Development and analysis of digital twins of production systems

AbstractProduction systems have to be adapted continuously to changing circumstances. This means that the responsible production planner has to frequently make decisions about reconfiguring complex systems under uncertainty whose outcome greatly affects the company's business success. Discrete-event simulation is one powerful tool to support the necessary analysis and scenario evaluation, but still remains time-consuming and tricky to set up and to maintain. When implemented as a digital twin of the production system, the simulation model can maintain a high degree of accuracy over a long time period. An approach to realise this potential is presented and illustrated in this paper with a use case from the automotive industry. This paper contributes several new methods and findings to the development of digital twins of production systems: Firstly, it demonstrates how exceptional events in the validation of the digital twin can be handled. Secondly, it shows how structural changes in the system can be discovered using data on machine activity and process mining. Thirdly, the paper introduces a possibility on how to assess the accuracy of the digital twin. Furthermore, it demonstrates how to assess the robustness of the digital twin to estimation errors in machine processing times.KEYWORDS: Digital twinmaterial flow simulationdata analyticsprocess miningmodel validation Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available from the corresponding author, L.O., upon reasonable request.Additional informationFundingWe thank Bosch Powertrain Solutions for supporting this research. The research stay of Leonard Overbeck at MIT was supported by the Karlsruhe House of Young Scientists (KHYS) in the framework of the Research Travel Grant. We extend our sincere gratitude to the KHYS. https://www.khys.kit.edu/english/research_travel_grant_doc.php.Notes on contributorsLeonard OverbeckLeonard Overbeck studied Industrial Engineering and Management at the Karlsruhe Institute of Technology (KIT), Universidad de Barcelona (Spain), and Virginia Tech (USA). He is a research assistant at wbk Institute of Production Science since 2019 and conducts his Ph.D. in the field of production system planning.Stephen C. GravesStephen C. Graves is currently the Abraham J. Siegel Professor, post tenure, at the MIT Sloan School of Management. He has a courtesy faculty appointment with the Mechanical Engineering Department at MIT. His primary research interests are in the design and planning of manufacturing systems and supply chains; recent efforts have considered supply-chain optimisation, the evaluation of manufacturing flexibility, and various tactical issues arising in online retailing.Gisela LanzaGisela Lanza is a member of the management board at the wbk Institute of Production Science of the Karlsruhe Institute of Technology (KIT). She heads the Production Systems division dealing with topics of global production strategies, production system planning, and quality assurance in research and industrial practice.