The manufacturing industry is undergoing a digital transformation in which an increasing range of digital technologies is available to support the development of smart production. However, realizing the potential of digital technologies depends not only on selecting suitable digital technologies but also on integrating them with existing production equipment, systems, processes, and people. Manufacturing companies commonly approach digital technology selection and integration through fragmented and technology-driven initiatives. This makes it difficult to establish coherent data flows, from raw data to usage, and realize the intended value over time. Moreover, production systems differ in their characteristics and needs. Digital technologies suitable in one context may therefore not be equally suitable in another. This, combined with the large number of digital technologies available, makes it challenging for manufacturing companies to determine which digital technologies to select and how to integrate them. This can result in unsustainable selection and integration processes that, for instance, are resource-demanding or fail to deliver the intended value. Against this background, the purpose of this PhD thesis is to manage the sustainable selection and integration of digital technologies to enable smart production. This was achieved by adopting a case study design in the manufacturing industry.

The findings show that the production system context influences sustainable digital technology selection and integration in several interrelated ways. First, it shapes requirements across the data value chain. This includes how data needs to be generated, communicated, stored, processed, and used to support decision-making. Second, contextual conditions and priorities influence which digital technology alternatives are feasible and what needs to be emphasized during selection and integration. Third, the production system context influences which selection and integration practices require emphasis and how they need to be applied. The findings further show that sustainable digital technology selection and integration is an iterative process of preparing, selecting, integrating, and ensuring value. Throughout this process, data value chain requirements need to be aligned with contextual conditions and priorities. Suitable selection and integration practices need to be applied and adapted, while competing demands are continuously managed. Since the implications of a specific production system context cannot always be fully anticipated, learning throughout the process may require earlier decisions to be revisited and refined.

The thesis contributes with an iterative process for managing the sustainable selection and integration of digital technologies in the context of the production system. The process provides manufacturing companies with guidance for moving from fragmented digital technology initiatives towards a more structured and adaptive approach to selection and integration of digital technologies. This can support manufacturing companies in accelerating smart production.