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Lufthansa Airlines

Lufthansa Airlines. Chris, Joe, Marty. Lufthansa Information. Flagship airline for Germany Largest airline in Europe in terms of passengers traveled & fleet Founding member of Star Alliance World's fourth largest airline Flies to 18 domestic destinations, 203 international in 78 countries

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Lufthansa Airlines

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  1. Lufthansa Airlines Chris, Joe, Marty

  2. Lufthansa Information Flagship airline for Germany Largest airline in Europe in terms of passengers traveled & fleet Founding member of Star Alliance World's fourth largest airline Flies to 18 domestic destinations, 203 international in 78 countries Brand is synonymous with quality, innovation, reliability, competence, and safety • http://en.wikipedia.org/wiki/Lufthansa

  3. Airline Industry Airline industry was affected by Recession 2008 Global air travel decreased from by 2.9% for the month of October in 2008 from the previous year African air travel decreased by 12.9% during this time frame Despite poor market conditions, Lufthansa actually increased passenger traffic during this time frame to all markets except a small decline in Europe

  4. \ Passenger traffic movement from October 2006 to September 2008 This data will help us forecast passenger traffic movement by using 4 methods: • 4 period Moving Average • Exponential Smoothing Average • Holt's Model • Winter's Model

  5. 4-period moving Average & Forecasting Error

  6. 4-period moving Average & Forecasting Error Moving average is used in situations when only deseasonalized data is preset. Trend and seasonality are not observed. The 4 period moving average is calculated by averaging the passenger traffic for the previous 4 periods. In the same table the Absolute Error, the Mean Squared Error, the Mean Absolute Deviation and the Mean Average Percentage Error are also shown.

  7. Exponential smoothing Like Moving Average, Smoothing Average is used only when level is observed ES tries to smooth the fluctuations in demand of different periods by using Alpha First: Calculate the Level of traffic flow of initial period by taking average traffic flow for last eight quarters (2 years) Alpha = 0.1 • Ft+1= Dt + 1- (Ft)

  8. Exponential smoothing

  9. Holts Model

  10. Winters Model

  11. Conclusion Having the whole supply chain in mind it is important to look at not only the supply side but also the demand side. • After observing demand we see that passenger traffic is not uniform throughout the year. In order to level out demand management can take a few steps. • Create more market strategies to develop demand in slower periods • Charge higher rates during peak periods to try and encourage some passengers to take their flight at a less busy time. • Offer discounted rates at the slower periods to encourage passengers to fly then. • Find new destinations that may be more appealing during slower periods.

  12. Using the moving average, simple exponential smoothing, and Holts and Winters models gives managers valuable information about demand in different quarters. • This information allows them to make changes to move with the demand of the customers. This study did not take into consideration the current economy and different security threats around the globe. • Because of this management will not be able to get the clearest picture of why market trends increase or decrease. • Future studies should take this into consideration. Winter's Model appears to have the lowest forecasting error and they have followed historical patterns most closely.

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