Facts of the Pre-Historic Predator Hyaenodon

Facts of the Pre-Historic Predator Hyaenodon Name: Hyaenodon (Greek for hyena tooth); pronounced hi-YAY-no-don Habitat: Plains of North America, Eurasia, and Africa Historical Epoch: Late Eocene-Early Miocene (40-20 million years ago) Size and Weight: Varies by species; about one to five feet long and five to 100 pounds Diet: Meat Distinguishing Characteristics: Slender legs; large head; long, narrow, tooth-studded snout About Hyaenodon The unusually long persistence of Hyaenodon in the fossil recordvarious specimens of this prehistoric carnivore have been found in sediments dating from 40 million to 20 million years ago, all the way from the Eocene to the early Miocene epochscan be explained by the fact that this genus comprised a large number of species, which ranged widely in size and enjoyed a nearly worldwide distribution. The largest species of Hyaenodon, H. gigas, was about the size of a wolf, and probably led a predatory wolf-like lifestyle (supplemented with hyena-like scavenging of dead carcasses), while the smallest species, the appropriately named H. microdon, was only about the size of a house cat. You might assume that Hyaenodon was directly ancestral to modern wolves and hyenas, but youd be wrong: the hyena tooth was a prime example of a creodont, a family of carnivorous mammals that arose about 10 million years after the dinosaurs went extinct and went extinct themselves about 20 million years ago, leaving no direct descendants (one of the biggest creodonts was the amusingly named Sarkastodon). The fact that Hyaenodon, with its four slender legs and narrow snout, so closely resembled modern meat-eaters can be chalked up to convergent evolution, the tendency for creatures in similar ecosystems to develop similar appearances and lifestyles. (However, bear in mind that this creodont didnt much resemble modern hyenas, except for the shape of some of its teeth!) Part of what made Hyaenodon such a formidable predator was its almost comically oversized jaws, which had to be supported by extra layers of musculature near the top of this creodonts neck. Like roughly contemporary bone-crushing dogs (to which it was only distantly related), Hyaenodon would likely snap the neck of its prey with a single bite, and then use the slicing teeth in the back of its jaws to grind down the carcass into smaller (and easier to handle) mouthfuls of flesh. (Hyaenodon was also equipped with an extra-long palate, which allowed this mammal to continue breathing comfortably as it dug into its meal.) What Happened to Hyaenodon? What could have edged Hyaenodon out of the spotlight, after millions of years of dominance? Thebone-crushing dogs referenced above are possible culprits: these megafauna mammals (typified by Amphicyon, the bear dog) were every bit as lethal, bite-wise, as Hyaenodon, but they were also better adapted for hunting scurrying herbivores across the wide plains of the later Cenozoic Era. One can imagine a pack of hungry Amphicyons denying a Hyaeonodon its recently killed prey, thus leading, over thousands and millions of years, to the eventual extinction of this otherwise well-adapted predator.

The film Black Swan explores and critiques cultural ideas concerning Essay

The film Black Swan explores and critiques cultural ideas concerning women - Essay Example In many ways, the ballet is the epitome of the misogynist fantasy, women who are represented as virginal and without sexuality, but with every movement of every muscle highlighted and visible. The great choreographer, George Ballanchine, wanted his ballerina’s to have no weight, to eat nothing, and to appear to be children rather than grown adults. Within the framework of the ballet, the dream of female fragility is played out through extremes of physical contortion and athleticism, which is presented to seem as if it is not. The film, Black Swan (2010), provides context for the nature of the life that women lead. Woman have always been constrained to live through the expectations put on them by society, trying to meet impossible standards without true recognition for how impossible the expectations that have been put on them are to meet. The expectation of perfection, the need to be all things, creates a pressure that often turns inward into self-mutilation and destructive be haviors. In the case of the lead character in Black Swan (2010), the critique on the culture of women explores the nature of striving for perfection and the dangers that lurk within the struggle to meet the expectations that are impossible to achieve. ... ecretaries for organizations, and do little within the home towards maintaining the daily chores, women are not allowed that luxury to let go of any sphere without facing failure. The ballerina, is first, an athlete. She performs on her toes, each movement of each muscle under her control so that she can turn, fly, twist, and land without appearing to be putting in any effort. She must be in peek condition, but her body must be starved of any body fat and lean without the bulge of muscle. Her representation is frail and fragile upon the stage, despite the strength and prowess that is required to create the dance. Her life is a contrast of needs, her impossible task set to see her fail. Few can achieve the balance, and all those who cannot, step away feeling like failures. The most notorious relationship in the recent history of the ballet is that between the choreographer George Balanchine and the ballerina Gelsey Kirkland. Kirkland wrote a book that described her experiences with Ba lanchine as he pushed his dancers to perform. He would tell his dancers â€Å"must see the bones†, â€Å"eat less†, and â€Å"eat nothing† (Freidler and Glazer 15). He led his dancers in what Kirkland is quoted as calling a â€Å"concentration camp aesthetic† (Gordon 124). Kirkland suffered from bulimia and anorexia nervosa, as well as drug addiction from taking pills to balance her lack of nutrition and energy. Her experience was painful, stressful, and full of the turmoil that women face as they strive to balance on their toes and be all things in the process. The metaphor of the ballet provides a rich textual dialogue about the difficulty of being a woman in today’s society. Unfortunately, as much as it is a metaphor it is also an explicit truth that the expectations in the ballet are

You choose the topic Essay Example | Topics and Well Written Essays - 1000 words - 6

You choose the topic - Essay Example Another issue that has caused problems is the different interpretations of body language. At times, Maria takes offense in prolonged eye contact and is sensitive to the distance maintained between us when communicating. The difference in stereotypes has affected our relationship. For instance, Maria feels like I hold her in low regard since she does not have strong English skills, an aspect that has affected her trust in me. Considering our relationship, the context component forms a basis for my analysis as it incorporates aspects of organization and culture, which involve the manner in which individuals from different cultures communicate and respond to both internal and external stimuli (Morreale, Spitzberg and Barge 38). In this case, the external stimulus’ source is casual communication, from where the cues of body language and effects of the accent differences affect the perception of the message. In addition, emotions and experiences are forms of internal stimuli that c ould cause different responses. The relationship allows proper integration of most of the factors that facilitate interpersonal communication. With reference to the cybernetics theory, the flow of information is limited with most of the information being barred from reaching either of us due to the inherent barriers (Mannan 60). In this case, the cultural aspects of the context component are not well negotiated in our relationship, with both of us failing to find a level ground that would accommodate each other’s cultural perspectives. On the other hand, the semiotics theory presents a clear analysis of the signs language and symbols applied as we communicate. As such, the differences in cultural backgrounds have provided for different applications of body language. This has in turn affected the way we interpret messages and culminate in the various instances

Literature review Essay Example | Topics and Well Written Essays - 2000 words - 9

Literature review - Essay Example The statement of aims and objectives in the main body is clearly provided and thus guarantees a score of 3. First and foremost is the research method used in collecting data for this study. According to the research paper data was collected from only those nursing students who had earlier participated in similar â€Å"peer learning partnerships†. This appears to be a narrow and restrictive criteria for carrying out this particular study since students who have already participated in such activities are generally conditioned to answer and/or show emotions a particular way as opposed to students who have never been part of such a study. Spontaneous responses, hence, are lessened to some extent. Moreover the paper fails to specify its data concerning the students involved within the study in a quantitative manner. There is no detailed mention of how many students joined in the research study or whether they were initially comfortable with the ground rules laid out for them. The study does not specify the number of dropouts (if any). There is also no detail why the students might have felt the need to be no longer part of the research study. The study does not make any mention of the response rate of the students under observation. It only mentions that in a moderated group environment the students tended to speak at the same time which again led to confusion when taking down responses in an organized manner. The nurses only provide data concerning their feelings and emotions when in a student-mentorship relationship. For most of the students in the group this would be classified as a positive experience since they would have decided it prior to joining the research study group that they were getting enrolled in this study as a positive experience and self study as well as self development. Hence, there are largely positive undercurrents to such a study as opposed to signs

