ids 2017 industrial data science conference · 2019-03-05 · data science use cases & best...
TRANSCRIPT
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Data Science Use Cases & Best Practices
in Industry
Digitization, Industry 4.0, the Internet of Things (IoT),
and the industrial internet are transforming industries
leveraging big data in various forms such as streaming
data, structured and unstructured data, text, image,
audio, and sensor data using advanced analytics, posing
significant challenges and offering enormous opportunities.
Join us at IDS 2017, to learn how advanced analytical
applications are being used by world-leading organizations
like ABB, Achenbach Buschhütten, Arcelor Mittal, BMW,
Daimler, DEW, Lufthansa, Miele, Volkswagen, and others to
get the most value out of their data, which challenges they
encountered, how they solved them, and which tools they
used to get a competitive advantage.
Twitter: #ids2017
Web: http://ids2017.rapidminer.com/
E-Mail: [email protected]
IDS 2017 – Industrial Data Science ConferenceDortmund, Germany | September 5th, 2017
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Welcome to
IDS 2017
Data Science Use Cases & Best Practices
in Industry
Ralf Klinkenberg
Co-Founder & Head of Data Science
RapidMiner
Twitter: #ids2017
Web: http://ids2017.rapidminer.com/
E-Mail: [email protected]
IDS 2017 – Industrial Data Science Conferece
Dortmund, Germany | September 5th, 2017
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Ralf Klinkenberg
Co-Founder & Head of Data Science Research
RapidMiner
IDS 2017 Conference Chairs
Prof. Dr.-Ing. Jochen Deuse
Head of the Institute of Production Systems (IPS)
TU Dortmund University
Dr. Stefan Michaelis
General Manager
Collaborative Research Center SFB 876
TU Dortmund University
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• Prof. Dr. -Ing. Jochen Deuse, Institute of Production Systems (IPS), TU Dortmund University
• Prof. Dr. Katharina Morik, Head of Artificial Intelligence Group & Speaker of Collaborative Research Center SFB 876, TU Dortmund Univ.
• Dr. Stefan Michaelis, General Manager of Collaborative Research Center SFB 876, TU Dortmund University
• Ralf Klinkenberg, Co-Founder & Head of Data Science Research, RapidMiner
• Dr. Martin Schmitz, Head of Data Science Services, RapidMiner
• Julian Schallow, General Manager, IPS Engineers GmbH
• Monika Gatzke, CPS.Hub NRW
• Jacqueline Schmitt, Institute of Production Systems (IPS), TU Dortmund University
• Anika Altmann, Event Management, RapidMiner
• Jennifer Paulsen, Event Management & Marketing, RapidMiner
• Mario Wiegand, Institute for Research and Transfer (RIF), TU Dortmund University
• Dr. Fabian Temme, Data Scientist, RapidMiner
• David Arnu, Senior Data Scientist, RapidMiner
• Edin Klapic, Data Scientist, RapidMiner
• Dr. Edwin Yaqub, Data Scientist, RapidMiner
• Philipp Schlunder, Data Scientist, RapidMiner
IDS 2017 Programme & Organization Committee
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IDS 2017 Supporting Organizations
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predicts that by 2018, more than half of large organizations globally
will compete using advanced analytics and proprietary algorithms, causing the
disruption of entire industries.
Real data science, fast and simple.
We l c o m e t o I D S 2 0 1 7
a n dO v e r v i e w o f
I n d u s t r i a l D a t a S c i e n c e
U s e C a s e s
Ralf Klinkenberg
www.rapidminer.com
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 7 -
Predictive Analytics Will DISRUPT Markets
©2016 RapidMiner, Inc. All rights reserved.
"Gartner predicts that by 2018, more than half of large
organizations globally will compete using advanced analytics
and proprietary algorithms, causing the disruption of entire
industries.”
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- 8 -©2016 RapidMiner, Inc. All rights reserved.
