Digital twins

This study aims to investigate the role of digital twins in enhancing the efficiency, quality, and competitiveness of small manufacturers, identifying key motivations, impacts, challenges, and future benefits. This qualitative study employed semi-structured interviews to gather in-depth insights from 28 key stakeholders in small manufacturing businesses that have implemented digital twin technology. Participants included business owners, operations managers, and IT specialists. The data were transcribed and analyzed using NVivo software, following a thematic analysis approach to identify and categorize recurring themes and concepts. The analysis revealed four main themes: Adoption of Digital Twins, Impact on Manufacturing Efficiency, Challenges and Barriers, and Future Outlook and Benefits. Key motivations for adoption included cost reduction, efficiency improvement, and competitive advantage. Digital twins significantly enhanced manufacturing efficiency through process optimization, quality control, and cost management. Major challenges encompassed technical barriers, financial constraints, organizational resistance, and regulatory issues. The future outlook for digital twins was positive, with potential long-term benefits and advancements in AI and IoT expected to further enhance their capabilities. Digital twins offer substantial benefits for small manufacturing businesses, including improved efficiency, quality control, and cost management. However, successful implementation requires addressing technical, financial, and organizational challenges. With continued technological advancements and strategic support, digital twins are likely to become integral to small manufacturers, driving innovation and competitiveness in the industry.

Background: Digital twin (DT) technologies have become significant enablers of urban management, utilising real-time information, data analytics, and IoT connectivity to manage challenging urban issues. Nonetheless, existing studies reveal the capacity of the DTs, while their generalization, flexibility, and cross-disciplinary application for various urban environments are not thoroughly studied yet. Objective: This article aims to evaluate the effectiveness of DT technologies in improving traffic management, energy efficiency, infrastructure maintenance, and public safety across six case study cities: There are Singapore, Helsinki, Barcelona, Dubai, New York, and Tokyo. The study examines how DTs can be extended and implemented to target urban issues and how their use operational performance might be optimized. Methods: The study used quantitative data processing, on-line data analysis with factorization and machine learning, and assessment of the case studies. Quantitative measures which included traffic flow, energy loss, down time, and response to emergency situations were investigated pre and post DT application. The improvements mentioned were statistically confirmed, and the metrics of scalability and adaptability were evaluated in the course of the cities. Results: DT technologies increased traffic flow by up to 42.9%, reduced energy losses by 35%, minimum down time was 42%, emergency response was 44.9%. This was the case because the network had high IoT coverage and because DTs were applied to the context when it specifically needed them. Conclusion: The study proves that DTs can be implemented in different environments due to their flexibility to accommodate different urban conditions. AI and cross domain integration can add to the effectiveness of DT in general and both are inarguably now crucial for the management of contemporary urban environment.