To read the full version of this content please select one of the options below:

Condition-based maintenance for major airport baggage systems

Frank Koenig (Buckingham Business School, University of Buckingham, Buckingham, UK)
Pauline Anne Found (Cardiff Business School, Cardiff University, Cardiff, UK)
Maneesh Kumar (Cardiff Business School, Cardiff University, Cardiff, UK)
Nicholas Rich (School of Management, Swansea University, Swansea, UK)

Journal of Manufacturing Technology Management

ISSN: 1741-038X

Article publication date: 17 July 2020

Issue publication date: 24 March 2021




The aim of this paper is to develop a contribution to knowledge that adds to the empirical evidence of predictive condition-based maintenance by demonstrating how the availability and reliability of current assets can be improved without costly capital investment, resulting in overall system performance improvements


The empirical, experimental approach, technical action research (TAR), was designed to study a major Middle Eastern airport baggage handling operation. A predictive condition-based maintenance prototype station was installed to monitor the condition of a highly complex system of static and moving assets.


The research provides evidence that the performance frontier for airport baggage handling systems can be improved using automated dynamic monitoring of the vibration and digital image data on baggage trays as they pass a service station. The introduction of low-end innovation, which combines advanced technology and low-cost hardware, reduced asset failures in this complex, high-speed operating environment.


The originality derives from the application of existing hardware with the combination of edge and cloud computing software through architectural innovation, resulting in adaptations to an existing baggage handling system within the context of a time-critical logistics system.



Koenig, F., Found, P.A., Kumar, M. and Rich, N. (2021), "Condition-based maintenance for major airport baggage systems", Journal of Manufacturing Technology Management, Vol. 32 No. 3, pp. 722-741.



Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited