Quality Data Management in the Next Industrial Revolution : A Study of Prerequisites for Industry 4.0 at GKN Aerospace Sweden

The so-called Industry 4.0 is by its agitators commonly denoted as the fourth industrial revolution and promises to turn the manufacturing sector on its head. However, everything that glimmers is not gold and in the backwash of hefty consultant fees questions arises: What are the drivers behind Industry 4.0? Which barriers exists? How does one prepare its manufacturing procedures in anticipation of the (if ever) coming era? What is the internet of things and what file sizes’ is characterised as big data? To answer these questions, this thesis aims to resolve the ambiguity surrounding the definitions of Industry 4.0, as well as clarify the fuzziness of a data-driven manufacturing approach. Ergo, the comprehensive usage of data, including collection and storage, quality control, and analysis. In order to do so, this thesis was carried out as a case study at GKN Aerospace Sweden (GAS). Through interviews and observations, as well as a literature review of the subject, the thesis examined different process’ data-driven needs from a quality management perspective. The findings of this thesis show that the collection of quality data at GAS is mainly concerned with explicitly stated customer requirements. As such, the data available for the examined processes is proven inadequate for multivariate analytics. The transition towards a data-driven state of manufacturing involves a five-stage process wherein data collection through sensors is seen as a key enabler for multivariate analytics and a deepened process knowledge. Together, these efforts form the prerequisites for Industry 4.0. In order to effectively start transition towards Industry 4.0, near-time recommendations for GAS includes: capture all data, with emphasize on process data; improve the accessibility of data; and ultimately taking advantage of advanced analytics. Collectively, these undertakings pave the way for the actual improvements of Industry 4.0, such as digital twins, machine cognition, and process self-optimization. Finally, due to the delimitations of the case study, the findings are but generalized for companies with similar characteristics, i.e. complex processes with low volumes