Data Driven Quality Control

Type of course:

Digital learning, Lesson

Language:

Duration:

15 minutes

Proficiency:

Intermediate

Target:

Manager, Professionals, Workers

SUMMARY

Quality control is at the heart of manufacturing excellence, ensuring that products meet the highest standards. But how do we move beyond traditional methods to harness the power of data?

In this lesson, you will explore fundamental quality control principles and how data-driven approaches revolutionize manufacturing processes. You’ll learn the key steps of a data-driven quality control project—from defining business goals to analyzing data and assessing model effectiveness. Additionally, we’ll uncover the challenges that come with implementing these modern systems, including data quality concerns and organizational resistance, and discover strategies to overcome them.

Whether you’re new to quality control or looking to integrate data-driven insights, this lesson will equip you with the knowledge to enhance efficiency, reduce defects, and drive continuous improvement in manufacturing.

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 define key quality control concepts and objectives, distinguishing between traditional quality control methods and data-driven approaches, and explain their significance in manufacturing processes.
By the end of this lesson, learners will be able to explain the standards steps of a data driven project, namely identifying business goals, preparing and analyzing relevant data, and evaluating the effectiveness of data-driven models, in the realm of quality control.
By the end of this lesson, learners will be able to identify common challenges and limitations associated with implementing data-driven quality control systems, including data quality issues and resistance to change, and propose strategies to mitigate these challenges effectively.