Comparing flexibility-based measures during different disruptions: evidence from maritime supply chains

2.1 Characteristics of disruptions

The nature of a disruption is an important dimension that influences how the disruption can be managed and can vary significantly. Delimiting the focus to our empirical setting, this paper pays specific attention to low-frequency, high-impact events, including natural disasters.

Before an event, companies can prepare to some extent, as restaurants in the United States have been observed to do a few months before the annual hurricane season (Ergun et al., 2010). A reason for preparation is that the probability of some events can be assessed (Lam and Su, 2015), either quantitatively through simulations based on historical data about natural disasters (Knemeyer et al., 2009) or qualitatively based on the perceptions of the relationships between ports and unions and the degree of union control (Berle et al., 2011). Likewise, such events are detectable and not surprising, at least on short time horizons (Ergun et al., 2010). By contrast, some events are unpredictable and surprising, including the lightning that ignited a fire at a microchips supplier that netted Ericson a loss of US $400 million due to its lack of a contingency plan (Norrman and Jansson, 2004).

A disruptive event can have different causes, often classified as man-made or natural (Macdonald and Corsi, 2013). Man-made events include political conflicts, wars, theft, sabotage and terrorism (Urciuoli et al., 2014). Because they involve decision-making, their duration may be short (e.g. terrorist attack) or long (i.e. war). Natural disasters, by contrast, typically do not last long but may cause a disruption that persists for years afterwards (Macdonald and Corsi, 2013). For some events, including hurricanes, a relationship between the duration and time to recovery has been observed (Verschuur et al., 2020). The severity of events can vary as well (Macdonald and Corsi, 2013; Dirzka and Acciaro, 2022). As a case in point, all earthquakes are not of the same magnitude, nor do they always occur in locations that affect supply chains.

The intensity of man-made events may also fluctuate due to decision-making, as witnessed in a port conflict (Rogerson et al., 2022) and the recent disruption of grain supplies from Ukraine. In the aftermath of such events, links and nodes in a supply chain might operate on an on-and-off basis. Furthermore, an event’s intensity can mean that a certain node works only in part, such as when a hurricane affects ports (Verschuur et al., 2020). The location of a disruption is of particular importance as well. The vessel Ever Given’s six-day blockage of the Suez Canal, in disturbing various supply chains (Wieland et al., 2023), made headlines in 2021, whereas Ever Forward’s 35-day grounding in the Chesapeake Bay on the US East Coast in 2022 was far less covered because it affected only companies with cargo on board. Location also includes geographical spread (Craighead et al., 2020), for it can affect a single node or a larger geographical area encompassing multiple ports (Verschuur et al., 2020).

In some cases, the relationship between an event and the time until its effects take hold is remarkably clear. For instance, an earthquake might damage the physical infrastructure of a port within minutes, if not seconds (Chang, 2000). However, in the case of the COVID-19 pandemic, it was unforeseeable how the disease would spread. In parallel, various types of uncertainty emerged regarding when and how companies would be affected by the disruption (Gunessee and Subramanian, 2020).

An event that disrupts a critical node or link can propagate other disruptions and have devastating effects across an entire system (Scheibe and Blackhurst, 2018). A disruption in a port, for instance, may have amplified effects for the regional or the global transport network (Verschuur et al., 2022). Along those lines, Verschuur et al. (2021) have found large disparities in the geographical and sectoral impacts of the COVID-19 pandemic. The severity of a disruption can be assessed in different ways, including in terms of breadth, depth and duration, all in relation to the focal firm (Hughes et al., 2022). Table 1 lists various characteristics of disruptions mentioned in the literature.

2.2 Flexibility as a mitigation strategy

The literature on supply chains describes various strategies for managing disruptions, including proactive risk management before and disruption management during and after events (Sheffi, 2005; Blackhurst et al., 2005; Macdonald and Corsi, 2013). Strategies can be classified as passive, internal, collaborative or integral (Revilla and Saenz, 2017) and can involve adding capacity or inventory, using redundant suppliers and/or increasing responsiveness, flexibility and/or capability (Chopra and Sodhi, 2004).

