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Dr. Michael AlexanderPartner
Munich, 15th November 2017
Industry 4.0 – Can the Electronics Assembly industry learn from Semiconductors, and vice versa?
220171115_SMT learning from SEMI_Vfinalgraphics.pptx
Contents Page
This document shall be treated as confidential. It has been compiled for the exclusive, internal use by our client and is not complete without the underlying detail analyses and the oral presentation.
It may not be passed on and/or may not be made available to third parties without prior written consent from .
© Roland Berger
A. A quick introduction to Roland Berger 3
B. Electronics Assembly in a nutshell 5
C. Industry 4.0 in Electronics Assembly 8
D. Learning from Semiconductors 14
E. A one-way road? 25
F. Summary 27
A. A quick introduction to Roland Berger
420171115_SMT learning from SEMI_Vfinalgraphics.pptx
Roland Berger is the only leading global consultancy of German heritage and European origin
Roland Berger at a glance
Founded in 1967 in Germany by Roland Berger
50 offices in 34 countries, with around 2,400 employees
Nearly 220 RB Partners currently serving
~1,000 international clients
Terra Numerata™ digital ecosystem joining forces with more than 30 leading digital firms
B. Electronics Assembly in a nutshell
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Flat overall Electronics Assembly market of ~ EUR 430 bn – Players leverage Industry 4.0 to extend service offerings & service quality
Source: IMS, Roland Berger
Key take-away Electronics Assembly market
> Electronics Assembly market is ~EUR 430 bn with ~1.0% CAGR 2011-15> Market is dominated by Electronic Manufacturing Services (EMS) and Original
Device Manufacturers (ODM) 1> EMS take ~75% or ~EUR 333 bn with ~7% CAGR 2011-15> Expected further slowing down of growth to ~5-6 %> ODM moving to EMS business to load their production capacities2> Most end-market segments growing at the same pace> Medical, Industrial, Automotive and other niches are ~20% of overall market> These appear to be the most profitable1) end-market segments, only slightly above 3C2)3> Price remains an important Key Buying Factor> Leading EMS players move towards extended service offerings and better service quality> Industry 4.0 is seen as a key enabler4
1) Based on an analysis of EMS players with over USD 100 m revenue / year, representing ~85% of the total market 2) Computers, Consumers, Communication
720171115_SMT learning from SEMI_Vfinalgraphics.pptx
Electronics Assembly mainly consists of Front-End (SMT) and Back-End manufacturing steps – FE more automated than BE
Simplified Electronics Assembly manufacturing process
Source: Interviews, desk research, Roland Berger
Assy Back-endAssy Front-end Modules
1) Surface Mount Technology; 2) Through Hole Technology
SMTassembly
Post-SMTassembly
Calibration/ Programming
Test Boxing/Assembly
> In-circuit testing (ICT) of the board
> Functional testing, i.e., testing that the board functions as designed
> Flashing of firmware
> Uploading of programs onto the microchips
> Mounting of THT2)
components> Gluing of
components, underfilling, coating of the PCB
> Mounting of electric SMT1) components that are placed on top of the copper pads of the Printed Circuit Board (PCB)
> Placing PCB intoa housing
> Assembly of the full mechatronic system
While the front-end production is highly automated, the back-end is often characterized by a significant larger share of manual work
Simplified
C. Industry 4.0 in Electronics Assembly
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Mechanization, Electrification, and Computerization have already influenced our working world radically – Industry 4.0 is next
Development stages of industrial manufacturing
Source: Bitkom/Fraunhofer, DFKI, Roland Berger
1784Mechanical weaving loom
First industrial revolution
100 %
1923Introduction of a moving assembly line at Ford Motors to support Taylorism
Second industrial revolution
100 %
1969First programmable logic controller
Third industrial revolution
100 %
201XReal time, self optimizing connected systems
Fourth industrial revolution?
<10 %
Time
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Major Industry 4.0 trends
Industry 4.0 is driven by technology, demand, and competitive trends with a strong impact on manufacturing
Source: Roland Berger, pictures: with courtesy of Grenzebach, Rethink Robotics, Google
Technology Demand Competition
Changes within the existing competitor landscape
Utilization of established/new SW technologies
Increasing degree of automation in the factory
Digitally connected factory
New players entering the market
Increasing flexibility of production systems
Advanced automation concepts
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Industry 4.0 scope comprises the entire value chain and business functions and has a huge impact on industry conduct and structure
The Industry 4.0 generic framework – Scope and impact
ImpactFlexibility/Mass customization
Direct client relationship
De-laborization
Higher asset rotation
Decentralization/ Regionalization
Faster product launches
Operator
Customer
Product Development
SupplierSmart Factory
Industry 4.0 scope
I
II
III
IV
V
VI
Shift of skillsetsVII
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SMTassembly
Control1)
In the Electronics Assembly industry we found ~ 20 potential SW and HW technology building blocks for Industry 4.0
Overview of main Industry 4.0 technology blocks in Electronics Assembly
Source: Interviews, desk research, Roland Berger
Fully automated equipment Automated material handling
Flexible workshop-based production
Predictive maintenance
Machine/line perf. opt.
