Risk Management and Process Optimization in Industry 4.0: Integrating Sensors with Critical Path and FMEA
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50022652" target="_blank" >RIV/62690094:18450/25:50022652 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-87908-1_7" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-87908-1_7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-87908-1_7" target="_blank" >10.1007/978-3-031-87908-1_7</a>
Alternative languages
Result language
angličtina
Original language name
Risk Management and Process Optimization in Industry 4.0: Integrating Sensors with Critical Path and FMEA
Original language description
In the era of Industry 4.0, there is a strong focus on automation, advanced smart technologies, and addressing emerging issues related to sustainability and efficient process management. This paper presents a new approach that integrates traditional risk analysis methods, such as Failure Mode and Effects Analysis (FMEA), with the Critical Path Method (CPM) and Business Process Model and Notation (BPMN). The contribution of this method is presented using a selected sample manufacturing process, namely pallet manufacturing. The goal is to show how sensors that monitor machine operation improve process optimisation and manufacturing system sustainability and function. Utilizing the hybrid FMEA-CPM approach and modelling the production line in BPMN allowed for a comprehensive examination of each activity. This analysis was compared with the outcomes obtained both with and without sensor integration. Sensor data were used for quality control at critical stages of the manufacturing process, reducing the risk of errors in the final product. The results show an over 80% reduction in risk (measured by RPN) and more than 80% improvement in process efficiency, significantly improving decision-making and risk management. This approach offers valuable insights into how incorporating modern technologies in Industry 4.0 can enhance the administration and control of complex production systems, thus improving efficiency and sustainability. © The Author(s) 2025.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EH23_021%2F0008402" target="_blank" >EH23_021/0008402: Multi-sector and Interdisciplinary Cooperation in Research and Development of Communication, Information and Detection Technologies for Control and Signalling Systems (CIDET)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Computer Science
ISBN
978-3-031-87907-4
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
18
Pages from-to
98-115
Publisher name
Springer Science and Business Media Deutschland GmbH
Place of publication
Cham
Event location
Aveiro
Event date
Nov 4, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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