Skip to main content
Kent Academic Repository

Improving demand forecasting in the air cargo handling industry: A case study

Magana Olmos, U., Mansouri, A., Spiegler, V.L.M. (2016) Improving demand forecasting in the air cargo handling industry: A case study. International Journal of Logistics Research and Applications, 20 (4). pp. 359-380. ISSN 1367-5567. E-ISSN 1469-848X. (doi:10.1080/13675567.2016.1263610) (KAR id:59234)

Abstract

Air transportation plays a crucial role in the agile and dynamic environment of contemporary supply chains. This industry is characterized by high air cargo demand uncertainty, making forecasting extremely challenging. An in-depth case-study has been undertaken in order to explore and untangle the factors influencing demand forecasting and consequently to improve the operational performance of an Air Cargo Handling Company. It has been identified that in practice, the demand forecasting process does not provide the necessary level of accuracy, to effectively cope with the high demand uncertainty. This has a negative impact on a whole range of air cargo operations, but especially on the management of the workforce, which is the most expensive resource in the air cargo handling industry. Besides forecast inaccuracy, a range of additional hidden factors that affect operations management have been identified. A number of recommendations have been made to improve demand forecasting and workforce management.

Item Type: Article
DOI/Identification number: 10.1080/13675567.2016.1263610
Subjects: H Social Sciences > H Social Sciences (General)
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Virginia Spiegler
Date Deposited: 29 Nov 2016 10:06 UTC
Last Modified: 19 Sep 2023 15:03 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/59234 (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.