The Industry 4.0 revolution is shifting the way that quality engineers, managers, and product developers must think about quality control. Super connectivity, IoT, and big data have enabled a transition from traditional “voice of the customer” surveys to the “voice of Big Data,” which communicates descriptive information from real customers about how they use products. Quality in the Era of Industry 4.0: Harnessing Data Analytics for Quality Engineering Applications guides readers on how that data can be leveraged to optimize products during use, to anticipate and mitigate the consequences of likely failures, and to build better and less expensive products in the future.

In a concise, straightforward style, this book offers readers a comprehensive framework for new quality management methods under Industry 4.0. This includes new techniques like using real-world data to improve product fit and performance, leveraging connectivity to make products responsive to changing needs and use cases, and drawing upon modern manufacturing to make cost-effective, bespoke solutions that can be produced more efficiently. Case examples featuring applications from the automotive, mobile device, home appliance, and healthcare industries are used to illustrate how IoT-enabled product usage data can be used to bench mark the product performances, durability, and detect design vulnerabilities. Automated Product Lifecycle Management, Predictive Quality Control, and defect prevention using technologies like smart factories, IoT, digital twins, machine learning, and sensors are also covered.