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Market Report | November 2018
INDUSTRY 4.0 & SMART MANUFACTURINGMARKET REPORT 2018-2023Enterprise Premium Edition
In-depth market report sizing the opportunity of the fast growing Industry 4.0 & Smart Manufacturing market from 2018-2023. The 375-page report includes market forecasts across 7 regions, 6 supporting technologies, and 6 connected industry building blocks. The report also details 38 case studies, pro iles 350+ leading suppliers, describes 79 trends, and analyzes 12 key use cases.
ii 0 o nal tics ll rights reser ed ii
INDUSTRY 4.0 & SMART MANUFACTURING 2018-2023
Date: November 2018
Authors: a he opata, ulian ic ert, nud asse ueth, adraig cull
iii 2018 IoT Analytics. All rights reserved. iii
Table of Contents1 Executive Summary ix 1.1 Overall Highlights x 1.2 Market Analysis x 1.3 Key Use Case Analysis xi 1.4 Nine Disruptive Trends xi 1.5 I4.0 Adoption Strategies xii
2 Introduction 1 2.1 History of Industry 4.0 5 2.2 Elements of Industry 4.0 10
3 Industry 4.0 Market Analysis 2018-2023 12 3.1 Overall I4.0 Market 13 3.2 Connected Industry Building Blocks Market 15 3.2.1 By Connected Industry Building Block 15 3.2.2 By Region 17 3.2.3 Regional deep-dive: North America 18 3.2.4 Regional deep-dive: Europe 19 3.2.5 Regional deep-dive: Asia 20 3.2.6 Regional deep-dive: Other 21 3.3 Supporting Technologies Market 22
4 Connected Industry Building Blocks 25 4.1 Hardware 26 4.1.1 Microchips 27 4.1.2 Sensors 31 4.1.3 Connectivity Hardware 38 4.2 Connectivity 50 4.2.1 Network Protocols 51 4.2.2 M2M/Network Services 71 4.3 Cloud, Platform, & Analytics 84 4.3.1 Hosting Environment 85 4.3.2 IoT Platforms 93 4.3.3 Data Analytics & AI 101 4.4 Applications 117 4.4.1 Application Development and AEPs 118 4.4.2 Industrial App Store & Distribution Methods 121
iv 2018 IoT Analytics. All rights reserved. iv
4.5 System Integration 122 4.6 Cyber Security 128 4.6.1 IT vs. OT Security 129 4.6.2 Overview of IoT Attack Surfaces 131 4.6.3 Common IoT Threats 134 4.6.4 Recent I4.0 Related Attacks 136 4.6.5 Industry 4.0 Trends 137 4.6.6 Leading Suppliers 138
5 Disruptive Trends 142 5.1 Trends Disrupting the 5-Layer Automation Pyramid 143 5.1.1 Trend 1: Software Applications and Data Are Moving to the Cloud 145 5.1.2 Trend 2: SCADA, MES, and ERP Systems Are Converging 152 5.1.3 Trend 3: New Edge Devices Are Connecting Directly to the Cloud 154 5.2 Other Disruptive Trends 161 5.2.1 Trend 4: PLCs Are Becoming Virtualized Software Programs 161 5.2.2 Trend 5: Manufacturing Capacity Is Being Sold as a Service 163 5.2.3 Trend 6: Machines are Being Sold as a Service 164 5.2.4 Trend 7: Production Setups Are Becoming Flexible 165 5.2.5 Trend 8: Value Chains Are Becoming More Integrated 165 5.2.6 Trend 9: New Distribution Methods Are Utilizing the Web 165
6 Supporting Technologies 166 6.1 Additive Manufacturing (AM) 167 6.1.1 Overview 168 6.1.2 I4.0 Applications 172 6.1.3 Market Size and Growth 176 6.1.4 Disrupted Industries 177 6.1.5 Trends 178 6.1.6 Leading Suppliers 181 6.2 Augmented and Virtual Reality 187 6.2.1 Overview 188 6.2.2 I4.0 Applications 190 6.2.3 Market Size and Growth 191 6.2.4 Disrupted Industries 192 6.2.5 Trends 193 6.2.6 Leading Suppliers 195 6.3 Collaborative Robotics 201 6.3.1 Overview 202 6.3.2 I4.0 Applications 204 6.3.3 Market Size and Growth 205 6.3.4 Disrupted Industries 206
v 2018 IoT Analytics. All rights reserved. v
6.3.5 Trends 206 6.3.6 Leading Suppliers 209 6.4 Connected Machine Vision 212 6.4.1 Overview 213 6.4.2 I4.0 Applications 220 6.4.3 Market Size and Growth 221 6.4.4 Disrupted Industries 222 6.4.5 Trends 222 6.4.6 Leading Suppliers 223 6.5 Drones/UAVs 225 6.5.1 Overview 226 6.5.2 I4.0 Applications 227 6.5.3 Market Size and Growth 228 6.5.4 Disrupted Industries 229 6.5.5 Trends 229 6.5.6 Leading Suppliers 231 6.6 Self-Driving Vehicles (SDVs) 233 6.6.1 Overview 234 6.6.2 I4.0 Applications 235 6.6.3 Market Size and Growth 236 6.6.4 Disrupted Industries 237 6.6.5 Trends 237 6.6.6 Leading Suppliers 238
7 Key Use Cases 240 7.1 Additive Production 243 7.1.1 Case Study: Mercedes Benz Trucks reduces costs with 3D printed spare parts 244 7.1.2 Case Study: Siemens accelerates repair process by a factor of 10 using 3DP parts 245 7.1.3 Case Study: Oreck uses AM to reduce manufacturing costs of fixtures by 65% 246 7.2 Advanced Digital Product Engineering 247 7.2.1 Case Study : SEAT cuts development time by 30% with virtual reality 248 7.2.2 Case Study: Volvo uses AM to cut cost and development time by ~90% 249 7.2.3 Case Study: Bausch + Ströbel uses VR + digital twins to reduce time to market by ~30% 250 7.2.4 Case Study: Ford moves towards digital twins to improve automotive design processes 251 7.3 Augmented Operations 252 7.3.1 Case Study: Bechtle reduces walking time by 50% using AR 253 7.3.2 Case Study: Bühler uses augmented reality to streamline operations 254 7.4 Data-Driven Asset/Plant Performance Optimization 255 7.4.1 Case Study: Audi uses advanced analytics to realize millions in cost savings 256
vi 2018 IoT Analytics. All rights reserved. vi
7.4.2 Case Study: KIANA Systems uses machine vision & analytics to drastically reduce error rate 257 7.4.3 Case Study: Wafios improves machine throughput using cloud analytics 258 7.4.4 Case Study: Stanley Black & Decker increases OEE by 24% and first pass
quality by 16% 259 7.4.5 Case Study: Massilly uses autonomous forklifts to increase production capacity 260 7.5 Data-Driven Inventory Optimization 261 7.5.1 Case Study: Schneider Electric identifies opportunity to reduce SKUs by ~30% 262 7.5.2 Case Study: AFI saves 2 man-months per year with smart bin system 263 7.6 Data-Driven Quality Control 264 7.6.1 Case Study: OPEL reduces programming and measuring time by >80% using optical quality control 265 7.6.2 Case Study: Daimler automates in-line quality control of multi-variant products 266 7.6.3 Case Study: Sturm uses connected machine vision to create fully digitized production 267 7.6.4 Case Study: Kemppi improves product quality while reducing development time by ~50% using IIoT technology 268 7.7 Everything-as-a-Service Business Models 269 7.7.1 Case Study: Standard Motor Products uses manufacturing-as-a-service marketplace to reduce lead time by up to 70% and costs by up to 90% 270 7.7.2 Case Study: PepsiCo use Protolabs to go from concept to market in < 6 months 271 7.7.3 Case Study: Heller differentiates product offering with machine-as-a-service 272 7.8 Human-Robot Collaboration 273 7.8.1 Case Study: Siemens uses collaborative robots to supplement operations 274 7.8.2 Case Study: Kuka uses robots to manufacture robots 275 7.8.3 Case Study: Hirotec strives for “lights-out production” with SDVs + Cobots 276 7.8.4 Case Study: SFEG improves output by 20% with portable collaborative 277 7.9 Predictive Maintenance 278 7.9.1 Case Study: HPE uses edge gateways + analytics to predict & prevent wind turbine failures 279 7.9.2 Case Study: Auto manufacturer saves ~$40M in downtime by sending data to the cloud 280 7.9.3 Case Study: Fero labs helps oil refinery increase revenue by $4M using PdM 281 7.10 Remote Asset Testing/Inspection/Certification 282 7.10.1 Case Study: INEOS lowers costs and improves safety by using drones for inspections 283 7.10.2 Case Study: Oil and gas operator uses Remotely Operated Aerial Vehicles (ROAV) to save hundreds of days of work 284 7.10.3 Case Study: Siemens uses drones to reduce wind turbine inspection
