Stavros Lounis

Digital technologies nowadays provide essential support for companies, making them a priority for businesses and a prominent area of study for researchers. In response to the increasing emphasis on sustainability and resilience, new information systems are developing to meet evolving business needs, namely circular and resilient information systems (CRISs). These systems integrate with traditional ones to optimise key performance indicators (KPIs) related to circularity and resiliency. Despite extensive methodologies for evaluating traditional information systems, systems designed for circularity and resiliency need to be assessed in parallel and in depth. Existing evaluations focus on efficiency and user satisfaction but often neglect the unique demands of circularity and resiliency. This study introduces a novel evaluation methodology for CRISs. Through a case study of an innovative system and the established literature, we address real-life needs and challenges in manufacturing. In particular, the system serves the needs of three distinct case studies: Carbon Fibre-Reinforced Polymer (CFRP) waste utilisation in drone manufacturing, recovery of magnets from Waste Electrical and Electronic Equipment (WEEE), and the repurposing of citrus processing waste into juice by-products. Our methodology is built on the 5W1H method to make our approach context-specific and aligned with each case’s unique requirements, making it also replicable for other industries. Our findings offer insights and a tool for practitioners and researchers to evaluate CRIS performance. The research highlights the importance of a two-fold evaluation approach for CRISs, evaluating both pilot-specific KPIs and the system’s technical performance. Policy implications suggest the need for regulatory frameworks and incentives to support the adoption, as well as evaluation, of CRISs and promote sustainable and resilient industrial practices.

Digital Twins (DTs) are a core enabler of Industry 4.0 in manufacturing. Cognitive Digital Twins (CDTs), as an evolution, utilize services and tools towards enabling human-like cognitive capabilities in DTs. This paper proposes a conceptual framework for implementing CDTs to support resilience in production, i.e., to enable manufacturing systems to identify and handle anomalies and disruptive events in production processes and to support decisions to alleviate their consequences. Through analyzing five real-life production cases in different industries, similarities and differences in their corresponding needs are identified. Moreover, a connection between resilience and cognition is established. Further, a conceptual architecture is proposed that maps the tools materializing cognition within the DT core together with a cognitive process that enables resilience in production by utilizing CDTs.