Keywords:Business process management Enterprise risk management Risk-aware business process management Model-driven engineering Meta-modeling Medication-use process a b s t r a c t Enterprise engineering deals with the design of processes which aim to improve the structure and efficiency of business organizations. It develops approaches based on modeling techniques, particularly on business process modeling, to ensure the quality and the global consistency of enterprise strategies and expectations. Nowadays, risk consideration in enterprise engineering is a growing concern since the business environment is becoming more and more competitive, complex, and unpredictable. To face this concern, a paradigm named risk-aware business process management (R-BPM) has recently emerged. It seeks to integrate the two traditionally isolated fields of risk management and business process management. Despite the significant benefits that can arise from the use of R-BPM, it suffers from a lack of solid scientific foundations and dedicated tooling. This present research work contributes to bridging that gap in a twofold way: (i) by establishing the BPRIM Business Process-Risk Integrated Method framework, and (ii) by designing a dedicated tool, named adoBPRIM which supports the efficient application of the BPRIM framework. This paper first comprehensively presents the foundation of BPRIM which is based on three main components and, secondly, its dedicated tool adoBPRIM which was designed using the ADOxx meta-modeling platform. An evaluation with a real case study in the health care domain shows the relevance of the methodological framework.

For the design of work and knowledge systems it is today common to revert to enterprise modeling methods. These methods not only support the representation and analysis of complex interactions between technical services and human actors. The resulting models also provide value through acting as knowledge bases themselves. Thereby, the formalization of modeling methods is essential to unambiguously define their structure, behavior, and semantics, and enable an intersubjective understanding and machine-processability.In this paper we analyze and compare six common enterprise modeling methods in regard to the formalization of their process-related aspects. From this comparison we derive implications for choosing an appropriate method when designing work and knowledge systems.

No abstract

No abstract

This paper motivates, describes, demonstrates in use, and evaluates the Open Models Laboratory (OMiLAB)-an open digital ecosystem designed to help one conceptualize and operationalize conceptual modeling methods. The OMiLAB ecosystem, which a generalized understanding of "model value" motivates, targets research and education stakeholders who fulfill various roles in a modeling method's lifecycle. While we have many reports on novel modeling methods and tools for various domains, we lack knowledge on conceptualizing such methods via a full-fledged dedicated open ecosystem and a methodology that facilitates entry points for novices and an open innovation space for experienced stakeholders. This gap continues due to the lack of an open process and platform for 1) conducting research in the field of modeling method design, 2) developing agile modeling tools and model-driven digital products, and 3) experimenting with and disseminating such methods and related prototypes. OMiLAB incorporates principles, practices, procedures, tools, and services required to address the issues above since it focuses on being the operational deployment for a conceptualization and operationalization process built on several pillars: 1) a granularly defined "modeling method" concept whose building blocks one can customize for the domain of choice, 2) an "agile modeling method engineering" framework that helps one quickly prototype modeling tools, 3) a model-aware "digital product design lab", and 4) dissemination channels for reaching a global community. In this paper, we demonstrate and evaluate the OMiLAB in research with two selected application cases for domain-and case-specific requirements. Besides these exemplary cases, OMiLAB has proven to effectively satisfy requirements that almost 50 modeling methods raise and, thus, to support researchers in designing novel modeling methods, developing tools, and disseminating outcomes. We also measured OMiLAB's educational impact.