System integration
HW-centric
SW-centric
Process control
Supply chain management
Smart Factory
PCB manufactur-
ing2)
Final packaging
Test / rework
Calibration / programm-
ing
Post-SMTassembly
Customer
Warehouse Shipping
Procure-ment tools
CRM
Asset performance mgmt. Quality mgmt.
Control1) Control1) Control1) Control1) Control1)Control1) Control1)
Prod. planning & schedulingProcess flow control
Process master control
Production scheduling
System integration
Product-to-machine communication
Machine-to-machine communication
Feedback loops
1) Machine control and/or line control including software interfaces 2) Usually not part of the electronics manufacturing process
QM systems
Automatic quotation tools
Order tracking
Supplier integration Digital twin
Material handling systems
Supplier
Sales tools
Design for manufacturing tools (Dfx)
Human-machine interfaces
8
9
Test
Prototyping
Product development
Plant eng.tools
Simulation
EDA/ECAD
PDM/PLM
Material handling
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20%
28%
31%
31%
41%
43%
44%
50%
31%
50%
52%
57%
Tier-1
Tier-1
Tier-3
Tier-1
Tier-2
Tier-3
Tier-2
Tier-4
Tier-4
Tier-4
Tier-2
Tier-2
The overall market for Industry 4.0 technology building blocks has huge potential – even leaders still have half the way to go
Relative Industry 4.0 maturity of selected EMS players
Even the leading players have about half the way to I4.0in front of them
Source: Interviews, desk research, Roland Berger
Note: Assessment based on RB I4.0 in EA technology building block segmentation and rating. Tier-1 players: turnover > USD 5 bn; Tier-2 players: USD 500 m < turnover < USD 5 bn ; Tier-3 players: USD 50 m < turnover < USD 500 m ; Tier-4 players: turnover < USD 50 m
Best in class
100%
D. Learning from Semiconductors
1520171115_SMT learning from SEMI_Vfinalgraphics.pptx
Generalmfg. SW
Diversified EDA
Electronics Assembly can learn from Semiconductors especially in Software
Electronics software player landscape
Smart
Factory
Productdevelopment
SMT EDASemi EDALong-tail of small players Specific PCB EDA long-tail of players
Long-tail of generalists &niche players
Offering both Semi and PCB design, as well as modules for System in Package
Semi Design
Semi front-end
Semi back-end
Assembly front-end
Assembly back-end
Modules
Note: Siemens refers to Camstar, Dassault to Apriso, SAP to Visiprise
Source: Interviews, Roland Berger
Semi mfg. SW
SMT mfg. SWSome smaller niche players
Majority developed inhouse;Some smaller niche players
Common ground especially in Semi back-end and SMT mfg. SW
1620171115_SMT learning from SEMI_Vfinalgraphics.pptxSource: Interviews, desk research, Roland Berger
SECS/GEM1) OIC2)
Unlike Semiconductors, different machine interface standards are used in Electronics Assembly – SECS/GEM has no "godfather"
Machine interface communication standards
Initiator
Hermes
Functionality > Interface to equipment and plant management SW
> Exchange machine data with plant management software
> Data exchange between machines
> Passing data within the line> Not designed for interfacing to
plant mgmt. SW
Importance > Limited importance -Only supported by small SMT players
> Widely used in Semiconductors
> Driven and supported by major players
> Widely used in Electronics Assembly, esp. SMT
> Driven and supported by major players
> Only focused on line control
> Relatively wide-spread in Electronics Assembly
Partners(selected)
1) SEMI Equipment Communications Standard / Generic Equipment Model; 2) Overall Inline Communication
1720171115_SMT learning from SEMI_Vfinalgraphics.pptx
Material handling, QM systems & Dfx tools are the mostly explored I4.0 SW technology building blocks in Electronics Assembly today
Average and best-in-class market maturity I4.0 SW technology building blocks
Best-in-class AverageTrend2016-21SW Technology Building Block
Source: Interviews, desk research, Roland Berger
Process master controlSystem assuring that products pass all processes in the right order; flexibly assigns operators to lines based on tasks
Machine/line perf. opt.Systems for visualization of machine KPIs, proposing recommendations, or even automate improving processes
Maturity
Production schedulingSystem generating an optimal production schedule taking capabilities, bottlenecks, changeover times, etc. into account
Predictive maintenanceSystems predicting machine defects to avoid unscheduled as well as scheduled maintenance
Material handling systemsSystem managing the overall material handling process including change overs and replenishments
