Ocean transport is the primary mode for global merchandise shipping and handles the largest share of international goods movement, especially in developing countries (UNCTAD 2023). Choosing the most appropriate shipping line and port combinations is therefore critical to obtaining improved logistics performance (Aaby 2012; Song 2011).

Logistics performance is typically measured through transit times, financial outlays and the consistency of service delivery. As transit time delays can be translated into cost (Haartveit, Kjøstelsen & Jacobsen 2007), cost remains the primary output measurement of logistics performance. Research on logistics costs emphasises monitoring and controlling expenditures at multiple levels (Andrejić et al. 2018). However, traditional frameworks often overlook indirect costs.

This study applies a ‘Total Economic Cost’ (TEC) model that converts delays in logistics processes into their equivalent economic cost. The TEC translates all time-related economic impacts into monetary terms, thus eliminating the need to account for time and cost separately. The TEC model incorporates opportunity costs, hidden logistics costs and externalities, providing a holistic measure of logistics performance (Hoffman, Mutendera & Venter 2023). The model accounts for a range of direct and indirect expenses, including financing charges tied to inventory, reductions in stock levels and the costs associated with stockouts. This study extends earlier research (Hoffman 2019; Hoffman, Lusanga & Bhero 2013; Minken & Johansen 2019 and most notably, Hoffman et al. 2023). Using a TEC model provides a comprehensive framework to quantify logistics performance costs, ensuring key metrics such as time delays and service reliability are accurately translated into economic terms, which is crucial for optimising decision-making in maritime logistics.

Because freight forwarders manage and integrate activities across the logistics chain, they are uniquely positioned to observe real-world operational processes and associated economic outcomes (see Özcan et al. 2024). Accordingly, the empirical evidence used in this analysis was sourced from a South African forwarding company and captures the conventional sequence of activities in an import process. Although this study focuses on South Africa, the methodology can be applied to any country participating in global trade.

The paper is structured as follows: the ‘Literature review’ section provides an overview of logistics performance and the TEC model. The ‘Objective of the total economic cost for this study’ section states the objective of this article. The ‘Methodology’ section outlines the assessment method and data collection. The ‘Total economic cost model specification’ section outlines the theoretical foundation of the TEC model, which underpins this study’s methodological approach. The empirical findings are presented in the ‘Results and findings’ section, and the ‘Conclusion, limitations and recommendations’ section offers concluding remarks together with recommendations for improving logistics efficiency.

Logistics networks are integral to extended supply chains and must maintain predictability, sustainability, reliability and agility. The coronavirus disease 2019 (COVID-19) pandemic placed exceptional stress on these capabilities, testing system resilience and revealing several system weaknesses (Notteboom, Pallis & Rodrigue 2021:180).

Notteboom et al. (2021) analysed the timing and geographic spread of COVID-19 supply and demand shocks and compared them with those of the 2008/2009 financial crisis. The evidence suggested enhanced resilience across shipping lines, terminal operators and ports, attributable to a combination of renewed risk recognition and structural organisational modifications. Also, COVID-19 reconfirmed the market position and bargaining power of shipping lines, with an upsurge in freight rates and a positive impact on their financial results (Notteboom et al. 2021:207). These developments emphasise the value of understanding logistics costs when navigating major disruptions.

However, they warned of the potential impact over the longer term amid ‘highly uncertain conditions generated by a new wave of COVID-19 cases and restrictions in countries around the world’ (Notteboom et al. 2021:186). Although the acute phase of COVID-19 was relatively short lived, Notteboom et al. (2021) noted that the broader uncertainty during the period could affect logistics networks beyond the immediate crisis. Similar observations were made in later work (e.g., Ciravegna & Michailova 2022:173), and the longer-term impact was also experienced in the logistics industry in developing contexts such as South Africa (see Grater & Chasomeris 2022). The pandemic therefore serves as a useful historical example of how rapidly logistics performance can deteriorate and why a comprehensive analysis of logistics costs remains essential for improving resilience and supporting informed decision-making.

Further research assessing logistics costs (e.g. Andrejić et al. 2018) emphasised the significance of monitoring and controlling logistics expenditures at global, national and organisational levels. However, while the existing research tends to cover major cost categories like transportation, warehousing and handling, it does not fully account for the full range of direct and indirect or implicit costs. A TEC model offers the potential to accurately quantify opportunity costs, hidden logistics costs and externalities caused by variable time delays, and in this regard, the TEC model provides a holistic measure of logistics performance.

Hoffman et al. (2023) applied a TEC model to transport corridors in the SADC region, using Zambia and Lusaka as examples. They found that variability in time delays significantly impacts total logistics costs, often more than only direct transport costs. For instance, the Beira corridor, despite its low direct transport cost, had high total costs because of time delay variability, making it less favourable compared to other corridors. Within this scenario, the financial impact of time delays could represent up to 80% of the total costs associated with the corridor.

Consequently, these findings highlight the usefulness of applying a TEC model to improve understanding of logistics costs. The following sections will explain how the TEC model is applied to the South African import trade lanes analysed in this study.

This study builds on previous work (Hoffman 2019; Hoffman et al. 2013, 2023; Minken & Johansen 2019) and uses a TEC model to quantify logistics performance costs. The TEC framework incorporates a combination of direct and indirect cost elements, including capital charges tied to inventory, shrinkage-related losses and the financial impact of stockouts.

