Original Research

Efficient trade lane selection: A total economic cost perspective on shipping lines

Alwyn Hoffman, Jacob van Rensburg, Sonja Grater
Journal of Transport and Supply Chain Management | Vol 20 | a1272 | DOI: https://doi.org/10.4102/jtscm.v20i0.1272 | © 2026 Alwyn Hoffman, Jacob van Rensburg, Sonja Grater | This work is licensed under CC Attribution 4.0
Submitted: 16 October 2025 | Published: 04 March 2026

About the author(s)

Alwyn Hoffman, School of Electrical, Electronic and Computer Engineering, Faculty of Engineering, North-West University, Potchefstroom, South Africa
Jacob van Rensburg, School of Economic Sciences, Faculty of Economic and Management Sciences, North-West University, Potchefstroom, South Africa; and, SAAF, Johannesburg, South Africa
Sonja Grater, School of Economic Sciences, Faculty of Economic and Management Sciences, North-West University, Potchefstroom, South Africa

Abstract

Background: Efficient logistics performance is vital for global trade, yet traditional cost assessments often overlook the economic impact of time delay variability. Especially in maritime logistics, these delays can generate substantial indirect costs. This study addresses a critical gap by integrating time-related uncertainty, which contains the implicit cost aspects, into logistics cost modelling to support better decision-making in trade lane selection.
Objectives: The study aims to quantify both direct and indirect logistics costs arising from time delays and variability across international shipping routes. Focusing on South Africa’s import trade, it introduces a replicable total economic cost (TEC) model that enables cargo owners and freight forwarders to optimise route and shipping line choices based on holistic cost performance.
Method: Using a dataset of 5374 import shipments (2017–2023) from a South African freight forwarder, the study segments total logistics chains into ocean, port and land legs. Time delays and their variability are analysed per segment. Direct and indirect costs – such as the cost of capital tied up in inventory, stock shrinkage and lost sales – are modelled using percentile-based TEC calculations across buffer stock strategies.
Results: The ocean leg was the largest contributor to time delays and cost variability. Shipping lines with lower delay variability enabled significantly lower TEC values and smaller buffer stocks. The TEC model revealed that variability-driven costs often exceeded direct logistics expenses.
Conclusion: Minimising delay variability, and not just transport time, can significantly reduce logistics costs. The TEC model supports better strategic alignment of shipping line and trade lane choices.
Contribution: This study provides a practical, data-driven methodology for quantifying total logistics cost under uncertainty and enabling optimal choices of trade lanes and service providers, addressing a key challenge in global supply chain optimisation.


Keywords

trade lane; logistics; total economic cost; time variability; statistical distributions

JEL Codes

C61: Optimization Techniques • Programming Models • Dynamic Analysis; L91: Transportation: General

Sustainable Development Goal

Goal 9: Industry, innovation and infrastructure

Metrics

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