Data analytics in quality 4.0: literature review and future research directions
Alexandros Bousdekis, Katerina Lepenioti, Dimitris Apostolou, Gregoris Mentzas
Abstract
The quality level in manufacturing processes increasingly concerns manufacturing firms, as they respond to pressures such as increasing complexity and variety of products, more complex value chains and shortened time-to-market. Quality management is becoming increasingly challenging as model variety, and highly complex products harbour the danger of distributing defective products in the market. Data analytics has started gathering the interest of quality researchers and practitioners, who investigate approaches, algorithms, and methods for supporting the manufacturing quality procedures in the context of Industry 4.0. This trend is facilitated by the wide expansion of sensory technology and the accelerated adoption of information systems by the manufacturing firms. Since quality and process control has been identified as one of the major challenges with a high potential of big data analytics, in this paper we investigated the manufacturing quality research field from a data analytics perspective. Specifically, we examined the existing literature, we provided clarity to the Quality 4.0 research field, we synthesized the literature review outcomes, and we identified the research gaps and challenges. On top of them, we proposed directions for future research.