Capturing and modeling complex manufacturing systems: extending and embedding BPMN in DES

Business process models are developed for the purpose of understanding the system behaviour and identifying the possible areas for performance improvement. Among existing process modelling languages, Business Process Model and Notation (BPMN) was selected to be assessed and extended, as it is the leading standard for business process modelling. The BPMN is currently gaining great attention in various business practices; it is an easy and flexible way to construct business process models, and thus it was hard to be overlooked by authors who are concerned with improving manufacturing processes. The introduction of BPMN to the manufacturing domain potentially allows all stakeholders to take advantage of the simplicity of this language in gaining full understandings for manufacturing processes through simple representations of the process models. Only limited work can be found addressing the use of BMPN in the modelling of manufacturing systems, and it is still not clear how powerful BPMN is in realizing performance improvements. This work proposes Manufacturing Process Model and Notation (MPMN) as an extension to BPMN; it offers a set of new and adapted notations that represent manufacturing processes. In order to add the ability to optimise or improve the system under study, MPMN is further extended and integrated with a full discrete-event simulation package to be able to easily build and simulate models of manufacturing systems. MPMN simulator is created to be a generic and reusable tool. It has a drag and drop library for non- simulation experts, to model and simulate MPMN models in the ExtendSim environment. This integration is considered a substitution to BPSim, the Business Process Simulation Standard that integrates BPMN and Simulation to model and execute business processes. The MPMN simulator combines both, models that are understood by all stakeholders and a simulation tool that is expressive enough to handle the varying levels of complexity in the manufacturing domain. This will strengthen the analysis and the evaluation of the current and future status of a system under study. The effectiveness of the developed system is demonstrated by application to two case studies covering different industry sectors