The Oxford Advanced Learner’s Dictionary defines flexibility as “the ability to bend easily without breaking”. In manufacturing and supply chains, it often means “the ability of a system to change or react with little penalty in time, effort, cost or performance” (Martínez Sánchez and Pérez Pérez, 2005). Most of the literature on flexibility focusses on the manufacturing industry (e.g. Slack, 1987, 2005; Oke, 2005) and supply chains from a broad perspective (Martínez Sánchez and Pérez Pérez, 2005). Even so, the need to study the concept from the standpoint of freight transportation is also emphasised in the literature (Naim et al., 2006; Mason and Nair, 2013a, b).

By extension, Dubey et al. (2021) have defined organisational flexibility as “the ability of organisations to deploy resources quickly, efficiently and effectively in response to sudden changes in the market conditions”, which establishes flexibility’s clear link with resilience. Flexibility is an inherent part of resilience (Peck, 2005) that allows organisations and supply chains to adapt to both foreseen and unforeseen changes in the environment (Rao Tummala et al., 2006; Gaudenzi et al., 2023) and navigate high degrees of uncertainty (Manuj et al., 2010).

The classification of flexibility-based measures differs depending on the context. In manufacturing systems, flexibility-based measures are classified as pertaining to new product flexibility, mix flexibility, volume flexibility and delivery flexibility (Slack, 2005). In supply chains, by comparison, flexibility-based measures can span production and product development and include the flexibility of logistics, the supply base and suppliers (Jin et al., 2014). However, in both contexts, the interface between inbound and outbound logistics activities – in a word, transportation – is obvious. That interface relates to Oke’s (2005) categorisation of internal and external flexibility, with the former meaning actions related to the manufacturer’s internal systems and the latter meaning actions visible to external parties and that define the firm’s perceived performance.

Departing from the literature on transport services, Naim et al. (2006) have introduced nine internal and five external dimensions of flexibility in freight transport that they subsequently applied to the ocean carrier industry during a global economic crisis and collapse in demand (Mason and Nair, 2013a, b). More recently, Rogerson et al. (2022) applied those transport flexibility-based measures in a similar context but refined the list by referring to logistics flexibility-based measures listed by Jafari (2015). Although the duality is similar, the descriptions of flexibility-based measures for transportation operations differ significantly because they are performed with a service mindset and within service supply chains. A selective, non-exhaustive list of transport flexibility-based measures is provided in Table 2 together with their brief definitions.

Most of the definitions in Table 2 were developed by Naim et al. (2006) and take a general perspective on transportation. However, the ones used by Mason and Nair (2013a, 2013b) and Rogerson et al. (2022) were adapted to the shipping context examined in their respective studies. For example, temporal flexibility was adapted to reflect the ability of rearranging the timing of delivery (Rogerson et al., 2022). Hence, different case settings call for the adaptation of flexibility-based measures that are observed.

Although the literature addresses flexibility in manufacturing, logistics and supply chains in general, how flexibility resonates in service supply chains, including transport chains, has yet to be explored. Recently, Rogerson et al. (2022) studied the impacts of a disruption in terms of flexibility and capacity during a port conflict in a maritime supply chain. By extension, this paper builds on their flexibility-based countermeasures in maritime supply chains by adapting them to similar strategies deployed by shipping lines during the COVID-19 pandemic.

3.1 The Gothenburg container port conflict, 2016–2017

The Swedish labour market is characterised by strong labour unions engaging in well-organised collective bargaining with employer organisations and strikes are relatively uncommon. For ports, however, there had been a long-term dispute with two competing labour unions that culminated in 2016–2017 with a lockout and strike at APM Terminals in Gothenburg, the only Swedish container port allowing direct calls by deep-sea vessels. The events prompted not only the closure of container port operations for a few days but also long, distressing periods of significantly reduced capacity. Analysing the effects of the conflict, Gonzalez-Aregall and Bergqvist (2019) have described mitigation strategies that involved moving cargo by truck or rail, thereby pinpointing the importance of hinterland transport surrounding the port. In similar work, Lindroth et al. (2020) have examined the mitigation strategies for the disruption used in the fashion retail industry, whilst Svanberg et al. (2021) have reported rerouting vessels to other ports and its effects on port efficiency. For example, amid reduced capacity in Gothenburg, containers were transshipped in other main ports using feeder services to smaller ports or truck, rail and ferry combinations to reach Sweden. More recently, Rogerson et al. (2022) have analysed problems with capacity related to the port conflict and the flexibility-based countermeasures applied. Ultimately, an agreement between both labour unions and the employer organisation was signed in 2019, and the current situation at the port is reasonably calm.

