Overview presentation at TU Munich - analytics overview (types, methods, roles) - industrie 4.0 overview - use cases

1. Machine Learning
and Industrie 4.0
TU München, 10. Januar 2018
Jens-Peter Schleinitz
Wilfried Hoge
IBM Deutschland GmbH
Photo by Eike Klingspohn

2. Economic
pressures
Interest rates
continue to
pressure the
bottom line and
product profitability
Regulatory
oversight
Regulations
globally require
a holistic approach
to managing risk
across the
enterprise
Consumers are
seizing control
Customers are
expecting more
from their providers
Data explodes
Organizations are
adopting strategic
data and analytics
strategies to
manage the data
used across the
enterprise
What is impacting the industry?
2

3. Data Science, AI, Machine
Learning, Deep Learning
Data Science
Data Mining
Predictive
Analytics
Artificial
Intelligence
CRISP-DM
Statistics
Machine
Learning
Deep Learning
• Hype topics of 2017 and 2018.
• In combination with Digitalization
one of the most important
techniques to cope with industry
challenges.
• What do all of these terms mean?
3

4. Artificial Intelligence
• Artificial Intelligence (AI)
is the science of making
things smart.
• Broad term for getting
computers to perform
human tasks.
• Definition constantly
changing over time.
4

5. AI current status
5
• The systems implemented today
are a form of narrow AI.
• They can only do just one thing
really good or better than humans.
• … like recognizing objects, natural
language understanding.

6. Machine Learning
6
• Approach to achieve AI through
systems that can learn from
experience to find patterns in a
set of data.
• Teaching/Learning instead of
programming:
- takes known data
- learn patterns from data
- classifies new data

7. Deep Learning
7
• A technique for implementing
Machine Learning.
• E.g. deep neural networks.
• Great results for image
recognition, natural language
processing.

8. History
8
Artificial Intelligence
Machine Learning
Deep Learning
• Artificial Intelligence
started in the 1950s.
• Popularity of Machine
Learning is rising
since 1980s.
• Deep Learning is
getting hot since
2010.

9. Machine Learning models
• A feature is a numerical representation of
raw data.
• A model is a mathematical “summary” of
features.
• Training of a model could be
• supervised: provide labeled training data to
learn pattern (e.g. regression)
• unsupervised: must learn pattern from
unlabeled data set (e.g. clustering)
Feature 1
Target
Feature 1
Feature2
Feature 1
Feature2
Regression
• Fit the target
value
Classification
• Decide between
classes
Clustering
• Group data
points tightly

10. Types of Machine Learning
Discovery
Finding patterns in data that may be used to
guide decisions
– Association Rule
– Clustering
Prediction
Using known results to create models that
can predict values
– Classification
– Regression
– Time Series
Classification models identify category
membership
Predict discrete values:
• Response to treatment
• Price, out-of-stock, time prediction
• Malfunction probability
• Segment membership
Clustering analysis groups similar objects
Group similar records:
• Segmentation
• Data exploration
• Text and image analysis
10

11. Use Case: Hyper or Radical
Personalization
Challenge:
– Offer the right product to the right customer
Solution:
– Analyze historical trends and transactions for
each customer and for the market
Benefit:
– Provide personalized content to each customer
every time
11

12. Use Case: Price and Product
Optimization
Challenge:
– Provide the right price so that each product is
competitive and revenue is maximized
Solution:
– Analyze historical trends and transactions for
each product/service/class and for the market
– Determine right price and product combination
given inventory, space and other constraints
Benefit:
– Optimal and dynamic pricing for each
product/service and time/location combination
12

13. Machine Learning pipeline
• Collect raw data from sensors, pictures,
machines, ERP, MES, people, etc.
• Extract relevant features
• Build machine learning model
• Deploy found model in production
• Use the model to predict future
• Iterative process needed to improve /
guarantee quality
• New roles (Data Scientist) and tools
Raw data
Features
Models
Deploy in
Production
Use / Predict

14. CRISP-DM: Cross Industry
Standard Process for Data
Mining
• Proven for Data Mining / Machine
Learning projects.
• Generally accepted Data Science
workflow.
14

