Offline digital twin synchronization using measurement data and machine learning methods

This tutorial paper discusses underrepresented practical perspectives in the operations research literature, which primarily focuses on theoretical advancements rather than documenting successful real-world applications. Drawing on experience in academia, business, and humanitarian organizations, the authors identify seven crucial—yet often overlooked—components essential for a successful analytics implementation. These components are change management, data management, model selection and validation, project management, promotion and advocacy, scoping, and software. Despite the prevalence of analytics research, these components are seldom discussed in academic literature, leaving a gap between theory and practice in analytics. We emphasize the importance of these components to achieve impactful analytics interventions and call for a more comprehensive approach to analytics research. By bridging the gap between hard analytics (mathematical modeling) and soft analytics (practical implementation), the tutorial highlights the need for scholars and practitioners to collaborate and exchange insights for more effective real-world applications of analytics. History: This paper was refereed.

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Offline digital twin synchronization using measurement data and machine learning methods

Cited by 4 publications

References 8 publications

Offline digital twin for simulation and assessment of product surface quality

Offline digital twin for simulation and assessment of product surface quality

Inspection Robot Based on Offline Digital Twin Synchronization Architecture

Tutorial: Addressing Underexposed Components in Operations Research Literature

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