Gothenburg is far from an exception because port workers are prone to labour strikes. Lam and Su (2015) have identified strikes and natural disasters as the two principal reasons for disruptions in Asian ports, whilst Farris (2008) has described conflicts at US West Coast ports. The critical role of ports in maritime supply chains also emerges when they close for reasons other than conflicts in the labour market. On that count, Childerhouse et al. (2020), for instance, have described strain on the logistics network for ports and facilities whilst modelling a yearlong closure of a port in New Zealand.

3.2 COVID-19 pandemic

The COVID-19 pandemic’s effects on container shipping are well-known and represent a rare example of freight transport being widely covered in mass media. In short, lockdowns prolonged the Lunar New Year holiday in China in early 2020, followed by a mid-March decline in global shipping demand due to the lack of raw materials, parts and workers, along with trade restrictions and closed manufacturing facilities. Shipping lines, meanwhile, responded with blank sailings and increased scrapping (Notteboom et al., 2021). Volumes recovered unexpectedly fast, however, primarily due to government stimuluses and shifts from spending on travelling and dining to buying consumer products online that spurred the need for increased shipping capacity (Altuntas Vural et al., 2021). As waves of COVID-19 followed, dealing with infections entailed isolation, port changes and cancellations (Altuntas Vural et al., 2021). Container shipping capacity, particularly in ports, became scarce, and as freight rates soared, near-shoring strategies were considered to increase resilience (Notteboom et al., 2021; Van Hassel et al., 2022). As restrictions were lifted and capacity came to match demand, freight rates sunk gradually during 2022 and reached pre-pandemic levels in 2023.

3.3 Data collection

Semi-structured interviews were conducted to collect data from multiple actors in maritime supply chains in order to ensure a supply network perspective. The same interview guide was used in all interviews, albeit with minor adaptations for each case. The interview guide focussed on three areas: effects of the disruption, countermeasures taken and lessons learnt. The guide was developed with reference to Macdonald and Corsi (2013) for themes, Blackhurst et al. (2005) for effects and Tang (2006), Van der Vorst and Beulens (2002) and Lam and Su (2015) for countermeasures. Initial interviews were conducted, and the phrasing of questions was discussed with other researchers before the guide was finalised. Not focussing on specific types of flexibility in the guide meant that the interviews were not limited to capturing preconceived aspects of the disruptions in question.

The supply chain actors to be interviewed were identified based on indications that they were influenced by the disruptions. The focus of the inquiry, however, was their ability to facilitate waterborne freight transport services. Thus, in both cases, the actor most affected by the disruption and its ability to facilitate that objective was selected as the point of departure. In Case 1 (i.e. the port conflict), ports were the point of departure, whilst the focus in Case 2 (i.e. the COVID-19 pandemic) was shipping lines that were heavily affected by the disruption. Early interviews during COVID-19 as part of a different study that focussed on another shipping segment indicated limited effects on Swedish ports also for container cargo flows. Following the interview with a Swedish port in this study, it was deemed sufficient with only one port interviewed in Case 2. In both cases, different actors representing maritime supply chains were sampled, but only the data explaining the flexibility-based countermeasures taken to continue facilitating waterborne freight transport services were considered, in line with this paper’s purpose. Thus, in Case 2, data saturation with certain actor types (e.g. shippers) was achieved relatively early, whereas further data elaboration was needed with other types of actors (e.g. shipping lines). The interviewees were selected for their knowledgeability regarding the disruption’s effects and countermeasures in each company, although their titles varied depending on the organisation.

Altogether, 29 companies were interviewed in Case 1 and 13 in Case 2, including companies importing and exporting goods, shipping lines, freight forwarders and port-terminal operators (Table 3). Lasting approximately 1–1.5 h, the interviews in each case were conducted from March to September 2018 and from November 2021 to January 2022, respectively, by one to three researchers, with one designated interview leader.

3.4 Data analysis

All interviews were audio-recorded and transcribed before being analysed using NVivo R1.6. Using that software enabled a systematic analytical process that allowed practical screening for data saturation. Two authors analysed the data and discussed it with the author team several times; in the case of disagreement, a third author was invited to read the interview in question, which was followed by a discussion to reach a consensus. As the analysis proceeded, the findings were discussed by all authors, as well as with industry representatives, to confirm the interpretations of the data (Marshall and Rossman, 2006). The measures of research quality that guided the study, adapted from Ellram (1996), Halldórsson and Aastrup (2003) and Strauss and Corbin (1998), are presented in Table 4.

