1、Global Industrial Automation Industry Primer 21 May 2019 EQUITY RESEARCH DISCLOSURE APPENDIX AT THE BACK OF THIS REPORT CONTAINS IMPORTANT DISCLOSURES, ANALYST CERTIFICATIONS, LEGAL ENTITY DISCLOSURE AND THE STATUS OF NON-US ANALYSTS. US Disclosure: Credit Suisse does and seeks to do business with c
2、ompanies covered in its research reports. As a result, investors should be aware that the Firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in making their investment decision European Capital Goods Te
3、am Andre Kukhnin, CFA +44 20 7888 0350 andre.kukhnincredit- Max Yates +44 20 7883 8501 max.yatescredit- Leo Carrington, ACA +44 20 7883 4532 leo.carringtoncredit- Iris Zheng, CFA +44 20 7883 5298 iris.zhengcredit- Artem Tokarenko +44 20 7888 2676 artem.tokarenkocredit- Specialist Sales: Andrew Bell
4、+44 20 7888 0459 andrew.bellcredit- US Electricals 2017 feedbacks 15 May 2017; 2016 feedbacks 29 Apr 2016 IIoT Webinar 5 Dec 2018; SPS IPC Drives 30 Nov 2018; Automatica 21 June 2018; IMTS Show-Automation 14 September 2016; Automatica Fair 27 June 2016; Automation Fair 1 December 2015 Source: Credit
5、 Suisse research, Company data Whats inside Slide 3 The primer contains the following: Global Industrial Automation Market Forecast slide 5 Global Industrial Automation Market Overview slides 6-8 Discrete vs. Process Automation Markets slides 9-10 Industrial Automation A structural Growth Story ? sl
6、ides 11-12 Peers: Competitive landscape and Financial benchmarking slides 13-14 Control Systems: Enterprise-level Controls closing the machine-to-machine loop is the ultimate goal. Discrete Automation cycle debate aside, we see re-shoring and generally rising adoption as positives but have concerns
7、over 1) automotive shift to EVs where drivetrain BOM is of ICEs and 2) increasing efforts in PLC virtualisation (unlikely for auto, in our view, but possible for new applications). Process Automation as most mature segment, we expect growth closer to General IP (c3-4% pa). Robotics auto capex cycle
8、headwind near term but overall rising adoption remains a structural driver; market shift towards cobots to watch. Potential strategic moves 1) acquisitions of independent industrial software players; 2) acquisitions of system integrators by lateral entrants to gain customer proximity; 3) IIoT platfo
9、rms consolidation; 4) cloud and analytics vendors to compete with IIoT platforms; 5) traditional robot makers acquiring into cobots. Source: Company data, Credit Suisse research 501001502002503002004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018G l o b a l I P In d e x Pro c
10、e s s Au t o m a ti o nDis c re t e Au t o m a ti o n In d u s tri a l Au to m a t i o n So ft wareR ob o ti c s-15%-10%-5%0%5%10%15%20%25%30%35%2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018A v e r a g e S o f t w a r e E B I T m a r g in A v e r a g e P A E B I T mar g inA v
11、 e r a g e D A E B I T m a r g i n A v e r a g e R o b o t ic s E B I T m a r g inCloud (AWS, Azure, Watson, etc.) Enterprise Level Systems Plant Level Controls Plant Instrumentation Industrial Automation “Hardware x Software” Crossover Slide 6 Source: Credit Suisse research Discrete Hybrid Process
12、ABB: AbilityGE: Predix Rockwell Automation: PTC partnership (ThingWorx)Siemens: MindSphere Schneider: Ecostruxure Industrial IoTplatformsConnectivity Industrial Automation Architecture Slide 7 Source: Credit Suisse research Ma j o r Pl a y e r s ER P: SA P, O ra cl e , M i cro s o f t , Sa g e , I n
13、 t u i t , C D C So f t w a re Pl a n t D e s i g n a n d Si m u l a t i o n : A v e v a , A s p e n , H O N , Sc h n e i d e rC A D (C o m p u t e r A i d e d D e s i g n )PL M : Si e m e n s , D a s s a u l t , PT C , SA P, O ra cl e , A u t o d e s k C A D : Si e m e n s , D a s s a u l t , PT C
