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Page 1: EMEA USERS CONFERENCE • BERLIN, GERMANY © Copyright …cdn.osisoft.com/osi/presentations/2016-users-conference... · 2016. 9. 28. · CPS cyber-physical systems M2M ... Wearables

© Copyright 2016 OSIsoft, LLC EMEA USERS CONFERENCE • BERLIN, GERMANY

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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

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2016 │ PI in the Sky │ Dr. Felix Hanisch 3

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2016 │ PI in the Sky │ Dr. Felix Hanisch 4

What‘s the

hype?

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2016 │ PI in the Sky │ Dr. Felix Hanisch 5

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2016 │ PI in the Sky │ Dr. Felix Hanisch 6

What‘s the

hype?

What does it

mean for

Chemicals?

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2016 │ PI in the Sky │ Dr. Felix Hanisch 7

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2016 │ PI in the Sky │ Dr. Felix Hanisch 8

What does it

mean for

Chemicals?

• Data

• Context

• Action

What‘s the

hype?

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2016 │ PI in the Sky │ Dr. Felix Hanisch 9

Have a look …

What‘s the

hype?

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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

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2016 │ PI in the Sky │ Dr. Felix Hanisch 11

In a state between goldrush and panic …

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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

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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.

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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

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2016 │ PI in the Sky │ Dr. Felix Hanisch 15

IN THE SKY PI E

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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

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2016 │ PI in the Sky │ Dr. Felix Hanisch 17

Big Data Analytics

… others are not.

? ?

?

?

?

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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

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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

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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

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2016 │ PI in the Sky │ Dr. Felix Hanisch 21

What does it

mean for

Chemicals?

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2016 │ PI in the Sky │ Dr. Felix Hanisch 22

What is Chemicals 4.0?

Are we different?

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2016 │ PI in the Sky │ Dr. Felix Hanisch 23

What is Chemicals 4.0?

Are we different?

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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

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2016 │ PI in the Sky │ Dr. Felix Hanisch 25

What is Chemicals 4.0?

Are we different?

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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!

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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

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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

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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

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2016 │ PI in the Sky │ Dr. Felix Hanisch 30

ONE OF THE

WORLD'S LEADING

POLYMER

PRODUCERS

Covestro – who we are

At a glance

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2016 │ PI in the Sky │ Dr. Felix Hanisch 31

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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?

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2016 │ PI in the Sky │ Dr. Felix Hanisch 33

Have a look …

• Data

• Context

• Action

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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

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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

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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

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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

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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

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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, … )

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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

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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

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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

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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

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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?

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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.

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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.

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covestro.com

Thank you for

your attention Felix Hanisch

[email protected]

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