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The projected annual growth of 6% until 2032 of the global reefer container fleet (1) clearly demonstrates the continued increase in the importance of perishable cargo. But it's not just the volume that's changing; the value and sensitivity are too. Refrigerated goods increasingly include pharmaceuticals, biologics, and other high-value products where even minor deviations can lead to total loss. Furthermore, controlled atmosphere containers add complexity even to conventional refrigerated goods, resulting in higher-value shipments due to optimised timing.
At the same time, global demand for temperature-controlled logistics is growing faster than that for general container freight. Consumption patterns are changing, and supply chains are becoming longer to offer goods year-round that were previously only available seasonally. This is leading to peak seasons in several parts of the world.
These developments are of paramount importance for the IT infrastructure of container terminals: reefer container transport is becoming a data problem. From their perspective, every refrigerated container is essentially a mobile sensor platform. It continuously generates data streams – temperature, humidity, setpoints, power status, alarms. However, in many terminals, this information is still underutilised, fragmented, or processed with a delay. The traditional model of manual checks and reactive interventions increasingly fails to meet the expectations of shipping companies and shippers, who demand real-time transparency and immediate response.
Terminal IT is becoming an essential enabler: Modern refrigerated container operations rely on reliable data flows between various systems: IoT-based monitoring solutions, terminal operating systems (TOS), monitoring platforms, and logistics systems. The challenge no longer lies solely in data collection, but also in contextualising, prioritising, and responding to the data in real-time (see also: Reefer management).
Breakdowns in reefer container transport are often due to technical malfunctions – such as a compressor failure, a defective sensor, or a power outage. However, the crucial factor is often a gap between processes, systems, and responsibilities at the transfer points. The Swedish Club’s refrigerated cargo analysis found that 69% of reefer claims occurred during the voyage, while 22% happened at the discharge port and 5% at the loading port. (2) Every transition carries its own risks: from warehouse to truck, from truck to terminal, from terminal to ship, and vice versa at the destination. At each of these points, responsibilities change. Data can be lost or delayed, and assumptions sometimes replace verified information.
Let's take, for example, the arrival of a reefer container on a truck at the terminal. If the identification of the container at the gate or the assignment of a parking space is delayed, valuable time is lost. If the connection process is not digitally confirmed—or worse, merely assumed—the container can remain without power longer than expected. A mechanical defect isn't even necessary here; the system simply didn't react correctly or quickly enough.
Of course, it's also possible that the container functions perfectly and the target values are met throughout the entire cold chain. It's just that they were incorrectly defined from the outset. Often, the error is only discovered at the end of the journey, when the goods are already defective or overripe.
Reefers send out alarms for good reason, but not all are equally important or urgent. In many operations, alarm lists become long, and prioritisation becomes difficult if it isn't defined. Operators are forced to prioritise a large number of alarms under time pressure. The predictable result: critical alarms may be overlooked.
Organisational boundaries add further complexity. Different actors have access to the data on different sections of the route. If an overview of the transport's progress so far is unavailable, everyone only sees their own section of the cold chain.
Added to this is the human factor. If manual processes and paper-based controls are still in use, the data is often not sufficiently reliable. Even well-trained teams make mistakes, especially in high-throughput environments. A missed inspection, a delayed response, or a simple misunderstanding can be enough to jeopardise the cold chain.
These are all systemic weaknesses. Container terminals are the place where errors can be identified and corrected or mitigated. This requires modern systems that close the gaps: seamless data flows (including access to trip logs documenting the journey's progress so far), real-time event tracking, and intelligent alarm prioritisation (see also: Reefer container temperature monitoring).
If there's one thing that distinguishes a controlled cold chain from a vulnerable one, it's monitoring. Not occasional checks, not manual logs, but reliable transparency about the processes inside every refrigerated container – at all times.
Without continuous monitoring, reefer container transport relies largely on individual checkpoints, both in terms of location and time. If temperature data is only recorded manually, reefers are essentially left on their own for hours, a period during which temperatures can rise relatively quickly under the scorching sun in the yard.
Given today's reefer volumes, the approach of monitoring at fixed intervals is not scalable and also doesn't correspond to the risk profile of today's cold chain. This requires a different model: one in which data is automatically collected, transmitted in near real-time, and continuously analysed.
The temperature is the central key parameter, but it is only one part of the overall picture. Besides the power status, which is essential for all reefer types, monitoring oxygen and carbon dioxide levels is indispensable for controlled atmosphere containers.
The sometimes overwhelming number of alerts can be mitigated by prioritisation. Simply distinguishing between warnings and actual alarms is helpful in this regard. This clarifies what is important and urgent, and which problems can wait a little longer but shouldn't be completely ignored.
The crucial difference to conventional monitoring lies in the shift from reactive to proactive monitoring: In a reactive model, an error occurs first, and then the system—or the operator—reacts. In a proactive model, deviations are detected early, patterns are identified, and measures are taken before thresholds are exceeded. This is where the value of data begins.
From an IT perspective, this presents new challenges. Data from various sources must be integrated: different reefer manufacturers, monitoring systems, and terminal systems. Formats must be standardised, timestamps synchronised—the data must be both accurate and up-to-date.
Simply displaying data is a thing of the past; today, the system must directly support workflows: alerting the right people, enabling rapid diagnostics, and tracking responses.
And once deployed, the data forms a treasure trove of historical information, providing a foundation for continuous improvement. It can uncover recurring problems, process inefficiencies, or patterns related to specific cargo types or routes.
Reefer monitoring at the container terminal is a critical operational capability that directly impacts risk, performance, and competitiveness. For IT leaders, this is a watershed moment. The question is no longer whether to invest in monitoring, but how to make it robust, integrated, and scalable enough to meet the demands of modern reefer container transport.