A History of Schlumberger

A History of Schlumberger Introduction Schlumberger is one of the world’s largest oil and gas industry. Where Schlumberger employs roughly 123,000 people in their company which representing over 140 nationalities and also working in more than 85 countries. It has its principal offices in Houston, Paris and Hague. Furthermore, Schlumberger provide the widest range of products in the industry and also the service from exploration through production. Hence, Schlumberger not just an innovation company but also invent, design, engineer, and apply technologies which help customers to find and produce oil and gas more efficient and safe. Schlumberger was founded in 1926 by the French brothers called Conrad and Marcel Schlumberger as the Electric prospecting company. They recorded the first-ever electrical resistivity well log also called borehole log which enable the recording of geologic formations penetrated by the borehole, in Merkwiller-Pechelbronn of France in 1927. In 1929, the company starts to grow quickly and logged its first well in the California, United States. Also they logged their first electrical logging in Japan in 1936. Schlumberger invested heavily in research industries, and it inaugurating the Schlumberger Research Center in Ridgefield, United States. In 1956, Schlumberger Limited was incorporated to hold the shares of multiple companies as a holding company for all Schlumberger businesses. Later on, Schlumberger continued to expand over years. In 1960, they formed Dowell Schlumberger consists of half of Schlumberger and half of Dow Chemical which expert in pumping services for oil industry. In 1962, the Schlumberger Limited appeared in the list on the New York Stock Exchange, the largest stock exchange in the world. Meanwhile, Schlumberger purchased an electronic instruments manufacturer, Daystorm in South Boston, Virginia which was sold to Sperry Hutchinson in 1971. In 1964, Schlumberger also purchased 50% of Forex and then created the Neptune Drilling Company by merging Forex with 50% of Languedocienne. The first computerized reservoir analysis program SARABAND, was introduced n the year of 1970. The remaining half of Forex was purchased by the following year, the name Neptune was changed to Forex Neptune Drilling Company. In 1979, Fairchild Camera and Instrument also joined the Schlumberger Limited included Fairchild Semiconductor. In 1981, Schlumberger also established its first international data links with e-mail. In 1983, Cambridge Research Center in Cambridge, England was inaugurated by Schlumberger and later on it was renamed as Schlumberger Gould Research Center after Andrew Gould, the former CEO of Schlumberger. In 1984, the SEDCO drilling company and 50% of Dowell of North America were purchased. Then Anadrill drilling segment was created by combining Dowell and The Analysts’ drilling segments. In 1985, Forex Neptune was merged with SEDCO to form the Sedco Forex Drilling Company and Schlumberger acquired Merlin and half of GECO at the meanwhile. In 1987, Schlumberger successfully complete their purchase on Neptune from North America, Bosco and Cori from Itali, and Allmess from Germany. Meanwhile, Fairchild Semiconductor under Schlumberger was acquired by National Semiconductor for 122 million dollars. In 1991, Schlumberger also purchased PRAKLA-SEISMOS and first created the use of geosteering to drill a borehole well more effectively. In 1992, Schlumberger purchased the software company GeoQuest System. In the 1990s Schlumberger also bought out the petroleum division, AEG meter and the ECLIPSE reservoir study team Intera Tehnologies Corporation. Furthermore, Oilphase and Camco International were also acquired by Schlumberger. Then a joint venture was formed between Schlumberger and Cable Wireless created Omnes, they will handle all Schlumberger’s internal IT business. In 2000, WesternGeco was created by merging Geco-Prakla division and Western Geophysical which Schlumberger held a stake of 70% and remaining for its competitor Baker Hughes. In the same year, Sedco Forex was left and merged with Transocean Drilling company. In 2001, Schlumberger spend 5.2 billion dollars to acquire the IT consultancy company Sema plc. In 2004, Schlumberger Business Consulting act as the companys management consultancy arm was launched. In 2005, Waterloo Hydrogeologic was acquired by Schlumberger which was followed by a few other groundwater industry related companies, for example Westbay Instruments and Van Essen Instruments. Schlumberger also relocated its corporate offices from New York to Houston. In 2006, Schlumberger complete purchased the remaining 30% of WesternGeco from its competitor Baked Hughes for 2.4 billion dollars. In the same year, the Schlumberger-Doll Research Center was moved to a newly built facility in Cambridge, Massachusetts. The facility united few other research centers run by the company in Cambridge, England; Moscow, Russia; Stavanger, Norway; and Dhahran, Saudi Arabia. In 2010, Schlumberger announced the acquisition of Smith International in an all stock deal priced at 11.3 billion dollars. This is the highest purchase in Schlumberger history. Schlumberger also announced planning to acquire the French-based company Geoservices which specialized in energy services, at the cost of 1.1 billion dollars with debt included. Schlumberger becomes the world leading oilfield company because of it can perform complete service in its field, including reservoir scouting, characterization, high technology drilling system, own production group and so on. Schlumberger owns a lots of daughter company which specialized in different field, and providing solutions. It also invested heavily in different research companies who improve high performance drilling equipments which can endure high pressure and temperature during drilling process. Sun Zi’s Art of War Theory Moral influence refers to measures and policies that align the people with the sovereign so as to be in complete agreement and harmony with each other. Weather refers to the contrasting changes of night and day, the coldness of winter and the heat of summer, and seasonal changes. Terrain means by distance, whether the ground is traversed with ease or difficulty, whether it is open or constricted and the chances of life or death (of an army). Generalship refer as the general’s qualities wisdom, trustworthiness, benevolence, courage and discipline. Doctrine law Refer to organization and control, management systems and procedures, and the command and control structure for the deployment of resources SWOT Analysis Strengths Schlumberger is the world’s largest leading oilfield services providers and one the global market leader in it industry which brings many benefits to it company. Generally, Schlumberger possess good company names, economies of scale, higher margins, revenues and other substantial benefits. Schlumberger not just the top 1 biggest oilfield services company, but also company that worldwide recognize in the oilfield industry. For example, Schlumberger ranked in the top 50 of the Engineering category of â€Å" The 2012 World’s Most Attractive Employers† that reflects all the students and professionals will try to seek job opportunities with the company. Besides that, Schlumberger also a company that consist excellent technology that strengthen the company’s name. Hence, it has technology innovation with 125 research and engineering facilities worldwide. It place strong importance on the developing innovation technology that increase the quality of product. In example, Schlumberger firmly holds their professionalism through years of experience in it field which to fulfil the customer needs and satisfaction by using its strong technologies. In addition, it creates more stable business and more likely to obtain other businesses as opportunities comes. Furthermore, Schlumberger has strong research capabilities which they always invested significant time and money on research and engineering as a long-term strategy to support and to expand their technology leadership. Schlumberger increase their services, products, tools, and equipment to maximize and extend production for the life of the reservoir. Schlumberger supplies various products and services including seismic, drilling services, well testing, well completions, subsea production, well production, and well intervention. Based from world energy news, Schlumberger generated 10.67 billion dollars in oil field services revenue with year to year increasing of eight percent. With these kind of technologies that Schlumberger can kind provide, it able to highly