Navigating Contextual Complexity in Smart and Sustainable Production: A Comparative Study on the Selection and Integration of Digital Technologies

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.