000100008000120008000Total780 008900098000115000113000p = 12 (even)Using a 12 period moving AverageYearMonthPeriodDemand DtLevelForecastEtAtbiasMSEMADPercent ErrorMAPETS1JAN120001FEB230001MAR330001APR430001 MAY540001JUN660001JUL770001AUG860001SEP9100001OC T10120001NOV11140001DEC12800065002JAN1330006583650 035003500350094230826911711713.002FEB144000666765832 583258360831351687435659114.002MAR153000666766673667 36679750215787065012210115.002APR1650006833666716671
- 17. 1091710917-229171758614130375568- 7.554DEC481200095839250-27502750- 256671737731530312367-8.475JAN4950009583958345834583- 210831745138930639268-6.885FEB5020009417958375837583- 1350018252500315337976- 4.285MAR5130009333941764176417- 708318701934321721479- 2.205APR5220009333933373337333250193764693296367870.0 85MAY5370009500933323332333258319113601327833850.795 JUN5460009417950035003500608318986497328258851.855JU L5580009250941714171417750018677778324818832.315AUG5 61000089179250- 75075067501835429132048812.115SEP572000092508917- 1108311083-43332018737833425581- 1.305OCT582000095839250-1075010750- 150832183177734705480-4.355NOV592200097509583- 1241712417-275002407485936215680- 7.595DEC6080009417975017501750-257502372465335902278- 7.176JAN61Forecasts9417Estimate of standard deviation of forecast error:44886FEB6294176MAR6394176APR6494176MAY659417 6JUN6694176JUL6794176AUG6894176SEP6994176OCT70941 76NOV7194176DEC729417 Comparison between Demand & Forecast 2000 3000 3000 3000 4000 6000 7000 6000 10000 12000 14000 8000 3000 4000 3000 5000 5000 8000 3000 8000 12000 12000 16000 10000 2000 5000 5000 3000 4000 6000 7000 10000 15000 15000 18000 8000 5000 4000 4000 2000 5000 7000 10000 14000 16000 16000 20000 12000 5000 2000 3000 2000 7000 6000 8000 10000 20000 20000 22000 8000 6500 6583.333333333333 6666.666666666667 6666.666666666667 6833.333333333333 6916.666666666667 7083.333333333333 6750 6916.666666666667 7083.333333333333 7083.333333333333 7250 7416.666666666667
- 18. 7333.333333333333 7416.666666666667 7583.333333333333 7416.666666666667 7333.333333333333 7166.666666666667 7500 7666.666666666667 7916.666666666667 8166.666666666667 8333.3333333333339 8166.666666666667 8416.6666666666661 8333.3333333333339 8250 8166.666666666667 8250 8333.3333333333339 8583.3333333333339 8916.6666666666661 9000 9083.3333333333339 9250 9583.3333333333339 9583.3333333333339 9416.6666666666661 9333.3333333333339 9333.3333333333339 9500 9416.6666666666661 9250 8916.6666666666661 9250 9583.3333333333339 9750 9416.6666666666661 9416.6666666666661 9416.6666666666661 9416.6666666666661 9416.6666666666661 9416.6666666666661 9416.6666666666661 9416.6666666666661 9416.6666666666661 9416.6666666666661 9416.6666666666661 9416.6666666666661 1. Start by reformatting data Calculate Errors Graph Using Winter's Model ExponentialSmoothingSimple Exponential SmoothingSalesYear 1Year 2Year 3Year 4Year 5JAN20003000200050005000FEB30004000500040002000MAR 30003000500040003000APR30005000300020002000MAY4000 5000400050007000JUN60008000600070006000JUL7000300070 00100008000AUG60008000100001400010000SEP10000120001 50001600020000OCT1200012000150001600020000NOV140001 6000180002000022000DEC8000100008000120008000Total780 008900098000115000113000p = 12 (even)Exponential