14、, A u t o d e s k, Be n t l e yvM ES : Sc h n e i d e r, C D C So f t w a re , A s p e n , R o ckw e l l , H O N , D a s s a u l tvv SC A D A : Si e m e n s , Sc h n e i d e r, A BBv v vvC o m p u t e ri z e d N u m e ri ca l C o n t ro l (C N C )3 D Pri n t i n g So f t w a reC N C : F a n u c, Si
15、e m e n s , M i t s u b i s h i 3 D Pri n t i n g So f t w a re : M T L Sv vH u m a n M a ch i n e I n t e rf a ce (H M I )vv vD ri v e s Se n s o rsR o b o t s M a ch i n e V i s i o nM o t o rsR e l a y s a n d s w i t ch e sM e t ro l o g y : H e x a g o n , F a ro , R e n i s h a w , Z e i s sEn
16、 t e rp ri s e R e s o u rce Pl a n n i n g (ER P)3 D Pri n t e r: X O N E, SS Y S, D D D M o t o rs : A BB , EM R , R BC , Si e m e n s , W EG , M i t s u b i s h i , T e co R o b o t s : A BB , F a n u c, KU KA , Y a s ka w a , KH I D ri v e s : A BB , F T V , ET N , M i t s u b i s h i , EM R , S
17、i e m e n s , R O K M a ch i n e V i s i o n : C o g n e x , Ke y e n ce M a ch i n e T o o l s : D M G M o ri , A m a d a , O ku m aPl a n t L e v e l C o n tr o l sPr o c e s s D i s c r e teD C S: A BB , H O N , y o ko g a w a , EM R , Sc h n e i d e r PL C : ET N , R O K, Si e m e n s , O m ro n
18、 , M i t s u b i s h i , Sc h n e i d e rD i s t ri b u t e d C o n t ro l Sy s t e m (D C S)Pro g ra m m a b l e L o g i c C o n t ro l l e r (PL C )Su p e rv i s o ry C o n t ro l a n d D a t a A n a l y s i s SC A D A - H M IM ES / C PM / M O MIndustrialIoTplatformIIoT pla t f or m : M i cro s o
19、f t A z u re , I BM W a s t o n , G o o g l e , A m a z o n , PT C T h i n g W ro x , I n t e l , G E Pre d i x , SA P, Si e m e n s M i n d Sp h e re , Sc h n e i d e r Ec o St ru x u re , A BB A b i l i t y M e t ro l o g y (3 D I n s p e ct i o n Green = Plant Level Controls; Blue = Instrumentati
20、on * Indicative splits from CS estimates * Indicative splits from CS estimates * Indicative splits from CS estimates Au to m o ti v e15%G e n e ra l In du s tri al s15%Con s u m e r goods11%C he m i c al s * * L V , M V a n d P o w e r Q u a l i tyD i g i t a l Pl a t f o r m E l e c t r i f i c a t
21、 i o n * *S e n si n g Self-organization of integrated production lines considering the entire value chain; Flexible decisions on production process on the basis of the current situation. We believe the key benefit of such a system is increased flexibility and efficiency of the production process: I
22、t allows significantly shorter time-to-market for new products given the integrated design and production system (as much as 50% time saving). It creates significant savings of energy and production resources due to better planning and organization. It allows for individualised mass production with
23、the help of additive manufacturing technology (3D printing) which caters for diversified individual demand. Source: Credit Suisse research, Company data, Siemens 0%5%1 0 %1 5 %2 0 %2 5 %3 0 %3 5 %01020304050602012201320142015201620172018E2019E2020ENum b e r o f Io T d e v i c e s c o n n te c te d y
24、 o y g ro wth - R H SSiemens estimates 50bn IoT devices to be connected by 2020 Industry 4.0 Industrial Internet of Things (ii) Slide 22 Source: IoT Analytics 5 types of Industrial IoT platforms Five types of IIoT platforms are (from bottom to the top): Connectivity platforms are a form of Platform-
25、as-a-Service that offer coverage capabilities and solutions for connecting the IoT devices, managing and orchestrating connectivity, and provisioning communication services for connected IoT devices. Device management platforms are a form of Platform-as-a-Service (or device cloud) that handle provis