Although the shipping environment is dynamic and conditions evolve continuously, the TEC model retains practical relevance because it functions as a flexible framework rather than a fixed prediction tool. Freight forwarders routinely capture the operational data required to apply the model, allowing TEC estimates to be updated as conditions change. Importantly, while many implicit costs – such as stock-in-transit financing, buffer-stock requirements and disruption risks – are recognised by practitioners, they are seldom evaluated collectively. The TEC consolidates these components into a single cost measure, enabling cargo owners and forwarders to compare trade lanes and carrier options systematically and to quantify the economic impact of variability in delivery performance. In this way, the model complements existing decision-making rather than serving as a purely theoretical construct.

The empirical evidence for this study is drawn from transaction-level records compiled by a South African freight forwarder for the period July 2017 to August 2023. The dataset covers around 5374 individual shipments and integrates information from technical service providers, including documentation process timelines, container-tracking data reflecting the physical movement of cargo and associated financial records.

Table 2 summarises the dataset that was obtained for this study.

The TEC model developed by Hoffman et al. (2023) was adapted for this study into the steps explained in Table 3.

As shown in Table 3, the TEC framework was adapted for this study to translate the detailed shipment-level data into a unified measure of total logistics cost across different trade lanes. The adaptation integrates direct charges, operational cost components and the time variability captured in the dataset to evaluate how delays and route-specific performance influence overall logistics efficiency. A more detailed description of the framework is published in Van Rensburg (2024). By aligning the model with the structure of the available freight-forwarder data, the TEC output provides a comparable cost measure for each origin–shipping line combination, allowing the identification of the factors that most strongly affect end-to-end trade lane performance.

This section discusses the results generated by the TEC model and presents the key findings derived from the analysis.

This study developed a TEC model to support more informed logistics decision-making in international trade. The TEC model quantifies the impact of time delay variability on direct and indirect logistics costs across different trade lanes. It built on previous work and added factors like time delay variability, shrinkage of stock and out-of-stock losses. Although the shipping environment is dynamic, the TEC framework remains operationally relevant because it can be recalculated as new data becomes available, enabling ongoing comparison of trade lanes and carrier performance. While the results show that shipping-line choice plays a particularly influential role given its strong impact on transit-time variability, the value of the TEC model extends beyond carrier selection alone. The framework also informs buffer-stock policies, highlights the cost implications of reliability differences across logistics segments and enables systematic comparison of trade-lane configurations as operating conditions evolve.

The findings reveal substantial differences in transit times across consignments, with delays ranging from only a few days to well over 100 days in certain cases. This underscores the need to quantify how delivery-time variability affects TEC from the cargo owner’s perspective. It also highlights the importance for freight forwarders to carefully evaluate trade-lane and shipping-line choices, as differences in delay performance can materially influence the overall economic cost of imported goods.

The ocean segment, averaging 35.97 days, had the strongest influence on total delays. Waterside and landside port operations contributed similarly, while inbound road transport had the smallest effect except for occasional large disruptions past the 96th percentile. The findings also suggest a review of the short-haul–long-haul inbound model, given the extended time many consignments spend in depots or warehouses.

Investigation of different buffer stock scenarios revealed that for each shipping line-trade lane combination, an ideal buffer stock period can be found. This results from the fact that interest and storage costs rise, while the cost of lost sales or production decreases, with increased buffer stock periods. For the available data set, an ideal buffer stock level of around 34 days minimised total logistics costs. For those shipping lines with low time delay variability, the buffer stock period could, however, be reduced to below 10 days. The findings emphasise the importance of aligning shipping lines with specific origin countries, as lower optimal buffer-stock requirements signal more reliable services and reduced TEC.

These results offer practical guidance for freight forwarders and cargo owners seeking to enhance logistics performance by selecting the most suitable shipping lines for specific routes, where sufficient operational and historical data are available. They also support the identification of appropriately calibrated buffer-stock levels by highlighting origin–carrier combinations that minimise TEC.

In addition, the variation in optimal buffer-stock requirements sheds light on how inventory decisions interact with key cost drivers affected by time delays – such as interest on stock in transit, storage costs and the risk of lost sales.

Despite the substantial dataset that was available for this research, several limitations remain.

Firstly, the dataset used, although substantial, represents only a sample of 5374 shipments, which may not fully capture the broader variability in global trade patterns. Secondly, the analysis focuses on limited combinations of country of origin and shipping line schedules, restricting the generalisability of the findings to other trade routes and logistical conditions. Thirdly, the TEC model relies on available data for specific elements, and gaps in comprehensive information, particularly indirect costs, could affect the model’s accuracy.

Lastly, the analysis is constrained by historical data and may not account for evolving trends in shipping practices or regulatory changes impacting logistics performance. These limitations suggest areas for further research to enhance the model’s robustness and applicability.

Regardless of these limitations, the study demonstrated that faster and more predictable movement of goods through the supply chain improves trade volumes and reduces logistics costs, highlighting the need for the industry to apply more robust data analytics in order to improve logistics performance. More specifically, where sufficient historical and operational data are available, the application of the TEC can significantly enhance the freight forwarder’s ability to make informed shipping-line selection decisions for specific trade routes.