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Smart and sustainable production is increasingly critical for companies aiming to reduce environmental impact while maintaining competitiveness. Digital technologies play a key role by enabling data-driven decision-making to optimize production processes, reduce waste, and extend product lifecycles through the deployment of circular strategies such as reuse and remanufacturing. However, realizing the full potential of digital technologies for smart and sustainable production requires thoughtful selection and effective integration, both of which must account for the contextual complexity of the production environment. Despite this, limited research has examined how factors such as multi-actor involvement, system heterogeneity, and data uncertainty influence the selection and integration of digital technologies. This paper addresses this gap by examining how contextual complexity influences the selection and integration of digital technologies in smart and sustainable production. A multiple case study design was applied, examining one case within a remanufacturing ecosystem and another focused on performance monitoring of production equipment. The study identifies seven dimensions of contextual complexity - spanning process maturity, organizational landscape, stakeholder environment, system architecture, data uncertainty, integration demands, and transformation challenges - that influence how technologies should be selected and integrated. The findings reveal that in low-to-medium complexity settings, greater emphasis should be placed on making a suitable technology selection, supported by standardized platforms and centralized governance. In contrast, high-complexity environments require stronger focus on integration, emphasizing interoperability, federated governance, and adaptable data strategies. Based on these insights, the paper presents a framework to guide platform strategy, visualization, governance, data storage, and data handling according to the complexity level of the deployment context. © 2026 The Author(s).

With the development towards Industry 5.0, manufacturing companies are developing towards Smart Production - namely, using data as a resource to interconnect the elements in the production system for a more resource-efficient and sustainable production. Selection and integration of digital technologies are crucial steps to ensure that suitable technology is chosen and properly introduced in the production system. However, having one digital technology is not enough; rather there is a need to combine several synergising technologies for smart production. There are many challenges when selecting and integrating a combination of synergising digital technologies for smart production. Therefore, the purpose of this paper is to support manufacturing companies in systematically selecting and integrating suitable digital technologies for efficiently benefiting data value chains for smart production. This paper employed a multiple case study involving manufacturing companies within different industries and of different sizes. The paper analyses the current challenges related to the selection and integration of digital technologies and proposes a data flow framework with possible ways of combining digital technologies. The proposed framework shows alternative data flows between a combination of technologies depending on what digital technologies are selected and how they are integrated.

With the development towards Industry 5.0, manufacturing companies are developing towards Smart Production, i.e., using data as a resource to interconnect the elements in the production system to learn and adapt accordingly for a more resource-efficient and sustainable production. This requires selecting and integrating digital technologies for the entire data lifecycle, also referred to as the data value chain. However, manufacturing companies are facing many challenges related to building data value chains to achieve the desired benefits of Smart Production. Therefore, the purpose of this paper is to identify and analyze the critical factors of selecting and integrating digital technologies for efficiently benefiting data value chains for Smart Production. This paper employed a qualitative-based multiple case study design involving manufacturing companies within different industries and of different sizes. The paper also analyses two Smart Production cases in detail by mapping the data flow using a technology selection and integration framework to propose solutions to the existing challenges. By analyzing the two in-depth studies and additionally two reference cases, 13 themes of critical factors for selecting and integrating digital technologies were identified.

Digital technologies offer great opportunities to increase efficiency and competitiveness in production. However, many manufacturing companies struggle to select and integrate digital technologies that generate both business and sustainability value. Research on digitalisation and sustainability has expanded rapidly, but the two areas are still often treated separately. As a result, decisions regarding digital technology selection and integration frequently prioritize technical performance or cost efficiency, while overlooking long-term social and environmental implications. The purpose of this paper is therefore to identify and categorize sustainability aspects affecting the selection and integration of digital technologies to achieve smart and sustainable production. Based on a literature review and workshops with industrial participants, the study synthesizes existing research on digitalization and sustainability and structures the findings based on two dimensions identified in literature. The first dimension is the data value chain, which includes data generation, communication, storage, processing, and usage. The second dimension are the three sustainability pillars: economic, social, and environmental sustainability. The resulting conceptual framework provides a holistic understanding of how the three sustainability pillars can be embedded in digitalization decisions throughout the data value chain. Moreover, the conceptual framework shows which sustainability aspects should be addressed during the selection of digital technologies and those that require attention during the integration. The paper contributes theoretically by linking sustainability perspectives with the data value chain and digital technology selection and integration literature, and practically by offering a structure for manufacturing companies to evaluate digital technologies beyond short-term efficiency gains.