Industry 4.0 & Smart Manufacturing Market

Market Report | November 2018

INDUSTRY 4.0 & SMART MANUFACTURING MARKET REPORT 2018-2023 Enterprise Premium Editon

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. INDUSTRY 4.0 & SMART MANUFACTURING 2018-2023

Date: November 2018

Authors: ahe opata, ulian icert, nud asse ueth, adraig cull

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Table of Contents

1 Executve Summary ix 1.1 Overall Highlights x 1.2 Market Analysis x 1.3 Key Use Case Analysis xi 1.4 Nine Disruptve Trends xi 1.5 I4.0 Adopton Strategies xii

2 Introducton 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 Supportng 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 Connectvity Hardware 38 4.2 Connectvity 50 4.2.1 Network Protocols 51 4.2.2 M2M/Network Services 71 4.3 Cloud, Platorm, & Analytcs 84 4.3.1 Hostng Environment 85 4.3.2 IoT Platorms 93 4.3.3 Data Analytcs & AI 101 4.4 Applicatons 117 4.4.1 Applicaton Development and AEPs 118 4.4.2 Industrial App Store & Distributon Methods 121

4.5 System Integraton 122 4.6 Cyber Security 128 4.6.1 IT vs. OT Security 129 4.6.2 Overview of IoT Atack Surfaces 131 4.6.3 Common IoT Threats 134 4.6.4 Recent I4.0 Related Atacks 136 4.6.5 Industry 4.0 Trends 137 4.6.6 Leading Suppliers 138

5 Disruptive Trends 142 5.1 Trends Disruptng the 5-Layer Automaton Pyramid 143 5.1.1 Trend 1: Sofware Applicatons 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 Connectng Directly to the Cloud 154 5.2 Other Disruptve Trends 161 5.2.1 Trend 4: PLCs Are Becoming Virtualized Sofware 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: Producton Setups Are Becoming Flexible 165 5.2.5 Trend 8: Value Chains Are Becoming More Integrated 165 5.2.6 Trend 9: New Distributon Methods Are Utlizing the Web 165

6 Supportng Technologies 166 6.1 Additve Manufacturing (AM) 167 6.1.1 Overview 168 6.1.2 I4.0 Applicatons 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 Applicatons 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 Collaboratve Robotcs 201 6.3.1 Overview 202 6.3.2 I4.0 Applicatons 204 6.3.3 Market Size and Growth 205 6.3.4 Disrupted Industries 206

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 Applicatons 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 Applicatons 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 Applicatons 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 Additve Producton 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 fxtures by 65% 246 7.2 Advanced Digital Product Engineering 247 7.2.1 Case Study : SEAT cuts development tme by 30% with virtual reality 248 7.2.2 Case Study: Volvo uses AM to cut cost and development tme by ~90% 249 7.2.3 Case Study: Bausch + Ströbel uses VR + digital twins to reduce tme to market by ~30% 250 7.2.4 Case Study: Ford moves towards digital twins to improve automotve design processes 251 7.3 Augmented Operatons 252 7.3.1 Case Study: Bechtle reduces walking tme by 50% using AR 253 7.3.2 Case Study: Bühler uses augmented reality to streamline operatons 254 7.4 Data-Driven Asset/Plant Performance Optmizaton 255 7.4.1 Case Study: Audi uses advanced analytcs to realize millions in cost savings 256

7.4.2 Case Study: KIANA Systems uses machine vision & analytcs to drastcally reduce error rate 257 7.4.3 Case Study: Wafos improves machine throughput using cloud analytcs 258 7.4.4 Case Study: Stanley Black & Decker increases OEE by 24% and frst pass quality by 16% 259 7.4.5 Case Study: Massilly uses autonomous forklifs to increase producton capacity 260 7.5 Data-Driven Inventory Optmizaton 261 7.5.1 Case Study: Schneider Electric identfes 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 tme by >80% using optcal quality control 265 7.6.2 Case Study: Daimler automates in-line quality control of mult-variant products 266 7.6.3 Case Study: Sturm uses connected machine vision to create fully digitzed producton 267 7.6.4 Case Study: Kemppi improves product quality while reducing development tme 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 tme 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 diferentates product ofering with machine-as-a-service 272 7.8 Human-Robot Collaboraton 273 7.8.1 Case Study: Siemens uses collaboratve robots to supplement operatons 274 7.8.2 Case Study: Kuka uses robots to manufacture robots 275 7.8.3 Case Study: Hirotec strives for “lights-out producton” with SDVs + Cobots 276 7.8.4 Case Study: SFEG improves output by 20% with portable collaboratve 277 7.9 Predictve Maintenance 278 7.9.1 Case Study: HPE uses edge gateways + analytcs to predict & prevent wind turbine failures 279 7.9.2 Case Study: Auto manufacturer saves ~$40M in downtme by sending data to the cloud 280 7.9.3 Case Study: Fero labs helps oil refnery increase revenue by $4M using PdM 281 7.10 Remote Asset Testng/Inspecton/Certfcaton 282 7.10.1 Case Study: INEOS lowers costs and improves safety by using drones for inspectons 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 inspecton

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 & ofer new services 288 7.11.3 Case Study: thyssenkrupp reduces service tme by 4x by using AR and remote connectvity 289 7.12 Virtual Training 290 7.12.1 Case Study: Normet uses VR and simulaton to improve operator efciency 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: Defniton of Digital Twins 294 7.14.2 Data-Driven Inventory Optmizaton: Defniton of Mult-Echelon Inventory Optmizaton 296

8 I4.0 Adopton Strategies 298 8.1 OEMs 298 8.1.1 Overview 299 8.1.2 Case Study: Liebherr feet management for constructon equipment 301 8.1.3 Case Study: Rolls-Royce conditon monitoring for aircraf engines 302 8.1.4 Case Study: Kärcher cleaning machinery feet management 303 8.1.5 Case Study: Heidelberg connected printng machines 304 8.1.6 Adopton Strategy Comparison: OEMs from Diferent Industries 305 8.1.7 Adopton 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 Automaton Suppliers 328 8.3.1 Overview 329 8.3.2 I4.0 Readiness Assessments of Top 5 Industrial Automaton 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

