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© Copyright 2016 OSIsoft, LLC EMEA USERS CONFERENCE • BERLIN, GERMANY
2016 │ PI in the Sky │ Dr. Felix Hanisch 2
PI in the Sky: What Cloud Computing,
Big Data and I4.0 Mean for the Process
Industry
Dr. Felix Hanisch
Covestro Deutschland AG
2016 OSIsoft EMEA User Conference
Berlin, Germany
2016 │ PI in the Sky │ Dr. Felix Hanisch 3
2016 │ PI in the Sky │ Dr. Felix Hanisch 4
What‘s the
hype?
2016 │ PI in the Sky │ Dr. Felix Hanisch 5
2016 │ PI in the Sky │ Dr. Felix Hanisch 6
What‘s the
hype?
What does it
mean for
Chemicals?
2016 │ PI in the Sky │ Dr. Felix Hanisch 7
2016 │ PI in the Sky │ Dr. Felix Hanisch 8
What does it
mean for
Chemicals?
• Data
• Context
• Action
What‘s the
hype?
2016 │ PI in the Sky │ Dr. Felix Hanisch 9
Have a look …
What‘s the
hype?
2016 │ PI in the Sky │ Dr. Felix Hanisch 10
Industrie 4.0 Buzzword Bingo
What is Industrie 4.0?
IoT internet of
things
smart
city
PAAS platform as a
service
Big
Data
Google cloud CPS cyber-
physical
systems
M2M machine-to-
machine
embedded
systems
Auto
PnP auto plug and
play
mobile social
machine
sensor data-
driven
operations
analytics
SoA service
oriented
architecture
(no)milk
in fridge I4.0
industrie 4.0
• Industrie 4.0 focuses on the production of
intelligent products, methods & processes
• Cyber-physical systems enable the intelligent
factory
• At its interfaces, the factory becomes part of an
intelligent infrastructure
• The Four V‘s of Big Data
Volume – scale of data
Velocity – streaming data analysis
Variety – different forms of data
Veracity – uncertainty of data
2016 │ PI in the Sky │ Dr. Felix Hanisch 11
In a state between goldrush and panic …
2016 │ PI in the Sky │ Dr. Felix Hanisch 12 1
2
2016 │ PI in the Sky │ Dr. Felix Hanisch
IN 2030, COMPUTERS
WILL BE MILLIONS
OF TIMES MORE
POWERFUL THAN TODAY. Source: Die Welt online
2016 │ PI in the Sky │ Dr. Felix Hanisch 1
3
Digital Revolution
OUR ANSWER:
Covestro develops materials to improve efficiency, user
friendliness, design and safety.
2016 │ PI in the Sky │ Dr. Felix Hanisch 14
Hype Cycle for Emerging Technologies 2015
Hype Cycle for Emerging Technologies, 2015
Source: Gartner (August 2015)
Innovation
Trigger
Peak of Inflated
Expectations
Trough of
Disillusionment Slope of Enlightenment
Plateau of
Productivity
As of July 2015
Plateau will be reached in:
EXPECTATIONS
Smart Dust
Virtual Personal Assistants
Digital Security People Literate technology
Bioacoustic Sensing
Quantum Computing
Brain-Computer Interface
Human Augmentation
Volumetric Displays 3D Bioprinting Systems for Organ Transplants
Smart Robots Affective Computing
Connected Home IoT Platform
Biochips Citizen Data Science
Neurobusiness Software–Defined Security
Digital Dexterity Micro Data Centers
Smart Advisors
Autonomous Vehicles Internet of Things
Speech-to-Speech Translation
Machine Learning
Wearables
Cryptocurrencies
Consumer 3D Printing
Natural-Language Question Answering
Hybrid Cloud Computing
Augmented Reality
Cryptocurrency Exchange Autonomous Field Vehicles
Virtual Reality
Gesture Control
Enterprise 3D Printing
less than 2
years
2 to 5
years
5 to 10
years
more than 10
years obsolete before
plateau
TIME
IoT Platform
Machine Learning
Augmented Reality
Advanced Analytics With Self-Service Delivery
2016 │ PI in the Sky │ Dr. Felix Hanisch 15
IN THE SKY PI E
2016 │ PI in the Sky │ Dr. Felix Hanisch 16
Internet of Things / Industrie 4.0
Some (I)IoT visions are long-term reality in the process industry …
Smart fridge orders milk Vendor managed inventory
2016 │ PI in the Sky │ Dr. Felix Hanisch 17
Big Data Analytics
… others are not.
