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.

8

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

44443332222221110

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Bra

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Italy

Mexic

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str

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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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an

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tes

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ina

2

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bal to

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ain

Ind

ia

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

10

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

11

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

13

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

23

4

5

6

7

8

9

1011

12

13

14

15

16

17

18

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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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Ready for the ride?

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