Predictive Analytics World for Manufacturing, Germany, Feb 2-3, Program highlights

Learn to expert presentations from leading companies (including IBM Watson, Microsoft, and Nokia Bell Labs) with a strong focus on real-world examples of deployed predictive analytics techniques.

Predictive Analytics World for Manufacturing - PAW Manufacturing - Programme highlights!

PAW Manufacturing Düsseldorf is getting closer!

The first European edition of Predictive Analytics World for Manufacturing, the leading vendor-independent expert conference for application-oriented Predictive Analytics in Industry 4.0, is just one month away! The dates to be saved are February 2nd & 3rd, 2017!

The conference programme is already live. Have a look at it here.

All presentations are projected from experts for experts with a strong focus on real-world examples of deployed predictive analytics techniques. With many international eminent experts from companies like IBM Watson IoT, Microsoft, Nokia Bell Labs and a full conference programme in English, PAW Manufacturing is a guaranteed opportunity to hear concrete application cases and business scenarios of Predictive Analytics in the context of industrial production and tap the power of predictive modelling to improve business outcomes.

Here some highlights of the programme: 

Nokia Bell Labs is doing intensive research to detect anomalies and potential root causes in highly complex data from mobile telecommunication providers. In the actual project researchers analyze data with approximately 300 attributes from 1.5 million subscribers using mobile services in a country wide network of 37.000 radio cells. This data is a base for intensive error and root cause analysis in near real time. For this purpose, algorithms are used for the analysis of time series, clustering, parameter correlation and for active and supervised learning.


Based on the current wellness trend IoT gadgets are part of our daily life. Almost every week a new tool or App is placed in the market with the respective hardware. Is this trend only for nerds and kids? Or can we apply some of the developments also in the manufacturing and energy industry? The project WorkRight, developed in cooperation with North Star BlueScope Steel and one of IBM’s Watson IoT research labs in Haifa, proved, that the wearable trend is not only suitable for private life but also in the current digitalization process of our industry. The primary target is to reduce industrial accidents and avoid status of employee’s illness. In addition to that the European Directive for workers safety (EU 89/391/EWG) forces the top management in all enterprises to find suitable, cost efficient and audit prove solutions to document workers safety on a daily basis. Explore more on the capabilities of predictive analytics and cognitive platforms based on the current Industrial Internet strategy.


Nowadays, everybody is following the hype around machine learning in general and around deep learning (DL) in particular. We are trying to use it for predicting unexpected down-times of machines, or to discover anomalies in data streams observing machines. What is usually missing is the magic. Most often DL is supervised, which means that someone is labelling some data which gets fed into some algorithm. But as an alternative, there is a new star at the horizon: Reinforcement Learning (RL). This is a concept using an agent and an incentive system to train an agent. By taking the incentives the agent can learn and improve his behavior. As a result, this is a self-learning system and only requires some simple rules. The combination of RL and DL eventually takes us to something we could consider as artificial intelligence. With AlphaGo we have seen how the combination of RL and DL can win a Go tournament. This is a very promising step in an interesting direction. This talk will provide an introduction into reinforcement learning. It shows how reinforcement learning and deep learning can be combined towards an AI system by providing some insights into existing projects. Starting with annotated data and using DL, it is possible to create a base model. This model gets refined with RL mechanisms. Finally, this talk will show how this approaches can be used to map it to Internet of Things and Industry 4.0 scenarios, such as a self-learning robot

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We invite you to take part in this extraordinary event to be informed and inspired, to learn about the latest research and advances and exchange ideas both professionally and personally.


You also have the chance to save up to 300€ on the full price if you book your ticket in advance. The Early Bird rate is available until January 13th!


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