9 Associatons, Foundatons, Commitees to watch 340 9.1 Platorm Industrie 4.0 341 9.2 Labs Network Industrie 4.0 342 9.3 Industrial Internet Consortum (IIC) 343 9.4 OPC Foundaton 344 9.5 Industrial Data Space Associaton 345 9.6 CyberValley of Baden Würtemberg 346 9.7 Center for the Development and Applicaton of Internet of Things Technologies 347 9.8 Manufacturing USA 348

10 Appendix 349 10.1 Market defniton, sizing, and methodology 349 10.1.1 Industry 4.0 defniton: 349 10.1.2 IoT defniton: 349 10.1.3 IIoT and Connected Industry defniton: 350 10.1.4 Connected Industry building blocks defniton: 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 369 About 373 Selected recent publicatons 373 Upcoming publicatons 374 Subscripton 374 Newsleter 374 Main Author 375 Other Authors: 375 Contact Us 375 Copyright 376

Eeute ummr

he ter ndustrie 0 0 as introduced b eran thought leaders at the 0 annoer air hibiton and has since been adopted around the globe as the coon ter to describe the th industrial reoluton hile there is no single idelaccepted defniton o the 0 aret, this report defnes the oerall 0 aret as the su o the onnected ndustr building blocs aret the anuacturing subset o the ndustrial nternet o hings o and the aret or other 0 supportng technologies.

his report highlights ho anuacturers are ipleentng these onnected ndustr building blocs and the si other 0 supportng technologies addite anuacturing, , collaborate robotcs, connected achine ision, droness, and seldriing ehicles to realie tele e use cases that are driing the th industrial reoluton

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er iits

• The overall I4.0 market reached $XXB in 20XX, with Connected Industry building blocks comprising o the aret and supporting technologies coprising o the aret 3

• The overall I4.0 market is growing at a CAGR of 37%, led by growth in the Connected Industry building blocs subset

• Advanced digital product engineering ill be the largest use case in 03 550 aret

• Additive production and augmented operations are epected to be the to astest groing use cases s

• roth in 0 adoption is largel drien b three tpes o alue generated b 0 use cases

1. Efficiency gains across the whole organization an industrial organiations hae estiatedproductiit gains ro inestents in 0 technologies to be

2. New revenue streams s are leeraging o technolog to create ne asaserice business odels hese paperoutcoe business odels better align s ith custoers obecties b incentiiing s to ae sure their achines are operating properl

3. More flexible, customer centric operations that XXX 0 technologiesenable anuacturers to be ore 1 o naltcs nterie anuacturing endusers beliee this nuber is possible and set it as goal

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ntroduton

Industry 4.02 0 and the ndustrial nternet o hings o are both ters describing disrupte technolog trends in industrial sengs he ters are soetes used interchangeabl hoeer, in order to ull coprehend the content o this report, it is iportant to understand the dierences beteen ndustr 0 and IIoT.

o is the industrial subset o the nternet o hings o t a high leel, o is about adoptng the internet in alost all econoic actites, and it ocuses on the technolog bacend or cross categor connectit and interoperabilit he eergence and si deelopent o the o is drien b the six major technological developments shown in Exhibit 1

1. ncreased adopton o obile deices

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

2ro no on used snonousl ith ndustr 0 3hrough the econoies o scale potental ro eg sartphone producton and operaton 4 illion nstructons er econd

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ExHIBIT 1: Technology drivers behind the Internet of Things

he ndustrial o o reers to hea industries such as anuacturing, energ, oil and gas, and agriculture in hich industrial assets are connected to the internet ithin o, dierent segents are ore industrial than others, and onnected ndustr, hich specifcall ocuses on anuacturing, is on the ost industrial end of the spectrum as shown in Exhibit 2.

Internet of Things (IoT) I4.0

Main Connected Buildings Smart Cities Natural Connected Retail Healthcare Insurance category Car & Living & Energy resources Industry • Digital signage • Adherence & • Health and life • Assisted & • Energy efficiency • Construction • Agriculture • Connected field • In-store offering support insurance autonomous & HVAC • Education • Mining • Digital factory & promotions • Clinical • Home insurance driving • Home • Energy • Oil & gas • Product design • Supply chain • Virtual care • Industrial • Fleet equipment & • Environmental & engineering • Smart ordering • Wellness & insurance management appliances • Roads, traffic & • Smart Industries/ & payment prevention • Vehicle • In-vehicle • Living assistance transport maintenance • Vending insurance infotainment • Safety & security • Social & Security • Supply chain Applications machines • Cross • Shared mobility • Vehicle-to- • Water & Waste management • Smart infrastructure navigation solutions • Vehicle • Workplace assistance operations

Less Industrial More Industrial ExHIBIT 2: IoT categories sorted from least to most industrial

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onnected ndustr is also the largest segent ithin o, coprising oer 30 o the aret in 07 onnected ndustr oerlaps ith the oerall 0 aret, but 0 has a broader scope it ais to optie the entre anuacturing alue chain and includes other 0 supportng technologies Exhibit 3 illustrates the oerlap o 0 ith o and highlights the other 0 supportng technologies

ExHIBIT 3: oparison o o and ndustr 0 in ters o industr and technolog scope adapted ro laor ndustrie 0

Industry 4.0 aret can be ieed as the cobinaton o the building blocs that aeup the Connected Industry market plus the market for other I4.0 supportng technologies

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3ndustr 0 aret nalsis 003

Chapter Overview This section quantifies the overall I4.0 market size as well as the market Overall Industry sizes for the two subsets of the overall I4.0 market: 4.0 Market 1. Connected Industry Building Blocks

2. Supporting Technologies Connected Supporting Industry Building Technologies Blocks Market Market

Section Overview Connected Industry Building Blocks 3.1 Overall I4.0 Market 3.2 Market 3.3 Supporting Technologies Market 3.2.1 By Connected Industry Building Block

3.2.2 By Region

Chapter Takeaways 1 The overall industry 4.0 market reached in 0 and is epected to reach 30 b 03, resulting in a o Connected Industry building blocks ade up 35 o the aret in 07, and supporting technologies ade up 2 The Connected Industry building blocks market reached 35 in 07 and is epected to gro at a o to in he biggest onnected ndustr building bloc in 07 as applications 3, olloed by hardware 3 he supporting technologies aret reached 3 in 07 and is epected to gro at a o 6 to 53 in 0 he biggest supporting technologies aret in 07 as additie anuacturing 9, olloed b connected machine vision 4 Growth is driven by 3 types of value deried ro 0 use cases cienc gains across the hole organiaton 2. New revenue streams 3 ore eible, custoer centric operatons that reduce tetoaret

