Artificial Intelligence & Data Science - Mews Partners

Xavier Brucker

Partner (Paris) | Polytechnique MIT

Markus Ripping

Partner (Hamburg) | Technische Universität Berlin

“AI is a set of methodologies particularly adapted to the world of industry. It is important to invest early in AI, to enrich internal skills and generate a strong competitive advantage.”

Xavier Brucker

Polytechnique MIT

A graduate of Ecole Polytechnique (X 1996), Telecom ParisTech (2001) and the Massachusetts Institute of Technology (MIT 2002), Xavier combines several international experiences with industry companies and start-ups. After a career in strategy consulting, he led a major defense program at Safran and then became director of the e-commerce and payments division of EquensWorldline. Xavier then spent several years in California, in the heart of start-up companies in Los Angeles and Silicon Valley. Upon his return to France, Xavier joined a Fintech start-up, which he supported in its growth. Today, within Mews Partners, he develops offers related to data science and artificial intelligence, which have become a real strategic challenge for industry and services. Furthermore, he writes on the topic of the scaling up of AI and smart factory in the blog of the experts from l’Usine Nouvelle.

Markus Ripping

Technische Universität Berlin

Markus has a Master of Science in Aeronautics, Astronautics & Transportation Technologies from Technische Universität Berlin.

He spent 13 years in Consulting at Prostep and Accenture working in cross-domain PLM, (Engineering) Process Optimization and Business Process Outsourcing, DMU & VR/AR. Markus focusses on international & multi-site collaboration, extended enterprise and M&A/PMI and their impact on Engineering and IT. His primary Industry know-how lies in Aerospace, Shipbuilding & Life Sciences. In the two and a half years before joining Mews Markus worked at Sartorius Stedim Biotech, a manufacturer of bio-pharmaceutical supplies and lab instruments, as Global PLM Process Owner and Manager “Idea to Product”.

He is currently an Associate Partner for Mews in Germany leading our Hamburg office.

The Artificial Intelligence (AI) and Data Science approach is particularly relevant in industrial contexts.

AI will monitor, predict, and optimize predefined KPIs and provide real-time answers. The complexity related to many different parameters will be easily manageable by a machine learning model. Man will still have added value in interpreting context and exceptions, and thus making the right decisions with the help of the machine.

An AI project consists in properly framing the subject and the KPIs that need to be optimized and in anticipating the interaction of these methodologies in the global process as well as with the people who lead it.


of production data is not used


of company data is not used


zettabytes of data in the world by 2020

What are the effects of Artificial Intelligence in organizations?

In organizations, Artificial Intelligence will first seek to improve operational efficiency. In practice, the most effective AI approaches are based on the analysis of KPIs related to the processes studied and seek to predict and optimize them on the basis of a large number of parameters. Products, services, processes and life cycle management can gradually be automated under the control of human operators. The company then transforms itself relying on predictive and prescriptive functionalities, capitalizing on past events. Robots, cobots, natural language and image processing, but also RPAs (robotic process automations) are among the methods mastered and deployed.

How to initiate an AI process?

It is essential to start with a business vision of the problem you are working on. What are the KPIs? What are the parameters that can influence these KPIs? What types of results would be useful?

The actual “data science” phase consists of gathering data, cleaning it if necessary, and injecting it into different models to find the best performance.

The last phase is crucial: it is the insertion of the model into the industrial process, taking into account the added value of human supervision, the performance and reliability of algorithms.

4 approaches

AI is particularly suitable for industrial projects.


Control of components in the factory

Decision support for the qualification of components on a production line. Image and parameter analysis for diagnosis.


Optimization of the Supply Chain

Prediction of stock requirements for a product launch, accurate definition of the safety stock, optimization of warehouses.


Optimization of the performance of a production line

Research for optimal parameters for a production line. Anticipation of problems and deviations.


Optimization of tests

Assistance in troubleshooting, review and prediction of test sequences, prediction of test results.

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Quotation and bookings optimization for a freight forwarder

We have developed with the client’s teams a tool to improve operational productivity by using algorithms and optimized user ergonomics.

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Stock optimization for a new product launch

Launching a new product, how to predict the need for stock?

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An Interview with Xavier Brucker in la Jaune et la Rouge Magazine


AI and Data Science within the frame of the smart factory.

Best-of the Conference Digital & Manufacturing


Relive the highlights of our conference!

Cocktail Afterwork Digital & Finance


Save the date! September 10th in Paris.

AI & Supply Chain video


Discover the video of the first edition of our AI & Supply Chain meetings.

AI in the Supply Chain business


Feedback on the first edition of the AI & Supply Chain Meetings.

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