strength their company income and also it can evolve to superior technology in days to come. In a nut shell, the deep knowledge of exploration and production that gained through more than 80 years of experience by Schlumberger enable it to have much higher company standard in oil and gas industry. With the company’s history, it consist a strong brand equity with worldwide recognition. Thus, the deep knowledge that gain the company but as the company obtain a great profits and revenues that have empower the company to achieve one the world’s leading oilfield company. Weaknesses Schlumberger is a company with many strength as in the oilfield industry. But doesn’t mean it don’t have any weaknesses in its field. Schlumberger have daughter companies over 140 nationalities, which give it a wide range of production and exploration. But with so many daughter companies, there is lots of possibility an error can occur in certain company. It is hard to tracking down the progress in each of the daughter company. For example, in certain daughter company may consist of drilling failure. This may cause inconvenience not just to the customer but also the company itself. On the other hand, there also legal issues involve with Schlumberger with the accusation of fracking price. According to the Bloomberg news, where the company were sued over claims Schlumberger and other oilfield industry which Halliburton and Baker Hughes collaborated to raise prices and crush oilfield service competitors in the booming U.S. market for hydraulic fracturing services. Therefore, with this kind of legal issue involve with the company will affected on the company’s good name and may worsen some relationship with certain company collaboration. Schlumberger may also contain another weakness that is not active in social media besides facebook and twitter. The company doesn’t use much of the social media to evolve their company’s name. For example, the company doesn’t promote much of commercialize video on it new innovative technologies or production to fascinate viewer or even new customers. Therefore, without increase the participation of social media will influence the good name of the company. Schlumberger announce for the full year result of oil field services in year 2013, land revenue decline 2% in the North America Area. Where land businesses experience some weakness in drilling, stimulation and wireline services. The cause of the weakness can be related to the company’s innovation technology has reach its limit. Hence, this kind of inconvenience the company facing would lead to decline revenues. The company able to increased service concentration, improve efficiency, market share gains, and new advanced technology. Opportunities Schlumberger consist plenty opportunities based from the company global market leader in the oil field industry. The company can acquire higher percentage of oil discovery. The company technologies additional innovative workflows, advance software, and technical experts to help the company clients to enhance characteristics of uncertain exploration. Thus the company deep understanding of the geology of their prospective areas which the company exploration crew can extract the maximum value from their exploration asset. Moreover, Schlumberger also have acquisitions by buying out their competitor which the company could offer a higher level of project management and integrated solution for oil and gas companies. While others individual suppliers involved with exploration and production add excessive cost that increase complexities. As an alternative, Schlumberger decided to work on that by broadening its technical capacity and also approach greater integration capabilities. Schlumberger also have better exposed to expand in international markets rather than their competitor. For example, Schlumberger exposure to Gulf of Mexico proved to be important, aiding the 3 percent revenue growth in North America. While Schlumberger also has been awarded an 18 moth multiple services contract by Oil India for designing, drilling, and completing six horizontal wells. Where Schlumberger consist of high probability making a bigger foray into other countries in the near future. Besides that, Schlumberger contain the peak of increasing oil supply which will happen in the future. This enable increasing the demand for oil extracting companies and outlying technologies that can discover oil in difficult locations. Manufacture of oil from coal and natural gas is potential benefits of peak in oil supplies which Schlumberger gained. In future, the importance of oil will increase and the demand for these sources will be more expensive. Threats Schlumberger have one of the best technology in the oil field services industry. Thus, the company maintain the service industry’s longest commitment to technology and innovation. But there are many challenges ahead towards the company that highly competition with other oil field services companies, with great technology in the company that some customers will need to consider on the budget. Because great quality also comes with a high price. Therefore, other competitor may provide much average technology cheaper budget that can already satisfy the technologies needed of several customers. Hence, that will enable the competitor to attract some the Schlumberger’s previous customer to dealt further businesses with competitor. Schlumberger will not only deal with other oil field services companies of the budget on technologies but also the products and services of the company. Where Schlumberger’s competitor Halliburton supplies oil and gas companies equipment and service that helps extract unprocessed oil and gas from the ground. Hence, they may offer the same products and services which also allow easier attract of new customer. Plus they will analyse and came up with new innovation technology to deal with Schlumberger. Furthermore, Schlumberger also concern about economic slowdown occur on it company. For example, the SchlumbergerSema revenue of 870 million USD for the quarter decreased 3 percent consecutively as a result of a 17 percent decrease in Cards revenue following lower demand in Europe. This economic slowdown can cause weak oil and gas demand and also will influence the pricing reduction in the company worldwide rig. In addition, it will also effect on the company’s production and exploration spending. In order to overcome economic slowdown, one of the method is by expand the company areas and attract new customer to sustain the company’s business which able to use various type of business commercialization strategies. Apart from that, the energy producers rising costs for raw materials such as steel had help Schlumberger contributed to 120 percent growth in production and exploration spending between year 2004 and 2007.Schlumberger also concern regarding the high energy prices have prompted governments worldwide to increase taxes and change investment terms to producer’s profits to gain a bigger share. Which can cause underinvestment and simply exacerbate the problems for the company. Conclusion Today Schlumberger is the world’s largest oilfield services provider, with employees representing over 140 nationalities. The company rapidly developed international origins and a worldwide presence, becoming the leading provider of subsurface surveys. Schlumberger is faithful to its legacy of technological innovation. After initially leading the industry with its subsurface survey methods, the company now offer a wide portfolio of leading technologies to meet the needs of our customers in the oil and gas industry. Schlumberger has grown both organically and through outside acquisitions. Along with new technologies and opportunities. Certainly, Knowledge, technological innovation and a truly global workforce remain the symbols of Schlumberger today. Based from the whole process, Schlumberger is the global market leader in oil and gas industry that consist a strong business strategies plan. The company is accurate in grasping the opportunities with their leading innovative technologies. Hence expand the company’s exploration and production and improve their revenue in the global market. Besides that, the company also amplifying their company by using the history of the company and the excellent name in their industry to maintain their leadership in global market. In fact, Schlumberger committed to providing services and products that enhance their customer’s performance. Schlumberger delivers excellence to their customers which the company treat all their customers in a consistent and transparent way. With their extensive experience in developing and deploying innovative technology solutions, its enable to maintain the satisfaction of the customer and also shareholders. Furthermore, Schlumberger also enable to maintain a greater oil service between their competitor with the company understand and support the direction ahead. In other hand, the company strongly apply service focus, continuous improvement, and self- development. Lastly, Schlumberger consist of a strong integrity which the company recognize the boundaries and have the courage to act responsibly and sincerely. Decision making is one the company integrity, the company observe the right time and also the risk before move forward to a certain exploration and production. The high responsibility also holds firmly by the company to able to gained greater trust from collaboration group and customers.

