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SMART FACE Shifting Automotive Production to Industry 4.0

Dr. Christian Schwede, Fraunhofer IML

Prague, June 9th, 2016

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Common Core of Recent Buzzwords and Upcoming Trends: Digitization of the World

Big Data

Cyber-Physical Systems

Industry 4.0

Social Media

Google Glass Smart

Devices

RFID

Cloud Computing

Service-Based Architectures

DronesAgent-Based

Systems

Internet of Things

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Election of the Pope 2005

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Election of the Pope 2013

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Industry 4.0 – The “classic” ladder of progress

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From truth to probability

Industry 3.0

• standard variants

• stable demands

• centralised organisation

• cycle driven work

• rigid

• focus on production volumes

Industry 4.0

• highly individual products

• uncertain global markets

• decentralised organisation

• autonomous decision making

• Flexible

• focus on the customer demand

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Car Production between Individualisation and Efficency

Number of variants

Output per variant

1850

1913

1955

1980

2000

Quelle: Koren (2010), cited in Bauernhansl (2014).Pictures: https://en.wikipedia.org (2015), https://www.impulse.de (2015), audi.de (2015), o2.co.uk (2015), computerbild.de

(2015).

Ford Model T

VW Käfer

Production

Audi Configurator

Mass-

production

Individualisation

»Shareconomy«

Complexity

Globalisation

iPhone

3D-Printed-Car

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Autonomous behaviour of everything is the core aspect of the revolution

Humansplan, control, interact…

Binsknow their content and control their processes

Containerorganize loads and routes on their own

Trucksno driver to transport goods

Vehicles and Forkliftsorganized in swarms

Racksorder their supplies autonomously

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SMART FACE is an approach towards a factory of the future

Development of a decentrally controlled small-scale production of electric vehicles

based on Industrie 4.0 technologies & concepts

Main Objective

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The Great Transformation on the shop floor -Current situation

Sequencing (part A 1:2)

Customer order

Weekly Program Planning

Frozen Zone

Restrictions (suppliers, assembly line) based on prognosis (e.g. 50% part A)

BodyPaint Shop

Buffer Buffer

Paint shop sequenceOverload

Floater

Distribution rows(buffer time 2 days)

Assembly sequence

assemblytime

Re-sequencing

Cycle time (e.g. 90 seconds)

Workstation boundaries

assembly time

Throughput time (e.g. 2-3 days)

racks racks

Pre-sequencing

Tuggertrain

Train Station

JIS call-off

JIS delivery

Super market

Large to small load carrier

LLC storageFinal call-off to supplier (3-4 weeks before)

suppliers

Rework (e.g. quota 40%, duration 1-2 days)Quality

analysis

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The Great Transformation on the shop floor -Current situation

Sequencing (part A 1:2)

Customer order

Weekly Program Planning

Frozen Zone

Restrictions (suppliers, assembly line) based on prognosis (e.g. 50% part A)

BodyPaint Shop

Buffer Buffer

Paint shop sequenceOverload

Floater

Distribution rows(buffer time 2 days)

Assembly sequence

assemblytime

Re-sequencing

Cycle time (e.g. 90 seconds)

Workstation boundaries

assembly time

Throughput time (e.g. 2-3 days)

racks racks

Pre-sequencing

Tuggertrain

Train Station

JIS call-off

JIS delivery

Super market

Large to small load carrier

LLC storageFinal call-off to supplier (3-4 weeks before)

suppliers

Rework (e.g. quota 40%, duration 1-2 days)Quality

analysis

What Problems do exists?

1. Managing unpredictable situation with an inflexible system

a. Manpower in planning and control

b. Resequencing, rework

2. Technical problems and overloads lead to stop of the whole assembly line

3. Huge stocks in distribution logistics due to instability

4. Production of stock orders without customers to fill the capacity gabs

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The Great Transformation on the shop floor –Industry 4.0

BodyPaint Shop

Buffer Buffer

Paint shop sequence

Distribution rows –transport assignment criteria for AGV

Final call-off to supplier (3-4 weeks before)

suppliers

Customer order

Volume cycle

Restrictions (suppliers) based on prognosis

Size

Time

Calculation of volume cylce

setting

Frozen Zone

Large to small load carrier

LLC storageConsumption-driven parts

(common parts)

Market Place

Demand-driven

(individual parts)

Size, value, variety

Quality analysis

racks

Rework performed by regular Workstations

By call-off

Kanban

Workstation

racks

racks

Part container

Assembly priority chart

AGV

Workstation

Station with various abilities

Cycle independent throughput times

JIT call-off

JIT delivery

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Simulation to evaluate the performance

• What do we expect?

1. Both systems running optimally

2. Run-up period

3. Instable volumes

4. Disruptions

Scenario/KPI

Reliability Stock inbound

Stock outbound

Capacityutilization

Resources Throughputtime

Working costs

1 0 - + 0 0 - -

2 0 - + + + - ?

3 0 - + + + - ?

4 + ? + + + ? -

- deterioration 0 unchanged + improvement

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Thank you for your attention!

www.smartfactoryplanning.de

Christian.Schwede@iml.fraunhofer.de