TY  - CPAPER
U1  - Konferenzveröffentlichung
A1  - Möhring, Michael
T1  - Digital twins in production: the integration of semi- and unstructured data
T2  - Proceedings of the 13th Conference on Learning Factories (CLF 2023), 9-11 May 2023, Reutlingen
N2  - Digital twins deployed in production are important in practice and interesting for research. Currently, mostly structured data coming from e.g., sensors and timestamps of related stations, are integrated into Digital Twins. However, semi- and unstructured data are also important to display the current status of a digital twin (e.g., of a machinery or produced good). Process Mining and Text Mining in combination can be used to support the use of log file data to understand the current state of the process as well as highlight issues. Therefore, issue related reactions can be taken more quickly, targeted and cost oriented. Applying a design science research approach; here a prototype as an artefact based on derived requirements is developed. This prototype helps to understand and to clarify the possibilities of Process Mining and Text Mining based on log data for production related Digital Twins. Contributions for practice and research are described. Furthermore, limitations of the research and future opportunities are pointed out.
KW  - digital twin
KW  - process mining
KW  - text mining
KW  - machine learning
KW  - production
KW  - industry 4.0
Y1  - 2023
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-42247
U6  - https://doi.org/10.2139/ssrn.4469903
DO  - https://doi.org/10.2139/ssrn.4469903
SP  - 1
EP  - 5
S1  - 5
PB  - Elsevier
CY  - Rochester, NY
ER  -