2026-04-20T23:12:52Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/14852024-02-29T15:01:33Zcom_20.500.11984_1143col_20.500.11984_1148

Data-driven Workflow Management by utilising BPMN and CPN in IIoT Systems with the Arrowhead Framework Larrinaga, Felix Kozma, Dániel Varga, Pal Digital Production Coloured Petri Nets BPMN Productive 4.0 Industry 4.0 Workflow management is realised in manufacturing at the Enterprise- and Production (workstation) levels. The characteristics of business processes in these levels differ enough to prevent the adoption of a conventional modelling or implementation solution. The conceptualisation of an adequate and adaptable workflow management model that over-arches the heterogeneous business process levels is challenging. However,one of the encouragements of Industry 4.0 is to provide easy-to-use models and solutions that enable the effective implementation of production goals. The current paper addresses this challenge and – as a proof-of-concept – it demonstrates how the goals mentioned above can be achieved by combining the different manufacturing process models. To accomplish this, a workflow control engine needs to be designed and standardised respectively. Arrowhead is an IIoT(Industrial IoT) framework that dynamically and flexibly sup-ports automated manufacturing processes following Industry 4.0 expectations. This paper describes how its workflow management system, i.e., the Workflow Choreographer implements automated production. Furthermore, it details what the functions of this system are and how it applies and combines BPMN and CPN in practice to provide a solution for the given challenge.To verify its feasibility, the paper showcases a demo application of the concept as well. 2019-10-31T09:13:55Z 2019-10-31T09:13:55Z 2019 http://purl.org/coar/resource_type/c_c94f 978-1-7281-0303-7 1946-0759 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=152146 https://hdl.handle.net/20.500.11984/1485 eng © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. IEEE