Blogs

Should you invest in developing your own analytics tool for manufacturing, or use an off-the-shelf solution?

Understand the difference between these two manufacturing software solutions and why we recommend investing in predictive quality first.

Six Sigma has been the dominant approach to quality control in modern times, but does it hold up amidst changing tech in Industry 4.0?

Manufacturing quality management in 2023 requires a proactive approach. But what does that involve?

Modern manufacturing generates a lot of data. But how much data is needed to take advantage of predictive quality insights from machine learning?

Acerta announces series B funding led by BDC Capital Industrial Innovation and Thrive Venture Funds, with participation OMERS Ventures and StandUp Ventures.

The ethics of AI depend on the intent of the humans applying it. What does this mean for automotive manufacturing applications?

Detecting anomalies can help automotive manufacturers improve critical metrics like OEE, scrap rates, and first time through yield (FTT)

Today’s manufacturers are leveraging smart Industry 4.0 technologies, such as artificial intelligence and machine learning, the Industrial Internet of Things, cloud and edge computing, virtual reality and augmented reality.

When analyzing msnufacturing data, anomaly detection is the process of identifying and observing rare items, events, patterns, and outliers that differ significantly from a dataset’s normal behavior.