Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.
Nächste SlideShare
×

Let the figures talk 1 7 w int1

Setting KPIs level for measuring Airports Performance,
this addressing the traffic flow for airports, and how to set the a KPIs scale to define the performance of airport

• Full Name
Comment goes here.

Are you sure you want to Yes No
• Als Erste(r) kommentieren

• Gehören Sie zu den Ersten, denen das gefällt!

Let the figures talk 1 7 w int1

1. 1. Setting KPIs Level For measuring Airports Performance Life is a continues learning course Dears, One of the key success factoris to have a targets, Target that the airport authority try to achieve them. One of these, is traffic flow, Yes it is hard to predict them, but we can move towards to achieved them by using excel spreadsheet. Setting targets for airports can be set for 1- Traffic Passengers 2- Number of Flights 3- Air Cargo Flow Moreover, KPIs level can be define by the difference between the forecasted figures and the achievable numbers. According to the following Performance Scale. GoodCase Studies Let the Figures Talk – Issues No. 1 to No. 7 Hope to enjoy Mohammed Salem Awad By: Mohammed Salem Awad Aviation Consultant
2. 2. 22 Let the Figures Talk – Issue No. 1 Defining Airport Performance in 2018 Case Study: Heathrow Airport Let the Figures Talk: This issue is addressing the answer, for the question, why we are forecast the traffic of the Airports. The forecasting analysis is FAIR, when R-square is greater than 90 %, and consequently the forecasted figures are set as TARGETS to be achieved. Errors: are the deviations between the Historical Data with Model Fitted, always addressing by a range percentage. Performance: are the deviations between the target figures and the actual figures for the proposed target period (2018). Results above targets are good performance and represented by a green color, and results below targets are poor one, represents by red color. However, airports should set up the right target level to define KPIs system. i.e less than (- 10.0 % ) should consider as a poor one. Case Study: Heathrow Airport By referring to issue no. 29 Heathrow Airport, R- Squared (monthly model) = 98.01 % Errors range: + 4.73 % - 3.82 % Analysis: The figures of seven months for LHR shows an excellent performance, keeping in mind the red color indicates below the targets level (very minor percentage),while the green one is good performance. The performance range – 0.57 % to +2.22 %
3. 3. 33 Let the Figures Talk – Issue No. 2 Defining Airport Performance in 2018 Case Study: Dublin Airport Let the Figures Talk: Each Airport Forecasting can be review, The forecasting analysis is FAIR, when R-square is greater than 90 %, and consequently the forecasted figures are set as TARGETS to be achieved. Errors: are the deviations between the Historical Data with Model Fitted, always addressing by a range percentage. Performance: are the deviations between the target figures and the actual figures for the proposed target period (2018). Results above targets are good performance and represented by a green color, and results below targets are poor one, represents by red color. However, airports should set up the right target level to define KPIs system. i.e less than (- 10.0 % ) should consider as a poor one. Case Study: Dublin Airport By referring to issue no. 30 Dublin Airport, R- Squared (monthly model) = 99.00 % Growth: 7.81 % Errors range: + 7.43 % - 4.26 % Analysis: The figures of six months for DUB shows an Good performance,keeping in mind the red color indicates below the targets level (only two readings make a significant impact -4.63% & -5.21 % ), while the green one is good performance. The performance range – 5.21 % to +1.13 %.
4. 4. 44 Let the Figures Talk – Issue No. 3 Defining Airport Performance in 2018 Case Study: ATHENS Int. Airport Let the Figures Talk: Each Airport Forecasting for 2018 can be reviewed, the forecasting analysis is FAIR, when R-square is greater than 90 %, and consequently the forecasted figures are set as TARGETS to be achieved. Errors: are the deviations between the Historical Data with Model Fitted, always addressing by a range percentage. Performance: are the deviations between the target figures and the actual figures for the proposed target period (2018). Results above targets are good performance and represented by a green color, and results below targets level are poor one, represents by red color. However, airports should set up the right target level to define KPIs system. i.e less than (- 10.0 % ) should consider as a poor one. Case Study: Athens Int. Airport By referring to issue no. 32 Athens Airport, R- Squared (monthly model) = 99.12 % Annual Growth = 8.48 % Errors range: + 6.75 % - 8.08 % Analysis: The actual data of eight months of 2018 for ATH shows an excellent performance, keeping in mind the red color indicates below the targets level (very minor percentage), while the green one is good performance. The performance range – 3.34 % to +7.03 %
