Table of contents:
A reefer alarm is triggered when the system detects a condition that requires attention. For instance, if a container's temperature moves outside the predefined range or the system detects a condition requiring attention, the alarm gives the terminal an opportunity to intervene before the cargo sustains damage. This immediate signal is essential for technicians and operations teams.
However, an alarm represents only a snapshot in time. It signals that something has happened “right now”. Yet, it does not necessarily reveal whether this is an isolated incident, a recurring issue, or the latest sign of a trend that has been developing for days.
Knowing this distinction is crucial. A unit that triggers an alarm might require nothing more than a simple check. Conversely, another unit might exhibit slight temperature fluctuations, unusual power consumption patterns, or repeated warnings over an extended period without ever triggering a critical alarm. Viewed in isolation, none of these signals appears urgent. Together, however, they might reveal that the unit is no longer behaving as expected.
This is precisely where the approach begins to evolve reefer management from purely reactive alarm handling to a proactive strategy. Instead of simply asking, “Which reefer containers are currently reporting an alarm?”, terminals can also ask, “Which containers are showing signs of altered behaviour?”
Answering this question requires reliable data and sufficient historical context. The result is a more targeted approach: technology enables earlier detection of potential deviations and prioritisation by risk, allowing technicians to be deployed where their attention is most valuable.
See also: Cold chain procedures and Reefer KPIs
A single temperature reading reveals little about why a reefer behaves a certain way. The same temperature deviation can have vastly different implications depending on the setpoint, the unit's operating history, its power status, and any alarms that have been triggered.
Historical data makes these relationships visible. Temperature data shows how consistently a reefer maintains the required conditions. Setpoint data provides the reference point against which you can evaluate performance. Reefer alarm history offers information on events already detected by the reefer's control system. Power and energy data can provide another piece of the puzzle, particularly when changes in electrical behaviour coincide with temperature deviations.
Time links these individual signals, making it possible to determine whether a deviation lasted minutes or hours, occurred once or repeatedly, and whether similar incidents had already taken place.
Consider a reefer that consistently shows a slight temperature rise after being connected to the power supply. This might simply reflect a normal operating cycle. However, if the same device increasingly struggles to return to the setpoint, repeatedly issues warnings, and exhibits a change in energy behaviour, this combination becomes more significant than any single measurement.
Over time, a picture emerges of what constitutes expected behaviour—not as a fixed, discrete value, but as an expected range of behaviour under specific operating conditions (see also: What is a cold chain monitoring system observing?).
Reefer alarm systems typically rely on fixed thresholds: if a reading exceeds a limit, an alarm is triggered. Modern systems also allow for warnings to be issued when a specific value is reached before the threshold itself is hit. This is both sensible and necessary for distinguishing between situations requiring an immediate response and those where continued monitoring of the unit suffices.
Detecting gradual changes in behaviour requires a comprehensive overview. Instead of merely checking whether a value has exceeded a predefined limit, analytical methods can examine whether a refrigerated container is behaving differently than would normally be expected.
This expectation can be based on the specific unit's history, comparable operating conditions, or patterns observed across the entire fleet. A minor temperature fluctuation might be insignificant in one situation yet unusual in another. Similarly, a single warning might require little attention, whereas repeated warnings—combined with increasingly unstable temperature control—could warrant an inspection.
This is where machine learning and other analytical methods come into play. Given sufficient and reliable historical data, algorithms can identify relationships and recurring patterns that would be difficult to spot simply by looking at individual readings or manually reviewing lengthy event logs.
The ultimate goal is to identify anomalies that warrant investigation. The system does not replace technical judgment; rather, it helps apply that judgment effectively, giving those responsible more scope to take timely action (learn more: Remote reefer monitoring system: How to integrate it?).
Predictive intervention begins with reliable visibility.
If reefer data is captured only during manual inspections or remains fragmented across disparate systems, the historical context needed to identify meaningful patterns is lacking. A more proactive approach requires continuous access to signals that describe each unit's operating status.
Automated reefer monitoring is the foundation. Temperature, setpoint, energy status, alarms, and other control data are continuously recorded—rather than only when someone manually checks the unit. Cloud-based monitoring makes this information available over time, thereby creating a history for each unit.
This historical data becomes even more valuable when you link different data points. For instance, you can view a temperature deviation in the context of the setpoint and alarm history. You can analyse changes in temperature behaviour alongside power interruptions or energy consumption. The chronological sequence of events reveals whether the issue is a one-off problem, a recurring event, or a worsening problem.
