Managing Data Interoperability Risks in Digital Twin-Enabled Battery Management System (BMS) Projects in Smart Manufacturing Industry

Abstract

This thesis investigates how complex projects such as Digital Twin (DT) enabled smart manu- facturing industry based projects can face data interoperability failures and how those can be identified as project-level risks and managed proactively. The research foundations is based upon development of a Electric Vehicles (EV) Battery Health Management System (BHMS), where multiple engineering disciplines team exchange data across organizations having in- compatible tools, standards, and organizational boundaries. Existing industry frameworks are exploring majorly “technical risk” aspects, however the project risk management aspect has been underexplored that classifies such failures under a generic label, obscuring the distinct root causes that require fundamentally different responses. A qualitative, literature-driven conceptual case study combined with innovative research based design science principles was adopted to analyze the delivery context of this project. Classic Project management tool has been explored and modified within the existing project risk management framework such as Work Breakdown Structure (WBS), Design Structure Matrix (DSM), and Risk Breakdown Struc- ture (RBS). Those tools were experimented against a six-level interoperability taxonomy to de- compose and figure out where and how reliability breaks down. Six distinct interoperability failure types were identified at specific stakeholder boundaries. The DSM analysis confirmed that every rework cycle in the BHMS case crosses at least one organizational boundary which establishes the fact that it is technically challenging to resolve interoperability failures indepen- dently as a team. These structural findings were later used to develop a three-phase Project Digital Twin (PDT) framework namely pre-deployment risk structuring, execution-phase mon- itoring through five project management based observable indicators, and level-specific re- sponse activation that replaces ad hoc investigation with targeted corrective logic. The frame- work is conceptual and has not been validated in a live project environment, which defines the scope for future empirical testing. The findings provide a framework establishing the fact that, interoperability risk can be made visible and required actions can be modified and adopted using standard project management instruments without requiring specialized data engineer- ing expertise. This creates a foundation for more proactive, structured risk control in complex DT-enabled manufacturing projects.