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Overall I4.0 Market er ret Overall I4.0 Market: By Technology

Global I4.0 Market Size in $B CAGR 17-23 350 310 I4.0 Supporting 26% 300 Technologies 53 Connected Industry 250 40% 226 Building Blocks 37% 200 42 164 150 33 120 256 87 26 100 184 64 21 131 48 16 50 94 13 67 35 48 0 2017 2018 2019 2020 2021 2022 2023 Year

Note: The overall market for I4.0 refers to global spending on the six connected industry building blocks and six I4.0 supporting technologies Source: IoT Analytics – October 2018 Xx% = Overall CAGR ExHIBIT 10: lobal 0 aret 0703 ource o naltics

he global aret or ndustr 0 solutions reached $48B in 2017 and is epected to gro at a CAGR of XX % to $XX in 20XX. The Connected Industry building blocks subset o the aret is epected 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 relatie aturit o the technologies that ae up this subset such as connected achine ision he groth o the aret or 0 solutions is largel drien b three tpes o alue deried ro the 0 use cases

1. Efciency gains across the whole organizaton an industrial organiatons hae estated productit gains ro inestents in 0 technologies to be 58.

Example: FANUC + Cisco and isco recentl partnered to create an

8o naltcs nterie anuacturing endusers beliee this nuber is possible and set it as goal 9ource Cisco

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onnected ndustr uilding locs

Chapter Overview This chapter explores the six connected industry buildings blocks that comprise the modern IIoT technology stack: 1. Hardware 4. Applications 2. Connectivity 5. System Integration 3. Cloud, Platform, & Analytics 6. Cyber Security Section Overview

4.1 Hardware 4.2 Connectivity 4.3 Cloud, Platform, & Analytics

4.1.1 Microchips 4.2.1 Network Protocols 4.3.1 Hosting Environment

4.1.2 Sensors 4.2.2 M2M/Network Service 4.3.2 IoT Platforms

4.1.3 Connectivity Hardware 4.3.3 Data Analytics & AI

4.4 Applications 4.5 System Integration 4.6 Cyber Security

4.4.1 Application Development and AEPs Industrial App Store & Distribution 4.4.2 Methods

Chapter Takeaways 1 XXXXX.

2 Companies are XXXXXXXXXXXXXXXXXXXXXhybrid cloud hosting environments. The market share of IoT plators hosted solel onpreise is proected to drop ro 3 in 07 to 3 in 03 3 IoT platformXXXXXXXXXXXXXXupporting AI technologies. o plator endors are including natie analtics tools eg icrosot ure achine earning, achine earning, oogle loud , etc hich allos custoers to build, train and deplo achine learning odels uicl and easil at scale 4 Platform specific edge computing agents are becoming more common. o plators are increasingl adding coputing and storage capabilities at the edge eg, aon reengrass, hich is leading to ore hbrid o deploents ith both edge and cloud architectures in place 5 AI algorithms stll require domain-specifc expertse. Suppliers of AI technologies hope to eventually develop odels that can be easil adapted beteen copanies and use cases hoeer, that ision has not et been realied an industrial solutons and algoriths are stll er dependent on industrspecifc training datasets and input ro subectaer eperts 6 Low cost/risk POCs gaining in popularity. s and to end users who are reluctant to allocate large budg I4.0 projects. For eaple, sstes integration irs li a 7 28% ofoering manufacture igital eperientsasaerice isco shoed thatganiations surattacs in the past ear the aerage 8 LoRa is the 2017 market leader in LPWAN technologies, olloed b igo, and o

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5isruptie rends

Chapter Overview 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. Section Overview

5.1 The 5-Layer Automation Pyramid 5.2 Other Disruptions

5.1.1 Trend 1: Software Applications and Data Are Moving to the Cloud 5.2.1 Trend 4: PLCs Are Becoming Virtualized Software Programs

5.1.2 Trend 2: SCADA, MES, and ERP Systems Are Converging 5.2.2 Trend 5: Manufacturing Capacity Is Being Sold as a Service

5.1.3 Trend 3: Edge to Cloud Connectivity 5.2.3 Trend 6: Machines Are Being Sold as a Service

5.2.4 Trend 7: Production Setups Are Becoming Flexible

5.2.5 Trend 8: Value Chains Are Becoming More Integrated

5.2.6 Trend 9: New Distribution Methods Are Utilizing the Web

Chapter Takeaways 1 TXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXajor trends: he igraton o soare applicatons to the cloud The convergence of SCADA, MES, and ERP systems e deices connectng directl to the cloud 2 New XXXXXXXXXXXXXXXXXXXXDA/MES cloud adoption. danceents in cellular counication 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 Manuates here ecess hoe capacit is adertised online, the eerging anuacturingasaserice ecosste allos connected anuacturers to sell their ecess anuacturing capacit online to custoers euipped ith digital product designs

4 Machine-as-a-Service business models bring new revenue and accountng challenges. More machines are being sold to anuacturers as serices nestors and s are grappling ith the reenue and accountng iplicatons o anuacturing custoers sitching to higher and loer businesses

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6upportng echnologies

Chapter Overview 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: 1. Additive Manufacturing 4. Connected Machine Vision 2. Augmented and Virtual Reality 5. Drones/UAVs 3. Collaborative Robotics 6. Self-Driving Vehicles Section Overview

6.1 Additive Manufacturing 6.2 Augmented and Virtual Reality 6.3 Collaborative Robotics Case Studies Case Studies Case Studies

6.4 Connected Machine Vision 6.5 Drones/UAVs 6.6 Self-Driving Vehicles Case Studies Case Studies Case Studies

Chapter Takeaways 1 AXXXXXXXXXXXXXXXXXXXXgies market. ith reenue at 9 in 07 and a proected o 5, additie anuacturing is and illsupporting technologies aret as more companies adopt the technology for more than just prototyping. 2 XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXrdware and CAD/CAM software providers. opanies lie assault and dare suppliers lie icrosot to create a ore sealess integrnd he end goal is or odels to be instantl created in the sotareurrentl ocusing on irtual realit and is partnering ith endors lie and culus