Essay on the Structure of William Faulkners A Rose for Emily

The Structure of A Rose for Emily    William Faulkner's "A Rose for Emily" is a story that uses flashbacks to foreshadow a surprise ending. The story begins with the death of a prominent old woman, Emily, and finishes with the startling discovery that Emily as been sleeping with the corpse of her lover, whom she murdered, for the past forty years. The middle of the story is told in flashbacks by a narrator who seems to represent the collective memory of an entire town. Within these flashbacks, which jump in time from ten years past to forty years past, are hidden clues which prepare the reader for the unexpected ending, such as hints of Emily's insanity, her odd behavior concerning the deaths of loved ones, and the evidence that the murder took place. Without bluntly saying it, Faulkner, in several instances, hints that Emily has gone mad. At a few points in the story, the narrator mentions Emily's Great Aunt Wyatt, who "had gone completely crazy at last" (paragraph 25). This is the narrator's insinuation that insa... ...was a desperate act of a lonely, insane woman who could not bear to loose him. The structure of this story, however, is such that the important details are delivered in almost random order, without a clear road map that connects events. The ending comes as a morbid shock, until a second reading of the story reveals the carefully hidden details that foreshadow the logical conclusion. Works Cited Faulkner, William. "A Rose for Emily". An Introduction to Literature, 11th ed. Ed. Barnet, Sylvan, et al. 287-294. Essay on the Structure of William Faulkner's A Rose for Emily The Structure of A Rose for Emily    William Faulkner's "A Rose for Emily" is a story that uses flashbacks to foreshadow a surprise ending. The story begins with the death of a prominent old woman, Emily, and finishes with the startling discovery that Emily as been sleeping with the corpse of her lover, whom she murdered, for the past forty years. The middle of the story is told in flashbacks by a narrator who seems to represent the collective memory of an entire town. Within these flashbacks, which jump in time from ten years past to forty years past, are hidden clues which prepare the reader for the unexpected ending, such as hints of Emily's insanity, her odd behavior concerning the deaths of loved ones, and the evidence that the murder took place. Without bluntly saying it, Faulkner, in several instances, hints that Emily has gone mad. At a few points in the story, the narrator mentions Emily's Great Aunt Wyatt, who "had gone completely crazy at last" (paragraph 25). This is the narrator's insinuation that insa... ...was a desperate act of a lonely, insane woman who could not bear to loose him. The structure of this story, however, is such that the important details are delivered in almost random order, without a clear road map that connects events. The ending comes as a morbid shock, until a second reading of the story reveals the carefully hidden details that foreshadow the logical conclusion. Works Cited Faulkner, William. "A Rose for Emily". An Introduction to Literature, 11th ed. Ed. Barnet, Sylvan, et al. 287-294.

Traffic Movement in Lufthansa Airlines: a Supply Chain Perspective

Journal of Services Research Volume 10 Number 2 October 2010 – March 2011 FORECASTING THE PASSENGER TRAFFIC MOVEMENT IN LUFTHANSA AIRLINES: A SUPPLY CHAIN PERSPECTIVE Aniruddh Kr Singh Faculty of Management Studies University of Delhi, India. Debadyuti Das Associate Professor, Faculty of Management Studies University of Delhi, India. The Journal of IIMT FORECASTING THE PASSENGER TRAFFIC MOVEMENT IN LUFTHANSA AIRLINES: A SUPPLY CHAIN PERSPECTIVE Aniruddh Kr Singh Debadyuti DasThe present paper attempts to find out the forecasted passenger traffic movement of Lufthansa Airlines on quarterly basis at a global level by employing four forecasting methods namely moving average, exponential smoothing, Holt's model and Winter's model with the help of published data pertaining to passenger traffic movement of Lufthansa Airlines. The study has also found out the forecasting errors of all the four methods through Absolute error (AE), Mean squared error (MSE), Mean absolute deviation (MAD ) and Mean absolute percentage error (MAPE).The study also carried out the comparative analyses of the above forecasting methods in the light of the available data. The findings reveal that the forecasting errors are the least in case of Winter's model. Further the forecasted values suggested by Winter's model more closely resemble the observed data of passenger traffic movement of Lufthansa Airlines. This provides a valuable insight to the top management as regards formulation of suitable strategies for addressing the varying demand of passenger traffic movement.Few strategies in respect of both demand side and supply side options have been suggested with a view to improving the overall supply chain profit of Lufthansa Airlines. INTRODUCTION irlines industry across the globe is currently undergoing recession due to severe financial crisis faced by the major economies of the world. As per the estimates of International Air Transport Association (IATA), globally air travel has declin ed by 2. 9% and 1. 3% during September and October, 2008 respectively compared to the same months in the previous year.Segment-wise passenger traffic estimates provided by IATA further reveal that the Asia Pacific Carriers and North American Carriers registered a decline in passenger traffic flow by 6. 1% and 0. 9% respectively in October, 2008 compared to the same month in the previous year. African Carriers recorded the largest decline in traffic flow by 12. 9% in October, 2008 Journal of Services Research, Volume 10, Number 2 (October 2010 – March 2011)  ©2010 by Institute for International Management and Technology. All Rights Reserved. A 4 Forecasting the Passenger compared to the same month in the previous year. The remaining segments namely European, Latin American and Middle Eastern Airlines experienced a moderate growth in its traffic flow to the tune of 1. 8%, 4. 5% and 3. 5% respectively in October, 2008 (IATA International traffic statistics, 2008a, 2008b). Howe ver, the financial crisis sweeping across the globe does not appear to have much negative impact on Lufthansa Airlines in respect of its passenger traffic flow till September, 2008 as revealed from the data provided in table 2a.A cursory observation into the table 2 further demonstrates that the passenger traffic flow in Lufthansa Airlines has been following a very systematic pattern since October, 2006 to September, 2008. There has been hardly any departure from the pattern observed in passenger traffic movement during the above period. Despite difficult market conditions, Lufthansa passenger Airlines was able to achieve a sales growth of 4. 