vii 2018 IoT Analytics. All rights reserved. vii
costs and failures 285 7.11 Remote Service 286 7.11.1 Case Study: TRUMPF reduces support costs and increases quality with remote service 287 7.11.2 Case Study: Heidelberg uses remote service to reduce costs & offer new services 288 7.11.3 Case Study: thyssenkrupp reduces service time by 4x by using AR and remote
connectivity 289 7.12 Virtual Training 290 7.12.1 Case Study: Normet uses VR and simulation to improve operator efficiency by 23% 291 7.12.2 Case Study: KOC reduces accidents & accelerates operator onboarding using VR 292 7.13 Other use cases 293 7.14 Use Case Appendix 294 7.14.1 Advanced Digital Product Engineering: Definition of Digital Twins 294 7.14.2 Data-Driven Inventory Optimization: Definition of Multi-Echelon Inventory
Optimization 296
8 I4.0 Adoption Strategies 298 8.1 OEMs 298 8.1.1 Overview 299 8.1.2 Case Study: Liebherr fleet management for construction equipment 301 8.1.3 Case Study: Rolls-Royce condition monitoring for aircraft engines 302 8.1.4 Case Study: Kärcher cleaning machinery fleet management 303 8.1.5 Case Study: Heidelberg connected printing machines 304 8.1.6 Adoption Strategy Comparison: OEMs from Different Industries 305 8.1.7 Adoption Strategy Deep-Dive: Elevator OEMs 306 8.2 Smart Factories 307 8.2.1 Overview 308 8.2.2 TRUMPF Smart Factory 309 8.2.3 GE Brilliant Factory 313 8.2.4 Audi Smart Factory 317 8.2.5 SmartFactoryKL320
8.2.6 SmartFactory OWL 322 8.2.7 Other Smart Factories 324 8.2.8 Deep-dive: Lean Manufacturing and Industry 4.0 326 8.3 Industrial Automation Suppliers 328 8.3.1 Overview 329 8.3.2 I4.0 Readiness Assessments of Top 5 Industrial Automation Vendors 331 8.3.3 Others Large Vendors ($3B+ I4.0 Related Revenue) 336 8.3.4 Smaller Vendors (<$3B I4.0 Related Revenue) 339
viii 2018 IoT Analytics. All rights reserved. viii
9 Associations, Foundations, Committees to watch 340 9.1 Plattform Industrie 4.0 341 9.2 Labs Network Industrie 4.0 342 9.3 Industrial Internet Consortium (IIC) 343 9.4 OPC Foundation 344 9.5 Industrial Data Space Association 345 9.6 CyberValley of Baden Württemberg 346 9.7 Center for the Development and Application of Internet of Things Technologies 347 9.8 Manufacturing USA 348
10 Appendix 349 10.1 Market definition, sizing, and methodology 349 10.1.1 Industry 4.0 definition: 349 10.1.2 IoT definition: 349 10.1.3 IIoT and Connected Industry definition: 350 10.1.4 Connected Industry building blocks definition: 350 10.1.5 Market Sizing: 354 10.1.6 Methodology: 356 10.2 List of Acronymst 358 10.3 List of Exhibits 363 10.4 List of Tables 369About 373 Selected recent publications 373 Upcoming publications 374 Subscription 374 Newsletter 374 Main Author 375 Other Authors: 375 Contact Us 375Copyright 376
i 0 o nal tics ll rights reser ed i
he ter ndustrie 0 0 as introduced b er an thought leaders at the 0 anno er air
hibition and has since been adopted around the globe as the co on ter to describe the th
industrial re olution hile there is no single idel accepted definition o the 0 ar et, this report
defines the o erall 0 ar et as the su o the onnected ndustr building bloc s ar et the
anu acturing subset o the ndustrial nternet o hings o and the ar et or other 0 supporting
technologies.
his report highlights ho anu acturers are i ple enting these onnected ndustr building bloc s
and the si other 0 supporting technologies additi e anu acturing, , collaborati e robotics,
connected achine ision, drones s, and sel dri ing ehicles to reali e t el e e use cases that
are dri ing the th industrial re olution
E e uti e umm r
0 o nal tics ll rights reser ed
er i i ts
• The overall I4.0 market reached $XXB in 20XX, with Connected Industry building blocks
comprising o the ar et and supporting technologies co prising o the ar et
3
• The overall I4.0 market is growing at a CAGR of 37%, led by growth in the Connected Industry
building bloc s subset
• Advanced digital product engineering ill be the largest use case in 0 3 5 50 ar et
• Additive production and augmented operations are e pected to be the t o astest gro ing use
cases s
• ro th in 0 adoption is largel dri en b three t pes o alue generated b 0 use cases
1. Efficiency gains across the whole organization an industrial organi ations ha e
esti ated producti it gains ro in est ents in 0 technologies to be
2. New revenue streams s are le eraging o technolog to create ne as a ser ice
business odels hese pa per outco e business odels better align s ith custo ers
ob ecti es b incenti i ing s to a e sure their achines are operating properl
3. More flexible, customer centric operations that XXX 0 technologies enable
anu acturers to be ore 1 o nal tics nter ie anu acturing end users belie e this nu ber is possible and set it as goal
1 0 o nal tics ll rights reser ed 1
ntrodu tionIndustry 4.02 0 and the ndustrial nternet o hings o are both ter s describing disrupti e technolog
trends in industrial se ngs he ter s are so eti es used interchangeabl ho e er, in order to ull
co prehend the content o this report, it is i portant to understand the di erences bet een ndustr 0
and IIoT.
o is the industrial subset o the nternet o hings o t a high le el, o is about adopting the internet in
al ost all econo ic acti ities, and it ocuses on the technolog bac end or cross categor connecti it and
interoperabilit he e ergence and s i de elop ent o the o is dri en b the six major technological
developments shown in Exhibit 1
1. ncreased adoption o obile de ices
2. Declining costs for hardware such as sensors3
3. Declining costs of bandwidth
4. eclining costs o data handling, such as processing 4 and data storage
5 Decreased size of hardware elements
6. Increased maturity of big data tools and infrastructure
2 ro no on used s non ousl ith ndustr 03 hrough the econo ies o scale potential ro e g s artphone production and operation4 illion nstructions er econd
2 0 o nal tics ll rights reser ed 2
ExHIBIT 1: Technology drivers behind the Internet of Things
he ndustrial o o re ers to hea industries such as anu acturing, energ , oil and gas, and agriculture
in hich industrial assets are connected to the internet ithin o , di erent seg ents are ore industrial
than others, and onnected ndustr , hich specificall ocuses on anu acturing, is on the ost industrial
end of the spectrum as shown in Exhibit 2.
