The Creation, Formalization, and Transfer of Expert Knowledge with Visual Analytics in Industrial Manufacturing Processes of Electrical Vehicles

Abstract

The 21st century will be heavily impacted by the capability to create value from recorded data of all kinds. In this regard, industrial manufacturing intuitions increasingly rely on new data-driven technologies, such as the Internet of Things or Machine Learning. In terms of data collection, manufacturing processes increasingly include sophisticated sensor equipment, which results in interconnected networks of manufacturing all assembling parts and producing data. However, manufacturing institutions currently face two challenges. First, large amounts of parts and hence data are produced during fully automated manufacturing processes. Second, due to the overwhelming amount of recorded data, it is particularly challenging to efficiently analyze manufacturing data. Hence, it is important to efficiently store and share gained knowledge from performed data analyses. Information visualization and Visual Analytics are two prolific branches of data analysis exploiting sophisticated visualization techniques to support the execution of analytical tasks and to store gained knowledge. This thesis looks at how data visualization approaches can help industrial manufacturing organizations to create value from large amounts of manufacturing data, as well as how to efficiently store and share knowledge from manufacturing data analyses. The goal is to understand how Visual Analytics can improve manufacturing processes in the context of knowledge management. Five Visual Analytics systems were designed, developed, and evaluated to tackle different domain problems that emerged from manufacturing setups. Findings from each system were used to carry out additional studies to enhance established theories of knowledge management. As a result, five success stories are provided of how Visual Analytics can significantly improve manufacturing processes and how knowledge can be efficiently created, formalized, and shared in an organization with Visual Analytics.

ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories

“…This form of externalizing knowledge is currently completely new at the BMW Group. It does not only help providers in easily performing their documentation tasks but also consumers involved in current analyses of manufacturing data or even outside the application domain [15]. Annotations of variables, for instance, can be queried by external stakeholders, such as suppliers in charge of developing specific manufacturing stations to evaluate how variables are related to specific regions of a part.…”

“…This knowledge, however, is provided by various stakeholder groups inside organizations, for example about the machine and product properties or data-driven analyses [13]. Hence, the efficient management of these distributed knowledge repositories is a key resource for the successful digitization of manufacturing processes [15], [59].…”

“…In this regard, Knowledge Management is a productive series of iterative and systematic exploitation and exploration activities, which aim to make information actionable and reusable [1], [16], [32]. Here, knowledge can be divided into two types: explicit knowledge that can be externalized easily, for example, in words or numbers; and tacit knowledge that is inherent to the individual [15], [18], [35], [52]. The latter is often not recognized by the individual as knowledge but rather expressed through action, commitment, and involvement, which renders it notoriously difficult to externalize [18].…”