Knowledge management and intellectual capital are essential factors in a company's success. An intrinsic element of knowledge management is knowledge exchange. Obtaining knowledge outside of a firm's boundaries, also known as open innovation, is necessary. Although these topics are well described in the literature, they seem less developed in a manufacturing context. The current economic context is favourable to the firm's merger and acquisition. This paper aims to understand how knowledge exchange can be supported in a multi-division manufacturing SME. During three months, thanks to a prototype platform, part of the knowledge exchanges and collaboration were tracked and evaluated in a Quebec aerospace multi-division manufacturing firm. Through this period, knowledge exchanges' quantity, success and collaboration symmetry were monitored. A semi-structured interview was conducted at the end of the experimentation with the members to gather the foundations and limitations of the prototype platform. This one proposes a supportive organizational structure and incentives which enhance knowledge exchanges among involved SMEs. This prototype platform approach should provide the foundations of more structured knowledge exchanges.

The digitization of technologies in product manufacturing results in the availability of large amounts of process and product data. To gain knowledge from this data and fully leverage its potential, its structuring and semantically annotation is essential. This allows preserving the context of data generation and makes the data machine-readable and interpretable. Contextualization is the key to generating FAIR (Findable, Accessible, Interoperable, Reusable) data. The documentation of research activities and provenance of generated data is usually achieved by protocols. However, there is often a tension between the desire to document data generation in a structured, semantically rich form and the need to design research and process parameters flexibly as experimental conditions change.

To resolve these contradictions, a dynamic model is described that allows to document research activities and implemented into a knowledge and research data management system to resolve these contradictions. The model allows a formal, semantic representation of research steps, parameters and gathered data, while also providing flexibility in the generation of protocol templates and individual experiments through the reuse of semantic building blocks. The approach is carried out within the context of a large collaborative research center, showcasing its use in managing and providing data for heterogeneous research tasks, documentation, and data types across interdisciplinary projects.

Knowledge management drives innovation within a group or organization. Once implemented through codification and personalization strategies, it becomes possible to add an additional set of tools to improve it. This set comes from the principles of gamification which aim to engage the members of the organization and motivate them to participate more effectively in the culture of knowledge transmission and sharing. This gamification is not sufficient on its own and requires to be based on already existing content. It is therefore to be seen as a real tool used to achieve the goals and objectives initially defined by an organization. Well-known gamification principles such as Yu-Kai Chou's Octalysis dynamics can be combined with Takeuchi and Nonaka's SECI matrix, which is central to the personalization strategy. Other tools such as serious games or simulations should also be taken into consideration as they are also based on game principles and can therefore be integrated into the knowledge management and transmission processes.