Anomaly Detection Use Cases and Benefits in Manufacturing

Artificial intelligence is driving a fundamental shift in manufacturing, propelling the industry toward greater efficiency, resilience, and innovation. Over the last decade, ML- and AI-driven solutions have rapidly moved from experimental to essential and investment in AI-driven manufacturing has skyrocketed. The global market of AI in manufacturing reached $5.94 billion in 2024 and is estimated to grow to $231 billion by 2034. 

Among the spectrum of AI technologies, anomaly detection is one of the most demanded. ML-powered anomaly detection systems are redefining quality, maintenance, and safety standards. What benefits do they provide to manufacturers, and how do they impact the business? 

How Does Anomaly Detection Work? 

Anomaly detection leverages the adaptive learning capabilities of machine learning (ML) to distinguish between normal operations and atypical events. Such systems “self-improve” with time, by learning through the received and processed data sets. These intelligent mechanisms can be built using several approaches: 

• Supervised learning: Models are trained on pre-labeled data in which anomalies are already marked, learning to differentiate between standard and abnormal outcomes.  
• Unsupervised learning: Based on unlabeled examples, algorithms uncover hidden patterns and flag outliers independently. This method is ideal for detecting unknown types of anomalies. 
• Semi-supervised learning: A hybrid method that uses a small set of labeled data and applies those patterns to a broader, unlabeled dataset, striking a balance between guidance and adaptability. 

Anomaly Detection Use Cases 

By selecting the appropriate ML method, you can address a range of complex production challenges. See the real-world anomaly detection examples and the practical value they bring. 

Quality Assurance 

Maintaining consistently high product standards is critical for customer satisfaction and brand reputation. Before the products reach customers, they should be inspected for quality, structure, and packaging. Human inspectors, while experienced, can be inconsistent and prone to errors. Automatic anomaly detection systems powered by high-resolution cameras and ML models monitor products throughout the production line, instantly flagging defects, mislabels, or irregularities in structure or packaging. Such systems minimize the risk of shipping subpar goods and allow manufacturers to address issues in real time. 

Predictive Maintenance 

Manufacturing downtime is becoming more expensive. A Siemens report states that the cost of one hour of unplanned plant downtime (all sectors combined) has doubled over the last five years. For the automotive sector, the idle production line costs around $695 million per year in 2024. In a heavy industry plant, it was $59 million, which is 1.6 times higher than in 2019. 

Anomaly detection can predict failures before they cause damage by analyzing data from IoT sensors, control systems, and operator inputs. When non-typical readings or patterns appear, the system triggers early warnings, empowering teams to conduct targeted maintenance and avoid unscheduled stoppages. 

Optimizing Equipment Utilization 

Identifying inefficiencies in equipment use is vital for maximizing productivity. ML-driven anomaly detection uncovers periods of underutilization or overload, giving manufacturers all necessary input to rebalance workloads, schedule preventive interventions, and optimize throughput. The result is increased uptime and lower maintenance costs. 

Workplace Safety Enhancement 

In the USA, manufacturing takes the third place by the number of nonfatal injury and illness cases  and the fifth place by the number pf deaths among all the industries. 

Anomaly detection solutions help prevent these alarming safety statistics. By analyzing video streams from the production floor, computer vision algorithms can automatically detect unsafe behaviors – like skipping equipment checks, not wearing protective gear, or engaging in improper actions – through object tracking, vehicle monitoring, and facial recognition. Such real-time monitoring helps identify risks before they cause harm. 

Another way to deal with dangerous behavior effectively is by using wearable IoT devices. They continuously track employee health data, enabling ML models to spot the sights of fatigue or illness. When anomalies are detected, automated alerts prompt supervisors to intervene, ensuring workers can take breaks or seek medical assistance when needed. 

Financial Fraud Detection 

According to the 2024 report by the Association of Certified Fraud Examiners, on average, organizations lose 5% of their annual revenue to fraud. In manufacturing, median losses have soared to a stunning $267,000 compared to $177,000 in 2022. Conventional fraud prevention methods often fall short, while anomaly detection technologies offer a more effective way to identify fraud and reduce these costly losses. 

With machine learning at its core, a powerful 24/7 monitoring system can oversee the activities vulnerable to breaches within the organization. By detecting unusual behaviors, these systems promptly flag suspicious incidents for management review. For example, 

• Log-on monitoring uses behavior modeling to identify irregular access patterns to financial data. These can be unfamiliar data requests, excessive log-in attempts, or logins from unexpected locations. The system triggers immediate alerts on such atypical activities. 
• Network intrusion detection uses ML to access security logs and behavior patterns, quickly spotting and responding to potential breaches, thus minimizing malware threats and protecting company finances. 
• Detecting abnormal financial activity helps address fraud by identifying false insurance claims, money laundering, out-of-pattern expenses, or suspicious transactions. The system flags anything that doesn’t match established norms and worker’s roles. 

Anomaly Detection in Manufacturing: Key Benefits 

Implementing anomaly detection in manufacturing is a strategic move that delivers measurable business value across operations. Here are the key benefits brought by such innovations. 

Quality Control 

The global automated industrial quality control market is steadily growing. It was $0.47 billion in 2024 and is projected to grow to $0.87 billion by 2033. 

Companies invest more in automation for quality control because it leads to better products. Anomaly detection across all the stages of the manufacturing process minimizes shipping details and brings higher customer satisfaction. 

Minimum Downtime 

As mentioned previously, unplanned downtime impacts companies far beyond just financial loss. It can damage reputation and cause delays in production. Unlike the traditional reactive approach, predictive maintenance transforms operations by cutting downtime by up to 15%, boosting labor productivity by nearly 20%, and lowering new equipment by 5% 

Optimized Efficiency 

All ML-driven anomaly detection use cases highlighted above help boost overall enterprise productivity. Improving equipment efficiency, minimizing idle time, strengthening safety, and ensuring product quality – all these improvements collectively raise operational performance across the board. 

Cost Savings 

Adopting anomaly detection technologies may require significant upfront investment. However, the long-term value far outweighs the initial cost. Take predictive maintenance, for instance – it delivers substantial savings, helping manufacturers save billions over time. 

What Could Manufacturers Save with Predictive Maintenance? 

Since private manufacturing sees an incidence rate of 3.1 per 100 full-time employees, prioritizing workforce safety is essential for reducing costs. Automated financial fraud detection also plays a crucial role in minimizing unexpected expenses, especially since fraud can be particularly damaging in the manufacturing industry. 

Conclusion 

Machine learning for anomaly detection has demonstrated its effectiveness across multiple aspects of manufacturing. If you haven’t yet integrated such solutions into your operations, it is an ideal time to start. Backed by years of expertise in advanced manufacturing solutions, MentorMate team is ready to support you with: 

• Anomaly detection ML solution development and customization
• ML algorithm enhancement
• Manufacturing digitalization and automation advisory

Our team is here to guide you every step on the way to safe and efficient production through mature tech advancements.