? ?
?
?
?
Digital strategies require integration from suppliers to customers
Identify biggest value potential in own operations and@interfaces up-/down stream
Supply Inbound Production stages Outbound Order Pull
B2B Integration
Supplier Logistics
Ve
rtic
al In
teg
rati
on
Logistics Customer Final
customer Unit i
Raws
Unit …
Intermediates
Unit n
Finished goods
Integration of own production sites and external partners
APS
ERP
MES / DCS
PSS
APS
ERP
MES / DCS
PSS
APS
ERP
MES / DCS
PSS
APS
ERP
MES / DCS
PSS
APS
ERP
MES / DCS
PSS
APS
ERP
MES / DCS
PSS
APS
ERP
MES / DCS
PSS
APS
ERP
MES / DCS
PSS
Industry 4.0
How to navigate digitization of the manufacturing sector
Virtually
guided
self-
service
Smart
energy
consump-
tion
Intelligent
IoTs Real–time
yield optimi-
zation
Routing
flexibility
Machine
flexibility
Remote
monitoring
and control
Predictive
maintenance
Augmented
reality for MRO1
Human–robot
collabo-
ration
Remote monitoring
and control Digital
performance manage-
ment Automa- tion of
knowledge work
In situ
3D
printing
Real–time SC
optimi-zation
Batch
size 1
Statistical process control (SPC)
Advanced process control (APC)
Digital quality
management
Data–driven
demand
prediction
Data–driven
design to
value
Customer
cocreation/open
innovation
Concurrent
engineering
Rapid experimen-tation and simulation
Predictive
mainte-
nance
Remote
mainte-
nance
Virtually
guided
self-
service
Resource /
process
Asset
utilization
Labor
Inventories
Quality
Supply/
demand
match
Time to
market
Service/
aftersales
Source: Industry 4.0 How to navigate digitization of the
manufacturing sector. McKinsey Digital 2015
Resource /
process
Asset
utilization
Labor
Inventories
Quality
Supply/
demand
match
Time to
market
Service/
aftersales
Industry 4.0
How to navigate digitization of the manufacturing sector
Source: Industry 4.0 How to navigate digitization of the
manufacturing sector. McKinsey Digital 2015
1. Cf. McKinsey Global Institute: Big data:
The next frontier for innovation, competition,
and productivity
2. McKinsey analysis
3. McKinsey analysis
4. Cf. McKinsey Global Institute: Disruptive
Technologies
5. See, for example, ABB case study
6. Cf. T. Bauernhansl, M. ten Hompel,
B. Vogel-Heuser (Hrsg.): Industrie 4.0
in Produktion/Automatisierung/Logistik
(2014)
Indicative quantification of value drivers
Productivity increase
by 3 – 5%5
30 – 50% reduction of
total machine
downtime2
45 – 55% increase of
productivity in technical
professions through
automation of knowledge
work4 Costs for inventory holding
decreased by 20 – 50%3
Costs for quality
reduced by 10 – 20%6
Forecasting
accuracy increased to
85+%3
20 – 50% reduction
in time to market1
10 – 40% reduction of
maintenance costs1
Resource /
process
Asset
utilization
Labor
Inventories
Quality
Supply/
demand
match
Time to
market
Service/
aftersales
2016 │ PI in the Sky │ Dr. Felix Hanisch 21
What does it
mean for
Chemicals?
2016 │ PI in the Sky │ Dr. Felix Hanisch 22
What is Chemicals 4.0?
Are we different?
2016 │ PI in the Sky │ Dr. Felix Hanisch 23
What is Chemicals 4.0?
Are we different?
2016 │ PI in the Sky │ Dr. Felix Hanisch 24
What is Chemicals 4.0?
Are we different?
chemical plant
20-30 years in
operation
cell phone dev cycle
car lease
operating system
~7 years
2016 │ PI in the Sky │ Dr. Felix Hanisch 25
What is Chemicals 4.0?
Are we different?
2016 │ PI in the Sky │ Dr. Felix Hanisch 26
What is Chemicals 4.0?
Are we different?
Long Asset Lifetime
High fixed capital Complex Production Networks
safe
+
secure!
