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Managing turnaround performance through Collaborative Decision Making

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  • Okwir, Simon
  • Ulfvengren, Pernilla
  • Angelis, Jannis
  • Ruiz, Felipe
  • Núñez Guerrero, Yilsy Maria

Abstract

The purpose of this paper is to explore turnaround performance as a resultant from both Collaborative Decision Making (CDM) processes and collaborative measures. This paper presents how CDM operates in the Turnaround Process (TAP) to propose a new method for managing the collaborative turnaround performance of all actors by predicting the most critical indicators. To achieve this, data from a CDM airport is used. Sample data of 6500 observations, taken from turnaround movements handled in 2014 at Madrid-Barajas Airport, were obtained from three separate databases and analyzed separately (in three databases). To predict turnaround performance, this paper also introduces a predictor dependent variable called “star values†as a measure of minimal delay conditions in order to predict time performance. The analysis shows that the proposed method unveils a new approach in determining how collaborative performance can be measured in the TAP and the predicted key performance indicators, which shows variations in the predicted CDM indicators. Results challenge managers and policymakers to find which improvements can be enacted for better usage of airport infrastructures and resources for optimum use as well as enhanced TAP. In terms of theory use and extension, the study reveals how CDM is an essential element in the literature on air traffic management.

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  • Okwir, Simon & Ulfvengren, Pernilla & Angelis, Jannis & Ruiz, Felipe & Núñez Guerrero, Yilsy Maria, 2017. "Managing turnaround performance through Collaborative Decision Making," Journal of Air Transport Management, Elsevier, vol. 58(C), pages 183-196.
    • Handle: RePEc:eee:jaitra:v:58:y:2017:i:c:p:183-196
    DOI: 10.1016/j.jairtraman.2016.10.008
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    1. Michael Schultz & Michael Schmidt, 2018. "Advancements in Passenger Processes at Airports from Aircraft Perspective," Sustainability, MDPI, vol. 10(11), pages 1-15, October.
    2. Lange, Anne & Sieling, Julian & Gonzalez Parra, Garoe, 2019. "Convergence in airline operations: The case of ground times," Journal of Air Transport Management, Elsevier, vol. 77(C), pages 39-45.
    3. Mota, Miguel Mujica & Boosten, Geert & De Bock, Nico & Jimenez, Edgar & de Sousa, Jorge Pinho, 2017. "Simulation-based turnaround evaluation for Lelystad Airport," Journal of Air Transport Management, Elsevier, vol. 64(PA), pages 21-32.
    4. Li, Max Z. & Ryerson, Megan S., 2019. "Reviewing the DATAS of aviation research data: Diversity, availability, tractability, applicability, and sources," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 111-130.
    5. Rott, Julian & König, Fabian & Häfke, Hannes & Schmidt, Michael & Böhm, Markus & Kratsch, Wolfgang & Krcmar, Helmut, 2023. "Process Mining for resilient airport operations: A case study of Munich Airport’s turnaround process," Journal of Air Transport Management, Elsevier, vol. 112(C).

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