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How Old is Old Economy?
Big Data in Production Industries - an Automotive Use Case
Agenda
Introduction 4
What is Big Data for us? 8
Automotive Production Use Case 13
23/13/2015
Agenda
Introduction 4
What is Big Data for us? 8
Automotive Production Use Case 13
33/13/2015
Our Name Reflects Our Consulting ApproachWe achieve optimal results for our clients by combining analytical
competence with strategic know-how.
4
A strategist with analytical know
how who acts fact-based and target
oriented.
strategos
Ancient Greek for
„strategist“ – a manager
who applies his knowledge
to act target oriented
thales
von Milet(624 – 546 BC)
Merchant and pioneer of
philosophical science as
well as discoverer of
mathematical relations
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Our USPWe distinguish ourselves from other consulting companies by combining
management consulting with analytical methods.
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Development Production Sales/Logistics CRM/Marketing
How Old is Old Economy?In all parts of the value chain the Big Data potential becomes more and
more important.
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Connected
cars/ M2M
communication
Car sharing
Autonomous
driving
Social Media
Analysis
Predictive
marketing
Real-Time
Decision-
making (RTD)
NBA/NBO in
CRM
Industry 4.0
(Smart
Factories)
Real-time
route
optimization
Crowd-based
pickup and
delivery
Demand
optimized
stock ordering
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Agenda
Introduction 4
What is Big Data for us? 8
Automotive Production Use Case 13
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Volume
What is Big Data?Social media, smart devices, location based systems and sensor data
create a tremendous amount of data.
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More data cross the internet every second than
were stored in the entire internet just 20 years ago.
2.5 Exabytes (2.5 x 106 TB) are created every day.*
* McAfee, A., & Brynjolfsson, E. (2012). Big data: the management revolution. Harvard business review, (90) Tag Cloud:www.olap.com
Velocity
Variety
http://www.domo.com/learn/data-never-sleeps-2
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Big Data in ContextBig Data is associated with different disciplines and technologies.
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Data Mining
Business Analytics
Business Intelligence
Computer Cluster/Parallel Computing
Cloud Computing
Sampling
BIG
DATA
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Challenges in the Practical Use of Big Data
10
IT Infrastructure Corporate culture Expertise Legal/Ethical
§§§
Access to data
Connection of
different types of
data
New database
systems required
Computation power
Analysis applications
Costs
Identification of
relevant data
sources
Knowledge in
statistical
methods
Visualization of
results
Fear of the „Black-
Box“
Trust in data
Evidence based
decision making
Data protection
Data superiority
Predictive crime
detection
„Data kraken“
To ensure a successful use of Big Data a holistic project team is required.
Challenges can arise from different areas within and outside the
organization.
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Professionals in Contact with Big Datathaltegos is focused on analytical consulting but has a broad
understanding for IT as well.
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IT InfrastructureBusiness
Understanding
Analysis
Implementation
Computer Scientist:
Transfer statistical algorithms,
requirements, and rule engines
in IT landscape
Data Scientist:
Preparation, statistical
analysis, visualization of
data, new algorithms
Manager/Consultants:
Adaption of results and
optimization of business
processes
IT Department:
Providing the necessary
infrastructure hard- and
software.
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Agenda
Introduction 4
What is Big Data for us? 8
Automotive Production Use Case 13
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Production Use CaseEvaluation of production and assembly quality of the front fender.
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Fender
90% good 65% goodBlack box
…
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Implementation of a Big Data SolutionSensor data is collected and processed in real time with a hadoop cluster.
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Pressing
Plant
Body in
white
Paint
ShopAssembly
Stage 1
Assembly
Stage 2
Final
Assembly
no data
DWH 1 DWH 2 DWH 3 DWH 4Vehicle data
ok
X
At every stage in the production process the
evaluation of previous measuring stations is available.
Production data
…
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Descriptive statistics
Implementation of a Big Data SolutionDescriptive statistics as well as explorative analysis can be conducted on
the computer cluster.
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Decision tree modelling helps to
identify critical combinations that lead
to a bad result in the end of the
production process.
Explorative analysis
Real time data visualization helps to
identify urgent problems and gives a
simple and comprehensive overview.
!Results from both parts can be joint to build an early alert system that reduces the
costs of correcting actions.
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Results and Practical ImplicationsThe production use case has proven the potential of a Big Data solution
led to significant improvements.
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Measurement process optimization
• Specific improvements could be identified
• Connection of all relevant data
Constant quality monitoring
• Automated and integrated quality assurance
• Visualization
Quick failure identification
• From trial and error to faster evidence based approach
Improvements in the production process
• Elimination of potential reasons for a failure
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Results and Practical ImplicationsThe production use case has proven the potential of a Big Data solution
led to significant improvements.
17
Measurement process optimization
• Specific improvements could be identified
• Connection of all relevant data
Constant quality monitoring
• Automated and integrated quality assurance
• Visualization
Quick failure identification
• From trial and error to faster evidence based approach
Improvements in the production process
• Elimination of potential reasons for a failure
Side fact:
Significantly less correctly produced cars
during the weeks of a local beer festival.
Causality?
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Our Contact Details
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Dr. Sebastian FuchsPartner | Management Consulting
thaltegos GmbH
Siegfriedstraße 8 • D-80803 München
Phone: +49 (0)89 9549 200 2
Mobile: +49 (0)176 176 178 70
Mail: [email protected]
Dr. Markus KickSenior Consultant | Management Consulting
thaltegos GmbH
Siegfriedstraße 8 • D-80803 München
Phone: +49 (0)89 9549 200 5
Mobile: +49 (0)176 176 178 75
Mail: [email protected]
3/13/2015
Thank you for your attention!