Internet of Things (IoT)
Retail
• Digital signage• In-store offering
& promotions• Supply chain• Smart ordering
& payment• Vending
machines
Healthcare
• Adherence & support
• Clinical• Virtual care• Wellness &
prevention
Insurance
• Health and life insurance
• Home insurance• Industrial
insurance• Vehicle
insurance• Cross
Connected Car
• Assisted & autonomous driving
• Fleet management
• In-vehicle infotainment
• Shared mobility• Smart
navigation• Vehicle
assistance
Buildings & Living
• Energy efficiency & HVAC
• Home equipment & appliances
• Living assistance• Safety & security• Vehicle-to-
infrastructure solutions
• Workplace operations
Smart Cities & Energy
• Construction• Education• Energy• Environmental• Roads, traffic &
transport• Social & Security• Water & Waste
Natural resources
• Agriculture• Mining• Oil & gas
Connected Industry
• Connected field• Digital factory• Product design
& engineering• Smart
maintenance• Supply chain
management
Main category
Industries/ Applications
I4.0
Less Industrial More Industrial
ExHIBIT 2: IoT categories sorted from least to most industrial
3 0 o nal tics ll rights reser ed 3
onnected ndustr is also the largest seg ent ithin o , co prising o er 30 o the ar et in 0 7
onnected ndustr o erlaps ith the o erall 0 ar et, but 0 has a broader scope it ai s to opti i e
the entire anu acturing alue chain and includes other 0 supporting technologies Exhibit 3 illustrates the
o erlap o 0 ith o and highlights the other 0 supporting technologies
ExHIBIT 3: o parison o o and ndustr 0 in ter s o industr and technolog scope adapted ro la or ndustrie 0
Industry 4.0 ar et can be ie ed as the co bination o the building bloc s that a e up the Connected
Industry market plus the market for other I4.0 supporting technologies
11 0 o nal tics ll rights reser ed 11
3 0 0 0 3
3 ndustr 0 ar et nal sis 0 0 3
This section quantifies the overall I4.0 market size as well as the market sizes for the two subsets of the overall I4.0 market:
1. Connected Industry Building Blocks
2. Supporting Technologies
Chapter Overview
Overall Industry 4.0 Market
Connected Industry Building Blocks Market
Supporting Technologies Market
Overall I4.0 Market3.1 Connected Industry Building Blocks Market
By Connected Industry Building Block
3.2
3.2.1
By Region3.2.2
Supporting Technologies Market3.3
Section Overview
Chapter Takeaways
1 The overall industry 4.0 market reached in 0 and is e pected to reach 3 0 b 0 3, resulting in a o Connected Industry building blocks ade up 35 o the ar et in 0 7, and
supporting technologies ade up
2 The Connected Industry building blocks market reached 35 in 0 7 and is e pected to gro at a o to in he biggest onnected ndustr building bloc in 0 7 as applications 3 , ollo ed
by hardware
3 he supporting technologies ar et reached 3 in 0 7 and is e pected to gro at a o 6 to 53 in 0 he biggest supporting technologies ar et in 0 7 as additi e anu acturing 9 , ollo ed b connected machine vision
4 Growth is driven by 3 types of value deri ed ro 0 use cases
cienc gains across the hole organi ation
2. New revenue streams
3 ore e ible, custo er centric operations that reduce ti e to ar et
12 0 o nal tics ll rights reser ed 12
3 0 0 0 3
er r et
50
0
300
150
200
100
250
350
Year
Global I4.0 Market Size in $B
2022 202320212018 20192017 2020
35
2116
13
256
9467
131
48
184
310
87
37%
164
120
226
42
33
2664
48
53
I4.0 SupportingTechnologies
Connected IndustryBuilding Blocks
Note: The overall market for I4.0 refers to global spending on the six connected industry building blocks and six I4.0 supporting technologies
CAGR 17-23
26%
40%
Overall I4.0 Market: By Technology
Overall I4.0 Market
Xx% = Overall CAGRSource: IoT Analytics – October 2018
ExHIBIT 10: lobal 0 ar et 0 7 0 3 ource o nal tics
he global ar et or ndustr 0 solutions reached $48B in 2017 and is e pected to gro at a CAGR of XX
% to $XX in 20XX. The Connected Industry building blocks subset o the ar et is e pected to gro
ro $XXX in 20XX to XXX in 2023 with a CAGR of 40%. The supporting technologies subset is projected
to grow from $XX in 2017 to $53B in 2023 with a more modest CAGR of 26% due to the relati e
aturit o the technologies that a e up this subset such as connected achine ision he gro th o
the ar et or 0 solutions is largel dri en b three t pes o alue deri ed ro the 0 use cases
1. Efficiency gains across the whole organization an industrial organi ations ha e esti ated producti it
gains ro in est ents in 0 technologies to be 5 8.
Example: FANUC + Cisco
and isco recentl partnered to create an
8 o nal tics nter ie anu acturing end users belie e this nu ber is possible and set it as goal9 ource Cisco
24 0 o nal tics ll rights reser ed 24
onnected ndustr uilding loc s
This chapter explores the six connected industry buildings blocks that comprise the modern IIoT technology stack:Chapter Overview
Connectivity
Network Protocols
4.2
4.2.1
M2M/Network Service4.2.2
Section OverviewHardware
Microchips
4.1
4.1.1
Sensors4.1.2
Connectivity Hardware4.1.3
Applications
Application Development and AEPs
4.4
4.4.1
Industrial App Store & Distribution Methods4.4.2
Cyber Security4.6
Cloud, Platform, & Analytics
Hosting Environment
4.3
4.3.1
IoT Platforms4.3.2
Data Analytics & AI4.3.3
System Integration4.5
1. Hardware
2. Connectivity
3. Cloud, Platform, & Analytics
4. Applications
5. System Integration
6. Cyber Security
Chapter Takeaways
1 XXXXX.
2 Companies are XXXXXXXXXXXXXXXXXXXXXhybrid cloud hosting environments. The market share of IoT plat or s hosted solel on pre ise is pro ected to drop ro 3 in 0 7 to 3 in 0 3
3 IoT platformXXXXXXXXXXXXXXupporting AI technologies. o plat or endors are including nati e anal tics tools e g icroso t ure achine earning, achine earning, oogle loud , etc
hich allo s custo ers to build, train and deplo achine learning odels uic l and easil at scale
4 Platform specific edge computing agents are becoming more common. o plat or s are increasingl adding co puting and storage capabilities at the edge e g , a on reengrass,
hich is leading to ore h brid o deplo ents ith both edge and cloud architectures in place
5 AI algorithms still require domain-specific expertise. Suppliers of AI technologies hope to eventually develop odels that can be easil adapted bet een co panies and use cases ho e er, that ision has not et been
reali ed an industrial solutions and algorith s are still er dependent on industr specific training datasets and input ro sub ect a er e perts
6 Low cost/risk POCs gaining in popularity. s and to end users who are reluctant to allocate large budg
I4.0 projects. For e a ple, s ste s integration ir s li a
o ering igital e peri ents as a er ice7 28% of manufacture isco sho ed that gani ations sur attac s in the past ear the a erage
8 LoRa is the 2017 market leader in LPWAN technologies, ollo ed b ig o , and o
140 0 o nal tics ll rights reser ed 140
5
5 isrupti e rendsChapter Overview
Other Disruptions
Trend 4: PLCs Are Becoming Virtualized Software Programs
5.2
5.2.1
Trend 5: Manufacturing Capacity Is Being Sold as a Service5.2.2
Trend 6: Machines Are Being Sold as a Service5.2.3
Trend 7: Production Setups Are Becoming Flexible5.2.4
Trend 8: Value Chains Are Becoming More Integrated5.2.5
Trend 9: New Distribution Methods Are Utilizing the Web5.2.6
Section OverviewThe 5-Layer Automation Pyramid
Trend 1: Software Applications and Data Are Moving to the Cloud
5.1
5.1.1
Trend 2: SCADA, MES, and ERP Systems Are Converging5.1.2
Trend 3: Edge to Cloud Connectivity5.1.3
I4.0 is commonly thought of as an evolution rather than a revolution, but I4.0 has the potential to disrupt a number of standards and industries in the long run. This chapter shows how the well-defined 5-layered technology architecture is already being disrupted with new connectivity models, and how other industrial processes and industries will also likely see significant changes.
