Artifical Intelligence can create signifcant advantages in many portions of the industry. Unfortunately, the road from artificial intelligence in research to AI application in companies is often a very long one. According to a Deloitte study on AI in 2020, 27% of German companies, when implementing AI solutions, have the central problem that they lack know-how and the trained professional personnel to adress the issue. This was our motivation to create an interactive series of events in cooperation with Merantix Labs – so that we can bring AI in all stages of the value chain to life.
Because there’s a simple fact: Using AI applications can generate impactful added value for the whole company.
The central question is: How?
Answers to that questions are provided by Nicole Büttner-Thiel (CEO and co-founder Merantix Labs) und Charlotte von Dryander (Project Manager Merantix Labs). Throughout four lectures, the two experts talk about the potentials of AI in combination with Smart Production, Smart Logistics, Smart Sales and Smart CRM – each with its own use case and specific advice on what to do.
In our series of articles, we’ll write about the different use cases so that you can see that advantges of AI on the value chain.
Because:
“I expect that artificial intelligence will have a similar far-reaching and lasting impact as the internet and software – in all branches of industry.”
Nicole Büttner-Thiel in an interview with Sebastian Borek (CEO Founders Foundation)
AI, ML, DL – What Does It All Mean?
Artifical Intelligence (AI): A broad term referring to machines which exhibit complex behavior or which solve complex problems.
Machine Learning (ML): The term refers to algorithms who iteratively evolve using example cases and who can make generalized deductions based on their learning.
Deep Learning (DL): Deep learning is a part of machine learning which uses so-called deep neural networks with millions of parameters to extract complex functions and relations from data.
Nicole Büttner-Thiel presents:
AI Use Case for Smart Production – Automated Visual Quality Assurance
Problem: The production industry is caught in an area of conflict. The goal of providing customers with high-quality products while at the same guaranteeing minimal production costs is most often realized by doing costly manual quality assurance. These processes require qualified employees, specialized training, and are, accordingly, a drain on resources. At the same time, manual visual quality assurance is characterized by a varied performance due to the involvement of different employees; the process is accordingly hardly reproducible. Ultimately, these repetitive work processes carried by people are often error-prone.
AI solution: Artifical intelligence offers many possibilites to automate visual testing, especially in the area of computer vision. AI algorithms are able to extract relevant markers from image data and evaluate accordingly. This leads to the detection, localization, and classification of relevant flaw types,
Results: The automation of repetitive work steps frees up resources which in turn can be conducted towards more goal-oriented tasks by the company. Establishing AI-based visual quality assurance increases the level of standardization in quality testing, accelerates processes, and raises customer satisfaction long-term. Aready today, such algorithms exceed the visual performance of humans and as such pose a promising tool for the digitalization of industrial companies.
Nicole Büttner-Thiel, CEO and co-founder of Merantix Labs is part of the 42.cx advisory committee. She develops technology-oriented solutions, is a member of the Digital Leaders for Europe Board des World Economic Forums, and is volunteering in the alumni board of the University of St. Gallen. She was awarded with Rising Talent award by the Women’s Forum and named a Young Leader by the Aspen Institute. She studied macroeconomics in St. Gallen, Stockholm, and Stanford.
Merantix Labs develops innovative, custom-built machine-learning solutions for companies from various sectors. They oversee the complete implementation of process and production automation for their customers – the guarantee the quality of their products.
What's next?
Learn about the potential of AI applications for the further stages of the value chain.
