BPMN and DMN for Easy Customizing of Manufacturing Execution Systems

Peinl, René; Perak, Ornella

doi:10.1007/978-3-030-37453-2_36

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Surprisingly, Gartner did not include Google App Maker in its evaluation although Google App Maker is rapidly gaining traction, both in research and practice. While the first seven tools account for 57% of the primary studies, the remaining 43% of the primary studies mention a constellation of 32 different tools: Temenos Quantum (P26 [50]), RESTsec (P4 [90]), Pega (G23 [41]) and MS Azure (P18 [73]) (2,3% each) and ZappDev (P12 [17]), TimeSeries (G13 [57]), Sysdev Kalipso (P15 [79]), Simplifier (P8 [44]), Siemens MindSphere (P10 [74]), Sagitec S3 (P20 [20]), PTCThingWork (P10 [74]), Node RED (P8 [44]), Nintex Workflow Cloud (P15 [79]), MIT App Inventor (P15 [79]), Lightening (P26 [50]), Lansa (G18 [73]), Kony (P23 [61]), KissFlow (P16 [59]), INTELLIT (P10 [74]), IDM Cloud (P10 [74]), HiCuMES (P17 [64]), Genio (G11 [2]), Cyclr (G9 [3]), ColdFusion Builder (G9 [3]), Camunda (P17 [64]), Axonivy (G14 [56]), AutoML (P18 [73]), Aurea (P3 [84]), AtmosphericIoT (P8 [44]), App Cloud (P11 [72]), AgilePoint (P20 [20]), and ADAMOS (P10 [74]) (1.1% each).…”

Section: Tools (Rq4)mentioning

confidence: 99%

Modelling in low-code development: a multi-vocal systematic review

2022

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In 2014, a new software development approach started to get a foothold: low-code development. Already from its early days, practitioners in software engineering have been showing a rapidly growing interest in low-code development. In 2021 only, the revenue of low-code development technologies reached 13.8 billion USD. Moreover, the business success of low-code development has been sided by a growing interest from the software engineering research community. The model-driven engineering community has shown a particular interest in low-code development due to certain similarities between the two. In this article, we report on the planning, execution, and results of a multi-vocal systematic review on low-code development, with special focus to its relation to model-driven engineering. The review is intended to provide a structured and comprehensive snapshot of low-code development in its peak of inflated expectations technology adoption phase. From an initial set of potentially relevant 720 peer-reviewed publications and 199 grey literature sources, we selected 58 primary studies, which we analysed according to a meticulous data extraction, analysis, and synthesis process. Based on our results, we tend to frame low-code development as a set of methods and/or tools in the context of a broader methodology, often being identified as model-driven engineering.

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Section: Tools (Rq4)mentioning

confidence: 99%

Modelling in low-code development: a multi-vocal systematic review

2022

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“…DMN is used to refer the decision maker to the correct component of the platform. In another example, Peinl and Perak (Peinl & Perak, 2019) describe how they use BPMN and DMN for the customization of Manufacturing Execution Systems (MES). They started from the observation that academic literature suggests to use as much standardized software as possible, but that this is difficult due to the industry-specific needs and the evolution towards lot-size one.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-based decision support for machine component design: A case study

Aerts

Deryck

Vennekens

2022

Expert Systems with Applications

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“…The potential benefits of BPM in manufacturing has been considered though, especially with the rise of smart technologies [50,51]. Indeed, BPM holds distinct advantages of interest in this research, especially for small and medium enterprises.…”

Section: Propositionmentioning

confidence: 99%

The HORSE Project: The Application of Business Process Management for Flexibility in Smart Manufacturing

et al. 2020

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Several high-tech manufacturing technologies are emerging to meet the demand for mass customized products. These technologies include configurable robots, augmented reality and the Internet-of-Things. Manufacturing enterprises can leverage these new technologies to pursue increased flexibility, i.e., the ability to perform a larger variety of activities within a shorter time. However, the flexibility offered by these new technologies is not fully exploited, because current operations management techniques are not dynamic enough to support high variability and frequent change. The HORSE Project investigated several of the new technologies to find novel ways to improve flexibility, as part of the Horizon 2020 research and innovation program. The purpose of the project was to develop a system, integrating these new technologies, to support efficient and flexible manufacturing. This article presents the core result of the project: a reference architecture for a manufacturing operations management system. It is based on the application and extension of business process management (BPM) to manage dynamic manufacturing processes. It is argued that BPM can complement current operations management techniques by acting as an orchestrator in manufacturing processes augmented by smart technologies. Building on well-known information systems’ architecting frameworks, design science research is performed to determine how BPM can be applied and adapted in smart manufacturing operations. The resulting reference architecture is realized in a concrete HORSE system and deployed and evaluated in ten practical cases, of which one is discussed in detail. It is shown that the developed system can flexibly orchestrate the manufacturing process through vertical control of all agents, and dynamic allocation of agents in the manufacturing process. Based on that, we conclude that BPM can be applied to overcome some of the obstacles toward increased flexibility and smart manufacturing.

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BPMN and DMN for Easy Customizing of Manufacturing Execution Systems

Cited by 5 publications

References 24 publications

Modelling in low-code development: a multi-vocal systematic review

Modelling in low-code development: a multi-vocal systematic review

Knowledge-based decision support for machine component design: A case study

The HORSE Project: The Application of Business Process Management for Flexibility in Smart Manufacturing

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