Chapter Takeaways
1 TXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXX XXXXXajor trends:
he igration o so are applications to the cloud
The convergence of SCADA, MES, and ERP systems
e de ices connecting directl to the cloud
2 New XXXXXXXXXXXXXXX XXXXXDA/MES cloud adoption. d ance ents in cellular co unication 5 , cyber security, and industrial gateways are making it more technically viable for companies to move their SCADA and MES systems to the cloud
3 Manu ates here e cess ho e capacit is ad ertised online , the e erging anu acturing as a ser ice eco s ste allo s connected anu acturers to sell their e cess anu acturing capacit online to custo ers e uipped ith digital product designs
4 Machine-as-a-Service business models bring new revenue and accounting challenges. More machines are being sold to anu acturers as ser ices n estors and s are grappling ith the re enue and accounting i plications o anu acturing custo ers s itching to higher and lo er businesses
164 0 o nal tics ll rights reser ed 164
6
6 upporting echnologies
This chapter explores the six I4.0 supporting technologies that are contributing (to varying degrees) to the rapid growth of
the overall I4.0 market:
Chapter Overview
Section Overview
Additive Manufacturing6.1
Case Studies
Augmented and Virtual Reality6.2
Case Studies
Collaborative Robotics6.3
Case Studies
Connected Machine Vision6.4
Case Studies
Drones/UAVs6.5
Case Studies
Self-Driving Vehicles6.6
Case Studies
1. Additive Manufacturing
2. Augmented and Virtual Reality
3. Collaborative Robotics
4. Connected Machine Vision
5. Drones/UAVs
6. Self-Driving Vehicles
Chapter Takeaways
1 AXXXXXXXXXXXXXXX XXXXXgies market. ith re enue at 9 in 0 7 and a pro ected o 5 , additi e anu acturing is and ill supporting technologies ar et as more companies adopt the technology for more than just prototyping.
2 XXXXXXXXXXXXXXX XXXXXXXXXXX XXXXXrdware and CAD/CAM software providers. o panies li e assault and d are suppliers li e icroso t to create a ore sea less integr nd he end goal is or odels to be instantl created in the so t are urrentl ocusing on irtual realit and is partnering ith endors li e
and culus
3 Higher labor costs and falling robot costs are driving collaborative robot adoption. obotics prices ill continue to all e en as ages increase in both de eloped and de eloping countries
4 Mobile collaborative robots are gaining in popularity. o panies are designing their collaborati e robots to be highl portable e en sel dri ing , allo ing or e tre el e ible anu acturing
5 Machine learning technology is moving closer to the edge with vision systems. Smart cameras from co panies li e ogne run trained achine learning al el es in order to achie e high speed pattern recognition
6 Regulations are constraining the growth of the drones/UAVs market. e ond isual line o sight regulations are constraining the gro th and nu ber o use cases or drones in certain regions
7 Suppliers of traditionXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXX XXXXX. As s ste s instead o to pro ide i ed path na igation guidance
238 0 o nal tics ll rights reser ed 238
7
7 e se ases
kawadarobotf
This chapter highlights 38 specific examples from a range of industries (10+ different end-user industries) including the automotive, consumer electronics, consumer packaged goods, and OEM industries. The examples are clustered into the 12 most common I4.0 use cases.
Chapter Overview
Section OverviewAdditive Production
Mercedes Benz Trucks reduces costs with 3D printed spare parts
7.1
7.1.1
Siemens accelerates repair process by a factor of 10 using 3DP parts
7.1.2
Oreck uses AM to reduce manufacturing costs of fixtures by 65%
7.1.3
Advanced Digital Product Engineering
SEAT cuts development time by 30% with virtual reality
7.2
7.2.1
Volvo uses AM to cut cost and development time by ~90%
7.2.2
Bausch+Ströbel uses VR + digital twins to reduce time to market by ~30%
7.2.3
Ford moves towards digital twins 7.2.3
Augmented Operations
Bechtle reduces walking time by 50% using AR
7.3
7.3.1
Bühler uses augmented reality to streamline operations
7.3.2
Data-driven Asset/Plant Performance Optimization
Audi uses advanced analytics to realize millions in cost savings
7.4
7.4.1
KIANA Systems uses machine vision & analytics to reduce error rate
7.4.2
Wafios improves machine throughput using cloud analytics
7.4.3
Stanley Black & Decker increases OEE by 24% and first pass quality by 16%
7.4.4
Massilly uses autonomous forklifts to increase production capacity
7.4.5
Data-driven Quality Control
OPEL reduces programming and measuring time by >80%
7.6
7.6.1
Daimler automates in-line quality control of multi-variant products
7.6.2
Sturm uses connected machine vision to create fully digitized production
7.6.3
Kemppi improves product quality and reduces development time by ~50%
7.6.4
Everything-as-a-Service Business Models
SMP uses Xometry to reduce lead time by up to 70% and costs up to 90%
7.7
7.7.1
PepsiCo use Protolabs to go from concept to market in < 6 months
7.7.2
Heller differentiates product offering with machine-as-a-service
7.7.3
Human-Robot Collaboration
Siemens uses collaborative robots to supplement operations
7.8
7.8.1
Kuka uses robots to manufacture robots 7.8.2
Hirotec strives for “lights-out production” with SDVs + Cobots
7.8.3
SFEG improves output by 20% with portable collaborative
7.8.4
Data-driven Inventory Optimization
Schneider Electric identifies opportunity to reduce SKUs by 30%
7.5
7.5.1
AFI saves 2 man-months per year with smart bin system
7.5.2
Remote Asset Testing/Inspection/ Certification
INEOS lowers costs and improves safety by using drones for inspections
7.10
7.10.1
Sky-Futures helps O&G operator save hundreds of man-days of work
7.10.2
Siemens uses drones to reduce wind turbine inspection costs and failures
7.10.3
Remote Service
TRUMPF reduces support costs and increases quality with remote service
7.11
7.11.1
Heidelberg uses remote service to reduce costs & offer new services
7.11.2
thyssenkrupp reduces service time by 4x by using AR + remote connectivity
7.11.3
Virtual Training
Normet uses VR and simulation to improve operator efficiency by 23%
7.12
7.12.1
KOC reduces accidents & accelerates operator onboarding using VR
7.12.2
Predictive Maintenance
HPE uses edge gateways + analytics to prevent wind turbine failures
7.9
7.9.1
Cisco helps automotive OEM save ~$40M in downtime7.9.2
Fero Labs helps oil refinery increase revenue by $4M using PdM
7.9.3
Chapter Takeaways
1 ut o the use cases identified as the top 0 use cases, advanced digital product engineering will be the largest use case b ar et si e in 0 3 ri en b additi e anu acturing and adoption, ad anced digital product engineering use cases are e pected to generate
2 Additive production and augmented operations will be the fastest growing use cases from 2018 to 2023, both ith s 5 o er cost and higher ualit additi e anu acturing technologies co bined
ith increased custo er de and or custo parts ill anu acturing or serial grations bet een
dri e au
96 0 o nal tics ll rights reser ed 96
0
0 doption trategiesChapter Takeaways
1 XXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXX XXXXXs are more likely to adopt IoT products and strategies.
2 are key focuses of smart actor initiati es
3 XXXXXXXXXXXXXXX XXXXX are leading the a in 0 readiness due to their o erings and in est ents in arious onnected ndustr building bloc s and 0 supporting technologies
E s
OEMs across a variety of industries are creating connected products and services to differentiate their offerings and create new service revenue streams.
