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Concept of a Multi-Agent System for Optimised and Automated Engineering Change Implementation

Published online by Cambridge University Press:  26 May 2022

O. Radisic-Aberger ,
T. Weisser ,
T. Saßmannshausen ,
J. Wagner  and
P. Burggräf
O. Radisic-Aberger*
Affiliation:
University of Siegen, Germany
T. Weisser
Affiliation:
University of Siegen, Germany
T. Saßmannshausen
Affiliation:
University of Siegen, Germany
J. Wagner
Affiliation:
University of Siegen, Germany
P. Burggräf
Affiliation:
University of Siegen, Germany
*
ognjen.radisic@uni-siegen.de

Abstract

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Engineering changes are necessary to stay competitive, unavoidable and occur more frequently with increased product complexity. Currently, scheduling of engineering changes into production and supply chain is a manual process. With new possibilities in the field of artificial intelligence, this publication presents the vision of a flexible multi-agent system with four agents and a single shared database. By autonomously scheduling changes and predicting KPI impacts of implementation dates, the agent-system provides additional capacity and decision-making support to the organisation.

Keywords

engineering changeartificial intelligence (AI)change managementmulti-agent systems
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1689 - 1698
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

References

Arnarsson, I.Ö., Frost, O., Gustavsson, E., Jirstrand, M. and Malmqvist, J. (2021), “Natural language processing methods for knowledge management—Applying document clustering for fast search and grouping of engineering documents”, Concurrent Engineering, Vol. 29 No. 2, pp. 142–152. 10.1177/1063293X20982973.Google Scholar
Barzizza, R., Caridi, M. and Cigolini, R. (2001), “Engineering change: A theoretical assessment and a case study”, Production Planning & Control, Vol. 12 No. 7, pp. 717–726. 10.1080/09537280010024054.Google Scholar
Bender, J., Kehl, S. and Müller, J.P. (2015), “A Comparison of Agent-Based Coordination Architecture Variants for Automotive Product Change Management”, in Müller, J.P., Ketter, W., Kaminka, G., Wagner, G. and Bulling, N. (Eds.), Multiagent System Technologies, Lecture Notes in Computer Science, Vol. 9433, Springer International Publishing, Cham, pp. 249–267. 10.1007/978-3-319-27343-3_14.Google Scholar
Beroule, B., Fougeres, A.-J. and Ostrosi, E. (2014), “Engineering change management through consensus seeking by fuzzy agents”, in 2014 Second World Conference on Complex Systems (WCCS), 10.11.2014 - 12.11.2014, Agadir, Morocco, IEEE, pp. 542–547. 10.1109/icocs.2014.7060920.Google Scholar
Bhuiyan, N., Gatard, G. and Thomson, V. (2006), “Engineering change request management in a new product development process”, European Journal of Innovation Management, Vol. 9 No. 1, pp. 5–19. 10.1108/14601060610639999.Google Scholar
Camarillo, A., Ríos, J. and Althoff, K.-D. (2017), “Agent Based Framework to Support Manufacturing Problem Solving Integrating Product Lifecycle Management and Case-Based Reasoning”, in Ríos, J., Bernard, A., Bouras, A. and Foufou, S. (Eds.), Product Lifecycle Management and the Industry of the Future, IFIP Advances in Information and Communication Technology, Vol. 517, Springer International Publishing, Cham, pp. 116–128. 10.1007/978-3-319-72905-3_11.Google Scholar
Diprima, M. (1982), “Engineering Change Control and Implementation Considerations”, Vol. 23, pp. 81–87.Google Scholar
Eckert, C., Clarkson, P.J. and Zanker, W. (2004), “Change and customisation in complex engineering domains”, Research in Engineering Design, Vol. 15 No. 1, pp. 1–21. 10.1007/s00163-003-0031-7.Google Scholar
Fricke, E., Gebhard, B., Negele, H. and Igenbergs, E. (2000), “Coping with changes: Causes, findings, and strategies”, Systems Engineering, Vol. 3 No. 4, pp. 169–179. 10.1002/1520-6858(2000)3:4<169:AID-SYS1>3.0.CO;2-W.Google Scholar
