First confirmed Sessions
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Towards Nature Inspired Predictive Analytics – Can Birds and Dolphins Help Humans Predict the Future?

Speakers:

Anasse Bari

Speakers:

Anasse Bari

Towards Nature Inspired Predictive Analytics – Can Birds and Dolphins Help Humans Predict the Future?

Summary:

Birds do not collide when they fly in flocks. We may wonder how they do not and how they flock in a self-organized and well-orchestrated movement. It is a collective intelligence that is encapsulated within the interactions between the birds and the environment. The cohesive self-organized movement of a biological swarm such as flocking birds is commonly studied. Such phenomena have had successful applications in robotics and autonomous vehicles, and it has attracted a renewed interest from the Artificial Intelligence and the Predictive Analytics communities. Social insects and animals such as ants, bees, birds, and dolphins can be viewed as a beautifully living intelligent problem-solving system that we can learn from to design new predictive analytics algorithms. This talk will give a survey of biologically inspired machine learning algorithms, from birds’ flocking behavior and dolphins’ echolocation to ant colonies pheromone communication, and other types of swarm intelligence. Prof. Anasse Bari will introduce a new predictive analytics paradigm inspired from Swarm Intelligence, what he refers to as “Nature-Inspired Predictive Analytics,” and he will explain a new theory of artificial swarms that can be applied in many domains such as manufacturing, logistics, energy and mobility.

How TecAlliance Built a B2B Recommender System for the Automotive Aftermarket with Simple Association Analysis & Web Analytics Data

How TecAlliance Built a B2B Recommender System for the Automotive Aftermarket with Simple Association Analysis & Web Analytics Data

Summary:

Judging by Amazon’s success, the recommender system works. Adopting such a system to the automotive aftermarket poses many challenges. Association analysis for vehicles is much more complicated than for simple consumer goods. Normally a recommender system is tailored towards the users’ subjective preferences whereas in the aftermarket the user follows an objective search approach. TecAlliance implemented a recommender system in its spare parts catalogue – known as TecDoc Catalogue – based on a simple association analysis and web analytics data that overcomes this challenge. Workshops don’t have to search interrelated articles manually anymore and get a suggestion of similar articles that are related to the viewed one. Overall, this leads to a massive time saving and better process design.

Predictive Maintenance in Data Centers with Self-Learning AI for NTT

Predictive Maintenance in Data Centers with Self-Learning AI for NTT

Summary:

NTT FACILITIES provides critical facility management services for more than 7,000 data centers worldwide. Cost-expensive action is taken to minimize risk of air con failure, like replacing critical components prior to their actual life time end. Activities were started to use self-learning multi-layer AI with the objective to maximize compressor run time, to decrease replacement and to avoid additional backups. This Japanese project was executed with the support of the German company IS Predict. The reliability of the solution was beyond NTT FACILITIES´ expectations who had already executed similar AI project. Accuracy in failure prediction for air condition system compressors of 98% was realized.

Making Sense Out of Sensor Data – Data Science at TAL-Group

Making Sense Out of Sensor Data – Data Science at TAL-Group

Summary:

Why are data science projects in an industrial context still rare? I made the experience that, especially in complex industry facilities, understanding the data and identifying a concrete use case requires a lot of domain expertise. Building up this domain expertise might be the hardest part for a data scientist. But without clear data understanding there won’t be a precise problem definition and sooner or later the project will fail. In this talk, Simon Kneller will explain the key success factors of a concrete data science project with TAL-Group, which is the operator of the transalpine oil pipeline transporting about 45 millions of tons of crude oil every year across the alps. Based on the experiences made in that project he will focus on two questions: 1. How did they overcome this “hardest part” from raw sensor data and to a concrete data science problem definition? 2. How did machine learning algorithms help in order to identify root causes for efficiency losses while pumping raw oil?

Predictive Analytics for Climate Risk Assessment

Predictive Analytics for Climate Risk Assessment

Summary:

Climate change is a systemic risk which impacts on all business sectors. It increases uncertainty and investment risk and endangers entire business models. Professional investors and asset managers are taking climate change more and more into account. Corporates are starting to quantify climate risk in their mid- and long-term strategies. Predictive analytics turns out to be key in making quantified assessments in mostly unexplored terrain: How are extreme weather risks impacting on production sites and physical assets of the firm? How is the upcoming carbon taxation impacting on the company now and in future? And how vulnerable is the global supply chain of the company against business interruption risks? This deep dive explores methods and tools for climate risk quantification.

Unlocking Inaccessible Data Sources with Active Learning at Utility Company Stadtwerke München

Unlocking Inaccessible Data Sources with Active Learning at Utility Company Stadtwerke München

Summary:

Important information is often buried in graphical plans, handwritten notes and other sources which are almost impossible to access at scale, even with the help of computer vision or optical character recognition. With Active Learning, you get a huge reduction in human effort in return for a somewhat lower precision. Sarah Frank and Dr. Michael Allgöwer demonstrate how they successfully applied Active Learning to an archive consisting of partly handwritten plans which can only be interpreted with expert knowledge on company specifics. They discuss which algorithms from deep learning and classical ensemble learning can be used together with this technique.

Automated Demand Forecasting in Production at Continental

Automated Demand Forecasting in Production at Continental

Summary:

In the Tire Division of Continental, demand planning is crucial as an input for the supply chain and previously involved mainly manual forecasting of almost 100 business experts. In this talk David Koll and Lars Schleithoff will reveal how the Continental Advanced Analytics team, together with Informationsfabrik, created a machine learning framework that – deployed on a state-of-the-art infrastructure – today automates large parts of this tedious task. In particular, they will give insight into the problem complexity, concrete improvements achieved as well as the technical and – importantly – organizational challenges that arise from automating manual processes.

Future Airport Prediction: How Advanced Analytics Supports Decision Making at Munich Airport

Future Airport Prediction: How Advanced Analytics Supports Decision Making at Munich Airport

Summary:

Munich Airport is one of the most important hubs in Europe and is again awarded as the best airport in 2019 like several years before. To achieve a comfortable passenger journey you have to provide excellent operational processes. To make this also possible in the future, predictive analytics helps to identify bottlenecks and calculates future scenarios. The case study by Dr. Heike Markus, Munich Airport, provides insights in the development of predictive models to quantify the impact of strategic decisions on major investment projects. She will talk about the challenges of implementing new data-driven work routines and the increasing requirements of flexibility and transparency of business models. She will explain what data analytics can achieve and what difficulties have to be overcome to be successful.

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