The data were coded in three steps: open coding, code matching and selective coding (Strauss and Corbin, 1998). In the first step, an iterative open coding process with constant comparison was followed. In a simultaneous process, previously coded data were continuously revisited as new codes emerged or certain patterns were observed. Any clear pattern between the dataset of effects and the dataset of countermeasures was recorded.

In the second step, the open codes from countermeasures were matched with previously reviewed flexibility-based measures in transport from the literature. That code-matching process revealed relationships between countermeasures and their similarities and differences with previously reviewed flexibility-based measures. The process also provided axial codes that were later used to construct higher-order categories.

In the third step, several flexibility-based measures were observed to be used either simultaneously or interdependently. Those relationships guided the selective coding process that allowed the grouping of flexibility-based measures into four higher-order categories (i.e. spatial, capacity, temporal and service flexibility). That categorisation is unique for maritime supply chains focussed on service provision and was necessary, given that literature on flexibility dimensions focusses heavily on manufacturing but neglects the service setting.

Last, the empirical examples of flexibility in the COVID-19 pandemic were listed next to those of the port conflict and compiled in tables listing similarities and differences. Those flexibility-based measures were subsequently analysed in relation to characteristics of disruptions reviewed in the literature (Table 1) and observed in the two cases. We coded the data for all characteristics listed in Table 1, but a few had only limited data, possibly owing to the type of disruptions studied, in which it may be easier to identify the time until effects occur and propagate other disruptive events. The difference may also indicate that other methods, including mathematical modelling, are better suited to studying certain characteristics – for instance, propagation, as studied by Ivanov and Dolgui (2021). In particular, our study elucidated how six characteristics related to flexibility-based measures: geographical spread, duration, the element of surprise, uncertainty, intensity and criticality. The findings regarding flexibility and characteristics of the disruptions were cross-checked to identify connections.

4.1 Flexibility as a countermeasure

The coding process resulted in four major groups of flexibility-based countermeasures – spatial, service, temporal and capacity – defined based on the different measures that they represent. First, spatial flexibility is mobilised when a disruption to the supply chain blocks the physical transport infrastructure and thus creates bottlenecks therein. For that reason, changes are needed with respect to loading and unloading locations, preferred nodes and links. Second, capacity flexibility involves the ability to expand or shrink the available space used to provide the transport service. Capacity is not always flexible, particularly when managed by strict contracts; however, there are ways to inject flexibility into maritime supply chains by utilising short-term contracts, adding other vehicles or vessels and/or lifting restrictions on authorised transport unit types. Third, temporal flexibility relates to the temporal dimension of the transport service, which is an important measure of transport performance. A blockage or disruption puts pressure on the temporal dimension, but that impact can be mitigated by expediting or slowing the service provision and by adapting the timing of required information on shipments. Furthermore, some effects from supply chain disruptions can be mitigated by changing the components of the service provided. In the cases that we examined, changes to different components of the transport service were observed. Fourth and finally, service flexibility can relate to the service provider, the type of vehicle enabling service provision, the prioritisation of services provided amongst different customer segments and the means of service provision.

Regarding flexibility-based measures applied in the Gothenburg port conflict versus the COVID-19 pandemic, spatial flexibility was exercised in both disruptions by changing ports. Given simultaneous problems in many ports during the pandemic, lower spatial flexibility was indicated, but shipping lines were also able to move vessels between routes. Compared with the port conflict, capacity flexibility-based measures during the pandemic were often described, including removing and adding vessels; however, overall capacity flexibility was lower and quickly reached the capacity ceiling. Although temporal flexibility existed for shipping lines in both disruptions (e.g. delaying deliveries), it was challenging for more actors during the pandemic than during the port conflict. For example, shipping lines expressed being negatively influenced by decisions made by ports. Concerning service flexibility, shipping lines adapted their service offerings more during the pandemic, meaning higher service flexibility, than during the port conflict. Even so, flexibility was lower for other actors such as forwarders, importers and exporters who changed carriers or traffic modes due to longer contract duration requirements. Details of the comparison of flexibility-based measures are presented in Table 5.

4.2 Flexibility and characteristics of disruptions

The two cases provided insights into the implications of characteristics of disruptions for flexibility-based countermeasures, which are described below and summarised in Table 6.