Section Overview
Subsection OverviewCase Studies
Liebherr LiDAT fleet management for construction equipment1Rolls-Royce condition monitoring for aircraft engines2Kärcher cleaning machinery fleet management 3Heidelberg connected printing machines 4
TRUMPF: sheet metal & laser cutting tools OEM I4.0 adoption strategy5Heidelberg: printing presses OEM I4.0 adoption strategy6
Krones: bottling and packing machines OEM I4.0 adoption strategy7Engel: injection molding machines OEM I4.0 adoption strategy8
thyssenkrupp: elevator OEM I4.0 adoption strategy9KONE: elevator OEM I4.0 adoption strategy10Otis: elevator OEM I4.0 adoption strategy11Schindler: elevator OEM I4.0 adoption strategy12
Section Takeaways
1 Tier 2/component suppliers and industries with moveable equipment, remote/high value assets, or data-driven products are more likely to adopt IoT products and strategies.
2 Construction equipment, elevator, and agricultural machinery OEMs are leading the a in o adoption, ith co panies iebherr, chindler, and laas i ple enting best in class solutions
339 0 o nal tics ll rights reser ed 339
9 , ,
P orm ndustrie
la or ndustrie 0pla or i 0 de
Founded: 2013Members: 6000+ ia the associations
he la or ndustrie 0 as ounded b the thee er an associations VDMA echanical ngineering ndustr ssociation , ZVEI regulator and econo ic polic authorit o the electrical and electronics industr and BITKOM er an s digital association and in 0 5 extended by stakeholders from politics ederal o ern ent inistries
o ducation esearch, cono ics echnolog , research raunho er esellscha , ational cade or cience and ngineering, er an esearch enter rtificial ntelligence and highl inno ati e co panies li e
to dri e the de elop ent o ndustrie 0 as a hole and possibilities or s in particular he pla or is organi ed in or ing groups on ollo ing topics
Our research and interviews with industry experts revealed that RAMI 4.0 is watched
with interest, but seems currently too academic and theoretical.
re erence architectures 0 , standards and nor sresearch and inno ation
3. security of networked systems4. legal framework5 or , education and trainingn arch 0 6, a collaboration ith the ndustrial nternet onsortiu as announced 164 uther cooperations
e ist ith the rench lliance ndustrie du utur ct 0 5 and ith hina ia the ino er an posiu cto 0 6 ll acti ities stri e to be pre co petiti e not de eloping ar et solutions , thus pro iding a sa e and
legal forum for a variety of industry players to discuss and resolve common problems.
164
164 ource la or ndustrie 0 engl
347 0 o nal tics ll rights reser ed 347
0
ppendir et de nition si in nd met odo o
ndustr de nition
he ter ndustrie 0 0 as introduced b er an thought leaders at the 0 anno er air hibition
and has since been adopted around the globe as the common term to describe the 4th industrial re olution
hile there is no single idel accepted definition o the 0 ar et, this report defines the o erall 0
ar et as the su o the onnected ndustr building bloc s ar et the anu acturing subset o the
ndustrial nternet o hings o and the ar et or other 0 supporting technologies
Overall Industry 4.0 Market
Connected Industry Building Blocks
Supporting Technologies
o de nition
he nternet o hings o is defined as a net or o nternet enabled ph sical ob ects, hich ai s
at integrating e er ob ect or interaction ia e bedded s ste s, net or co unications, bac end
co puting, and applications t picall in the cloud t allo s ob ects to co unicate ith each other, access
in or ation on the nternet, capture store and retrie e data, and interact ith users as ell as other s ste s
and applications, creating s art connected en iron ents
348 0 o nal tics ll rights reser ed 348
0
o nd onne ted ndustr de nition
ndustrial o o is a subset o the nternet o hings o hich re ers to hea industries such as
manufacturing, energy, oil and gas, and agriculture in which industrial assets are connected to the internet.
ithin o , di erent seg ents are ore industrial than others, and onnected ndustr , hich specificall
ocuses on anu acturing, is on the ost industrial end o the spectru as sho n in the e hibit belo
Internet of Things (IoT)
Retail
• Digital signage• In-store offering
& promotions• Supply chain• Smart ordering
& payment• Vending
machines
Healthcare
• Adherence & support
• Clinical• Virtual care• Wellness &
prevention
Insurance
• Health and life insurance
• Home insurance• Industrial
insurance• Vehicle
insurance• Cross
Connected Car
• Assisted & autonomous driving
• Fleet management
• In-vehicle infotainment
• Shared mobility• Smart
navigation• Vehicle
assistance
Buildings & Living
• Energy efficiency & HVAC
• Home equipment & appliances
• Living assistance• Safety & security• Vehicle-to-
infrastructure solutions
• Workplace operations
Smart Cities & Energy
• Construction• Education• Energy• Environmental• Roads, traffic &
transport• Social & Security• Water & Waste
Natural resources
• Agriculture• Mining• Oil & gas
Connected Industry
• Connected field• Digital factory• Product design
& engineering• Smart
maintenance• Supply chain
management
Main category
Industries/ Applications
I4.0
Less Industrial More Industrial
onne ted ndustr ui din o s de nition
he onnected ndustr ar et can be bro en do n in to si building bloc s that together or onnected
ndustr solutions
1. Cyber Security: security tools, technologies, and methods used throughout all building blocks
2. Hardware: the chips, sensors, & gateways used to build and connect smart devices
3. Connectivity: the protocols and ser ices re uired to achie e connect industrial e uip ent
4. Cloud, Platform, & Analytics hosting en iron ents, o pla or s, and data anal tics
5 Applications: so are progra s that are built on top o o pla or s
6. System Integration: the ser ices associated and ith designing, planning, building, and operating 0
solutions
3 9 0 o nal tics ll rights reser ed 3 9
0
he table belo describes the sub ele ents o each onnected ndustr building bloc
loud, la or , nal tics
o la or ss ha e specific eatures that o er rule engine e ent
anage ent, s or business apps, integration s or endpoints, integrated de elop ent en iron ent and application ar etplace
loud, la or , nal ticso la or s
pla or s support de ice onitoring and anage ent, bidirectional co and control, o er the air updates and application management
loud, la or , nal tics
IoT Cloud backends public, pri ate
o are bac end that aggregates inbound strea ing data, handles processing and storage in databases or ultiple data odels or ats e g , relational, non relational, e alue, etc and scales as re uired
loud, la or , nal tics
o onnecti it la or s
onnecti it pla or s support di erent protocols data or ats, ensuring bidirectional co unication ith all de ices and abilit to
onitor net or usage generating notifications and alerts
loud, la or , nal tics
d anced anal ticsd anced nal tics pla or s ha e specific eatures that allo or
ad anced anal tics on o data through achine learning, strea ing anal tics and co ple algorith s
o unicationsellular icensed
traditional Operator services related to 2G, 3G and 4G technologies in the licensed spectrum
o unicationsellular icensed perator ser ices related to technologies in the licensed
spectru o ,
o unicationsCellular - Unlicensed perator ser ices related to technologies in the unlicensed
spectru e g , ora, ig o , ngenu, etc
o unications ellular 5 perator ser ices related to 5 technologies in the licensed spectru
o unications Satellite Operator services related to satellite technologies
o unications ireline perator ser ices related to ireline technolog i i
o unications Other ther operator ser ice re enue e g , esh net or s, etc
ard are Chips e iconductors used in o de ices and co unications e uip ent
ard are Sensors Sensors used in IoT devices
ard are perating ste perating s ste s used in o de ices
ard are dge applicationspplications that are de eloped and run specificall on gate a s and
devices
ard are dge anal ticsnal tics ser ices that are de eloped and run specificall on gate a s
and devices
ard areo unications
modulesac age o antenna, chipset, etc that allo s or connecti it
ard are SIM cards SIM cards
Table 81: onnected ndustr building bloc sub ele ents
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0
ard are Routers & Gateways Routers & Gateways
ard areoards and s all
componentsCircuit boards, transistors, capacitators and other electronics e uip ent
ard areOther hardware components
ther hard are re uired or o de ices e g , screens, spea ers, la ps, etc
System ntegration
onsulting
