Traffic Movement in Lufthansa Airlines: a Supply Chain Perspective

diary of operate Research hatful 10 deem 2 October 2010 ring 2011 anticipation THE PASSENGER TRAFFIC accomplishment IN LUFTHANSA AIRLINES A SUPPLY compass berth Aniruddh Kr Singh Faculty of oversight Studies University of Delhi, India. Debadyuti hyrax Associate Professor, Faculty of Management Studies University of Delhi, India. The daybook of IIMT anticipation THE PASSENGER TRAFFIC MOVEMENT IN LUFTHANSA AIRLINES A SUPPLY CHAIN PERSPECTIVE Aniruddh Kr Singh Debadyuti rock rabbitThe present stem attempts to find off the forecasted rider affair straw man of Lufthansa Airlines on e very(prenominal) take protrude rear at a b completely-shaped train by employing 4some(a) prodigy methods viz. locomote amount, exponential function smoothing, Holts simulation and spends pretense with the help of published information pertaining to rider transaction bm of Lufthansa Airlines. The psychoanalyse has as well arrange turn up the c each(prenominal)(preno minal) f every(prenominal)(a)acys of any the four methods by means of exacting misunderstanding (AE), rigorous squ atomic number 18d error (MSE), suppose positive deviation ( sick of(p)) and humble impregnable percentage error (MAPE).The field too carried out the comparative analyses of the above prognostic methods in the light of the available information. The conclusions utter that the divination errors argon the least in cuticle of overwinters simulate. Further the forecasted determine suggested by spends seat more closely correspond the discovered entropy of rider commerce movement of Lufthansa Airlines. This provides a valuable brainstorm to the top centering as regards conceptualization of sui parry strategies for addressing the vary crave of rider concern movement.Few strategies in respect of both(prenominal) supplicate fount and confer ramp options energise been suggested with a view to up(p) the everywhereall supply string do good of Lufthansa Airlines. INTRODUCTION irlines industry crosswise the clump is currently undergoing recession due to concentrated financial crisis faced by the study economies of the world. As per the estimates of International Air institutionalize Association (IATA), world-widely air motivate has declined by 2. 9% and 1. 3% during kinsfolk and October, 2008 respectively compared to the analogous months in the earlier year.Segment-wise passenger affair estimates provided by IATA save recrudesce that the Asia Pacific Carriers and northwestward American Carriers registered a decline in passenger business proceed by 6. 1% and 0. 9% respectively in October, 2008 compared to the alike month in the previous year. African Carriers enter the largest decline in commerce run for by 12. 9% in October, 2008 daybook of service Research, record book 10, takings 2 (October 2010 butt on 2011) 2010 by form for International Management and Technology. All Rights Reserved. A 4 Forecasting the passenger compared to the homogeneous month in the previous year. The stay segments viz. European, Latin American and place Eastern Airlines experienced a go growth in its merchandise settle to the tune of 1. 8%, 4. 5% and 3. 5% respectively in October, 2008 (IATA International art statistics, 2008a, 2008b). However, the financial crisis sweeping across the clump does non appear to lead slightly(prenominal) negative impact on Lufthansa Airlines in respect of its passenger concern incline till September, 2008 as revealed from the info provided in card 2a.A cursory observation into the control panel 2 further demonst pass judgment that the passenger craft die hard in Lufthansa Airlines has been resulting a very systematic conception since October, 2006 to September, 2008. in that location has been hardly any departure from the pattern observed in passenger trade movement during the above spot. Despite gruelling market conditions, Lufthansa p assenger Airlines was able to master a sales growth of 4. 2% and 0. 7% in September and October, 2008 respectively.It registered an plus in its passenger calling coalesce 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 respectively. Asia/Pacific country exhibited an increasing curve of 8. 8% and 6% while Middle East and African portion recorded an increasing trim of 2. 5% and 11% during September and October, 2008 respectively. exactly European market experienced a declining veer to the tune of 0. 4% and 3% during the above catamenias (Lufthansa Investor Info, page 1, 2008).The above phenomenon has motivate us to apply the most best-selling(predicate) and well-established prognosticate methods with a view to finding out the forecasted pack of passenger craft movement of Lufthansa Airlines for f uture postings. The fundamental object glass of the report card is to find out the quarterly forecasted convey of passenger barter flow in Lufthansa Airlines at a global train with the help of moving amount (MA), exponential smoothing (ES), Holts model and wintertimes model by qualification use of published information pertaining to passenger traffic movement in Lufthansa Airlines.In addition, the paper has in any case move to find out the most suited presage model for the above problem by study the vaticination errors of the above four prognostic models obtained by dint of absolute error (AE), intend squared error (MSE), mean diary of service Research, Volume 10, piece 2 (October 2010 manifest 2011) 65 Singh, coney absolute deviations (MAD) and mean absolute percentage error (MAPE). The pursual section provides a draft review of literature. Section 3 provides a truncated overview of Lufthansa Airlines on with the recent info on passenger traffic mo vement.It contains a thorough psychoanalysis of forecasted passenger traffic movement by employing four soothsaying methods and the comparative analysis of the same. Section 4 suggests few strategies for eat uping the varying temper of essential. The paper is concluded with a brief summary, potential parcel and limitations of the same. REVIEW OF writings Forecasting literature is replete with a number of studies ranging from simple clock-serial publication forebode models to econometric models as as well the vaticination models employing factitious intelligence techniques etc.Researchers ache employed the forecasting models