Data for Quality Control

Type of course:

Digital learning, Lesson

Language:

Duration:

15 minutes

Proficiency:

Intermediate

Target:

Manager, Professionals, Workers

SUMMARY

In modern manufacturing, data is the key to maintaining high product quality and minimizing defects. But what types of data matter most, and how do they contribute to effective quality control?

This lesson will guide you through the different data types used in manufacturing, from numerical sensor readings to visual inspections captured by cameras. You’ll discover how each data type plays a crucial role in monitoring product consistency, identifying defects, and predicting potential failures. We’ll also explore common data sources—such as machine sensors, operator logs, and real-time imaging systems—and how they work together to enhance early anomaly detection and process optimization.

By the end of this lesson, you’ll have a deeper understanding of how manufacturing data fuels smarter quality control strategies, helping businesses achieve higher efficiency and reliability.

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 Internet of Things (IoT) and Machine Learning (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, learners 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, learners 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, learners 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.