IGNITE 2015 Valentijn de Leeuw - Industry 4.0: The industrial Internet of Things

The Contribution of Supply Chain Networks

Smart Manufacturing and Industrial IoT

Elemica Ignite Sept 15th, 2015

Valentijn de Leeuw Vice President

ARC Advisory Group [email protected]

2 © ARC Advisory Group

What ARC Does

t  ARC helps Suppliers •  Accelerate Revenue Growth & Manage Costs •  Bring Products & Services to Market Faster and more

Effectively t  ARC helps Industrial Companies

• Understand the Value of Emerging Technologies •  Choose Appropriate Suppliers for their Unique Needs •  Implement Operational Best Practices

Blog: Newsletter: http://industrial-iot.com http://industrial-iot.com/subscribe-to-newsletter/


Contents

1.   Smart Manufacturing for economic growth 2.   Key Initiatives SMLC, Industrie 4.0 and Horizon 2020

Innovator’s IIoT application examples Their implications for supply chain networks

3.   New approaches to analytics Supply Chain analytics

4.   Human-machine integration The role of the Human in all this?


Smart manufacturing: the growth strategy?

t  Manufacturing fuels the supply chain t  Smart Manufacturing increases manufacturing

growth


Manufacturing growth and competitiveness

Manufacturing Resilience Competitiveness Growth

High degree of Technology intensity Technology/manufacturing complexity Quality

DE

Complexity index 2010 versus 1995

SE UK

FR IT

ES

Impacted by Smart Manufacturing


Smart Manufacturing Initiatives

Smart Manufacturing Leadership Coalition (US) (High Value Manufacturing) Catapult (UK) Industrial Internet Consortium (International) Industrie 4.0 (Germany, Intl.) Industrie du Futur (France) Horizon 2020 (EU)

SPIRE (Sustainable process industries by Resource and Energy Efficiency Factory of the Future

Alliance for IoT Innovation (EU) Confederation of Indian Industries’ Smart Manufacturing (India) Made in China 2025 (China)

Different visions for different outcomes


vo•cab•u•la•ry (vō-kăbˈyə-lĕrˌē)

t  Smart Manufacturing • Advanced Manufacturing

• …

• Smart Manufacturing Technologies • Industrial Internet of Things (IIoT) • …

t  Smart Manufacturing Initiatives


Copyrighted by ARC Advisory Group

t  Key Characteristics •  Revitalize US manufacturing

since 2006, innovation •  Oil and Gas, Process and

Hybrid focused •  Engineering, Manufacturing

and Supply Chain •  Private-public partnerships •  Open SM platform, test

beds, market place (standards)

•  Step-change improvements •  Project cost and

duration •  Efficiency, productivity,

cost reduction •  Flexibilty and agility •  Sustainability and safety

Smart Manufacturing Leadership Coalition



Shorten SCM implementation time dramatically

t  SMLC Testbed General Mills

t  Complex integrated solution requirement •  Each implementation

iteration takes years •  Objective: reduce

application building and integration to a few months.



Industrie 4.0 t  Key Characteristics

•  German > International •  Rather discrete focused •  PLM, Manufacturing and SC •  Private-public partnerships •  Technology / Approach

•  Digitalization •  IT/OT/Process integration •  Ubiquitous sensing / CPS •  Big data – analytics

•  Stepchange or gradual change •  Industry growth, biz

models •  Project cost and duration •  Efficiency, productivity,

cost reduction •  Flexibilty and agility •  Sustainability and safety



Industrie 4.0 t S

up

ply

Ch

ain

In

teg

rati

on

• In

tra-

com

pany

• 

Inte

r-co

mpa

ny

• In

ter

disc

iplin

ary



Traditional Supply Chain t A

dap

tive

Pro

du

ctio

n

• D

eman

d pu

ll • 

Mat

eria

l sup

ply

coul

d be

pr

ovid

ed v

ia S

CO

N

Source: Poetter, Namur General Assembly 2013



Industrie 4.0: Cyber-physical systems t V

MI

usi

ng

cyb

er-

ph

ysic

al s

yste

ms

• Re

quires

rea

l-tim

e op

erat

ing

SCO

N!

Sou

rce:

Poe

tter

, N

amu

r G

ener

al A

ssem

bly

20

13



Increased throughput in existing plant

t  Industrie 4.0 at ThyssenKrupp

t  Supply chain integration •  Thyssen-Krupp and

clients •  Pull manufacturing •  Throughput

increase •  Avoid equipment/

surface size increase



Fine Chemicals and Life Sciences Modular production technolgy

t M

odu

lar

pro

du

ctio

n

• EU

co-

spon

sore

d re

sear

ch

• 7

F3 F

acto

ry c

ase

stud

ies



Modular equipment, lines and production units Revolution in engineering, construction and production

(Fine) Chemicals, Polymers and Pharmaceuticals

Modular reactor

Docking a modular plant

Details: “Advanced” Manufacturing



Future Implications of Modular/Mobile Production

t  Modularity of plants / exchangeable units •  P&S must take all possible routings into account, also within

the plant and production lines •  This is the “self-organizing” plant of Industrie 4.0 •  Plant size would not be a constraint anymore: line up/line

down

t  Mobile production lines •  Production network becomes flexible

•  place the production unit where it creates maximum value/minimum cost



Other Smart Manufacturing Impacts

t  Digitization • Digital twin of the

plant enables fast delocalization

t  Energy and feedstock price volatility • Multi energy supply • Multi feedstock supply

t  Greening •  Increasing use of

biological, living materials

•  Biomass as feedstock •  CO2-based feedstock



Your Grandfather’s BI & Analytics…

Operational Systems

(ERP, MES, SCM, Financials etc.)

