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Purpose Standard Business Process Model and Notation (BPMN) lacks native constructs to manage the physical tools and materials involved in manual work, creating a digital-physical divide. This research proposes the Passive Resource-integrated Modeling Extension (PRiME), a framework extending BPMN to formally model, execute, and monitor the lifecycles of passive resources in cyber-physical systems. Design/methodology/approach Following the Design Science Research Methodology we developed a formal ontology for passive resources, distinguishing between consumable materials and reusable tools. The resulting artifact of mapping the ontology to the BPMN 2.0 metamodel is a standard-compliant, executable extension validated through a prototype implementation and demonstrated in two scenarios. Findings PRiME provides a machine-readable mechanism to execute resource checks directly within the process engine. The framework enables (1) runtime support by generating real-time resource checklists and shortage alerts to prevent operational delays, and (2) design-time analysis via a Sankey-based visualization of material flows. Performance benchmarks confirm that the extension imposes negligible overhead, with sub-second processing of the analysis pipeline. Research limitations/implications This research establishes a foundation for Physical Business Process Management by elevating physical assets to first-class citizens within process orchestration. By enforcing algorithmic availability checks and visualizing material consumption, the framework addresses nonproductive time and supports sustainability (SDG) goals through precise waste tracking. Furthermore, it shifts the cognitive burden of resource management from the worker to the system, augmenting skilled labor and aligning operational efficiency with responsible consumption standards. Originality/value Unlike previous conceptual proposals or architectural frameworks, this research provides a fully executable BPMN extension for passive resources. It combines a formal ontological foundation with a practical, standard-compliant implementation that bridges the gap between theoretical process modeling and physical execution.
Poss et al. (Tue,) studied this question.