The EthonAI Manufacturing Analytics System is a powerful software suite to achieve operational excellence at scale. Our products are used by world-leading manufacturers and have already proven to reduce production waste by up to 50% while increasing job satisfaction of factory workers. We aim for nothing less than setting a new gold standard for AI in manufacturing. To learn more about EthonAI, visit www.ethon.ai.

ETH Zurich Foundation

Wingman Ventures

Acequia Capital (AceCap)

La Famiglia

Earlybird Venture Capital

EthonAI

Excited to share our inaugural paper on the Manufacturing Analytics System written by Julian, Bernhard, Roland and Torbjørn. The paper will be presented at the IFAC INCOM 2024!

This paper introduces a three-layer architecture for advanced manufacturing analytics:

1. Context Layer:
   - Gathers real-time data from sensors and machines.
   - Ensures accurate and comprehensive data acquisition.
   - Facilitates seamless integration with existing systems.

2. Model Layer:
   - Employs advanced algorithms to process and analyze data.
   - Utilizes AI and machine learning for analytics.
   - Provides actionable insights for decision-making.

3. Application Layer:
   - Transforms processed data into user-friendly applications.
   - Enhances operational efficiency and quality control.
   - Supports real-time monitoring and optimization.

These layers work together to create a robust system that enhances production quality, reduces downtime, and improves overall efficiency.

Read the full paper here: https://lnkd.in/dvHfEKiJ

#Manufacturing #Analytics #AI #Industry4.0 #ETHZurich #IFAC2024

Last Thursday, we announced our #SeriesA investment round of $16.5 million, led by Index Ventures with participation from General Catalyst, Earlybird Venture Capital, and Founderful. I want to take this opportunity to reflect on the past two years.

The manufacturing industry faces a significant problem, with one in every five dollars going to waste. Despite collecting terabytes of data, producers struggle to leverage big data for process improvements. Many enterprises are "data rich, but information poor." To address this, we have invested early in the development of causal AI tools.

Amidst the buzz around generative AI, at EthonAI, we have recognized the significant impact of causal AI. We see causal AI as a major driver of both top-line and bottom-line performance.  By uncovering the underlying cause-and-effect relationships within complex manufacturing processes, causal AI helps increase output and reduce costs and waste. This financing round positions us at the forefront of manufacturing analytics. The funds will enable us to advance our foundational work on AI models that target the industry's most challenging problems.

I would like to express my gratitude to our amazing team and our loyal customers who have believed in us from the very beginning. Your support has been essential to our progress.

I am looking forward to working with Katharina Wilhelm and Carlos Gonzalez-Cadenas from Index Ventures to accelerate our growth and continue innovating in the manufacturing sector.

Thank you also to Leif Danielsen and Acequia Capital and to everyone involved in this journey. We are excited about the future and the opportunities ahead.

Julian Senoner Pascal Mathis Antonia Albert Herbert Bay Dr. Andre Retterath Sélim Benayat Judith Dada Laurin Class Samuel Beyer Roland Philippsen #AI #CausalAI #Manufacturing #Innovation #Digitization #industry40 #smartmanufacturing #changemanagment #iiot #iot #manufacturinganalytics #digitalmanufacturing #ai


Today, we’re excited to announce our CHF 15m Series A financing round led by Index Ventures. Our existing investors General Catalyst, Earlybird Venture Capital, and Founderful also participated.

EthonAI was founded with the vision to redefine operational excellence in manufacturing. To pursue this ambitious goal, we have built a new category of software: The Manufacturing Analytics System (MAS). Today's financing round places us in a leading position to expand our platform offering, grow our presence globally, and drive productivity improvements across the industry.

Our Co-Founder Julian spoke with Martin Coulter from Reuters about this milestone. Read the full article here: https://lnkd.in/dxh7NvF3

We’re just getting started. If our mission excites you, join us!

#MAS #SeriesA #AI #Manufacturing #VC

"What if my data does not cover all potential root causes?" is a question we frequently encounter from industry experts. Our Co-Founder, Julian Senoner, explains that useful insights can still be gained from the data that most manufacturers collect today. And in tests where sensor data is deliberately omitted, the EthonAI Analyst still identifies key root causes.

🔍 Read the full article here: https://lnkd.in/dbb852SD

#RootCauseAnalysis #QualityImprovement #RCA #Analyst

The MIT Technology Review named data quality issues as a main barrier for AI use cases in manufacturing. How can it be that, eight years into #industry40, we are still grappling with these challenges?

Data acquisition, in general, is an iterative and complex process. Typically, it begins with data collection, followed by cleaning, modeling, deploying, and evaluating. This cycle is refined and repeated continually - not just once. However, in the manufacturing sector, data collection is particularly cumbersome and costly. It often requires physical labor to connect machines and secure the architecture, making it a substantial investment. 

In practice, I frequently observe a common issue: considerable effort is spent on data acquisition due to its complexity, yet the data often remains unused once stored. Modeling, deployment, and evaluation often have to wait for years. The problem with this approach is twofold:

➜ First, if the data isn’t modeled and evaluated, errors may go unnoticed. Even if initial checks are done, the data can degrade over time without anyone realizing it. Issues such as unnoticed changes in machine timestamps or calibration errors can render the collected data useless.

➜ Second, gaps in data only become apparent with a specific objective in mind. For example, aiming to improve quality/throughput with incomplete data may not yield immediate KPI improvements. However, it indicates where further data collection efforts should focus, ultimately saving time and money.

Based on my experience, several practices can foster good data quality:

𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞-𝐃𝐫𝐢𝐯𝐞𝐧 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧: Collect data with clear, measurable goals in mind, such as reducing waste by 20% or increasing throughput by 5%. Even if your data collection is not yet at the point where you can achieve those objectives, using a clear goal will accelerate your data acquisition process and improve data quality, allowing you to achieve the goals faster.

𝐅𝐚𝐬𝐭 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬: Ensure that data, once collected, is easily accessible to all relevant personnel. Process engineers, line managers, and other key stakeholders should be able to extract KPIs, create plots, and run initial analyses as soon as the data is available. Their feedback is crucial for refining data collection and cleaning processes.

𝐍𝐨-𝐂𝐨𝐝𝐞 𝐀𝐜𝐜𝐞𝐬𝐬: Providing no-code tools for data access increases the likelihood that the data will be actively used and analyzed.

Do you think I missed something, or do you disagree? I welcome any comments or suggestions on this topic, as collaboration and knowledge sharing are key to overcoming challenges in our industry.

EthonAI #industry40 #smartmanufacturing #changemanagment #iiot #iot #manufacturinganalytics #digitalmanufacturing #ai

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