Vertical Integration Analytics


Advanced Analytics applications, in particular Machine Learning, get more and more common in production environments, for instance in the form of Predictive Maintenance. However, so far the analyses are mostly focussed on single companies and their “data silos“. The common usage of data promises better results and consequently an increased efficiency. All participants of the business network, in particular the customers can benefit from this increased efficiency.

In the project Vertical Integration Analytics we investigate how this potential can be realized vertically along a value chain. Though, the sharing of production and meta data is critical due to data protection concerns. This issue can be addressed for example by the usage of abstracted or encrypted information which does not contain confidential information anymore. On this basis, it is assessed to what extent the efficiency gains can be realized, when Machine Learning is applied across company borders.

One use case for this is to leverage data regarding the production of machining tools to facilitate an optimal usage of this products by the customer. For instance, the customer receives recommendations how and how long concrete tools can be used.


Ceratizit Austria GmbH & Plansee SE


01.06.2018 – 30.11.2021


Ceratizit Plansee

Involved DSI Researchers

Jannis Walk

Niklas Kühl


Journal Articles
Artificial intelligence for sustainability: Facilitating sustainable smart product-service systems with computer vision
Walk, J.; Kühl, N.; Saidani, M.; Schatte, J.
2023. Journal of Cleaner Production, 402, Art.-Nr.: 136748. doi:10.1016/j.jclepro.2023.136748
Conference Papers
A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems
Zschech, P.; Walk, J.; Heinrich, K.; Vössing, M.; Kühl, N.
2021. 29th European Conference on Information Systems (ECIS 2021), June 14 - 16, 2021 - Marrackech, Morocco
An Uncertainty-based Human-in-the-loop System for Industrial Tool Wear Analysis
Treiss, A.; Walk, J.; Kühl, N.
2021. Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V. Ed.: Y. Dong, 85–100, Springer. doi:10.1007/978-3-030-67670-4_6
Conference Papers
Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation based on Deep Learning in the Machining Industry
Walk, J.; Kühl, N.; Schäfer, J.
2020. Proceedings of the 53rd Hawaii International Conference on System Sciences (HICSS-53), Grand Wailea, Maui, HI, January 7-10, 2020