- 19. SmoothingAlpha = 0.6 TALLURI: TALLURI: used solver to get the optimal value for alpha. Minimize MAD by restricting alpha to be less than equal to 1YearMonthPeriodDemand DtLevelForecastEtAtbiasMSEMADPercent ErrorMAPETS082171JAN1200044878217621762176217386469 4462173113111.001FEB23000359544871487148777032042856 13852501802.001MAR3300032383595595595829813736917276 6201273.001APR43000309532382382388536103168332134897 4.001MAY5400036383095- 90590576318417218188823824.041JUN6600050553638- 2362236252697944143196739752.681JUL7700062225055- 1945194533247349573196428681.691AUG86000608962222222 223546643704217464602.031SEP91000084366089-39113911- 365742150519873958-0.181OCT1012000105748436-35643564- 3929794989621453055-1.831NOV11140001263010574- 34263426-7355829408822612452- 3.251DEC12800098521263046304630-2725938908024585853- 1.112JAN13300057419852685268524127122782412796228661. 482FEB1440004696574117411741586711617668272144652.16 2MAR1530003679469616961696756411034985265357642.852 APR16500044713679- 13211321624210454443256926622.432MAY17500047894471- 52952957149855912244911592.332JUN18800067154789- 3211321125029881324249240581.002JUL193000448667153715 37156217100878012556124612.432AUG20800065944486- 35143514270410200761260444601.042SEP211200098386594- 54065406-27021110642827374560- 0.992OCT2212000111359838-21622162- 48641081409827111858-1.792NOV23160001405411135- 48654865-97291137292428053057- 3.472DEC2410000116221405440544054- 56751158385628574156- 1.993JAN25200058491162296229622394714823523312848173
- 21. 730730418116588604334512671.255JUL55800070835708- 22922292188916382521332629660.575AUG561000088337083- 29172917-10281624190733192965- 0.315SEP5720000155338833-1116711167- 121941814461434565665-3.535OCT58200001821315533- 44674467-166611817576634742264- 4.805NOV59220002048518213-37873787- 204481811073534791764- 5.885DEC60800012994204851248512485- 796220406946362915665-2.196JAN61Forecasts8780Estimate of standard deviation of forecast error:45376FEB6247126MAR6336856APR6426746MAY655270 6JUN6657086JUL6770836AUG6888336SEP69155336OCT7018 2136NOV71204856DEC7212994 Comparison Between Demand and Forecast 2000 3000 3000 3000 4000 6000 7000 6000 10000 12000 14000 8000 3000 4000 3000 5000 5000 8000 3000 8000 12000 12000 16000 10000 2000 5000 5000 3000 4000 6000 7000 10000 15000 15000 18000 8000 5000 4000 4000 2000 5000 7000 10000 14000 16000 16000 20000 12000 5000 2000 3000 2000 7000 6000 8000 10000 20000 20000 22000 8000 8216.6666666666661 4486.6666666666661 3594.6666666666665 3237.8666666666668 3095.1466666666665 3638.0586666666668 5055.2234666666664 6222.0893866666665 6088.8357546666666 8435.5343018666663 10574.213720746666 12629.685488298666 9851.8741953194658 5740.749678127786 4696.2998712511144 3678.5199485004459 4471.4079794001782 4788.5631917600713 6715.4252767040289 4486.1701106816117 6594.4680442726449 9837.7872177090576 11135.114887083622 14054.045954833449 11621.618381933 38 5848.6473527733524 5339.4589411093411 5135.7835764437368
- 22. 3854.3134305774947 3941.7253722309979 5176.6901488923995 6270.6760595569594 8508.2704238227834 12403.308169529113 13961.323267811646 16384.52930712466 11353.811722849863 7541.5246891399456 5416.6098756559786 4566.6439502623916 3026.6575801049567 4210.6630320419827 5884.2652128167929 8353.7060851267179 11741.482434050688 14296.592973620274 15318.637189448109 18127.454875779244 14450.981950311698 8780.3927801246791 4712.157112049872 3684.8628448199488 2673.9451379279799 5269.5780551711923 5707.8312220684766 7083.1324888273903 8833.2529955309565 15533.301198212383 18213.320479284954 20485.328191713983 8780.3927801246791 4712.157112049872 3684.8628448199488 2673.9451379279799 5269.5780551711923 5707.8312220684766 7083.1324888273903 8833.2529955309565 15533.301198212383 18213.320479284954 20485.328191713983 12994.131276685594 Calculate Errors Graph Using Winter's Model 1. Start by reformatting data Holt'sModelHolts ModelSalesYear 1Year 2Year 3Year 4Year 5JAN20003000200050005000FEB30004000500040002000MAR 30003000500040003000APR30005000300020002000MAY4000 5000400050007000JUN60008000600070006000JUL7000300070 00100008000AUG60008000100001400010000SEP10000120001 50001600020000OCT1200012000150001600020000NOV140001
