Authors

Mark Bennett

Abstract

Containerized cargo growth has underpinned recent seaborne container trade. However published techniques for developing forecasts of containerized throughput at seaports, which are often quantitative, are rarely understood or deployed by practitioners. To fill this research gap, this research aimed to identify how UK practitioners make forecasts and to evaluate the role of forecasts in port management. The research applied a mixed methods approach using a sequential exploratory design which involved qualitative research in phase one, and Delphi methods in phase two. Phase one involved conducting semi-structured interviews with experts and container port managers in the UK. In phase two, an opinion based long-term investigation of trends deployed Delphi methods. Template analysis of phase one interviews indicated common concepts, practitioners who use short-term basic extrapolation; some who use demand estimates supplied by cargo shippers, and others who do not predict future container cargo throughput but instead seek to maximise throughput by collaborating with key partners and shipping lines. Template analysis offers a practical method to identify and categorise concepts derived from qualitative data and facilitates comparative analysis. Delphi surveys recruited specialist logistician panellists with long-term career commitments, who expected flows through UK ports to slow slightly after 2030, but to almost double every 15 years. Increasingly UK container ports will probably function either as intra-regional feeder hubs linked to Rotterdam or will host coastal services linked to UK hubs. Individual port prospects will depend on publicizing facilities on offer internationally, as much as investing in them. The operational worth of traditional statistical forecasting techniques and market shares modelling will depend on dubious assumptions that the shipping system and its fundamental components remain relatively unchanged. Ad hoc Delphi panels offer a useful alternative approach to long term forecasting.

Document Type

Thesis

Publication Date

2020-01-01

DOI

10.24382/869

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