Digital Technologies for Enabling Smart Production : Examining the Aspects of Selection and Integration

Abstract: With the development towards Industry 5.0, manufacturing companies are developing towards smart production. In smart production, data is used as a resource to interconnect different elements in the production system to learn and adapt to changing production conditions. Common objectives include human-centricity, resource-efficiency, and sustainable production. To enable these desired benefits of smart production, there is a need to use digital technologies to create and manage the entire flow of data. To enable smart production, it is essential to deploy digital technologies in a way so that collected raw data is converted into useful data that can be applied by equipment or humans to generate value or reduce waste in production. This requires consideration to the data flow within the production system, i.e., the entire process of converting raw data into useful data which includes data management aspects such as the collection, analysis, and visualization of data. To enable a good data flow, there is a need to combine several digital technologies. However, many manufacturing companies are facing challenges when selecting suitable digital technologies for their specific production system. Common challenges are related to the overwhelming number of advanced digital technologies available on the market, and the complexity of production system and digital technologies. This makes it a complex task to understand what digital technologies to select and the recourses and actions needed to integrate them in the production system.Against this background, the purpose of this licentiate thesis is to examine the selection and integration of digital technologies to enable smart production within manufacturing companies. More specifically, this licentiate thesis examines the challenges and critical factors of selecting and integrating digital technologies for smart production. This was accomplished by performing a qualitative-based multiple case study involving manufacturing companies within different industries and of different sizes. The findings show that identified challenges and critical factors are related to the different phases of the data value chain: data sources and collection, data communication, data processing and storage, and data visualisation and usage. General challenges and critical factors that were related to all phases of the data value chain were also identified. Moreover, the challenges and critical factors were related to people, process, and technology aspects. This shows that there is a need for holistic perspective on the entire data value chain and different production system elements when digital technologies are selected and integrated. Furthermore, there is a need to define a structured process for the selection and integration of digital technologies, where both management and operational level are involved.