pec 2017 6 aprile | industry 4.0_santino
TRANSCRIPT
1MILANO, 6 APRILE 2017
Marco Santino @ PEC 2017
Operations & Footprint 4.0: Impatti e Prospettive per la Supply Chain
2
Industry 4.0: the fourth level of the industrial (r)evolutionDevelopment stages of the industry from the loom to cyber-physical-systems of tomorrow
Late 18th century Early 20th century Early 1970s already started...
...introduction of mechanical
production plants using water and
steam power
...introduction of work-division
mass production using electrical
energy
...use of electronics and IT to foster
automated production
... E2E connected & adaptive value
chain, using cyber-physical systems
(CPS) and dynamic data processing
Industrial
revolution1.
Historical loom
Automatic animal feeding
system in mass production
Industrial
revolution2.
Industrial
revolution3.
Industrial
revolution4.
Automated industrial robot in
manufacturing
Connection between
physical and digital systems
3
Nine technology drivers driving Industry 4.0
Several applications already exist for all technology drivers
Industry
4.0
Advanced robotics
Simulation
Horizontal/vertical
software integration
Augmented reality
Big Data and analytics
Additive
manufacturing,
e.g. 3D printing
Cloud
Industrial Internet
(network of hardware-
integrated sensors)
Cyber-security
Advanced robotics
SimulationAugmented reality
Big Data and analytics
4
Technological drivers: expected evolution (I)
Autonomous
robots
Now Industry 4.0
Autonomous, cooperating industrial robots
Numerous integrated sensors
Standardized interfaces
Intelligent robots with sensors
Take on complex assignments with
flexible programming
Usually with proprietary interfaces
Simulation and optimization of comprehensive, complex, and
value networks based on real-time data from intelligent
systems
Data-driven (3D) simulation of single products and materials
widespread
Simulation of production processes and first digital factories
Cross-company, universal data integration based on
communication and data transfer standards
Requirement for fully automatic value chain
(from supplier to customer, from management to shop floor)
Vertical and horizontal data integration realized
in part
Numerous communication gaps within and between corporate
functions and beyond
the company
Complete network of machines, products, processes, and
systems in real time
Multidirectional communication between networked objects
Machine and system network and connectivity available in
large-scale industry
Connectivity recognized as central requirement for
generating pools of data
Networked, open systems
High level of networking between intelligent machines,
products, and systems leads to especially high security
requirements
Separate management systems and unconnected production
systems (closed systems)
Cybersecurity necessary due to system
Internet connections
Simulation
Horizontal/
vertical
integration
Industrial
Internet
Cyber-security
5
Transfer machine data via cloud
Automation software partially in private cloud
Cloud-based real-time communication is also possible for
production systems
Use proprietary private clouds
Focus on management software
First cloud-based analytic tools as SaaS
3D printing along with individual products also available for
mass production
High-performance, decentralized 3D production systems to
reduce transport distances and stock
on hand
Application in prototyping
Production of individual product components from additive
production processes (e.g., aviation industry, medical
technology)
Virtually augmenting reality for many complex tasks (e.g.,
helicopter maintenance)
Display supporting information directly in field
of sight possible (e.g., standard, industrial use
of AR glasses)
Various pilots AR-based support systems (e.g., package finder
or repair instructions from augmented reality on mobile devices
Forerunner models to AR (e.g., pick by voice, pick by color)
popular as assistance systems
Comprehensive evaluation of available data sources (e.g.,
analysis of combined ERP, SCM, MES, CRM, and machine data)
Real-time decision-making support and optimization
Intelligent algorithms (analytics) for evaluating large,
structured, and unstructured volumes
of data (data lakes)
Focus on looking at one object
Cloud
Additive
manufacturing /
3D printing
Augmented
reality
Big data and
analytics
Technological drivers: expected evolution (II)
Now Industry 4.0
6
Increased flexibility
… e.g., through machines and
robots that can execute the
production steps for a large
number of products
Increased speed
… from the first idea to the
finished product through
consistent data and, e.g., new
simulation opportunities
Increased productivity …
e.g., through a higher level of
automation and shorter setup
times and smaller stocks
Increased quality
… through more sensors and
actuators that monitor the
current production in real
time and quickly intervene in
case of errors
I
Flexibility
II
Speed
III
Productivity
IV
Quality
Central
requirements
from
production
SafetyWorking
conditionsCollaboration
Environm.
protection
Innovative
capability
More occupational safety
through increased
automation
Better working conditions
through ergonomically
adapted workstations
Increased collaboration in the
production network through
consistent data availability
Better environment protection
through optimized use of resources
(e.g., more energy-efficient
operation of machinery)
Increased innovative
capability through new
technological possibilities
in manufacturing
Manufa
ctu
ring
condit
ions
Industry 4.0: step change in production performance
7
Low-cost countries: still a valid concept?