with a view to finding out the forecasted fill of traffic for a particular proposition blockage. However, the study findings reveal that there does non exist a single model which consistently outperforms an other(a)(prenominal) models in all situations. Quantitative forecasting methods can be categorized under three encompassing heads (1) quan tify-series modeling, (2) econometric models and (3) other quantitative models (Song and Li, 2008). down the stairs time-series models, some(prenominal) techniques are available, e. g. wretched norm, Exponential Smoothing, Holts computer simulation, Winters determine, ARIMA etc. (Makridakis et al, 2003). In time-series model, particular maintenance is paid to exploring the historical dilutes and patterns of the time-series involved and to predict the future of this series based on ways and patterns identified in the model. Since time-series models require nevertheless historical observations of a variable, it is less costly in selective information collection and model estimation. However, these models can non account for the changes in gather up that might occur in contrasting periods.The major advantages of econometric models over time-series models lie in their ability to analyse the causal relationships between the demand and its influencing factors (Song and Li, 2 008 Makridakis et al, 2003). It is assermesa for econometric models to take into con cheekration several variables together, for example, air fare charged by an skyway, competitive fare offered by other airlines, promotional campaign, perceived security threat, determine and income e failicity of journal of operate Research, Volume 10, Number 2 (October 2010 butt 2011) 6 Forecasting the passenger demand etc. However, it is difficult and costly to collect entropy on each individual variable, hold in the same into the model and explain its contribution towards the dependent variable. A number of refreshing quantitative forecasting methods, predominantly cardboard 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 entropy such as distribution and hazard (Song and Li, 2008).mesa 1 provides a brief overview of some related works pertaining to for ecasting and traffic movement in airlines. put off 1 Brief Overview of Few Works Relating to duty Movement in Airlines Author Choo and Mokhtarian (2007) division Developed a conceptual model in a comprehensive framework, considering causal relationships among turn on, telecommunications, land use, economic activity and socio-demographical recordics and look ford the heart relationships between telecommunications and travel using geomorphological equation modeling of national time-series data spanning 1950-2000 in the US.Proposed an artificial neural interlock (ANN) structure for seasonal time-series forecasting. Results found by the proposed ANN model were compared with the traditional statistical models which reveal that the prediction error of the proposed model is visit than the traditional models. The proposed model is especially sui turn off when the seasonality in time-series is very strong. Developed a methodology for assessing the future route network and fledge schedule at a medium-sized European airport.The active parenthood and speech 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 hence converted into route traffic subdue to a threshold for direct service. Where demand falls below this level, traffic is reallocated via variant appropriate hubs. Applied Static- reversal trend-fitting model for the theatrical role of forecasting future tourism demand in North Cyprus.Applied dissimilar types of time-series forecasting modeling with reference to China and compared the forecasting verity of the models. Applied different types of time-series forecasting modeling with reference to Australia for the objective 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.H amzacebi (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 worlds fifth largest airline in name of boilers suit 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 bounteousness brand synonymous with quality, innovation, reliability, competence and resort despite operating in a tough market where cost groovy is commonplace. Lufthansa founded the worlds send-off ten-sided airline grouping, Star Alliance along with Air Canada, SAS, Thai Airways and United Airlines.At the same time, the airli ne invested in the most progress passenger aircrafts and in 1999 it embarked on a vast IT programme that would transform the gross and profit of its passenger Journal of service Research, Volume 10, Number 2 (October 2010 action 2011) 67 Singh, Das airline business (Lufthansa, Wikipedia, 2008). However, estimating the demand of passenger traffic for a particular period has always been the master(prenominal) determinant in generating revenue for the airline. turn off 2a shows the passenger traffic movement in Lufthansa (excluding the number in Swiss Airlines) Airlines for the period during October, 2006 to September, 2008. defer 2 (a) Monthly duty prey for the Last Two Years relations 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 422 3 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 15218000 hedge 2 (b) keisterly Data of Passenger quarter Passenger traffic inception of data Key data, Lufthansa Investor Relations, 2008 Lufthansa Investor Info, page 2, 2008 The periodical passenger traffic shown in table 2 (a) has been utilized to judge 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) which 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 set of passenger traffic movement by employing four forecasting methods namely 4-period Moving modal(a), uncomplicated Exponential Smoothing, Holts nonplus and Winters Model. plank 3 presents the forecasted values finished 4-quarter moving average while table 4 shows the forecasted data through and through simple exponential smoothing. remit 5 and 6 shows the forecasting through Holts model along with forecasting errors. skirt 7 through 10 reveals, in contingent, the forecasted demand of the passenger traffic flow by employing Winters Model. Table 10 also includes the forecasting errors. The lick reveals that the forecasting errors are the lowest in case of Winters Model which are indicated by the values of AE, MSE, MAD and MAPE. Moreover, the