QM systemsSystem responsible for tracing all consumables used in the production and capturing all testing data
Design for manufacturing (Dfx)Software supporting design for manufacturing and assembly tasks by analyzing schematics
1820171115_SMT learning from SEMI_Vfinalgraphics.pptx
Within the next years, accelerating I4.0 adoptions will foster Process Control and Asset Performance Management software solutions
Average and best-in-class market maturity I4.0 SW technology building blocks
Source: Interviews; desk research; Roland Berger
Best-in-class AverageTrend2016-21SW Technology Building Block
Process master controlSystem assuring that products pass all processes in the right order; flexibly assigns operators to lines based on tasks
Machine/line perf. opt.Systems for visualization of machine KPIs, proposing recommendations, or even automate improving processes
Maturity
Production schedulingSystem generating an optimal production schedule taking capabilities, bottlenecks, changeover times, etc. into account
Predictive maintenanceSystems predicting machine defects to avoid unscheduled as well as scheduled maintenance
Material handling systemsSystem managing the overall material handling process including change overs and replenishments
QM systemsSystem responsible for tracing all consumables used in the production and capturing all testing data
Design for manufacturing (Dfx)Software supporting design for manufacturing and assembly tasks by analyzing schematics
1920171115_SMT learning from SEMI_Vfinalgraphics.pptx
Illustration
According to market participants Process Master Control is one of the most important levers to increase labor and capex efficiency
Source: Interviews, desk research, picture on top: Mycronic – Photographer Magnus Elgquis, second from top: Bosch Rexroth, Roland Berger
Process master control system
SMT Line 1
Flying probe tester 2
Dispensing system 1
Assembly station 7
Process master control
"The process master control system assures that each product moves through all process steps in the right order. It thereby reduces the risk of errors and enables us to process different products at the same workstation simultaneously"
Tier-3 EMS player
"A process master control system enables us to handle the complexity of workshop-based production in the back-end and significantly increases the capex utilization in low volume environments"
I4.0 maturity leading Tier-2 EMS player
"The ability to pool operators across several SMT lines instead of assigning them to a certain line would enable us to further reduce the labor intensity of our production process – This requires a system that flexibly assigns tasks to operators according to their priority"
Tier-3 EMS player
Program provisioning
Step by step work instructions
Transportation routes
Machine operator tasks
2020171115_SMT learning from SEMI_Vfinalgraphics.pptx
Illustration
Future Production Scheduling Systems need to be cross-vendor compatible to achieve full impact
Production scheduling
Source: Interviews, desk research, Roland Berger
Job 1
Production plan
A B C D
Job 1
AJob 2
B
Job 3
C
Job 4
D
A BJob 2
C D
Line 1
Job 2
Line 2
Job 1 Job 2
Line 1
Job 1
Line 2
Job 2
Equip. 1
A1 A2
Equip. 2
A3
Equip. 1
A1
Equip. 2
A2 A3
Grouping production orders with feeder setup
Optimal assignment of jobs to lines to maximize line performance
Optimal assignment of pick & place tasks to different machines
High-level planning of products
"Production scheduling in SMT is quite complex since production and changeover times are highly depended on the specific machine/feeder setup – Finding the optimum between production time and changeover time is key to increase equipment utilization"
Tier-3 EMS player
"Most market solutions currently focus on specific parts of the scheduling problem, while true multivariate optimization is difficult it would be a significant improvement"
Tier-2 EMS player
"There is a large potential in optimizing the production schedule across lines from different machine vendors, however, third-party solutions can only approximate the machine behavior and usually underperform the solutions from machine manufacturers"
Tier-2 EMS player
2120171115_SMT learning from SEMI_Vfinalgraphics.pptx
Illustration
Systems visualizing KPIs, proposing recommendations or auto-matically improving processes will penetrate the market quickly
Machine/line performance optimization
Source: Interviews, desk research, picture: Mikroelektronika, Roland Berger
Level 1 Visualization of process parameters
Level 2 Advances analytics and recommendations
Level 3 Process Feedback
Level 1 Level 2 Level 3
OK?
