Selected publications and current work on Digital Twins, AI, value realisation, stakeholder alignment, and manufacturing.
Why it matters Shows how a deliberately low-fidelity Digital Twin can create operational value through real-time monitoring and transparency without unnecessary modelling complexity.
Why it matters Shows through a real industrial implementation how a Digital Twin can be developed and implemented to maximise value in an operational manufacturing environment.
Why it matters Explores how Large Language Models and Digital Twins can be used synergistically in production logistics and assembly to create new capabilities and value.
Why it matters Presents recurring Digital Twin challenges and practical measures in a form designed for industrial application.
Why it matters Shows why communication between stakeholders is one of the most underestimated yet critical challenges in Digital Twin projects.
Why it matters Challenges technology-centred narratives and synthesises research directions with particular relevance to industrial development and adoption.
Why it matters Integrates value orientation, stakeholder alignment, fidelity, and implementation into a coherent approach for Digital Twins in manufacturing companies.
Why it matters Provides a systematic method for calculating the fidelity level that maximises net benefit for a defined objective.
Why it matters Shows why Digital Twin fidelity is a central design variable that directly shapes both potential benefits and costs.
Why it matters Identifies the technical, organisational, and methodological challenges that hinder Digital Twin adoption in manufacturing.
Why it matters Defines how fidelity requirements should be derived from a Digital Twin’s purpose before development begins.