Predictive Analytics Transforms Insight into ACTION
Descriptive
Diagnostic
Predictive
Prescriptive
OBSERVEWhat happened
EXPLAINWhy did it happen
ANTICIPATEWhat will happen
ACTOperationalize
Value
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 9 -
Predictive Analytics is GAME CHANGING
ANTICIPATE TOMORROW
• Make HIGHLY ACCURATE predictions, forecasts or classifications
• OPERATIONALIZE in mission-critical systems to drive decisions and actions in near real time
©2016 RapidMiner, Inc. All rights reserved. - 9 -
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 10 -
2007 2010 2013 2015
2,500 15,000 75,000 125,000
#1 Open Source Community
250,000+
EMERGING EXPANDING INTENSIFYING
2017
- 10 -©2016 RapidMiner, Inc. All rights reserved.
Number of Registered Users
http://community.rapidminer.com/
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 11 -
Analysts
RapidMiner Highlights
By the numbers
#1 Open-Source Platform Last five years in a row
Data Mining &Analytics Software Poll
Strong Performer 2015 & 2017
Forrester Wave on Big Data Predictive Analytics 2015
/Data Science Platforms 2017
#1
Open Source Data Science Platform
250,000+
Engaged Community Members
250+
GlobalClients
Channel Partners
50+
Innovation Winner 2015
Wisdom of Crowds for Advanced & Predictive Analytics, Big Data Analytics
& End-User Data Prep
Leader 2014, 2015, 2016 & 2017 Gartner Magic Quadrant for
Advanced Analytics Platforms
Accolades
CB InsightsThe AI 100, 2017
“100 Startups Using Artificial Intelligenceto Transform Industries”
VENTANA RESEARCH2016 Technology Innovation
Awards Winner Predictive Analytics
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- 19 -©2016 RapidMiner, Inc. All rights reserved.
Data Science Use Cases
©2016 RapidMiner, Inc. All rights reserved.
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 20 -
Sample Customer Use Cases
Voice of the Customer
Automated Customer Feedback Text Analysis for Automated E-Mail / Social
Media, Categorization, Triage & Routing
Manufacturing –Predictive Maintenance
High Value Assets - Silicon, Cars, Trucks, Aircraft, Turbines, IT
Infrastructure,…
Maximizing Customer Lifetime Value
CRM applications including optimization of direct marketing
campaigns, automated generation of product recommendations for cross-
selling and up-selling, customer churn prevention, and fraud detection
Manufacturing –Production Optimization
Optimization Of Production Logistics & Flows, Quality, Yield, Product Mix,
Process Mining
Fraud Detection
Fraud detection in retail network historical data on service usage,
transaction history, customer profiles, usage logs, and known
cases of fraudulent behavior
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 21 -
Predictive Maintenance
Customers Using RapidMiner for Predictive Maintenance, i.e. for Predicting & Preventing Machine Failures before they happen:• Major German Car Manufacturer:
Text Analytics of Repair & Service Reports to IdentifyCar Quality & Car Maintenance Issues
• Major European & South American Airplane Manufacturersand Major International Airplane Operators:Sensor Data & Text Mining Repair & Service Reports forPredictive Airplane Maintenance & Resource Allocation
• Major European Cement Producer:Cement Mill Failure Prediction & Prevention
• Major Chinese Energy Provider:Wind Turbine Failure Prediction & Prevention
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 22 -
Predictive Maintenance: Cement Mills
One of the largest two cement producers world-wide:
• RapidMiner-based solution for predicting and preventing drilling machine failure
• RapidMiner-based solution for simulating drilling machine behavior when changing system parameters
• 40 persons trained to deploy the RapidMiner-based solution in their cement mills world-wide
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 23 -
OPTIMAL
MIXTURE
OF
INGREDIENTS?
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 24 -
Optimizing Mixtures of Ingridients
• Which mixtures will produce high quality products?• Which mixtures will lead to quality issues?• How much of particular expensive additives is needed?• How to lower costs while ensuring high product quality?• How to increase production process reliability & product quality?• What variables are correlated to product quality and how?• How to predict and ensure product quality?• How much of each ingredient is optimal?• How to configure the production process and machines?• => Automated Predictions & Alerts & Action Recommendations• => Lower Cost & Lower Risk & Higher Reliability & Higher Quality
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- 25 -©2016 RapidMiner, Inc. All rights reserved.