2% and 0. 7% in September and October, 2008 respectively.It registered an increase in its passenger traffic flow in three major markets namely America (North/South), Asia/ Pacific, and Middle East & Africa both during September and October, 2008. American segment recorded a growth rate of 6. 9% and 1% during September and October, 2008 respecti vely. Asia/Pacific region exhibited an increasing trend of 8. 8% and 6% while Middle East and African region recorded an increasing trend of 2. 5% and 11% during September and October, 2008 respectively. Only European market experienced a declining trend to the tune of 0. 4% and 3% during the above periods (Lufthansa Investor Info, page 1, 2008).The above phenomenon has motivated us to apply the most popular and well-established forecasting methods with a view to finding out the forecasted demand of passenger traffic movement of Lufthansa Airlines for future periods. The main objective of the paper is to find out the quarterly forecasted demand of passenger traffic flow in Lufthansa Airlines at a global level with the help of moving average (MA), exponential smoothing (ES), Holt’s model and Winter’s model by making use of published data pertaining to passenger traffic movement in Lufthansa Airlines.In addition, the paper has also attempted to find out the most suitable forecasting model for the above problem by comparing the forecasting errors of the above four forecasting models obtained through absolute error (AE), mean squared error (MSE), mean Journal of Services Research, Volume 10, Number 2 (October 2010 – March 2011) 65 Singh, Das absolute deviations (MAD) and mean absolute percentage error (MAPE). The following section provides a brief review of literature. Section 3 provides a brief overview of Lufthansa Airlines along with the recent data on passenger traffic movement.It contains a thorough analysis of forecasted passenger traffic movement by employing four forecasting methods and the comparative analysis of the same. Section 4 suggests few strategies for absorbing the varying nature of demand. The paper is concluded with a brief summary, potential contribution and limitations of the same. REVIEW OF LITERATURE Forecasting literature is replete with a number of studies ranging from simple time-series forecasting models to economet ric models as also the forecasting models employing artificial intelligence techniques etc.Researchers have employed the forecasting models with a view to finding out the forecasted demand of traffic for a particular period. However, the study findings reveal that there does not exist a single model which consistently outperforms other models in all situations. Quantitative forecasting methods can be categorized under three broad heads: (1) time-series modeling, (2) econometric models and (3) other quantitative models (Song and Li, 2008). Under time-series models, several techniques are available, e. g.Moving Average, Exponential Smoothing, Holt’s Model, Winter’s Model, ARIMA etc. (Makridakis et al, 2003). In time-series model, particular attention is paid to exploring the historic trends and patterns of the time-series involved and to predict the future of this series based on trends and patterns identified in the model. Since time-series models require only historica l observations of a variable, it is less costly in data collection and model estimation. However, these models cannot account for the changes in demand that might occur in different periods.The major advantages of econometric models over time-series models lie in their ability to analyze the causal relationships between the demand and its influencing factors (Song and Li, 2008; Makridakis et al, 2003). It is possible for econometric models to take into consideration several variables together, for example, air fare charged by an airline, competitive fare offered by other airlines, promotional campaign, perceived security threat, price and income elasticity of Journal of Services Research, Volume 10, Number 2 (October 2010 – March 2011) 6 Forecasting the Passenger demand etc. However, it is difficult and costly to collect data on each individual variable, incorporate the same into the model and explain its contribution towards the dependent variable. A number of new quantitati ve forecasting methods, predominantly Artificial Intelligence (AI) techniques, have emerged in forecasting literature. The main advantage of AI techniques is that it does not require any preliminary or additional information about data such as distribution and probability (Song and Li, 2008).Table 1 provides a brief overview of some related works pertaining to forecasting and traffic movement in airlines. Table 1: Brief Overview of Few Works Relating to Traffic Movement in Airlines Author Choo and Mokhtarian (2007) Contribution Developed a conceptual model in a comprehensive framework, considering causal relationships among travel, telecommunications, land use, economic activity and socio-demographics and explored the aggregate relationships between telecommunications and travel using structural equation modeling of national time-series data spanning 1950-2000 in the US.Proposed an artificial neural network (ANN) structure for seasonal time-series forecasting. Results found by the p roposed ANN model were compared with the traditional statistical models which reveal that the prediction error of the proposed model is lower than the traditional models. The proposed model is especially suitable when the seasonality in time-series is very strong. Developed a methodology for assessing the future route network and flight schedule at a medium-sized European airport.The existing origin and destination demand from the base airport across the world is considered. In addition, the growth rates by country or region is also taken into account. The future origin and destination demand in then converted into route traffic subject to a threshold for direct service. Where demand falls below this level, traffic is reallocated via various appropriate hubs. Applied Static-regression trend-fitting model for the purpose of forecasting future tourism demand in North Cyprus.Applied different types of time-series forecasting modeling with reference to China and compared the forecasting accuracy of the models. Applied different types of time-series forecasting modeling with reference to Australia for the purpose of forecasting business tourism and compared the forecasting