A Comprehensive Data Model for Resilience Assessment in Industrial Production Systems

Abstract

Resilience is becoming an increasingly important factor in industrial production systems due to growing risks of disruption across all levels. This paper presents a semantic data model that supports the assessment of resilience in supply chains, shopfloor environments, and individual resources. The study builds on a review of key resilience concepts and current assessment methods. From this, a set of core data requirements is derived.An evaluation of existing standards, such as those from the Industrial Digital Twin Association and the OPC Unified Architecture, shows that current models do not fully meet these requirements. To address this gap, the paper introduces a knowledge graph-based model structured around three main domains, namely: employee, incident and simulation, and the industrial production system. The proposed model allows for consistent and scalable resilience assessments across different production levels and data types. It contributes to the development of datadriven methods for improving the resilience and adaptability of industrial production systems.