onsulting ser ices are ad isor ser ices b outsourced pro iders that help businesses identi o opportunities create business cases and road aps assesses organi ational readiness, go ernance, ris , legal ra ifications, securit and business process redesign and select the product, vendor or technology. In doing so, these services help align technology strategies with business or process strategies. These ser ices support o initiati es b pro iding strategic, architectural, operational and i ple entation planning
System ntegration
ple entation
ple entation ser ices custo i e or de elop o solutions, assets and processes, and then integrate these solutions, assets and processes
ith e isting in rastructure and processes he also include product engineering o sensors e bedded de ices, sensor installation, hard are so are net or i ple entation, and application and de ice testing in arious conditions
System ntegration
perations
perations ser ices pro ide da to da anage ent and operation o o assets and processes hese include related so are and hard are
support services, as applicable. They may include infrastructure anage ent, application anage ent, de ice anage ent,
per or ance onitoring, re ote diagnostics, authentication, billing and custo er support nal tics operations, hich see to leverage data associated with sensor readings and networked s ste s operational state data, are a e part o operations he anal tics e orts see to s nthesi e ra operational data, as ell as create predicti e algorith s into actionable in or ation and reco endations
pplicationsSmart phone applications
uilding and aintaining applications that a e use o o data on s artphones esp ndroid and pple
pplicationseb based
applicationsuilding and aintaining applications that a e use o o data in eb
browser environments
pplicationspplication
development environments
pecific de elop ent tools that let co puter scientists progra , test, and deplo applications in the cloud and on the de ice
pplications ac end integrationntegration o o applications and data to other enterprise ser ices e g , , s ste s including progra ing o standard s and
s and other data translation
Table 81 (Continued): onnected ndustr building bloc sub ele ents
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0
pplications ther applicationspecific applications that a e use o o data hich are not
programmed on the web or in the smartphone
Cyber Security Device securitypecific securit eatures including hard are e g , s, circuit
shielding and so are solutions e g , secure boot that enhance the level of security on the device layer.
Cyber Securityo unications
security
pecific securit eatures that ensure data is sa el encr pted hile in transit e g , , and un anted intrusions are detected pre ented e g , fire alls,
Cyber Security Cloud security
pecific securit eatures that protect sensiti e in or ation stored in the cloud i e , dis encr ption or data at rest and ensures onl authori ed access is granted i e , pla or application 3rd part erification
Cyber Securityi ec cle
management
ontinuous processes re uired to eep the securit o an o solution up to date ro deplo ent to deco issioning e g , acti it
onitoring, regular securit updates patches
Table 81 (Continued): onnected ndustr building bloc sub ele ents
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0
upporting technologies definition
here are si supporting technologies that are deplo ed alongside onnected ndustr building bloc s in 0
solutions
• Additive Manufacturing: the process o oining aterials pol ers, etals, cera ics, etc ro 3
models to make industrial prototypes or low volume products
• Augmented and Virtual Reality: tools that immerse users in digital worlds in order to help them design
and operate industrial products and systems
• Collaborative Robotics: s art, e ible, eas to train robots hich enable sa e hu an achine interaction
in factories without the need for fences or cages
• Connected Machine Vision ad anced industrial ca eras that si ultaneousl co unicate ith
industrial control s ste s and higher le el i age anage ent and anal tics s ste s
• Drones/UAVs re otel controlled aerial ehicles re uentl e uipped ith ca eras and other sensors
to collect data from hard to reach industrial assets
• Self-Driving Vehicles a subset o the auto ated guided ehicle ar et that incorporates 0
technologies such as achine ision and ad anced anal tics to e ibl na igate actor oors ithout
dependence on ph sical ar ers and fi ed paths
he supporting technologies ar et is seg ented into these si categories and includes all products and
ser ices associated ith 0 applications o the technolog
r et i in
o nal tics ar et si ing or the overall I4.0 market is based on a data model augmented by the input
o industr e perts and a thorough re ie o econo ic and re enue data to or ulti ear pro ections
on e pected re enue changes he onnected ndustr building bloc s portion o the ar et is seg ented
b both building bloc and region, and the supporting technologies portion o the ar et is seg ented b
supporting technolog onl he o erall 0 ar et odel is based on the su o the onnected ndustr
building bloc s ar et and the supporting technologies ar et
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0
The Connected Industry building blocks market model for 2017 is based on both a top down as well as
a bo o up approach he top do n approach starts ith the o erall o ar et and then esti ates the
proportion o that ar et that alls under the onnected ndustr categor he o erall o ar et is based on
o nal tics global orecast odel that has been de eloped and alidated ith arious industr e perts o er
the last 5 ears he bo o up approach is based on arious o nal tics deep di es e g , o la or s,
redicti e aintenance in hich actual and esti ated re enue nu bers ro e ar et participants ere
added up to form a total market size. Regional and building block splits are based both on the results of the
inter ie uestionnaire as ell as through the use o eb indicators or regional and seg ent specific o
acti it e g , nu ber o e plo ees or ing on o solutions in a specific countr
The supporting technologies market model for 2017 is based on a top-down approach and is calculated based
on arious e pert inputs and an esti ation o the 0 proportion o each supporting technolog ar et or
instance, the rones s in 0 use cases ar et is de eloped b ta ing the o erall drones s ar et
and subtracting out all o the non 0 use cases, such as ilitar drones and consu er applications nputs to
the supporting technologies ar et odel include e pert inter ie s, publicl a ailable financial state ents,
3rd part research reports, and o nal tics internal intelligence
The key use cases ar et odel is a deri ati e odel based on the o erall 0 ar et odel he proportion
o re enues associated ith particular use cases are esti ated based on sur e s, o pro ects lists, e pert
inter ie s, and o nal tics internal intelligence
35 0 o nal tics ll rights reser ed 35
0
et odo o
he ain ob ecti es o this research are
• o define and seg ent the technological co ponents that co prise the ndustr 0 ar et
• o esti ate the orld ide ndustr 0 ar et si e ith seg entation b technolog or both onnected
ndustr building bloc s and supporting technologies and b region or onnected ndustr onl
• o understand e 0 technolog trends and ho these trends are disrupting e isting industries
• o identi ho co panies are i ple enting 0 technologies to reali e e 0 use cases and esti ate
the market size and growth rates of those key use cases
• o e a ine the 0 adoption strategies o s, actories, and industrial auto ation suppliers
his report is the result o al ost t o ears o research including
• elect insights and statistics ro e isting o nal tics reports and sur e s on o securit , o pla or s,
predicti e aintenance, , and s art cities
• nter ie s o 00+ e perts co ering a ariet o 0 technologies and industries, including
• 5+ e pert inter ie s ith e sta eholders in the o securit ar et technolog endors and
technolog users
• 0+ industr inter ie s and endor briefings ith e ecuti e le el o solution e perts
• 0+ inter ie s and briefings ith endors and users o industrial edge connecti it solutions
• 0+ inter ie s ith endors, integrators, and end users
• 0+ industr inter ie s and endor briefings ith e ecuti e le el o solution e perts, all ocused
on
• 5+ leading o and 0 con erences e g , o olutions orld ongress, ilan, anno er
esse, osch onnected orld, ndustr o hings orld, ri es, nternet o anu acturing, o
ech po, o orld, itachi , i e or , etc
• econdar research in ol ed ainl des top research e a ining annual reports, press releases,
hitepapers, co pan products and ser ices por olios, go ern ent and econo ic data, regulations
and roadmaps, and industry case studies.