- 23. 6000180002000022000DEC8000100008000120008000Total780 008900098000115000113000p = 12 (even)Smoothing Constants TALLURI: TALLURI: note that the values of the smoothing constants alpha and beta are obtained by treating them as changing variables and minimizing the MAD value in solver subject to the constraint that their values are less than equal to 1.Alpha = 0.5Beta =0.5Deseasonalized Demand RegressionYearMonthPeriodDemand DtLevelTrendForecastEtAtbiasMSEMADPercent ErrorMAPETSSUMMARY OUTPUT05997701JAN120004034- 94760684068406840681654460840682032031.00Regression Statistics1FEB230003044- 9683087878741558276099207731032.00Multiple R0.97492561931MAR330002538-7372075- 92592532305802515169331791.91R Square0.95047996311APR430002400-4371800- 1200120020304711661157040691.29Adjusted R Square0.94940344061MAY540002981721963-20372037- 7459928816635166-0.00Standard Error226.90147155071JUN6600045278093053-29472947- 2954527978518774963- 1.57Observations481JUL77000616812255335-16651665- 4618492140018472457- 2.501AUG860006696877739213921392- 3226454859317902353- 1.80ANOVA1SEP910000878614837573-24272427- 5653469772618612450-3.04dfSSMSFSignificance F1OCT101200011135191610270-17301730- 7383452727718481446- 4.00Regression145456339.830369245456339.8303692882.9169 1716281.14477843987011E-311NOV111400013525215313051- 949949-833241975931766743- 4.72Residual462368276.7784270951484.27779189331DEC1280 00118392341567976797679-653876133522599647-
- 24. 0.29Total4747824616.60879632JAN1330007536- 203512073907390738420144195282783302673.032FEB144000 4751- 2410550215021502992113550661269138653.69CoefficientsSta ndard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%2MAR1530002670-22452341- 659659926212676255255622623.62Intercept5997.26051768227 9.193407475775.72928996046.12849862068368E- 505837.85260875486156.66842660975837.85260875486156.66 842660972APR1650002713-1102425- 45754575468713192105268291641.75X Variable 170.2457844842.36407008729.71391790331.14477843986988E - 3165.48716276175.00440620765.48716276175.0044062072MA Y1750003305-2541611- 33893389129813091721272468640.482JUN1880005526983305 1-49494949-36511372501528476264- 1.282JUL1930004754106650835083508- 14213650507288211767-0.052AUG20800064308914860- 31403140-32821346095928953965-1.13Avg. Seasonal Factors2SEP2112000966020617321-46794679- 79611386258829803964-2.672OCT221200011860213011721- 279279-8240132360092857261- 2.88JAN0.4272NOV231600014995263313991-20092009- 102501283604428201359- 3.63FEB0.4752DEC2410000138147261762876287628- 26221472566730217660-0.87MAR0.4633JAN2520008270- 24091454012540125409918204263453401627822.92APR0.3983 FEB2650005430- 262458618618611077919669198330417803.26MAY0.6213MAR 2750003903-20762806- 21942194858519119017326344792.63JUN0.8343APR28300024 14-17831827- 11731173741218485332318839772.32JUL0.8533MAY29400023 15-940631- 33693369404218239320319484771.27AUG1.1513JUN30600036
- 26. 462380986098609818773261880843890305794.835MAR513000 1713-3980426- 257425741619925804495386486804.195APR522000-133-2913- 2267- 4267426711933256583183872213823.085MAY5370001977- 401-3046- 10046100461887270784683988144830.475JUN5460003788705 1576-44244424-25382693953639967483- 0.645JUL558000624615824492-35083508- 60452667341039884482-1.525AUG5610000891421257828- 21722172-82182628135339552281- 2.085SEP572000015519436511039-89618961- 171792722917440434581-4.255OCT582000019942439419884- 116116-17295267599363975179- 4.355NOV59220002316838102433623362336- 149592639887439471178-3.795DEC60800017489- 9352697818978189784019319616024198237810.966JAN61Fore casts16554Estimate of standard deviation of forecast error:52476FEB62156206MAR63146856APR64137516MAY651 28166JUN66118826JUL67109476AUG68100126SEP6990786O CT7081436NOV7172096DEC726274 