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Managing turnaround performance through Collaborative Decision Making
KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Industrial Management. Universidad Politécnica de Madrid, Spain.ORCID iD: 0000-0001-6887-6859
KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Industrial Management.ORCID iD: 0000-0002-9709-540X
KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Industrial Management. Research Institute of Industrial Economics, Sweden.ORCID iD: 0000-0003-0904-5822
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2017 (English)In: Journal of Air Transport Management, ISSN 0969-6997, E-ISSN 1873-2089, Vol. 58, p. 183-196Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 58, p. 183-196
Keywords [en]
Collaborative Decision Making, Collaborative measures, Turnaround process, Performance measurement
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-199470DOI: 10.1016/j.jairtraman.2016.10.008ISI: 000389294200019Scopus ID: 2-s2.0-84995906521OAI: oai:DiVA.org:kth-199470DiVA, id: diva2:1067868
Note

QC 20170123

Available from: 2017-01-23 Created: 2017-01-09 Last updated: 2017-05-29Bibliographically approved
In thesis
1. Collaborative Measures: Challenges in Airport Operations
Open this publication in new window or tab >>Collaborative Measures: Challenges in Airport Operations
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Over the last 20 years, internal use of Performance Management(PM) within organizations has become much more complex in terms measurement techniques and approaches as well as their deployment within different organizational structures. In contrast to the traditional use of PM as an intra-organizational system, the emergence of networked operations, has extended organizational boundaries of Performance Management System (PMS) to new operational settings where actors often deal with a challenge of Collaborative Measures. Consequently, there is a significant lack of feedback and feedforward reporting mechanisms. This raises an important question for Performance Measurement & Management (PMM) literature. How do actors manage operations through inter- organizational performance measures? Hence, the purpose of this thesis is to investigate the management of collaborative measures in a quest to attain better operational performance for inter- organizational PM.

The thesis builds on four studies investigating a collaborative PMS for capacity enhancements in airport operations. Due to their operational complexity and highly networked subsystems, airport operations provided a fitting empirical scene for studying PM that transcends organizational boundaries. Within the context of this thesis, airports are viewed as a System of System (SoS), and inter- organizational PM is investigated with the dimensions Organizational Complexity, Continuous Improvement and Social system. The studies use a multimethod approach, including longitudinal action research, multiple-case study, Systematic Literature Review (SLR), Classification and Regression Tree method (CART) and Artificial Neural Network (ANN) Method.

The findings show that that (1) inter- organizational performance is affected by intrinsic Performance Measurement Complexity (PMC) which aggregates as interactive complexity with many actors. (2) The challenge of feedback and feedforward mechanisms as a dual control for collaborative performance is analysed with four cybernetic functions: sensor, commander, actuator and process. (3) The concept of Reflective Performance Measurement System (RPMS) is introduced with general conditions to facilitate collaborative decision-making within such platforms.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017. p. 76
Series
TRITA-IEO, ISSN 1100-7982 ; 7
Keywords
Performance Measurement, Airport Operations, Collaboration Decision Making
National Category
Business Administration
Research subject
Industrial Economics and Management; Planning and Decision Analysis; Transport Science; Industrial Information and Control Systems
Identifiers
urn:nbn:se:kth:diva-207780 (URN)978-91-7729-331-6 (ISBN)
Public defence
2017-06-14, F3, Lindstedtsvägen 26, Stockholm, 09:00 (English)
Opponent
Supervisors
Projects
MASCA- Managing System Change in Aviation
Note

This research was conducted within the framework of the European Doctorate in Industrial Management—EDIM, which is funded by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission. QC 20170524

Available from: 2017-05-24 Created: 2017-05-23 Last updated: 2017-06-07Bibliographically approved

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Okwir, SimonUlfvengren, PernillaAngelis, Jannis

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