Data for Quality Control (Pilot)

Type of course:

Digital learning, Lesson

Language:

Duration:

10 minutes

Workload:

2 hours

Proficiency:

Intermediate

Target:

Manager, Professionals, Workers

SUMMARY

“How can the data you collect today shape a more reliable, efficient manufacturing process tomorrow?”

This course, Data for Quality Control in Manufacturing, dives into the essentials of using data to elevate quality control processes in industrial manufacturing. It’s designed for forward-thinking professionals who want to harness data to detect anomalies, improve product consistency, and prevent costly errors in real time.

Today’s manufacturing environments generate vast amounts of data—from images and video to sensor readings, machine logs, and operator notes. But how do you turn this data into actionable insights? In this course, we’ll break down the types of data critical for quality control, and explore real-world examples. Through practical examples and case studies, you’ll learn how companies are using data to monitor key quality metrics, detect anomalies, and even predict potential failures before they happen.

Ready to transform your approach to quality control? This course is your next step.

About The Author

Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using IoT and ML techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.

Learning outcomes

By the end of this lesson, students will be able to classify different data types used in manufacturing, including visual, numerical, categorical, text, and temporal data, and explain their relevance in monitoring product quality.
By the end of this lesson, students will be able to explain how each data type—such as sensor readings, camera images, and operator logs—contributes uniquely to quality control by providing specific insights for detecting anomalies or assessing product consistency.
By the end of this lesson, students will be able to identify common data sources in a manufacturing environment, such as cameras, sensors, and machine logs, and understand how these sources align with different data types to enhance quality control and early anomaly detection.