355 0 o nal tics ll rights reser ed 355
0
he research is based on a rigorous process ith acade ic and industr recogni ed ethodologies such as
eb based anal tics, trends anal sis, and publicl a ailable data on the ar et e g , annual reports, co pan
ebsites he insights gained through these ethodologies ere enhanced b o e pertise ro internal
research anal sts and the consulting tea
356 0 o nal tics ll rights reser ed 356
0
ist o ron ms
3DP 3 rinting
5G FWA 5th eneration i ed ireless ccess
AM dditi e anu acturing
AES d anced ncr ption tandard
AMQP d anced essage ueuing rotocol
AWS a on eb er ices
AEP pplication nable ent la or
API pplication rogra ing nter ace
AI rtificial ntelligence
AR Augmented Reality
AGV utono ous uided ehicle
AMR Autonomous Mobile Robot
BLE luetooth o nerg
BPO usiness rocess utsourcing
Cobot ollaborati e obot
CAGR Compound annual growth rate
CoAP onstrained pplication rotocol
CRM usto er elationship anage ent
DCS Distributed Control System
DDoS Distributed Denial of Service
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0
ERP Enterprise Resource Planning
xML tensible ar up anguage
FTP File Transfer Protocol
GB Gigabytes
GSM lobal ste or obile o unication
GSMA roupe peciale obile ssociation
HMI u an achine nter ace
HTTP per e t rans er rotocol
Wi-Fi 0 i i lliance
IIC ndustrial nternet onsortiu
IIoT Industrial Internet of Things
ISM ndustrial, cientific, edical
I4.0 Industry 4.0
IT n or ation echnolog
IaaS Infrastructure as a Service
IO Input Output
I/O nput utput
IDE Integrated Development Environment
IEC nternational lectrotechnical o ission
IoT Internet of Things
IP Internet Protocol
35 0 o nal tics ll rights reser ed 35
0
IDS ntrusion etection ste
IPS ntrusion rotection ste
JSON a ascript b ect otation
LAN ocal rea et or
LoRa ong ange lo po er net or
LTE ong er olution o g co unication standard
LTE-M onger er olution or achines
LPWAN o o er ide rea et or
ML achine earning
M2M Machine to Machine
MES anu acturing ecution ste
MB Megabytes
MBSE odel ased ste s ngineering
MQTT essage ueueing ele etr ransport
MEMS Micro Electro Mechanical Systems
MCU Microcontroller
MPU Microprocessor
NEMS Nanoelectromechanical Systems
NB-IoT Narrowband IoT
OPC DA or rocess ontrol ata ccess
OPC or rocess ontrol or pen la or o unications
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0
OPC UA pen la or o unications nified rchitecture
OS perating ste
OT perational echnolog
OEM riginal uip ent anu acturer
OTA Over-The-Air
OEE erall uip ent ecti eness
PC Personal Computer
PLM roduct i ec cle anage ent
PLC rogra able ogic ontroller
PoC Proof of Concept
RFID adio re uenc dentification
RPMA ando hase ultiple ccess
RTU Remote Terminal Units
REST epresentational tate rans er
ROI Return On Investment
SSL ecure oc ets a er
SDV el ri ing ehicle
SaaS o are as a er ice
SDK o are e elop ent it
SPC tatistical rocess ontrol
SQL tructured uer anguage
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0
SCADA uper isor ontrol and ata c uisition
SI System Integrator
TSN i e ensiti e et or ing
TCP Transmission Control Protocol
TLS ransport a er ecurit
TPM rusted la or odule
US United States
UAV n anned erial ehicle
UDP User Datagram Protocol
VFD ariable re uenc ri e
VPN irtual ri ate et or
VR irtual ealit
WAN ide rea et or
WLAN ireless ocal rea et or s
WEF orld cono ic oru
361 0 o nal tics ll rights reser ed 361
0
ist o E i its
hibit Technology drivers behind the Internet of Things
hibit IoT categories sorted from least to most industrial
hibit 3 Comparison of IoT and Industry 4.0 in terms of industry and technology scope adapted ro la or ndustrie 0
hibit elationship bet een 0, onnected ndustr building bloc , and supporting technologies.
hibit 5 onnected ndustr building bloc s and supporting technologies enable 0 use cases
hibit 6 he our industrial re olutions based on
hibit 7 lobal interest in ndustr 0 and related ter s o er the last fi e ears
hibit er ie o other 0 s art anu acturing nitiati es around the globe
hibit 9 Core elements of Industry 4.0
hibit 0 lobal 0 ar et 0 7 0 3 ource o nal tics
hibit lobal onnected ndustr building bloc s ar et, b building bloc ource o nal tics
hibit lobal onnected ndustr building bloc s ar et, b region ource o nal tics
hibit 3 orth erican onnected ndustr building bloc s ar et ource o nal tics
hibit uropean onnected ndustr building bloc s ar et ource o nal tics
hibit 5 sian onnected ndustr building bloc s ar et ource o nal tics
hibit 6 lobal 0 supporting technologies ar et, b supporting technolog ource o nal tics
hibit 7 a ples o o de ices that use s or s
hibit a ple sensor deplo ent in a pac aging achine
hibit 9 apid gro th o s art in sensors since 0
362 0 o nal tics ll rights reser ed 362
0
hibit 0 s art ensor
hibit a ple esh net or o er asense sensors
hibit art ridge b epperl+ uchs sends in data to s art de ices ia luetooth, which can then forward the data directly to the cloud
hibit 3 obotic beetle de eloped b olls o ce in collaboration ith specialists ro ar-ard ni ersit and the ni ersit o o ngha
hibit ensor data collection using single purpose hard are and so are
hibit 5 e ote sensor data collection ith o gate a
hibit 6 Gateways as bridge between OT and IT
hibit 7 ndustrial connecti it p ra id
hibit protocol sends digital signals o er sa e ire as analog signals
hibit 9 in aster odules can be dais chained and read alues ro non in sensors
hibit 30 ireless et or e a ple
hibit 3 a ple industrial net or containing both ieldbus and ndustrial thernet de ices
hibit 3 0 industrial auto ation protocol o er ie
hibit 33 ireless fieldbus e a ple ro hoeni ontact
hibit 3 OPC-DA compared to OPC UA
hibit 35 er ie o co unications structure ource oundation
hibit 36 he hapers ho are or ing on
hibit 37 Three types of cloud architectures
hibit 3 lobal o la or s ar et 0 7 0 3 b hosting en iron ent ource o nal tics
hibit 39 he technolog behind an o pla or
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0
hibit 0 d anced anal tics hierarch
hibit lassification o anal tics
hibit elect results ro a recent industrial anal tics sur e ource o nal tics
hibit 3 ogne i i uses on pre ise deep learning or industrial i age anal sis
hibit s atson o la or le erages both cloud and on pre ise anal tics
hibit 5 o parison o di erent anal tics architectures
hibit 6 rocess o o industrial application de elop ent using s
hibit 7 o parison o ndustrial o app stores denotes app stores here 90 o the applications include transparent pricing applications listed as eta or oon were not counted. PTC apps without support were not counted, Siemens MindCon-nect and ind ccess o erings ere not counted
hibit I4.0 services value chain
hibit 9 IoT security survey results
hibit 50 tatus uo o o c ber securit solution ele ents and securit co ponents
hibit 5 o on threats ulnerabilities across the o a ac sur ace ser, e ice, ate-a , onnection, loud, and pplication
hibit 5 Convergence of IT and OT
hibit 53 n erse relationship bet een the tendenc or an application to be hosted in the cloud and the i portance o co unication ith
hibit 5 nducti e uto ation cloud architecture
hibit 55 icroso s la or as a er ice o ering
hibit 56 oc ell uto ation s ne line o o pact ogi s includes indo s 0 o and nati e connecti it to ure
hibit 57 le aa o ering
364 0 o nal tics ll rights reser ed 364
0
hibit 5 our categories and eight acti ities or hich applications are used ource
hibit 59 raditional co unication stac
