Contenu connexe Similaire à IGNITE 2015 Valentijn de Leeuw - Industry 4.0: The industrial Internet of Things (20) IGNITE 2015 Valentijn de Leeuw - Industry 4.0: The industrial Internet of Things1. The Contribution of Supply Chain Networks
Smart Manufacturing and Industrial IoT
Elemica Ignite
Sept 15th, 2015
Valentijn de Leeuw
Vice President
ARC Advisory Group
vdeleeuw@arcweb.com
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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:
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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?
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Smart manufacturing: the growth strategy?
t Manufacturing fuels the supply chain
t Smart Manufacturing increases manufacturing
growth
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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
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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
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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
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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
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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.
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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
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Industrie 4.0t SupplyChain
Integration
• Intra-company
• Inter-company
• Interdisciplinary
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Traditional Supply Chaint AdaptiveProduction
• Demandpull
• Materialsupplycouldbe
providedviaSCON
Source: Poetter, Namur General Assembly 2013
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Industrie 4.0: Cyber-physical systemst VMIusingcyber-
physicalsystems
• Requiresreal-timeoperating
SCON!
Source:Poetter,NamurGeneralAssembly2013
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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
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Fine Chemicals and Life Sciences
Modular production technolgy
t Modularproduction
• EUco-sponsoredresearch
• 7F3Factorycasestudies
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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
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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
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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
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Your Grandfather’s BI & Analytics…
Operational
Systems
(ERP, MES, SCM,
Financials etc.)
Data
Warehouse
12
6
39
1
2
5
4
7
8
10
11
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Add Velocity, Volume and Variety…
Operational
Systems,
M2M Data,
Partner
Data, Public
Data, Textual…
Data
Warehouse
12
6
39
1
2
5
4
7
8
10
11
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…Has Too Much Latency for IIoT
Operational
Systems
(ERP, MES, SCM,
Financials etc.)
Data
Warehouse
Events Insight
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Cutting Latency
Operational
Systems,
M2M Data,
Partner
Data, Public
Data, Textual…
Data
Warehouse
1. Merged
Database
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Cutting Latency
2. Stream Processing (CEP)
3. Predictive Analytics
Operational
Systems
(ERP, MES, SCM,
Financials etc.)
Data
Warehouse
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Complex Event Processing
Complex Event Processing
(aka Event Streaming)
Real-Time
Automated
Decisions
Data Streams
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What Predictive Analytics Isn’t…
3834
5117
6448
7908
9181
11497
10788
10021
8341
Dow Jones
Industrial Average
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Value from Variety (Unstructured Data)
Operational
Systems,
M2M Data,
Partner
Data, Public
Data, Textual…
Data
Warehouse
4. Text Analytics
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Unstructured Brings New Perspective
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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
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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
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Emerging SM/IIoT Architecture – SC Analytics
Plant Operations
CorporatePurchasingEngineering
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
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Analytics Levels and Methodologiest Level1:historicalreporting
• reporting
t Level2:PredictiveAnalytics
• Forecasting
t Level3:Prescriptiveanalytics
• Recommendations,optimization
Tim Sharpe, Energy management at Sabic UK, Sabisu, EIF 2015
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t Negotiation, collaborative forecasts, engaged social
networking, motivation, decision making …
We continue to need unique human skills
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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
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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
36. Acknowledgement
David White
Senior Analyst
ARC Advisory Group
dwhite@arcweb.com
@addicted2data
IIoT Newsletter:
http://industrial-iot.com/
subscribe-to-newsletter/
Thanks to David for the analysis and survey
on IIoT, big data and analytics