Proposal of BPMN extensions for modelling manufacturing processes

Abouzid, Ihsane; Saidi, Rajaa

doi:10.1109/icoa.2019.8727651

Cited by 9 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A set of fundamental graphic elements is envisaged, generally sufficient to model the simplest processes, to which the standard envisages the possibility of adding other elements in order to increase the level of detail of the representation. Abouzid & Saidi [27] analyzed the application of the BPMN to the manufacturing process, whereas Djatna & Munichputranto [28] used the BPMN to design the integration of an OEE data collection application in the manufacturing process.…”

Section: Bpmn For Representation Of To-be Processesmentioning

confidence: 99%

A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process

Luisi,

Di Pasquale,

Pietronudo

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Monitoring production systems is a key element for identifying waste and production efficiency, and for this purpose, the calculation of the Key Performance Indicator (KPI) Overall Equipment Effectiveness (OEE) is validly recognized in the scientific literature. The collection and analysis of the cause of the interruption of the plants is particularly useful in this sense. The use of Internet of Things (IoT) technology in order to automate data collection for the purpose of calculating the OEE and the causes of interruption is effective. Furthermore, the existing literature lacks research studies that aim to improve the data quality of important process data that cannot be collected automatically. This study proposes the use of IoT technologies to request targeted and intelligent information inputs from the operators directly involved in the process, improving the completeness and accuracy of the information through the real-time and smart combination of manual and automated data. The Business Process Model and Notation (BPMN) methodology was used to analyze and redesign the collection data process and define the architectural model with a deep knowledge of the specific process. The proposed architecture, designed for application to a plastic injection molding production line, comprises several elements: the telemetry of the injection molding machine, an intervention request system, an intervention tracking system, and a human–system interface. Furthermore, a dashboard was developed using the Power BI software, 2.122.746.0 version, to analyze the information collected. Reducing the randomness of manual data makes it possible to direct production efficiency efforts more effectively, helping to reduce waste and production costs. Reducing production costs appears to be strongly linked to reducing environmental impacts, and future studies will be able to quantify the benefits obtained from the solution in terms of environmental impact.

show abstract

Section: Bpmn For Representation Of To-be Processesmentioning

confidence: 99%

A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process

Luisi,

Di Pasquale,

Pietronudo

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…BPMN is the de facto standard for business process modeling; however, it requires extensions to support manufacturing processes in which physical entities are included (in comparison to business processes where information processing is dominant). A few extensions have already been proposed with the focus on including physical objects and activities in the models [45], or on modeling IoT-aware processes [46][47][48].…”

Section: Bpmn Modelingmentioning

confidence: 99%

Novel Approach Using Risk Analysis Component to Continuously Update Collaborative Robotics Applications in the Smart, Connected Factory Model

et al. 2022

View full text Add to dashboard Cite

Building on the idea of Industry 4.0, new models of the highly connected factory that leverage factory-generated data to introduce cost-effective automation and involve the human worker for creating higher added value are possible. Within this context, collaborative robots are becoming more common in industry. However, promises regarding flexibility cannot be satisfied due to the challenging process of ensuring human safety. This is because current regulations and standards require updates to the risk assessment for every change to the robotic application, including the parts involved, the robotic components, and the type of interaction within the workspace. This work presents a novel risk analysis software tool that was developed to support change management for adaptive collaborative robotic systems in the connected factory model. The main innovation of this work is the tool’s ability to automatically identify where changes have been made to components or processes within a specific application through its integration with a connected factory architecture. This allows a safety expert to easily see where updates to the risk assessment are required, helping them to maintain conformity with the CE marking process despite frequent changes. To evaluate the benefits of this tool, a user study was performed with an exemplary use-case from the SHOP4CF project. The results show that this newly developed technology for risk assessment has better usability and lower omission errors when compared to existing methods. Therefore, this study underlines the need for tools that can help safety engineers cope with changes in flexible robotics applications and reduce omission errors.

show abstract

Dynamic Process Synchronization Using BPMN 2.0 to Support Buffering and (Un)Bundling in Manufacturing

Traganos

Spijkers

Grefen

et al. 2020

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

Proposal of BPMN extensions for modelling manufacturing processes

Cited by 9 publications

References 8 publications

A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process

A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process

Novel Approach Using Risk Analysis Component to Continuously Update Collaborative Robotics Applications in the Smart, Connected Factory Model

Dynamic Process Synchronization Using BPMN 2.0 to Support Buffering and (Un)Bundling in Manufacturing

Contact Info

Product

Resources

About