Comparison Between Demand & Forecast 2000 3000 3000 3000 4000 6000 7000 6000 10000 12000 14000 8000 3000 4000 3000 5000 5000 8000 3000 8000 12000 12000 16000 10000 2000 5000 5000 3000 4000 6000 7000 10000 15000 15000 18000 8000 5000 4000 4000 2000 5000 7000 10000 14000 16000 16000 20000 12000 5000 2000 3000 2000 7000 6000 8000 10000 20000 20000 22000 8000 6067.5063021662454 3087.122360025568 2075.1497989488371 1800.3760686732626 1962.8951863671593 3053.4309486223178 5335.3410925943172 7392.4608914317378 7572.9055679925132 10269.901514274774 13050.92410884721 15678.704378921626 12072.918419228427 5501.7958345747202
- 27. 2340.7855836041877 425.08406221787391 1610.9622859702488 3051.1608263538737 6508.4698899572177 4860.0069492695857 7320.7737416083728 11720.963702375673 13990.817757165405 17628.040345268921 14539.641553003448 5860.5317686198514 2805.8439342730885 1827.039033531436 630.87682477775047 1375.0765142064702 3903.4072303692124 6441.7207808582798 10100.447360888244 15654.698810681166 18268.149832907337 21007.837885793586 14125.722440788315 6903.2341080886008 2066.1814147165928 131.10971435144165 - 1369.2035644189946 972.94068730053596 4650.7776413351667 9327.0017080186917 14833.363314355782 18878.20328893538 20181.072453991335 22787.238923021479 17393.512426781177 8098.2710719657343 426.08262656657826 -2266.5322527746439 - 3046.2066292515942 1575.5078398228291 4492.4881144043329 7827.8562230940015 11038.576221665335 19884.292165534669 24336.077096085672 26977.950287339758 16554.39931113186 15619.823478593838 14685.247646055817 13750.671813517798 12816.095980979779 11881.520148441758 10946.944315903736 10012.368483365717 9077.7926508276978 8143.2168182896767 7208.6409857516555 6274.0651532136362 Calculate Errors Graph Using Winter's Model
- 28. 1. Start by reformatting data Winter'sModelWinter's ModelSalesYear 1Year 2Year 3Year 4Year 5JAN20003000200050005000FEB30004000500040002000MAR 30003000500040003000APR30005000300020002000MAY4000 5000400050007000JUN60008000600070006000JUL7000300070 00100008000AUG60008000100001400010000SEP10000120001 50001600020000OCT1200012000150001600020000NOV140001 6000180002000022000DEC8000100008000120008000Total780 008900098000115000113000p = 12 (even)Smoothing Constants TALLURI: TALLURI: note that the values of the smoothing constants alpha, beta, and gamma are obtained by treating them as changing variables and minimizing the MAD value in solver subject to the constraint that their values are less than equal to 1.Alpha = 0.0003957008Beta =0.9320232176Gamma =0YearMonthPeriodDemand DtLevelTrendSeasonal Factor StForecastEtAtbiasMSEMADPercent ErrorMAPETSDeseasonalized Demand Regression0599770SUMMARY OUTPUT1JAN120006067700.4325885885885883462845882929 1.001FEB230006137700.472912- 88885011769993383161.48Regression Statistics1MAR330006207700.462871- 1291293721235222684121.39Multiple R0.97492561931APR430006277700.402499-501501- 1291553063261713-0.39R Square0.95047996311MAY540006347700.623944-5656- 185124877272111-0.68Adjusted R Square0.94940344061JUN660006418710.835355-645645- 8301734083341111-2.48Standard Error226.90147155071JUL770006490710.855534-14661466- 22964555514962112- 4.63Observations481AUG860006560711.15755315531553- 7436999226282614-
