UNIX SHELLS Essay Example | Topics and Well Written Essays - 500 words

UNIX SHELLS - Essay Example Hence, interpreting login commands that will result to the anticipated result. Undeniably, UNIX serves a critical role in the current technological knowhow though to date it has undergone varied and essential developments that give it an advantage over, which this study seeks to highlight. Compared to other operating systems as well as varied computer technological enhancements, what I like regarding UNIX is its speed (Liu, Yue & Guo, 2011). However, historically this has been improving since its inception in 1960s up to date to the extent of being able to numerous daemons that known to operate manually or slowly in UNIX’s absence (Liu, Yue & Guo, 2011). This is quite advantageous especially currently, whereby almost each venture has almost completely embraced technology in order to evade commercial opposition from competitors. Additionally, it possesses the capability of aiding multiple segments that necessitate usage of varied network cards by the firewall (Liu, Yue & Guo, 2011). Mostly, this is at least 32, which is contrary to the practical Netware’s support that has a limit of 16 and windows strictly limited to four (Parker & Morley, 2014). Another aspect that I like about this system encompasses its compatibility whereby poses a capability of suppo rting word processors including WordPerfect as well as StarOffice (Liu, Yue & Guo, 2011). In customizing this system, it entails adequate knowledge coupled how to navigate with ease to ensure the intended adjustments work as necessitated (Robbins, Hannah, Lamb & Lamb, 2008). However, there are varied ways of customizing this system whereby on my side I would encompass setting the right margin such that it will be prompting to wrap sentences automatically (Robbins, Hannah, Lamb & Lamb, 2008). This is without one or user having to execute that command with the aid of a ENTER key thus saving on time (Robbins, Hannah, Lamb & Lamb,

Business Law Case Study Example | Topics and Well Written Essays - 250 words - 1

Business Law - Case Study Example During the exam, Grothe asked Eelbode to do an exercise and during it Eelbode experienced immediate sharp and burning pain in his lower back and down the back of his right leg. Consequently, Eelbode filed a suit in a Washington state court against Grothe and Chec, claiming that he was injured because of an improperly administered back torso strength test. Based on the exculpatory document signed by Eelbode, Grothe and Chec filed a motion for summary judgment. The issue is whether the court should grant the motion and consider the exculpatory clause valid. RULE:Â   An adhesion contract is a legally binding contract, presented by one party to the other in a standardized form which contains clauses written exclusively by one party, without them being properly negotiated with the other party. This is the result of unequal bargaining power and causes unfair consequences to the adhering party. APPLICATION: The agreement signed between Norbert Eelbode and Grothe and Chec is an adhesion contract which did not offer Norbert the opportunity to negotiate its clauses. Norbert did not have a choice to sign or not to sign the exculpatory clause as he needed to pass the medical exam in order to get a job at Travelers Inn. Therefore, he was forced by the circumstances to sign the contract. Agreeing to that clause would not have been a problem if the physical injuries suffered by Norbert would not have occurred during the improper strength test. The exculpatory clause signed by Norbert actually forced him to give up any legal method of protecting his rights and repairing the suffered damages. This is unfair to Norbert, and, therefore, the agreement signed by him and Grothe and Chec is an adhesive one and should not be enforced. CONCLUSION: The court should consider the agreement signed by Norbert Eelbode an adhesive one and should not enforce it. Therefore, the exculpatory clause should not be considered

