Artificial Intelligence & Data Science | Mews Partners

Xavier Brucker

Partner | Polytechnique MIT

Christophe Bressange

Senior Partner | EMLyon

Jong-Mo Allegraud

Senior Data Scientist | ISAE

“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

Partner | Polytechnique MIT

Herr Brucker hat an der École Polytechnique (X 1996), an der Telecom Paris Tech (2001) und am Massachusetts Institute of Technology (MIT 2002) studiert und kann auf vielfältige internationale Einsätze bei Industrieunternehmen und Start-ups verweisen. Nachdem er seine Laufbahn in der Strategieberatung begonnen hatte, leitet er bei Safran ein großes Verteidigungsprogramm und war im Anschluss daran Leiter des Geschäftsbereichs E-Commerce und Zahlungsabwicklung bei EquensWorldline. Danach verbrachte Xavier Brucker mehrere Jahre in Kalifornien, mitten unter den Start-ups in Los Angeles und im Silicon Valley. Nach seiner Rückkehr nach Frankreich begleitete er das Wachstum eines Fintech-Start-ups. Bei Mews Partners entwickelt er derzeit die Angebote in den Bereichen Data Science und KI, die für die Industrie und den Dienstleistungssektor zu einer echten strategischen Herausforderung geworden sind.

Christophe Bressange

Senior Partner | EMLyon

Herr Bressange legte sein Diplom an der EM LYON ab und bringt mehr als 15 Jahre Erfahrung aus Beratungsgesellschaften mit Schwerpunkt Supply Chain mit. Darüber hinaus übte er sieben Jahre lang Betriebs- und Managementfunktionen bei der Geodis- und bei der Casino-Gruppe aus. Herr Bressange kam zu unserem Bereich Operations, weil er seine Kenntnis der Sektoren CPG, Retail, Transport & Logistik sowie seine Erfahrung aus einigen Transformationsprojekten für eine internationale Lieferkette einbringen wollte.

Jong-Mo Allegraud

Senior Data Scientist | ISAE

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.

30%

of production data is not used

99%

of company data is not used

40

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.

1

Control of components in the factory

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

2

Optimization of the Supply Chain

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

3

Optimization of the performance of a production line

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

4

Optimization of tests

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

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