- 29. 1.181SEP9100006631711.7311492149214927498694497241514 1.03ANOVA1OCT10120006701711.7811916- 84846647832176601131.01dfSSMSFSignificance F1NOV11140006772702.121438138138110467252376353121.6 5Regression145456339.830369245456339.8303692882.9169171 6281.14477843987011E-311DEC1280006842711.097490- 5105105366864426246110.86Residual462368276.77842709514 84.27779189332JAN1330006913710.432949- 51514856338375802110.84Total4747824616.60879632FEB1440 006984710.473314-686686-2016221595881711- 0.342MAR1530007055710.463264264264645853315669110.11 CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%2APR1650007129730.402838- 21622162-20998409616664313-3.15Intercept5997.2605176822 TALLURI: TALLURI: y-intercept79.193407475775.72928996046.12849862068368E- 505837.85260875486156.66842660975837.85260875486156.66 842660972MAY1750007202730.624474-526526- 26248077396581113-3.99X Variable 170.245784484 TALLURI: TALLURI: slope2.36407008729.71391790331.14477843986988E- 3165.48716276175.00440620765.48716276175.0044062072JUN 1880007276740.836070-19301930-45549697457282413- 6.252JUL1930007349730.85626932693269- 1285148111186210918- 1.492AUG2080007421731.158543543543-7421421813846718- 0.882SEP21120007494721.731298798798724514004898538170. 29Avg. Seasonal Factors2OCT22120007566721.781345314531453169814327988 8012171.932NOV23160007638722.12162202202201918137261 18511172.25JAN0.4272DEC24100007710731.098440- 15601560358141681388116160.41FEB0.4753JAN25200077827 10.433320132013201679142986489966181.87MAR0.4633FEB2 650007854720.473727-
- 31. 0.775APR5220009740700.4038791879187911721559780932942 01.265MAY5370009810700.626095- 905905266154581493213200.295JUN5460009879690.83824422 4422442510161040695637212.635JUL5580009948690.8584844 84484299415853929476203.165AUG561000010016681.151153 1153115314525159891595815204.725SEP572000010085691.73 17476- 252425242001168259998513202.035OCT582000010154691.781 8055- 19451945561718846100210200.065NOV592200010224692.122 1712-288288-2321691121990120- 0.235DEC60800010292681.091126832683268303618409411028 41202.956JAN61Forecasts0.434420Estimate of standard deviation of forecast error:12856FEB620.4749496MAR630.4648566APR640.4042076 MAY650.6266076JUN660.838929Winter's model seems to result in lowest forecast error6JUL670.8591856AUG681.15124766SEP691.73189016OC T701.78195146NOV712.12234526DEC721.0912164 Comparison Between Demand and Forecast 2000 3000 3000 3000 4000 6000 7000 6000 10000 12000 14000 8000 3000 4000 3000 5000 5000 8000 3000 8000 12000 12000 16000 10000 2000 5000 5000 3000 4000 6000 7000 10000 15000 15000 18000 8000 5000 4000 4000 2000 5000 7000 10000 14000 16000 16000 20000 12000 5000 2000 3000 2000 7000 6000 8000 10000 20000 20000 22000 8000 2588.4588635671421 2912.1698193122206 2871.2862368542915 2499.3397834116895 3943.8058865340809 5354.9707883453557 5534.2530799650031 7552.5846575124306 11491.864097846497 11915.563432523853 14381.370929336594 7490.4023902666349 2949.204389593192 3314.1807701977355 3264.0707822246491 2837.7293684565234 4474.462006428209 6070.272648766796
- 32. 6268.8987980761704 8543.2670304313142 12986.925095033599 13453.025935469643 16220.209992040151 8440.0416512641368 3320.2587516386275 3726.6783351149497 3666.9771072360541 3186.0570723636442 5016.6965826460855 6797.1077490684402 7009.1866042853535 9543.7072014166624 14493.462477195671 14999.747612567948 18069.35468291926 9393.8723350789987 3691.3175404525305 4141.4573078368967 4071.1180671541288 3533.2857269372985 5556.6324176273274 7521.2973769555374 7748.4804447300485 10542.443971329858 15999.997808090397 16546.303946498076 19917.250693881102 10347.005169387035 4063.8709260189962 4556.2820682078109 4474.2701737068874 3879.1063529206995 6094.6963985057819 8243.6081503056703 8484.3971050899363 11530.591381057318 17476.334130268264 18054.507617802545 21711.84429126243 11268.078655676818 4419.766826595901 4948.8330003833562 4856.0047883950647 4206.9898755919849 6606.6746248859536 8929.1669109002505 9185.3726691382417 12476.279969527408 18900.742566816538 19513.792996175973 23451.615189666551 12163.636157683737 1. Start by reformatting data Calculate Errors Graph Using Winter's Model Graph
- 33. Using Winter's Model Given Data Red Forecast Color coding