A BPMN extension for modeling Cyber-Physical-Production-Systems in the context of Industry 4.0

Bocciarelli, Paolo; D’Ambrogio, Andrea; Giglio, Andrea; Paglia, Emiliano

doi:10.1109/icnsc.2017.8000159

Cited by 40 publications

(16 citation statements)

References 8 publications

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“…BPMN lacks structure, expressiveness, and flexibility for expressing the requirements brought by the integration of the CPS into the business process. Bocciarelli et al (2017) proposed an extension of BPMN for specification and management of the business processes supporting cyber-physical production systems (CPPSs). It is composed of PyBPMN, a BPMN extension that annotates performability-oriented properties in BPMN models, and eBPMN, a platform for business process simulation.…”

Section: Bpmnmentioning

confidence: 99%

A survey of model-driven techniques and tools for cyber-physical systems

Liu

Zhang

Cao

et al. 2020

Front Inform Technol Electron Eng

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Cyber-physical systems (CPSs) have emerged as a potential enabling technology to handle the challenges in social and economic sustainable development. Since it was proposed in 2006, intensive research has been conducted, showing that the construction of a CPS is a hard and complex engineering process due to the nature of integrating a large number of heterogeneous subsystems. Among other approaches to dealing with the complex design issues, model-driven design of CPSs has shown its advantages. In this review paper, we present a survey of research on model-driven development of CPSs. We are concerned mainly with the widely used methods, techniques, and tools, and discuss how these are applied to CPSs. We also present comparative analyses on the surveyed techniques and tools from various perspectives, including their modeling languages, functionalities, and the challenges which they address in CPS design. With our understanding of the surveyed methods, we believe that model-driven approaches are an inevitable choice in building CPSs and further research effort is needed in the development of model-driven theories, techniques, and tools. We also argue that a unified modeling platform is needed. Such a platform would benefit research in the academic community and practical development in industry, and improve the collaboration between these two communities.

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Section: Bpmnmentioning

confidence: 99%

A survey of model-driven techniques and tools for cyber-physical systems

Liu

Zhang

Cao

et al. 2020

Front Inform Technol Electron Eng

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“…Many publications present solutions for an integration of IoT and Cyber Physical Systems (CPS) into the BPMN meta model. Work often concentrates on concepts for physical entities, sensors and actuators, adding new task types, events and attributes to the meta model (Meyer et al, 2013), (Meyer et al, 2015), (Graja et al, 2016), (Bocciarelli et al, 2017). Quality of Information (QoI) of physical resources is focused in (Martinho and Domingos, 2014).…”

Section: Related Workmentioning

confidence: 99%

Resilient BPMN: Robust Process Modeling in Unreliable Communication Environments

Nordemann

Tönjes

Pulvermüller

2020

Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development

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Process modeling languages help to define and execute processes and workflows. The Business Process Model and Notation (BPMN) 2.0 is used for business processes in commercial areas such as banks, shops, production and supply industry. Due to its flexible notation, BPMN is increasingly being used in non-traditional business process domains like Internet of Things (IoT) and agriculture. However, BPMN does not fit well to scenarios taking place in environments featuring limited, delayed, intermittent or broken connectivity. Communication just exists for BPMN-characteristics of message transfers, their priorities and connectivity parameters are not part of the model. No backup mechanism for communication issues exists, resulting in error-prone and failing processes. This paper introduces resilient BPMN (rBPMN), a valid BPMN extension for process modeling in unreliable communication environments. The meta model addition of opportunistic message flows with Quality of Service (QoS) parameters and connectivity characteristics allows to verify and enhance process robustness at design time. Modeling of explicit or implicit, decision-based alternatives ensures optimal process operation even when connectivity issues occur. In case of no connectivity, locally moved functionality guarantees stable process operation. Evaluation using an agricultural slurry application showed significant robustness enhancements and prevented process failures due to communication issues.

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“…It provides a rich set of element types that can fully represent the context of a business process. Moreover, this standard can be easily extended, and it has been applied to modeling problems in various domains, such as wireless sensor network [18], healthcare processes [19], and manufacturing processes [20,21]. This section describes a construction procedure for manufacturing process models from the fundamental data of manufacturing processes.…”

Section: Bpmn-based Manufacturing Process Modelingmentioning

confidence: 99%

“…The BPMN standard has a variety of its extensions, but it lacks modeling notations for the manufacturing domain. Although a few studies presented extensions for manufacturing processes [20,21], their extensions do not cover the full context of the manufacturing domain due to Sustainability 2019, 11, 2560 6 of 18 the absence of uniformity. Accordingly, we define a BPMN extension that has a minimal set of element types but suffices to model the examples we present in this paper.…”

Section: Bpmn-based Manufacturing Process Modelingmentioning

confidence: 99%

“…The agglomerative clustering method builds a hierarchy from the process models being clustered (see the algorithm below). Initially, each model is assigned to its own cluster (line 4-7) and then the clustering algorithm proceeds iteratively, at each step merging the two most similar clusters, continuing until all the models are merged into a single cluster (line [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Hierarchical Clustering Algorithmmentioning

confidence: 99%

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A Similarity-Based Hierarchical Clustering Method for Manufacturing Process Models

Ahn

Chang

2019

Sustainability

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As the adoption of information technologies increases in the manufacturing industry, manufacturing companies should efficiently manage their data and manufacturing processes in order to enhance their manufacturing competency. Because smart factories acquire processing data from connected machines, the business process management (BPM) approach can enrich the capability of manufacturing operations management. Manufacturing companies could benefit from the well-defined methodologies and process-centric engineering practices of this BPM approach for optimizing their manufacturing processes. Based on the approach, this paper proposes a similarity-based hierarchical clustering method for manufacturing processes. To this end, first we describe process modeling based on the BPM-compliant standard so that the manufacturing processes can be controlled by BPM systems. Second, we present similarity measures for manufacturing process models that serve as a criterion for the hierarchical clustering. Then, we formulate the hierarchical clustering problem and describe an agglomerative clustering algorithm using the measured similarities. Our contribution is considered on the assumption that a manufacturing company adopts the BPM approach and it operates various manufacturing processes. We expect that our method enables manufacturing companies to design and manage a vast amount of manufacturing processes at a coarser level, and it also can be applied to various process (re)engineering problems.

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A BPMN extension for modeling Cyber-Physical-Production-Systems in the context of Industry 4.0

Cited by 40 publications

References 8 publications

A survey of model-driven techniques and tools for cyber-physical systems

A survey of model-driven techniques and tools for cyber-physical systems

Resilient BPMN: Robust Process Modeling in Unreliable Communication Environments

A Similarity-Based Hierarchical Clustering Method for Manufacturing Process Models

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