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BPM Analytics

Anomaly Detection for Proactive Risk Mitigation in Manufacturing

Manufacturing is a domain where precision, efficiency, and quality are of paramount importance. However, even the most sophisticated production systems are susceptible to occasional disruptions—whether caused by equipment malfunctions, production defects, or unforeseen variables in the supply chain. Anomalies such as these can increase costs, impact downtime, reduce production efficiency, and more.


What is anomaly detection? 

It is a powerful tool that leverages data analytics and ML (Machine Learning) to identify outliers and irregular patterns in real time. Anomaly detection systems continuously monitor production processes and alert manufacturers about potential issues before they escalate. This facilitates quicker response times and more proactive management.


How Machine Learning-Based Anomaly Detection Works?

Machine learning (ML)-based anomaly detection is a powerful technique to identify unusual patterns, behaviours, or events that deviate from the expected norm in a dataset. These anomalies can indicate equipment failure, production defects, supply chain disruptions, etc. ML-based anomaly detection uses data-driven approaches to spot irregularities.


ML-based anomaly detection goes through the following steps:

Step 1: Data collection and preprocessing

The first step in ML-based anomaly detection is collecting large volumes of data from the manufacturing process. This data could include sensor readings, production statistics, machine logs, environmental factors, etc. The data is preprocessed to ensure it’s clean, normalised and formatted properly.


Step 2: Feature engineering

Feature engineering is the process of transforming raw data into meaningful attributes that can be used by ML algorithms.


Step 3: Training the ML models 

At this stage ML models are trained to detect anomalies using the following techniques:

  • Supervised Learning: This model distinguishes between normal and anomalous behaviour based on historical examples. 
  • Unsupervised Learning: In most manufacturing scenarios, anomalies may be rare and not always labelled. Unsupervised learning techniques are therefore more common. These models identify unusual patterns by learning the inherent structure of the data without any prior knowledge of anomalies.
  • Semi-supervised learning: It is an approach that works with a large set of unlabelled data and a smaller amount of labelled data. The model is trained mostly on non-anomalous data and then used to identify instances that deviate significantly from the learned patterns.

Step 4: Anomaly detection

Once the model is trained, it can detect anomalies in real-time or on historical data. The model will output a score or probability indicating how likely a given data point is to be anomalous. 


Step 5: Real-time anomaly detection and action

The model is deployed to continuously monitor incoming data in real time, providing instant alerts when it detects something unusual. These alerts may trigger automatic corrective actions, such as shutting down a machine, adjusting production parameters, or notifying maintenance personnel. Machine learning models can also be set up to evolve and adapt over time as new data is collected, improving the accuracy of detection as the system learns from fresh patterns of normal and anomalous behaviour.


Step 6: Feedback loop and continuous improvement

If an anomaly is correctly identified and resolved, the feedback can reinforce the model's learning. If the anomaly detection is inaccurate the system can be adjusted and retrained to improve its performance.


Benefits of ML-based anomaly detection in manufacturing 

Increased production efficiency: ML-based anomaly detection streamlines operations for maximum throughput by identifying bottlenecks within the manufacturing processes.

Enhanced quality control: Anomaly detection helps spot defects in products before they leave the factory floor. This greatly enhances product quality.

Optimised predictive maintenance: ML algorithms forecast possible machine failures which facilitates performing maintenance preemptively thereby reducing costly downtime.

Streamlined supply chain: Supply chain operations can be refined with anomaly detection which enhances logistics and inventory management.

Minimised production costs: Production costs can be reduced by leveraging ML-based anomaly detection because it not only identifies inefficiencies but also pinpoints wasted resources.

Optimised energy utilisation: Anomaly detection systems monitor energy consumption across the manufacturing process and identify wasteful energy power usage patterns.

Adaptive manufacturing processes: ML-based anomaly detection facilitates swift adjustments in the manufacturing process which helps meet changing customer demands and fluctuations in the market.

Improved risk management: Machine Learning algorithms can predict and mitigate potential risks which ensures seamless manufacturing with minimal operational disruptions.


In conclusion

Machine Learning-powered anomaly detection has disrupted traditional anomaly detection systems. It has streamlined manufacturing processes bringing in higher efficiencies and enhanced product quality.


How can Infosys BPM help?

Infosys BPM offers customised anomaly detection services to clients in the manufacturing sector. We leverage the power of data with domain expertise and help clients streamline their manufacturing processes.


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