INDUSTRY 4.0 & SMART MANUFACTURING MARKET REPORT … · 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

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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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.


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


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


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


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


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


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


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


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


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


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


0

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


0

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


0

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


0

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


0

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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INDUSTRY 4.0 & SMART MANUFACTURING MARKET REPORT … · 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