5.1 Flexibility-based countermeasures

Whilst Rogerson et al. (2022) have focussed on a relatively large set of actors when describing flexibility-based countermeasures, this paper shows how such countermeasures can be applied by shipping lines and how their effects spread across various actors in maritime supply chains. It also offers a new categorisation of flexibility-based countermeasures – spatial, capacity, service and temporal – that departs from the literature by focussing on service supply chains. That focus is important because the resilience-based countermeasures of service supply chains in general and of maritime supply chains in particular have either been underexamined or studied from the perspective of manufacturers. Therein, flexibility has mostly been analysed from the perspective of production capacity, supply capacity or flexibility in the supply base, all of which are less relevant in maritime supply chains. That being said, maritime supply chains are prone to significant disruptions. A bottleneck occurring in those chains has rippling effects on global manufacturing supply chains, which depend on the uninterrupted, on-time flow of goods around the world. For that reason, an in-depth understanding and special categorisation of flexibility from the perspective of maritime supply chains can help with grasping nuances that are specific to those chains and their actors.

Our results corroborate Dirzka and Acciaro (2022) findings showing the forms of flexibility available to shipping lines when the maritime industry has needed to withstand network disruptions. Similar to Dirzka and Acciaro (2022), we observed how shipping lines have used service rescheduling and cancellation, although we have described those approaches in terms of flexibility. Flexibility clearly exists but also comes at a cost, whether in terms of money, time, or both. As described by Rožić et al. (2022), freight rates increased dramatically during the COVID-19 pandemic. A striking similarity between the pandemic and the port conflict was that some actors presented positive financial results. Those positive results, notably for shipping lines and haulers, relate to the mismatch between available capacity and demand. Although that dynamic may be viewed as good disruption management – for instance, that shipping lines managed the pandemic much better than the financial crisis a decade earlier (Notteboom et al., 2021 – that view is problematic when considering the entire network (Ivanov and Dolgui, 2020; Rogerson et al., 2022), because capacity is in fact not sufficient for the demand. Per our findings, other actors had to pay the price for shipping lines’ limited capacity flexibility (e.g. not finding the desired space for their cargo), whilst the shipping lines were able to increase freight rates and profit from the lack of capacity. Thus, limited flexibility was not necessarily adverse for the shipping lines. That finding agrees with what Chua et al. (2022) found: that reducing capacity was an effective strategy for the carriers that they studied but sacrificed the level of service provided to shippers and end customers.

This paper, by adopting the lens of flexibility, can expand current understandings of decisions underlying changes in traffic flows that have been described in the literature, including variations in maritime traffic for container vessels in major trade lanes during the initial months of the COVID-19 pandemic (March et al., 2021). Another example concerns spatial flexibility, which relates to the connectivity of the container port network and choice of hub, as mapped by Yap and Yang (2022). The paper also nuances understanding of problems with positioning empty containers put forward by Toygar et al. (2022).

In the vast amount of literature on supply chain disruptions, the dominant perspective is that of transport buyers and their mitigation of supply chain disruptions – for example, by redesigning supply chains, including by incorporating the concept of the plastic response (Hughes et al., 2022) or resilience in managing products, partnerships and processes (Cohen et al., 2022). However, whereas that literature takes a long-term perspective, this paper contributes by offering in-depth descriptions of mitigation strategies during disruptive events and in the acute phase of their impacts with a particular transport provider focus.

5.2 Flexibility considering various characteristics of disruptions

This paper illustrates how characteristics of disruptions in supply chains may influence flexibility in maritime supply chains, particularly by drawing on examples from and comparing the port of Gothenburg conflict and the COVID-19 pandemic. On that count, six characteristics were particularly revealing: geographical spread, duration, uncertainty, criticality, the element of surprise and intensity. The ease of applying various flexibility-based countermeasures given various characteristics of disruptions is illustrated in Figure 1.

Spatial flexibility is easier to apply when the disruption has a local geographical spread, is expected, has low uncertainty and has low criticality. If a disruption is less surprising (i.e. more expected), then companies can have mechanisms in place to divert vessels and identify alternatives. Meanwhile, if uncertainty is low, then it is easier to plan rerouting, and if criticality is low, then alternatives are available. Beyond that, a local geographical spread typically means that problems can be bypassed. That understanding can be compared with what Gunessee and Subramanian (2020) found in the local disruption of flooding in Thailand in 2011: that sourcing flexibility existed such that companies could shift to facilities in alternative locations. Conversely, spatial flexibility is relatively difficult to apply when a disruption has a long duration, high uncertainty and high criticality. In our study, both disruptions had long durations and thus needed long-term flexibility. That result aligns with Cariou and Notteboom (2022) finding that whilst supply chain disruptions in the United States of America due to natural disasters (e.g. hurricanes) resulted in limited possibilities to substitute ports, the longer disruption of the COVID-19 pandemic involved lockdowns and slowdowns at major ports.