Remote access to control data introduces another practical dimension. If a unit requires inspection, technicians can review the relevant operating data before even arriving on site. Modern systems also allow setpoints to be adjusted remotely. In addition, the system supports automated workflows—for example, by monitoring the duration of a power disconnection and triggering an escalation if the expected reconnection timeframe is exceeded.
Integration with the Terminal Operating System (TOS) goes a step further. Reefer container status need not remain in an isolated monitoring environment; relevant information can be integrated into the terminal's operational workflows. This makes it possible to link reefer status with operational processes such as arrival, storage and pickup, while also supporting workflows for technician intervention.
The same architecture is suitable for terminals with varying levels of digitalisation. A smaller depot might initially monitor a limited number of reefers, while a larger terminal can extend monitoring across a much larger fleet and use the accumulated data for more complex analysis.
Analytical methods constitute another layer. Based on historical data, it is possible to define expected behaviour, identify recurring anomalies, and highlight units requiring attention. Over time, machine learning methods can support more informed recommendations by recognising patterns that are difficult to capture through rigid rules alone.
The architecture can therefore best be understood as a sequence:
Continuous monitoring → context-aware data → historical patterns → anomaly detection → risk prioritisation → human intervention.
The more closely these elements are interconnected, the easier the transition becomes from simply knowing that a reefer has triggered an alarm to understanding which units may require attention—even before a serious alarm occurs.
The more a terminal relies on data for operational decisions, the less room there is for unreliable information. This is especially true when AI is employed. While AI can process far larger volumes of data than a human, it cannot always reliably compensate for missing, inconsistent, or misattributed information. If temperature readings are incomplete, timestamps are unreliable, or data is assigned to the wrong reefer container, a sophisticated model may—in a worst-case scenario—simply generate a misleading recommendation more quickly.
Certain fundamental prerequisites are crucial; the terminal needs to know:
Only when continuous temperature readings and information regarding events—such as alarms or power supply issues—are available do datasets become historically comparable, allowing changes to be meaningfully identified.
Furthermore, it must be recognised that temperature fluctuations do not always require the same treatment; the surrounding circumstances are decisive. Without sufficient context, an analysis system might mistakenly classify normal operational fluctuations as anomalies—or overlook a significant combination of signals.
When all these prerequisites are met, analytics can help transform vast amounts of operational data into valuable insights: fewer aimless inspections, better prioritisation, and the early detection of behavioural patterns requiring closer investigation.
While technologies may become increasingly sophisticated, the fundamental premise remains unchanged: good recommendations rely on good data.
Not every deviation requires the same response. A brief, isolated fluctuation might call for closer observation rather than immediate action, whereas recurring deviations, persistent temperature instability, or multiple warning signs may warrant a more thorough review. Data-driven monitoring helps create a prioritised inspection list based on factors such as frequency, duration, severity, and operational history. This lets technicians focus on cases that need investigation, rather than inspecting every refrigerated container with the same level of urgency.
Possible criteria are:
Predictive intervention does not begin with an algorithm. It begins with reliable, continuous data.
When data regarding temperature, setpoints, alarms, performance, and energy usage are collected over an extended period and linked to specific refrigerated containers, patterns emerge that a single alarm cannot reveal. Analytical methods help distinguish between isolated incidents and recurring or evolving behavioural trends, thereby supporting a more targeted inspection strategy.
The goal is not to predict every failure with certainty. Rather, the aim is to give technicians better information early on: Which units require special attention? Why might they be behaving unusually? How has their behaviour changed over time?
Despite increasingly sophisticated predictive techniques, the fundamental principle remains simple: better context enables better intervention.
Delve deeper into one of our core topics: Reefer monitoring
Analytical methods are systematic techniques for extracting insights from data to support modelling, prediction, and decision-making. They include statistical analysis, exploratory data analysis, feature engineering, dimensionality reduction, clustering, classification, regression, and evaluation metrics. These methods prepare data, select and tune models, and validate results, forming the quantitative foundation for machine learning and AI systems. (3)
An event log is a time-ordered record of discrete events generated by systems, applications, or devices. Each entry typically includes a timestamp, a source, an event type or identifier, and a description of what happened. Event logs support monitoring, auditing, troubleshooting, and security analysis, and they also serve as input for process mining, where they reveal how business processes actually execute. (4)
References
(1) https://www.sciencedirect.com/science/article/pii/S0306437921000247
(2) https://link.springer.com/article/10.1186/s41072-022-00110-z
(3) Ghavami, Peter (2019): Big Data Analytics Methods: Analytics Techniques in Data Mining, Deep Learning and Natural Language Processing. De Gruyter.
(4) van der Aalst, Wil M.P.; Carmona, Josep (2022): Process Mining Handbook. Springer.
Note: This article was partly created with the assistance of artificial intelligence to support drafting.