accuracy of the models. Employed autoregressive distributed lag model (ADLM) for the purpose of forecasting tourism demand at Greece.Hamzacebi (2008) Dennis (2002) Bicak, Altinay and Jenkins (2005) Kulendran and Shan (2002) Kulendran and Witt (2003) Dritsakis and Athanasiadia (2000) THE CASE OF LUFTHANSA AIRLINES Deutsche Lufthansa (Lufthansa), the third largest airlines of Europe, is the world’s fifth largest airline in terms of overall passengers carried and operating services to 209 destinations in 81 countries. It has the 6th largest passenger airline fleet in the world.Lufthansa is headquartered in Cologne, Germany with its main base and primary traffic hub at Frankfurt International Airport in Frankfurt and a second hub at Munich International Airport. Lufthansa has built a premium b rand synonymous with quality, innovation, reliability, competence and safety despite operating in a tough market where cost cutting is commonplace. Lufthansa founded the world’s first multilateral airline grouping, ‘Star Alliance’ along with Air Canada, SAS, Thai Airways and United Airlines.At the same time, the airline invested in the most advanced passenger aircrafts and in 1999 it embarked on a vast IT programme that would transform the revenue and profit of its passenger Journal of Services Research, Volume 10, Number 2 (October 2010 – March 2011) 67 Singh, Das airline business (Lufthansa, Wikipedia, 2008). However, estimating the demand of passenger traffic for a particular period has always been the principal determinant in generating revenue for the airline. Table 2a shows the passenger traffic movement in Lufthansa (excluding the number in Swiss Airlines) Airlines for the period during October, 2006 to September, 2008.Table 2 (a): Monthly Traffic F low for the Last Two Years Traffic Year – Month Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-08 Sep-08 Passenger traffic (in thousands) 4936 4327 3969 3851 3820 4668 4635 4991 5003 5241 5067 5193 5241 4604 4132 4141 4223 4625 5031 5152 5203 5171 4883 5164 2006 Q- 4 2007 Q- 1 2007 Q- 2 2007 Q- 3 2007 Q- 4 2008 Q- 1 2008 Q- 2 2008 Q- 3 13232000 12339000 14629000 15501000 13977000 12989000 15386000 15218000Table 2 (b): Quarterly Data of Passenger Quarters Passenger traffic Source of data: Key data, Lufthansa Investor Relations, 2008; Lufthansa Investor Info, page 2, 2008 The monthly passenger traffic shown in table 2 (a) has been utilized to calculate the quarterly data of passenger traffic for the last two years Journal of Services Research, Volume 10, Number 2 (October 2010 – March 2011) 68 Forecasting the Passenger (from Quarter 4, 2006 to Quarter 3, 2008) w hich has been shown in table 2 (b).With the help of these quarterly data of passenger traffic for the last two years, we have attempted to find out the forecasted values of passenger traffic movement by employing four forecasting methods namely 4-period Moving Average, Simple Exponential Smoothing, Holt’s Model and Winter’s Model. Table 3 presents the forecasted values through 4-quarter moving average while table 4 shows the forecasted data through simple exponential smoothing. Table 5 and 6 shows the forecasting through Holt’s model along with forecasting errors.Table 7 through 10 reveals, in detail, the forecasted demand of the passenger traffic flow by employing Winter’s Model. Table 10 also includes the forecasting errors. The exercise reveals that the forecasting errors are the lowest in case of Winter’s Model which are indicated by the values of AE, MSE, MAD and MAPE. Moreover, the quarterly forecasted values suggested by Winter’s Mode l closely follow historical pattern which is clearly depicted in figure 1. FORECASTING THROUGH 4-PERIOD MOVING AVERAGE (MA) Moving Average method is generally employed in a situation in which only level, i. e. eseasonalized demand is present and neither trend nor seasonality is observed. We took the average traffic flow of four quarters starting from the 4th quarter of 2006 and continued the exercise till the 3 rd quarter of 2008 for the purpose of finding out the forecasted passenger traffic movement in the immediate following quarter. Table 3 presents the forecasted values of passenger traffic movement through four-quarter MA method. In the same table, the values of forecasting errors measured in terms of AE, MSE, MAD and MAPE are also shown. Journal of Services Research, Volume 10, Number 2 (October 2010 – March 2011) 9 Singh, Das Table 3: Forecasting through 4-Period Moving Average & Forecasting Errors Period(t) 1 2 3 4 5 6 7 8 Quarters Traffic (D) Level (L) Forecast (F) Four Period Moving Average Method Absolute Error Mean Squared Error Error (E) (AE) (MSE) Mean Absolute Deviation (MAD) 2006 Q- 4 13232000 2007 Q- 1 12339000 2007 Q- 2 14629000 2007 Q- 3 15501000 13925250 2007 Q- 4 13977000 14111500 13925250 2008 Q- 1 12989000 14274000 14111500 2008 Q- 2 15386000 14463250 14274000 2008 Q- 3 15218000 14392500 14463250 -51750 1122500 -1112000 -754750 51750 1122500 1112000 754750 2678062500 6. 31342E+11 8. 3076E+11 7. 67219E+11 51750 587125 762083. 3333 760250 % Error MAPE Forecasted Traffic F9=F10=F11=F12=14392500 0. 37025113 0. 37025113 8. 64192779 4. 50608946 7. 22734954 5. 41317615 4. 95958733 5. 29977895 Formula used Systematic demand = Level Lt= (Dt + Dt-1+†¦.. Dt-n+1)/N Ft+1=Lt Ft+n=Lt (Chopra and Meindl, 2007) FORECASTING THROUGH EXPONENTIAL SMOOTHING (ES) Like moving average method, exponential smoothing is also used in a situation, in which only level is observed. However, ES attempts to smoothen the fluctuations observed in demand data o f different periods through smoothing constant (alpha).We first calculated the level of passenger traffic flow of the initial period by taking the average of actual traffic flow for the last eight quarters, which has been considered as the forecasted value of passenger traffic flow for quarter 1. Table 4 demonstrates the forecasted values through simple ES. The same table also contains the values of forecasting errors expressed in terms of AE, MSE, MAD and MAPE. Journal of Services Research, Volume 10, Number 2 (October 2010 – March 2011) 70 Forecasting the Passenger Table 4: Forecasting through Simple Exponential Smoothing & Forecasting Errors Period(t) 0 1 2 3 4 5 6 7 8 % Error 7. 0479897 13. 9977916 5. 02789835 9. 89599461 1. 02611209 8. 60018261 9. 04478131 7. 12621269 2006 Q- 4 2007 Q- 1 2007 Q- 2 2007 Q- 3 2007 Q- 4 2008 Q- 1 2008 Q- 2 2008 Q- 3 MAPE 7. 00479897 10. 5012953 8. 67682963 8. 98162087 7. 39051912 7. 5921297 7. 79965136 7. 71547153 Formula used Systematic de mand = Level Ft+1=Lt Ft+n=Lt Lt+1=alpha(Dt+1)+(1-alpha)Lt alpha=0. 