A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process
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 ca...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/22/12145 |
| _version_ | 1797460352501284864 |
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| author | Gerardo Luisi Valentina Di Pasquale Maria Cristina Pietronudo Stefano Riemma Marco Ferretti |
| author_facet | Gerardo Luisi Valentina Di Pasquale Maria Cristina Pietronudo Stefano Riemma Marco Ferretti |
| author_sort | Gerardo Luisi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T17:04:50Z |
| format | Article |
| id | doaj.art-bafb0b7fa2594325bb86e3981960a705 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:04:50Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bafb0b7fa2594325bb86e3981960a7052023-11-24T14:26:23ZengMDPI AGApplied Sciences2076-34172023-11-0113221214510.3390/app132212145A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding ProcessGerardo Luisi0Valentina Di Pasquale1Maria Cristina Pietronudo2Stefano Riemma3Marco Ferretti4Department of Management and Quantitative Studies, Parthenope University of Naples, 80133 Napoli, ItalyDepartment of Industrial Engineering, University of Salerno, 84084 Fisciano, ItalyDepartment of Management and Quantitative Studies, Parthenope University of Naples, 80133 Napoli, ItalyDepartment of Industrial Engineering, University of Salerno, 84084 Fisciano, ItalyDepartment of Management and Quantitative Studies, Parthenope University of Naples, 80133 Napoli, ItalyMonitoring 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.https://www.mdpi.com/2076-3417/13/22/12145overall equipment effectiveness (OEE)production downtimeinternet of thingsmanual data collection |
| spellingShingle | Gerardo Luisi Valentina Di Pasquale Maria Cristina Pietronudo Stefano Riemma Marco Ferretti A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process Applied Sciences overall equipment effectiveness (OEE) production downtime internet of things manual data collection |
| title | A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process |
| title_full | A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process |
| title_fullStr | A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process |
| title_full_unstemmed | A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process |
| title_short | A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process |
| title_sort | hybrid architectural model for monitoring production performance in the plastic injection molding process |
| topic | overall equipment effectiveness (OEE) production downtime internet of things manual data collection |
| url | https://www.mdpi.com/2076-3417/13/22/12145 |
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