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dAIvelopers at Henkel

Big data, artificial intelligence, Internet of things – Henkel is actively looking into developing new technologies and digital concepts. Strengthening our digital competencies is the aim of Henkel dAIvelopers.

Big Data has never been as exciting as today!

Our Henkel dAIveloper team is looking for answers revolving around big data: How will sales develop in a specific region? When do goods need to leave the factory to be delivered to the customer on time? How and when should we approach which consumers? But they are careful to not create the so-called “transparent consumer.”

"This job requires a different, very agile and creative way of working – combined with mathematical-statistical and technical knowledge", explains Thomas Zeutschler, team lead of the Henkel Data & Application Foundation and expert in data science, software development & operations (DevOps). "The term data science describes it quite accurately."

For example, one topic the team has been working on is hair colorants. These products have very strict specifications, because even the smallest variations in composition can produce a different finish. The aim was to reduce the so-called rework rate – which is to say the work required to reach the perfect dosage – and be able to achieve the ideal result in the first production run. Thus, several data sources were combined to do this: What will be produced, what are the formulations? What do the sensors on the machines in production say, and how is the quality of the latest samples compared to earlier ones? Connecting these bits of information from the different data sources was a very complex task. Roughly about 80% of the job lies in processing the data in such a way that it can be combined. The developed system can make predictions and identify potential problems in production ahead of time based on historical data. Amazingly, a similar approach to data analysis can also be applied in completely different areas, such as logistics. 

Even today, smart technologies can personalize the experiences we make with products. In industry, "Machine Learning" – one part of Artificial Intelligence – helps to optimize production processes. Artificial intelligence has long since ceased to be just a future vision. With each search on the internet, each GPS activation on the phone, each like and friend connection on Social Media, we leave a trail of data behind. And that is what AI feeds on.

Facts & Figures: AI

In 1966 Joseph Weizenbaum
developed the first chatbot
program, which was
called ELIZA.

Robots like “Paro” are used
for therapeutic care. The doll
looks like a seal and reacts to
touch and voice input by
moving and making sounds.

The term “artificial intelligence”
was first used in 1956 by
John McCarthy.

The health care and automotive
sectors are most affected by AI:
According to a PwC study, it is
estimated that AI has the biggest
influence in those industries.

In 1996, IBM’s “Deep Blue”
chess computer became the
first machine to defeat the
reigning world chess champion,
Garry Kasparov.

Speech recognition is the
AI technology most
frequently used by
businesses.

Based on extrapolated data,
Gartner forecasts that by 2020,
20.8 billion IOT devices will
already be in use worldwide.

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