3 Higher labor costs and falling robot costs are driving collaboratve robot adopton. obotcs prices ill contnue to all een as ages increase in both deeloped and deeloping countries 4 Mobile collaboratve robots are gaining in popularity. opanies are designing their collaborate robots to be highl portable een seldriing, alloing or etreel eible anuacturing 5 Machine learning technology is moving closer to the edge with vision systems. Smart cameras from copanies lie ogne run trained achine learning aleles in order to achiee high speed pattern recognition 6 Regulatons are constraining the growth of the drones/UAVs market. eond isual line o sight regulatons are constraining the groth and nuber o use cases or drones in certain regions 7 Suppliers of traditionXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX. As sstes instead o to proide iedpath naigation guidance

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7e se ases

Chapter Overview 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. Section Overview

7.1 Additive Production 7.5 Data-driven Inventory Optimization 7.9 Predictive Maintenance

Mercedes Benz Trucks reduces costs with 3D printed Schneider Electric identifies opportunity to reduce HPE uses edge gateways + analytics to prevent wind 7.1.1 7.5.1 7.9.1 spare parts SKUs by 30% turbine failures Siemens accelerates repair process by a factor of 10 AFI saves 2 man-months per year with smart bin 7.1.2 7.5.2 7.9.2 Cisco helps automotive OEM save ~$40M in downtime using 3DP parts system Oreck uses AM to reduce manufacturing costs of Fero Labs helps oil refinery increase revenue by $4M 7.1.3 7.9.3 fixtures by 65% using PdM

7.2 Advanced Digital Product Engineering 7.6 Data-driven Quality Control 7.10 Remote Asset Testing/Inspection/ Certification

SEAT cuts development time by 30% with virtual OPEL reduces programming and measuring time by INEOS lowers costs and improves safety by using 7.2.1 7.6.1 7.10.1 reality >80% drones for inspections Volvo uses AM to cut cost and development time by Daimler automates in-line quality control of multi- Sky-Futures helps O&G operator save hundreds of 7.2.2 7.6.2 7.10.2 ~90% variant products man-days of work Bausch+Ströbel uses VR + digital twins to reduce time Sturm uses connected machine vision to create fully Siemens uses drones to reduce wind turbine 7.2.3 7.6.3 7.10.3 to market by ~30% digitized production inspection costs and failures Kemppi improveskawadarobotf product quality and reduces 7.2.3 Ford moves towards digital twins 7.6.4 development time by ~50%

7.3 Augmented Operations 7.7 Everything-as-a-Service Business Models 7.11 Remote Service

SMP uses Xometry to reduce lead time by up to 70% TRUMPF reduces support costs and increases quality 7.3.1 Bechtle reduces walking time by 50% using AR 7.7.1 7.11.1 and costs up to 90% with remote service Bühler uses augmented reality to streamline PepsiCo use Protolabs to go from concept to market in Heidelberg uses remote service to reduce costs & 7.3.2 7.7.2 7.11.2 operations < 6 months offer new services Heller differentiates product offering with machine- thyssenkrupp reduces service time by 4x by using AR + 7.7.3 7.11.3 as-a-service remote connectivity

7.4 Data-driven Asset/Plant Performance Optimization 7.8 Human-Robot Collaboration 7.12 Virtual Training

Audi uses advanced analytics to realize millions in cost Siemens uses collaborative robots to supplement Normet uses VR and simulation to improve operator 7.4.1 7.8.1 7.12.1 savings operations efficiency by 23% KIANA Systems uses machine vision & analytics to KOC reduces accidents & accelerates operator 7.4.2 7.8.2 Kuka uses robots to manufacture robots 7.12.2 reduce error rate onboarding using VR Wafios improves machine throughput using cloud Hirotec strives for “lights-out production” with SDVs + 7.4.3 7.8.3 analytics Cobots Stanley Black & Decker increases OEE by 24% and first SFEG improves output by 20% with portable 7.4.4 7.8.4 pass quality by 16% collaborative Massilly uses autonomous forklifts to increase 7.4.5 production capacity

Chapter Takeaways 1 ut o the use cases identfed as the top 0 use cases, advanced digital product engineering will be the largest use case b aret sie in 03 rien b additie anuacturing and adoption, adanced digital product engineering use cases are epected to generate 2 Additve producton and augmented operatons will be the fastest growing use cases from 2018 to 2023, both ith s 5 oer cost and higher ualit additie anuacturing technologies cobined ith increased custoer deand or custo parts ill anuacturing or serialgrations beteen drie au

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0 dopton trategies

Chapter Takeaways 1 XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXs are more likely to adopt IoT products and strategies. 2 are key focuses of smart actor initiaties 3 XXXXXXXXXXXXXXXXXXXX are leading the a in 0 readiness due to their oerings and inestents in arious onnected ndustr building blocs and 0 supporting technologies

Section Overview OEMs across a variety of industries are creating connected products and services to differentiate their offerings and create new service revenue streams.

Subsection Overview

Case Studies Liebherr LiDAT fleet management for TRUMPF: sheet metal & laser cutting thyssenkrupp: elevator OEM I4.0 1 5 construction equipment tools OEM I4.0 adoption strategy 9 adoption strategy Rolls-Royce condition monitoring for Heidelberg: printing presses OEM I4.0 KONE: elevator OEM I4.0 adoption 2 6 aircraft engines adoption strategy 10 strategy Kärcher cleaning machinery fleet Krones: bottling and packing machines Otis: elevator OEM I4.0 adoption 3 management 7 OEM I4.0 adoption strategy 11 strategy Heidelberg connected printing Engel: injection molding machines OEM Schindler: elevator OEM I4.0 adoption 4 machines 8 I4.0 adoption strategy 12 strategy

Secton 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 Constructon equipment, elevator, and agricultural machinery OEMs are leading the a in o adopton, ith copanies iebherr, chindler, and laas ipleentng best in class solutons

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Porm ndustrie

laor ndustrie 0 plaori0de Founded: 2013 Members: 6000+ ia the associatons he laor ndustrie 0 as ounded b the thee eran associatons VDMA echanical ngineering ndustr ssociaton, ZVEI regulator and econoic polic authorit o the electrical and electronics industr and BITKOM erans digital associaton and in 05 extended by stakeholders from politcs ederal oernent inistries o ducaton esearch, conoics echnolog, research raunhoer esellscha, atonal cade or cience and ngineering, eran esearch enter rtfcial ntelligence and highl innoate copanies lie

to drie the deelopent o ndustrie 0 as a hole and possibilites or s in partcular he plaor is organied in oring groups on olloing topics

Our research and interviews with industry experts revealed that RAMI 4.0 is watched with interest, but seems currently too academic and theoretcal.

reerence architectures 0, standards and nors research and innoaton 3. security of networked systems 4. legal framework 5 or, educaton and training n arch 06, a collaboraton ith the ndustrial nternet onsortu as announced164 uther cooperatons eist ith the rench lliance ndustrie du utur ct 05 and ith hina ia the inoeran posiu ct o 06 ll actites strie to be precopette not deeloping aret solutons, thus proiding a sae and legal forum for a variety of industry players to discuss and resolve common problems.