If a disruption has high uncertainty, then it remains unclear how ports will be affected, which makes it more difficult to plan. Meanwhile, the high criticality of the node and network (Craighead et al., 2007; Knemeyer et al., 2009) means increased difficulty with finding alternatives. That understanding can be compared with the conclusion of Guerrero et al. (2022) that large ports and small but densely interconnected ones better resisted the disruption of the COVID-19 pandemic. In particular, densely interconnected ports may imply particularly high spatial flexibility. Verschuur et al. (2020) have described barriers to substituting ports, including contracts and specialised equipment, which consequently reduce spatial flexibility. Dirzka and Acciaro (2022) have described the flexibility available to shipping lines in terms of service rescheduling and cancellation despite the global pandemic but have also highlighted that a disruption occurring in various locations instead of emanating from a specific geographical cluster, such as during the pandemic, would have been more challenging. Meanwhile, Pais-Montes et al. (2023) found that hub ports were more easily substitutable and that large ports were more often cancelled during the pandemic. Reducing the element of surprise (Azadegan et al., 2021) of the effects of disruptions can also allow shipping lines to develop strategies for best utilising spatial flexibility (e.g. diverting vessels and identifying alternatives).

Capacity flexibility is easier to apply when a disruption has a local geographical spread, a shorter duration and low uncertainty. A local spread means that capacity is available elsewhere, whilst a short duration implies that capacity can be re-allocated. Even so, it should be noted, as stated by Verschuur et al. (2020), that spare capacity in potential substitute ports may be less if the disruption is not local. When uncertainty is low, it is easier to plan the use of resources. Conversely, capacity flexibility is more difficult to apply when there is high uncertainty and high criticality. We found a reluctance to add capacity if it remained uncertain how that capacity should be designed. The case studies illustrate that high uncertainty makes decision-making more difficult regarding use of flexibility (e.g. misjudging the situation), such that shipping lines initially reduced capacity, as described by Notteboom et al. (2021). In that vein, this paper contributes by describing how increasing the capacity again was less straightforward. Also, given high criticality, there were fewer alternatives available. Furthermore, both cases involved insufficient replacement capacity. Even so, a global geographical spread, a long duration and high intensity produced favourable conditions for adding capacity. As pinpointed by Dirzka and Acciaro (2022), the COVID-19 pandemic was a unique, unprecedented disruption, whereas mitigation in recurring disruptions can be easier. In rare disruptions, it is difficult to motivate overcapacity in scarce resources (e.g. ports, vessels and containers) and may also be difficult to anticipate warning signs that aid the detection of disruption (Bradley, 2014). Both disruptions also entailed varying degrees of fluctuating intensity, which for flexibility means that peak capacity is needed that will not be needed later.

Service flexibility is easier to apply when a disruption has a long duration. When durations were long, we found that long-term commitments and contracts became more prevalent, meaning that it became more difficult for shipping lines to make changes between trade lanes. With high uncertainty, shipping lines could prioritise certain lanes and take advantage of the situation. Although service flexibility existed for shipping lines, it was less available to other actors. Conversely, Özcan and Yumurtacı Hüseyinoğlu (2023) have reported that 3PLs used a flexibility strategy during the COVID-19 pandemic that involved switching to RoRo and intermodal transportation to overcome uncertainties, including port congestion. In our study, such flexibility was lowered due to the scope of the disruption, which influenced many transport segments.

The implications of characteristics of disruptions on temporal flexibility were relatively few. Although the global spread of COVID-19 resulted in less temporal flexibility, fewer possibilities to store containers and lower vessel utilisation, it was possible to change speed due to the scale of the disruption, and the extreme revenues footed the hefty bills for fuel. Furthermore, there were interdependencies between the various types of flexibility. Therefore, the effects of characteristics of disruptions on temporal flexibility can be somewhat expected. For example, Verschuur et al. (2020) have highlighted that production recapture (i.e. increasing work in ports to move delayed cargo) depends on the duration of the disruption.