Drinking and driving Essay Example for Free

Drinking and driving Essay On Jan.8, 2002, President Bush signed the No Child Left Behind Act of 2001 (NCLB) that became the education-reform bill. The No Child Left Behind is most sweeping education-reform bill since 1965 that made changes to the the Elementary and Secondary Education Act. The No Child Left Behind plays a big part in the life’s of students, parents, teachers, and the future of the educational system. â€Å"No longer content to provide access to education for traditionally extended students populations, we are now demanding that these students receive equally good educations. † In other words, administrations are now demanding equality of quality. President Bush thinks that all students are title to high quality education, treated equal, fair, and to be safe while at school. The No Child Left Behind places significant responsibilities on state educational agencies, school districts, principals, and the teachers. â€Å"In 2002 the federal government returned to the force front in potentially historic fashion.† With the passage of No Child Left Behind, supported by bipartism majorities in Congress, the nation committed itself to the achievement of every student in America. The No Child Left Behind in the federal system the United States for every education state and school districts fail to meet the requirements of No Child Left Behind Act that will be held accountable, with the opportunity to improve their down fall. Each state makes their own standards for what a child should know and learn for grades, for math and reading the standards should be developed first. Every student should be tested by the standards. All school districts should make adequate yearly progress toward meeting their state standards. Schools that fail need all the support they can get to improve their progress. The school or District shall come up with an idea that’s going to meet all necessary to make higher goals. The No Child Left Behind has a standardize by making sure that all teachers were high. The No CLB Act has In the article (Teacher’s Views on No child left behind) teachers was the no child left behind law â€Å"The federal legislation provides considerable discretion to states that can develop their own academic content standards, choose the tests they will administer, and specify the minimum scores students must obtain to be declared â€Å"proficient†. The no child left behind law increased the attention to many schools that pay to academic achievement and to disadvantage children that make it better. â€Å"As a result, the skills, and knowledge of the subgroups of children that historically have not increased as rapidly under many state tests would suggest. Second, no child left behind has increased the efforts of schools scores, third, adequate yearly progress rules; some states increased the migration of experienced teachers out of school serving high concentrations of low – performing students. No child left behind represents that parents of students who are attending title I schools are given the option to transfer their students to another school in the district for improvement status, If a student requested to be transferred shall be allowed to transfer. The No Child Left Behind, has two new educational options, -supplemental educational services and school choice for title I schools for restricting, improvement, and corrective action by the options depends on parental decisions. Parents will know their student assessments. If the school needs improvement the parent will be informed. Conclusion: The No Child Left Behind Act is great. Every child should have the right to receive an education, and be safe while in school. There so many jobs and opportunities will be required to have a college degree in order for a student to have success in life they need an education. The No Child Left Behind Act helps and gives students the impossible they did not have. The No  Child Left Behind made school districts accountability achievement. ON January 8, 2002 President Bush signed the No Child Left Behind (NCLB), Act the reform bill improves student’s goals on the state- wide testing, and The No Child Left Behind has admirable goal of improvement in the educational system. President Bush thinks that no child should be left behind and that all children are entitled to education, treated fair and to be safe. No matter what the student is, their race, where they live, they should be entitled to an equal education. References’ Abernathy, S. (2007). No child left behind and the public schools {electronic resource} / Scott Franklin Abernathy. Ann Arbor: University of Michigan Press, c2007. Michigan Press. In 2001 the author researcher at Ann Arbor: University of President George W. Bush’s education reform legislation, the no child left behind act (H.R. 1). Testing and accotability provisions Chubb, J.E. (2009). Learning from no child left behind {electronic resource}: how and why the nation’s most important but The Author research stand ford, California. : hoover institution Murnane, R., Papay, J. (2010). Teacher’s views on no child left behind: support for the principles, concerns about the practices. Journal of Economic Perspectives, 24(3), 151- 166 Programs, all other Miscellaneous Schools Believes that the school should not be accountable for teaching all children well. The No child left behind is to improve all students’ performance. Put students’ performance in data gives the parents opportunity to see the child’s performance. Students that attend low-performing schools start to develop discipline issues, their want to be transferred to a better – performing school. School that doesn’t need their goals will offer including free tutoring, and after school instruction. Randolph, K., Wilson – Younger, D. (2012). †Is No Child Left Behind Effective For All Students?† Parents don’t think so. Online submission. The author’s researchers Database: ERIC. Since the No child left behind is to discuss the advantage of the core requirements for its implementation. Parents have concerns whether the children are really learning. Zimmer, R., Gill, B., Raquin, Booker, K., Lockwood, J., Department of education, w.c (2007). State and Local Implementation of the â€Å"No Child Left Behind† (N ls-â€Å"nclb†). The author researchers us department of  education. The key component the no child left behind for the parent children that were attending title I school options for corrective action, improvement, failure achieve toward meeting state standards.

Cameras are an everyday commodity

Cameras are an everyday commodity Introduction / history Cameras are an everyday commodity and are commonly used today. It is a much simpler and advance device compared to the first ever camera made; the camera obscura. It was a sliding wooden box camera with a dark chamber which consists of an optical device. The first photograph taken with the camera obscura was by Joseph Nicephore Niepce in year 1814 but the image was only temporary. Soon after, the first practical photograph method was invented by Louis Jacques Daguerre and named it â€Å"daguerreotype†. However the process was perfected by William Fox Talbot and named it the â€Å"Calotype† in 1840. It is the first negative-positive process making multiple copies possible. The use of photographic film was found by George Eastman. His first camera called â€Å"Kodak† was a very simple box camera with a fixed focus lens and single shutter speed. It came along with a relatively low price which appealed to many average consumers. In 1900, He broadens mass market photography by introducing the concept of snapshot also known as â€Å"the Brownie†. George Eastman is known to bring photography and cameras general to the public. In 1913, the first 35mm camera develops. Oskar Barnack explored using 35mm cine film while building a compact camera capable for high quality enlargements. It gains immediate popularity as the format of choice for high end compact cameras. The 35mm cameras were expensive for most people and remain rollfilm as the format of choice for mass market cameras. However, 35mm film came to dominate the market when the cheapest cameras using rollfilm was discontinued. As conventional cameras became more sophisticated, a new type of camera appeared in the market. Edwin Land invented instant cameras; Polaroid photography. The Polaroid camera allows photographer to remove a developing print after the image is snapped. It was later made available to the public in 1948. In 1986, Fuji introduced disposable cameras. It is a camera that recycles parts which later was named â€Å"single use cameras†. In the late 1980s, digital photography entered the market of commerce. Kodaks introduction on the professional digital camera system (DSC) allows photojournalists to take electronic pictures. However, the first real digital camera introduced to consumers was in 1990. The camera was able to store compressed images and later downloaded into a PC or a Mac via cable. Photography became common to consumers and made available to consumers by the creator of Macintosh computer. It is a color digital camera with a 50mm fixed focus lens. Also able to store images into its memory, this had set some quality for digital cameras in the future.

