How to Detect Manufacturing Anomalies

In this era of inflation, volatile supply chains, labor shortages, and increased environmental and compliance concerns, manufacturers must control what they can to find new or expanded revenue streams, reduce operating costs, evolve capabilities, and reduce risks.

Anomaly detection, when implemented with a strategic partner with deep domain expertise, is a step towards resilience and data savviness that will serve any manufacturer well. In fact, manufacturers with mature anomaly detection capabilities achieve substantial operational cost reductions from reduced defect and scrap rates, prevention of unplanned equipment downtime, and even optimized energy consumption.

Typically, most data streams simply confirm normal operations and provide no new actionable information. However, when data shows an anomaly, it can indicate something has changed or is behaving abnormally—leading to actionable insights about how to correct any issues before they become widespread or time-consuming.

Download this whitepaper to learn about industry solutions, applications, and autoencoder machine learning models for detecting anomalies.

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