The focus of IT is shifting from system administration to data orchestration.
Integration
Despite standardisation efforts such as ISO 10368, whose adoption is voluntary and which is often considered incomplete, interoperability issues across the industry are not yet fully resolved. There are still different data formats, communication protocols, and levels of detail. Bringing all this data together in a unified view requires more than simple interfaces – it requires a well-designed data architecture. If this doesn’t happen, the result is a fragmented data view: multiple dashboards, inconsistent timestamps, and gaps in the data chain.
Context
The raw data – temperature readings, alarm codes, power status – are only meaningful for the current moment and that single unit. Only when combined with operational data such as container location, dwell time, ship schedules, cargo type, and priority levels do they unfold their true potential. This contextualisation transforms them into well-founded decision support. For example, the same temperature deviation might trigger immediate action in one case for two differently sensitive loads, and in the other case, it might remain under monitoring. Without context, systems cannot differentiate—and operators are left to make that judgement manually.
Event management
On a large terminal with thousands of reefer slots, dozens, if not hundreds, of events can occur every hour. Most are likely routine, but some are critical. The IT department's task is to identify, prioritise, and correctly route these critical events without overloading operations. This requires intelligent filtering, rule-based logic, and increasingly, machine-assisted decision-making.
Digital twins and AI
The more data is available, the more practical new big data technologies become. A digital twin creates a virtual representation of a container and combines real-time data with models of expected behaviour. AI can analyse historical and current data to recognise patterns, predict failures, and identify anomalies that wouldn't trigger conventional alarms. These technologies require high data quality and consistency. Without reliable input data, even the most advanced models will deliver unreliable results.
Cybersecurity
The increasing interconnectedness of systems expands the attack surface. Integration with external platforms, remote access capabilities, and IoT systems creates new vulnerabilities that extend beyond traditional terminal IT environments. Cybersecurity must therefore be integrated into system design from the outset. This includes secure communication protocols, strict access controls, and continuous monitoring of system activity.
Remote access
Efforts to provide more insights for logistics partners and customers introduce complexity. Permissions must be clearly defined, actions traceable, and conflicts between stakeholders avoided. IT's role is to enable access while ensuring control and transparency.
Sustainability: Energy management
Refrigerated container operations are energy-intensive and therefore a key focus of sustainability initiatives. Terminals are also increasingly required to monitor and optimise energy consumption – for both cost and environmental reasons. This includes detailed recording of electricity consumption, identifying inefficiencies, and balancing loads within the infrastructure. However, energy data is only useful for meaningful optimisation when combined with information such as container type, dwell time, and utilisation. IT systems must therefore bridge this gap and enable terminals to reduce energy consumption without compromising cargo safety.
A refrigerated container (reefer) is an actively cooled unit with its own refrigeration system. As long as it is powered, it maintains a precise temperature regardless of external conditions.
An insulated container, on the other hand, does not have an active cooling system. It relies solely on insulation to slow down temperature fluctuations. It can maintain the internal temperature for a limited time, but it cannot actively cool or heat the cargo. Its performance depends heavily on the initial cargo temperature and the duration of transport.
A non-operational refrigerated container (NOR) is generally an intentional and planned condition. The container's refrigeration system is deliberately switched off, and the container is used like a standard dry container. This is common practice when relocating equipment or transporting non-temperature-sensitive cargo. Everyone involved understands that the container is not designed for temperature control.
A malfunctioning refrigerated container, on the other hand, indicates a problem. The system should be running—but it isn't. This could be due to a technical defect, a power outage, or a faulty component. In this case, the container can no longer maintain the set conditions, and if it is transporting temperature-sensitive cargo, there is a risk of cargo loss.
The success of reefer container transport today is defined not only by the equipment but also by data generation and utilisation. For IT at container terminals, the focus is shifting from simply monitoring containers to understanding the processes, detecting harmful events, and taking the right action at the right time.
This requires integration, context, and intelligent event management. Those who implement this correctly evolve from reactive processes with blind spots to controlled processes where even minor problems are not overlooked. In a networked cold chain, transparency alone is not enough. The real advantage lies in transforming this transparency into control.
Delve deeper into one of our core topics: Reefer Monitoring
ISO 10368 is an international standard for remote condition monitoring of freight thermal containers, especially reefer containers. It defines the information, interfaces, data-logging formats, and message protocols needed for a central monitoring system to communicate with remote devices from different manufacturers. In practice, this standard helps refrigerated containers transmit operating data such as temperature, alarms, and equipment status in a consistent way, so terminals, carriers, and service providers can monitor reefers across systems and brands. The 2006 edition is the published version currently referenced in many technical discussions; the 1992 edition was withdrawn. (3)
Load balancing refers to the process of distributing electrical power demand evenly across a system to prevent overloads, optimise efficiency, and reduce peak costs. In power grids, it coordinates generation and consumption to maintain frequency stability, using techniques like demand response, energy storage, and dynamic scheduling. This avoids blackouts, minimises curtailment of renewables, and lowers reliance on expensive peaker plants. (4)
References:
(1) https://www.qyresearch.com/reports/6189346/reefer-container-fleet
(2) https://www.swedishclub.com/uploads/2023/12/TSC_Container_focus_Reefers_2022_WEB.pdf
(3) https://www.containerhandbuch.de/chb_e/wild/index.html
(4) Ela, Erik et al. (2017). Grid Integration of Variable Renewable Energy. Routledge.
Note: This article was partly created with the assistance of artificial intelligence to support drafting.