2016 │ PI in the Sky │ Dr. Felix Hanisch 27
Europe‘s chemical industry in a squeeze:
% of global sales in Europe to drop from 33% in 2000 to just 13% in 2035
33%
19%
13%
0% 20% 40% 60% 80% 100%
2000
2015e
2035e
Europe
Asia
North America
Latin America
Rest of World
Source: Roland Berger, Chemicals 2035, 05/2015
2016 │ PI in the Sky │ Dr. Felix Hanisch 28
The „Big Five“:
Challenges to Europe‘s chemical industry along the value chain
Feedstock End Use Chemical Production Application Manufactrg.
Feedstock
disadvantage 1
Chemical clusters
outside EU 2
56% is the amount by which EU regulations for the chemical industry have increased since 2008
– driving up costs in Europe and creating an uneven international playing field.
3
EU’s shrinking
manufacturing base 4
Demand shift 5
Source: Roland Berger, Chemicals 2035, 05/2015
2016 │ PI in the Sky │ Dr. Felix Hanisch 29
Is “Chemicals 4.0” the life vest in a rising tide?
Evolution of the chemical industry:
Age of Feedstock Making the most
out of feedstock
< 1980
Age of Value
Chain Focus Focus on core businesses
1980 – 2000
Age of
Life Sciences Focus on value-added specialties
and profitable growth
2000 – 2015
Age of Application:
Chemicals 4.0
> 2015
Source: Roland Berger, Chemicals 2035, 05/2015
2016 │ PI in the Sky │ Dr. Felix Hanisch 30
ONE OF THE
WORLD'S LEADING
POLYMER
PRODUCERS
Covestro – who we are
At a glance
2016 │ PI in the Sky │ Dr. Felix Hanisch 31
2016 │ PI in the Sky │ Dr. Felix Hanisch 32
Covestro approach to “digital”
3 horizons of implementation
Optimize supply Innovate how we do daily
business
1.
Predictive Maintenance
& E2E supply chain
Leverage growth Innovate how customers do
business with us
2.
Customer experience
& channels
Start a new game Business models digitally
enabled
3.
How would Google
run the business?
2016 │ PI in the Sky │ Dr. Felix Hanisch 33
Have a look …
• Data
• Context
• Action
2016 │ PI in the Sky │ Dr. Felix Hanisch 34
Optimize supply – why? Safe, reliable, efficient product supply is key for Covestro and our customers.
increase uptime / improve quality
ensure safety
reduce cost
Optimize supply – how? Putting data into context and making it actionable turns data into value.
approach in the past:
- focus on systematic data aquisition
- engineered solutions providing context +
analytics
powerfull, but often costly to maintain and
transfer to other plants & sites slow
digitalization strategy:
- systematically evaluate & prioritize gaps in
all 3 sectors and all 3 layers
faster + more cost-efficient roll-out
increased penetration
reduce cost
ensure safety
increase uptime
/ improve quality
Piece of cake: standardizing on the PI System
Today, 58 production units on 55 PI Servers globally, recording ~1.6 million tags
good old times standardize! PIER = PI System Enterprise Rollout I4.0
# tags on PI System
IP21
PI
System
legacy/other
2007
?
2016
2016 │ PI in the Sky │ Dr. Felix Hanisch 37
Sustainability
Clear objectives – targets
Reduce impact on the planet …
… and increase profit for society
Our
operations
Our
logistics
Customer
operations
Use of
product
End-of-life
Our raw
materials
Drive development of PPP product solutions (People, Planet, Profit)
Reduce impact from
Reduce impact from…
Foster Life Cycle Thinking
Reduce impact from
Enable our partners
CO2
CO2
PEOPLE
PLANET
PROFIT
STRUCTese®
For sustainable energy efficiency development
A three Step Approach
Online Energy Monitoring
• Visualization against Best Demonstrated Practice
(BDP)
• Control Room Awareness 2
Energy Efficiency Check
• Process analysis, detailed energy review
• Identifies efficiency projects
• Categorization, Action plans 1
Energy Loss Cascade
• Reporting of EnPIs
• Visualization of energy losses
• Target setting and monitoring 3
1.325
TEO CP IS PEO EL ES IO OEO PM PL DT OP LS CEC
Spez.
Consum
ption k
Wh P
E/
t P
rodct
The Energy Loss Cascade
Effort pays off for energy intensive productions
Big Data Management
Plan Do Check Act 3
PI System - data
Daily averages
STRUCTese-PI-Server: Local PI Servers (60 units)
> 4000 tags
(20 – 200 tags/unit)
Load, CEC, Fouling, etc.