26、ioning tasks to ensure connected devices are deployed, configured, and kept up-to-date with regular firmware/software updates. Cloud platforms (IaaS backends) are a form of Infrastructure-as-a-Service that offer a scalable enterprise-grade backend for data management of IoT applications and services
27、. Application Enablement Platforms (AEPs) are a form of Platform-as-a-Service that also offer Software-as-a-Service solutions enabling developers to rapidly create, test, and deploy an IoT application or service. Advanced analytics platforms are a form of Platform-as-a-Service that also offer Softwa
28、re-as-a-Service solutions for sophisticated analytics tools including machine-learning techniques and streaming analytics capabilities to extract actionable insights from IoT data. IIoT platforms deployment by customer type: Large factories: often already have connectivity deployed Small/Medium fact
29、ories: often look for end-to-end solutions Industry 4.0 Industrial Internet of Things (iii) Slide 23 Source: IoT Analytics Large factories: IIoT platform scope Different-sized factories have different needs: Large factories often already have connectivity and plant network deployed, and manufacturer
30、s derive value by integrating and analysing existing data sources. Small/Medium factories: OEMs need end-to-end solutions in order to create connected products and services. SEMs can leverage cloud-based solutions to realise IIoT use cases without investing in IT infrastructure. Plant networks alrea
31、dy existed Small/Medium factories: IIoT platform scope End-to-end solutionIndustry 4.0 Industrial Internet of Things (iv) Slide 24 Source: IoT Analytics Industrial 4.0 use case size (12 key use cases) and growth rates Industrial IoT platforms play a key role in realising many of the I4.0 use cases:
32、Advanced Digital Product engineering the biggest I4.0 use case: refers to the use of I4.0 technologies to reduce the time and cost required to bring new products to market. Data-driven Asset/Plant Performance Optimization: refers to use cases implementing I4.0 technologies to increase asset utilizat
33、ion. Predictive Maintenance: seeks to prevent the estimated $657bn in downtime losses that occur each year, and is regarded as one of the most important use cases of I4.0 technology. Data-driven quality control: refers to the use of I4.0 technologies to produce a greater number of higher-quality pro
34、ducts. Remote service: refers to the use of I4.0 technologies to provide remote troubleshooting and optimization services to customers and maintenance personnel. Everything-as-a-service: refers to selling products as services instead of or in combination with physical products. 45%40% 50%020%15% 25%
35、 35%30%2 0 2 3 Mark e t Siz e ( $ B)CA GR ( 20 18 - 20 2 3 )D a t a - d r iv en In v en t o ry Op t imiz a t ionAd v an c ed D igit al Pr o d u c t En gin ee rin g R em o t e Asse t T es t in g / In sp ect ion/ Cert if ic a t ionAd d it iv e Pr od u c t ionH u man R o b o t Collab o r a t ionPr ed i
36、c t iv e M ain t en a n c eR em o t e Se r vic eE v eryth in g - as - a - Servic e B u siness M od elsD a t a - d r iv en Qua lit y Con t r o lV irt u al T r ain in gD a t a - d r iv en Asse t / Pla n tP erf orman c e O p t imiz a t ionAu gmen t ed Op er a t ion s$1 - 5B$25 - 50B$5 - 10B$10 - 25BInd
37、ustry 4.0 Industrial Internet of Things (v) Slide 25 Source: IoT Analytics Global IoT Platforms market size Manufacturing is the largest market segment for IoT platforms (37% of overall in 2018). The manufacturing segment is growing at 42% p.a. faster than overall market growth of 37%. The total glo