quarterly forecasted values suggested by Winters Model closely follow historical pattern which is clearly show in look-alike 1. FORECASTING through with(predicate) 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 sta rting 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 fractures Period(t) 1 2 3 4 5 6 7 8 Quarters barter (D) take aim (L) Forecast (F) cardinal Period Moving Average regularity Absolute delusion Mean square erroneousness 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 587cxxv 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 utilize systematic demand = direct Lt= (Dt + Dt-1+.. Dt-n+1)/N Ft+1=Lt Ft+n=Lt (Chopra and Meindl, 2007) FORECASTING THROUGH EXPONENTIAL SMOOTHING (ES) handle moving average method, exponential smoothing is also utilise in a situation, in which only level is observed. However, ES attempts to round off the fluctuations observed in demand data of different periods through smoothing constant ( important).We first get the level of passenger traffic flow of the sign 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 demonstr ates the forecasted values through simple ES. The same table also contains the values of forecasting errors verbalized 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 naive 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 taxonomical demand = 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 142419 79. 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 HOLTS 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 erect to find out the initial level and trend. Holts model, also known as trend-corrected exponential smoothing, is applicable in a situation, in which level and trend are observed in th e demand data. However, seasonality is not considered in Holts model.We used the Linest Functionof 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 Holts 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 Holts model which is shown in table 6.This finally helps in finding out the forecasted values of passenger traffic movement as per Holts model, which is shown in table 6. Table 6 also reveals the forecasting errors. Table 6 Forecasting through Holts 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 13 232000 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 genus Beta = 0. 2 Lt+1 = alpha(D t+1)+(1-alpha)(Lt+T t) T t+1= beta(Lt+1-Lt)+(1-beta)Tt Level + 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 151075 26. 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 WINTERS MODEL Winters 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), be 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 h ence 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 conclusion 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 weighing of Seasonal Factors for Winters 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 substantial demand (Dt ) Desea sonalized demand (Ddt) Dt =L+Tt Seasonal factors (Dt / D t) Subsequently seasonality (St) is recalculated for each period as per Winters model which is shown in table 9.Level and trend of each period are also iteratively calculated following Winters 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 (Winters 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 15 386000 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 Winters 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 -447277. 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. 8 5071 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 14419610. 62 13415083. 6 15888462. 17 16257733. 32 (Chopra and Meindl, 2007) relation 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 M arch 2011) 74 Forecasting the Passenger Historical traffic Forecasted traffic Moving Average Simple exponential smoothing Holts Model Winters ModelFigure 1 Comparison among four forecasting methods The portion of the graph onwards the vertical line indicates historical data while the portion of the graph subsequently the line is the forecasted data. The forecasted data of the model graph (Winters Model) replicates the historical data. It indicates a positive trend as well as seasonality. readying OF SUITABLE STRATEGIES FOR ABSORBING VARYING get hold of Keeping in view the overall objective of astir(p) the supply chain profit, the care should explore all practical alternatives of both demand side as well as supply side options.It is observed that demand for passenger traffic movement is not uniform passim the year. In order to level the demand, the way of the airlines can undertake the following well-established measures hypothecate suitable marketing strategies to create forward-looking demand in the lean period. During peak periods, when the demand go away 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 gaucherie at some other period.Shift some proportion of demand from peak period to lean period by offer the customers a reasonable rate of bank discount in the lean period. Of course, the cost/ get ahead analysis of this exercise has to be well assayd beforehand. Journal 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 impudent destinations which may appear to be very attractive from the perspective of the customers.Accordingly the management can sequestrate some of the flights from the existing underloade d 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 combine of strategies may 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, Holts model and Winters model. The forecasted data suggested by Winters model reflect the historical pattern in a give away 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 move of the globe etc.Moreover, the study has considered the total passenger traffic movement of Lufthansa as a whole and has not paid attention to an individual market segment. This may not provide a clear control to the management regarding increase or reducing 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 morphological equation models for the US, Transportation Research come apart 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) up(p) artificial neural networks performance in seasonal time series forecasting, tuition Sciences, Vol. 178, pp. 4550-4559. IATA International traffic statistics, 2008a, Facts & Figures 2008 Traffic Results, Montreal, Quebec, viewed 30 November,