TY - GEN

T1 - Recommendation of Mass Customized Products with a Multi-agent System

T2 - 11th World Mass Customization and Personalization Conference

AU - Plappert, Stefan

AU - Becker, Christian

AU - Pusch, Felix

AU - Heuer, Sören

AU - Gembarski, Paul Christoph

N1 - Funding Information: Acknowledgements. Funded by the European Union under Grant Agreement No 101092073. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency (HaDEA). Neither the European Union nor the granting authority can be held responsible for them. Funding Information: Acknowledgments. This research and development project is funded by the German Federal Ministry of Education and Research (BMBF) within the “The Future of Value Creation - Research on Production, Services and Work” program (funding number 02L19C250) and managed by the Project Management Agency Karlsruhe (PTKA). The authors are responsible for the content of this publication. Funding Information: Data acquisition and DVSM analysis of future states can be realized by integrating simulation into approaches that automatically generate real-time DVSM based on data collected from smart objects in the factory. A conceptual framework was developed by [37] to build a real-time VSM supported by a simulation-based model which allows to visualize future state maps. Data is collected from common database systems such as ERP and MES, and then linked to geographical data captured by RFID. Then, data analytics and simulation are used to recognize improvement potential in current and future state maps. An industrial application was proposed by [58], who present an approach for DVSM in the context of the SEAMLESS project, an initiative funded by the German Federal Ministry of Education and Research. Data regarding different product-related processes from various levels is automatically extracted and fed into the value stream model, including BOM information, ERP data and machine-related data. Based on the concept of digital twin, the information about the behavior of the production system is transferred into a platform that provides the simulation model of the value streams. The platform allows to users to work on the model without having to connect to the real production plant, enabling simulation runs at the value stream level [58]. Funding Information: Acknowledgments. This research was funded by the Mercedes-Benz Group AG and accompanied by the Department Organisation of Production and Factory Planning, University of Kassel. Funding Information: Acknowledgement. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC-2023 Internet of Production— 390621612. Funding Information: Acknowledgment. The authors would like to acknowledge the financial support by the Federal Ministry for Economic Affairs and Climate Action (BMWK) within the program “Future Investments for Vehicle Manufacturers and Supplier Industry” (KoPa 35c) for the project SkaLab (grant number: 13IK025B). Funding Information: Acknowledgements. This research is supported by Flanders Make, the strategic research centre for the manufacturing industry in Flanders, Belgium. Funding Information: Acknowledgements. This research was carried out within the funding framework funded by the European Union – NextGenerationEU. Funder: Project funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3 - Call for tender No. 341 of 15/03/2022 of Italian Ministry of University and Research funded by the European Union – NextGenerationEU. Award Number: Project code PE0000003, Concession Decree No. 1550 of 11/10/2022 adopted by the Italian Ministry of University and Research, CUP D93C22000890001, Project title “Research and innovation network on food and nutrition Sustainability, Safety and Security – Working ON Foods” (ONFoods). Funding Information: Acknowledgment. We extend our sincere thanks to the German Federal Ministry for Economic Affairs and Climate Action (BMWK) for supporting this research project 13IK001ZF “Software-Defined Manufacturing for the automotive and supplying industry https://www.sdm4fzi.de/”. Funding Information: Acknowledgments. This research has been partially funded by the H2020 EU Project DIMO-FAC – Digital Intelligent MOdular FACtories. Funding Information: Acknowledgment and Statements. The authors would like to express their appreciation to the IDEKO Research Center for the support and to the Karlsruhe House of Young Scientists (KHYS) for sponsoring of the abroad stay. The project “AgiProbot” is funded by the Carl Zeiss Foundation. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023

Y1 - 2023

N2 - To satisfy individual customer requirements, products with many options are offered in mass customization. Particularly in the case of variant designs for small batch production, the design or configuration of the possible options implies additional effort. Therefore, a knowledge-based engineering system as a multi-agent system is created in this paper. This offers the advantage of an orchestration of the solution space exploration using constraint satisfaction problems and a CAD implementation. To actively involve the customer in the decision-making process and provide recommendations, the integration of the customer via chat client is investigated. For the application of the multi-agent system, an extruder for plastics manufacturing was set up as a case study.

AB - To satisfy individual customer requirements, products with many options are offered in mass customization. Particularly in the case of variant designs for small batch production, the design or configuration of the possible options implies additional effort. Therefore, a knowledge-based engineering system as a multi-agent system is created in this paper. This offers the advantage of an orchestration of the solution space exploration using constraint satisfaction problems and a CAD implementation. To actively involve the customer in the decision-making process and provide recommendations, the integration of the customer via chat client is investigated. For the application of the multi-agent system, an extruder for plastics manufacturing was set up as a case study.

KW - Case study

KW - Constraint satisfaction problem

KW - Design automation

KW - Knowledge-based engineering systems

KW - Multi-agent system

UR - http://www.scopus.com/inward/record.url?scp=85172359432&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-34821-1_11

DO - 10.1007/978-3-031-34821-1_11

M3 - Conference contribution

AN - SCOPUS:85172359432

SN - 9783031348204

T3 - Lecture Notes in Mechanical Engineering

SP - 93

EP - 100

BT - Production Processes and Product Evolution in the Age of Disruption

A2 - Galizia, Francesco Gabriele

A2 - Bortolini, Marco

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 20 June 2023 through 23 June 2023

ER -