164

164ource laor ndustrie 0 engl

0 o naltcs ll rights resered 339 0

ppendi

ret deniton siin nd metodoo

ndustr deniton

he ter ndustrie 0 0 as introduced b eran thought leaders at the 0 annoer air hibiton and has since been adopted around the globe as the common term to describe the 4th industrial reoluton hile there is no single idelaccepted defniton o the 0 aret, this report defnes the oerall 0 aret as the su o the onnected ndustr building blocs aret the anuacturing subset o the ndustrial nternet o hings o and the aret or other 0 supportng technologies

Overall Industry 4.0 Market

Connected Supporting Industry Technologies Building Blocks

o deniton

he nternet o hings o is defned as a netor o nternetenabled phsical obects, hich ais at integratng eer obect or interacton ia ebedded sstes, netor counicatons, bacend coputng, and applicatons tpicall in the cloud t allos obects to counicate ith each other, access inoraton on the nternet, capture store and retriee data, and interact ith users as ell as other sstes and applicatons, creatng sart connected enironents

0 o naltcs ll rights resered 347 0

o nd onneted ndustr deniton

ndustrial o o is a subset o the nternet o hings o hich reers to hea industries such as manufacturing, energy, oil and gas, and agriculture in which industrial assets are connected to the internet. ithin o, dierent segents are ore industrial than others, and onnected ndustr, hich specifcall ocuses on anuacturing, is on the ost industrial end o the spectru as shon in the ehibit belo

Internet of Things (IoT) I4.0

Less Industrial More Industrial

onneted ndustr uidin os deniton

he onnected ndustr aret can be broen don in to si building blocs that together or onnected ndustr solutons

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. Connectvity: the protocols and serices reuired to achiee connect industrial euipent

4. Cloud, Platorm, & Analytcs hostng enironents, o plaors, and data analtcs

5 Applicatons: soare progras that are built on top o o plaors

6. System Integraton: the serices associated and ith designing, planning, building, and operatng 0 solutons

0 o naltcs ll rights resered 348 0

he table belo describes the subeleents o each onnected ndustr building bloc

s hae specifc eatures that oer rule engine eent loud, laor, o laors anageent, s or business apps, integraton s or endpoints, naltcs integrated deelopent enironent and applicaton aretplace plaors support deice onitoring and anageent, loud, laor, naltcs bidirectonal coand control, oertheair updates and applicaton o laors management oare bacend that aggregates inbound streaing data, handles loud, laor, IoT Cloud backends processing and storage in databases or ultple data odelsorats naltcs public, priate eg, relatonal, nonrelatonal, ealue, etc and scales as reuired onnectit plaors support dierent protocolsdata orats, loud, laor, o onnectit ensuring bidirectonal counicaton ith all deices and abilit to naltcs laors onitor netor usage generatng notfcatons and alerts danced naltcs plaors hae specifc eatures that allo or loud, laor, danced analtcs adanced analtcs on o data through achine learning, streaing naltcs analtcs and cople algoriths ellular icensed Operator services related to 2G, 3G and 4G technologies in the licensed ounicatons traditonal spectrum ellular icensed perator serices related to technologies in the licensed ounicatons spectru o, Cellular - Unlicensed perator serices related to technologies in the unlicensed ounicatons spectru eg, ora, igo, ngenu, etc ounicatons ellular 5 perator serices related to 5 technologies in the licensed spectru ounicatons Satellite Operator services related to satellite technologies ounicatons ireline perator serices related to ireline technologii ounicatons Other ther operator serice reenue eg, esh netors, etc ardare Chips eiconductors used in o deices and counicatons euipent ardare Sensors Sensors used in IoT devices ardare peratng ste peratng sstes used in o deices pplicatons that are deeloped and run specifcall on gateas and ardare dge applicatons devices naltcs serices that are deeloped and run specifcall on gateas ardare dge analtcs and devices ounicatons ardare acage o antenna, chipset, etc that allos or connectit modules ardare SIM cards SIM cards Table 81:onnected ndustr building bloc subeleents

0 o naltcs ll rights resered 39 0

ardare Routers & Gateways Routers & Gateways oards and sall Circuit boards, transistors, capacitators and other electronics ardare components euipent Other hardware ther hardare reuired or o deices eg, screens, speaers, ardare components laps, etc onsultng serices are adisor serices b outsourced proiders that help businesses ident o opportunites create business cases and roadaps assesses organiatonal readiness, goernance, ris, System legal raifcatons, securit and business process redesign and select onsultng ntegraton the product, vendor or technology. In doing so, these services help align technology strategies with business or process strategies. These serices support o initates b proiding strategic, architectural, operatonal and ipleentaton planning pleentaton serices custoie or deelop o solutons, assets and processes, and then integrate these solutons, assets and processes System ith eistng inrastructure and processes he also include product pleentaton ntegraton engineering o sensorsebedded deices, sensor installaton, hardaresoarenetor ipleentaton, and applicaton and deice testng in arious conditons peratons serices proide datoda anageent and operaton o o assets and processes hese include related soare and hardare support services, as applicable. They may include infrastructure anageent, applicaton anageent, deice anageent, perorance onitoring, reote diagnostcs, authentcaton, System peratons billing and custoer support naltcs operatons, hich see ntegraton to leverage data associated with sensor readings and networked sstes operatonal state data, are a e part o operatons he analtcs eorts see to snthesie ra operatonal data, as ell as create predicte algoriths into actonable inoraton and recoendatons Smart phone uilding and aintaining applicatons that ae use o o data on pplicatons applicatons sartphones esp ndroid and pple eb based uilding and aintaining applicatons that ae use o o data in eb pplicatons applicatons browser environments pplicaton pecifc deelopent tools that let coputer scientsts progra, test, pplicatons development and deplo applicatons in the cloud and on the deice environments ntegraton o o applicatons and data to other enterprise serices pplicatons acend integraton eg, , sstes including prograing o standard s and s and other data translaton Table 81 (Contnued):onnected ndustr building bloc subeleents

