Use Case: Roasting Process Environment
Anomalyse worked with a medium-sized Food & Beverage business to help reduce maintenance and process issues across their roasting facilities.
An intense roasting process involving much heat, heavy machinery and steam presents this customer with a number of challenges daily. Wanting to improve plant efficiency by reducing unplanned downtime and optimising processes, this partner approached Anomalyse to explore a new way of understanding their assets, beyond SCADA monitoring and basic vibration sensors. By using the Anomalyse platform to monitor a number of key assets across the roast house, a key first step in the production process, a number of potential maintenance events have been avoided and the time taken to investigate process issues has been greatly reduced.
For more detail, see full use case write up.
What is Anomaly Detection? (And Why It Matters in Manufacturing)
In any manufacturing process, consistency is key. Machines are calibrated, processes are standardised, and outputs are expected to meet strict quality requirements. But what happens when something unusual slips through?
That’s where anomaly detection comes in.
What is Anomaly Detection?
Anomaly detection is a way of identifying things that don’t look “normal.” In the context of machine learning, it means using data to automatically spot unusual patterns, behaviours, or events - often in real time.
Instead of explicitly programming every possible fault or failure, machine learning models learn what normal looks like from historical data. Then, when something deviates from that pattern, it gets flagged as an anomaly.
Think of it like a seasoned operator who knows when a machine “just doesn’t sound right” - except now it’s powered by data and can scale across entire factories.
Example 1: Machine Maintenance (Predicting Failures Early)
Imagine a critical machine on a production line equipped with sensors measuring vibration, temperature, and rotation speed.
Individually, each reading might look fine. But together, they form a pattern that represents normal operation.
A machine learning anomaly detection system can:
Learn the typical combination of these signals during healthy operation
Detect subtle shifts across multiple sensors
Flag early signs of wear or failure
For example, a slight increase in temperature combined with a small change in vibration might indicate a bearing issue - something that wouldn’t trigger a simple threshold-based alert.
This allows maintenance teams to act early, reducing unplanned downtime and costly repairs.
Example 2: Process Consistency (Keeping Production Stable)
Consider a manufacturing process such as mixing materials or controlling a chemical reaction. The process depends on multiple variables: temperature, pressure, flow rate, and timing.
Even if each variable stays within its acceptable range, the relationship between them matters.
An anomaly detection system can:
Learn how these variables behave together during stable production
Identify unusual combinations that signal a drift in the process
Alert operators before product quality is affected
For instance, a slightly higher temperature might be fine, unless it happens alongside a lower flow rate. These kinds of interactions are difficult to capture with simple rules but are naturally handled by machine learning.
Example 3: Energy Consumption (Spotting Inefficiencies)
Energy usage is a major cost in manufacturing. Machines typically have predictable energy consumption patterns based on their workload and operating conditions.
By analysing data such as:
Power usage
Production output
Machine state
Environmental conditions
an anomaly detection system can learn what “normal” energy usage looks like.
If a machine starts consuming more energy than expected for the same level of output, the system can flag it. This might indicate:
Equipment inefficiency
Wear and tear
Suboptimal operating conditions
Catching these anomalies early helps reduce energy waste and improve sustainability.
Why Multivariate Machine Learning Matters
Traditional approaches often rely on simple rules, like “alert if temperature > 80°C.” But real-world systems are more complex than that.
The Anomalyse anomaly detection platform uses multivariate machine learning, which means it:
looks at many variables (features) at the same time
understands how those variables interact with each other
detects issues that wouldn’t be visible when looking at a single measurement
In other words, more data - when used effectively - leads to better insights.
A single sensor might not tell you much. But when you combine multiple sensors, you get a much richer picture of what’s really happening.
The Value of Anomaly Detection
In manufacturing, anomaly detection can help:
Improve product quality
Reduce waste and rework
Prevent equipment failures
Lower energy costs
Increase operational efficiency
Most importantly, it allows teams to focus their attention where it matters most, on the small number of events that could signal a bigger problem.
Anomaly detection isn’t about replacing human expertise - it’s about augmenting it. By combining data-driven insights and model explainability values with domain knowledge, manufacturers can spot issues earlier, act faster and run more reliable operations.
At Anomalyse, we help organisations turn their data into actionable asset insights, so anomalies don’t become problems.
Why AI and Machine Learning are the next big thing to close the Productivity Gap
How Anomaly Detection can be used to improve productivity in your site.
The UK's productivity is relatively low compared to other developed countries. According to the Organisation for Economic Co-operation and Development (OECD), the UK's productivity per hour worked was 15.1% below the average for G7 countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) in 2019. In fact, the UK's productivity gap has widened in recent years, and it has consistently been lower than countries such as Germany, France, and the United States.
In terms of manufacturing productivity specifically, the UK's performance has been mixed. Some sectors, especially in the high-tech industry such as pharmaceuticals, have higher productivity levels than other countries. However, the UK's overall manufacturing productivity remains lower than countries such as Germany and the United States.
To meet this challenge of bridging the productivity gap, many companies are now exploring how they can better use the data that is being captured from their plant assets. There is also a shift away from utilising dated rules-based approaches, as many look towards adopting solutions underpinned by AI and Machine Learning.
One form of machine learning which is growing in popularity is Anomaly Detection, where a model is trained to classify data as either ‘normal’ or ‘abnormal’ based on what has been previously observed. Anomaly Detection has many applications which can lead to improved productivity in manufacturing, including:
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Anomaly Detection can help detect unusual behaviour in machines, equipment, or products at an early stage, before they become a major problem. This helps to address issues quickly, minimising the impact on production.
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Anomaly Detection can help predict potential equipment failure by identifying patterns that are indicative of equipment failure. This can help manufacturers perform preventive maintenance and schedule repairs before a failure occurs, reducing unplanned downtime and increasing productivity.
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Anomaly Detection can help identify defects and abnormalities in products during production, enabling manufacturers to improve product quality and reduce waste.
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Anomaly Detection can help optimise the use of resources (e.g., raw materials, energy or labour) by identifying areas where resources are being wasted or underutilised.
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Anomaly Detection provides manufacturers with data and insights that can be used to improve production processes, leading to increased efficiency and productivity.
The use of Anomaly Detection in manufacturing helps improve overall productivity by reducing downtime, increasing product quality, and optimising the use of resources.
To hear more about how the Anomalyse platform can help you close the productivity gap using Anomaly Detection at your site, contact our team today.