38、bal IoT platforms market is forecast to reach $22bn in 2023, close to 5x the size in 2018 of $4.5bn. 02 0 ,0 0 02 5 ,0 0 01 5 ,0 0 05 ,0 0 01 0 ,0 0 0Y ea rGl o b al I o T Pl a tf o rms Mark e t Siz e in $ B20212020 202320222017 2018 201913 ,03 62,4 241,6 701,1 463,9 052,8 191,9 939,9 127,3 865,4 32
39、4 ,4 8 91 6 ,6 3 02 2 ,2 8 46 ,3 2 98 ,8 6 11 2 ,2 2 86,7 184,8 423,4 293 ,1 3 939%9,2 48M an uf act uringNon - M an uf act uringCA GR 1 7 - 2337%42%Industry 4.0 Industrial Internet of Things (vi) Slide 26 Source: Credit Suisse research, Company data User cases of Industrial IoT platforms Industrial
40、 IoT platform vendors IIoT platform vendors: GE, Siemens and Schneider appear more advanced in IIoT than ABB. Siemens MindSphere runs an open architecture and does not need to be tied to just Siemens physical devices. However, customers are encouraged to use Siemens connected devices to better fit i
41、nto the ecosystem. Connected IIoT devices: We find elevator OEMs are the front-runners in deploying connecting elevators and IIoT platforms. The global top 4 elevator OEMs have all launched their IIoT platform offerings based on IaaS vendors and are pushing for incremental income from IIoT and impro
42、ved operational efficiency. Other industrial players such as Legrand, Atlas and Wartsila have also developed IIoT offerings, but they lag that of elevator OEMs, in our view. T e c h p a r t n e rK O N E 24 / 7 C o nn ec t ed S er vi ce s 2017 15 0,000 e le vat o r s an d escal at o r s IBM Wat so nS
43、 c h i n d l e r S ch in d le r A he ad 2016 n/ a GE Pr ed ix, H uawe iO t i s Ot is On e 2018 3 00 , 00 0 e le vat o r s an d escal at o r s A T A u g u s t 2 0 1 5 ( l a u n c h e d c l o u d s e rv i c e )$ 1 b n + P re d i x - p o w e re d re v e n u e i n 2 0 1 7 ;$ 1 2 b n d i g i t a l re v e
44、 n u e b y 2 0 2 0n /a 3 5 , 0 0 0 + d e v e l o p e rs n /a A p p l e , I B M Wa t s o n , M i c ro s o f t A z u reH o n e y w e l l Se n t i e n c e 1 2 /1 6 ; H o n e y w e l l F o rg e e x p e c t e d Q 2 1 9$ 2 . 6 B o f So f t w a re Re v e n u e ( e m b e d d e d a n d s t a n d a l o n e )H
45、 O N - s p e c i f i c 2 , 0 0 0 + i n t e rn a l d e v e l o p e rs n /aRo c k w e l l F a c t o ry T a l kI n n o v a t i o n Su i t e2 0 1 6 C o n n e c t e d E n t e rp ri s e$ 3 0 0 m n i n 2 0 1 8 i n I n f o rm a t i o n So l u t i o n s a n d C o n n e c t e d Se rv i c e sn /a n /a P T C ,
46、M i c ro s o f t , C i s c oSc h n e i d e r E c o St ru x u re 2 0 0 8 ; N o v e m b e r 2 0 1 6 ( l a u n c h e d e n h a n c e d v e rs i o n )So f t w a re re v e n u e 650 m i n 2017 ( 450 m b e f o re A V E V A a c q u i s t i t i o n )10+ 9 , 0 0 0 s y s t e m i n t e g ra t o rs 58 M i c ro
47、s o f t A z u re , I n t e lSi e m e n s M i n d Sp h e re B e f o re 2 0 1 1 ( o ri g i n a t e d f ro m D i g i t a l F a c t o ry ) ; M a rc h 2 0 1 6 ( l a u n c h e d o p e n Sm a l l re l a t i v e t o o t h e r d i g i t a l re v e n u e s( D ig ita l re v e n u e s 5 . 2 bn , o f w h ic h 4
48、b n s o ftw a re a n d 1 . 2 b n d i g i t a l s e rv i c e s )20+ 9 0 0 s o f t w a re d e v e l o p e rs 17 I B M Wa t s o n , A t o s , M i c ro s o f t A z u re , SA P , A c c e n t u re T e c h p a r t n e r s# o f C o m p a n y A p p l i c a t i o n s L a u n c h d a t e Re v e n u eIoTplatformsIndustry 4.0 Industrial Internet of Things (vii) Source: IoT Analytics Slide 27 C om po ne nt s M i c r os of t I B M G oo gl e A m a z on pt c I nt e l GE S A PV i s u a l i z a t i o n B u s i n e s s S y s t e m I n t e g r a t i o n