hibit 60 Four emerging ethods or connecting edge de ices directl to the cloud
hibit 6 o is enabling cloud based ar etplaces or anu acturing utili ation
hibit 6 3 printing in the artner pe cle ource artner 0 , 0 3, 0 5
hibit 63 dditi e anu acturing alue chain
hibit 6 3 sur e results percentage o respondents indicating that hurdles e ist 900 co panies
hibit 65 ndustries that appl 3 printing toda and ill appl it in the uture, i the intended adoption is i ple ented
hibit 66 he additi e anu acturing ar et in 0 use cases ar et ource o nal tics
hibit 67 rea e en points based on cost per part or di erent anu acturing ethods illustrati e
hibit 6 ru rint 5000 includes three 500 a lasers
hibit 69 i erences bet een and s art glasses
hibit 70 i erences bet een and technologies ith respect to hard are costs and typical use cases
hibit 7 he in 0 use cases ar et ource o nal tics
hibit 7 esults ro a sur e o 00 industrial enterprises using hing or tudio as ing about the desired benefits ro adopting technolog
hibit 73 ndustrial robots doubled auto oti e or er producti e ro the late 70 s to earl 90 s
hibit 7 uppl o industrial robotics b industr
hibit 75 he e olution o robotics in anu acturing
hibit 76 he collaborati e robotics in 0 use cases ar et ource o nal tics
365 0 o nal tics ll rights reser ed 365
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hibit 77 nit sales o robotics b region
hibit 7 ourl cost o robots s hu an operators in logistics rance
hibit 79 he connected achine ision in 0 use cases ar et ource o nal tics
hibit 0 Overview of drone markets and enterprise use cases
hibit he drones s in 0 use cases ar et ource o nal tics
hibit s s s ource OTTO Motors
hibit 3 he s in 0 use cases ar et ource o nal tics
hibit irtual con e or s ste ro etch obotics
hibit 5 a s in hich 0 use cases deri e alue or organi ations ource o nal tics
hibit 6 Projected market sizes and growth rates of key I4.0 use cases
hibit 7 TRUMPF Telepresence architecture
hibit isual nline upport
hibit 9 Digital twins of products and processes can create closed-loop systems of continuous i pro e ent
hibit 90 Digital thread for tractor manufacturing
hibit 9 er ie o fi e s art actories eatured in this section
hibit 9 art actor in hicago,
hibit 93 Overview of machinery at the TRUMPF Smart Factory
hibit 9 rilliant actor in une, ndia
hibit 95 er ie o s rilliant actor concept
hibit 96 Audi Smart Factory
hibit 97 3 printing at udi s tool a ing di ision
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hibit 9 udi s logistics depart ent is e peri enting ith s and drones
hibit 99 Overview of SmartFactory technology and vendors
hibit 00 art actor acilit hosting actor ac 0 7
hibit 0 esearch and solution areas o the art actor
hibit 0 0 readiness ran ing o top industrial auto ation suppliers
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ist o es
able eading chip suppliers
able eading sensor suppliers
able 3 eading suppliers o ndustrial ate a s, rotocol on erters, et or ing and o puters
able eading suppliers o o ponents ode s, odules, antennas, connectors, etc
able 5 er ie o to ield e el rotocols
able 6 Comparison of Fieldbuses
able 7 Industrial Ethernet protocols
able doption head inds tail inds or database transactions
able 9 doption head inds tail inds or
able 0 doption head inds tail inds or
able endors adopting the protocol
able Popular wireless protocols by range and data rate
able 3 Short range wireless protocols
able ong range ireless protocols
able 5 eading suppliers o ong range ireless solutions
able 6 eading suppliers o atellite solutions
able 7 Unlicensed protocols in co parison
able icensed protocols in co parison
able 9 Key operators of unlicensed technologies
able 0 e operators o licensed technologies
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able ecurit considerations or arious technologies indicates optional controls ust be i ple ented to achie e rating
able echnical suitabilit o s or fi e e industrial use cases
able 3 eading osting ro iders
able eading uppliers o o la or s ith ndustrial ocus
able 5 eading suppliers o connecti it pla or s
able 6 eading uppliers o ndustrial dge nal tics
able 7 eading suppliers o d anced ndustrial nal tics on la or , o ocus
able eading suppliers o d anced ndustrial nal tics on la or , eneral ocus + IIoT Use Cases
able 9 eading suppliers o o la or s ith ati e d anced nal tics , 3 5 ea-ding suppliers o s ste integration
able 30 rigin, t pe, and ertical ocus o select s ste s integrators
able 3 eading suppliers o s ste integration
able 3 Common Cyber Security Threats
able 33 eading suppliers o o securit solutions
able 3 eading suppliers o n rastructure securit
able 35 eading suppliers o ndpoint ecurit
able 36 eading suppliers o loud securit
able 37 raditional di erences bet een and technologies
able 3 hree eatures re uired or co unications bet een and and and the technical solutions that are helping to achie e these eatures or cloud -
s ste s
able 39 actors that application endors should consider hen creating cloud based o erings
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able 0 AM methods for polymers and metals
able 0 use cases or additi e anu acturing
able eading suppliers o s ste s ol er
able 3 eading suppliers o s ste s etal
able eading suppliers o ser ices
able 5 eading suppliers o o are
able 6 eading suppliers o aterials
able 7 use cases in ndustr 0
able a ples o and hard are partnerships
able 9 eading suppliers o ug ented ealit glasses
able 50 eading suppliers o irtual ealit glasses
able 5 eading suppliers o pplication e elop ent o are la or
able 5 eading chip suppliers
able 53 i erences bet een traditional industrial robots and collaborati e robots
able 5 se cases or collaborati e robotics
able 55 eading suppliers o upporting echnologies
able 56 o on applications, sensing technologies, architectures, and co unication methods for machine vision systems
able 57 er ie o si actor oor ision applications
able 5 Overview of three types of machine vision sensing technologies
able 59 Comparison of two machine vision architectures
able 60 Overview of three types of machine vision interfaces
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able 6 onnected achine ision use case e a ples
able 6 eading suppliers o hard are so are
able 63 Drone use cases
able 6 eading suppliers o rones
able 65 Use cases for self-driving vehicles
able 66 eading suppliers o sel dri ing robotic ehicles
able 67 o adoption b industr based on the nu ber and aturit o pro ects
able 6 o parison o arious digitali ation strategies
able 69 o parison o the digitali ation e orts o our a or ele ator s
able 70 0 technologies concepts i ple ented at the art actor
able 7 0 technologies concepts i ple ented at s rilliant actories
able 7 0 technologies concepts i ple ented at udi s art actor
able 73 0 technologies concepts i ple ented at the art actor
able 7 0 technologies concepts i ple ented at art actor
able 75 he orld cono ic oru s list o so e o the best actories in the orld
able 76 ist o er an research institutes concentrating on ndustr 0
able 77 otentiall positi e and negati e aspects o the integration o 0 and lean progra s
able 7 enefits o 0 technolog on data collection or ean anu acturing
able 79 sti ated 0 i pact on lean production principles b ndustr 0 pro ect lea-ders at an auto oti e co pan
able 0 Components of Overall I4.0 Readiness score
able Connected Industry building block sub-elements
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