0
120
130
80
140
100
110
90 87
2014
86
2004
96
Manufacturing-cost index, 2004 versus 20141 (U.S. = 100)
+10
+7
+25
+12+9
2014
107
2004
97
2014
101
2004
94
2014
123
2004
97
2014
99
2004
OtherElectricityLabor2 Natural gas
China Czech RepublicPolandRussia Brazil
Sources: U.S. Economic Census; BLS; BEA; ILO; Euromonitor; EIU; BCG.Note: Index covers four direct costs only. No difference assumed in “other” costs (for example, raw-material inputs, machine and tool depreciation); cost structure calculated as a weighted average across all industries. 1Changes in the index from 2004-2014 are rounded to the nearest percentage point. 2Productivity-adjusted.
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Robotics as a game changer in global manufacturing Labor-cost evolution and productivity gains due to robotics heavily impacting countries' competitiveness
Potential change in manufacturing cost-competitiveness index1 due to robotics, 2014 – 2025
1BCG's Global Manufacturing Cost-Competitiveness Index shows how competitive the top 25 export economies are in manufacturing. BCG measures each economy relative to the US. Above, a one-point gain vs. the US means that the direct manufacturing costs of the country in question will become one percentage point cheaper relative to the US by 2025. For further background, see BCG's August 2014 report, The Shifting Economics of Global Manufacturing. Sources: STAN Bilateral Trade Database, US Bureau of Labor Statistics, BCG analysis
Conservative
Aggressive
Scenarios
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Gain ground vs.
the US
Lose ground vs.
the US
(11) (12) (3) (5) (4) (2) 0 0 (6) 1 (5) 1 1 (2) (2) 2 (0) 0 0 1 2 6 6 7 7
(4) (0) (1) (0) 0 (1) 0 (0) (0) (0) (0) 1 1 0 0 1 0 0 1 1 1 1 1 2 2
Robotics offer an opportunity for both high- and low-wage countries to make competitiveness gains
Advanced Robotics
9
Can
ad
a
So
uth
Ko
rea
Structural impact on labor costsBy 2025, ~25% of all 'automatable tasks' will be automated through robotics, driving ~16% in global labor-cost savings
1BCG estimates that by 2025, the portion of automatable tasks done by robots will surpass 23% for all mfg industries worldwide. Select heavy-adopting industry-country pairs are expected to near steady-state maximum automation levels of ~60% in 2030 or later. 2China figures based on YRD region. Sources: STAN Bilateral Trade Database, US Bureau of Labor Statistics, BCG analysis
Conservative
Aggressive
Scenarios
00
3
6777888999
131416
181820
2121222224
25
33
0
10
20
30
40
Labor-cost savings from adoption of advanced industrial robots (%, 2025)
Average global labor-cost savings ~16%
Ind
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Ru
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Mexic
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47 40 35 32 33 30 41 39 34 35 30 24 26 30 29 27 26 14 25 24 23 22 19 7 0 0
21 15 12 10 12 10 6 6 9 5 8 8 4 6 6 5 5 4 5 5 5 4 4 1 0 0
Advanced Robotics
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Digital Supply Chain: the technology is already there
... key questions to clarify
What does digital supply chain mean and
how does it differ from conventional?
What are relevant technology trends, what
are best practice applications?
How do these trends impact my supply
chain?
Which value does digital bring to my
supply chain?
How do I transform my supply chain to
digital – is there any standard approach?
The time is now ...
Cost of sensors
$1.30avg. cost .60over the past ten years
Cost of bandwidth
40xover the past ten years
Enough IP addresses
IPv6 3.4 x 1038
IP addresses=
Cost of
processing power
50xover the past ten years
Cloud infrastructure
20xcost per MB
over the past ten years
✓
✓
✓
✓
✓Data
90%of global data generated
in the last 2 years
✓
Source: BCG research
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How to read Digital Supply Chain evolution
Benefit
dimensions
Application areas
Levers
Technology trends
Implications
Benefits of digital SC
What business targets are
focused?
Application areas
Which business processes are
affected?
Levers
What levers could be
taken?
