A Framework for Understanding Intellectual Property Risks to Collaboration When Using Digital Twin Based Decision Support Systems

Digital technologies that enable Digital Twin Systems are driving demand for more sophisticated system-of-systems decision support services which depend on the collaboration of a complexity of stakeholders, for managing data, developing algorithms and managing the service infrastructure. Such systems are based on the commercial investment of design and know-how by competing design and manufacturing organisations. Traditionally the intellectual property (IP) and trade secrets were contained within design teams in a mix of formats and disparate data sources, but the greater connectivity of data could alter the risk profile from both legitimate and illegitimate causes. Some risks can be anticipated, such as revealing greater understanding of the design and behaviour to a wider stakeholder group, but other risk mechanisms may not be well understood and risk ambiguity could be a barrier to collaboration. There are advantages to manufacturers, in understanding how their systems perform, and for users, to plan maintenance, but to realise these benefits, risks need to be identified, understood and managed despite the early stage of adoption.
This research study was designed and implemented to construct a framework for understanding how IP can influence the risk to multi-stakeholder collaboration using Digital Twins for life-cycle decision support; and to explore how it could be applied to manage risk.
The constructed risk framework contributes an improved understanding of the inter-related factors that underpin the evaluation and mitigation of IP risks to collaborations using Digital Twins. Such issues were not well understood at the start of the research study. The new framework also supports evidencing life-cycle management of risks, linking cause-consequences with mitigating tools as the basis of providing evidence supported assurance.
The research study has originality in the model, data and application. The methodology includes application of a concurrent mixed methods research design dominated by qualitative analysis based on Charmez (Charmez, 2014) constructive grounded theory in a systems engineering context. The framework model uniquely brings together legal and systems engineering viewpoints to support the assessment and mitigation of stakeholder dependency risks. Novel data was sourced and analysed in development of the framework from semi-structured interviews and questionnaires and emerging case studies. The framework was uniquely applied to rail rolling stock cases with application inferred more generally.