CEC,PM,PL,DT,OP,LS
STRUCTese-Cascade-Tool:
Central Data Base (GLAMOUR) 1.325
TEO CP IS PEO EL ES IO OEO PM PL DT OP LS CEC
Spez.
Consum
ption k
Wh P
E/
t P
rod
ct
monthly cascades
(PE, power, steam, … )
Sustainability Achievements
STRUCTese®: For sustainable energy efficiency development
Covestro on Track to Meet Specific Targets
• STRUCTese® implemented in 60 plants
• BMS AG EnMS certified since 2009
(DIN 16001, ISO 50001)
• 30 % reduction of specific energy
consumption since 2005 (2030: 50%)
• 39 % reduction of specific CO2-emissions
since 2005 (2025: 50%)
• Sustainable savings from
energy efficiency projects
• 450 000 t CO2e/a
• 1,55 Mio. MWh PE/a
STRUCTese®/EnMS
& investment in new
process technologies
2016 │ PI in the Sky │ Dr. Felix Hanisch 41
Condition Monitoring
Automate routine checks for common equipment using asset model + alerts
redundant
sensor
diagnostics
sensor warning
plant data
sensor malfunction detection
• fouling
• freezing
• drift/offset
preprocessing (filtering, standard deviation)
tuning
parameters
pump runtime
monitoring
• switch redundant pumps
based on runtime
• runtime based
maintenance
• identify frequent on/off
operations
Konstante Constant (Property)
Ausgang Output (Alias)
Konstante Constant (Property)
Ausgang Output (Alias)
heat exchanger
fouling monitoring
• calculate heat transfer +
fouling continously
• alert on limit values
• common module for
several types of HX
on/off valves
• cycle count
• monitor opening &
closing times
• monitor correct
performance of partial
stroke tests
open
closed
Challenge #1: linking data / fast deployment
Asset information is distributed over many data sources how to link it for fast roll-out?
Leverkusen
Brunsbüttel
Dormagen
Antwerp
Baytown Shanghai
Map Ta Phut
Krefeld-Uerdingen
other asset
data… covestro main
production sites –
a true global player
Tough cookie: data model & streamlined process landscape
Value is captured via process – data reference
Data
Data
usage
Appli-
cations
Process
Value
Business
view
Eng. Op. Maint. Sup.
Process model Pain points Use cases
Application Appl. 1 Appl. 2
Usage-to-application mapping
System recommendation and selection
Data Process-to-data mapping
Data flow, data owner
Data-to-data mapping
Leading systems, integration
Data
usage Appl.
Data-to-usage mapping
Leading system functionalities
Phase 1
Phase 2
Challenge #2: different innovation cycles
How can we leverage tech developments more quickly on a broad base?
DCS / SIS
Installed base
MES /
PI System
Mobile ERP Office Cloud
FI M New equipment FI
external
partners
“clay layer of automation”
• slow and incomplete adoption
of standards (e.g. OPC UA)
• how to bypass / tunnel?
• how to retrofit installed base?
• how to build in security?
…
Summary: What does cloud computing, big data, I4.0 mean
for the process industry?
• Process industry has a long history of collecting, connecting
and analyzing data and turning it into value – don‘t panic!
• Current opportunities arize by systematically evaluating the
value chain vertically and horizontically – integrate your own
operations, integrate with partners!
• Having a common data infrastructure puts you ahead of the
game, but you also need a good + consistent asset model for
fast deployment – big data is more than a lake or 42.
• Covestro, a global player in polymers, has reduced specific
CO2 emissions by 40% by leveraging PI System data in
energy management.
• This presentation is over.
Forward-looking statements
This presentation may contain forward-looking statements based on current assumptions and forecasts made
by Covestro AG.
Various known and unknown risks, uncertainties and other factors could lead to material differences between
the actual future results, financial situation, development or performance of the company and the estimates
given here. These factors include those discussed in Covestro’s public reports, which are available on the
Covestro website at www.covestro.com.
The company assumes no liability whatsoever to update these forward-looking statements or to adjust them
to future events or developments.
covestro.com
Thank you for
your attention Felix Hanisch
felix.hanisch@covestro.com
© Copyright 2016 OSIsoft, LLC EMEA USERS CONFERENCE • BERLIN, GERMANY
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