EthonAI
Assembly Line
EthonAI raises $16.5M for Manufacturing AI
EthonAI announced that it has raised CHF 15 million ($16.5 million) in a Series A round of funding led by Index Ventures, with participation from General Catalyst, Earlybird and Founderful.
Founded out of Zurich in 2021 by CEO Julian Senoner and CTO Bernhard Kratzwald, EthonAI can train AI models for specific use cases, for instance in electronics manufacturing where the customer supplies imagery of defect-free products and EthonAI’s Inspector software can then identify surface defects in the products during the manufacturing and assembly process.
The effect of unmeasured root causes in problem-solving
There is a common misconception that for AI-based root cause analysis to be effective, the data must be perfect. This is not the case. While it is true that unmeasured variables limit the ability to make process improvements, useful insights can still be gained from the data that most manufacturers collect today. The presence of unexplained variation does not preclude the value of such analyses. Imperfect models can still enhance process understanding. In this article, we will explore an example demonstrating how, despite the absence of some sensors, robust algorithms are capable of reliably identifying key root causes amidst unexplained variation.
Using process mining to improve productivity in make-to-stock manufacturing
This paper proposes a data-driven procedure to improve productivity in make-to-stock manufacturing. By leveraging recent developments in information systems research, the paper addresses manufacturing systems with high process complexity and variety. Specifically, the proposed procedure draws upon process mining to dynamically map and analyse manufacturing processes in an automated manner. This way, manufacturers can leverage data to overcome the limitations of existing process mapping methods, which only provide static snapshots of process flows. By bridging data and process science, process mining can exploit hitherto untapped potential for productivity improvement. The proposed procedure is empirically validated at a leading manufacturer of sanitary products. The field test leads to three concrete improvement suggestions for the company. This research contributes to the literature on production research by demonstrating a novel use of process mining in manufacturing and by guiding practitioners in its implementation.