From Unstructured Product Descriptions to Structured Data for Industry 4.0 with ChatGPT
Christof Tinnes, Marko Ristin, Uwe Hohenstein, Kiavash Fathi, Hans Wernher van de Venn · 2024
The digital transformation of manufacturing - Industry 4.0 - has sparked interest in digital twins, which are virtual replicas of physical assets. One popular structure for these digital twins is the Asset Administration Shell (AAS), which has been widely adopted. However, the large-scale conversion of asset data into AAS structures is not trivial, particularly when the asset data is unstructured. In this study, we investigate the use of large language models such as ChatGPT for this task. Our approach involves clustering product descriptions into product categories, structuring manually parts of each cluster, and then using ChatGPT to generalize this structuring to unseen product descriptions. We evaluate our methodology and compare it to alternative approaches. Our findings indicate that large language models can effectively produce structured AAS v3.0 data. For some product categories full automation is possible-at worst, $17 \%$ of the structures need to be manually corrected. We provide a novel real-world industrial data set as well as tools for the comparison of AAS structures for future studies.