5. 5. 55 Let the Figures Talk – Issue No. 4 Defining Airport Performance in 2018 Case Study: CagliariInt. Airport Let the Figures Talk: Each Airport Forecasting for 2018 can be reviewed, the forecasting analysis is FAIR, when R-square is greater than 90 %, and consequently the forecasted figures are set as TARGETS to be achieved. Errors: are the deviations between the Historical Data with Model Fitted, always addressing by a percentage range. Performance: are the deviations between the target figures and the actual figures for the proposed target period (2018). Results above targets are good performance and represented by a green color, and results below targets level are poor one, represents by red color. However, airports should set up their own right target levels to define KPIs system. i.e less than (- 5.0 % ) should consider as a poor one. Case Study: Cagliari Int. Airport By referring to issue no. 98 Cagliari Int. Airport, R- Squared (monthly model) = 98.67 % Annual Growth = 5.95 % Errors range: + 8.39 % - 4.46 % Analysis: The actual data of eight months of 2018 for CAG shows an excellent performance, keeping in mind the red color indicates below the targets level (but greater than – 5.0 %), while the green one is good performance. The performance range – 3.72 % to +1.68 %
6. 6. 66 Let the Figures Talk – Issue No. 5 Defining Airport Performance in 2018 Case Study: Schiphol Amsterdam Airport Let the Figures Talk: Each Airport Forecasting for 2018 can be reviewed, the forecasting analysis is FAIR, when R-square is greater than 90 %, and consequently the forecasted figures are set as TARGETS to be achieved. Errors: are the deviations between the Historical Data with Model Fitted, always addressing by a percentage range. Performance: are the deviations between the target figures and the actual figures for the proposed target period (2018). Results above targets are good performance and represented by a green color, and results below targets level are poor one, represents by red color. However, airports should set up their own right target levels to define KPIs system. i.e less than (- 5.0 % ) should consider as a poor one. Case Study: Amsterdam Airport By referring to issue no. 39 Schiphol Amsterdam Airport, R- Squared (monthly model) = 99.03 % Annual Growth = 8.27 % Errors range: + 4.23 % - 2.72 % Analysis: The actual data of Seven months of 2018 for AMS shows a the performance of the airport, at -5 % threshold level, keeping in mind the red color indicates below the targets level (but greater than – 5.0 %), while the green one is good performance. The performance range – 9.28 % to +1.15 %. Should we review the threshod level to -10 % !!!, it depends on the airport policy and top management decision to review & setup these targets level.
7. 7. 77 Let the Figures Talk – Issue No. 6 Defining Airport Performance in 2018 Case Study: Aéroportsde Montréal (Int. Pax) Let the Figures Talk: Each Airport Forecasting for 2018 can be reviewed, the forecasting analysis is FAIR, when R-square is greater than 90 %, and consequently the forecasted figures are set as TARGETS to be achieved. Errors: are the deviations between the Historical Data with Model Fitted, always addressing by a percentage range. Performance: are the deviations between the target figures and the actual figures for the proposed target period (2018). Results above targets are good performance and represented by a green color, and results below targets level are poor one, represents by red color. However, airports should set up their own right target levels to define KPIs system. i.e less than (- 5.0 % ) should consider as a poor one. Case Study: Montreal Airport- Int. Pax By referring to issue no. 68 Montreal Airport, R- Squared (monthly model) = 98.01 % Annual Growth = 10.01 %, Errors range: + 8.25 % - 6.41 % Analysis: The actual data of eight months of 2018 for YUL shows the performance of the airport, at -5 % threshold level, keeping in mind the red color indicates below the targets level (but greater than – 5.0 %), while the green one is good performance. The performance range : – 6.70 % to +4.97 %.
8. 8. 88 Let the Figures Talk – Issue No. 7 Defining Airport Performance in 2018 Case Study: MiamiInternationalAirport Let the Figures Talk: Each Airport Forecasting for 2018 can be reviewed, the forecasting analysis is FAIR, when R-square is greater than 90 %, and consequently the forecasted figures are set as TARGETS to be achieved. Errors: are the deviations between the Historical Data with Model Fitted, always addressing by a percentage range. Performance: are the deviations between the target figures and the actual figures for the proposed target period (2018). Results above targets are good performance and represented by a green color, and results below targets level are poor one, represents by red color. However, airports should set up their own right target levels to define KPIs system. i.e less than (- 5.0 % ) should consider as a poor one. Case Study: MIA Int. Airport By referring to issue no. 75 Miami Int. Airport, R- Squared (monthly model) = 81.60 % Annual Growth = 00.00 %, Errors range: + 3.45 % - 4.49 % Analysis: The actual data of seven months of 2018 for MIA shows the performance of the airport, at -5 % threshold level, keeping in mind the red color indicates below the targets level (but greater than – 5.0 %), while the green one is good performance. The performance range : – 1.73 % to + 3.96 %.