1 Forecasted Traffic F9=F10=F11=F12=14241980 13232000 12339000 14629000 15501000 13977000 12989000 15386000 15218000 Quarters Traffic (D) Level (L) 14158875 14066187. 5 13893468. 75 13967021. 8 14120419. 69 14106077. 72 13994369. 95 14133532. 95 14241979. 66 14158875 14066187. 5 13893468. 75 13967021. 88 14120419. 69 14106077. 72 13994369. 95 14133532. 95 926875 1727187. 5 -735531. 25 -1533978. 1 143419. 688 1117077. 72 -1391630. 1 -1084467 926875 1727187. 5 735531. 25 1533978. 125 143419. 6875 1117077. 719 1391630. 053 1084467. 048 8. 59097E+11 1. 92114E+12 1. 46109E+12 1. 68409E+12 1. 35139E+12 1. 33413E+12 1. 42021E+12 1. 38969E+12 926875 1327031. 25 1129864. 583 1230892. 969 1013398. 313 1030678. 214 1082242. 762 1082520. 98 Forecast (F) Simple Exponential Smoothing Method Absolute Error Error (E) (AE) Mean Squared Error (MSE) Mean Average Deviation (MAD) (Chopra and Meindl, 2007) FORECASTING THROUGH HOLT'S MODEL We carried out a regression analysis wherein Time period was considered on X-axis and passenger traffic data was taken on Y-axis in order to find out the initial level and trend. Holt's model, also known as trend-corrected exponential smoothing, is applicable in a situation, in which level and trend are observed in the demand data. However, seasonality is not considered in Holt's model.We used the â€Å"Linest Function†of Microsoft Excel to calculate the values of L0 and T0, which is shown in table 5. Table 5: Regression to Find Initial Level and Trend for Holt's Model x (Period) 1 2 3 4 5 6 7 8 270154. 7619 T0 y (Traffic) 13232000 12339000 14629000 15501000 13977000 12989000 15386000 15218000 12943178. 57 L0 Journal of Services Research, Volume 10, Number 2 (October 2010 – March 2011) 71 Singh, Das Once the initial values of level of trend are found, the subsequent values of the level and trend of each period are iteratively calculated following Holt's model which is shown in table 6.This finally helps in finding out the forecasted values of passenger traffic movement as per Holt's model, which is shown in table 6. Table 6 also reveals the forecasting errors. Table 6: Forecasting through Holt's Model Period(t) 0 1 2 3 4 5 6 7 8 2006 Q- 4 2007 Q- 1 2007 Q- 2 2007 Q- 3 2007 Q- 4 2008 Q- 1 2008 Q- 2 2008 Q- 3 13232000 12339000 14629000 15501000 13977000 12989000 15386000 15218000 Quarters Traffic (D) Trend(T) 270528. 095 Level (L) 13215200 Forecast (F) 13213333. 33 13485728. 1 13618648. 82 13987484. 49 14436906. 91 14679788. 95 14765767 15095251. 1 Error (E) -18666. 67 1146728. 1 -1010351 -1513516 459906. 91 1690788. 9 -620233 -122748. 1 Absolute Error (AE) 18666. 66667 1146728. 095 1010351. 181 1513515. 506 459906. 9118 1690788. 949 620232. 9957 122748. 0864 T8=269916. 6 15377443 15647360 15917276 16187193 Formula used Systematic demand = Ft+1=Lt+T t alpha =0. 1 Beta = 0. 2 Lt+1 = alpha(D t+1)+(1-alpha)(Lt+T t) T t+1= beta(Lt+1-Lt)+(1-beta)Tt Lev el + Trend Ft+n =Lt+nT t Mean Squared Error (MSE) 348444444. 4 6. 57667E+11 7. 78714E+11 1. 15672E+12 9. 67677E+11 1. 28286E+12 1. 15455E+12 1. 01211E+12 270154. 762 12943178. 7 247593. 533 13371055. 29 267800. 557 13719683. 94 298070. 867 14138836. 04 288872. 729 14390916. 22 255056. 95 267461. 61 14510710. 05 14827790. 3 269916. 571 15107526. 72 Mean Average Deviation (MAD) 18666. 66667 582697. 381 725248. 6476 922315. 3622 829833. 6721 973326. 2183 922884. 3294 822867. 299 % Error 0. 141072148 9. 293525369 6. 906495187 9. 763986233 3. 290455117 13. 0170833 4. 031151668 0. 806598018 MAPE 0. 141072148 4. 717298758 5. 447030901 6. 526269734 5. 879106811 7. 068769558 6. 634824146 5. 90629588 L8=15107527 F9 F10 F11 F12 Forecasted Traffic Chopra and Meindl, 2007) FORECASTING THROUGH WINTER'S MODEL Winter’s model, also known as trend and seasonality-corrected ES, is generally employed in a situation in which all characteristic features of demand data, i. e. level (Lt), trend (Tt) and seasonality (St) are observed. The actual demand (Dt), being seasonal in nature, is transformed into deseasonalized demand (Ddt ). The deseasonalized demand data and corresponding time periods are employed to run regression analysis in order to calculate the initial level (L0) and trend (T0) which is shown in table 7.The values of L0 and T0 are then used to find out the estimated deseasonalized demand (Dt) of passenger traffic of different time periods. Seasonal factors for each period are calculated using the formula Dt /(Dt) as shown in table 8. Journal of Services Research, Volume 10, Number 2 (October 2010 – March 2011) 72 Forecasting the Passenger Table 7: Regression Analysis for Finding out the Deseasonalized Demand X (Period) 3 4 5 6 140439. 5 Y (Deseasonalized demand)(Ddt) 14018375 14192750 14368630 14427880 13619931 T0 L0 Table 8: Calculation of Seasonal Factors for Winter's ModelPeriod(t) 0 1 2 3 4 5 6 7 8 2006 Q- 4 2007 Q- 1 2007 Q- 2 2007 Q- 3 2007 Q- 4 2008 Q- 1 2008 Q- 2 2008 Q- 3 13232000 12339000 14629000 15501000 13977000 12989000 15386000 15218000 14018375 14192750 14368630 14427880 13760370. 5 13900810 14041249. 5 14181689 14322128. 5 14462568 14603007. 5 14743447 0. 961602015 0. 887646116 1. 041858846 1. 093029187 0. 97590243 0. 898111594 1. 053618578 1. 032187385 Quarters Actual demand (Dt ) Deseasonalized demand (Ddt) Dt =L+Tt Seasonal factors (Dt / D t) Subsequently seasonality (St) is recalculated for each period as per Winter's model which is shown in table 9.Level and trend of each period are also iteratively calculated following Winter's model which have been mentioned in detail in table 9. Finally table 10 demonstrates the forecasted data of passenger traffic flow along with forecasting errors. Journal of Services Research, Volume 10, Number 2 (October 2010 – March 2011) 73 Singh, Das Table 9: Determination of Level, Trend and Seasonal Factors (Winter's Model) Period(t) Quarters Actual Traffic (Dt) Deseasonalized demand (Ddt) Estimated deseasonalized demand (Dt) 13760370. 5 13900810 14018375 14192750 14368630 14427880 14041249. 5 14181689 14322128. 14462568 14603007. 5 14743447 Seasonality St Level(L) Trend(T) 0 1 2 3 4 5 6 7 8 9 10 11 12 2006 Q- 4 2007 Q- 1 2007 Q- 2 2007 Q- 3 2007 Q- 4 2008 Q- 1 2008 Q- 2 2008 Q- 3 13232000 12339000 14629000 15501000 13977000 12989000 15386000 15218000 0. 968752222 0. 892878855 1. 047738712 1. 062608286 0. 968072702 0. 892415518 1. 047252432 1. 065603208 0. 968770988 0. 892874843 1. 047722994 1. 062255808 13619931 13755292. 34 13891430. 02 14027555. 72 14187811. 57 14334567. 79 14480348. 88 14626058. 49 14744278 140439. 5 139931. 6844 139552. 284 139209. 6254 141314. 2474 141858. 4444 142250. 709 142596. 999 140158. 8902 Table 10: Forecasting through Winter's Model and the Forecasting Errors Forecast(F) 13330389. 5 12406751. 72 14700803. 