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pecifc applicatons that ae use o o data hich are not pplicatons ther applicatons programmed on the web or in the smartphone pecifc securit eatures including hardare eg, s, circuit Cyber Security Device security shielding and soare solutons eg, secure boot that enhance the level of security on the device layer. pecifc securit eatures that ensure data is sael encrpted hile in ounicatons Cyber Security transit eg, , and unanted intrusions are detectedpreented security eg, frealls, pecifc securit eatures that protect sensite inoraton stored in the cloud ie, dis encrpton or data at rest and ensures onl Cyber Security Cloud security authoried access is granted ie, plaorapplicaton3rd part erifcaton ontnuous processes reuired to eep the securit o an o soluton ieccle Cyber Security uptodate ro deploent to decoissioning eg, actit management onitoring, regular securit updatespatches Table 81 (Contnued):onnected ndustr building bloc subeleents

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upportng technologies defniton

here are si supportng technologies that are deploed alongside onnected ndustr building blocs in 0 solutons

• Additve Manufacturing: the process o oining aterials polers, etals, ceraics, 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

• Collaboratve Robotcs: sart, eible, eas to train robots hich enable sae huanachine interacton in factories without the need for fences or cages

• Connected Machine Vision adanced industrial caeras that siultaneousl counicate ith industrial control sstes and higherleel iage anageent and analtcs sstes

• Drones/UAVs reotel controlled aerial ehicles reuentl euipped ith caeras and other sensors to collect data from hard to reach industrial assets

• Self-Driving Vehicles a subset o the autoated guided ehicle aret that incorporates 0 technologies such as achine ision and adanced analtcs to eibl naigate actor oors ithout dependence on phsical arers and fed paths

he supportng technologies aret is segented into these si categories and includes all products and serices associated ith 0 applicatons o the technolog

ret iin

o naltcs aret siing or the overall I4.0 market is based on a data model augmented by the input o industr eperts and a thorough reie o econoic and reenue data to or ultear proectons on epected reenue changes he onnected ndustr building blocs porton o the aret is segented b both building bloc and region, and the supportng technologies porton o the aret is segented b supportng technolog onl he oerall 0 aret odel is based on the su o the onnected ndustr building blocs aret and the supportng technologies aret

0 o naltcs ll rights resered 35 0

The Connected Industry building blocks market model for 2017 is based on both a top down as well as a booup approach he topdon approach starts ith the oerall o aret and then estates the proporton o that aret that alls under the onnected ndustr categor he oerall o aret is based on o naltcs global orecast odel that has been deeloped and alidated ith arious industr eperts oer the last 5 ears he booup approach is based on arious o naltcs deepdies eg, o laors, redicte aintenance in hich actual and estated reenue nubers ro e aret partcipants ere added up to form a total market size. Regional and building block splits are based both on the results of the interie uestonnaire as ell as through the use o eb indicators or regional and segent specifc o actit eg, nuber o eploees oring on o solutons in a specifc countr

The supportng technologies market model for 2017 is based on a top-down approach and is calculated based on arious epert inputs and an estaton o the 0 proporton o each supportng technolog aret or instance, the roness in 0 use cases aret is deeloped b taing the oerall droness aret and subtractng out all o the non0 use cases, such as ilitar drones and consuer applicatons nputs to the supportng technologies aret odel include epert interies, publicl aailable fnancial stateents, 3rd part research reports, and o naltcs internal intelligence

The key use cases aret odel is a deriate odel based on the oerall 0 aret odel he proporton o reenues associated ith partcular use cases are estated based on sures, o proects lists, epert interies, and o naltcs internal intelligence

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etodoo

he ain obectes o this research are

• o defne and segent the technological coponents that coprise the ndustr 0 aret

• o estate the orldide ndustr 0 aret sie ith segentaton b technolog or both onnected ndustr building blocs and supportng technologies and b region or onnected ndustr onl

• o understand e 0 technolog trends and ho these trends are disruptng eistng industries

• o ident ho copanies are ipleentng 0 technologies to realie e 0 use cases and estate the market size and growth rates of those key use cases

• o eaine the 0 adopton strategies o s, actories, and industrial autoaton suppliers

his report is the result o alost to ears o research including

• elect insights and statstcs ro eistng o naltcs reports and sures on o securit, o plaors, predicte aintenance, , and sart cites

• nteries o 00+ eperts coering a ariet o 0 technologies and industries, including

• 5+ epert interies ith e staeholders in the o securit aret technolog endors and technolog users

• 0+ industr interies and endor briefngs ith eecute leel o soluton eperts

• 0+ interies and briefngs ith endors and users o industrial edge connectit solutons

• 0+ interies ith endors, integrators, and end users

• 0+ industr interies and endor briefngs ith eecute leel o soluton eperts, all ocused on

• 5+ leading o and 0 conerences eg, o olutons orld ongress, ilan, annoer esse, osch onnectedorld, ndustr o hings orld, ries, nternet o anuacturing, o ech po, o orld, itachi , ieor, etc

• econdar research inoled ainl destop research eaining annual reports, press releases, hitepapers, copan products and serices porolios, goernent and econoic data, regulatons and roadmaps, and industry case studies.