Hamraz, B., Caldwell, N.H.M. and Clarkson, P.J. (2013), “A Holistic Categorization Framework for Literature on Engineering Change Management”, Systems Engineering, Vol. 16 No. 4, pp. 473–505. 10.1002/sys.21244.Google Scholar
Hevner, Alan R, March, Salvatore T and Sudha (2004), Design Science in Information Systems Research, Vol. 28.Google Scholar
Huang, G., Yee, W. and Mak, K. (2003), “Current practice of engineering change management in Hong Kong manufacturing industries”, Journal of Materials Processing Technology, Vol. 139 No. 1-3, pp. 481–487. 10.1016/S0924-0136(03)00524-7.CrossRefGoogle Scholar
Jarratt, T., Clarkson, J. and Eckert, C. (2005), “Engineering change”, in Clarkson, J. and Eckert, C. (Eds.), Design process improvement: A review of current practice, Springer, London, pp. 262–285. 10.1007/978-1-84628-061-0_11.CrossRefGoogle Scholar
Kehl, S., Stiefel, P. and Mueller, J.P. (2015), “Changes on Changes: Towards an Agent-Based Approach for Managing Complexity in Decentralized Product Development”, DS 80-3 Proceedings of the 20th International Conference on Engineering Design (ICED 15) Vol 3: Organisation and Management, Milan, Italy, 27-30.07.15, pp. 219–228.Google Scholar
Ma, S., Jiang, Z. and Liu, W. (2017), “Multi-variation propagation prediction based on multi-agent system for complex mechanical product design”, Concurrent Engineering, Vol. 25 No. 4, pp. 316–330. 10.1177/1063293X17708820.Google Scholar
Moon, Y.B. and Wang, B. (2009), “Agent-Based Modeling and Simulation of Resource Allocation in Engineering Change Management”, in Proceedings of the 11th International Conference on Enterprise Information, 06.05.2009 - 10.05.2009, Milan, Italy, SCITEPRESS - Science and Technology Publications, pp. 281–284. 10.5220/0001852302810284.Google Scholar
Ouertani, M.Z. (2008), “Supporting conflict management in collaborative design: An approach to assess engineering change impacts”, Computers in Industry, Vol. 59 No. 9, pp. 882–893. 10.1016/j.compind.2008.07.010.Google Scholar
Potdar, P. and Jonnalagedda, V. (2018), “Design and development of a framework for effective engineering change management in manufacturing industries”, International Journal of Product Lifecycle Management, Vol. 11 No. 4, p. 368. 10.1504/ijplm.2018.097880.CrossRefGoogle Scholar
Radisic-Aberger, O. (2021), Engineering Change Management - Classification Appendix of Literature Review. 10.17632/VNS2KP3ZT3.1.Google Scholar
Russell, S.J. and Norvig, P. (2016), Artificial intelligence: A modern approach, Always learning, Third edition, Global edition, Pearson, Boston, Columbus, Indianapolis.Google Scholar
Sharp, M.E., Hedberg, T.D., Bernstein, W.Z. and Kwon, S. (2021), “Feasibility study for an automated engineering change process”, International Journal of Production Research, Vol. 59 No. 16, pp. 4995–5010. 10.1080/00207543.2021.1893900.Google Scholar
Shivankar, S.D., Kakandikar, G.M. and Nandedkar, V.M. (2015), “Implementing engineering change management through product life cycle management in automotive field”, International Journal of Product Lifecycle Management, Vol. 8 No. 2, p. 132. 10.1504/ijplm.2015.070579.Google Scholar
Wänström, C., Lind, F. and Wintertidh, O. (2006), “Creating a model to facilitate the allocation of materials planning resources in engineering change situations”, International Journal of Production Research, Vol. 44 No. 18-19, pp. 3775–3796. 10.1080/00207540600622506.Google Scholar
Weißer, T., Wagner, J., Burggräf, P. and Lichtenwalter, D. (2021), “Support-vector classification of downstream problem effects during physical product development and ramp-up”, Procedia CIRP, Vol. 99, pp. 621–626. 10.1016/j.procir.2021.03.084.CrossRefGoogle Scholar
Wooldridge, M., Jennings, N.R. and Kinny, D. (2000), “The Gaia Methodology for Agent-Oriented Analysis and Design”, Autonomous Agents and Multi-Agent Systems, Vol. 3 No. 3, pp. 285–312. 10.1023/A:1010071910869.Google Scholar