33 15053722. 24 13871635. 54 12918987. 41 15313552. 98 15737526. 24 Error(E) 98389. 50148 67751. 71749 71803. 33314 -447 277. 7569 -105364. 4571 -70012. 58968 -72447. 01855 519526. 2416 Absolute Error(AE) 98389. 50148 67751. 71749 71803. 33314 447277. 7569 105364. 4571 70012. 58968 72447. 01855 519526. 2416 Mean Squared Error (MSE) 9680494002 7135394612 6475502625 54870974917 46117113697 39247888533 34390843099 63830427174 Mean Average Deviation (MAD) 98389. 0148 83070. 60949 79314. 85071 171305. 5772 158117. 3532 143433. 226 133292. 3392 181571. 577 % Error 0. 743572411 0. 549085967 0. 490828718 2. 885476788 0. 753841719 0. 539014471 0. 470863243 3. 413893032 MAPE 0. 743572411 0. 646329189 0. 594495699 1. 167240971 1. 084561121 0. 993636679 0. 91895476 1. 230822044 L8=14407445 T8=3284577 Formula used Systematic component of demand =(level+demand)*seasonal factor Ft+1 = (Lt+T t)St+1 Ft+i=(Lt+iTt)St+i L t+1 = alpha (Dt+1/St+1)+(1-alpha)(Lt+Tt) T t+1= Beta (Lt+1 – Lt) + (1- Beta)T t St+p+1= gamma (Dt+1/Lt+1) + (1-gamma)St+1 Alpha = 0. 5 beta=0. 1 gamma=0. 1 Forecasted traffic F9 F10 F11 F12 14419 610. 62 13415083. 6 15888462. 17 16257733. 32 (Chopra and Meindl, 2007) COMPARISON AMONG FOUR FORECASTING METHODS The following figure gives an interesting revelation regarding the behaviour of forecasted data by comparing the quarterly forecasted demand of passenger traffic obtained through all four methods. Journal of Services Research, Volume 10, Number 2 (October 2010 – March 2011) 74 Forecasting the Passenger Historical traffic Forecasted traffic Moving Average Simple exponential smoothing Holt’s Model Winter’s ModelFigure 1: Comparison among four forecasting methods The portion of the graph before the vertical line indicates historical data while the portion of the graph after the line is the forecasted data. The forecasted data of the model graph (Winter's Model) replicates the historical data. It indicates a positive trend as well as seasonality. FORMULATION OF SUITABLE STRATEGIES FOR ABSORBING VARYING DEMAND Keeping in view the overall objective of impr oving the supply chain profit, the management should explore all possible alternatives of both demand side as well as supply side options.It is observed that demand for passenger traffic movement is not uniform throughout the year. In order to level the demand, the management of the airlines can undertake the following well-established measures: †¢ †¢ Formulate suitable marketing strategies to create new demand in the lean period. During peak periods, when the demand will exceed capacity, the management needs to offer seats to the customers who will pay the highest fares. Of course, other customers need to be motivated and informed that they would probably be charged less fare, if they undertake their trip at some other period.Shift some proportion of demand from peak period to lean period by offering the customers a reasonable rate of discount in the lean period. Of course, the cost/benefit analysis of this exercise has to be thoroughly examined beforehand. †¢ Journa l of Services Research, Volume 10, Number 2 (October 2010 – March 2011) 75 Singh, Das †¢ Considering the lean periods of the airline in different routes and destinations, the top management needs to explore new destinations which may appear to be very attractive from the perspective of the customers.Accordingly the management can withdraw some of the flights from the existing underloaded routes and ply the same in the new routes. Alternatively the management needs to examine the passenger traffic data of different routes on monthly/quarterly basis. If it is found that during the same period, some destinations experience very high demand while others have low demand, the management may withdraw some of the flights from underutilized routes and introduce the same in the heavily loaded routes. †¢In all cases, the detailed cost/benefit analysis of different alternatives is to be thoroughly examined. Then a particular course of a strategy or a combination of strategies m ay be adopted by the management. CONCLUSION The present study has attempted to find out the quarterly forecasted demand of passenger traffic flow of Lufthansa Airlines by employing the four forecasting methods, viz. moving average, simple exponential smoothing, Holt's model and Winter's model. The forecasted data suggested by Winter's model reflect the historical pattern in a better manner than three other forecasting methods.This gives a valuable insight to the managers regarding formulation of appropriate strategies in order to absorb varying nature of demand in different quarters. The same kind of study can be replicated in other airlines with suitable modifications. Of course, the present work have not taken into consideration important factors, for example, the prevailing slowdown in the global economy, perceived security threat in the wake of terrorist strikes at different parts of the globe etc.Moreover, the study has considered the total passenger traffic movement of Lufthan sa as a whole and has not paid attention to an individual market segment. This may not provide a clear picture to the management regarding increase or decrease in traffic flow in a particular segment. Future study should take care of this aspect. Journal of Services Research, Volume 10, Number 2 (October 2010 – March 2011) 76 Forecasting the Passenger The implications of varying demand on supply side need to be thoroughly examined and accordingly suitable strategies should be adopted for improving the profit across the whole supply chain.REFERENCES Bicak, H. A. , Altinay, M. & Jenkins, H. (2005) ‘Forecasting tourism demand of North Cyprus', Journal of Hospitality and Leisure Marketing, Vol. 12, pp. 87-99. Chopra, S and Meindl, P (2007) Supply Chain Management: Strategy, Planning & Operation, 3rd edition, Pearson Education, New Delhi. Choo S. and Mokhtarian, P. L. (2007) ‘Telecommunications and travel demand and supply: Aggregate structural equation models for the US', Transportation Research Part A, 41 pp. 4 -18. Dennis, N. P. S. 2002) ‘Long-term forecasts and flight schedule pattern for a medium-sized European airport', Journal of Air Transport Management, Vol. 8, pp. 313-324. Dritsakis, N. and Athanasiadis, S. (2000) ‘An econometric model of tourist demand: The case of Greece', Journal of Hospitality and Leisure Marketing, Vol. 7, pp. 39-49. Hamzacebi, C. (2008) ‘Improving artificial neural networks' performance in seasonal time series forecasting', Information Sciences, Vol. 178, pp. 4550-4559. IATA International traffic statistics, 2008a, Facts & Figures – 2008 Traffic Results, Montreal, Quebec, viewed 30 November,