Technology trends
What technical trends
enable the possible levers?
Implications
What are the pre-
requisites and changes to
my organization?
BCG digital supply chain framework
12
Big data
Shift to
the cloud
Internet
of things
Auto-
nomous
control
systems
Cognitive
computing
Auxiliary
systems
3D
printing
Planning and
visibility
Procure-
ment
Production
After
sales
Sales &
customer
service
Logistics
People and capabilities
Pro-
cesses
Systems
and tools
Structures
Advanced analytics
forecasting
Advancedinventory
mgmt.
Control tower & real-time
optimizationPredictive diagnostics
Remoteservicing
Predictive spare parts management
Sensor driven replenishment
Demand driven SCM
Warehouse operations automation
Geo analytics based network optimization
Vision picking
Protoyping
Predictive maintenance
Processsimulation
Customer platforms
Supplier
platforms
Supplier
collaboration
Spend
analytics
Service
Cost Revenue Agility
Risk management
Digital supply chain @ a glance
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Clear emerging "new" levers by application area
Planning and
visibility
Procurement
Production
After sales
Sales &
customer service
Logistics
Advanced analytics
forecasting Advanced
inventory
mgmt
Control
tower & real-time
optimization
Sensor driven
replenishment
Demand
driven SCM
Warehouse operations
automation Geo analytics
based network
optimization
Vision
picking
Simulation
Predictive analytics
New production
technologies
Customer
platforms
Application
areas and
levers
Predictive
diagnostics
Remote
servicing
Predictive
spare parts
management
Buyer
platforms
Supplier
collaboration
Spend
analytics
1
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Example: Control towers Enabling end-to-end transparency and real-time supply chain optimization
Description
Central data hubs and team with data access across functions, locations,
and external partners managing key aspects of the logistics flow
• Centrally controls and optimizes product flows
• Determines and implements optimal inventory
• Improves load efficiency
• Acts as central contact for all stakeholders in the supply
chain
Benefits & impact
• Transparency across the supply chain
• Real-time information on various parameters
• Bundled responsibility in one team
• Lower inventories and improved allocation
• Improved material & parts availability
• Optimized transportation and logistics flows
Labor & logistics costs, working capital requirements
Product availability
Reduction in ~2-5%1 of total costs=
Note: While machine to machine communication not essential for use case, it can significantly improve sequence stability 1. Steady state defined as time it takes to implement all necessary measures for the execution of use case (potentially 5-10 years); Source: BCG analysis, expert interviews
Planning & visibility Procurement Production Logistics Sales & customer service
After sales
Other examples
Opened remote operations
centre for real time visibility
& decision making
Single platform used with
partners for monitoring &
network analysis
Use case in action
Analytics & Innovation
division to produce insights
through analytics
15
Virtual product design, next frontier of simulation
Source: Company website, BCG analysis
PLM software the integrates an synchronizes product/project data of different source-systems and allows multiple users access
and edit rights.
Basic PLM functions Further application possibilities
Global synchronization of engineering data
of different CAD-, CAM and CAE-systems –
directly linked to production
Team-wide workload planning and
milestone definition (esp. for design and
development)
Harmonization and synchronization of
different BoM lists allowing for quick analysis
and audits
Global document management, integrated
in existing desktop applications, e.g. MS
Office
Continuous monitoring of product (target)
costs through integrated design and BoM
data
Supplier integration during product design
through constant data exchange, e.g. of
requirement
Quality mgmt. through systematically
investigating, analyzing and resolving quality
issues
Integration of MRO data already during
early design phases for individual parts
Example: Teamcenter (HD-PLM)
17
Production: today and tomorrow Example automotive: Using autonomous robots leads to more flexible production processes
Industry today … … and tomorrow (Industry 4.0)
Holding device
Programming
Individual, automated
industrial robots
Autonomous, cooperating industrial robot
(groups)
Fixed clamping device affixes the workpiece
for processing
Adaptable industrial robots hold and spin the
workpiece according to individual requirements
Set, programmed movements and activities
for robot arms
Programmed sequence of motions for the
processed workpiece
Flexibility of
production lines
Multiple inflexible production lines
for one car model each
Flexible and individually adaptable production
lines for multiple models
CommunicationReal-time communication to industrial control
systems
Instantaneous communication within the robot
group and to industrial control systems
Applicability of
production lines
Product and plant engineering for up to two
product life cycles
per model
Product and plant engineering for multiple
product life cycles
and models
Technolo
gy
Advanta
ges
Architectural
components used
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