0 o naltcs ll rights resered 35 0

he research is based on a rigorous process ith acadeic and industr recognied ethodologies such as ebbased analtcs, trends analsis, and publicl aailable data on the aret eg, annual reports, copan ebsites he insights gained through these ethodologies ere enhanced b o epertse ro internal research analsts and the consultng tea

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ist o ronms

3DP 3 rintng

5G FWA 5th eneraton ied ireless ccess

AM ddite anuacturing

AES danced ncrpton tandard

AMQP danced essage ueuing rotocol

AWS aon eb erices

AEP pplicaton nableent laor

API pplicaton rograing nterace

AI rtfcial ntelligence

AR Augmented Reality

AGV utonoous uided ehicle

AMR Autonomous Mobile Robot

BLE luetooth o nerg

BPO usiness rocess utsourcing

Cobot ollaborate obot

CAGR Compound annual growth rate

CoAP onstrained pplicaton rotocol

CRM ustoer elatonship anageent

DCS Distributed Control System

DDoS Distributed Denial of Service

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ERP Enterprise Resource Planning xML tensible arup anguage

FTP File Transfer Protocol

GB Gigabytes

GSM lobal ste or obile ounicaton

GSMA roupe peciale obile ssociaton

HMI uan achine nterace

HTTP peret ranser rotocol

Wi-Fi 0 ii lliance

IIC ndustrial nternet onsortu

IIoT Industrial Internet of Things

ISM ndustrial, cientfc, edical

I4.0 Industry 4.0

IT noraton echnolog

IaaS Infrastructure as a Service

IO Input Output

I/O npututput

IDE Integrated Development Environment

IEC nternatonal lectrotechnical oission

IoT Internet of Things

IP Internet Protocol

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IDS ntrusion etecton ste

IPS ntrusion rotecton ste

JSON aascript bect otaton

LAN ocal rea etor

LoRa ong ange lo poer netor

LTE ong er oluton o g counicaton standard

LTE-M onger er oluton or achines

LPWAN o oer ide rea etor

ML achine earning

M2M Machine to Machine

MES anuacturing ecuton ste

MB Megabytes

MBSE odelased stes ngineering

MQTT essage ueueing eleetr 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 laor ounicatons

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OPC UA pen laor ounicatons nifed rchitecture

OS peratng ste

OT peratonal echnolog

OEM riginal uipent anuacturer

OTA Over-The-Air

OEE erall uipent ecteness

PC Personal Computer

PLM roduct ieccle anageent

PLC rograable ogic ontroller

PoC Proof of Concept

RFID adioreuenc dentfcaton

RPMA ando hase ultple ccess

RTU Remote Terminal Units

REST epresentatonal tate ranser

ROI Return On Investment

SSL ecure ocets aer

SDV elriing ehicle

SaaS oare as a erice

SDK oare eelopent it

SPC tatstcal rocess ontrol

SQL tructured uer anguage

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SCADA uperisor ontrol and ata cuisiton

SI System Integrator

TSN ieensite etoring

TCP Transmission Control Protocol

TLS ransport aer ecurit

TPM rusted laor odule

US United States

UAV nanned erial ehicle

UDP User Datagram Protocol

VFD ariable reuenc rie

VPN irtual riate etor

VR irtual ealit

WAN ide rea etor

WLAN ireless ocal rea etors

WEF orld conoic oru

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ist o Eiits

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 laor ndustrie 0

hibit elatonship beteen 0, onnected ndustr building bloc, and supportng technologies.

hibit 5 onnected ndustr building blocs and supportng technologies enable 0 use cases

hibit 6 he our industrial reolutons based on

hibit 7 lobal interest in ndustr 0 and related ters oer the last fe ears

hibit erie o other 0sart anuacturing nitates around the globe

hibit 9 Core elements of Industry 4.0

hibit 0 lobal 0 aret 0703 ource o naltcs

hibit lobal onnected ndustr building blocs aret, b building bloc ource o naltcs

hibit lobal onnected ndustr building blocs aret, b region ource o naltcs

hibit 3 orth erican onnected ndustr building blocs aret ource o naltcs

hibit uropean onnected ndustr building blocs aret ource o naltcs

hibit 5 sian onnected ndustr building blocs aret ource o naltcs

hibit 6 lobal 0 supportng technologies aret, b supportng technolog ource o naltcs

hibit 7 aples o o deices that use s or s

hibit aple sensor deploent in a pacaging achine

hibit 9 apid groth o sart in sensors since 0

0 o naltcs ll rights resered 361 0

hibit 0 s art ensor

hibit aple esh netor o erasense sensors

hibit artridge b epperl+uchs sends in data to sart deices ia luetooth, which can then forward the data directly to the cloud

hibit 3 obotc beetle deeloped b ollsoce in collaboraton ith specialists ro ar- ard niersit and the niersit o ongha

hibit ensor data collecton using single purpose hardare and soare

hibit 5 eote sensor data collecton ith o gatea

hibit 6 Gateways as bridge between OT and IT

hibit 7 ndustrial connectit praid

hibit protocol sends digital signals oer sae ire as analog signals

hibit 9 in aster odules can be dais chained and read alues ro nonin sensors

hibit 30 ireless etor eaple

hibit 3 aple industrial netor containing both ieldbus and ndustrial thernet deices

hibit 3 0 industrial autoaton protocol oerie

hibit 33 ireless feldbus eaple ro hoeni ontact

hibit 3 OPC-DA compared to OPC UA

hibit 35 erie o counicatons structure ource oundaton

hibit 36 he hapers ho are oring on

hibit 37 Three types of cloud architectures

hibit 3 lobal o laors aret 0703 b hostng enironent ource o naltcs

hibit 39 he technolog behind an o plaor

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hibit 0 danced analtcs hierarch

hibit lassifcaton o analtcs

hibit elect results ro a recent industrial analtcs sure ource o naltcs

hibit 3 ogne ii uses onpreise deep learning or industrial iage analsis

hibit s atson o laor leerages both cloud and onpreise analtcs

hibit 5 oparison o dierent analtcs architectures

hibit 6 rocess o o industrial applicaton deelopent using s

hibit 7 oparison o ndustrial o app stores denotes app stores here 90 o the applicatons include transparent pricing applicatons listed as eta or oon were not counted. PTC apps without support were not counted, Siemens MindCon- nect and indccess oerings ere not counted

hibit I4.0 services value chain

hibit 9 IoT security survey results

hibit 50 tatusuo o o cber securit soluton eleents and securit coponents

hibit 5 oon threats ulnerabilites across the o aac surace ser, eice, ate- a, onnecton, loud, and pplicaton

hibit 5 Convergence of IT and OT

hibit 53 nerse relatonship beteen the tendenc or an applicaton to be hosted in the cloud and the iportance o counicaton ith

hibit 5 nducte utoaton cloud architecture

hibit 55 icrosos laorasaerice oering

hibit 56 ocell utoatons ne line o opactogi s includes indos 0 o and nate connectit to ure