"Visualization of process parameters like the OEE is a simple method to increase transparency and improve the overall manufacturing efficiency – Therefore this is standard in our plants"
Tier-3 EMS player
"While analyzing data with basic statistical methods can significantly help operators in fine tuning machine parameters in the NPI process, new algorithms like machine learning will bring this to the next level"
Tier-2 EMS player
"In the future machine parameters will be automatically optimized based on data without the need of human experts – Feedback loops are already the first step into this direction"
Industry Expert
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Nanotronics combines nanoscale imaging with artificial intelligence to create automated defect detecting microscopes
Machine/line performance optimization
Source: Nanotronics, desk research, Roland Berger
> Manual inspection times: 30 min to look at 50 chips
> Manual inspection often error prone> Learning via data sharing limited
Current process
> Nanotronics microscopes can analyze up to 100,000 chips/min
> Automatic error and defect detection with feedback loop
> Article Intelligence (AI) software to detect new error types and improve inspection algorithms
Nanotronics solution
AI to identify new error types
AI to improve detection algorithms
Automatic optical detection
Integration via SECS/GEM
Conventional error reporting
Product offering – Wafer quality inspection
Founded 2010
Headquarters New York, NY
Revenue[2016, USD m]
n/a
Funding [USD m]
30.0 Series D, Oct 2017
Board of directors containsPeter Thiel, Beth Comstock, Jaan Tallinn
Data repository
Manual inspection stations
Inspiration story
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Prediction algorithms using big data
Database containing historic & future equipment data
The predictive maintenance offering is currently still limited – Many new players entering the field
Predictive maintenance
Source: Interviews, desk research, Roland Berger
Illustration"The predictive maintenance offering by machine manufacturers is currently quite limited – We had to develop our own solution for our electrical testing equipment"
Tier-3 EMS player
"Analyzing machine behavior and failures is not only providing value to operators, it is also key for future machine development and can provide a significant competitive advantage to machine suppliers"
Equipment manufacturers
"While machine manufacturers have a key advantage in predictive maintenance, many new players are still entering this field"
Industry Expert
Pick & place
Oven
Asset performance management
Dispenser
Robot Machine
Plant m – EMS 1
Plant n – EMS 1
Machine Machine Machine
Asset performance management
Machine Machine
2420171115_SMT learning from SEMI_Vfinalgraphics.pptx
Cassantec offers enhanced analytics solutions using basic sensors that are already included by the manufacturer at point of sales
Predictive maintenance
Source: Cassantec, desk research, Roland Berger
Alert / Alarmfor immediate response
Monitoring
> Vibration, ultrasonic, infrared etc. sensors & devices
> Software for data illustration, mapping, projection, trending
> Lubricant, vanish and filter debris lab services
Insightfor work orders
Diagnostic
> Advanced software based on equipment-specific models
> Specialized consultants, field technicians interpreting results
> Experts making predictions ("predictive analytics")
Prognosis
> Objective, condition-based info on remaining life (RUL)
> Computed risk profiles over a significant, future time horizon
> Online solution utilizing data and functions already available
Foresightfor long-term planning
Founded 2007
Headquarters Zurich, Switzerland
Revenue[2016, USD m]
n/a
Funding [USD m]
n/a
Collaborations with leading international research institutions as Stanford, RWTH Aachen and EPFL Lausanne
Product offering – Predictive maintenance on long-term horizon
Inspiration story
E. A one-way road?
2620171115_SMT learning from SEMI_Vfinalgraphics.pptx
Not much can be seen vice versa – Electronics Assembly is still way back in Industry 4.0 maturity from Semiconductors and FPD
Industry 4.0 maturity in different industries
Source: Interviews, Roland Berger
Indicative
Scale of production
Batch size one production vs. mass production
Degree of customization
High levels of customization vs. standardization
Capital intensiveness
High capital intensiveness in production vs. low capital intensiveness
Production discreteness
Discrete production vs. process production
Drivers
Low maturity High maturity
Surface-mount
technology
Semi-conductor
manufacturingFlat panel manu-
facturing
Maturity level
F. Summary
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Summary: Electronics Assembly on its way towards Industry 4.0 can learn a lot from Semiconductors – Especially in the SW domain
Industry 4.0 scope comprises entire businesses. I4.0 driven by technology, demand, and competitive trends will have strong impact on factory automation in Electronics Assembly
In the Electronics Assembly industry we found ~ 20 potential technology building blocks for Industry 4.0. Most of this market is yet immature. Even EMS leaders still have half the way to go towards Industry 4.0
Electronics Assembly can learn from Semiconductors especially in Software
> Unlike Semiconductors, different machine interface standards are used in Electronics Assembly – SECS/GEM has no "godfather"
> Material handling, QM systems, and Dfx tools are the mostly explored I4.0 SW technology building blocks today
> Within the next years, accelerating I4.0 adoptions will foster Process Control and Asset Performance Management SW
Not much learning can be seen vice versa
The overall ~EUR 430 bn Electronics Assembly market is flat – Industry 4.0 will help extending service offerings & service quality
2920171115_SMT learning from SEMI_Vfinalgraphics.pptx
Your contacts for further information regarding SMT 4.0
Partnermichael.alexander@rolandberger.com
Consultantjonas.zinn@rolandberger.com
Dr. Michael
AlexanderJonas
Zinn
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