Data Warehouse

12

6

3 9

1 2

5 4

7 8

10 11



Add Velocity, Volume and Variety…

Operational Systems,

M2M Data, Partner

Data, Public Data, Textual…

Data Warehouse

12

6

3 9

1 2

5 4

7 8

10 11



…Has Too Much Latency for IIoT

Operational Systems


Data Warehouse

Events Insight



Cutting Latency


M2M Data, Partner


Data Warehouse

1. Merged Database



Cutting Latency

2. Stream Processing (CEP) 3. Predictive Analytics

Operational Systems


Data Warehouse



Complex Event Processing

Complex Event Processing (aka Event Streaming)

Real-Time Automated Decisions

Data Streams



What Predictive Analytics Isn’t…

3834

5117

6448

7908

9181

11497 10788

10021

8341

Dow Jones Industrial Average



Value from Variety (Unstructured Data)


M2M Data, Partner


Data Warehouse

4. Text Analytics



Unstructured Brings New Perspective



From MRPII to Advanced P&S

Collaborative Forecasting

and Demand

Management

Supply &

Demand Balancing

Scheduling And

Capable to Promise

Rough Cut Capacity Planning

Distribution Requirements Planning

Sales and Operations Planning

Master Production Scheduling

Material Requirements Planning

Infinite Capacity Scheduling Available to

Promise

Statistical Forecastin

g



From MRPII to Advanced P&S and Analytics

Collaborative Forecasting

and Demand

Management

Supply &

Demand Balancing

Scheduling And

Capable to Promise

Rough Cut Capacity Planning

Distribution Requirements Planning

Sales and Operations Planning

Master Production Scheduling

Material Requirements Planning

Infinite Capacity Scheduling Available to

Promise

Statistical Forecastin

g

Towards Predictive

Supply Chain Analytics and

Network Optimization



Emerging SM/IIoT Architecture – SC Analytics

Plant Operations

Corporate Purchasing Engineering

XYZ Chemical XYZ Chemical XYZ Chemical

Enterprise

Maintenance

XYZ Chemical

Device buses

Production Management

Logic & Motion

Discrete ControlProcess Control

Infrastructure (Networks…)

Wireless

HMI / Workstations

Fieldbus

Application Specific

Appliances

Safety

XYZ Chemical

Client

3rd Parties

Supplier

Physical asset with sensors, actuators

Local IoT Compute and Communicate module

Smart Machine

IoT Smart Module

Emerging Option: Connect Assets Using New Technologies

New IoT Analytics and Applications

Purdue Hierarchy

IIoT Hierarchy

Enterprise



Analytics Levels and Methodologies t 

Leve

l 1:

his

tori

cal r

epor

tin

g

• re

port

ing

t 

Leve

l 2:

Pre

dic

tive

An

alyt

ics

• Fo

reca

stin

g t 

Leve

l 3:

Pre

scri

pti

ve a

nal

ytic

s • 

Rec

omm

enda

tions

, op

timiz

atio

n

Tim Sharpe, Energy management at Sabic UK, Sabisu, EIF 2015



t  Negotiation, collaborative forecasts, engaged social networking, motivation, decision making …

We continue to need unique human skills



Human-Machine Integration t  Human

•  Provide data to the system •  Need to develop trust •  Assesses, delegates,

interprets, judges and decides with consciousness and skill

Cognitive agents unload the human

Semantic interaction: meaningful human-

system communication

Source: Maurice Wilkins, Valentijn de Leeuw

t  Machine •  Allows focusing on problem

solving and decision making •  Provide context •  Ecological interface design

Predictive analytics proposes actions

Acts ethically and with compassion



Implementation Strategies

t  Target radical efficiency improvements •  Start small

t  Choose areas of innovation in line with business strategy and sector needs •  Per production type, process or plant type

t  Set goals, define KPI’s •  Improve product, material, substance performance if possible •  Innovate business models (e.g. circular) and value creation ecosystem •  Sustainability

t  Assessment methodology and Roadmap •  Maturity model, business case, roadmap •  Feasible roadmap, with regular updates

Acknowledgement

David White Senior Analyst ARC Advisory Group [email protected] @addicted2data

IIoT Newsletter: http://industrial-iot.com/subscribe-to-newsletter/

Thanks to David for the analysis and survey on IIoT, big data and analytics

IGNITE 2015 Valentijn de Leeuw - Industry 4.0: The industrial Internet of Things

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Transcript of IGNITE 2015 Valentijn de Leeuw - Industry 4.0: The industrial Internet of Things