RapidMiner Predictive Analytics Platform
©2016 RapidMiner, Inc. All rights reserved.
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 26 -
The RapidMiner Competitive Advantage
Lightning FastMachine Learning
Powerful, visual & guided use of 1,500 data prep and ML functions & third party ML
libraries
UnifiedPlatform
Prototype – Substantiate –Operationalize – seamless,
high performance orchestration
#1 Marketplacefor Data Science
Expertise
On-demand consultants, algorithms & extensions;
global presence & domain expertise in every industry
Real data science, fast and simple.
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- 27 -©2016 RapidMiner, Inc. All rights reserved.
DATA MASHUP ENGINE
MODERN, AGILE ENTERPRISE PLATFORM
Ingestion
Blending
Cleansing
Best Practice Recommendations
Unified Workflows Intelligent UtilizationIn Hadoop In-Memory In Database
PRESCRIPTIVE DECISION ENGINE
Diagnostic Relationships
Predictive Insights
Prescriptive Actions
Business Processes & Applications
AnyData SourceData at Rest and Data in Motion
OPERATIONALIZATION ENGINE
High-Velocity Scoring
Honest Validation
Process Integration
Automation Services
WISDOM OF CROWDS ADVISOR
EFFORTLESS WORKFLOW DESIGNER
FEDERATED ANALYTICS DRIVER
Marketplace Innovations & Extensions
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 28 -
RapidMiner Studio
Lightning Fast Visual interface for rapidly building complete analytic workflows
PowerfulRich library of algorithms and functions to build the strongest possible model for any use case
Open & ExtensibleOpen source innovation keeps pace with changing business needs
All-In-One Data Science Workflow Designer
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 29 -
RapidMiner ServerCollaborate + Compute + Deploy + Maintain
Process Execution Engine
Process Scheduler
Data and Process Repository
User/Group Access Rights management
Web App Portal
Web
Ser
vice
s
RapidMiner Web Applications
Integrate using Web Service and SQL operators
Application (BI, ERP,CRM…) / Portal
Java SE/EE ApplicationServer Application
Databases / DWHs
RapidMiner StudioVisual Workflow Designer
Process Execution Engine
Workflow Builder
RapidMiner RadoopCompile + Execute in Hadoop
RapidMiner Market PlaceIndustry, Application & ML Extensions
RapidMiner MarketplaceIndustry, Application & ML Extensions
RapidMiner RadoopCompile + Execute in Hadoop
The RapidMiner Platform
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- 30 -©2016 RapidMiner, Inc. All rights reserved.
Current State & Challenges & Barrierersfor Data Science in Industry
©2016 RapidMiner, Inc. All rights reserved.
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 31 -
Current State of Industrial Data Science
• Insular solutions – only few and isolated use cases
• Data silos – data not sufficiently used across the organization
• Experts of different fields rarely communicate enough
• Almost no truely completely data-driven companies– but Google and Tesla create pressure on automotive sector
– but Google NEST creates pressure on utility providers
– but high risk of others gaining a significant competitive advantage
• The opportunities are enormous, but most companies do not dare to move ahead fast enough but wait for others– Listen closely today – You will see others are already moving ahead!
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 32 -
Challenges & Barrierers for IDS
• Different mind sets of domain experts and data analysts
• Lack of communication and lack of mutual understanding
• Lack of ideas for use cases and potential opportunities
• Risk-averseness and lack of investment
• Lack of openness for innovation
• Lack of leadership and management support
=> IDS 2017 wants to inspire and show opportunities andsuccessful examples to make you move ahead faster!
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- 33 -©2016 RapidMiner, Inc. All rights reserved.
CRISP-DMCRoss-Industry Standard Process
for Data Mining
©2016 RapidMiner, Inc. All rights reserved.
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CRISP-DM – The Standard for DM Projects
CRoss-Industry Standard Process for Data Mining:• Structured and proven process for a
close cooperation of domain experts, data experts, and data scientists
• Cooperative iterative process with communication during all phases of the process fostering mutual understanding
• Best combination of domain exprtiseand human knowledge with data-driven machine learning based models
• Goal-, validation-, and deployment-oriented
Image Source: Wikipedia:https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining
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- 35 -©2016 RapidMiner, Inc. All rights reserved.