Essay --

Every morning I wake up at the last minute. I press snooze from the moment my alarm goes off at 6:50 a.m. to 7:45 a.m. I go to bed early, but I can’t seem to get enough sleep. Teens are supposed to sleep for at least eight to nine hours every night. Having school start at 8:30 a.m. doesn’t help because teens like me have to work at night. After work I get home late and have homework to do before school the next morning, which keeps me up past midnight sometimes. Studies show that even getting a half an hour more of sleep improves children’s learning ability from eighteen to forty-four percent (School Really). If school started later teens would improve their grades and their health. Concentration from students is obviously one of the most important aspects in learning new things. It has been proven that if students sleep in, even if it’s only a half hour longer than normal, they’re more likely to have better concentration, behavior, and overall health. â€Å"A sleep disorder not only results in a sleepy, cranky, and often poor-performing student at school, but also an irritable, unhappy child or teenager at home† (Debatewise). Teenagers have a lot of stress put on them. One of the main stresses in teen’s lives is not getting enough sleep. Most teenagers have a job to help support their families and pay for their car insurance, gas, and other things they need. On top of having a job, many teenagers are in sports that occupy a lot of their free time. Teenagers are also pressured into getting all of their homework done in short amounts of time just to get good grades. The last thing that teenagers are worried about is sleep. Teenagers don’t realize how important sleep is not only to their education, but their health in general. ... ...l should start later varies. For one, it’s dangerous to drive to school tired. Automobile accidents are most commonly caused by inexperienced drivers. Imagine being inexperienced and being so tired to the point where you can barely keep your eyes open all because you have to be at school bright and early. Cutting back the time teens arrive at school has been proven to lower the risk of car crashes. There was a school that had cut car accidents by 17% just by changing the time school begins (3 Reasons). Being tired also causes depression, depression makes it harder for students to concentrate on what’s most important, their homework (3 Reasons). I can’t stress enough how important it is to get good grades. Getting good grades and having a good GPA, not only helps you become who you want to be when you get older, but it helps you get into any college you like.

Internet Advertising †A World Wide Waste of Time Essay -- Argumentati

Internet Advertising – A World Wide Waste of Time As I approach the Philadelphia bus shelter, I'm struck by the gaze of a familiar, yet ominous strongman. His aging muscles are backlit by a powerful fluorescent light. Intrigued, I approach the advertisement and begin to look at the copy. As my eyes scan below the title of the film, an irritating, unavoidable, and inevitably cryptic message glows: http://www.warnerbros.com/eraser. Here is another website for the fearless compujunkies to surf at three in the morning and another piece of litter along the information superhighway. While computer users like myself have the choice to turn off their computers or stop their net searches, it is more and more difficult to remove the strands of the net from our personal lives. When we return home from a long day of work, television now flashes an endless barrage of corporate web addresses at us. When we pick up our newspapers to browse the comics, it is difficult to flip past a handful of pages without spotting a '...dot- com' along the way. The web's marketing frenzy is no longer limited to literature, entertainment, and computer browsing. Within the past few months I have noticed the web's subtle migration from the virtual into the physical. Products ranging from children's cereal to artificial sweeteners all boast web addresses printed on them. Open up your pantry and you may be surprised to see how many munchies you own with web support. Children can check out the 'You Rule School' page for cereal kingpins General Mills, Inc. where you can play with food and view the trix rabbit, the honey bee, the leprechaun, and the cocoa pebbles creature. If that drives you coocoo you can turn to the history of Snicker's bars or Mars' conc... ...s it may seem, works. It is unlikely that the trend of releasing the virtual upon the real will end because of corporate guilt. In the meantime, enjoy the simplicity of having only a hundred channels to watch, reading electronic magazines before your web connection backs up for hours, and take a walk after work to relieve stress. If you need to find a recipe, call a friend or ask a colleague, and avoid the Ragu woman. Work on the development of a V-chip (that would be Vidiot) to block out sites of no value for your children. If you happen to run into Arnold Schwarzenegger, tell him to stick to the cinema; he loses his zest on a 13 inch monitor. References 'Web ads mark 7nd birthday with decisive issues ahead' by Debra Aho Williamson Advertising Age August 2003 'Outlook ‘04: Will Web ads go mainstream?' by Debra Aho Williamson Advertising Age October 2003

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,