hibit 57 le aa oering

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hibit 5 our categories and eight actites or hich applicatons are used ource

hibit 59 raditonal counicaton stac

hibit 60 Four emerging ethods or connectng edge deices directl to the cloud

hibit 6 o is enabling cloudbased aretplaces or anuacturing utliaton

hibit 6 3 printng in the artner pe cle ource artner 0, 03, 05

hibit 63 ddite anuacturing alue chain

hibit 6 3 sure results percentage o respondents indicatng that hurdles eist 900 copanies

hibit 65 ndustries that appl 3 printng toda and ill appl it in the uture, i the intended adopton is ipleented

hibit 66 he addite anuacturing aret in 0 use cases aret ource o naltcs

hibit 67 reaeen points based on costperpart or dierent anuacturing ethods illustrate

hibit 6 rurint 5000 includes three 500a lasers

hibit 69 ierences beteen and sart glasses

hibit 70 ierences beteen and technologies ith respect to hardare costs and typical use cases

hibit 7 he in 0 use cases aret ource o naltcs

hibit 7 esults ro a sure o 00 industrial enterprises using hingor tudio asing about the desired benefts ro adoptng technolog

hibit 73 ndustrial robots doubled autoote orer producte ro the late 70s to earl 90s

hibit 7 uppl o industrial robotcs b industr

hibit 75 he eoluton o robotcs in anuacturing

hibit 76 he collaborate robotcs in 0 use cases aret ource o naltcs

0 o naltcs ll rights resered 364 0

hibit 77 nit sales o robotcs b region

hibit 7 ourl cost o robots s huan operators in logistcs rance

hibit 79 he connected achine ision in 0 use cases aret ource o naltcs

hibit 0 Overview of drone markets and enterprise use cases

hibit he droness in 0 use cases aret ource o naltcs

hibit s s s ource OTTO Motors

hibit 3 he s in 0 use cases aret ource o naltcs

hibit irtual coneor sste ro etch obotcs

hibit 5 as in hich 0 use cases derie alue or organiatons ource o naltcs

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 contnuous iproeent

hibit 90 Digital thread for tractor manufacturing

hibit 9 erie o fe sart actories eatured in this secton

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 erie o s rilliant actor concept

hibit 96 Audi Smart Factory

hibit 97 3 printng at udis toolaing diision

0 o naltcs ll rights resered 365 0

hibit 9 udis logistcs departent is eperientng ith s and drones

hibit 99 Overview of SmartFactory technology and vendors

hibit 00 artactor acilit hostng actor ac 07

hibit 0 esearch and soluton areas o the artactor

hibit 0 0 readiness raning o top industrial autoaton suppliers

0 o naltcs ll rights resered 366 0

ist o es

able eading chip suppliers

able eading sensor suppliers

able 3 eading suppliers o ndustrial ateas, rotocol onerters, etoring and oputers

able eading suppliers o oponents odes, odules, antennas, connectors, etc

able 5 erie o to ield eel rotocols

able 6 Comparison of Fieldbuses

able 7 Industrial Ethernet protocols

able dopton headindstailinds or database transactons

able 9 dopton headindstailinds or

able 0 dopton headindstailinds or

able endors adoptng 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 solutons

able 6 eading suppliers o atellite solutons

able 7 Unlicensed protocols in coparison

able icensed protocols in coparison

able 9 Key operators of unlicensed technologies

able 0 e operators o licensed technologies

0 o naltcs ll rights resered 367 0

able ecurit consideratons or arious technologies indicates optonal controls ust be ipleented to achiee ratng

able echnical suitabilit o s or fe e industrial use cases

able 3 eading ostng roiders

able eading uppliers o o laors ith ndustrial ocus

able 5 eading suppliers o connectit plaors

able 6 eading uppliers o ndustrial dge naltcs

able 7 eading suppliers o danced ndustrial naltcs onlaor, o ocus

able eading suppliers o danced ndustrial naltcs onlaor, eneral ocus + IIoT Use Cases

able 9 eading suppliers o o laors ith ate danced naltcs, 35 eading suppliers o sste integraton

able 30 rigin, tpe, and ertcal ocus o select sstes integrators

able 3 eading suppliers o sste integraton

able 3 Common Cyber Security Threats

able 33 eading suppliers o o securit solutons

able 3 eading suppliers o nrastructure securit

able 35 eading suppliers o ndpoint ecurit

able 36 eading suppliers o loud securit

able 37 raditonal dierences beteen and technologies

able 3 hree eatures reuired or counicatons beteen and and and the technical solutons that are helping to achiee these eatures or cloud - sstes

able 39 actors that applicaton endors should consider hen creatng cloudbased oerings

0 o naltcs ll rights resered 368 0

able 0 AM methods for polymers and metals

able 0 use cases or addite anuacturing

able eading suppliers o sstesoler

able 3 eading suppliers o sstesetal

able eading suppliers o serices

able 5 eading suppliers o oare

able 6 eading suppliers o aterials

able 7 use cases in ndustr 0

able aples o and hardare partnerships

able 9 eading suppliers o ugented ealit glasses

able 50 eading suppliers o irtual ealit glasses

able 5 eading suppliers o pplicaton eelopentoare laor

able 5 eading chip suppliers

able 53 ierences beteen traditonal industrial robots and collaborate robots

able 5 se cases or collaborate robotcs

able 55 eading suppliers o upportng echnologies

able 56 oon applicatons, sensing technologies, architectures, and counicaton methods for machine vision systems

able 57 erie o si actor oor ision applicatons

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 o naltcs ll rights resered 369 0

able 6 onnected achine ision use case eaples

able 6 eading suppliers o hardare soare

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 driing robotc ehicles

able 67 o adopton b industr based on the nuber and aturit o proects

able 6 oparison o arious digitaliaton strategies

able 69 oparison o the digitaliaton eorts o our aor eleator s

able 70 0 technologiesconcepts ipleented at the art actor

able 7 0 technologiesconcepts ipleented at s rilliant actories

able 7 0 technologiesconcepts ipleented at udis art actor

able 73 0 technologiesconcepts ipleented at the artactor

able 7 0 technologiesconcepts ipleented at artactor

able 75 he orld conoic orus list o soe o the best actories in the orld

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• IoT Security Market Report 2017-2022

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• Smart Parking Market Report 2018-2023

• ndustrial onnectit 09

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