Industrial Data Science Use Cases & Best Practices
Presented at IDS 2017
©2016 RapidMiner, Inc. All rights reserved.
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IDS 2017 Theme following CRISP-DM
• Thought Leaders – from Research to Industry Applications
• Data & Data Analysis Platforms, Approaches, Use Cases
• Data Preprocessing & Transformation and Use Cases
• Modeling & Prediction and Use Cases
• ... all presented along industry use cases & best practices
• ... intermitted by breaks & networking opportunities
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Prof. Dr. Katharina Morik
• Head of Artificial Intelligence Unit,TU Dortmund University
• Speaker of the Collaborative Research Center SFB 876
• Member of AcaTech• Internationally Acknowledged
Expert for Machine Learning• Initiator of Machine Learning
Research in Germany• Broad Experience in Algorithmic
Research as well as Industry Applications
Machine Learning and Data Science: Research and Applications in Industry
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Prof. Dr.-Ing. Jochen Deuse
• Head of Institute of Production Systems (IPS), TU Dortmund University
• Held senior management positions in the Bosch Group in Germany and Australia
• Head of the chair of Industrial Engineering since 2005, which in 2012 merged with the chair of Industrial Robotics and Production Automation to form the Institute of Production Systems (IPS) under his direction.
• Member of the board at the industry network NIRO e.V.
• Expert for Industrial Engineering and Data Science Applications in Industry
From Industrial Engineering towards Industrial Data Science
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11:00-11:30h Coffee Break & Networking
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 40 -
From Prototype to Operative Software –Data Analytics @ Lufthansa
Dr. Fabian Werner
Data Science Consultant
Lufthansa Industry Solutions
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Optilink – A Big Data Platform for Industrial Cloud Applications
Roger Feist
Head of Automation
Achenbach Buschhütten
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Dynamic Bottleneck Forecasting in Flexible Manufacturing Systems
Ferdinand Klenner
Project Leader Predictive Analytics and Optimization Flexible Manufacturing Systems
BMW
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13:00-14:00h Lunch & Networking
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Predicting Assembly Times & Assembly Plans for New Product Designs & Variants Project “Pro Mondi
– Predicting Assembly Plans in Digital Factories” sponsored by the German Government
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Application of Data Mining for Prospective Assembly Time Determination
Dr. Olga Erohin
Corporate Development Division Professional Technology
Miele
Ralf Kretschmer
Director Segment Professional Laundry Technology
Miele
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 46 -
Data-Mining-Based Generation of Assembly Work Plans to Accelerate Product Emergence Processes
Dr. Regina Wallis
Strategic Series Management
Daimler
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Prediction Model for Agile Rework Scheduling
Sven Krzoska
Expert for Industrial Engineering and Data Mining
Volkswagen
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 48 -
15:30-16:00h Coffee Break & Networking
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 49 -
EVERYTHING OK?
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!
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 51 -
BIG DATA CAN BE
OVERWHELMING
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 52 -
Holistic Development of Industrial Big-Data Applications and Services
Marcel Dix
Scientist
Industrial
Data Analytics
ABB
Dr. Benjamin Klöpper
Principal Scientist
Industrial
Data Analytics
ABB
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 53 -
Detecting, Predicting, and Preventing Exceeded Emissions & Critical Situations
Project “FEE” sponsored by the German Government
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 54 -
Predicting Product Quality as Early as Possible in the Production Process
Dr. Gabriel Fricout
Head of Surface Properties, Data and Signal Processing
Arcelor Mittal
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 56 -
Quality Prediction in a Rolling Mill from Sensor Data Streams
Daniel Lieber
Deputy Operations Manager Rolling Mill and Forging Shop Witten
DEW - Deutsche Edelstahlwerke Specialty Steel GmbH & Co. KG
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© 2017 RapidMiner, GmbH & RapidMiner, Inc.: all rights reserved. - 57 -
17:30-19:30h Networking Reception