15. Data Science Life:
Skillset of the Data
Scientist
Process Automation
Parallel Computing
Software Development
Database Systems
Mathematics Background
Analytic Mindset
Domain Expertise
Business Focus
Effective Communication
Software
Engineer
Statistician
Business
Analyst
ML
danger zone research
unicorn
• Skillset of a Data
Scientist is hard to find
in a single person.
• Data Science is a team
sport.
15

16. Use Case: End-to-End Supply
Chain Management
Challenge:
– Manage the supply chain end to end (from
supplier, to manufacturing, to warehouse, to
store to consumer)
Solution:
– Forecast on demand, lead times and
transportation times
– Plan supplier orders, manufacturer schedules,
product inventory and product flow
Benefit:
– Avoid out-of-stocks and slow turning inventory
– Quickly update plans and schedules to respond
to changing business needs 16

17. Use Case: Optimize infrastruc-
ture and distribution assets
Challenge:
– Reduce accidents in petrochemical complexes
due to aging equipment, engineering resource
shortages. Improve reliability of refineries.
Solution:
– Predictive asset optimization applied to
underground pipe deterioration, cooling tower
temperature variation, gas turbine fuel nozzle
performance and onsite pipe deterioration.
Benefit:
– Developed predictive models for prediction of
equipment failures and improve safety.
– Help prevent operational issues in the refinery
through analysis and evaluation 17

18. Predictive
Maintenance
Condition Based
Maintenance
Predictive
Quality
After
Sales
Marketing
Industrial Use Cases

19. PREDICTIVE
QUALITY
19
25% increase in overall
productivity of cylinder-
head line
50% reduction in time
required to achieve
process target levels
100% payback achieved
within two years

20. DIGITAL
TRANSFORMATION
20
• Efficient Production
• Smart Products
• New Business Models

21. Mass Customization
From one car for all to a unique car for each of
us!
Increasing demand for customization, combined with pressure to
lower costs and time to market, are driving Industrie 4.0
21
In the beginning, we just made them all
(efficient, but it didn’t meet all customers’ needs)
… or we built them one-by-one for each new
customer
(it met the needs, but was very expensive)
Now customers demand increasingly
specialized and customized products
and costs are going through the roof!
Source: http://minicoopergraphics.com/

22. • Ability to connect and manage devices
• Near real-time data collection
• Insights of what is happening
• New business models
Internet of Things
• Embedded in equipment, products
and services
• Predict what may happen
• Prescribe actions for best
outcomes
• Self learning
• Communicate in natural language
Analytics & Cognitive
• Flexible machines
• 3D printing
• Machine to machine
• Mixed human / robotics
• New standards and protocolls
• Vertical and horizontal
integration
Flexible manufacturing
• New delivery channels and
business models
• Integration across value chains
• The API economy
Internet of Services
• Smart &networked products
• Ability to communicate
thru the Internet
• Self diagnose / self awareness
Autonomous Systems
Industrie 4.0
Context &
Components

23. Final report of the Industrie 4.0 working group
23

24. Horizontal / Vertical
Integration in Industrie 4.0
Smart Factory
• Fast setup and change of manufacturing
lines
• Lotsize 1 with +ROI
• +ROI in high labour country
• Provides and organizes partner network
• www.smartfactory-kl.de

25. Big Data Analytics
25
• Large amounts of data
• Structured data (sensors)
• Unstructured data (images,
videos)
• Intensive use of ML
technologies to learn from
data and predict
• Edge analytics to minimize
data transfer

26. Prozess Industrie
SIDAP
26
Quelle: Computerwoche

27. NAMUR*
Questionnaire
Results Data
Integration
*User Association of Automation Technology in
Process Industries
27

28. ERP
CYBER-PHYSICAL
PRODUCTION SYSTEMS
Infrastructure for
analytics is
changing with
Industrie 4.0
• from siloed
components in
layers
• to integrated
systems with
well defined
interfaces

29. Standards
Domains
29
• VDI 5600 (MES)
• DIN EN 62541 (OPC UA)
• EN 61970-407 (CIM)
• DEXPI
• PlantXML
ISO 15926
PandIX
• IEC 62424
Industry IoT

30. ZENTRALES
DATENMODEL
30

31. 31
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