Global Decision Support for Airport Performance and Efficiency Assessment

[:en]

Global Decision Support for Airport Performance and Efficiency Assessment

(Conference Proceeding)

Conference: 20TH ATRS World Conference
Year: 2016
Location: Rhodes, Greece

Abstract

Airport benchmarking depends on airport performance and efficiency indicators and it is an important issue for business, operational management, regulatory agencies, airlines and passengers. Using the MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) approach, a hierarchical additive value model was constructed with criteria weights and value scales derived from experts judgments of comparison of different reference levels and profiles of performance. This model enables managers to measure the performance and efficiency of any airport not only in a global, but also to peer benchmark it within a set of direct competitors or to self-benchmark itself during a certain period of time. This structure resulted from the analysis and discussion of the Airport Council International (ACI) reports that divide the airport in six Key Performance Areas (KPAs) each one associated to several Key Performance Indicators (KPIs). Integrated in a management system, the GDS (Global Decision Support) model outputs permit the identification of deficiencies requiring urgent intervention and corrective measures for its continuous improvement. Enabling the decision makers to act upon any key performance area that doesn´t complies with established goals or achievements, GDS model is also essential for consolidating historical time series, and can also be used to build airport projections and scenarios. This approach aims at supporting airport decision makers in selecting the best alternative to ensure the best assessment of airport performance and efficiency during the decision process; it will be more user-friendly than traditional management tools and will integrate the interests of all the engaged stakeholders.

Keywords

First Author

Maria E. Baltazar
Maria E. Baltazar
Jorge Silva
Jorge Silva
[:PT]

Global Decision Support for Airport Performance and Efficiency Assessment

(Comunicação em Conferência)

Conferência: 20TH ATRS World Conference
Ano: 2016
Localização: Rodes, Grécia

Resumo

Airport benchmarking depends on airport performance and efficiency indicators and it is an important issue for business, operational management, regulatory agencies, airlines and passengers. Using the MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) approach, a hierarchical additive value model was constructed with criteria weights and value scales derived from experts judgments of comparison of different reference levels and profiles of performance. This model enables managers to measure the performance and efficiency of any airport not only in a global, but also to peer benchmark it within a set of direct competitors or to self-benchmark itself during a certain period of time. This structure resulted from the analysis and discussion of the Airport Council International (ACI) reports that divide the airport in six Key Performance Areas (KPAs) each one associated to several Key Performance Indicators (KPIs). Integrated in a management system, the GDS (Global Decision Support) model outputs permit the identification of deficiencies requiring urgent intervention and corrective measures for its continuous improvement. Enabling the decision makers to act upon any key performance area that doesn´t complies with established goals or achievements, GDS model is also essential for consolidating historical time series, and can also be used to build airport projections and scenarios. This approach aims at supporting airport decision makers in selecting the best alternative to ensure the best assessment of airport performance and efficiency during the decision process; it will be more user-friendly than traditional management tools and will integrate the interests of all the engaged stakeholders.

Palavras-chave

Primeiro Autor

Maria E. Baltazar
Maria E. Baltazar
Jorge Silva
Jorge Silva
[:pt][/vc_column_text][/vc_column][/vc_row]

Global Decision Support for Airport Performance and Efficiency Assessment

(Comunicação em Conferência)

Conferência: 20TH ATRS World Conference
Ano: 2016
Localização: Rodes, Grécia

Resumo

Airport benchmarking depends on airport performance and efficiency indicators and it is an important issue for business, operational management, regulatory agencies, airlines and passengers. Using the MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) approach, a hierarchical additive value model was constructed with criteria weights and value scales derived from experts judgments of comparison of different reference levels and profiles of performance. This model enables managers to measure the performance and efficiency of any airport not only in a global, but also to peer benchmark it within a set of direct competitors or to self-benchmark itself during a certain period of time. This structure resulted from the analysis and discussion of the Airport Council International (ACI) reports that divide the airport in six Key Performance Areas (KPAs) each one associated to several Key Performance Indicators (KPIs). Integrated in a management system, the GDS (Global Decision Support) model outputs permit the identification of deficiencies requiring urgent intervention and corrective measures for its continuous improvement. Enabling the decision makers to act upon any key performance area that doesn´t complies with established goals or achievements, GDS model is also essential for consolidating historical time series, and can also be used to build airport projections and scenarios. This approach aims at supporting airport decision makers in selecting the best alternative to ensure the best assessment of airport performance and efficiency during the decision process; it will be more user-friendly than traditional management tools and will integrate the interests of all the engaged stakeholders.

Palavras-chave

Primeiro Autor

Maria E. Baltazar
Maria E. Baltazar
Jorge Silva
Jorge Silva
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